The Sound of Exercise: A Randomized Controlled Pilot Study Investigating Music Listening as an

Intervention in Group-based Cardiorespiratory Exercise for Inpatient Stroke Survivors

Writtenby:

DanielSkakMazhari-Jensen

Master’sthesisMusictherapy,10thsemester

DepartmentofCommunicationandPsychology Aalborg University

TitelbladDanielSkakMazhari-JensenAalborgUniversitet,InstitutforMusikterapispecialeKommunikation,Musikterapi

Lyden af træning: Et randomiseret kontrolleret pilotstudie af

musiklytning som musikintervention i gruppebaseret konditionstræning for

patienter i fase-ll-hjerneskaderehabilitering

Billedet på forsiden er tegnet af

Jane Krebs.

Rapportens samlede antal tegn

(med mellemrum & fodnoter): 191.885

Svarende til antal normalsider af 2400 tegn: 79.95

Hvoraf artiklen er 47.897 og resten er 143.988 tegn

Aalborg Universitet

15. februar 2019

Daniel Skak Mazhari-Jensen: 20134847

Vejleder: Stine Lindahl Jacobsen

Referencestil: American Psychological

Association (APA), 6th Edition

10. semester, Musikterapi

Specialeopgave

________________________________________________________

Daniel Skak Mazhari-Jensen

Acknowledgements

Acknowledgments Først vil jeg takke Rikke Østerbye, for at stille et vigtigt spørgsmål, som jeg ikke kunne svare på under min 6. semesters praktik. Med denne opgave kan jeg nu

give et mere kvalificeret svar.

Af hjertet tak til Helle Rovsing, for at have gjort dette projekt muligt og holde mig ved ilden samt været mit forbillede på hvor meget man kan brænde for sit felts

forskning og klinisk praksis.

Til min vejleder Stine Lindahl Jacobsen, er jeg særligt taknemmelig for hendes enorme engagement og skønne humør – det har været uundværligt. Også tak

for, at lade et naivt og ambitiøst projekt blive realiseret, på trods af mange udfordringer, spørgsmål og nødvendige dispensationer gennem processen.

Tak til Kira Vibe Jespersen, for at tage sig tid til at være censor til min

kandidateksamen. Det er jeg utroligt beæret over, og jeg har glædet mig længe hertil.

Særligt tak til Dorte Jelsbak, Palma Egholm, Carina Wested og Sisse Andersen, for at deltage engageret og professionelt i opstarten og udførelsen af projektet.

Tak til Anja Børkild Nielsen og Nicki Møller Larsen, for at guide mig gennem de

pragmatiske udfordringer som opstår i en klinisk virkelighed på et hospital.

Alle ansatte og ledelsen på ”institutionen” (anonymiseret), skal have en stor tak for deres utroligt store rummelighed til mig og nysgerrighed på min profession.

Det gav mig muligheden for at udvikle mig, samt skabe noget innovativt og nytænkende i min kliniske praksis.

Tak til min kære mor, for at læse mere end 7000 tal op, som jeg skulle nedskrive

og omregne af flere gange i processen.

Også tak til min kæreste Stine, for at hjælpe med de mange regnestykker, og for at lægge øre til de talløse timers monologer jeg har holdt om projektet. Du har

været min største støttet gennem min uddannelse, og jeg glæder mig til at støtte dig gennem din.

Til slut et stort tak til drengern’, mine venner og min nære omgangskreds, for at støtte mig i specialeprocessen, diskuterer tingene over en øl samt give mig mod

og inspiration til at fortsætte.

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Quotes

“I think I should have no other mortal wants, if I could always have plenty of music. It seems to infuse strength into my limbs, and ideas into my brain. Life seems to go on

without effort, when I am filled with music.” (6.3.10).

– George Eliot (1819-1880), from The mill of the Floss, where the character Maggie explains her love of music.

“Music produces a kind of pleasure which human nature cannot do

without.” ― Confucius, from The Book of Rites

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AbstractDanielSkakMazhari-JensenAalborgUniversitet,InstitutforMusikterapispecialeKommunikation,Musikterapi

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The Sound of Exercise: A Randomized Controlled

Pilot Study Investigating Music Listening as an Intervention in Group-based Cardiorespiratory

Exercise for Inpatients Stroke Survivors

Abstract Background: Aerobic exercise has been suggested to play an important role in cardiovascular fitness, cognitive abilities, quality of life, and other health outcomes among stroke patients (Han et al., 2017). In clinical recommendations from the Danish Health Organization (Sundhedsstyrelsen, 2014a), cardiorespiratory exercise is recommended with the highest available level of recommendation. As a moderate level of fitness, endurance, and mobility is necessary for engaging in the recommended physical intensity levels, such exercise might be a catalyst for improving other aspects of patients’ physical functioning. However, the intensity demand of physical training requires dedication and motivation from the patient and can in reality be hard to achieve. Especially the higher intensities in the therapeutic window is hard to obtain (Billinger et al., 2014). Music has in previous studies been shown to invigorate healthy subjects and athletes, thereby enhancing mood, increasing motivation, and performance, as well as attenuating bodily discomfort (Bigliassi, Karageorghis, Wright, Orgs, & Nowicky, 2017; Karageorghis & Priest, 2012a, 2012b). Considering these findings, music interventions may be hypothesized as a suitable and cost-efficient tool for supporting an efficient and motivating exercise session in inpatient stroke survivors’ neurorehabilitation. Objective: This article based master’s thesis investigated the effects of music-supported aerobic exercise for inpatient stroke survivors in group-based aerobic exercise. The objective was to compare the immediate affective, experiential, and behavioral effects of listening to two different music conditions: local radio and a tailored playlist, compared to a non-music control condition. Methods: A three-armed crossover within-subject design was used. Three groups consisting of 5-8 participants participated in three weekly exercise sessions for three weeks. The condition order was randomly assigned to each group. A total of 19 participants were included and analyzed. Outcome measures consisted of training duration (seconds), duration of recommended intensity (≥40%HRR), affective valence state using the Feeling

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AbstractDanielSkakMazhari-JensenAalborgUniversitet,InstitutforMusikterapispecialeKommunikation,Musikterapi

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Scale (Hardy & Rejeski, 1989), the Borg Rating of Perceived Exertion scale, and rating the overall experience of the exercise sessions. The tailored music listening intervention was based on scientific recommendations (Karageorghis, Terry, Lane, Bishop, & Priest, 2012) and a meta-theoretical framework (Clark, Baker, & Taylor, 2016b). Results: Results show significant differences between pre-post scores in Feeling Scale for music conditions (Radio: p = .036; Playlist: p = .042), whereas non-music condition did not (p = .369), however, no difference was found between conditions. Only radio condition showed statistically significant differences between non-music control condition in overall experience (p = .018), with playlist showing strong trend (p = .057), and no difference between music conditions. No differences was found in Borg’s Rating of Perceived Exertion between any conditions. Significant differences comparing both music conditions to non-music condition showed prolonged training duration (p < .0001, Radio: β = 486.318; Playlist: β = 452.815). However, a ceiling effect restricts findings and makes the effect size and beta estimate misguiding. The study found significant difference for duration of recommended intensity when considering participants’ Functional Independence Measure (FIM) gait score as a predictor. This showed prolonged duration of recommended training intensity for the playlist condition (p < .0001, β = 534.358). In addition, a strong tendency in the interaction effect for participants with lower gait functioning measured by the FIM was found (p = .057). However, there was no significant difference observed in the univariate test based on marginal means. Conclusion: This study supports the hypotheses of music enhancing mood and heighten exercise experience in aerobic exercise for inpatient stroke survivors. Correspondingly, music may prolong the training duration of patients with difficulties to endure a full training session, but insufficient data restricted the findings. Only tailored music was found to increase duration in recommended cardiovascular intensity, when FIM gait scores were applied as a predictor. No difference was found in perceived exertion. This pilot study contributes to the empirical research supporting the beneficial effects of music in clinical aerobic exercise, lending support to further investigation of music in cardiorespiratory exercise for (inpatient) stroke survivors. Keywords Stroke, aerobic exercise, neurorehabilitation, music intervention, music therapy

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DanskresumeDanielSkakMazhari-JensenAalborgUniversitet,InstitutforMusikterapispecialeKommunikation,Musikterapi

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Dansk resume:

Rationale: Konditionstræning er associeret med at spille en vigtig rolle i øget kardiovaskulær fitness, kognitive funktioner, livskvalitet, samt andre sundhedsparametre (Han et al., 2017). Konditionstræning er, ifølge de nationale kliniske retningslinjer for ergo- og fysioterapeutisk arbejde i hjerneskaderehabilitering (HSR), blandt de to højst anbefalede genoptræ-ningsaktiviteter (Sundhedsstyrelsen, 2014). Moderat kondition, udholdenhed og mobilitet er nødvendig for at deltage i de anbefalede daglige fysiske aktiviteter, derfor kan konditionstræning være katalysator for at forbedre andre aspekter af patientens fysiske funktionsniveau. Det kan være svært at opnå det anbefalede niveau i de kliniske retningslinjer, hvor der i et omfattende review, lavet af den amerikanske hjerte- og slagtilfældeforening (Billinger et al., 2014), bl.a. nævnes problemer med at fastholde træningen i det terapeutiske vindue. Musik har i tidligere studier vist sig effektive i at øge motivation i normalbefolkningen og eliteatleters sport og træning, hvorigennem personerne har oplevet højnet humør, bedre præstationer, samt mindsket kropslig ubehag og anstrengelse (Bigliassi et al., 2017; Karageorghis & Priest, 2012a, 2012b). På baggrund af denne viden kan musikinterventioner tænkes at være anvendelig og kost-effektiv for at støtte en effektiv og motiverende trænings session for patienter i fase-II-hjerneskaderehabilitering. Formål: Dette artikel-baseret speciale undersøgte effekten af musiklytning i gruppebaseret konditionstræning for apopleksipatienter i fase-II-hjerneskaderehabilitering. Formålet var at måle den øjeblikkelige effekt på humør, oplevelse og adfærd når deltagerne lytter til musik, samt hvorvidt special-designet musikinterventioner havde en større effekt end lokalradio. Desuden var der inkluderet en kontrol, hvor deltagerne ikke fik musik. Metode: Et tre-armet crossover within-subject design blev brugt i den kliniske undersøgelse. Tre træningshold af 5-8 deltagere deltog i tre ugentlige træningssessioner. De tre lydmiljøer blev anvendt I tilfældig rækkefølge for hver gruppe. I alt var der 19 deltagere, som alle blev inkluderet i analysen. Der blev målt på trænings tid (sekunder), tid i den anbefalede træningszone (≥40%HRR), affekt-valens ved brug af Feeling Scale (Hardy & Rejeski, 1989), Borg’s anstrengelsesskala, samt en helhedsoplevelsen af hver session. Resultater: Resultaterne fremhævede signifikant forskel mellem før- og eftermålingerne i Feeling Scale i begge musikinterventioner (Radio: p = .036; Playliste: p = .042), hvorimod det musikløse miljø ikke viste forskel (p = .369). Dog var der ikke signifikant forskel mellem de tre lydmiljøer. Kun radio condition viste statistisk signifikant forskel sammenlignet med ingen musik condition i helhedsoplevelsen fra træningen (p = .018), hvor playlisten viste en stærk tendens (p = .057). Ingen forskel blev fundet mellem

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DanskresumeDanielSkakMazhari-JensenAalborgUniversitet,InstitutforMusikterapispecialeKommunikation,Musikterapi

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musikinterventionerne. Der blev ikke fundet en forskel i analyserne af Borgs skala for oplevelse af anstrengelse. Ved at sammenligne hver af de to musik interventioner til det musikløse miljø, blev der fundet signifikant længere træningstid i musikinterventionerne (p < .0001, Radio β = 486.318; Playlist: β = 452.815). Dog gjorde en ceiling effekt at resultaterne er misvisende i effektstørrelse og betakoefficient. Ved at inddrage deltagernes Functional Independence Measure (FIM) scoring af gangkvalitet, som en prædiktor, viste analyserne signifikant længere træningstid i den anbefalede pulszone for playliste conditionen (p < .0001, β = 534.358), samt en stærk tendens for en interaktionseffekt for deltagere med lav gangfunktion målt med FIM, hvor playliste conditionen viste signifikant længere træningstid i den anbefalede pulszone sammenlignet med det musikløse lydmiljø (p = .057). Dog fandt dette studie ingen signifikant forskel for tid i den anbefalede træningspuls-zone i en univariat analyse baseret på estimated marginal means. Konklusion: Dette studie understøtter hypotesen om, at musikinterventioner anvendt i træning kan forbedre humøret og oplevelsen af træningssessionen for apopleksipatienter i fase-II-hjerneskaderehabilitering. Ydermere kan musik være med til at forlænge træningstiden for patienter som har udfordringer med at have nok udholdenhed til en træningsseance, men data er utilstrækkelig for at konkludere noget. Kun den skræddersyede playliste viste signifikant længere træningstid i den anbefalede pulszone, når FIM gangscoren var anvendt som prædiktor. Ingen forskel blev fundet i den oplevede anstrengende ud fra Borgs skala. Dette pilotstudie bidrager til den empiriske forskning, som understøtter hypotesen om musikinterventioners gavnlige virkninger i klinisk konditionstræning. Studiet understøtter yderligere interesse for at undersøge musik i konditionstræning for apopleksipatienter (i fase-II). Nøgleord Apopleksi, konditionstræning, hjerneskaderehabilitering, musikintervention, musikterapi

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TableofContents

Table of Contents Aknowledgements..........................................................................................................i

Quotes...............................................................................................................................ii

Abstract...........................................................................................................................iii

EnglishAbstract.......................................................................................................................................................iiiDanskResume.............................................................................................................................................................v

Figures,Tables,andPaper...........................................................................................x

Abbreviations................................................................................................................xi

Chapter1–Introduction.............................................................................................1

1.1Motivation.............................................................................................................................................................21.2AreaofInterest...................................................................................................................................................31.2ResearchQuestion............................................................................................................................................4

1.2.1DelimitationsandScope.................................................................................................................41.2.2Sub-ResearchQuestions.................................................................................................................61.2.3Hypotheses............................................................................................................................................7

1.3DefiningMusicInterventions......................................................................................................................71.4StructureofTheThesis...............................................................................................................................11

TheThesisArticle.......................................................................................................12

Chapter2–TheoryandLiteratureReview..........................................................12

2.1Epidemiology-ApoplexiaCerebralisinDenmark........................................................................132.1.ClinicalNeedsandGoals.................................................................................................................13

2.2AerobicExerciseinStrokeRehabilitation.........................................................................................142.3ApplyingMusicinClinicalExerciseorPhysicalActivity...........................................................16

2.3.1IntegrativeLiteratureReview..................................................................................................172.3.2AMeta-theoreticalFramework................................................................................................192.3.4EmpiricalStudies.............................................................................................................................252.3.5KeyFindings-MethodologicalandPracticalImplications/Recommendations..............................................................................................................................................................................28

Chapter3–MethodologyandProtocol.................................................................303.1TheoryofScience...........................................................................................................................................303.2Design...................................................................................................................................................................313.3Subjects................................................................................................................................................................31

3.3.1EthicalConsiderations..................................................................................................................313.4InterventionProtocol...................................................................................................................................32

3.4.1Music-supportedAerobicExercise........................................................................................333.4.2TheMusicInterview......................................................................................................................333.4.3ChoosingtheRadio.........................................................................................................................343.4.4ProtocolforDesigningthePlaylist.........................................................................................343.4.5ProtocolforClinicalApplication..............................................................................................35

3.5OutcomeMeasuresandMaterials.........................................................................................................363.5.1Borg’sRatingofPerceivedExertionScale..........................................................................36

TableofContents

3.5.2PortableHeartRateMonitor.....................................................................................................373.5.3HeartRateReserve–TheIntensityMarker......................................................................37

3.6Randomization,Blinding,SampleSizeandAllocation................................................................393.7Statistics..............................................................................................................................................................39

Chapter4–Results.....................................................................................................414.1ParticipantFlowandRecruitment........................................................................................................414.2PreliminaryAnalysis–InitialDataAnalysis....................................................................................43

4.2.1ReevaluationofMaxHeartRateReserve............................................................................434.2.2InitialDataAnalysisofObservations(rawdata)............................................................434.2.3CheckingModelAssumptionsfortheLinearMixedEffectModel.........................444.2.4ParticipantDemographicsandClinicalCharacteristics..............................................454.2.5PreferenceofSoundMilieu........................................................................................................45

4.3Numbersanalyzed–Attrition..................................................................................................................464.4OutcomesandEstimation..........................................................................................................................46

4.4.1Borg’sRatingofPerceivedExertion......................................................................................464.4.2FeelingScale.......................................................................................................................................464.4.3OverallExperience..........................................................................................................................474.4.4TrainingDuration............................................................................................................................484.4.5DurationofRecommendedIntensity....................................................................................49

4.5Ancillaryanalysis...........................................................................................................................................524.6Harms...................................................................................................................................................................53

Chapter5–Discussion...............................................................................................53

5.1Findings...............................................................................................................................................................535.1.1BriefSynopsisoftheKeyFindings.........................................................................................535.1.2ConsiderationofPossibleMechanismsandExplanations.........................................56

5.2Methodology,Procedure,andAnalysis...............................................................................................585.2.1LimitationsofthePresentStudy.............................................................................................585.2.2ProtocolandOutcomeMeasures............................................................................................615.2.4FutureRecommendations..........................................................................................................63

5.3ClinicalIndicationsforPatientsandInterventionists.................................................................645.4Perspectives......................................................................................................................................................67

Chapter6–Conclusion..............................................................................................67

OtherInformation.......................................................................................................68

Registration...............................................................................................................................................................68Protocol.......................................................................................................................................................................68Funding........................................................................................................................................................................68

References.......................................................................................................................1

Attachments....................................................................................................................1

ThesisArticle...............................................................................................................................................................1

Appendices......................................................................................................................1

1–TheAuthor’sViewonMusicInterventionsWithinStrokeRehabilitation............................1.1APhilosophicalViewonTreatmentandTherapyinNeurorehabilitation.................1.2MusicTherapyApproachinNeurorehabilitation...................................................................

TableofContents

1.3TheClinicalContextofThisPaper..................................................................................................2–JournalGuidelines...............................................................................................................................................3–CompleteBookletoftheInterventionProcedure“MusikstøttetKonditionstræning”....

3.1–Deltagerinformation...........................................................................................................................3.2–Protokol–Guidelinesforindsamlingafdata.........................................................................3.3–MusikstøttetkonditionstræningIneurorehabilitering–CaseReportForm........3.4–UndersøgelsesspørgsmåltilMusikalskInterview...............................................................3.5–Terapeutiskforholdemådeundersessionerne.....................................................................3.6–Rengøringafpulsmonitor................................................................................................................3.7–OpsætningafGarminForerunner735XTindenudlevering..........................................3.8–Træningsprogressionafintensitet/intervaller–30minsession...............................

4–AssessmentandQuestionnaireBookletforSessions.......................................................................4.1–Borg’sRatingofPerceivedExertion...........................................................................................4.2–FeelingScale(Hardy&Rejeski,1989)......................................................................................4.3–OverallExperiencescale...................................................................................................................4.4–Aestheticappraisalofthemusicalsoundmilieu..................................................................

5–ScoringSheetforGroup-sessions...............................................................................................................6–ChecklistforSessions........................................................................................................................................7–InformationCriteriaforLinearMixedModels.....................................................................................8–InitialDataAnalysisofRawData................................................................................................................9–GraphsforCheckingModelAssumptionsforLinearMixedEffectModels...........................

9.1–TrainingDuration.................................................................................................................................9.2–DurationofRecommendedIntensity.........................................................................................

10–PatientDemographicandClinicalCharacteristics..........................................................................11–NumbersAnalyzedinAllSessions...........................................................................................................

Figures,Tables,andPapers

Table of Figures Figure1Sub-researchQuestions......................................................................................................................6Figure2ConceptualFrameworkforMusic,Health,andWell-Being.............................................7Figure3TheRelationofEverydayUseofMusic.....................................................................................8Figure4TheRelationinMusicTherapy.......................................................................................................9Figure5TheHierarchicalStructreofTreatmentsUsingMusicMedicine...............................10Figure6FlowChartofMaster’sThesisStructure.................................................................................12Figure7LiteratureSearchOutcomes..........................................................................................................18Figure8Meta-TheoryontheModulatingEffectofMusicListeningOnExerciseandPhysicalActivity.....................................................................................................................................................20Figure9RelationshipBeteenComplexityandPleasyreandGrooveforHealthyandStrokeSurvivorsPopulations..........................................................................................................................24Figure10Music-SupportedAerobicExerice...........................................................................................33Figure11Set-uptheExerciseRoom............................................................................................................36Figure12CONSORTFlowChartofParticipantRecuitment............................................................42Figure13SoundMilieuPreferenceScores...............................................................................................45Figure14FeelingScaleScoresPre-andPostsession.........................................................................47Figure15OverallExperienceScores...........................................................................................................48Figure16TrainingDuration............................................................................................................................48Figure17DurationofRecommendedIntensity.....................................................................................50Figure18PerCentofTimeinRecommendedZones..........................................................................51

Table of Tables Table1:Borg’sRatingsofPerceivedExertion........................................................................................46Table2:TrainingDurationFortheFourParticipantsNotHittingTheCeilingEffect........49Table3:PercentageofSessionMeetingRecommendationsOfIntensitiesPrecentedin10MinuteBouts......................................................................................................................................................51Table4:ParticipantsinEachConditionMeetingRecommendationsPresentedIn10MinuteBouts............................................................................................................................................................52Table5:NumberNeededtoTreatInEachConditionMeetingRecommendationsPresentedIn10minutesBouts.......................................................................................................................52

List of Papers See attachment: Mazhari-Jensen, D.S. & Jacobsen, S.L. (2019) The Sound of Exercise: Affective, Experiential and Behavioral Effects of Music Listening in the Context of a Cardiorespiratory Exercise Session in Inpatient Stroke Rehabilitation. Manuscript not submitted for publication.

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Abbreviations

Abbreviations Abbreviations Definition AIC Akaike Information Criteria ANOVA Analysis of Variance BIC Bayesian Information Criteria BPM Beats Per Minute CONSORT Consolidated Standards of Reporting Trials CVA Cerebrovascular accident dB(A) dB: Decibel. The A in dB(A) referrers to the frequential weight of

the relative loudness of sounds in air as perceived by the human ear.

DuRI Duration of Recommended Intensity FS Feeling Scale: an assessment developed by Hardy & Rejeski

(1989). FIM Functional Independence Measure HR Heart Rate HRH Heart Rate Reserve LMEM Linear Mixed Effect Model MST Music-supported Therapy NMT Neurologic Music Therapy OE Overall Experience scale RAS Rhythmic Auditory Stimulation RM Repeated Measures RHR Resting Heart Rate RPE Rating of Perceived Exertion. Refers to Borg’s RPE scale. TD Training Duration RPM Rotations Per Minute VO2Max Peak Oxygen Consumption QoL Quality of Life

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Chapter 1 – Introduction

Interview with Hugo, a 51-year-old stroke survivor, on “how may music motivate you in your physical training of your stroke rehabilitation?” translated from Mazhari-Jensen (2016, appendix, p. 12). Now I actually come to think of it - I haven’t thought of this at all, but ... I haven’t thought of music that way, or anything, but I’m doing cycling training every day! And I actually noticed that there is a radio in there [exercise room]. When it’s turned off, I think it’s that endurance it’s, it’s harder, maybe, when there is quiet in the room. But when I go there in the evening, when there is no one else, I turn on the radio. And when there is happy music on the radio. Then, it’s almost, like, it brings a rhythm to the body, and I get more energy that helps me push - I make it, yeah, a longer duration. And I actually have increased my time and the kilometer amount when I ride. And it may be because I put on music every night because it helps, like, well it peps you up a bit! [laughs] - if you can say that. It really does! Something happens, it’s like, you kind of find the rhythm in it and listen for it, and then you really kind of forget the time and place you are, and you just go along! You ride along, - the flow… the task you do - without perhaps feeling: now I’m really beginning to be like “that bad leg is becoming tired now". But you give it just a notch more. So it [music] does! I could almost sign my name on it, I believe! So yeah, it does something. Can music really alter the dynamics of physical exercise, ameliorate exertion and increase endurance? And do all stroke survivors experience music as positive in aerobic exercise, enhancing affect and the exercise experience? Aerobic exercise has been suggested to play an important role in improving health outcomes among stroke survivors (Han et al., 2017). In the clinical guidelines by the Danish Health Organization (Sundhedsstyrelsen, 2014a), cardiorespiratory exercise is recommended with the highest available level of recommendation. As a moderate level of fitness, endurance and mobility is necessary for engaging in the recommended physical intensity levels, such exercise might be a catalyst for improving other aspects of patients’ physical functioning. However, difficulties in meeting the recommendations for physical activity and cardiorespiratory intensity are commonly reported (Bernhardt, Dewey, Thrift, & Donnan, 2004; De Wit et al., 2005), which is a major health concern, as sedentary behavior and general inactivity is associated with poor functional outcome after stroke (Askim, Bernhardt, Salvesen, & Indredavik, 2014), and approximately one in two stroke survivors report difficulties participating in activities of daily living (ADL), which leads to poor quality of life (QoL) and

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dissatisfaction with treatment after rehabilitation programs have ended (Hartman-Maeir, Soroker, Ring, Avni, & Katz, 2007; McKevitt et al., 2011). Physical inactivity is presumably caused by both physical and psychological impairment (Billinger et al., 2014). The demand for higher cardiovascular exercise intensity requires physical stamina and motivation from the patient, and can in reality be hard to achieve due to both physiological and psychological impairments after stroke. Here, researchers highlight deconditioning or motor impairments as well as personal factors such as lack of interest/motivation, perceived exertion, pain and depression as causes for the decline in activity (Billinger et al., 2014, p. 2543; Billinger, Boyne, Coughenour, Dunning, & Mattlage, 2015). In summary, physical inactivity after stroke is highly prevalent, causing physical and psychological functioning to decrease, which leads to even higher difficulties for participating in physical activity and meeting recommendations, creating a vicious cycle of sedentariness. Thus, a novel and innovative approach targeting both affective, perceptual, and behavioral aspects of rehabilitation is warranted to aid patients reach higher training intensities of both frequency, length and cardiovascular intensity, hence, achieving greater therapeutic benefits and gaining a higher independence and autonomy in their everyday lives. Music has been documented to be an important tool for self-boosting performances for elite athletes (Laukka & Quick, 2013). In everyday life, music is an integral part of many people’s training program (DeNora, 2000; Hallett & Lamont, 2014, 2017), appropriating the affording qualities offered by music (constructs proposed by Gibson’s ecological theory of perception, as used by DeNora, 2000) to regulate inner states. Spinning, Zumba and step are common examples for cardiorespiratory exercise with music being an essential part of the exercise, and a simple Spotify search of “workout” or “exercise” reveals an overwhelming amount of playlists promising to invigorate you, infuse strength into your limbs, and ultimately allowing you to perform and endure to your absolute limit. But could this also be a viable option for increasing efficiency of aerobic exercise in clinical neurorehabilitation for stroke survivors, and is all music equally effective?

1.1 Motivation

In 2016, when I did my first clinical internship at a neurorehabilitation unit, a physiotherapist asked me if I would come and share my insight in applying music in cardiorespiratory exercise. She even invited me to attend her group-

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based session, consisting of cycle ergometry and circuit training, so that I could illustrate my practice and recommendations. However, the only problem was that I did not know anything about applying music in aerobic exercise. I turned to my books and searched the Internet for literature, only to find plenty of statements on music as a motivational factor in exercise, leaving no cited empirical study or practical statements. Applying music would seem almost mundane and common-sense to many of us, but as a matter of fact, through a quick Google Scholar search and by looking in my main text books, I was unable to find any empirical evidence for applying music in cardiorespiratory training in stroke rehabilitation. Later that day I did my first clinical session in aerobic exercise, collaborating with the physiotherapist. This area of interest became the driving motivation for my two years master’s degree. Six months later, in January 2017, I had the first meeting with the neuro unit going into this master’s thesis. The aim was specifically to examine the use of music as an aid to keep up motivation during aerobic exercise, to feel empowered, and if possible to increase patients’ cardiovascular intensity and physical activity level. Efficiency by increasing the duration of intensity of the exercise session was the main goal, as the therapists at the neuro unit were concerned that patients did not meet recommendations for physical activity and cardiovascular intensity of aerobic exercises. The present master’s thesis is a culmination of approximately one and a half years’ worth of field research and conducting a feasibility study.

1.2 Area of Interest

As illustrated above, music is thought to be helpful and beneficial in physical exercise by heightening the affective state, boosting motivation and providing diversion/dissociation, thus ameliorating perceived exertion and fatigue. This has become common-sense, leading some clinicians to apply music intuitively in their clinical physical exercise programs, based on their own experience of using music in exercise. However, this approach lacks systematism and scientific rationale for guiding the use of music in clinical physical training. Thus, more research is needed to provide evidence-based guidelines of effectiveness and efficiency and to provide specifications for delivery of intervention and dosage. Using my 8th semester feasibility study as a stepping stone, I want to continue my research in the field of music supporting clinical aerobic exercise in neurorehabilitation.

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1.2 Research Question

The purpose of this master’s thesis is to investigate whether music listening (as an intervention) can enhance psychological factors in addition to promote efficiency in an exercise session for inpatient stroke survivors. Music listening will be examined by assessing the influence on affective valence using the Feeling Scale (Hardy & Rejeski, 1989), subjective experience using the Overall Experience Scale, and with the Borg Rating of Perceived Exertion Scale in the exercise session. Efficiency will be examined by the outcomes of training duration (TD) and duration of cardiovascular intensity following clinical recommendations. TD and cardiovascular intensity are variables included in the clinical recommendations (Billinger et al., 2014; Sundhedsstyrelsen, 2018) and are considered predictors for the functional outcome of neurorehabilitation, as they increase the individual’s ability to achieve higher level of functioning, become more independent and autonomous I ADL, and thus achieve greater QoL (Ibid). Affective and experiential state have been proposed to be a leading predictor for future motives of physical activity and exercise behavior (Rhodes, Fiala, & Conner, 2009; Rhodes & Kates, 2015). Besides having a control condition with no music, music listening is divided into two types of interventions: a systematic and tailored music playlist designed by a music therapist and listening to the local radio channel. Effectiveness of each music intervention will be compared on the abovementioned outcomes. Summarized, my research question is as follows: Research Question Using PICOS:

1.2.1 Delimitations and Scope Delimitations are necessary for defining the boundaries of the paper. First, there are different modalities of participating in tasks or activities that may facilitate intensities sufficient for inducing moderate to high-intensity of cardiorespiratory fitness. In clinical guidelines, gait, cycle ergometry and circuit

Using a crossover randomized controlled trial, and based on a cross-disciplinary theoretical framework, how efficiently can music listening, compared to a non-music control, prolong training duration and duration of recommended heart rate intensity, as well as enhance affect, the subjective experience, and attenuate perceived exertion for stroke survivors in group-based cardiorespiratory exercise? And how effective is a tailored receptive music intervention compared to listening to the local radio channel?

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training are the primary modalities recommended for aerobic exercise (Billinger et al., 2014; Sundhedsstyrelsen, 2014a). However, gait is a task which requires multi-dimensional observations with qualitative measure-ments, and this task requires one-to-one therapy. Furthermore, a large group of stroke survivors experience lower extremity paresis, causing gait impairments, which makes gait an ineffective cardiorespiratory activity for these patients, especially in the higher intensities. Likewise, circuit training has diverse movement patterns, higher cognitive demands in regard to remembering the exercise tasks, thereby increasing the complexity of measuring the effects on the different tasks. Hence, I chose cycle ergometry as this task requires less therapists per patient (cost-efficient), requires less complex measurements and outcome variables, in addition to the capability of including a greater variety of patients (since patients with lower extremity paresis or postural imbalance still are able to participate). Second, the clinical protocol is based on a feasibility study by Mazhari-Jensen (2017), which trialed a structured and tailored playlist designed by a music therapist compared to a preferred radio channel on healthy students. Here, the music listening environment was evaluated, and recommendations for clinical applications and outcomes were discussed. Third, only immediate effects were measured. This essentially means that the long-term effect on functional outcomes as well as QoL and independence is not included in the scope of this thesis. Therefore, predictors for long-term outcomes highlighted in the clinical guidelines were chosen as the outcome variables. Meeting these recommendations are essentially predicting patients to achieve the long-term effects stated above. Therefore, an exercise session may be considered as more efficient than others. Finally, the study was purely based on quantitative methods, which has huge delimitations for the ontological and epistemological stance, regarding methods and possible outcomes. One could easily have conducted a mixed methods research, as outcomes such as affect and experience are included in the scope of this study. However, as the objective and the research question are based around how much and not in which ways, quantitative measures are needed.

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1.2.2 Sub-Research Questions

Figure 1 – Sub-Research Questions

1.Canmusiclisteningimmidiatelyaffecttheexperienceandtrainingefficiencyofaerobic

exercise?

1aDostrokesurvivorsprefersoundmilieuwithmusicornomusicwhileexercising?

1bCanamusicalsoundmilieuaffectexperientialandaffectivevalencestate,aswellasperceivedexertion?

1cIstrainingintensityaffectedbyamusicalsoundmilieu?

2.Iftheformeristhecase,does

thetypeofmusichavedifferenteffectivenesson

exerciseoutcomes?

2aDostrokesurvivorspreferalocalradiochanneloratailoredplaylisttothegroup-members?

2bDothedifferentmusicalsoundmilieuaffect

experientialandaffectivevalancestatedifferently?

2cIstrainingintensityaffecteddifferentlybythemusical

soundmiieu?

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1.2.3 Hypotheses

1.3 Defining Music Interventions In this paper, I will be using the term music intervention as an umbrella-term, describing an intervention that can be carried out by different actors with the aim to achieve intentional health-related improvements or changes to the recipient(s). The Author’s personal view on music therapy in neurorehabilitation is presented in appendix (1). In the following section, I briefly define a set of subordinate categories within the realm of music interventions that is relevant to the present study. A Conceptual Model

Figur 2 – Conceptual Framework for Music, Health and Well-being by MacDonald, 2013.

Hypotheses 1. Participants will prefer the sound milieu of the playlistcondition, compared to the radio and non-music (control)sound milieus.

2. Participants will in the playlist condition have enhancedexperiental and affective valence state, compared toparticipating in the radio and non-music (control) condition.

3. Participants will in both music conditions have prolongedtraining duration compared to participating in the non-music(control) condition.

4. Participants will in the playlist condition prolong theirrecommended intensity duration, compared to participatingin the radio and non-music (control) condition.

5. Participants will in both music conditions experience anattenuation in ratings of perceived exertion compared toparticipating in the non-music (control) condition.

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In the present master’s thesis, only the three segments: music therapy, music medicine and everyday use of music will be of relevance. Everyday Use of Music Refers to the act of listening to music based on no specific requirements and with no specific task accompanying the act. It is mainly applied by the individual itself, or as a passive exposure by others, who listen to the music (see Figure 3).

Figur3–TheRelationofEverydayuseofmusic

The everyday use of music has grey areas adjacent to the field of music therapy, as music may be appropriated by affording health and well-being (DeNora, 2000), affecting the listener psychologically and/or physiologically. Furthermore, individual music preferences are important in some receptive or community singing music therapy practices. In the context of neurorehabilitation, researchers have reported significant findings suggesting that daily music listening can increase neural recovery and increase cognitive functioning (Sarkamo et al., 2008; Särkämö et al., 2014; Särkämö, Tervaniemi, & Huotilainen, 2013). Music Therapy Music therapy is commonly defined referring to Kenneth Bruscia’s definition from 1998:

“Music therapy is a systematic process of intervention wherein the therapist helps the client to promote health, using music experiences and the relationships that develop through them as dynamic forces of change” (Bruscia, 1998, p. 20).

There is consensus upon the importance of a therapeutic relation to define a music-based intervention as being music therapy (Gold et al., 2011). Elaborating this, music-based interventions centered around the concept of music therapy will be treatment in a clinical setting, performed by a certified music therapist, and utilizing different medias and elements, e.g. singing, musicing, movement to music, or receptive methods.

IndividualMusic

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Accordingly, the relation is between client and music therapist, but also as a common and shared focus revolving around the music (see Figure 4).

Figur4–TheRelationinMusicTherapy

Functional music, an auxiliary level of the didactic music therapy practice as described by Bruscia (1998), is “the use of music to influence physical states, behaviors, moods, attitudes etc. outside of a therapy context, that is, in commercial, industrial, work, educational, or home settings.” Bruscia (1998, p 180). Bruscia’s rationale of (background) music listening is to affect the listeners environmental experience or induce a revitalizing effect on the individual, as well as stimulate neural networks. This field in music therapy is described by Paul and Ramsey (Paul & Ramsey, 2000), as well as in clinical practice of Rhythmic Auditory Stimulation (RAS). Hence, music therapy has grey areas adjacent to the field of music medicine, as music therapists may help clients appropriate music as a receptive intervention for regulating perceptual experience and arousal levels. There is also elements of overlap across everyday use of music, as music listening is a modality of receiving music interventions in music therapy. In the context of neurological rehabilitation, scientists have often used the term Music-Supported Therapy (MST) for describing the instrumental performance (or musicing) as an activity for physical activity and functional gross or fine motor skill rehabilitation (Altenmüller & Schlaug, 2013; Grau-Sánchez et al., 2013; Rodriguez-Fornells et al., 2012; Sihvonen et al., 2017). MST has been described and carried out by non-music therapists, even though this intervention do involve classic music therapy defining elements (musicing and having a relation in the music with the patient). Music Medicine Music medicine is the specific use of music listening to induce change towards, facilitate a certain effect in, or accompany a specific health-related objective. Similar to music therapy, music medicine also has a health-related

Patient

MusicTherapist

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goal but lacks the therapeutic relation and the interpersonal and mutual interactions or communication characteristic of music therapy (Gold et al., 2011). Usually, music medicine is performed by healthcare professionals, e.g., doctors, physiotherapists or psychologists – but can also be performed by music therapists, as long as the therapeutic relation is non-present. Music medicine is commonly produced to a broader population, (e.g. children) with a general goal (e.g. music-induced analgesia). As the composer and producer do not know the individual needs, a generalized assumption of the specific population’s needs is comprised for creating the content and deciding on musical parameters and effects. Hence, there are usually no direct interaction from client to producer, and the relation is solely between the client and the fixed music, thereby not allowing for tailored musical parameters to accompany individual clients’ needs and goals (see Figure 5).

Figur5–Thehierarchicalstructureoftreatmentsusingmusicmedicine

MacDonald states that an:

“important point is that most music medicine interventions use ‘‘prescribed music’’ and so clinicians make informed assumptions about the effects of particular pieces of music upon the patients’ psychological and physiological functioning” (MacDonald, 2013, p. 5).

Therefore, music medicine also has cross areas to music therapy, as both main goals and music as medium are overlapping across. Music medicine can even be delivered by a music therapist, making the grey area even more pronounced. Here, the primary modality of receiving interventions is music listening. Music interventions in the field of music medicine are reviewed in a recent white paper by Vuust and Gebauer (Vuust & Gebauer, 2014).

Healthcareprofessional

Music

PatientA

PaitientB

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Rhythmic auditory stimulation (RAS), probably the most well renowned music intervention in neurorehabilitation, is a technique used in the rehabilitation of movements that are intrinsically rhythmic, e.g., gait (C. Thaut & Rice, 2014). In RAS, a series of auditory cues are presented at a fixed rhythm, and movements are synchronized (entrained) to the rhythm. Even though it qualifies as music medicine, using music listening as the mode of receiving it, it is described by music therapists, and most commonly associated with Neurologic Music Therapy.

1.4 Structure of The Thesis The disposition follows the Consolidated Standards Of Reporting Trials (CONSORT) guidelines. The present master’s thesis consists of 6 chapters and one thesis article: Chapter 1 will present the foundation of this master’s thesis, including the area of interest, the research objective, hypothesis and a prelude consisting of the author’s a priori knowledge. Based on the introduction, the article follows (see attachment). The thesis article is designed as a publishable paper, which makes it a full piece and stand-alone paper. The following chapters will elaborate on the article and add to the paper. Chapter 2 begins with describing the clinical population and pathology of stroke, followed by a brief empirically founded description of aerobic exercise as an intervention in stroke rehabilitation. Finally, an integrative literature review of music interventions in clinical aerobic exercise, including a meta-theoretical framework of applying music in clinical aerobic exercise and empirical studies. Theory of science is presented in chapter 3, followed by an elaborated methodology and study protocol. Chapter 4 consists of additional results, including Borg’s RPE, preliminary analyses and a categorical perspective on the main outcomes. Results are discussed in chapter 5 and are put in relation to the additional chapters in the linking text. Following the discussion, a conclusion is offered for the master’s thesis in chapter 6. Finally, a literature list and appendices are offered, which are referenced throughout the thesis article and linking text. For a flow chart of the disposition, see Figure 6. As the article is designed as a stand-alone paper, it includes a separate abstract and references, which follow the criteria of the journal Music and Medicine (see appendix 1).

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Figure 6 – Consort Flow Chart of the Papers Disposition Using the Consort Checklist Items.

The Thesis Article

See attachment or send me an e-mail for a copy of the article.

Chapter 2 – Theory and Literature Review

This chapter includes a brief introduction to the clinical population of stroke in Denmark (2.1), as well as a rationale for aerobic exercise as a treatment for stroke survivors (2.2). Following, 4 sections will target the application of music interventions in aerobic exercise with emphasis on, but not limited to, stroke rehabilitation (2.3). First, the methodology and findings of a literature review

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are presented in a condensed format (2.3.1). The second and third part will appraise the literature, starting with a theoretical framework (2.3.2), followed by empirical studies (2.3.3). Finally, key findings concerning this master’s thesis are presented, with emphasis on recommendations in methodology and procedure (2.3.4).

2.1 Epidemiology - Apoplexia Cerebralis in Denmark Apoplexia cerebralis (lat.), commonly referred to as stroke or cerebrovascular accident (CVA), is categorized as a cerebrovascular disease (I60-69 in the ICD-10) (World Health Organization, 2016). WHO defines stroke as “rapidly developing clinical signs of focal (or global) disturbance of cerebral function, lasting more than 24 hours or leading to death, with no apparent cause other than that of vascular origin” (Sacco et al., 2013). Causes of stroke are: infarct due to cardiac emboli, intracerebral haemorrhage or subarachnoid haemorrhage after aneurysm rupture (Sundhedsstyrelsen, 2018), manifested by sudden focal neurological outcomes based on either an ischemic lesion or a non-traumatic brain haemorrhage (Sørensen, Paulson, & Gjerris, 2016, p. 336). The risk of stroke is rapidly increasing with age (ibid.), where the average age of stroke is 75 years, and only 20% of people with stroke are under the age of 65 (Sundhedsstyrelsen, 2018). Case fatality, the mortality due to stroke within the first month, is 17% for haemorrhagic stroke and 4% for iscaemic stroke in Denmark (Stevens, Emmett, Wang., McKevitt, & Wolfe, 2017), which in relation to cardiovascular diseases are the second most common cause of death in Denmark, only surpassed by cancer (Sundhedsdatastyrelsen, 2018). An increased proportion of elderly people in the population will lead to more strokes in the coming years. In 2009, the Danish National Patient Register registered 10,185 novel stroke unsets in adults, while the National Indicator Project (NIP-apopleksi) registered 11.421 (Sundhedsstyrelsen, 2011b, p. 68), corresponding to the annual incidence, and more than 75,000 people live in Denmark with a diagnosed stroke outside the hospital walls (prevalence) (Møller, Nielsen, & Kjær, 2014, p. 7). Approximately 60% of all acquired brain injuries are caused by stroke (Sundhedsstyrelsen, 2011b, p. 68), of which half will have chronic life lasting handicaps and deficits, and one out of four never achieve an independence level high enough, for them to return to their own home (Hjernesagen, 2018). Consequently, stroke is the leading cause of fulltime caregiving in the somatic part of the Danish healthcare system (Kruuse, Kristensen and Andersen, 2017).

2.1. Clinical Needs and Goals Our brain is involved in all human activity and helps us understand and interact with the world around us. Therefore, in case of insults to the brain, we can see deficits in all our functions and abilities, and even completely lose them after

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damage to a specific area. Thus, stroke can lead to many serious consequences for the exposed person, both physically, mentally and socially (Sundhedsstyrelsen, 2011b, p. 60). In symptoms of an acquired brain injury, one distinguishes between:

• Physical changes • Cognitive changes • Emotional changes • Changes in social behavior

(Erichsen, 2005, p. 4; Forchhammer & Nøhr, 2013, p. 5). Often the person will experience several of the aforementioned symptoms, depending on the location of the brain injury (Forchhammer & Nøhr, 2013; Sundhedsstyrelsen, 2011b, p. 69), with 75-85% of stroke survivors having hemiparesis or paralysis of upper and lower extremities and about 20-25% suffering from aphasia (Kruuse, Kristensen, & Andersen, 2017). After an acquired brain injury, a spontaneous remission usually occurs, where the person will get better. Despite this effect, it is nevertheless necessary to help these people recover and rehabilitate, which is the goal of the neurorehabilitation process (Johansen, Rahbek, Møller, & Jensen, 2004). 51% of the population having a CVA living in Denmark receive stroke unit care (Stevens et al., 2017). Here, the focus on treatment is constantly changing in the more acute phase, as needs and objectives constantly change according to response to treatment or spontaneous regression (Forchhammer & Nøhr, 2013; Sundhedsstyrelsen, 2011b, p. 69). Thus, the patient group is described as heterogeneous, and the needs vary from none to very extensive within each area (Ibid.).

2.2 Aerobic Exercise in Stroke Rehabilitation Evidence for Aerobic Exercise A recent Cochrane Review (Saunders et al., 2016) concludes that cardiorespiratory training may reduce disability during or after usual stroke care, which could be mediated by improved mobility and balance. In other meta-reviews, aerobic exercise has been suggested to play an important role in cardiovascular fitness, cognitive abilities, QoL, mobility, and other health outcomes among stroke patients (Han et al., 2017), as well as reduces the risk for subsequent cardiovascular events (Billinger et al., 2014; Sundhedsstyrelsen, 2011b). In aerobic exercise, frequency, type, time, and the level of cardiovascular intensity are important factors, which are linked to elicit greater therapeutic outcomes (Billinger et al., 2015). Especially high-intensity aerobic exercising has shown to be the primary predictor for inducing significant positive effects in peak oxygen uptake (VO2Max), gait endurance and velocity, postural control,

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QoL, self-reported mobility, and aerobic capacity (ibid.), hence, higher intensities are associated with greater improvement. Based on this, clinical guidelines recommend that aerobic exercise should be no less than three days a week, at least 30 minutes pr. session (or multiple bouts of 10 consecutive minutes), with a cardiovascular intensity of 40-80% heart rate reserve (HRR) (Billinger et al., 2014; Sundhedsstyrelsen, 2014b, 2018). A statement for healthcare professionals from the American Heart Association/American Stroke Association recommends that physical activity goals and exercise prescriptions for patients need to be tailored and customized to the patient’s recovery stage, environment, tolerance, social support, and preferences for activity (Billinger et al., 2014, p. 2535). The most commonly reported motivational factors for stroke patients to participate are: meeting other peers, which can give stroke survivors psychological and social support, as well as receiving professional support in guiding and facilitating the physical activities (Billinger et al., 2014, p. 2543). Thus, group-sessions and therapist-supervised aerobic exercise are associated with higher motivation for participating in aerobic exercise.

Mechanisms Explaining the Therapeutic Effect of Cardiorespiratory Exercise For stroke survivors to adequately participate in the neurorehabilitation, patients must have a moderate level of aerobic fitness, cardiovascular fitness, endurance, balance, and mobility to take part in the recommended physical activities.

VO2Max, maximal walking velocity, and endurance in post-stroke patients have been reported to values only 50% of an aged-matched healthy population (Kelly, Kilbreath, Davis, Zeman, & Raymond, 2003), resulting in the population having less energy to get them through the rehabilitation process. Muscle weakness and loss of coordination may be the primary impairments that affect physical functioning after a stroke, however, in the case of gait a limited walking endurance and velocity caused by impaired cardiorespiratory fitness may secondarily affect gait performance (Kelly et al., 2003). Thus, cardiorespiratory exercise is important for gait and other ADL activities. The high energy consumption is associated with an inefficient movement pattern and spasticity (Sundhedsstyrelsen, 2018). Aerobic training can presumably break the vicious cycle by increasing the cardiorespiratory fitness and lower the necessary energy consumption (Ibid.), thereby increasing the population’s overall physical functioning and ability to participate in and complete a rehabilitation program.

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Looking more specifically at the aerobic modality of leg cycling exercises, cycling and locomotion have been proposed to have similar muscle activation patterns and may be generated by the same central neural network (Christensen et al., 2000). Similar to locomotion, the rhythmic and repetitive nature of cycling kinematics allows patients to generate temporal symmetrically and reciprocal forces from both limbs (Lin, Chen, & Lin, 2013).

2.3 Applying Music in Clinical Exercise or Physical Activity Of the current trends in stroke rehabilitation, music has been recognized as a multimodal stimulus, as it activates multiple brain structures and can stimulate complex cognition and multisensory integration, facilitating brain plasticity (Johansson, 2011, 2012; Särkämö et al., 2013). Music interventions have been proposed as a non-pharmacological intervention in various aspects of neurorehabilitation, where music interventions have been shown to modulate the perceptions and ergonomics of physical activity in stroke patients (Kim & Koh, 2005; Magee, Clark, Tamplin, & Bradt, 2017; Moumdjian, Sarkamo, Leone, Leman, & Feys, 2017; Sihvonen et al., 2017; M. H. Thaut & Hoemberg, 2014). Music interventions in neurorehabilitation have already documented encouraging effects in gait rehabilitation (Magee et al., 2017), upper extremity motor skills (Grau-Sánchez et al., 2013; Kim & Koh, 2005; Schneider, Schönle, Altenmüller, & Münte, 2007) and in raising mood (Magee & Davidson, 2002; Sarkamo et al., 2008) and adherence to other treatments in the rehabilitation program (Nayak, Wheeler, Shiflett, & Agostinelli, 2000; Wheeler, Shiflett, & Nayak, 2003). However, applying music in the context of stroke patients cardiovascular exercise has not attracted much attention. Nonetheless, music has been shown to invigorate healthy subjects and athletes in aerobic exercise activities, thereby enhancing mood, increasing motivation and performance, as well as attenuating bodily discomfort, and attaining efficient movements, ultimately leading to improved performance in various activities and tasks (Karageorghis, 2016; Karageorghis & Priest, 2012b, 2012a). The power of music is wide-ranging and is beyond the scope of the present master’s thesis. Furthermore, theories on music and exercise psychology are not specific to stroke survivors, even though specific concepts are more important than others for this patient group (e.g. neural plasticity and neuromuscular connectivity). Hence, the following sections will be less specific on pathological concerns, even though I only target relevant theoretical constructs and mechanisms.

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2.3.1 Integrative Literature Review A comprehensive systematic review would be the gold standard prior to conducting any empirical research. However, this is not within the scope and resources of this master’s thesis. In my limited search, I prioritized databases and only described findings in relation to my own primary inspiration moving to the empirical study. The included literature is multidisciplinary, spanning from medicine and epidemiology to sports- and exercise psychology, cognitive neuroscience, and music therapy theory. As both a theoretically and empirically based review was of interest, I chose to conduct a small and flexible integrative review. An integrative review is “a specific review method that summarizes past empirical or theoretical literature to provide a more comprehensive understanding of a particular phenomenon or healthcare problem” (Whittemore & Knafl, 2005, p. 546, with reference to Broome, 1993). The methodology is flexible compared to a classic systematic review (Whittemore & Knafl, 2005). The information relevant to the area of interest is organized in categories (Abbott, 2016).

Databases and Search Words Databases EbscoHost (incl. all sub-databases, e.g., Academic Search Premier and CINAHL), Medline, PubMed and Scopus were chosen, based on a recommended list for music therapy databases provided by Aalborg University Library. These where chosen based on the scope of music medicine and include a vast number of sub-databases. Guided by a thesaurus, three main keywords were developed with different synonyms, which were implemented in all search engines. This combination of search words was used in all databases:

(Music* OR Music Therapy OR Music intervention OR music medicine) AND (Clinical* OR inpatient) AND (aerobic exercise OR aerobic training OR physical activity OR exercise OR physical exercise)

Inclusion- and Exclusion Criteria Inclusion:

• Literature (theoretical and empirical) examining music interventions in clinical populations.

• Music-supported exercise as the primary intervention compared to another active exercise intervention (with or without music).

• Literature explicitly investigating aerobic exercise or physical activity with the aim to improve training intensity or psychological factors, as well as adherence in aerobic exercise as primary outcomes.

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• Participants must be a clinical population, with a diagnosed disease or illness.

• Literature in English language. Exclusion:

• Literature that is not eligible to the inclusion criteria above. • Literature primarily investigating functional outcomes of lower- and

upper extremities or the respiratory system. • Literature on healthy, obese, community dwelling, or elderly

populations. • Papers that compare music in exercise to non-exercise intervention,

e.g., waitlist, conversation or other. • Protocols that do not include a clinical trial.

Thus, studies on rhythmic auditory stimulation (RAS), music-supported movement therapy and similar studies that solely assess limb functioning and gait qualities are not included. This delimitation was made partly due to the overwhelming number of studies that have accumulated over the last 30 years with RAS, and partly because specific limb functioning is not a proxy of cardiovascular intensity and thus not deemed relevant for the present study. Figure 7 – Literature Search Outcomes

Through the integrative literature review, I was able to find a total of 13 articles relevant to the present topic of music in clinical exercise. The studies concerned diabetes (Hutchinson, Karageorghis, & Black, 2017), fibromyalgia (Espí-López, Inglés, Ruescas-Nicolau, & Moreno-Segura, 2016), chronic obstructive pulmonary disease (COPD) (Lee, Dolmage, Rhim, Goldstein, & Brooks, 2018; Reychler et al., 2015), autism spectrum disorder (Woodman et

1.• Medline,PubMed,Scopus,andEbscoHost• 833eligibledocuments

2.• 332wasduplets• 501abstractswasscreened

3.• 477wasdropedafterscreeningabstracts• 24full-textwasassessed

4.• 11wasdropedbasedonfull-text• 13remainedandwasincluded

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al., 2018), cardiac patients (Alter et al., 2015; Clark, Baker, Peiris, Shoebridge, & Taylor, 2017; Miller & Terbizan, 2017), and dementia (Johnson, Deatrick, & Oriel, 2012). In addition, one paper was found studying older adults’ music preferences in cardiac rehabilitation (Clark, Baker, & Taylor, 2016a), and two literature reviews were found, one only assessing studies in CPOD patients (Lee, Desveaux, Goldstein, & Brooks, 2015) and one in multiple clinical populations (Ziv & Lidor, 2011). Finally, one meta-theoretical paper was found (Clark et al., 2016b) describing the modulating effects of music. In addition, another theoretical paper was included through prior knowledge (Karageorghis, 2016). Based on these findings I chose to make three delimitations moving on to critically appraising the literature and including it in the thesis. First, the literature will not be reviewed in detail, but mainly be presented as a corpus of literature relevant for comparison to this study, presented in a brief narrative synthesis Second, on grounds of parsimony I will not describe studies already included in the two reviews, as these studies have already been presented with an overview of the literature (leaving 9 empirical studies, ). Third, I was able to find two theoretical models, one being primarily developed for athletes and healthy people (Karageorghis, 2016), and one primarily designed for clinical populations (Clark et al., 2016b). Both models have strengths and limitations in explaining the antecedents, modulating and consequential effects of applying music in exercise, but for the present master’s thesis, the clinical model seemed more appropriate as a meta-framework. Hence, only this framework is presented.

2.3.2 A Meta-theoretical Framework The present study utilizes a meta-theoretical framework published by Clark, Baker & Taylor (2016b), seeking to explain the modulating effects of music in health-related exercise and physical activity in adults. The meta-theory is based on a systematic review including 23 theoretical texts. The model integrates multifaceted theories from different disciplinary contexts describing similar interrelated mechanisms. Using this corpus of literature, the theorists interpreted themes, concepts, and hypotheses to support a meta-theory. Through a thematic analysis, the theorists identified three different contexts: therapeutic effects (mainly music therapy literature), sports and exercise performance (from sports psychology), and auditory-motor processing (emanating from neuroimaging studies and cognitive neuroscience) (Ibid.). The model consists of a feedforward and feedback between auditory-motor processing communicating with the body of the exercisers (Figure 8). This loop interacts with sub-cortical and cortical structures, creating neural stimulation

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and response. In this loop, the model includes three main areas of interest: physiological arousal, subjective experience, and behavioral response.

Figure 8 – Meta-theory on the modulating effects of music listening on exercise and physical activity from Clark, Baker & Taylor (2016). In the following part of this section, I will give a condensed presentation of the key theories that together form the meta-theory. As the sub-theories and empirical papers are not targeting neurorehabilitation, I have integrated additional theoretical papers, and conversely left some out, for focusing on relevance for neurorehabilitation. The three main areas and the sub-categories are consistent with the original paper by Clark and colleagues. Physiological Arousal Physiological arousal includes two sub-categories concerning rhythmic entrainment and the neurophysiological response to music. Entrainment, “the synchronization of internal rhythm processes (such as neuronal oscillations) or behavior (such as tapping or dancing) to external, periodic events (e.g., the beats in a rhythm)” (Cameron & Grahn, 2016, p. 363). The tendency to move synchronously to the rhythm of music is seen in every culture and arises without explicit training (Ibid., p. 356), and perception of beat has been proposed as an innate ability (Winkler, Haden, Ladinig, Sziller, & Honing, 2009). Rhythm reliably elicits auditory and motor activity even without overt behavioral responses (Chen, Penhune, & Zatorre, 2008; Grahn & Brett, 2007). It is assumed that an auditory-motor circuit in the brain is particularly sensitive to rhythms and thereby stimulates and facilitates physical movement based

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on auditive stimuli and cues (M. H. Thaut, 2013; M. H. Thaut & Hoemberg, 2014; M. H. Thaut, McIntosh, & Hoemberg, 2014). It is further assumed that cueing of the central pattern generators may be partially responsible, which activate the muscles from rhythmic cues (Ibid.). All three disciplines (i.e., music therapy, sports psychology, and neuroscience) have examined the physiological effects elicited by music interventions, resulting in different theoretical frameworks for explaining the phenomenon. In sports and exercise psychology, researchers have focused on the synchronous and asynchronous use of music. These concepts relate to the exercisers’ movements either mirroring the musical beat or not (Karageorghis & Priest, 2012a, 2012b). Exercisers and athletes have been found to appropriate music as an external resource to synchronize movement patterns (DeNora, 2000). Sports psychologists generally attribute increased ergogenic effects to the synchronization approach. Theorists in neuroscience have similarly acknowledged entrainment, specifying effects as: reducing variability in muscle recruitment, resulting in increased symmetry/coordination, postural control, and energy efficiency (Cameron & Grahn, 2016; Crasta, Thaut, Anderson, Davies, & Gavin, 2018; Nombela, Hughes, Owen, & Grahn, 2013b; Rodriguez-Fornells et al., 2012). The theoretical framework of the majority of the techniques in Neurologic Music Therapy is driven by the neurobiological foundations of rhythmic entrainment and the motor system for creating a temporal frame or a pattern sensory enhancement (M. H. Thaut, 2013; M. H. Thaut & Hoemberg, 2014; M. H. Thaut et al., 2014). As briefly mentioned in section 2.3, music has the ability to excite or inhibit multiple neural processes, which can inflict neurophysiological responses. These responses manifest themselves in the mesolimbic dopaminergic reward pathway (leading to the feeling of reward) (Koelsch, 2014), as well as in the autonomic nervous system and the neuroendocrine system (Ibid.). On a basic level, music is auditory stimuli that can affect our brain. Theorists have categorized four fundamental levels of influence: the physiological, the semantic, the semiotic, and the transcendental level (Bonde, 2011, p. 30). Correspondingly, music is recognized being able to induce almost global neural activation, making music a potent neural stimulation and recognized as having great indications for stroke rehabilitation (Johansson, 2011, 2012; Särkämö et al., 2013), as activation is causing connectivity (Hebb’s theory). Neural transmitters (dopamine, oxytocin, and serotonin) are affecting various part of our brain, including movement, reward, and emotions (Chanda & Levitin, 2013). Through these neurochemical processes, the neural connectivity of our brain is affected and eventually reshaped, which is why we are able to experience physiological and psychological effects, such as music-

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induced analgesia or a sense of belonging and connecting with others in a group (Vuust & Gebauer, 2014). The theory of regulating the autonomic nervous system was early adapted to music therapy practice for regulating patients in both affective states (often referencing affective attunement, or in this case affective retuning, see e.g. Holck (2014, p. 136) or in arousal states (often using Yerkes-Dodson’s law or Stern again, see Holck (2014, p. 133), often used as a (seductive) transition where the client is guided by the therapist. Subjective Experience The subjective experience comprises of three sub-categories involving personal impact, psychological response, and diversion. There are several theories to how and why we develop and change musical preferences (for more, see; Bonde (2011, chapter 12)). Where musicologist traditionally studied the music (music as an autonomous art form or artifact), music therapists, sports psychologists and neuroscientists all study music in a more constructivist approach. The personal impact of music preference is affected by extrinsic and intrinsic factors, as well as cultural and extra-musical factors. In sports psychology, Costas Karageorghis and colleagues have developed theories which integrate musical factors (antecedents) as well as personal and situational factors (modelling factors). The situational factors comprise of the environment and task-related factors, including: personality, age, gender, musical upbringing, peer group influence, musical preferences, familiarity, rhythmic ability, personal associations, attention style, training status, hearing acuity, and beat deafness (Karageorghis, 2016). Music therapy theorists ascribe to this, but in addition to these factors, they build a bridge between the musical preferences and the construction of self and one’s narrative (DeNora, 2000; Ruud, 1997). Thus, music is not merely personal, but also a part of one’s personality. We know from the theory of predictive coding (Koelsch, Vuust, & Friston, 2019), that neural models for compassing and predicting music is inherently constructing our musical experience. Theorists have proposed that musical inherent factors as well as cultural and extra-musical factors are important in creating the computational neural model for appraising and predicting music as an aesthetic medium (Ibid.). Congruently, familiarity with the music presented has been shown to reduce cognitive demand and is correlated to improved tempo matching and velocity in gait of healthy subjects (Leow, Rinchon, & Grahn, 2015).

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Regulation of mood, affect, and emotion are all psychological responses one may experience during exercise. Music has known properties, not only affecting motion, but emotions. People appropriate music by its affording qualities to regulate their emotions, mood, and affects in their everyday lives, leading theorists to describe music as a ‘technology of the self’ (DeNora, 2000). Based on consistent findings by theorists in the cognitive neuroscience of music-evoked emotions, theorists propose that music can evoke real emotions, and not merely subjective feelings (Koelsch, 2014). As previously mentioned, music therapists have developed a practice of manipulating specific musical parameters to evoke music-induced feelings (affective attunement or retuning, see Holck (2014, p. 136). This is possible because we attribute certain musical parameters with affective values. Here Zatorre and colleagues illustrate that:

”…the acoustical features of typically sad or subdued music (containing slow tempo, lower pitched sounds and smooth transitions between sounds) are compatible with the physical expression of sadness, which involves slow, low-intensity movements. The reverse applies to music typically associated with happiness or excitement, which tends to be loud, fast and high-pitched, and is hence associated with rapid, high-energy movements, such as can be observed in spontaneous dancing to music” (Zatorre, Chen, & Penhune, 2007, p. 555).

Zatorre and colleagues are closing the gap between motion and emotion. By using expressions of movement and intensity, music is able to induce very specific emotions to the listener. A similar approach is used by psychologist Daniel Stern in his forms of vitality, describing motions and emotions as dynamic forces (also applicable to music notation), which is by theorists considered the foundation of improvisational music therapy (Hannibal, 2014, p. 128). Based on empirical data, theorists propose a reversed u-shaped model for choosing the optimal harmonic and rhythmic complexity for increasing pleasure and desire to move (Matthews, Witek, Heggli, Penhune, & Vuust, 2019; Witek, Clarke, Wallentin, Kringelbach, & Vuust, 2014). It is hypothesized that stroke patients having lesions in the basal ganglia may have difficulties with cognitive processes in the beat and rhythm perception (Nombela, Hughes, Owen, & Grahn, 2013a), leading to a skewness in their reversed u-shaped model towards the left, resulting in peak pleasure and desire to move in lower complexities.

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Figure 9 – The Relationship Between Complexity and Pleasure and Groove for a Healthy and Stroke Survivor Populations In relation to the harmonic and rhythmic complexity, studies by sports psychologists have demonstrated a relationship between heart rate (as a proxy of intensity) and preference for musical tempo, which is characterized by a series of inflection points (Karageorghis, Jones, & Low, 2006). The preferred musical tempo fell within a narrow range of 125–140 beats per minute (BPM). Given that maximum heart rate reduces considerably with age, this relationship may possibly be different for the elderly population and has yet to be examined among older exercisers. Diversion is a key mechanism for inducing the perceptual regulation. Effects have been demonstrated on ratings of perceived exertion (RPE), pain and other bodily discomfort and motivation, as well as boredom during repetitive tasks (Karageorghis & Priest, 2012a, 2012b; Lim, Miller, & Fabian, 2011). Thus, music listening may be described as a dissociative cognitive strategy that diverts attention away from internal experiences of pain and discomfort. Behavioral Response Finally, the behavioral response has no sub-categories and revolves around the fact that physiological arousal and subjective experience (as described above) can lead to behavioral changes. In everyday exercising, music is used to afford incentives of invigorating, motivating and engaging people in their exercise, thereby affecting adherence (DeNora, 2000; Karageorghis & Priest, 2012a, 2012b). Through the neurophysiological processes and through the pattern sensory enhancement presented in the entrainment, stroke patients may have increased neural plasticity, resulting in enhanced motor learning (Altenmüller & Schlaug, 2013; Grau-Sánchez et al., 2013; Rodriguez-Fornells et al., 2012). This may lead to the experience of mastery and increased confidence (Clark

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et al., 2016b), ultimately increasing adherence and performance (Karageorghis & Priest, 2012a, 2012b). Applying an asynchronous approach (i.e. background music) elicits psychological (enhanced affect and distraction), ergogenic (improved muscle activation) and psychophysiological (RPE) effects as mentioned above (Karageorghis & Priest, 2012a, 2012b; Lim et al., 2011). However, exercising using a synchronous approach may benefit the exerciser by increasing neuromuscular efficiency, prolonging endurance, and reducing oxygen consumption compared to the asynchronous approach (Karageorghis, 2016; Karageorghis & Priest, 2012a, 2012b). Theorists argue that when coupled with individual needs and preferences, well-selected and task-specific music may further enhance affective states, reduce variability in muscle recruitment, synchronize movements (entrainment), induce distraction and attenuate patients’ perceptions of effort during exercise (Clark et al., 2016b; Karageorghis, 2016). This concludes the theoretical framework. The following section will continue the integrative literature review by targeting the empirical studies.

2.3.3 Empirical Studies On the ground of parsimony, the following literature will not be reviewed in detail but mainly be presented as a corpus of literature relevant for comparison to the present study, and the prior empirical study designs and findings will be presented in a brief categorical synthesis. For a systematic approach, I will be using the TIDieR checklist (Hoffmann et al., 2014) (without item 10 and 12, modifications and the planned part of How Well) combined with Robb, Burns and Carpenter’s guidelines for reporting music-based interventions (Robb, Burns, & Carpenter, 2011). Additionally, I will add outcomes, as these may also be important for comparison. Why - Intervention Theory Only two studies (Clark et al., 2017; Hutchinson et al., 2017) did explicitly have a theoretical foundation for their empirical study. Some studies (n = 3) provided a neurobiological foundation, with empirical neuroimaging studies or music psychology studies (Alter et al., 2015; Johnson et al., 2012; Miller & Terbizan, 2017), while others only provided empirical studies (n = 4) as their rationale (Espí-López et al., 2016; Lee et al., 2018; Reychler et al., 2015; Woodman et al., 2018). What (materials and procedure) and Tailoring - Intervention Content Person Selecting the Music:

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In two studies, music was researcher-selected based on knowledge of the sample demographic (Johnson et al., 2012; Woodman et al., 2018), while two were guided participant-selected (Alter et al., 2015; Clark et al., 2017). Some studies (n = 3) had a restricted patient-selected approach (Espí-López et al., 2016; Lee et al., 2018; Miller & Terbizan, 2017). One study used a focus group interview (Hutchinson et al., 2017), and one study did not report the selection process (Reychler et al., 2015). What Music: A minority of studies provided sufficient information on what music was chosen. Among the explicit criteria, the included studies used rhythmical music (n = 1) (Johnson et al., 2012), and music based on restricted BPM, (n = 2) (Reychler et al., 2015; Woodman et al., 2018) with no other criteria described. One study used preference music (Clark et al., 2017), one study manipulated preference music to specific tempo and embedded with RAS (Alter et al., 2015), and three studies had a list of 50-100 songs that participants could choose from (Espí-López et al., 2016; Lee et al., 2018; Miller & Terbizan, 2017) without detailing what it consisted of. Finally, one study referenced the BASES expert statement for choosing appropriate music (Hutchinson et al., 2017). Music Delivery Method (live or recorded): In all studies, recorded music was used. Intervention Strategies: Two studies explicitly used a synchronous approach (Johnson et al., 2012; Miller & Terbizan, 2017), while five studies use an asynchronous approach or background non-explicit approach (Clark et al., 2017; Hutchinson et al., 2017; Lee et al., 2018; Reychler et al., 2015; Woodman et al., 2018). Two studies tested both approaches (Alter et al., 2015; Espí-López et al., 2016). Who Provided - Interventionist Interventionists for designing the music intervention were (sports) psychologists (n = 3) (Hutchinson et al., 2017; Miller & Terbizan, 2017; Woodman et al., 2018), doctors (n = 1) (Alter et al., 2015), physiotherapists (n = 4) (Espí-López et al., 2016; Johnson et al., 2012; Lee et al., 2018; Reychler et al., 2015), and music therapists (n = 1) (Clark et al., 2017). The number of music interventionists were not reported in any of the studies. How - Unit of Delivery Groups or Individuals:

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In four studies, the intervention was delivered to individuals (Alter et al., 2015; Clark et al., 2017; Lee et al., 2018; Miller & Terbizan, 2017), while five studies used groups-based settings (Espí-López et al., 2016; Hutchinson et al., 2017; Johnson et al., 2012; Reychler et al., 2015; Woodman et al., 2018). Where - Setting Location: The majority of studies (n = 7) were in settings with a professional staff at a location relative to the treatment of the clinical population (Espí-López et al., 2016; Hutchinson et al., 2017; Johnson et al., 2012; Lee et al., 2018; Miller & Terbizan, 2017; Reychler et al., 2015; Woodman et al., 2018). Two studies used exercise ambulant or in prescribed exercise monitored occasionally by a professional (Alter et al., 2015; Clark et al., 2017). Ambient Sound: Only two studies reported the sound level. Here, the music volume was at 70dB(A) (Reychler et al., 2015) and 75dB(A) (Hutchinson et al., 2017). When music was delivered individually sound level was adjusted individually (n = 3) (Alter et al., 2015; Clark et al., 2017; Lee et al., 2018). The rest (n = 4) did not include information on this matter (Espí-López et al., 2016; Johnson et al., 2012; Miller & Terbizan, 2017; Woodman et al., 2018). When and How Much - Intervention Delivery Schedule Training Type: Running, walking or treadmill were the dominating aerobic modalities (n = 5) (Alter et al., 2015; Clark et al., 2017; Lee et al., 2018; Miller & Terbizan, 2017; Woodman et al., 2018). Others included body movements (n = 1) (Johnson et al., 2012) and multiple aerobic- and resistance training modalities (n = 3) (Espí-López et al., 2016; Hutchinson et al., 2017; Reychler et al., 2015) Frequency, Intensity and Interval: The intervention schedules ranged from once pr. week for 30 minutes (Johnson et al., 2012), to twice per week for 55-60 minutes (Espí-López et al., 2016; Hutchinson et al., 2017). Four studies only investigated the immediate effect (within 1-10 trials and 10-30 minutes) (Lee et al., 2018; Miller & Terbizan, 2017; Reychler et al., 2015; Woodman et al., 2018). Two studies assessed adherence and activity in individual training (Alter et al., 2015; Clark et al., 2017). How Well - Treatment Fidelity (controls) Control and Multiple Music Interventions:

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Non-music control was the dominant study design (n =4) (Clark et al., 2017; Johnson et al., 2012; Lee et al., 2018; Reychler et al., 2015). Some studies had non-music in addition to fast vs. slow music (Woodman et al., 2018), sedentary vs. simulative (Miller & Terbizan, 2017), music-only vs. music + TV (Hutchinson et al., 2017), synchronous vs. asynchronous (Espí-López et al., 2016), and embedded RAS (Alter et al., 2015). Outcomes Psychological: Outcomes include: affect valence pre, in, and post session (Hutchinson et al., 2017; Miller & Terbizan, 2017), anxiety (Reychler et al., 2015), QoL (Espí-López et al., 2016), depression (Espí-López et al., 2016), state attentional focus (Hutchinson et al., 2017) and exercise self-efficacy (Clark et al., 2017) Perceptual/Psycho-physiological: Outcomes include: perceived exertion (Hutchinson et al., 2017; Lee et al., 2018; Miller & Terbizan, 2017; Reychler et al., 2015) and pain/discomfort (Espí-López et al., 2016). Physiological: Outcomes include: aerobic intensity by heart rate (Alter et al., 2015; Hutchinson et al., 2017; Miller & Terbizan, 2017; Reychler et al., 2015), blood pressure (Miller & Terbizan, 2017), blood glucose (Hutchinson et al., 2017), and cardiovascular risk factors (blood sample) (Clark et al., 2017). Behavioral: Outcomes include: training duration or endurance (Johnson et al., 2012; Lee et al., 2018; Woodman et al., 2018), adherence and general physical activity (Alter et al., 2015; Clark et al., 2017), dyspnea (Lee et al., 2018; Reychler et al., 2015), balance (Espí-López et al., 2016), and Six Minute Walk test (Clark et al., 2017).

2.3.4 Key Findings - Methodological and Practical Implications/Recommendations The following section will briefly sum up the chapter and, based on the literature, make suggestions for methodology. Stroke survivors have diverse global ranging functioning, consequently, individually tailored goals in each person’s neurorehabilitation are paramount (Billinger et al., 2014, 2015). Aerobic exercise on cycle ergometers is highly recommended based on evidence-based clinical guidelines (Billinger et al., 2014; Sundhedsstyrelsen, 2011b, 2014a, 2018), where special emphasis is on adherence and duration of recommended training intensity (Ibid.). Supervision of exercise and group-

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based sessions, including peers, is considered motivating and reflects best practice (Billinger et al., 2014). Applying music in physical activity and exercise is a relatively well-described phenomenon theoretically, with multidisciplinary theoretical models (Clark et al., 2016b). Yet, empirical studies have not reported to use theoretical frameworks, and as a result studies have applied numerous methodological approaches in regard to music, dose, and measurements, limiting comparison of findings. Finally, studies have been applied to a number of diverse populations but with a limited number of replications. Based on the literature, the clinical field of music in exercise is still in its infancy. Through the integrative literature review, I was not able to find prior empirical studies on stroke survivors. Furthermore, research applying multiple music interventions is similarly scarce. Studies have reported conflicting findings on the effect on heart rate, which may be caused by the diverse music interventions in the literature. Finally, the majority of studies lack a rigid design, and did not control whether patients actually preferred the music interventions. Therefore, this study will investigate a common and easily applicable source of music: the local radio channel. This will be compared to a music intervention following a rigid protocol for selecting and delivering music, targeted towards the receivers, but guided by research recommendations (Karageorghis et al., 2012) and theory (Clark et al., 2016b). Outcomes will be based on clinical recommendations (Billinger et al., 2014; Sundhedsstyrelsen, 2018). By referencing back to the research question, the objectives for the thesis are as follows: Research Objective Using PICO: Building on this chapter, the following section will elaborate on the study design – the S in PICOS – including the methodology and protocol.

Based on theory of music therapy, sports psychology, and neuropsychological and -biological musicology, and by assessing heart rate intensity and training duration as well as using the Feeling Scale (Hardy & Rejeski, 1989) and the Overall Experience Scale, and the Borg Rating of Perceived Exertion Scale this study will investigate the effects of music listening, comparing a tailored playlist to radio and no music, in group-based cardiorespiratory exercise on cycle ergometers for medium to severely injured stroke survivors.

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Chapter 3 – Methodology and Protocol

Chapter three includes the methodology that was less addressed in the thesis article. This includes a brief description of the author’s choice of theory of science (3.1), more detail on the design of the clinical trial (3.2), and ethical considerations (3.3). Section 3.4 will give a more comprehensive look into the intervention protocol, followed by an introduction to the materials and outcome measurements (3.5). Finally, randomization, blinding, sample size, and allocation will briefly be reviewed (3.6), and the chapter is ended with a look at statistical procedures and approaches (3.7).

3.1 Theory of Science The theory of science, on which the present thesis is based, is post-positivistic using a hypothetico-deductive model. Classic positivism has a clear ontological stance of an 'absolute truth' in reality, also called naïve realism, which can be found through empiricism and control of variables (Hiller, 2016). Here, post-positivism differs from classic positivism by having a critical realism (Mertens, 2005, p. 10). This has a direct impact on the epistemological view, as post-positivism thus recognizes the researcher as a subject. One of the results of critical realisms is, that the researcher can only reach a part of the "truth", and that research therefore requires replications and repetitive trials in order to review the evidence. For this exact reason, it is essential in post-positivism to standardize the intervention procedure in order to achieve a less biased and more generalizable result. It is absolutely fundamental for the method that the researcher actively eliminates bias, evaluates the reliability and validity of his/her study, and tries to falsify the hypothesis (Ridder & Bonde, 2014). The falsification procedure can be achieved by either replication of the entire study, namely, to reexamine the hypothesis and by evaluating the findings to those of previous studies, or by statistical analysis of the null hypothesis to obtain statistical significance (Coolican, 2014, pp. 407–437; Ridder & Bonde, 2014). Despite a more liberal ontological view, acknowledging the subjectivity of the researcher's part in the scientific process, it is nevertheless the aim of the research to be able to generalize the results on an entire (specified) population. Still, in practice the epistemology and aim of explaining cause-effect are identical to classic positivism. The basic epistemological thesis of positivism is, that all knowledge originates from perceptual experiences, and thus is exclusively empirically founded (Thisted, 2010). In line with the standpoints of post-positivism, this project does not want to generate data from a laboratory and then generalize to a naturalistic setting, but to commence from an ecological field study. Thus, by opting for an ecological setting, several confounding variables will inarguably be uncontrolled, and the methodological

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quality of the study will be prioritized differently than in the classic positivist approach.

3.2 Design The design is classified as a randomized controlled trial (RCT) constructed as a three-armed crossover design (categorized under longitudinal designs). A crossover design can be a RCT when randomizing the treatment conditions (DeLoach, Wheeler, & Murphy, 2016). In the present study, block randomization with a fixed number of participants was used to determine the intervention sequence, with pragmatic allocation/enrollment in the block groups. Generally, the design is an experimental design that maintains three conditions consisting of a control condition and two intervention conditions: local radio channel and group-tailored playlist (collectively referred to as treatments). It exposes the same subjects to different conditions manipulating the independent variable (intervention on sound milieu), hence, categorized as a repeated measures design (RM). It differs from the other RCTs and experimental designs by not adhering to randomization in the allocation (Ibid.). As accumulated effect can affect the results, crossover designs often include a washout period, though, it was intentionally avoided in this study. The design targets within-subject factors. Here, the difference of interest is the difference between multiple test scores within each participant’s repeated measurements, and differences between participants are no of interest (Coolican, 2014, p. 85). All subjects were allocated to all three conditions with the same number of treatments.

3.3 Subjects

As the topic of describing the subjects, inclusion criteria and procedure of eligibility are sufficiently provided in the thesis article, this section will only concern the ethics of the clinical trial.

3.3.1 Ethical Considerations The welfare and safety of all participants in the study were of utmost importance, and the interdisciplinary team monitored vital numbers and physical stress continually throughout the study. As all eligible participants were hospitalized, they were in a similar environment with similar opportunities for consulting a healthcare professional 24/7. None of the participants, nor anyone else, were deliberately exposed to any harm or danger, and if any unintended events occurred, they were addressed and documented in the patients’ records. During the course of the study, the multidisciplinary team was attentive to the health and well-being of the participants, and the trial-supervising physiotherapists provided basic needs.

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All participants were given the ability to decline participation before, during and after the project, but after write-up of the thesis (February 15, 2019) it was no longer possible to have their data deleted in the published project. Data security followed laws and regulations provided by The Danish Data Protection Agency. For meeting these requirements, we insured that:

• Informed consent was required for all participants. • All confidential content is anonymized. • All data is treated confidentially and was locked up during writeup. • Sensitive data was destroyed after writeup.

After recruitment, each individual was invited to a meeting with the music therapist. Here, information about the study and the content of the study was delivered to the participants. After finishing participation in the clinical trial, the music therapist facilitated a meeting with all group-members and discussed with the group how they had experienced the exercise sessions, and if they had any comments on their experience or questions for the study and publishing. Potential Risks of Participation During exercise, the blood flows away from the cochlear and down to the active muscles. This increases the risk of hearing loss and injury (Karageorghis, 2017, p. 215). The instructor organizations IDEA Health & Fitness Association recommends a maximum volume of 85 dB(A) (which is measured at the ear) and a voice volume of 95dB(A) (IDEA Health & Fitness Inc., 2002). This is in conformity to the recommendations by WHO (Krug et al., 2015). As music volume is aimed at approx. 65-75 dB(A) in the present study, this adheres to the normed values for safe volume.

3.4 Intervention Protocol

As the three conditions are described in detail in the thesis article, there is no need for further explanations. However, a clarification of the role of the therapists is warranted due to the clinical and practical implications of how and why certain interventionists may be more qualified for conducting such interventions. On grounds of parsimony, the comprehensive protocol for conducting the music interventions will not be elaborated in the present master’s thesis. Nonetheless, this is an essential part of the study. For a deeper understanding of the intervention and practical concerns, the reader is directed to the feasibility study by Mazhari-Jensen (2017), on which this study’s protocol is based. As the protocol was designed using the TIDieR checklist (Hoffmann et

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al., 2014), all 12 items are presented and discussed in the original feasibility study. The following section will focus on the specifications for this particular population and how the protocol was changed to suite the requirements of the present study.

3.4.1 Music-Supported Aerobic Exercise The tailored playlist intervention, referred to as music-supported aerobic exercise, was based on three sub-elements: 1) a musical interview, 2) designing the playlist, and 3) the clinical application (see Figure 10).

Figure 10 – Music-Supported Aerobic Exercise

In the following sections, the sub-elements will be elaborated as appropriate in relation to the thesis article.

3.4.2 The Music Interview

In order to guide the participants in choosing the optimal music, a semi-structured interview was designed. This semi-structured interview allowed for the music therapist to tune in on the individual participant’s preferences and life experiences with music. In the original study protocol and feasibility study, Mazhari-Jensen (2017) conducted the music interview in plenum with the group of exercisers. This was, however, not applicable in this study, as deficits after stroke (e.g. communicative and cognitive impairments) can make it challenging for patients to communicate their musical life stories. Thus, a planetary dialog was not applicable, forcing the music therapist to do the interviews individually.

Music-supportedAerobicExercise

MusicInterview

ClinicalApplication

DesignofPlaylist

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As the group is encouraged to inspire each other’s musical selection and to attune a homogeneous playlist acceptable by all individuals, this process was complicated. For achieving this step, the music therapist would revisit participants for updating them on the musical selection and to let them express their feelings about others’ selections. If contraindicative musical pieces were present (demotivating or associated with negative emotions to a group-member), the music therapist would mediate between the individuals. Apart from this, the aim of the interview and the role of the therapist were the same: being encouraging and aiding the client in selecting motivating and enjoyable music for cycling and doing aerobic exercise. The material recommended in the protocol was not applicable, as participants may be impaired by visual and cognitive deficits. Still, patients were required to choose at least three musical pieces that were considered appropriate based on theory (Clark et al., 2016b) and research (Karageorghis et al., 2012) (see chapter 2).

3.4.3 Choosing the Radio Instead of having all groups select a preferred radio channel as described in the original protocol, is was argued that selecting only one radio channel was the more systematic approach, and easier to compare different groups. Thus, a sample of representative subjects (patients at the hospital) was interviewed prior to the clinical trial. Based on numerous responses, the local radio channel, Radio Brønderslev, was selected, as is contains primarily music, covering both modern pop and Danish Top music to more rock and up-tempo repertoire, with a relatively low saturation of commercials.

3.4.4 Protocol for Designing the Playlist

The protocol for designing the playlist was identical to the feasibility study. To give the reader a brief recapitulation, the aim of the playlist was to produce an aesthetic and motivating sound milieu, in addition to regulate the exercisers’ arousal. The playlist designer followed a systematic approach of designing the playlist for fitting the exercise program and the intensities to instill the maximum effect. Thus, the playlist designer was in contact with the supervising physiotherapists to discuss intensity and progression. First, the procedure included two different listening techniques (open and analytical), cutting out sections that were not following theory or research recommendations (see chapter 2) (e.g. non-rhythmic sections), or cognitively demanding sections (e.g. varying meter). Second, at least one or two of each participant’s selected pieces (depending on the group size) were included.

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Third, playlists were edited and cut in Garage Band (v. 10.1.6 on a MacBook Pro, 2015), creating a segued playlist with transitions fitting in tempi and rotations per minute (RPM). Finally, the gestalt of the playlist was assessed by the designer, having both aesthetically pleasing results, as well as meeting all requirements in the procedure.

3.4.5 Protocol for Clinical Application

The music therapist supervised the sessions in the feasibility study. In the present study, the supervising therapists was physiotherapists with specialization in neurorehabilitation. This was essential, as working with stroke patients requires a specialized knowledge of the clinical characteristics. Furthermore, the protocol aspired for best practice, meeting the most common motivating factors: group-based exercise supervised by professionals (see section 2.3.5). The groups were closed to strive for identical environments for the group dynamics and constellation, and used the same equipment in each exercise session. A protocol with guidelines was provided for the therapists to inquire data (See appendix 3.2), as well as treatment guidelines for roles and standard phrases, instructions and a checklist for each session (see appendix 3.5). Essentially, supervising therapists were told to not give pep-talks or motivational speeches, but always help participants, both physically and verbally, in participating, e.g., regulate resistance on the cycle ergometer or communicate to individuals for informing the intensity level within the exercise program. Intensity levels were informed both verbally and visually, supported by a poster of Borg’s Rating of Perceived Exertion (See appendix 4.1).

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Setup of Music and Cycle Ergometers

Figure 11 – Setup of the exercise room

The aerobic exercise was performed on cycle ergometers (Monark Ergomedic 828E or Cardio Care 927E, to accommodate difficulties for mounting the cycle). The supervising physiotherapists helped set up the resistance, height and fixate the upper or lower extremities to the cycle ergometer to support individuals with hemiparesis. Cycle ergometers were spatially placed in a circle or oval, and with the music placed at a safe distance (without a cycle directly in from of it) as illustrated in Figure 11. An iPhone 5c linked to a Bose SL3 speaker was used to play either radio or a MP3-file with the playlist. Loudness was targeted at 65-75 dB(A), being at the level of the ear. Audio volume was monitored by the Sound Meter app and was on average 65dB(A) and peaked at 75dB(A), which is in conformity with the recommendations by Karageorghis (Karageorghis, 2017, p. 215).

3.5 Outcome Measures and Materials

This section will elaborate on the outcome measures and materials that were used to inquire the outcomes. However, since an extensive description was offered for demographics and clinical characteristics, FS, OE, TD and parts of the DuRI in the thesis article, this section will exclusively provide information for Borg’s RPE. Furthermore, as some of the technical parts were left out in the thesis article, I will provide a more detailed explanation of the methodology behind the heart rate measurements, the data transformation, and how heart rate data was obtained.

3.5.1 Borg’s Rating of Perceived Exertion Scale Borg’s Ratings of Perceived Exertion (RPS)-scale was designed to investigate the subjective feeling of exertion experienced in a specific physical activity. It has frequently been used in the literature (Terry, Curran, & Karageorghis, 2014).

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The test is considered having high validity and reliability and scores high on correlation with heart rate and blood pressure (Hassmén, Hassmén, & Plate, 2005, s. 200-207). It is commonly used in both public gyms, as well as by clinical professionals in hospital settings and in private physiotherapy practice. The version used was the original (Danish translation), ranging from 6-20.

3.5.2 Portable Heart Rate Monitor Using a medical electrocardiogram (ECG) is considered the gold standard of measuring heart rate, however, often this is not feasible when applied to several people who need monitoring at the same time. Therefore, consumer wearable heart rate monitors have been increasingly investigated for use in research and clinical settings (Ge et al., 2016; Gillinov et al., 2017). The technology used in wrist-worn hart rate monitors is based on photoplethysmography (PPG), however, it is possible to purchase a wireless chest strap, which uses ECG signal (for a detailed explanation of the technology, see Weiler, Villajuan, Edkins, Cleary, & Saleem, 2017). Overall, it appears that the chest strap utilizing ECG signal is more accurate (Ge et al., 2016; Gillinov et al., 2017), with lower variance of difference to medical ECG (Weiler et al., 2017), and with a much lower limit of agreement (Claes et al., 2017) than the optical devices utilizing PPG for monitoring heart rates while exercising, and thus electrode-containing chest monitors should be used when accurate HR measurement is imperative (Ge et al., 2016; Gillinov et al., 2017). Straps have been reported as feasible and comfortable to wear by elderly populations (Ehmen et al., 2012).

3.5.3 Heart Rate Reserve – The Intensity Marker HRR is a value primarily used to measure training intensity. It is calculated using the max HR and subtracting the resting heart rate (RHR).

!"" = max!" − "!"

Thus, the HRR is a value representing the interval between the lowest and highest heart frequencies. This is particularly a good value for measuring percentage of HR-intensities. Compared to only using the maxHR, which assumes a null-value of 0 BPM (which is not applicable in exercise settings), the HRR also integrates the resting heart rate (RHR), giving a more intuitive and precise understanding of the interval between the lowest and highest heart frequencies possible for that specific person. Furthermore, RHR will vary based on fitness (VO2Max in the blood-oxygen uptake), whereas max HR mostly depends on age and sex. As two individuals at age 45 may have same max HR, depending on their fitness level, one person’s RHR might be lower, and thus has a greater range of HR

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frequencies, leading to a higher HRR. In this understanding of heart frequency potential, the formula incorporates two personal values creating a more specific and tailored band of HR intensities. The HRR% and max HR% is applied to calculate heart rate zones (HRZ). HRZ approximates the optimal HR in a specific activity for a specific subject. Having a larger HRR will result in greater gaps between HRZ. Putting this in context, the Danish Health Authority (Sundhedsstyrelsen, 2016) defines moderate training intensity as:

40-59 % of HRR, or 64-76 % of max HR, or 12-13 on Borg’s RPE, or Physical activity, in which the exerciser is slightly out of breath, but can talk to others meanwhile.

High intensity is defined as:

60-84 % of HRR, or 77-93 % of Max HR, or 14-16 on Borg’s RPE, or Physical activity, in which the exerciser is out of breath, and has difficulties having a conversation meanwhile.

Based on the American Heart Association’s and the Danish Health Authority’s recommendations (Billinger et al., 2014; Sundhedsstyrelsen, 2011b, 2018), the target heart rate zone was defined as ≥40%, being the recommended therapeutic zone, where the duration of recommended intensity (DuRI) was measured. HRZ were defined using HRR and calculated to represent per cent of HRR: ()*+,-,-.012101

0113 ∙ 100 = 789:;:;<!""%

(Sundhedsstyrelsen, 2014, s. 50). Resting Heart Rate As RHR is sufficiently described in the thesis article, I will avoid repeating this procedure and the relevant measurements. Maximum Heart Rate MaxHR differs from peak HR in two important ways, the first being that maxHR is the maximum HR that the individual’s heart is capable of, and the second that it is not activity specific. Frequently, maxHR-tests are measuring peakHR in a specific exercise, often an cycle ergometer. However, the individual’s

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peak-HR in this activity may not be the same as the peak HR when, e.g., running or rowing. This is because different muscle groups consume different amounts of oxygen (Hassmén et al., 2005). The theoretical calculated maxHR is of the individual’s capacities and is not restricted by any confounding variables such as the psychological state of the day, pain/injuries, nutritional factors and so on. As an empirical test of maxHR is extremely exhaustive and tough on the body, often a theoretical estimation is calculated using a basic formula. Here, the Tanaka has by researchers been favoured when applied to an elderly population, as is have high validity and good correlation to the actual mx HR (Camarda et al., 2008). Therefore this formula was applied. The Tanaka formula is stated as follows:

>9?:@A@ℎC98D89DC = 208 − 0.7 ∙ 9<C (Tanaka, Monahan, & Seals, 2001). Based on a feasibility study (Mazhari-Jensen, 2017), the peak training heart rate would act as the max heart rate of the participants’ whose heart rate should exceed their Tanaka max HR value in the monitored sessions.

3.6 Randomization, Blinding, Sample Size and Allocation Sample size was determined by pragmatic reasons. There were no power calculations, as no prior studies have investigated the effects of music on heart rate intensity in stroke patients. Instead, the three months of interventions served as the point of ending the data enquiry. As described in the thesis article, randomization was applied to the condition order of the three sound milieus. The group allocations and choosing which participants to group were decided by pragmatic conditions. Participants were not blinded, as this is not possible in a crossover design when applying different conditions that are easily discriminated between by the subjects. Neither was rater-blinding applicable.

3.7 Statistics An intention-to-treat approach was followed in all analyses (McCoy, 2017). Thus, no outliers or dropouts was left out in any analysis. Borg’s Ratings of Perceived Exertion For Borg’s RPE, a Friedman’s test with post-hoc Wilcoxon’s was applied to all listwise and pairwise sessions, respectively. Similar to FS and OE in the thesis article, I included all sessions instead of calculating medians for the purpose of minimizing deletion of data and keeping

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the variance of the original data. As Borg’s RPE is a measure of state perceived exertion, correlation was not assumed between different measurements. Thus, the analysis compared session number 1v1, 2v2 and 3v3, and treated each session number as independent within subjects (even though this is not exactly true). η2 (eta squared) was calculated as a measure of relative treatment effect. Linear Mixed Models As Linear Mixed Effect Models (LMEMs) was used to analyze TD and DuRI, these analyses are described in the thesis article. However, as this statistical approach is not common in music therapy literature, I will offer a brief introduction. The LMEMs constructed in the thesis article are both characterized as a repeated measures (RM) LMEM with random intercept for subjects. This basically means that the model controls for clustered data (multiple data from the same subject) in the repeated measures, as well as targeting only the within-subject effects. The flexibility in LMEMs makes this approach advantageous in the analysis of RM data compared to, e.g., RM-ANOVA, as they are capable of using all available data (no listwise or pairwise deletion), thus handling missing data more appropriately, as well as adequately account for correlations between RM on the same subject, and have greater flexibility to model time effects (Gueorguieva & Krystal, 2004). The only disadvantage is the ease of implementation and computational complexity, which is ranked as hard and high, respectively (Ibid.). Despite its superior flexibility making it a complex toolset, it is important to recognize that the use of LMEM is by no means restricted to complex grouping designs, and that it can also be used for studies with a single grouping factor of participant or subject (Magezi, 2015). LMEMs have in many cases been found superior to RM-ANOVA based on these flexible traits (Krueger & Tian, 2004), which is also true in the case of small sample data (Muth et al., 2016). As described in the thesis article, several predictors were trialed in the model and evaluated based on Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) (as appropriate, depending on the model being nested). To test the assumptions of homoscedasticity, plots of the predicted values against the residuals were made.

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Chapter 4 – Results

Chapter four will follow CONSORT standards presented in five sections. As main outcomes were presented in the thesis article, this chapter will include categorical results that were left out and give a brief recapitulation of the main findings. First, participant flow and recruitment are reintroduced (4.1), followed by baseline demographics and clinical characteristics (4.2). Continuing, the following section will present numbers analyzed (4.3) before introducing outcomes and estimation (4.4). The final sections will include ancillary analyses (4.5), and finally present harms (4.6).

4.1 Participant Flow and Recruitment Participant flow and recruitment is presented in the thesis article. For a brief recapitulation, the flow chart is reprinted:

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Figure 12 – Consort Flow Chart ; Abbreviations: NM = Non-music (control) condition; R = Radio condition; P = Playlist condition

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Of the 19 subjects originally recruited into the study, 17 completed all three weeks with at least one measurement of each condition (silent n = 19, radio n = 17 and playlist n = 19). Of the two dropouts, one was due to decannulation of tracheostomy (resulting in contraindication for aerobic exercise) and one due to early discharge from the hospital.

4.2 Preliminary Analysis – Initial Data Analysis The following initial data analysis (IDA) integrates more information than a typical IDA would. I have chosen to include analyses and results of the raw data, and checking the assumptions of the LMEM, as this is an important and often underrecognized part of validating quantitative studies.

4.2.1 Reevaluation of Max Heart Rate Reserve As expected from findings in the feasibility study (Mazhari-Jensen, 2017), some participants managed to exceed the theoretically estimated max HR. This error creates a distorted interpretation of the HRR percentage (>100%), which may course interpretation errors. Therefore, I chose to calculate a new max HRR based on the actual max HR for the monitored in-task sessions, making the peak HR = max HR (100%). This correction was made for six participants (31,58%) of the sample, two of which had their max HR calculated by the beta-blocker correction, leaving only one with this estimated formula.

4.2.2 Initial Data Analysis of Observations (raw data) As described in section 3.7, the model is evaluated based on the BIC or AIC. Based on these findings, I chose the model with the lowest information criteria, with a >2 points as negative results, 2-6 as a positive, 6-10 as strong and >10 as very strong (Kass & Raftery, 1995, p. 777). A table including the model components with its information criteria and number of parameters for evaluating models are presented in appendix (7). However, before relying solely on the information criteria in the model, the raw data was visually analyzed for its covariance structure of the repeated measures and possible (systematic) outliers. The full IDA is presented in appendix (8), but summarizing results, it was evident that there is a ceiling effect for training duration (TD), and it may not be safe to analyze the data, as outcomes can differ quite significantly from the true normal distribution. Conversely, as a standard exercise session is 30 minutes, this data may be closer to clinical reality and hence representing normal distribution within a clinical session of 30 minutes. Thus, I will continue with the analysis with this criticism and being mindful about this limitation. DuRI shows no clear sign of correlation, and outcomes seem to be randomly increasing and decreasing.

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Based on both a fit line of the raw data and the BIC and AIC of the LMEM with restricted maximum likelihood and full maximum likelihood (used as appropriate for evaluating fixed and random parameters), the data favors a scaled identity (non-correlated) covariance structure.

4.2.3 Checking Model Assumptions for the Linear Mixed Effect Model Training Duration First, as illustrated on the simple scatter plot of residuals by predicted values normality plot, the residuals are x-axis unbalanced, with multiple outliers scattered to the left side. This is caused by the ceiling effect, having the majority of observations on the maximum value. This is not a flaw in the model but rather a simple premise for the study design. However, on the y-axis, the results are relatively symmetrically distributed, but with a wide range of errors. Second, similar results are shown in the normality test with no skewness of data but with a very high kurtosis, resulting in significant p-values for the normality test. Finally, a scatterplot with fit line of observations by predicted values illustrates the coefficient of determination (R2 =0,677). This implies that 67.7% of the variability of the observed value has been accounted for, and the remaining 1/3 of the variability is still unaccounted for by the model predictions. Duration of Recommended Intensity First, as illustrated on the simple scatter plot of residuals by predicted values normality plot, the residuals are relatively symmetrical, with 3 outliers to the right, suggesting predictions are underestimating these observed score. Other than the symmetrical properties of both axes, the plot is characterized by a wide range of errors in the model. Second, similar results are shown in the normality test, with significant p-value for the Shapiro-Wilk, but a non-significant result for the Kolmogorov-Smirnov. The disagreement may be caused by the outliers. Finally, a scatterplot with fit line of observations by predicted values illustrates the coefficient of determination (R2 =0,785). Thus, 78,5% of the variation in the raw observations can be explained by the variability in the predictions by the LMEM. Summarized In conclusion, the IDA and preliminary test illustrates that the normality of the data is compromised by outliers and a wide range of disagreement from the predicted values to the observed values (residuals). Nonetheless, the coefficient of determination is relatively high, suggesting a positive correlation between observations and the model predictions. With these limitations in mind, the models will be used for further analyses. For a graphic representation, see appendix (9).

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4.2.4 Participant Demographics and Clinical Characteristics To compare the baseline demographic and patient characteristics of the three groups, I used one-way ANOVAs, Kruskal-Wallis’ and χ2-tests. The groups did not differ significantly on any parameter, except on the memory sub-score of the FIM (see appendix 10). Later analysis found no correlation for FIM score of memory with any of the depended variables. As this was sufficiently addressed in the thesis article, this topic will not be elaborated further in this paper.

4.2.5 Preference of Sound Milieu In the thesis article, we presented a statistical approach of observing the preference. Here, I provide a graphical view, which can serve as a reference for the individual responses. Figure 13 illustrates all participants (x-axis) and their ratings of the sound milieu (y-axis) for the different conditions (colors). The graph illustrates higher ratings of the playlist condition, although, three outliers (participant 5, 6 and 7) prefer the non-music condition.

Figure 13 – Sound Milieu Preference Scores. In 82% of the exercise sessions, radio scored higher preference than non-music, and in 58% of the sessions, playlist scored higher than radio (90% being higher or equal).

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4.3 Numbers Analyzed – Attrition As 19 subjects were included, a total of 171 sessions data were plausible. 32 exercise sessions of the 171 were missing data (patient was not able to participate in the session). 139 sessions are equal to 81,3% of the plausible sessions, making 18,7% missing data. Reasons for not attending sessions included surgery, meetings with other hospital units, appointments with the doctor and other pre-scheduled health-related appointments. For the heart rate data, two sessions, both in the playlist condition, were lost due to technical difficulties. In the LMEMs, all data were included for the analysis. However, for the Wilcoxon’s and Friedman’s tests, listwise and pairwise deletion restricts a similar approach. Tables are presented in appendix 11 showing number of sessions (n) for the 19 participants with (%) of the observations for the three conditions and three statistical procedures. Briefly summarized, Friedman’s test only analysed 73% of available data in all cases, while the Wilcoxon’s test ranged from only 77% to 100%.

4.4 Outcomes and Estimation

4.4.1 Borg’s Rating of Perceived Exertion There was no statistically significant difference between conditions in Borg’s RPE of the aerobic exercise session in relation to the sound milieu whilst cycling, χ 2(2) = .470, p = .791. Despite no need for post hoc analysis I want to provide an effect size estimate for the intervention. Median (IQR) score of the Borg’s RPE for the non-music, radio and playlist cycling trial were 15 (14 to 16), 14 (12 to 15) and 14 (12 to 15), respectively. The three tests revealed no significant difference in Borg’s RPE between the non-music and radio cycling trials (Z = -0.750., p = .453, η2 = 0.007), between the silent and playlist trial (Z = -0.534, p = .593, η2 = 0,003), or between the radio and playlist cycling trials (Z = -0.206, p = 0.837, η2 = 0,0006). Table 1 Borg’s Ratings of Perceived Exertion of the aerobic exercise sessions For 19 subjects, 34 exercise sessions Non-music Radio Playlist P-value Overall Experience (n = 34) 15 ± (14-16) 14 ± (12-15) 14 ± (12-15) 0,453 Effect size (η2) 0.007 0,003 0,0006

Means and ± (IQR). *: the variable is significantly different between groups. Data is based on 19 subjects but in tests, sessions was used.

4.4.2 Feeling Scale In the thesis article, we presented a statistical approach of observing the Feeling Scale (FS). Here, I provide a graphical view, which can serve as a reference for the individual responses. Figure 14 illustrates pre-post scores (x-axis) of FS ratings (y-axis) for the different conditions (colors). The graph illustrates that all conditions increases,

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but FS scores post session are higher for both music conditions, compared to non-music condition. In addition, a steeper slope for both music conditions are observed, compared to the non-music condition.

Figure 14 – Feeling Scale scores pre and post sessions.

4.4.3 Overall Experience A statistical approach of observing the Overall Experience ratings were presented in the thesis article. Here, I provide a graphical view, which can serve as a reference for the individual responses. Figure 15 illustrates all participants (x-axis) and their ratings of the OE (y-axis) for the different conditions (colors). The graph illustrates general higher ratings in the music conditions, though, there are two outliers (participant 15 and 17) who all preferred the non-music condition.

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Figure 15 – Overall Experience scores.

4.4.4 Training Duration Here, I provide a graphical view, which can serve as a reference for the individual responses, in relation to the statistical analysis in the thesis article. Figure 16 illustrates all participants (x-axis) and their mean TD (y-axis) for the different conditions (colors). The graph illustrates a higher TD of the playlist conditions, though, there is one outlier (participant 17) who performed the best in the radio condition.

Figure 16 – Training duration

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Early withdraws from sessions were due to fatigue (n = 22) or the patient not being interested in participating for a longer amount of time the given day (n = 1). As the training duration (TD) was affected by a ceiling effect, the data are not optimal for statistical analysis. Looking at the results from a categorical perspective, four out of 19 participants endured longer TD in music condition, compared to their non-music control outcome, ranging from 2,5 to 14 minutes (on avg. above 6.5 minutes). All four participants, irrespective of the condition order (all three were included), scored higher in the music conditions relative to the non-music condition. Table 2 – Training Duration For the Four Participants Not Hitting the Ceiling Effect Participant Non-music Radio Playlist 8 1120 1630 1800 14 1228 1473 1744 15 1327,67 - 1474 17 757 1607,5 956,5 Average 1108,17 1570,17 1493,63

When exploring the characteristics of the four participants, all four had FIM-gait scores below 6 (1, 3, 5 and 5, resulting in non-independent gait).

4.4.5 Duration of Recommended Intensity In the thesis article, we presented a statistical approach of assessing the difference in DuRI. Here, I provide a graphical view, which can serve as a reference for the individual responses. Figure 17 illustrates all participants (x-axis) and their DuRI (y-axis) for the different conditions (colors). The graph illustrates no clear difference of conditions, and the general effects seems to be varying in different patients.

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Figure 17 – Duration of Recommended Intensity. Per Cent of Time in Recommended Heart Rate Zones Figure 18 shows per cent of time in the recommended heart rate zones. Overall, looking at every participant the participants spent only 52,38%, 54,01%, and 55,10% in the recommended intensity for non-music, radio and playlist, respectively. Looking at each FIM-gait category, participants with a FIM-gait score of 7 (n = 5) spent 73.16%, 70.31% and 65.69% in the non-music, radio and playlist condition, respectively. Participants with a FIM-gait score of 6 (n = 6) spent 48.57%, 53.22% and 49.99% in the non-music, radio and playlist condition, respectively. Participants with a FIM-gait score of 5 (n = 4) spent 26.83%, 25.37% and 45.19% in the non-music, radio and playlist condition, respectively. The only patient with a FIM-gait score of 3 (n = 1) spent 30.47 %, 1.92% and 0% in the non-music, radio and playlist condition, respectively. Participants who scored the minimum FIM-gait score of 1 (n = 3) spent 44.86%, 63.96% and 72,31% in the non-music, radio and playlist condition, respectively.

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Figure 18 – Per Cent of Time in Recommended Zones Looking at the results from a categorical perspective, we may divide sessions in per cent of how many sessions that met recommendations of at least 10 minutes in DuRI, how many who met recommendations of 20 minutes, and how many who did not meet the minimum level of 10 consecutive minutes of DuRI in one exercise session. Such a table (3) is presented below. Table 3 - Percentage of Sessions Meeting Recommendations of Intensities Presented in 10 Minute Bouts Not met ≥10 min ≥20 min Non-music 67,3% 24,5% 8,2% Radio 67,4% 16,3% 16,3% Playlist 64,4% 17,8% 17,8% Average 66,4% 19,7% 13,9%

Looking at the results from a effectiveness-oriented approach, specifically targeting the effect on each participant, table 4 shows how many of the 19 participants that met duration of recommendation (DuRI) of at least 10 minutes in DuRI, how many who met recommendations of 20 minutes, and how many who did not meet the minimum level of 10 consecutive minutes of DuRI in one exercise session.

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Table 4 – Participants in Each Condition Meeting Recommendations Presented in 10 Minute Bouts Not met ≥10 min ≥20 min Non-music n = 5 n = 14 n =8 Radio n = 8 (6)* n = 11 n = 7 Playlist n = 5 n = 14 n = 11 Average n = 6 n = 13 n = 8,7

*two participants dropped out of the radio condition, but using the ITT-principle they should be included. In addition, an alternative interpretation in the form of Number Needed to Treat (NNT) was used, which signifies how many stroke survivors that should receive the treatment/music intervention before one patient would meet the recommendations of additional 10 minutes. To calculate NNT, the following formula was used:

II7 =1

JK − JL=

1M""

(Mendes, Alves, & Batel-Marques, 2017). Here, π0 equals the risk control group and π1 equals the risk in treatment group, also expressed as absolute risk reduction (ARR). Table 5 – Number Needed to Treat in Each Condition Meeting Recommendations Presented in 10 Minute Bouts Comparisons of control vs intervention ≥10 min ≥20 min Non-music control vs radio intervention -6,3 -19 Non-music control vs playlist intervention ∞ 6,3 Radio as active control vs playlist intervention 6,3 4,8

All treatments are compared to their relative control condition, meaning if numbers are negative that the control condition will be the beneficial treatment.

4.5 Ancillary Analysis In the thesis article, we included ancillary analyses to explore the effect of time as a predictor in repeated measures on the dependent variables. Briefly summarized, the results indicated that time was not significantly effecting training duration (TD) nor the duration of recommended intensity (DuRI). The results showed no statistically significant changes in the linear relation in performance comparing treatment order in either TD (F(2, 116.014) = 2.235, p = .112), or in DuRI (F(2, 114.370) = .091, p = .913). There was, however, a statistically significant simple effect from the first treatment order in TD (p = .040; β = -209.457816), showing that the first session had significantly lower TD compared to the later sessions, but that this

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was not an adherent effect in relation to any other sessions. Conversely, there was no relation in DuRI, showing fluctuation at random.

4.6 Harms No harms where reported during the entire study.

Chapter 5 – Discussion

In this master’s thesis, I have investigated the theoretical literature for explaining the effect and mechanisms in applying music in aerobic exercise for clinical populations, with an emphasis on stroke patients. Furthermore, a thesis article was presented, investigating the immediate effect of a tailored music playlist for inpatient stroke survivors, compared to listening to the local radio channel and a non-music (control) condition. In the clinical trial, the outcomes assessed included affective valence pre- and post-sessions, the overall experience of the exercise, as well as training duration (TD) and duration of recommended intensity (DuRI) in the exercise session. The linking text included perceived exertion as a post session measure.

5.1 Findings Aside from Borg’s RPE, key findings are presented in the thesis article. A summary of findings will address the present master’s thesis’s hypotheses and sub-questions raised in chapter 1. In addition, this section will introduce considerations of possible mechanisms and explanations, but will however not include comparison to other studies’ findings, as this was elaborated in the thesis article.

5.1.1 Brief Synopsis of the Key Findings Looking at my hypotheses (in section 1.2.3), hypotheses 1-4 was answered in the thesis article. For a brief recapitulation, the results support hypothesis 1, but there were more complex findings in hypothesis 2-4: radio showed significantly different experiences (hypothesis 2), significant results were found for TD, but a ceiling effect blurred the efficiency (compared to no music control) and effectiveness (comparing music interventions) (hypothesis 3), and DuRI was only prolonged in patients with non-independent gait scores by the Functional Independence Measure (FIM) (hypothesis 4). Borg’s Rating of Perceived Exertion Contrary to the hypothesis, patients’ RPE did not ameliorate in the music conditions, measured by the Borg RPE scale (hypothesis 5). The median score of the Borg RPE showed an overall tendency (non-statistical) for a decrease of one point in both music conditions, suggesting a marginal improvement. This should be seen in context of the median being 14

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and 15, which is the level participants should be at (arguably even higher), considering one should score 14-16 for being in the high-intensity domain (Sundhedsstyrelsen, 2016). However, this does not match cardiovascular intensity, as only 33,6% of the sessions (mean for all conditions) met the recommendations of 10 minutes or more with a heart rate intensity of ≥40%HRR in each session (Table 3). Hence, results indicate that the participants’ RPE are reported higher than normative data imply. No conclusions can be drawn from this small sample, but stroke survivors may experience and rate exercise to be more exertive than a normal population, based on participants’ relatively high RPE scores. Conversely, the maxHR could have been miscalculated, creating unrealistically high expectations to participants’ heart rates (HR). However, as 31.58% had initially been calculated with a too low maxHR, and that even more participants at some point hit close to their maxHR, this is not considered probable. Continuing the synopsis of key findings, the following will focus on results from a categorical perspective in the other outcomes. Affect and experience Both ratings from the Feeling Scale and the Overall Experience Scale showed higher scores in both music conditions, suggesting a positive effect. The statistical results are presented in the thesis article. Training Duration The study’s results indicate that music may be effective for prolonging TD in aerobic exercise for inpatient stroke survivors. The limitations of the study design and analyses, however, pose interpretation errors for effect size and effectiveness, as a ceiling effect prohibited further investigation for 15/19 participants (reason: participating in all 30 minutes). The 15 participants who had this ceiling effect all had FIM gait scores ≥ 6 FIM. This systematic ceiling effect for patients with high gait functioning makes results from the linear mixed effect model (LMEM) less valid as only participants who did not endure the maximum TD (n = 4) was the only driving force for the (statistical) change in TD. Furthermore, this makes the interaction results between FIM gait scores misguiding, as only participants with low FIM gait scores changed. Therefore, no valid conclusion can be drawn to weather the FIM gait score is a predictor for increased TD. Nevertheless, results from the LMEM indicated a significant effect for both music conditions, encouraging further investigation. However, looking at the difference in TD in the LMEM (Radio: β = 486.318; Playlist: β = 452.815), and by pairwise comparison with estimated marginal means (average increase of 82,95 seconds and 77,06 seconds for radio and playlist, respectively), the results reflect a relatively minor improvement. However, when considering that only four participants improved (caused by

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the ceiling effect), the results are misguiding and should not be used to draw conclusions. Calculating the four patients’ raw mean in TD, a mean improvement of 462 seconds was found in the radio condition and 385,46 seconds in the playlist condition. Thus, for making assumptions for the music-induced effect on cardiorespiratory endurance, the study is simply using an insufficient design or needs more specified inclusion criteria. The effect observed in these four cases shows an increase ranging from 2,5 to 14 minutes of prolonged TD in the music conditions, compared to the non-music control. Taking a pragmatic perspective, the clinical reality may only provide a 30-minute cycle ergometry exercise session, making these results more ecological and closer to the proximity of an actual effectiveness. By thinking in these terms, the results indicate that patients with poorer lower-extremity functioning may be profiting more from the music intervention, as they are the only patients not capable of enduring the entire duration of a standard aerobic exercise session. Duration of Recommended Intensity Considering that the playlist showed significantly prolonged DuRI, compared to the non-music control, music may have an effect on stroke survivors’ ability to increase cardiorespiratory intensity. This could potentially increase efficiency of the training duration by spending more time in the recommended heart rate zone (HRZ). However, the effect was not generalizable for all participants, since the univariate analysis was not significant between conditions. There seem to be a connection between lower gait functioning (measured by the FIM score) and more positive effects on DuRI. This was evident both through statistical analysis of the interaction effect (as illustrated in the thesis article), and by visual inspection (section 4.4.5). First, an efficiency-oriented analysis compared per cent of time in the recommended HRZ. Results indicate that the playlist condition had a slightly higher DuRI. However, when dividing participants in FIM gait sub-categories, the analysis found varying efficiency in the training duration, with lower FIM gait scores to have considerably higher DuRI in the playlist condition. This is in conformity with the analysis with LMEM provided in the thesis article. Second, taking an effectiveness-oriented view of the three treatments, I calculated the number of patients who met the recommendations of the minimum of ≥10 minutes in the recommended intensity (≥40HRR%), in ≥20 minutes, and how many participants that did not meet the recommendations, comparing means in each condition type (Table 4). Results indicate that only the playlist condition had a notable increase of participants that met the

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recommendations of ≥20 minutes, compared to the non-music condition. Finally, a number needed to treat (NNT) was calculated for the comparison. From this simplistic analysis we saw that for every 6,3 patients receiving the playlist intervention, compared to the non-music control, one patient will increase his or hers DuRI from the lower recommendation of ≥10 to the higher recommendation of ≥20. Here, the radio showed negative findings compared to both the playlist intervention and the non-music control, however, two participants dropped out in the radio condition, which may affect results (following the intention-to-treat principle, they were analyzed as not meeting recommendations due to not receiving the treatment). Conclusion In summary, I will answer my sub-research questions (Figure 1). Based on the clinical trial, stroke survivors preferred music sound milieus over a non-music sound milieu (1a), and this seems to affect participants’ experiential and affective valence state (1b), as well as in training intensity (1c), but not in perceived exertion (1b). Therefore, music interventions might immediately affect the experience and training intensity of aerobic exercise (1). Taking a deeper look into how different music sound milieus altered the effects, based on the clinical trial, stroke survivors preferred a tailored playlist compared to listening to the local radio channel (2a). However, marginal differences were observed in participants’ experiential and affective valence state, as well as perceived exertion (2b), which was also the case in TD, even though findings were blurred by a ceiling effect (2c first part). However, looking at DuRI, the playlist showed higher scores but only for patients with lower gait functioning, measured by the FIM score (2c final part).

5.1.2 Consideration of Possible Mechanisms and Explanations Applying the theoretical framework (section 2.3.2) to the findings, some of the possible mechanisms and explanations will be discussed and hypothesized below. Affect and Experience Exposing patients to individual-specific pleasant music excites specific brain areas (Koelsch, 2014), causing stimulation of dopaminergic and serotonergic neurons to activate neurotransmitter release (Chanda & Levitin, 2013), leading to a feeling of reward and well-being (Vuust & Gebauer, 2014). Furthermore, music can evoke empowering associations and may be specially effective in increasing motivation (Clark et al., 2016b; Karageorghis & Priest, 2012a, 2012b; Karageorghis et al., 2012). All these factors may explain the affective and experiential effects.

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Although the tailored playlist scored significantly higher on the music preference scale, there was no difference in FS or OE scores. This may be caused by the playlist not being individualized (Ibid.). Ratings of Perceived Exertion Contrary to theories, participants in this study did not experience lower RPE. It could be that the music conditions did not evoke dissociative experiences in the participants. Researchers have noted that the distractive effect wears off in higher intensities, as an internal focus increases (Karageorghis & Priest, 2012a, 2012b) and that sedative music has been reported to produce a larger effect on RPE (Miller & Terbizan, 2017). Thus, both the musical selection, being primarily simulative and invigorating, as well as the exercise intensity might not be optimal for causing music-induced analgesic effects and lower RPE. Cardiovascular Intensity and Endurance As participants were free to choose a synchronous or asynchronous approach, the findings on TD and DuRI may or may not be explained by the beneficial effects of entrainment. Still, psychological effects, associative effects, and desire to exercise more intensely would be present anyhow. When listening to music, patients can be inclined to increase motivation and thereby intensity (Clark et al., 2016b), as affect and desire to move increases (Matthews et al., 2019; Witek et al., 2014). Concurrently, prior research has shown rhythmic synchronization to reduce variability in muscle activation, hence increasing activation potential (Grau-Sánchez et al., 2013; Rodriguez-Fornells et al., 2012; Sihvonen et al., 2017), increasing efficiency of movement patterns (Crasta et al., 2018; M. H. Thaut, 2013). Through complex neural mechanisms, engaging both emotion, perceptual and motor areas (Altenmüller & Schlaug, 2013; Grau-Sánchez et al., 2013; Rodriguez-Fornells et al., 2012; Särkämö et al., 2013), music interventions might increase confidence in physical exercise (Clark et al., 2016b), ultimately increasing adherence and performance (Karageorghis & Priest, 2012a, 2012b). However, the theoretical framework was not sufficient in explaining how the clinical characteristics of patients experienced significantly different effects, as seen in the present study with DuRI. Here, participants with lower FIM gait scores showed significantly more effect in DuRI, than patients with independent gait. Thus, antecedents in clinical characteristics may be an important factor for estimating the efficacy and clinical effectiveness. One theory might be that familiar and well-selected music is superior in facilitating efficient neural activation of the muscle, as shown in neuroimaging studies (Leow et al., 2015; Nombela et al., 2013b), which is less inherent in

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patients with lower extremity paresis and gait deficits. Thus, patients with impaired neural connectivity to lower extremities may experience more of an impact, as they have higher benefits of increased neural activation for compensating for insufficient movement patterns and potential muscle activity. Hence, increased TD and DuRI may be hypothesized stemming from either increased self-efficacy, arousal and motivation (Clark et al., 2016b; DeNora, 2000) or increased muscle activation and lower muscle variability (M. H. Thaut, 2013) – the modulating effects could very well be a combination of all the above. Ending Notes As the mechanisms are mainly based on hypotheses and theories derived from small samples in neuroimaging studies, as well as observations and empirical studies on behavior in healthy participants, one may really only guess what the explanations and mechanisms might be for the effects of music-supported exercise. Stroke survivors have deficits in functional connectivity and in activating focal impaired brain areas. Consequently, neural mechanisms might be different from healthy younger populations. A theoretical framework, including predictors that can inform researchers and clinicians on how to make choices about inclusion and exclusion criteria, could improve both research quality and help clinical practice in prioritizing or targeting specific clinical characteristics for receiving the intervention.

5.2 Methodology, Procedure, and Analysis

5.2.1 Limitations of the Present Study Initially, it is important to note that this study was limited to an immediate effect only. Although an immediate effect is important to investigate and may be feasible as a predictor of future outcomes and effectiveness, this is just a proxy of the essential outcomes, such as QoL, self-efficacy, and autonomy. For meeting this scope, a longitudinal study would be necessary, and a within-subject (e.g. crossover) design would not be applicable. This section will mainly focus on the scope of the present study. Group-based or Individualized Music Interventions As mentioned in section 5.1.2., music may be more effective when individualized for meeting individual needs and preferences. However, studies recommend training with peers in environments professionally supervised, based on the increase of motivation brought about from training with other patients (Billinger et al., 2014), which makes tailoring music individually a challenge. Delivering the music intervention in headphones would degrade verbal interventions or instructions from the therapist, and the social aspect

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would vanish. Thus, music delivered though a speaker into the room, creating a common sound milieu seems more appropriate as well as feasible in clinical practice and affordable in a social-economic perspective. Evaluation of the Design Utilizing a crossover design comes with both advantages and disadvantages. In relation to the present study, I will discuss: the appropriate use of crossover in outcomes measured, spontaneous remission in stroke survivors, carryover effects, in addition to ethics and statistical power.

A crossover design was chosen based on recommendations from a feasibility study (Mazhari-Jensen, 2017), as it has some advantages over both a parallel study and a non-crossover longitudinal study. First, the influence of confounding covariates is reduced because each crossover patient serves as his or her own control. In a non-crossover study, even randomized, it is often the case that different treatment-groups are particularly challenging to balance on some covariates. In randomized controlled crossover designs, such imbalances are accounted for (unless the covariate is capable of changing during the study). As stroke survivors are a heterogeneous population, having matched groups on all covariates, especially in small n studies, would be challenging. Furthermore, not only clinical characteristics but also personal characteristics are important for predicting physical exercise. Having two different groups and comparing results would not be sensitive to these covariates (suggesting a within-subject design). Second, the study did not utilize a washout period, as all measurements were targeting state affect, experience and RPE, as well as in-task physical performance. Patients were not able to ‘forget or unlearn’ the sound milieu they were exposed to. However, crossover designs also come with disadvantages, as carryover effects and variables changing over time will be affected in this design. The design may have its limitations in relation to the population, as sub-acute patients still experience some level of spontaneous neurological recovery. Therefore, one needs to be cautious not to overinterpret the beneficial effects of the interventions, given the potential impact of spontaneous recovery. Even though time between allocation to all treatments was within a short time scale of three weeks, some patients may have experienced recovery and improvements of, e.g., motor functions. This may have resulted in a bias towards the later conditions/sessions in comparison to the first conditions/sessions. This is, however, taken into account by the experimental design with randomized condition order. To further address this potential bias, ancillary

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analyses, using time as a predictor for effect, was carried out (see thesis article). Summarized, there was no significant difference in either outcomes of TD or DuRI. There was however a trend for TD to improve, but as the ceiling effect makes the data less valid, it is not possible to conclude anything. There was no trend for DuRI to change over time. A washout period would not have contributed as all parameters were measured as in-task performances and would have led to further bias of the recovery, hence it was not applied. Finally, optimal crossover designs are statistically efficient and require fewer subjects than do non-crossover designs (or even other repeated measures designs). Therefore, such designs may be more convenient for the researcher, and the ethics in recruiting vast numbers of patients to experimental treatments are also decreased, which are factors that should be considered too. Confounding Variables Due to the real world research and ecological nature of the clinical trial, several variables were not controlled, causing third variables. Variables such as nutrition, hydration, heart rate variability, and menstruation are all antecedent variables that can have a profound effect on heart rate (Hassmén et al., 2005). Moderating variables such as group dynamics and interpersonal relations or conversations also influence arousal and motivation. These factors are acknowledged as confounding variables, which may have great importance for the results. Though they are acknowledged, they are also a part of a real world environment and will inevitably affect the dependent variables. These confounding variables are not easily manipulated by scientists and are a byproduct of ecological validity and real world research. As individuals shared musical pieces, and thereby personal artifacts or arguably a part of their own personality (DeNora, 2000; Ruud, 1997), interpersonal interactions took place concerning who chose a specific musical piece, and when others liked (or possibly disliked) it, it would influence the person selecting it. However, as the music therapist had facilitated a pre-agreeable playlist, participants already had a common ground and were aware of the personality and privacy of the individual pieces. Attrition and its Impact on Statistical Procedures As attrition rates are also a limitation, especially in statistical procedures, the following section targets these concerns. The amount of missing data is relatively high (32 session = 18,7%), however, it is expected, as this population has multiple medical health concerns, resulting in surgeries and examinations by other health-care professionals that may be prioritized and rescheduled from day to day. Importantly, there was no

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observable pattern, and all missing data had a documented reason from the hospital prior to the session to which the participants did not show up. Thus, attrition is accounted for, and data is presumed missing at random. The approach of analyzing every session as independent (instead of using a median) utilized the data more sufficiently. Only using medians would conversely make less variance in effect size, discard larger amounts of data (in contrast to the ITT-principle) and possibly inflate type I-error. Whereas Friedman’s test is dependent on a listwise deletion, the Wilcoxon’s test is capable of using a pairwise deletion, utilizing the data more efficiently. Efficient data utilization was also one of the main arguments for using LMEMs in the behavioral data. The relatively high count of deleted data might explain the lack of significant results in the Friedman’s test analyzing differences between FS scores in the three conditions, arguably causing less statistical power leading to a type-II error. Similarly, Wilcoxon’s post-hoc test for Overall Experience score in playlist vs. non-music may have produced a type-ll error, as the playlist condition was found to have higher scores than radio, but did not show statistical significance to non-music (which was the case in radio vs. non-music). The LMEM provided a relevant statistical procedure, that analyzed all available data without deletion, nor had rigid assumptions of covariance and time between repeated measures. However, using generalized estimating equation (GEE) or even generalized linear mixed model (GLMM) could have been an even more robust approach, as these generalized statistical procedures have no assumption for normative distributions. As the normality assumptions of residuals were arguably barely met, the validity of the models is compromised. However, using classic procedures, such as RM-analysis of variance (ANOVA), would not yield more robust results, as RM-ANOVA is a rigid statistical approach with multivariate assumptions for normality, compound symmetry covariance structure for time between repeated measures, as well as assumptions of homoscedasticity, leading to flaws in the model fit and assumptions.

5.2.2 Protocol and Outcome Measures In the thesis article, we discussed the group-based session. The following will take the discussion and evaluation of the protocol further, as well as the reliability and validity of the outcome measures. Evaluating the Protocol The limitations of 30 minute sessions did have a strong impact on the results. As the majority of the participants (79%) were able to endure the time limit,

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outcomes on TD is based on an small sample (4 subjects, 23 sessions). Similar issues were not observed in the duration of recommended intensity (DuRI). For maintaining the ecological validity, clinical sessions provide 30 to 60 minutes of exercise sessions, and time limits may be used as appropriate for the specific population or sample size. If opting for longer sessions, a broader time scale may decrease the effectiveness in cardiovascular intensity for conserving energy, as stroke patients have limited cardiovascular endurance (Billinger et al., 2015). In the music interviews, some patients heavily relied on guidance from the music therapist, as deficits after stroke (e.g. communicative and cognitive impairments) made it difficult for patients to communicate their musical life stories. As the music therapist framed the interview, sufficient time and non-verbal guidance was used. This was also the primary reason for doing the interviews one-on-one, as patients may otherwise not be able to interact and produce sufficient information for creating the tailored playlist. Validation and Reliability of Outcome Measures Measurements were feasible in practice, but may be insensitive to the subtle changes induced by different music interventions. The following section will discuss all included measurements, as well as discuss important outcomes not included in the clinical trial. The heart rate monitors were not of clinical standard, but similar equipment has been used in other studies (e.g., Hutchinson et al., 2017) and have been validated in studies (Ge et al., 2016; Gillinov et al., 2017). The present study utilized heart rate reserve (HRR) for evaluating cardiovascular intensity. This calculation has the capabilities of individualizing heart rate zones (HRZ), which validates results and makes them more generalizable between subjects. Furthermore, HRR% is the recommended measurement by clinical guidelines (Sundhedsstyrelsen, 2014b). Secondly, the FS is validated and widely used in the sports and exercise domain (Terry et al., 2014) but has not traditionally been applied in a stroke population. Studies most commonly report using the Profile of Mood States (POMS) (e.g. Magee & Davidson, 2002; Särkämö et al., 2008), which requires a doctor or psychology license for administrating. Furthermore, the Feeling Scale (FS) version we used was translated for the purpose and was not standardized or validated in the current state. Conversely, Borg’s RPE is a well-validated assessment tool and has traditionally been used in stroke rehabilitation (Hassmén et al., 2005). As mentioned in the thesis article, the majority of studies (e.g., Hutchinson et al., 2017; Miller & Terbizan, 2017) measured Borg’s RPE and FS scores pre-,

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in-, and post task. Their results indicate that the most profound effect (by far) happened in-task. As this study did not utilize in-task questionnaires, such effects were not included. Measuring FS and Borg’s RPE pre-task, in-task (halfway through) and post-task may tell us more about the critical time point within the session, where we believe the largest effect is happening. Finally, the OE scale is not a standardized assessment scale, which leads to issues about interpreting the results, validation problems, test-retest reliability and psychometric properties. However, the fact that it consists of only one item and was specially constructed to be simple to administrate by the patients creates a pseudo-face validity and content validity. The present study’s scope did not include ergogenic effects. One may hypothesize that patients could be performing better on distance or watts (measured by the cycle ergometer) when exercising on cycle ergometers with music, hence having an unevaluated ergogenic effect. However, ergogenic measures are not included in the clinical recommendations (Billinger et al., 2014; Sundhedsstyrelsen, 2018), as this is a non-generalizable value. Therefore, in this pilot study an ergogenic effect was not regarded as a predictor for clinical outcomes.

5.2.4 Future Recommendations Recapitulating the thesis article, following recommendations were addressed: better inclusion criteria, longitudinal studies on functional outcomes, applying music interventions in other modalities of aerobic exercise, and encouraged for multidisciplinary collaborations for grasping the complexity in the field. Additional recommendations will be offered below. A suggestion for future studies would be to apply methodology that allows for easy comparison of treatments. This could be achieved by including a larger sample and allocate all subjects to a baseline consisting of non-music for one week. Afterwards, a randomization procedure could allocate half of the sample to a music intervention, having groups receive either control or intervention for additional one to three weeks. Results could then benefit from time*condition interactions, analyzing if one group significantly differed by comparing baseline scores to post-allocation scores, and follow-up analyses would be possible for longitudinal studies. However, as stroke patients are a heterogeneous population, paired groups are strongly recommended. Another recommendation, based on the present study’s methodology, is to select a more dichotomous music intervention, if applying two music interventions for comparison or active control. Using RAS-embedded music has previously shown promising results, compared to music only (Alter et al., 2015).

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Finally, using ergogenic outcomes such as total accumulated watts or distance travelled could be an important variable for analyzing functional outcomes in longitudinal studies, or explaining effects observed in cardiovascular intensity.

5.3 Clinical Indications for Patients and Interventionists In the thesis article, we discussed the clinical indications of whom the interventionist might be in such clinical settings. However, no discussion of if one should use music in clinical aerobic exercise was presented. The following section adds to both of these topics. Should Music Listening be Used as an Intervention in Clinical Aerobic Exercise? Based on the findings from this pilot study and by using the meta-theoretical framework (Clark et al., 2016b), music may be hypothesized to increase intensity, leading to more efficient training, as well as enhance participants’ overall satisfaction with aerobic exercise in stroke rehabilitation, resulting in adherence and the longevity of participation. Considering the study’s results, different music interventions may achieve different goals, hence having different effectiveness. Here, a tailored and systematic playlist might be more effective, as the intervention targets specific individual needs and preferences. Comparing the two music interventions, the only difference was an increase in duration of recommended intensity (DuRI). The radio condition was indeed significantly different to the non-music condition, having comparable effects on affective valence and experiential state, as well as training duration (TD) to the playlist condition. However, the effectiveness on cardiovascular intensity was superior in the playlist condition. Meeting recommendations and increasing DuRI lead to more efficient exercise sessions. This could potentially lead to an economical benefit, decreasing the hours needed to exercise before meeting clinical guidelines. Here, a systematic and tailored sound milieu may be prioritized by healthcare professionals. When patients experience higher affect and greater overall experience of the exercise session, they are more inclined to raise self-efficacy and participate in more activities alike (Rhodes et al., 2009). As evidence strongly emphasize the link between repetitions, frequency, time and intensity (Billinger et al., 2015), one may hypothesize that patients would gain higher level of functioning, independence and QoL. Thus, music may have an effect in a longer time perspective. However, as music did not show to increase cardiorespiratory intensity for all participants in this study, there may be some limitations for an immediately effect generalizable to the entire population.

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In an economic perspective, music interventions are low-cost and often fast to implement. Therefore, music can be cost-efficient if effects, as the present study propose, are replicable. This study’s findings suggest that music was indeed significantly different to no music, affective valance and experiential state, as well as raising training intensity in training duration and duration of recommended intensity for patients with lower gait functioning, in aerobic exercise when applied to inpatient stroke patients. However, the findings of this study should be interpreted with caution based on small sample size and other limitations. Why a Music Therapist? An important question to address in this master’s thesis is: why a music therapist? This topic was also included in the article, but a further discussion is included in the following. As the playlist condition in the present study partly consisted of a music interview with the music therapist, as well as tailoring of a playlist, the playlist intervention might be characteristic of music therapy practice. There was a therapeutic relationship between the therapist and the group which resembles a receptive music intervention. Every participant in the group was requested by the therapist to share a musical piece with the group. The piece should reflect personal qualities of motivation and associations of increased exercise intensity. The music therapist was responsible for facilitating a conversation between group members and for designing the playlist. However, other non-music therapists supervised the actual music intervention in the exercise sessions, characterizing a music medicine intervention. This highlight a grey area between the fields of music therapy and music medicine (illustrated in Figure 2). As described in the thesis article, music therapists have been proposed to prescribe music listening with exercise as a prevention or health promotion intervention for populations in medical and community contexts (Clark et al., 2016b; Paul & Ramsey, 2000). Theorists have proposed using a meta-theory as a framework to inform music therapy practice (Clark et al., 2016b). In accordance with this literature, music therapists are trained in selecting music meant to promote specific clinical effects and outcomes for individual participants or groups. The combination of a clinical therapist and being professionally trained in music psychology and music theory is certainly rare if not unique to the music therapy profession. However, the knowledge and practice of music interventions are not by any means restricted to music therapists.

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Music therapists can guide music listening interventions and supervise other healthcare practitioners, e.g., physiotherapists, psychologists and doctors, in applying music in clinical exercise. Broadening the use of music in clinical exercise may further enable more patients in participating in physically demanding exercise sessions. As demonstrated in the present study, the music intervention consisting of a tailored playlisted guided by a music therapist might have slightly different effectiveness. To achieve the effect of desire, music therapists can increase effectiveness and consistency of music interventions. This was illustrated by the higher cardiovascular intensity only found in the playlist condition. Furthermore, some participants was restricted by communicative impairments, making the music interviews challenging. Therefore, a clinical therapist might be essential for facilitating a conversation and engaging the patients in the music-selection process. One might ask why it is relevant for music therapists to protocolize and research music interventions that are not at the core of music therapy practice, but rather supervise other professions applying music medicine? For a therapy to be successful, the process of change should not only be happening and revolving around the therapy sessions but be a continuous process of change reflected in the clients’ daily living. To aid the client in this process, we as therapists can provide (transitional) objects for the client to facilitate the change outside of the therapeutic room. Furthermore, the therapist may even be able to provide guidelines for other actors, such as caregivers, relatives, or even the clients themselves to enable and empower the client outside of the therapeutic room. By researching and protocolizing music interventions that may be applicable by other professions we as music therapists contributes to the global healthcare and well-being of the clients’ needs. In the specific case of the playlist intervention, this might be characterized as music as an augmentative level (Bruscia, 1998, p. 163) in which music or music therapy is used to enhance the efforts and make supportive contributions to the therapeutic benefits from the physical exercise sessions. Bonde (2014, pp. 222–227) argues that even though music medicine and music therapy is two distinct fields, music therapy as a profession is a well-established scientific field that can contribute in aiding the medical field and other health professionals in using music, e.g., in clinical guidelines and for public health benefits. Bonde concludes by speculating that music therapists may in the future play a more central role in supervising, educating and

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designing tools for clinical use of clinicians such as physiotherapists, occupational therapists, doctors, nurses and other healthcare professionals. The present study’s protocol might be viewed as an additional small step in the direction of an interdisciplinary approach of collaboration between music therapy and our professional colleagues in healthcare settings.

5.4 Perspectives Sedentary behaviour has been recognised as a leading contributor to global mortality (World Health Organization, 2010). Throughout recent years there have been accumulated significant knowledge of the evidence-based foundation for physical training as a treatment for a large number of diseases, including pathologies that do not primarily manifest themselves as disorders of the musculoskeletal system (Sundhedsstyrelsen, 2018). These patient groups may be viable populations for integrating similar music interventions to affect both psychological factors, such as enhancing the affective experience of participating in exercise, and behavioural factors, including increased efficiency by regulating intensity, and increasing adherence.

Chapter 6 – Conclusion

Based on a meta-theoretical framework and by assessing heart rate intensity and time duration as well as using the Feeling Scale (Hardy & Rejeski, 1989) and the Overall Experience Scale, and the Borg Rating of Perceived Exertion Scale, this study investigated the effects of music listening, comparing a tailored playlist to radio and no music, in group-based cardiorespiratory exercise on cycling ergometers for medium to severely injured stroke survivors. Through, a randomized controlled clinical trial, utilizing a crossover design, this master’s thesis analyzed and compared the outcomes stated above. Both music listening conditions differed significantly, compared to no music, on effects in affective valence and overall experience as well as TD. For DuRI, the univariate analysis showed no difference, but when considering participants’ FIM gait scores, significant improvement for the tailored music was found. Applying music in clinical aerobic exercise may have varying effectiveness depending on patient characteristics and the quality of music used. Based on this pilot study, music may instantaneously increase training intensity and longevity of training duration for stroke patients with gait deficits, as well as raise participants’ overall affect and experience with aerobic exercise in just a single session. However, more rigorous trials are needed.

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Based on these findings, this master’s thesis provides the first empirical foundation for receptive beneficial effects, lending support to further investigation of music in cardiorespiratory exercise for (inpatient) stroke survivors.

Other Information

Registration The clinical trial has no registration number or name of trial registry.

Protocol The full protocol is available by sending the author an email request or by downloading it at Aalborg University’s webpage.

Funding No funding was applied for the making of the study.

Jeg kan vel ikke konkluderer på, om der er effekt og hvad litteraturen sigereview NU!

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