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U N I V E R S I TAT I S O U L U E N S I S

MEDICA

ACTAD

D 1533

AC

TAM

aisa Niem

elä

OULU 2019

D 1533

Maisa Niemelä

TEMPORAL PATTERNS OF PHYSICAL ACTIVITY AND SEDENTARY TIME AND THEIR ASSOCIATION WITH HEALTH AT MID-LIFETHE NORTHERN FINLAND BIRTH COHORT 1966 STUDY

UNIVERSITY OF OULU GRADUATE SCHOOL;UNIVERSITY OF OULU,FACULTY OF MEDICINE;INFOTECH OULU;MEDICAL RESEARCH CENTER OULU;OULU UNIVERSITY HOSPITAL;OULU DEACONESS INSTITUTE

ACTA UNIVERS ITAT I S OULUENS I SD M e d i c a 1 5 3 3

MAISA NIEMELÄ

TEMPORAL PATTERNS OF PHYSICAL ACTIVITY AND SEDENTARY TIME AND THEIR ASSOCIATION WITH HEALTH AT MID-LIFEThe Northern Finland Birth Cohort 1966 study

Academic dissertation to be presented with the assent ofthe Doctoral Training Committee of Health andBiosciences of the University of Oulu for public defence inAuditorium F101 of the Faculty of Biochemistry andMolecular Medicine (Aapistie 7), on 21 November 2019,at 12 noon

UNIVERSITY OF OULU, OULU 2019

Copyright © 2019Acta Univ. Oul. D 1533, 2019

Supervised byProfessor Timo JämsäDoctor Maarit KangasProfessor Raija Korpelainen

Reviewed byAssistant Professor Sarah Kozey KeadleAssociate Professor Jari Viik

ISBN 978-952-62-2365-0 (Paperback)ISBN 978-952-62-2366-7 (PDF)

ISSN 0355-3221 (Printed)ISSN 1796-2234 (Online)

Cover DesignRaimo Ahonen

JUVENES PRINTTAMPERE 2019

OpponentProfessor Malcolm Granat

Niemelä, Maisa, Temporal patterns of physical activity and sedentary time andtheir association with health at mid-life. The Northern Finland Birth Cohort 1966studyUniversity of Oulu Graduate School; University of Oulu, Faculty of Medicine; University ofOulu, Infotech Oulu; Medical Research Center Oulu; Oulu University Hospital; Oulu DeaconessInstituteActa Univ. Oul. D 1533, 2019University of Oulu, P.O. Box 8000, FI-90014 University of Oulu, Finland

Abstract

Physical activity reduces mortality and morbidity and improves physical and psychological health.Lately, the detrimental health associations of excess sedentary time have also been acknowledged.It is still unknown how temporal patterns of physical activity and sedentary time are associatedwith health, as previous studies have mainly focused on summary metrics of these behaviors; forexample, the weekly duration of moderate to vigorous physical activity.

This study aimed to investigate the associations between the amount and temporal patterns ofphysical activity and sedentary time and health at mid-life. Physical activity and sedentary timewere objectively measured for two weeks using an accelerometer-based activity monitor in theNorthern Finland Birth Cohort 1966 46-year follow-up (n=5,621). Participants attended clinicalexaminations and completed health and behaviour questionnaires. A machine learning method (X-means cluster analysis) was used to identify distinct groups of participants with different patternsof physical activity and sedentary behaviour based on the activity data.

A positive, dose-response association was found with perceived health and self-reportedleisure time and objectively measured moderate to vigorous physical activity. Higher prolongedsedentary time was associated with better heart rate variability but not with resting heart rate orpost-exercise heart rate recovery. Four distinct physical activity clusters (inactive, evening active,moderately active and very active) were recognised. The risk of developing cardiovascular diseasewas significantly lower in the very active cluster compared to the inactive, and in women also inthe moderately active cluster compared to the inactive and evening active clusters. On average, thecardiovascular disease risk was low, indicating good cardiovascular health in the study population.

Prolonged sedentary time was associated with cardiac autonomic function, which in this studywas not explained by physical activity or cardiorespiratory fitness level. Higher cardiovasculardisease risk was found in the activity clusters in which the amount of physical activity was lowerand in women took place later in the evening. Results of the study can be used for designingfeasible interventions for risk groups with unhealthy physical activity and sedentary behaviourpatterns.

Keywords: accelerometer, cardiac autonomic function, cardiovascular diseases,perceived health, physical activity, sedentary behaviour

Niemelä, Maisa, Fyysisen aktiivisuuden ja paikallaanolon ajallisen jakautumisenyhteys terveyteen keski-iässä. Pohjois-Suomen vuoden 1966 syntymä-kohorttitutkimusOulun yliopiston tutkijakoulu; Oulun yliopisto, Lääketieteellinen tiedekunta; Oulun yliopisto,Infotech Oulu; Medical Research Center Oulu; Oulun yliopistollinen sairaala; OulunDiakonissalaitosActa Univ. Oul. D 1533, 2019Oulun yliopisto, PL 8000, 90014 Oulun yliopisto

Tiivistelmä

Fyysinen aktiivisuus vähentää sairastavuutta, kuolleisuutta sekä parantaa fyysistä ja psyykkistäterveyttä. Viime aikoina on lisäksi tunnistettu liiallisen paikallaanolon terveyshaitat. Vielä ei tie-detä, miten fyysisen aktiivisuuden ja paikallaanolon ajallinen jakautuminen päivän aikana vai-kuttaa terveyteen, koska aiemmat tutkimukset ovat keskittyneet enimmäkseen tiettyihin summa-muuttujiin kuten kohtuullisesti kuormittavan liikkumisen määrään viikossa.

Työn tarkoituksena oli tutkia fyysisen aktiivisuuden ja paikallaanolon määrän ja ajallisenjakautumisen terveysyhteyksiä keski-iässä. Fyysinen aktiivisuus ja paikallaanolo mitattiin kiih-tyvyysanturipohjaisella aktiivisuusmittarilla kahden viikon ajan Pohjois-Suomen vuoden 1966syntymäkohortin 46-vuotistutkimuksessa (n=5621). Tutkittavat osallistuivat kliinisiin tutkimuk-siin ja täyttivät kyselyitä terveydentilastaan ja käyttäytymisestään. Koneoppimismenetelmällä(X-means cluster analysis) tutkittavat luokiteltiin aktiivisuusdatan perusteella ryhmiin, joissafyysisen aktiivisuuden määrä ja ajallinen jakautuminen päivän aikana poikkesi mahdollisimmanpaljon ryhmien välillä.

Positiivinen annos-vasteyhteys löydettiin koetun terveyden ja itseraportoidun vapaa-ajan lii-kunnan sekä mitatun kohtuullisesti kuormittavan liikkumisen väliltä. Suurempi pitkittynyt pai-kallaanoloaika oli yhteydessä parempaan sykevälivaihteluun mutta ei leposykkeeseen tai harjoi-tuksen jälkeiseen sykkeen palautumiseen. Neljä aktiivisuusryhmää tunnistettiin (inaktiiviset,ilta-aktiiviset, kohtuullisen aktiiviset ja erittäin aktiiviset). Sydän- ja verisuonitautien sairastu-misriski oli merkitsevästi pienempi erittäin aktiivisessa ryhmässä verrattuna inaktiiviseen ryh-mään ja lisäksi naisilla kohtuullisen aktiivisessa ryhmässä verrattuna inaktiiviseen ja ilta-aktiivi-seen ryhmään. Sairastumisriski oli keskimäärin matala viitaten hyvään sydänterveyteen tutki-musjoukossa.

Pitkillä paikallaanolojaksoilla oli yhteys sydämen autonomiseen säätelyyn, jota tässä työssäei selittänyt fyysinen aktiivisuus tai aerobinen kunto. Korkeampi sydän- ja verisuonitautien riskilöydettiin aktiivisuusryhmistä, joissa fyysisen aktiivisuuden määrä oli vähäisempää ja naisillapainottunut myöhäisempään iltaan. Tutkimuksen tuloksia voidaan hyödyntää interventioidensuunnittelussa riskiryhmille, joiden fyysisen aktiivisuuden ja paikallaanolon piirteet ovat tervey-delle haitallisia.

Asiasanat: fyysinen aktiivisuus, kiihtyvyysanturi, koettu terveys, paikallaanolo,sydämen autonominen säätely, sydän- ja verisuonitaudit

All things are so very uncertain, and that’s exactly what makes me feel reassured. – Tove Jansson

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Acknowledgements

This work was carried out at the Research Unit of Medical Imaging, Physics, and

Technology at the University of Oulu during the years 2015–2019.

I express my gratitude to my supervisors, Professor Timo Jämsä, Professor

Raija Korpelainen, and PhD Maarit Kangas for academic guidance and mentoring

throughout the project. You set a bar high, you were supportive and demanding,

and always arranged time for a meeting when I had lost myself somewhere in the

literature or statistics.

I want to thank my co-authors PhD Riikka Ahola, MHSc Anna-Maiju

Leinonen, MSc Vahid Farrahi, Professor Tuija Tammelin, Docent Antti Kiviniemi,

MD, PhD Juha Auvinen, MSc Eeva Vaaramo, Professor Sirkka Keinänen-

Kiukaanniemi, and PhD Katri Puukka for all the help I received. Special thanks go

to Eeva, you always helped and untangled my data issues. I wish to thank the head

of the NFBC project centre, PhD Minna Ruddock, and especially all the

participants in the NFBC1966 46-years follow-up; this research would not have

been possible without your participation.

I want to acknowledge the whole physical activity research group for sharing

ideas and reminding me about the bigger picture. Vahid, thank you for your

inspirational and enthusiastic attitude and all the help with anything related to data

analytics and beyond. I greatly appreciate your effort with this thesis. Petra, you

have shared all the ups and downs with me and your company at conferences and

outside the office has been very important. Thank you for your cheerful comments

and help. Anna-Maiju, your research has been an inspiration and helped me on my

own journey – thank you for the encouragement and support. Antti, thank you for

your strong expertise and generous help with cardiac autonomic function data and

revising the manuscripts.

I am very grateful that I had the opportunity to visit Northeastern University in

Boston, United States in summer 2017. I want to thank my hosts Professor Holly

Jimison and Professor Misha Pavel from the College of Information and Computer

science who supervised my work with a curious and supportive attitude. I am

grateful to the whole research group there for taking me in as a part of your big

family.

Special thanks go to Assistant Professor Sarah Kozey Keadle and Associate

Professor Jari Viik for pre-examination of the thesis. Your valuable comments and

advice greatly improved this thesis. I want to thank my follow-up group members,

10

Docent Matti Kinnunen and Adjunct Professor Arto Hautala for their help and ideas

about my research and this thesis.

I owe a debt of gratitude to my colleagues in the MIPT research unit. I want to

thank Anna and Jari for sharing the few square meters we had in our room and the

daily thoughts about research and (mostly) other things. Thank you Maarit for your

support related to research and teaching. Without you, this thesis would not have

been finished I’m sure. You have been a great example on how everything is

possible without forgetting life and exercise outside the confines of work.

Finally, I wish to express loving gratitude to my friends and family. Without

you, this journey would have ended long ago. I appreciate your genuine interest in

my work. Mom, thank you for believing in me and always helping with grammar;

without you, the commas would have been misplaced many times. Hannu, you have

been there for me all the time. Your encouragement and support made this possible.

This study was financed by the Ministry of Education and Culture in Finland,

Oulu University Hospital, Infotech Oulu, and the Tauno Tönning Foundation.

Oulu, September 2019 Maisa Niemelä

11

Abbreviations and symbols

ρ Spearman correlation coefficient

χ2 Chi-square test

AC Accelerometer

BMI Body mass index

BP Blood pressure

CI Confidence interval

CO2 Carbon dioxide

CRF Cardiorespiratory fitness

CVD Cardiovascular disease

DLW Doubly-labelled water

DBP Diastolic blood pressure

EE Energy expenditure

fat% Body fat percentage

g Gravitational unit, 1 g=9.81 ms−2

GPAQ Global physical activity questionnaire 2H Deuterium

HbA1c Glycated haemoglobin

HDL High-density lipoprotein

H2O Water

HR Heart rate

HRpeak Peak heart rate during cardiorespiratory fitness test

HRR60 Heart rate recovery during the first 60 seconds in the recovery

phase of the cardiorespiratory fitness test

HRrec Heart rate at 60 seconds in the recovery phase of the

cardiorespiratory fitness test

HRrest Resting heart rate

HRslope Steepness of the largest change in heart rate in the 30 seconds

during the first 60 seconds of the recovery phase of the

cardiorespiratory fitness test

HRV Heart rate variability

IPAQ International physical activity questionnaire

K Number of cluster centres

LCA Latent class analysis

LDL Low-density lipoprotein

LF/HF Ratio of low frequency and high frequency power

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LPA Light physical activity

LTPA Leisure time physical activity

MEMS Micro-electro-mechanical system

MET Metabolic equivalent

ML Machine learning

mPED Mobile phone-based physical activity education

MVPA Moderate to vigorous physical activity

MVPA10 Duration of at least 10-minute bouts of moderate to vigorous

physical activity

NFBC1966 Northern Finland Birth Cohort 1966

NHANES The National Health and Nutrition Examination Survey

O2 Oxygen molecule 18O Oxygen isotope

OR Odds ratio

PA Physical activity

PH Perceived health

r Pearson correlation coefficient

R2 Coefficient of determination

RRi R-R intervals

rMSSD Root mean square of successive differences in R-R intervals

SBP Systolic blood pressure

SD Standard deviation

SED Sedentary time

SED30 Duration of at least 30-minute bouts of sedentary time

SED60 Duration of at least 60-minute bouts of sedentary time

ST Sitting time

TS Traditional statistics

VCO2 Volume of carbon dioxide

VO2 Volume of oxygen

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List of original publications

This thesis is based on the following publications, which are referred to throughout

the text by their Roman numerals:

I Niemelä, M., Kangas, M., Ahola, R., Auvinen, J., Leinonen, A-M., Tammelin, T., . . . Jämsä, T. (2019). Dose-response relation of self-reported and accelerometer-measured physical activity to perceived health in middle age—the Northern Finland Birth Cohort 1966 Study. BMC Public Health 19: 21. doi: 10.1186/s12889-018-6359-8

II Niemelä, M., Kiviniemi, A., Kangas, M., Farrahi, V., Leinonen, A-M., Ahola R., . . . Jämsä, T. (In press). Prolonged bouts of sedentary time and cardiac autonomic function in midlife. Translational Sports Medicine. doi: 10.1002/tsm2.100

III Niemelä, M., Kangas, M., Farrahi, V., Kiviniemi, A., Leinonen, A-M., Ahola, R., . . . Jämsä, T. (2019). Intensity and temporal patterns of physical activity and cardiovascular disease risk in midlife. Preventive Medicine 124: 33–41. doi: 10.1016/j.ypmed.2019.04.023

Contributions to research for publications: In all publications I participated in the

planning and design of the studies using previously collected data. I had the main

responsibility for accelerometer-measured physical activity data pre-processing

and data analysis (I–III). I performed the statistical analyses for all the publications,

with statistician advice (I). I was responsible for drafting the manuscripts (I–III).

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15

Contents

Abstract

Tiivistelmä

Acknowledgements 9 

Abbreviations and symbols 11 

List of original publications 13 

Contents 15 

1  Introduction 17 

2  Review of the literature 19 

2.1  Physical activity ...................................................................................... 19 

2.2  Measurement of physical activity ........................................................... 20 

2.2.1  Measurement of energy expenditure ............................................ 21 

2.2.2  Accelerometer .............................................................................. 22 

2.2.3  Analysis of accelerometer data ..................................................... 25 

2.3  Sedentary behaviour ................................................................................ 26 

2.4  Measurement of sedentary behaviour ..................................................... 27 

2.5  Temporal and accumulation patterns of physical activity and

sedentary behaviour ................................................................................ 29 

2.5.1  Compositional analysis ................................................................. 30 

2.5.2  Profiling physical activity and sedentary behaviour ..................... 31 

2.6  Perceived health ...................................................................................... 34 

2.7  Role of physical activity and sedentary behaviour in cardiac

autonomic function and cardiovascular diseases .................................... 35 

3  Aims of the study 37 

4  Materials and methods 39 

4.1  Study population ..................................................................................... 39 

4.2  Methods ................................................................................................... 39 

4.2.1  Questionnaires .............................................................................. 39 

4.2.2  Measurement of physical activity and sedentary behaviour

(I–III) ............................................................................................ 41 

4.2.3  Clinical examination ..................................................................... 43 

4.3  Statistical methods .................................................................................. 45 

5  Results 47 

5.1  Study population characteristics ............................................................. 47 

5.2  Accelerometer-measured physical activity ............................................. 47 

5.3  Physical activity and perceived health (I) ............................................... 48 

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5.4  Cardiac autonomic function and prolonged sedentary time (II) .............. 51 

5.5  Temporal patterns of physical activity and cardiovascular disease

risk (III) ................................................................................................... 53 

6  Discussion 59 

7  Conclusions 65 

References 67 

Appendix 85 

Original publications 87

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1 Introduction

Physical activity (PA) has many well-established health benefits, including

decreased mortality and morbidity. To achieve the health benefits, PA guidelines

have been established suggesting that individuals should reach 150 minutes of at

least moderate intensity (or 75 min of vigorous intensity) PA per week (World

Health Organization, 2010). Recent research has questioned whether it is sufficient

to only focus on achieving this PA goal, as the detrimental health effects of

excessive sedentary time (SED), even after controlling for moderate to vigorous PA

(MVPA), have been acknowledged (Biswas et al., 2015; Matthews et al., 2012). In

addition to every third individual being physically inactive determined by not

meeting the PA guidelines (Guthold, Stevens, Riley, & Bull, 2018), over half of all

waking hours are spent doing sedentary pursuits in developed countries (Loyen et

al., 2016). In the recent updated PA guidelines in the United States, individuals have

been encouraged to sit less in addition to engaging PA to gain further health benefits

(Piercy et al., 2018).

It seems evident that all PA behaviour occurring in waking hours, in addition

to the amount of MVPA suggested in global guidelines, contributes to health. Lower

intensity PA (Saint-Maurice, Troiano, Berrigan, Kraus, & Matthews, 2018) and

short bouts of very strenuous exercising when the amount is not fulfilling the PA

recommendation (Glazer et al., 2013; Metcalfe, Babraj, Fawkner, & Vollaard, 2012)

have positive health associations. Additionally, not only the total amount of SED

but the patterns in which it occurs can mediate or amplify its deleterious effects

(Diaz et al., 2017; Dunstan et al., 2011; Jefferis et al., 2014). Recently, more

advanced methods for capturing PA patterns occurring throughout the day have

been introduced, which can be utilised for finding the behavioural patterns that are

most beneficial or harmful for health (Lewis, Napolitano, Buman, Williams, &

Nigg, 2017; Liu, Gao, & Freedson, 2012; Troiano, McClain, Brychta, & Chen,

2014).

In adulthood, the levels of PA decrease and SED increase with increasing age,

(Hallal et al., 2012; Matthews et al., 2008). Thus, recognising those subgroups of

people with deleterious PA behaviours in middle age, such as prolonged SED and

decreased amount of light PA (LPA) or MVPA, is vital for creating interventions

that aim for permanent changes in PA and thus healthier ageing.

The main objective of this study was to investigate the health associations of

both the amount of PA and SED and their temporal patterns measured with an

accelerometer among middle-aged Finnish adults. The main hypothesis was that in

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addition to the total volume of PA and SED, the accumulation patterns of these

behaviours are also associated with health. For evaluating health, we measured

health perception, cardiac autonomic function and risk of cardiovascular disease.

The study population consisted of over 5,000 individuals who participated in the

Northern Finland Birth Cohort 1966 46-years follow-up study.

In this thesis, the term sedentary time is used for stationary behaviours in a

sitting, lying or reclining posture that are associated with low energy expenditure

(Tremblay et al., 2017). Sitting time is used for sedentary time which is known to

take place in a sitting posture, e.g. measured by the orientation of wearable sensors

or question item inquiring about sitting time.

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2 Review of the literature

2.1 Physical activity

Physical activity is defined as any bodily movements produced by skeletal muscles

requiring energy expenditure (EE) (Caspersen, Powell, & Christenson, 1985).

MVPA in particular is known to have numerous health-enhancing associations

(Wen et al., 2011; World Health Organization, 2010). Based on these health benefits

the World Health Organization (2010) has introduced PA guidelines that suggest

performing at least 150 minutes of moderate or 75 minutes of vigorous intensity

PA regularly per week. When these guidelines are fulfilled, the risk of total

mortality and morbidity of type 2 diabetes, cardiovascular diseases, certain cancers

and depression is reduced (World Health Organization, 2010). In addition, lower

levels of PA (amount not fulfilling the PA recommendation) and LPA contribute to

health compared to total inactivity, although higher PA levels bring more health

benefits (Saint-Maurice et al., 2018; Wen et al., 2011).

One third of the entire adult population is physically inactive, i.e. not meeting

the PA recommendation (Hallal et al., 2012). Physical inactivity is the fourth

leading cause of death from non-communicable diseases worldwide (World Health

Organization, 2009). Physical inactivity increases the risk of many diseases,

shortens the lifespan over three years and causes a high economic burden to society

(Ding et al., 2016; Lee, I. M. et al., 2012; Wen et al., 2011). The overall prevalence

of physical inactivity has remained at the same level from 2001 onwards and thus

effective interventions for reducing physical inactivity are warranted, especially in

high-income countries where insufficient PA levels are most prevalent (Guthold et

al., 2018).

Age-related changes in physical activity and sedentary behaviour

Physical activity decreases with increasing age. The proportion of individuals not

meeting the PA recommendation grows steeply after 60 years (in some regions

already after 45 years) especially in high-income countries (Hallal et al., 2012). At

the same time, higher levels of sedentary behaviour often occur among middle-

aged and older adults (Matthews et al., 2008; Strain, Kelly, Mutrie, & Fitzsimons,

2018). Ageing causes many health-related changes, such as a decline in aerobic

fitness and muscle mass (Janssen, Heymsfield, Wang, & Ross, 2000; Paterson,

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Cunningham, Koval, & St. Croix, 1999). High PA and fitness levels play a

protective role against functional disabilities; for example, the prevalence of

functional limitations in everyday activities was half that in men aged over 60 with

high levels of fitness compared to younger men (50–59 years) in the lowest fitness

group (Huang et al., 1998).

Physical activity among middle-aged and older adults is associated with

healthier ageing (Lafortune et al., 2016; Paterson & Warburton, 2010) including

positive associations with PA in middle age and cognitive and physical functioning

(Brach et al., 2003; Chang et al., 2010), mobility (Tikkanen et al., 2012), and mental

health (Steinmo, Hagger-Johnson, & Shahab, 2014) in later life. In addition, those

participating in leisure time or occupational PA in early middle age are more likely

to engage in leisure time physical activity (LTPA) also when ageing (Borodulin et

al., 2012).

2.2 Measurement of physical activity

Methods to measure physical activity can be divided into subjective and objective

approaches. Subjective methods rely on an individual’s own evaluation of PA

performed, for example PA questionnaires and diaries. Objective PA measures

include wearable monitors like pedometers, heart rate monitors and accelerometers

(Hills, Mokhtar, & Byrne, 2014). Doubly-labelled water (DLW) and indirect

calorimetry can be used for measuring EE of PA and are often utilised in research

and for calibrating and validating other measuring modalities (Moreira da Rocha,

Alves, & da Fonseca, 2006; Schoeller & van Santen, 1982).

Subjective measures

Subjective methods of PA include questionnaires, PA diaries and logs. Many of the

questionnaires are validated such as International Physical Activity Questionnaire

(IPAQ) and Global Physical Activity Questionnaire (GPAQ) and available in

several languages. Questionnaires are cost-effective and feasible in large-scale

studies and many of them have acceptable reliability, but their validity has been

questioned (Helmerhorst, Brage, Warren, Besson, & Ekelund, 2012; Lee, P. H.,

Macfarlane, Lam, & Stewart, 2011). With just a few PA questions it is possible to

distinguish active and inactive individuals, but with additional questions more

detailed information about PA behaviour can be achieved (Godin & Shephard, 1985;

Philippaerts, Westerterp, & Lefevre, 2002). Questionnaires usually examine

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activities from the past one to seven days, the last month or even the past year

(Jacobs, Ainsworth, Hartman, & Leon, 1993). Often simple questionnaires have

given better estimates of the actual PA compared to more complex ones due to their

easier interpretability (Philippaerts et al., 2002). One advantage of self-reported PA

is the information gained about PA domain and type, such as work-related PA,

walking, household chores or sports. However, recall and social desirability bias

are inherent with self-reported PA measures (Ainsworth & Levy, 2004).

Differences have been reported when comparing PA outcomes from

questionnaires and objective PA measures. When questionnaires are used for

evaluating EE, the results often differ compared to DLW, which is considered a

golden standard for EE estimation. Better EE estimates are achieved if

questionnaires have items about many different PA behaviours as well as SED and

sleeping time (Neilson, Robson, Friedenreich, & Csizmadi, 2008). In addition, PA

questionnaires often estimate a higher proportion of individuals fulfilling PA

recommendations compared to accelerometer measured PA levels (Steene-

Johannessen et al., 2016). In addition, the time spent performing light and moderate

intensity activities seems to differ when comparing measures from questionnaires

and accelerometers, as these behaviours often occur many times a day and are not

as specific in terms of intensity and duration compared to more intensive PA like

sport exercises, and therefore recalling them precisely might be more challenging

(Jacobs et al., 1993). Previously, a stronger agreement between different PA

measures when measuring higher intensity PA has been reported (Besson, Brage,

Jakes, Ekelund, & Wareham, 2010; Ishikawa-Takata et al., 2007).

2.2.1 Measurement of energy expenditure

Doubly-labelled water is considered the golden standard for evaluating the total EE

of an individual in a free-living environment. Total EE includes basal metabolic

rate, the thermic effect of diet-induced EE and the energy cost of PA (Westerterp,

2013). DLW is based on the isotopes of hydrogen and oxygen and their different

elimination paths from the body after ingestion. 18O leaves the body as water (H2O)

and carbon dioxide (CO2) while deuterium (2H) leaves the body only as water. Thus,

CO2 production can be calculated by subtracting 2H elimination from 18O

elimination. The measurement period is often from 4 to 21 days and starts with

baseline urine and/or saliva samples and administration of the isotope dose. Urine

samples are then collected after 24 hours and at the end of the measurement period.

The costs of DLW are high due to expensive oxygen isotope and the required

22

analysis equipment (Committee on Military Nutrition Research Food and Nutrition

Board, 1997; Schoeller & van Santen, 1982).

Indirect calorimetry measures EE based on measured gas exchange (O2 and

CO2) while breathing through a mouthpiece. The expired gas is collected at a

known temperature with specific gas sensors, calculated into volume values for

CO2 (VCO2) and O2 (VO2), and then transformed into EE. Indirect calorimetry can

be used for measuring resting EE and with a portable measuring device also during

exercise for evaluating the EE and thus the intensity of different activities (Moreira

da Rocha et al., 2006). Measuring equipment is expensive and requires careful

calibration but is often used for validating wearable measuring devices (Moreira da

Rocha et al., 2006).

When evaluating the EE of different physical activities, metabolic equivalent

(MET) values can be used. 1 MET corresponds to resting EE whilst awake and is

described as oxygen consumption of 3.5ml/kg/min (equivalent of energy

consumption of 1 kcal/kg·h) irrespective of body size. Different exercises and

physical tasks have different MET values: walking at a speed of 5 km/h is

equivalent to around 3.5 METs, and thus causes 3.5-fold EE compared to rest for

an individual (Ainsworth et al., 2000). Very strenuous exercises like running or

skiing at high speed can have an intensity of over 15 METs, which would lead to

EE of over 1050 kcal/h during the exercise for an individual weighing 70kg.

2.2.2 Accelerometer

Physical activity can be evaluated in terms of accelerations occurring due to body

movements using accelerometers (Kavanagh & Menz, 2008; Mathie, Coster,

Lovell, & Celler, 2004). The measuring mechanism of the accelerometer is often

described as a mass-spring system operating under Hooke’s law (F=kx) and

Newton’s second law of motion (F=ma) (Kavanagh & Menz, 2008). Movement

causes compression or stretching force to the system and the spring will generate a

proportional restoring force. Because the stiffness and mass of the spring can be

controlled, the acceleration of the mass element can be determined from its

displacement:

𝐹 𝑘𝑥 𝑚𝑎, thus 𝑎 , (1)

in which F is force, k is the stiffness factor of the spring, x is displacement, m is

mass of the element, and a is acceleration. In practice, the acceleration is quantified

23

using many different techniques and the transducers most often used in

accelerometers for PA measurements are piezoelectric, piezoresistive or capacitive

(Kavanagh & Menz, 2008).

Piezoelectric acceleration sensors include seismic mass and piezoelectric

elements (single crystal or ceramic). Acceleration creates movement of the seismic

mass, which causes compression and bending in the element. Adaptation of the

element generates an electric current, which creates a charge or voltage difference

proportional to the applied acceleration. Piezoresistive sensors include a

polysilicon structure with polysilicon springs. Acceleration forces change the

electrical resistance of the springs, and voltage in proportion to the amplitude of

the acceleration is produced using a Wheatstone bridge. In capacitive sensors, the

seismic mass is enclosed between two electrodes. Deflection of the seismic mass

between the two electrodes creates differential proportional capacitance. (Yang &

Hsu, 2010) Nowadays, both piezoresistive and capacitive sensors use micro-

electro-mechanical system technology (MEMS), resulting in the sensors being

miniature and low-cost instruments with lower power consumption. Both types

require an external power supply that allows the accelerometer to respond to static

accelerations (Mathie et al., 2004).

A uniaxial accelerometer records acceleration in a single direction (often the

vertical direction of the body is selected), biaxial in two orthogonal directions

(often vertical and medio-lateral directions are selected), and triaxial in all three

orthogonal directions (vertical, medio-lateral and anterior-posterior) (Mathie et al.,

2004). Accelerometers can be attached to many different parts of the body,

including the waist and hip (near the centre of mass), extremities such as ankles

and wrists, and less often-used sites such as the foot, head or tibia (Kavanagh &

Menz, 2008).

Recorded accelerations originate from several sources: body movement,

gravity, external vibrations, and bouncing of the sensor due to a loose attachment.

Of these, the last two cause unintentional “noise” to the acceleration signal and

require filtering. The gravitational component of the output signal is dependent on

the orientation of the measurement direction of the sensor in the gravitational field

and varies between −1 and 1 gravitational units (g). Because piezoresistive and

capacitive sensors are able to measure static response and thus also the gravity

component, they are therefore most often used in human movement applications.

Acceleration occurring due to body movement (kinematic component) is dependent

on the body part and thus is affected by the sensor placement. In addition, body

24

posture and sensor orientation can influence the kinematic acceleration component.

(Bouten, Koekkoek, Verduin, Kodde, & Janssen, 1997)

Accelerations caused by body movement have relatively low frequencies and

amplitudes. Frequencies are higher in a vertical direction compared to medio-

lateral or anterior-posterior directions during locomotion and in caudal parts of the

body compared to cranial parts. Thus, lower body exercises like running and

jumping are expected to cause large accelerations with high frequencies (Bouten et

al., 1997). Acceleration occurring during walking (5 km/h) in a vertical direction

when a sensor is attached to the hip is equivalent to about 0.8 g, running at moderate

speed (9 km/h) to 3.2 g and fast running (13 km/h) to 4.2 g of acceleration (Jämsä,

Vainionpää, Korpelainen, Vihriälä, & Leppäluoto, 2006). Often the measuring

range in the accelerometers is set somewhere between 0.1 and 10 g (Chen & Bassett,

2005) but the sensor placement should be taken into account as peak acceleration

measured during running varied from 4 to even 12 g depending on the sensor

placement (Bhattacharya, Mc Cutcheon, Shvartz, & Greenleaf, 1980).

Frequencies during walking and running rarely exceed 10 Hz (Antonsson &

Mann, 1985; Winter, Quanbury, & Reimer, 1976). However, in some arm

movements 25 Hz frequency can be achieved. Thus, commercial accelerometers

used in PA measurements usually have a sampling frequency range of 1 to 64 Hz.

Sensor output is filtered using a band pass filter, which enables both very low

frequency spectrum (gravitational component) and electrical noise occurring at

higher frequency domains (≥60 Hz) to be cut off. The currently used ranges are

between 0.25 and 7 Hz after filtering. (Chen & Bassett, 2005)

Advantages of the accelerometers for estimating PA is their small size and light

weight. They are also wireless and non-invasive and thus easy to use, and they do

not infere with body movements. Measurement periods can range from days to

weeks, which makes data collection in free-living environments feasible. However,

there might be bias regarding the correct measurement of specific activities if the

body part where the sensor is attached stays relatively still during the activity. For

example, measuring cycling with waist- or wrist-worn monitors can be challenging.

In addition, accelerometers do not recognise carried loads or muscle work and thus

lifting heavy weights or training in the gym might be underestimated in terms of

EE. However, it has been presumed that during free-living, the contribution of these

activities to the overall EE of PA is small. However, for individuals regularly taking

part in these types of sports, the estimated EEs might underestimate the actual PA

levels (Chen & Bassett, 2005; Trost & O'Neil, 2014).

25

2.2.3 Analysis of accelerometer data

Accelerometers provide a raw acceleration signal of the activity being measured,

which is often converted to a proprietary metric called activity counts. Higher

acceleration provides greater counts. To reduce the amount of data, a raw

acceleration signal measured at a relatively high frequency is often transformed to

activity counts and analysed using epoch length varying from seconds to several

minutes (Esliger, Copeland, Barnes, & Tremblay, 2005). The epoch lengths often

used are 30 and 60 seconds. Eventually, this causes problems when outcomes of

different accelerometers are compared and a lack of agreement between

accelerometer brands and models (Esliger et al., 2005; Leinonen et al., 2017).

For categorising different physical activities by their intensity, many sets of

different cut-points have been suggested. One of the most popular sets has been

introduced by Freedson et al. (1998) using a hip-worn accelerometer, which

categorises SED as ≤1.5 METs, LPA as 1.5–3 METs, moderate PA as 3–6 METs

and vigorous PA ≥6 METs. Many different cut-points for activity counts have been

proposed for the classification of PA behaviours in these intensity categories. These

sets of cut-points are often derived from regression-based analyses (Crouter,

Churilla, & Bassett, 2006; Esliger et al., 2005). However, their comparability with

each other is often poor (Crouter, DellaValle, Haas, Frongillo, & Bassett, 2013;

Watson, Carlson, Carroll, & Fulton, 2014). To overcome the issues concerning PA

counts, it has been suggested among researchers to move towards analysing raw

acceleration data (Troiano et al., 2014). The use of cut-point based analytical

approaches probably still continues due to operational costs and to ensure

comparability with earlier data collections (Troiano et al., 2014). However, some

differences have also been reported in raw acceleration data obtained from different

activity monitor brands and models. Montoye et al. (2018) reported a higher

correlation between ActiGraph GT3X+ and GT9X Link accelerometers for the

count data compared to raw data, when comparing all three measuring axes and

vector magnitude between the models. In addition, differences in mean vector

magnitude have been reported between ActiGraph GT3X+ and GENEA

accelerometers (John, Sasaki, Staudenmayer, Mavilia, & Freedson, 2013).

Recently, more sophisticated methods based on machine learning (ML) for

identifying PA type and intensity from acceleration data have gained popularity. In

ML approaches, calculated metrics and patterns from the acceleration data in time

and frequency domain are used to predict activity intensity directly or activity type

and then mapping activities based on their intensity (Farrahi, Niemelä, Kangas,

26

Korpelainen, & Jämsä, 2019; Montoye, Westgate, Fonley, & Pfeiffer, 2018;

Rosenberger et al., 2013). Varying levels of accuracy have been reported in

predicting activity intensity (directly or indirectly) using ML methods (Ellis, Kerr,

Godbole, Staudenmayer, & Lanckriet, 2016; Montoye et al., 2018; Montoye et al.,

2017; Rosenberger et al., 2013). The performance of ML methods is often limited

in terms of training the model with acceleration data collected in the laboratory. For

categorising PA types, most often specific tasks are carried out in a laboratory

within a short period of time and with a limited amount of study participants. When

these models are tested in a free-living setting, their performance is decreased due

to activities not occurring in the training phase or individual variations in

performing the activities (Bastian et al., 2015; Farrahi et al., 2019). Often the

predictive accuracy of ML models increases when data collected in a free-living

environment is used in the model training (Bastian et al., 2015; Fullerton, Heller,

& Munoz-Organero, 2017). There is no consensus in the previous literature as to

which ML method (e.g. Artificial Neural Network, Decision Tree, Support Vector

Machine) is the most feasible in terms of PA prediction accuracy and computational

requirements (de Almeida Mendes et al., 2018).

2.3 Sedentary behaviour

Sedentary behaviour is defined as any waking behaviour with low energy

expenditure (≤1.5 MET) in a sitting, lying or reclining posture (Tremblay et al.,

2017). In many developed countries, over half of all waking hours are spent

sedentary (Loyen et al., 2016; Matthews et al., 2008; Strain et al., 2018). Globally,

over 40% of all people report sitting for four or more hours per day (Hallal et al.,

2012). High SED has been associated with deleterious health effects on

cardiometabolic health, metabolic health, and increased mortality even after

controlling for MVPA time (Biswas et al., 2015; Healy, Matthews, Dunstan,

Winkler, & Owen, 2010; Matthews et al., 2012). Global guidelines for reducing

sitting and overall SED are still currently lacking.

Studies reporting the independent detrimental effects of SED on health

conditions have been critiqued (Maher, Olds, Mire, & Katzmarzyk, 2014; van der

Ploeg & Hillsdon, 2017), as often accounting for the effect of PA has only included

controlling for MVPA and, thus, LPA has been neglected. The association between

SED and cardiometabolic biomarkers have been shown to diminish when

controlling for total PA (Maher et al., 2014) and very high levels of MVPA (60–75

min/d) have been reported reducing mortality risks related to excess SED (Ekelund

27

et al., 2016). Thus, reducing SED might be most beneficial for less physically active

populations.

2.4 Measurement of sedentary behaviour

Sedentary behaviour, similarly to PA, has mostly been assessed via questionnaires

enquiring about sitting times (ST) in different domains, e.g. at work, watching

television or while commuting, or as a total ST per day. As the questionnaire items

provide adequate reliability, their validity is often poorer, for example sedentary

questions in IPAQ yielding moderate reliability (Spearman ρ>0.7) but only poor to

moderate convergent validity (Spearman ρ<0.5) when compared to accelerometry

(Craig et al., 2003). Sitting while watching television has been used as a proxy

measure of total SED and is reported to have many deleterious health effects (Clark

et al., 2009). It has been questioned whether television-watching reflects the total

SED and questionnaires including a wide spectrum of sedentary behaviours or total

SED are warranted (Atkin et al., 2012; Biddle, Gorely, & Marshall, 2009). When

comparing self-reported and objectively measured SED, sometimes even

substantial differences have been reported. Individuals tend to underestimate their

total SED and often the SED questionnaires omit some habitual sedentary

behaviours yielding smaller estimates of total SED than when measured with the

accelerometer (Copeland, Clarke, & Dogra, 2015; Gupta et al., 2017; Jefferis et al.,

2016).

Accelerometers can be used as an objective method for measuring SED.

However, those monitors designed for PA measurements often have difficulties in

differentiating between postures, because they measure the intensity of body

movements and not the posture itself. Thus, differentiating between sitting and

standing, both still activities, with wrist or hip-worn monitors might be challenging

and prone to error (Byrom, Stratton, Mc Carthy, & Muehlhausen, 2016; Hart,

Ainsworth, & Tudor-Locke, 2011; Lyden, Kozey Keadle, Staudenmayer, &

Freedson, 2012). Rather than trying to separate SED from PA using different cut-

points for acceleration data or activity counts, measuring the posture itself has been

recommended, as it is known that even still activities like standing and sitting can

have different physiological responses and therefore different health outcomes

(Fanchamps, van den Berg-Emons, Stam, & Bussmann, 2017; Henson et al., 2016;

Katzmarzyk, 2014; Sievänen & Kujala, 2017).

Regarding posture recognition, an accelerometer attached to the thigh and

measuring the sensor inclination (activPAL) showed higher accuracies in measuring

28

SED compared to the waist-worn accelerometer (ActiGraph GT3X) (Kozey-

Keadle, Libertine, Lyden, Staudenmayer, & Freedson, 2011; Lyden et al., 2012;

Varela Mato, Yates, Stensel, Biddle, & Clemes, 2017). One limitation of activPAL

is that it does not separate lying from sitting because the thigh orientation

(inclination angle) in these two postures is similar (horizontal). Two-sensor systems,

including an accelerometer attached to both thigh and torso, have also been used to

measure posture (Bassett et al., 2014; Lyons, Culhane, Hilton, Grace, & Lyons,

2005). The inclination of the accelerometers at two body sites and their orientation

in the gravitational field can differentiate between all three postures: lying, sitting

and standing (Bassett et al., 2014; Lyons et al., 2005).

Transitions between postures, e.g. from sitting to standing, have also been

captured with a single monitor attached to chest, using both the acceleration signal

and inclination of the monitor (Godfrey, Bourke, Ólaighin, van de Ven, & Nelson,

2011; Najafi et al., 2003). These measuring modalities were tested over a relatively

short period of time and their suitability for longer recordings should be studied. In

addition, the ActiGraph GT3X accelerometer has an inclinometer function, which

is said to measure posture when the monitor is worn on the hip and attached in a

perfectly vertical direction. However, low accuracies in correctly measuring sitting

apart from other activities or postures have been reported when using this function

(Carr & Mahar, 2012; Kim, Y., Barry, & Kang, 2015).

For measuring SED and transitions between postures, ML-based algorithms

have also been developed (Bastian et al., 2015; Lyden, Keadle, Staudenmayer, &

Freedson, 2014). If SED is measured with a single accelerometer that only utilizes

acceleration signal, their performance in differentiating between postures is partly

subject to the same problems as the traditional analysis approaches (De Vries, Garre,

Engbers, Hildebrandt, & Van Buuren, 2011; Kim, Y. et al., 2015). In some studies,

high levels of accuracy have been reported when measuring sitting and standing

from acceleration data using ML algorithms. When activities and postures have

been measured in the laboratory, over 95% accuracy have been achieved in

correctly classifying sitting and standing (Ellis et al., 2016). In a free-living

environment, lower accuracies have been reported (between 80 to 90%) (Wullems,

Verschueren, Degens, Morse, & Onambélé, 2017).

29

2.5 Temporal and accumulation patterns of physical activity and

sedentary behaviour

Objective measures of PA have gained popularity as they have become accessible

and feasible for large-scale studies to obtain certain summary metrics of PA such

as time obtained in LPA, MVPA, and when sedentary (Lee, I. M. & Shiroma, 2013;

Westerterp, 2009). However, whether any single metric, like achieving moderate

intensity activity for 150 min/wk is enough for categorising an individual as active

has been questioned, as it is possible to obtain very high levels of MVPA and still

spend the majority of the day in a sedentary position (Thompson, Peacock, Western,

& Batterham, 2015). Thus, a broader approach for evaluating the PA patterns

throughout the day might be more useful in differentiating populations with

detrimental or beneficial PA behaviours.

In the global PA guidelines, it has been stated that “activity should be

performed in bouts of at least 10 minutes duration” (World Health Organization,

2010). This bout criterion has been omitted from PA questionnaires and aims to

provide flexibility in achieving the recommended amount of PA. However, no

specific physiological effect has been found to support it, and bouts of MVPA (in

bouts of at least 5 or 10 minutes) and sporadic MVPA (total time with no bout

restriction) have been reported to have almost identical effects in reducing total

mortality (Saint-Maurice, Troiano, Matthews, & Kraus, 2018). No additional health

benefits of bouts of MVPA compared to sporadic MVPA have been found in terms

of cardiovascular health and metabolic syndrome (Glazer et al., 2013; Robson &

Janssen, 2015). A recent review of 23 studies sums up MVPA of any bout duration

being positively associated with numerous health outcomes including, for example

all-cause mortality, metabolic syndrome, glycemic control, and lipid concentrations

(Jakicic et al., 2019). Recently, this bout criterion has been removed from the

updated version of the PA guidelines for Americans and thus also bouts of activity

of less than 10 minutes should be encouraged (Piercy et al., 2018).

PA distribution over a week has been studied and relatively similar health

benefits have been reported from obtaining MVPA on three or more days in shorter

bouts of exercise or squeezing the same volume into one or two more intensive

exercise days (for so-called weekend warriors) (O'Donovan, Lee, Hamer, &

Stamatakis, 2017). The timing and duration of different activities during a single

day, such as short bouts of intense activity (an amount not fulfilling the activity

recommendation) (Glazer et al., 2013; Metcalfe et al., 2012) and breaking up

prolonged SED also contribute to health (Dunstan et al., 2011; Jefferis et al., 2014).

30

Almost half of the daily SED has been reported to accumulate in periods lasting

for 30 minutes or longer and prolonged sedentary periods of 90 minutes or longer

account for 14% of the total SED among middle-aged people (Diaz et al., 2016).

Prolonged uninterrupted sedentary periods (in addition to total SED) have been

suggested to increase the risk of all-cause mortality independently of MVPA (Diaz

et al., 2017). Nevertheless, a recent study by the same group found both short and

long sedentary bouts to be associated with a mortality risk of equal magnitude.

Moreover, only substituting SED with LPA or MVPA was associated with reduced

risk (Diaz et al., 2019). Many studies have reported the detrimental health effects

of prolonged SED on different cardiovascular and metabolic health markers like

insulin sensitivity, glucose tolerance, and triglyceride levels, with little evidence on

changes in BP, cholesterol level and fasting insulin and glucose (Larsen et al., 2014;

Lyden, Kozey Keadle, Staudenmayer, Braun, & Freedson, 2015; Saunders,

Larouche, Colley, & Tremblay, 2012).

When prolonged SED has been intentionally increased, even after short

exposures (3–7 days) significant changes in insulin sensitivity have been

acknowledged and insulin resistance has been reported after just one week of

increased uninterrupted SED (Larsen et al., 2015; Lyden et al., 2015). Breaking up

prolonged SED is associated with reductions in insulin levels. Breaks containing

LPA, MVPA or standing have been found to be beneficial for preventing negative

sitting-induced changes in glycemia (Dunstan et al., 2011; Holmstrup, Fairchild,

Keslacy, Weinstock, & Kanaley, 2013; Peddie et al., 2013; Thorp et al., 2014; van

Dijk et al., 2013). However, there is still debate on the detrimental health effects of

prolonged bouts of SED (Chastin, Thorlene, Leask, & Emmanuel, 2015;

Stamatakis et al., 2019) and many of the studies finding breaking SED to be

beneficial to health have not accounted for increased EE related to PA breaks

(Dunstan et al., 2011; Peddie et al., 2013; van Dijk et al., 2013). Thus, these studies

fail to prove per se, whether prolonged time in a sitting posture would cause

detrimental health effects but confirm the breaks with LPA or MVPA healthy for

glucose metabolism (Chastin et al., 2015; Stamatakis et al., 2019).

2.5.1 Compositional analysis

The independent role of specific activity in disease prevention or health promotion

has remained obscure due to the difficulty in studying these behaviours

independently in a free-living setting (Chastin, Palarea-Albaladejo, Dontje, &

Skelton, 2015; Maher et al., 2014). Time during the day is finite and increasing

31

time in one activity will inevitably decrease it in one or several others. For

estimating the health effects of allocating time between activities, compositional

analyses have been implemented. It has been suggested that replacing SED with

MVPA in particular decreases cardiovascular disease (CVD) risk (Buman et al.,

2014) and all-cause mortality (Matthews et al., 2015; Stamatakis et al., 2015).

However, these methods rely on statistical modelling and have been used in cross-

sectional designs, thus they do not actually measure the temporal effect of replacing

one activity with another but rather give an estimate of what the health associations

of reallocating time between different activities are.

2.5.2 Profiling physical activity and sedentary behaviour

For evaluating the PA behaviour of an individual, different profiling methods have

gained popularity. Previous studies have implemented multiple different profiling

approaches (see Table 1). A simplistic method is the use of medians or other

quantiles of different PA intensity levels and categorising participants accordingly

(Bakrania et al., 2016; Gubelmann, Vollenweider, & Marques-Vidal, 2017;

Maddison, Jiang, Foley, Scragg, & Direito, 2015). For example, if using the

medians for SED and MVPA, four mutually exclusive PA behaviour groups can be

formed, named in the previous literature as Couch potato (high SED, low MVPA),

Sedentary exerciser (high SED, high MVPA), Light mover (low SED, low MVPA)

and Busy bee (low SED, high MVPA) (Maddison et al., 2015). Also, MVPA tertiles,

or achieving the PA recommendation (MVPA duration ≥150 min/wk), have been

used as boundary conditions for categorising individuals as active or inactive

combined with quantiles of the ratio between LPA and SED to achieve

corresponding PA behaviour groups (Bakrania et al., 2016; Gubelmann et al., 2017;

Loprinzi, Lee, & Cardinal, 2014). These methods are easy to implement, but they

often create unevenly-sized groups (Bakrania et al., 2016; Gubelmann et al., 2017;

Loprinzi et al., 2014; Maddison et al., 2015). Comparison with other similar studies

is difficult, as the MVPA and SED average levels depend greatly on the study

population. For example, Busy bees and Sedentary exercises in a study by Bakrania

et al. (2016) had on average 20min/d more MVPA compared to similar groups by

Maddison et al. (2015). Differences have been reported in CVD risk, cholesterol

levels, body composition and C-reactive protein levels between the PA groups in

favour of those groups with higher amount of MVPA (Bakrania et al., 2016;

Loprinzi et al., 2014; Maddison et al., 2015).

32

Other studies have used the decision tree to recognise different participant

subgroups with different behaviours, such as Lakerveld et al. (2017), who studied

the sociodemographic correlates related to SED. They reported extensive SED

(>7.5 h/d, self-reported) most likely occurring in those individuals with high levels

of education, white-collar jobs and good wealth.

Latent class analysis (LCA) has also been used for identifying distinct PA and

SED groups (Evenson, Wen, Metzger, & Herring, 2015; Metzger et al., 2010). LCA

assigns participants into mutually exclusive classes based on the associations

among observed variables (Evenson et al., 2015). For example, using The National

Health and Nutrition Examination Survey (NHANES) data, Evenson et al. (2015)

found 5–7 groups based on the amount and accumulation of MVPA (total time,

MVPA proportional to wear time and MVPA bouts) and similarly 5–7 groups when

using the features of SED. Differences in the risk factors for metabolic syndrome

were found between the groups (Metzger et al., 2010).

Cluster analysis has also been proposed for PA profiling. In K- and X-means

cluster analysis, the number of clusters (profiles) is predefined (K-means) or

automatically detected (X-means) and the algorithm iteratively assigns the

participants to the closest cluster so that the clusters are distinct but have high inter-

cluster homogeneity (Hastie, Tibshirani, & Friedman, 2008; Pelleg & Moore, 2000;

Wardlaw, Silverman, Siva, Pavord, & Green, 2005). Lee et al. (2013) found two

different clusters (active and less active) when continuous acceleration data was

used to find differing temporal patterns in PA behaviour. Fukuoka et al. (2017)

reported three different PA clusters among inactive women, which differed by the

timing and intensity of PA (afternoon engaged, morning engaged and unengaged),

and differences in the prevalence of depressive symptoms were found between the

clusters.

33

Table 1. Studies using accelerometer-measured physical activity for identifying

physical activity profiles.

Study N Study

population

Profiling method and

data features

Profiles Results

Metzger et

al. (2010)

3,458 NHANES 03–

04, all ≥20

years old

ML, LCA with AC data

from 7 days, used

features: MVPA min/d

5 classes Four more active

classes were

associated with lower

risk factors for

metabolic syndrome

compared to the least

active class.

Lee et al.

(2013)

1,714 Hong Kong

Jockey Club

FAMILY Project

Cohort Study,

all ≥15 years

old

ML, k-means cluster

analysis, AC data from

4 days

Active, Less active Higher prevalence of

chronic health

conditions in the Less

active profile compared

to Active profile.

Loprinzi et

al. (2014)

5,580 NHANES 03–

06, all ≥20

years old

TS, AC data from ≥4

days. MVPA at least or

lower than 150 min/wk,

positive vs. negative

LPA/SED ratio

High MVPA and

positive LPA/SED

ratio, high MVPA

and negative ratio,

low MVPA and

positive ratio, low

MVPA and

negative ratio

Profiles with high

MVPA had more

favourable levels for

BMI, waist

circumference, C-

reactive protein, white

blood cells, and

neutrophils compared

to less active profiles.

Evenson et

al. (2015)

7,931 NHANES 03–

06, all ≥18

years old

ML, LCA with AC data

from ≥3 days, used

features: counts/min,

proportion of MVPA,

proportion of MVPA

bouts, proportion of

SED, proportion of

SED bouts

5–7 latent classes

were recognised

depending on the

feature used

Based on total volume

of PA, older adults were

most often present in

the least active class

and younger adults in

the most active class.

Maddison et

al. (2015)

6,499 NHANES 03–

06, all 30–75

years old

TS, AC data from ≥1

days. MVPA at

least/lower than

median, SED at

least/lower than

median

Busy exercisers,

Techno-Actives,

Potterers, Couch

potatoes

Lower CVD risk was

found in more active

profiles compared to

less active profiles.

34

Study N Study

population

Profiling method and

data features

Profiles Results

Bakrania et

al. (2016)

2,131 Health survey

for England 08,

all ≥18 years

old

TS, AC data from ≥4

days. MVPA at least or

less than 150 min/wk,

Quartiles of SED/LPA

ratio (1st vs. 2nd, 3rd

or 4th)

Busy bees,

Sedentary

exercisers, Light

movers, Couch

potatoes

Higher HDL cholesterol

in more active profiles

compared to Couch

potatoes. MVPA ≥150

min/wk was associated

with lower BMI and

higher HbA1c.

Fukuoka et

al. (2017)

215 mPED

Intervention

trial, 25–69-

year-old

inactive women

ML, k-means cluster

analysis, AC data from

7 days

Afternoon

engaged, Morning

engaged,

Unengaged.

Unengaged profile had

higher depressive

symptoms compared to

others.

Gubelmann

et al. (2017)

2,592 CoLaus study

in Switzerland,

all 45–86 years

old

TS, AC data from ≥7

days. MVPA tertiles (1

vs. 2 or 3), Tertiles of

SED/LPA ratio (1st or

2nd vs. 3rd)

Busy bees,

Sedentary

exercisers, Light

movers, Couch

potatoes

Higher employment

level in more active

profiles compared to

Couch potatoes.

Gubelmann

et al. (2017)

2,592 CoLaus study

in Switzerland,

all 45–86 years

old

TS, AC data from ≥7

days. Tertiles of MVPA

weekend/week ratio

(1st or 2nd vs. 3rd),

tertiles of MVPA time

(1st vs. 2nd or 3rd)

Weekend warrior,

Regularly active,

Inactive

Higher employment

level in Weekend

warriors, lower

educational level in

Regularly active

compared to two other

profiles.

NHANES=The National Health and Nutrition Examination Survey, ML=Machine learning, LCA=Latent

class analysis, AC=accelerometer, MVPA=Moderate to vigorous physical activity, TS=Traditional

statistics, LPA=Light physical activity, SED=Sedentary time, BMI=Body mass index, CVD=Cardiovascular

disease, HDL=High-density lipoprotein, HbA1c=Glycated haemoglobin, mPED=Mobile Phone-Based

Physical Activity Education

2.6 Perceived health

Perceived health (PH, also known as self-perceived health and self-rated health) is

an inexpensive and widely used subjective health measure. PH includes physical,

mental, functional and emotional health aspects (Shields & Shooshtari, 2001).

Often a single-item question asking the individuals to rate their health using 4- or

5-point scale from poor to excellent is used (Shields & Shooshtari, 2001). Due to

35

its simplicity, PH measures have been used in research for many decades (Maddox

& Douglass, 1973).

Determinants of PH include features beyond physiological health status such

as lifestyle, social and work-related factors (Lindström, 2009; López del Amo

González, Benítez, & Martín-Martín, 2018; Wu et al., 2013), and thus it has been

acknowledged that individuals with similar health conditions may perceive their

health differently (Krause & Jay, 1994). Nevertheless, PH has been shown to be

associated with mortality, morbidity and objective health status and to predict

mortality after controlling for objective health status (Idler & Benyamini, 1997; Wu

et al., 2013). In some cases, it has been an even stronger predictor of survival than

medical records (Mossey & Shapiro, 1982).

Both self-reported and objectively measured PA have a positive, dose-response

association with PH (Abu-Omar & Rütten, 2008; Galán, Meseguer, Herruzo, &

Rodríguez-Artalejo, 2010; Hamer & Stamatakis, 2010; Malmberg, Miilunpalo,

Pasanen, Vuori, & Oja, 2005). It has been suggested that the PA domain has an

effect on the association, with LTPA being more strongly associated with good PH

compared to PA in other domains (e.g. household or occupational PA) (Abu-Omar

& Rütten, 2008; Bogaert, De Martelaer, Deforche, Clarys, & Zinzen, 2014; Lera-

López, Ollo-López, & Sánchez-Santos, 2017). No association between SED and

PH or well-being has previously been found (Hamer & Stamatakis, 2010; Withall

et al., 2014).

2.7 Role of physical activity and sedentary behaviour in cardiac

autonomic function and cardiovascular diseases

Cardiac autonomic function is regulated by the autonomic nervous system,

including sympathetic and parasympathetic systems. Usually, the two branches of

the autonomic nervous system are in dynamic balance; the sympathetic system

being associated with the mobilisation of energy and the parasympathetic system

being responsible for restorative functions. However, environmental demands

cause modulation in the system responses. (Thayer, Yamamoto, & Brosschot, 2010)

The balance between these systems can be analysed non-invasively by measuring

heart rate variability (HRV), defined as oscillation in the interval between

consecutive heartbeats (Malik et al., 1996). Cardiac autonomic dysfunction is

associated with lower resting HRV and is a risk factor for all-cause mortality and

CVD morbidity (Dekker et al., 1997; Thayer et al., 2010).

36

Lower levels of HRV are found in older adults compared to their younger peers

due to the normal ageing process, which causes a decline in cardiac vagal

modulation reducing the parasympathetic activity (Bonnemeier et al., 2003;

Jandackova, Scholes, Britton, & Steptoe, 2016; Zhang, 2007). High PA levels and

good cardiorespiratory fitness (CRF) are associated with higher HRV (Föhr et al.,

2016; Kiviniemi et al., 2017). PA also seems to have a protective role in the age-

related decline of HRV (Buchheit et al., 2005; Schuit et al., 1999). Evidence on the

associations between SED, independently of MVPA, and HRV are scant. High

occupational ST was associated with reduced HRV, but the same association was

not found for sitting during leisure time among blue-collar workers (Hallman et al.,

2015). In recent work by the same research group no significant association was

found between HRV and leisure or occupational ST after controlling for leisure

time MVPA, BMI, age, and smoking (Hallman et al., 2019). It has been suggested

that high levels of ST might induce unfavourable cardiovascular changes leading

to poorer autonomic regulation (Thayer et al., 2010).

Cardiovascular diseases are a group of detrimental pathologies in the heart and

vascular system including coronary artery diseases (e.g. myocardial infraction) and

other CVDs like stroke, heart failure and peripheral artery disease (World Health

Organization, 2017). The incidence of CVD increases with increasing age and

major risk factors include high blood pressure (BP), dyslipidaemia, smoking and

physical inactivity, the last of which is responsible for 6% of the total CVD burden

globally (Lee, I. M. et al., 2012; Lowe et al., 1998; Vasan et al., 2005; World Health

Organization, 2017; Yusuf et al., 2004). CVD is a leading cause of death, causing

around 30% of all deaths worldwide (World Health Organization, 2017).

PA decreases the risk of CVD (Kohl, 2001) and even low levels of PA have

been found to be beneficial to cardiac health compared to total inactivity (Lachman

et al., 2018). Positive, independent and dose-response association between SED

and CVD regardless of the activity level has been reported (Carter, Hartman, Holder,

Thijssen, & Hopkins, 2017; Katzmarzyk, Church, Craig, & Bouchard, 2009).

Negative changes in traditional CVD markers (BP, glucose tolerance, high-density

lipoprotein cholesterol) as well as direct vascular effects like reduction in mean and

antegrade blood flow and shear rate in response to prolonged sitting potentially

explain this association (Carter et al., 2017; Stamatakis, Hamer, Tilling, & Lawlor,

2012; Thosar, Bielko, Mather, Johnston, & Wallace, 2015).

37

3 Aims of the study

The main objective was to study the associations between the amount and temporal

patterns of accelerometer-measured PA and SED and the different health outcomes

among middle-aged Finnish adults. The specific aims were:

1. To assess the relationship between self-reported and objectively measured PA

and perceived health in mid-life.

2. To evaluate the association of prolonged objectively measured SED and

cardiac autonomic function, accounting for PA and cardiorespiratory fitness

level.

3. To identify the intensity and temporal patterns of PA and SED based on the

accelerometer-measured PA data and to study their association with CVD risk.

38

39

4 Materials and methods

4.1 Study population

The study population consisted of those individuals from the Northern Finland

Birth Cohort 1966 (NFBC1966) participating the follow-up at 46 years in 2012–

2014 (n=10,321). Originally, the NFBC1966 included all those born in 1966 in the

two northernmost provinces of Finland, Oulu and Lapland (n=12,058 live births).

Since birth, information about these individuals has been collected through health

care records, clinical examinations and questionnaires, including data collected on

their parents and offspring (Northern Finland Cohorts). The 46-year follow-up was

approved by the Ethical Committee of the Northern Ostrobothnia Hospital District

in Oulu, Finland (94/2011) and the study was performed in accordance with the

Declaration of Helsinki. Subjects provided written consent for the study. Personal

identity information was encrypted and replaced with identification codes. At the

46-year follow-up, participants attended clinical examinations, performed a

cardiorespiratory fitness test, filled in questionnaires and their PA was objectively

measured with a wrist-worn accelerometer over a two-week measurement period

(Fig. 1).

4.2 Methods

4.2.1 Questionnaires

Postal questionnaires were sent to all participants with known addresses in 2012–

2014. Questionnaires included items about health, behaviour and social

background. Marital status, education level, employment status, annual household

income, and prevalence of diagnosed diseases were also addressed. Smoking and

drinking habits were enquired about with multiple questions.

Leisure time PA was asked about with questions on the frequency and duration

of light PA (described to cause no sweating or breathlessness) and brisk PA

(described to cause at least some sweating or breathlessness). PA frequency had the

following response options: 1) once a month or less often, 2) 2–3 times a month, 3)

once a week, 4) 2–3 times a week, 5) 4–6 times a week, and 6) daily. PA duration

per exercise time was asked: 1) not at all, 2) less than 20 minutes, 3) 20–39 minutes,

4) 40–59 minutes, 5) 1–1.5 hours, 6) more than 1.5 hours. (Tammelin, 2003)

40

Weekly averages of MET-minutes of light and brisk PA were calculated by

multiplying the PA volume (duration×frequency) by its intensity (light PA 3 METs

and brisk PA 5 METs) (Suija et al., 2013).

Daily ST was asked about (“How much time do you spend sitting on a normal

weekday?”) and composed as sum of STs in different domains (at work, at home

watching TV or video, at home in front of computer, in a vehicle, and in another

place) (Peltonen et al., 2008). Those reporting ST of more than 18 h/d (n=27) were

excluded from analyses concerning ST.

Perceived health was asked about with the question “How would you describe

your health at the moment?” The response options were 1) very good, 2) good, 3)

fair, 4) poor and 5) very poor. The PH responses were dichotomised as good (very

good and good) and other (fair, poor, and very poor) (Idler & Benyamini, 1997).

Fig. 1. Participants of the 46–year follow-up in the Northern Finland Birth Cohort 1966.

(Modified from I)

41

4.2.2 Measurement of physical activity and sedentary behaviour (I–

III)

PA was objectively measured with a wrist-worn, waterproof accelerometer-based

activity monitor (Polar Active, Polar Electro Oy, Kempele, Finland). The monitor

includes a uniaxial capacitive accelerometer (VTI Technologies, Vantaa, Finland;

currently Murata Electronics) (Kinnunen et al., 2019). Polar Active uses the height,

weight, gender and age of the user as predefined inputs and provides MET values

every 30 seconds based on the acceleration sensed and reflects the intensity of daily

PA, Fig. 2 (Hautala et al., 2012). Polar Active has been shown to correlate with the

double-labeled water technique, assessing EE during exercise training and in daily-

living (correlation coefficients 0.86 and 0.88 respectively) (Kinnunen, Tanskanen,

Kyröläinen, & Westerterp, 2012; Kinnunen et al., 2019). The monitor gave no

feedback to the user but only showed the time of day. Participants got the monitors

and padded envelopes during the scheduled visit at the clinical examinations and

were instructed to mail them back after the measurement period. Participants were

asked to wear the activity monitor for 24 hours a day for at least 14 days on the

wrist of the non-dominant hand.

Daily averages (min/d) of time spent in different activity intensity levels (very

light: 1–1.99 MET, light: 2–3.49 MET, moderate: 3.5–4.99 MET, vigorous: 5–7.99

MET and very vigorous ≥8 MET) were calculated for all participants (Jauho et al.,

2015). SED was assessed as the duration of the lowest PA intensity level. MVPA

was assessed as all activity at an intensity of at least 3.5 METs. In our earlier

comparison of different accelerometry-based methods, these intensity levels for

Polar Active provided more comparable results than often used limits defined using

hip-worn accelerometer (SED ≤1.5 METs, LPA 1.5–3 METs, moderate PA as 3–6

METs) for SED, and light and moderate intensity activity levels when using the

above-mentioned limits for ActiGraph (model GT3X) (Leinonen et al., 2017). For

example, the threshold <2 MET for Polar Active provided similar results as the

threshold <100 counts per minute for ActiGraph, and the mean difference between

methods was 7.0 min/d (95% confidence interval from −17.8 to 31.7 min/d)

(Leinonen et al., 2017).

The daily average of total volume of PA was assessed as all activity with an

intensity of at least 2 METs, and each MET value was multiplied by its duration

(MET min/d). Volume of LPA was assessed including all activity with intensity 2–

3.49 MET and calculated by multiplying each MET value with its duration (vLPA,

MET min/d). Volume of MVPA was calculated by multiplying each MET value

42

(intensity of at least 3.5 METs) by its duration (vMVPA, MET min/d). The daily

average time obtained in MVPA bouts was calculated as at least 10 minutes of

consecutive MET values with an intensity of at least 3.5 METs and not allowing

any lower MET values in between (MVPA10, min/d). The daily average time

obtained in prolonged SED was calculated as bouts of at least 30 and 60 minutes

of consecutive MET values in between 1–1.99 METs and not allowing any higher

or lower MET values in between (SED30 and SED60, min/d).

Wear time during waking hours was calculated as a sum of all five activity

intensity levels (min/d). The day the monitor was given to the participant was

excluded from the analysis and those participants with at least four valid days (wear

time ≥600 min/d) were included in the analysis (I and II). Seven consecutive days

of valid PA data was required for cluster analysis (III).

Fig. 2. Intensity of daily physical activity, metabolic equivalent values from Polar Active,

example of one measured day.

Clustering (III)

X-means clustering (Pelleg & Moore, 2000) was used to identify subgroups of

people with different daily activity patterns. In clustering, the aim is to form groups

in which the intra-cluster homogeneity is high and inter-cluster homogeneity is low

(Wardlaw et al., 2005). In K-means clustering, the desirable number of cluster

centres (K) needs to be predefined and the K-means algorithm iteratively moves

the centres so that the total within-cluster variance is minimised (Hastie et al., 2008).

X-means clustering is an expansion of this algorithm as it efficiently defines the

43

number of clusters automatically and thus is more practical when the number of

clusters is not known a priori (Pelleg & Moore, 2000). In X-means clustering, the

number of clusters is based on the best-scoring centroid set.

Those participants who provided valid PA data (wear time ≥600 min/d) from

seven consecutive measured days starting from the second day were included in the

cluster analysis (n=4,582) (III). For those participants, 10-minute averages of the

original MET data (MET values for every 30 sec) for the one-week measurement

period were calculated. Because participants received the activity monitors on

different weekdays, the MET data was reorganised temporally from Monday to

Sunday to standardise the effect of weekends on the habitual PA. A total of 1,008

average values (144 for each day) from each participant were used for clustering.

The number of cluster centroids from 1 to 7 was tested. For clustering, Weka,

version 3.8.1 (Waikato Environment for Knowledge Analysis, University of

Waikato, Hamilton, New Zealand) was used.

4.2.3 Clinical examination

Participants (n=5,852) attended a clinical examination where trained nurses

conducted comprehensive medical examinations. Participants’ height and weight

were measured and body mass index (BMI) was calculated as weight (kg) divided

by height squared (m2). The participants’ body composition was analysed after

overnight fasting with a bioelectrical impedance measurement device (Inbody 720;

Inbody, Seoul, Korea), and body fat percentage (fat%) and visceral fat area (cm2)

were reported.

In the laboratory, venous blood samples were drawn after overnight fasting for

the analysis of triglycerides, serum total cholesterol, low-density lipoprotein (LDL)

and high-density lipoprotein (HDL) cholesterol levels, which were determined by

using enzymatic assay methods (Advia 1800; Siemens Healthcare Diagnostics Inc.,

Tarrytown, Ny, USA). The samples were analysed in NordLab Oulu, a testing

laboratory (T113) accredited by Finnish Accreditation Service (EN ISO 15189).

Systolic (SBP) and diastolic blood pressures (DBP) were measured three times in

a seated position (the two latter measurements averaged; Omron M10, Omron 124

Healthcare, Kyoto, Japan) after 15 minutes of rest.

44

Measurement of cardiorespiratory fitness (II)

Cardiorespiratory fitness was measured by a sub-maximal four-minute single-step

test on a bench without shoes. The height of the bench was adjusted to 33/40 cm

for women and men respectively. For pacing, the stepping rate of 23 ascents/min

with a metronome was used. The protocol was the same as in the previous follow-

up and is based on the Astrand-Ryhming sub-maximal step test. Heart rate (HR)

was measured continuously (RS800CX, Polar Electro Oy, Kempele, Finland)

during the test and was transformed into moving 10-beat median data. Peak HR

during the test (HRpeak, beats per minute) and recovered HR value 60 seconds

after the test (HRrec, bpm) measured in a seated position were reported. Heart rate

recovery at 60 seconds (HRR60) was calculated (HRpeak – HRrec, bpm).

Steepness of the largest change in heart rate in the 30 seconds during the first 60

seconds of the recovery phase was calculated (HRslope, bpm·s−1). (Kiviniemi et al.,

2017) Peak HR obtained from a submaximal exercise test can be used to evaluate

somewhat reliably an individual’s aerobic capacity (Åstrand & Ryhming, 1954).

Those participants who terminated the test because of exhaustion or some other

reason (n=277) and who did not perform the test due to impaired health status or

unwillingness (n=534) or who had technical problems with HR measurement (n=31)

were excluded from the analysis concerning CRF.

Measurement of cardiac autonomic function (II)

A description of the protocol was provided to each participant whilst they sat on a

chair having the instrumentation they needed attached. In order to record R-R

intervals (RRi), a HR monitor (RS800CX) was used. Before recordings, a rest

period of at least one minute was held to allow for stabilisation of HR. This short

stabilization period has been reported to provide robust HRV measurements from

even as short as a 1-minute recording (Flatt & Esco, 2016). HR was recorded for

three minutes in a seated position followed by three minutes standing. The

participants were allowed to breathe spontaneously. Spontaneous breathing was

allowed because it requires less familiarization from the participant and breathing

frequency has been reported to have only a small impact on one of the main HRV

variable; the root mean square of successive differences in RRi (rMSSD) (Penttilä

et al., 2001). The first 150 seconds of recording in a seated position and the last 150

seconds of recording in a standing position were analysed. Artefacts and ectopic

beats were removed and replaced by the local average (Hearts 1.2, University of

45

Oulu, Finland) and sequences with ≥10 consecutive beats of noise or ectopic beats

were deleted. The RRi series with ≥80% accepted data were included in the

analyses (Kiviniemi et al., 2017).

A total of 5,679 subjects took part in the RRi recordings and of these 5,473

(96%) had eligible HRV data. Mean heart rate (HRrest), rMSSD describing the

cardiac vagal activity, and the ratio between low frequency and high frequency

power (LF/HF), describing the balance between sympathetic and parasympathetic

activation, were analysed from measurements in seated position. Participants

taking antihypertensive medication in the form of beta blockers (n=766) or having

type I or II diabetes (n=235) were excluded from the analysis (II).

4.3 Statistical methods

The descriptive data is presented in counts and proportions, means and standard

deviations (SD), or medians, and 25th and 75th percentiles for skewed data.

Univariate associations between continuous variables and two groups were

analysed using the independent-samples t-test, with the Tukey post hoc test for

normally distributed variables and with the independent-samples Mann-Whitney U

test for skewed data. For multiple group comparisons analysis of variance with

Tukey post hoc tests for normally distributed variables and with Kruskal–Wallis

tests for skewed data were used. Differences in categorical variables between

groups were analysed using the Chi-square (χ2) test, and the Z-test with Bonferroni

correction was used for post hoc analysis. Correlation between two continuous

variables was calculated using the Pearson correlation coefficient (r).

Cardiorespiratory autonomic function variables (rMSSD, LF/HF ratio),

objectively measured PA variables (moderate, vigorous, very vigorous intensity

activity, MVPA, sedentary bouts and MVPA bouts) and other variables (alcohol

consumption, triglycerides, total cholesterol, HDL cholesterol and CVD risk) with

non-Gaussian distribution were natural-log transformed before statistical analyses.

Non-transformed values are presented in the text and tables.

The associations between PA and PH were studied through binary logistic

regression analysis (I). PH was controlled for gender, marital status

(married/cohabiting, unmarried), employment status (working, unemployed, other),

education (vocational or no vocational education, polytechnic/university degree),

prevalence of diagnosed diseases (CVD, diabetes mellitus, cancer, musculoskeletal

diseases or mental disorder), smoking (non-smoker, former smoker, current

smoker), heavy alcohol consumption (men ≥40 g/d, women ≥20 g/d), BMI, income

46

(quartiles of yearly household income, €k/year), and ST (quartiles, min/d). Model

1 included accelerometer-measured MVPA (quartiles, min/wk), and Model 2 self-

reported LTPA (quartiles, min/wk). Odds ratios (OR) and 95% confidence intervals

(95% CI) for good PH were calculated, and ORs were adjusted for all the variables.

The significance of the models was evaluated using the likelihood ratio test and

Nagelkerke coefficient of determination (R2) value.

Associations between total SED and sedentary bouts (SED30, SED60) and

autonomic function variables (those with linear relationship with sedentary

variables) were studied through multivariate linear regression analysis separately

for men and women (II). Dependent variables were adjusted for potential

confounders, including HRpeak (bpm), fat%, SBP (mmHg), HDL and LDL

cholesterol and triglycerides (mmol/l), alcohol consumption (g/d), smoking, and

vLPA and vMVPA (MET min/d). Diastolic BP was highly correlated with SBP and

was therefore excluded. Models were adjusted first for all covariates in block 1

(stepwise method). Total SED and sedentary bout variables were then each added

separately to the model in block 2 (enter method).

Statistical significance was set to p<0.05, and statistical analyses were

performed with IBM SPSS Statistics for Windows, version 24.0 (IBM Corporation,

Armonk, USA).

47

5 Results

5.1 Study population characteristics

Anthropometric, demographic and behavioural information of the study population

(n=7,132) is presented in Table 2.

Table 2. Characteristics of the study population.

Variable Men (n=3,291) Women (n=3,841)

Height, cm 178.5 (6.3) 164.8 (6.0)

Weight, kg 87.2 (14.9) 72.0 (14.9)

BMI, kg/m2 27.3 (4.3) 26.5 (5.3)

Education, n (%)

No professional education 148 (5) 106 (3)

Vocational/college level education 2,174 (72) 2,339 (66)

Polytechnic/university degree 699 (23) 1,125 (31)

Employment status, n (%)

Employed 2,571 (87) 3,051 (87)

Studying 33 (1) 68 (2)

Unemployed 220 (8) 182 (5)

Other 122 (4) 207 (6)

Smoking, n (%)

Non-smoker 1,366 (45) 2,086 (57)

Former smoker 923 (30) 872 (24)

Current smoker 762 (25) 697 (19)

Alcohol consumption

Total consumption, g/d 8.5 (2.4–21.8) 2.9 (0.6–8.1)

Heavy users, n (%)a 330 (11) 291 (8)

Values are mean (SD) or median (25th–75th percentiles) if not otherwise stated, BMI=body mass index, aAlcohol consumption in men ≥40 g/d, in women ≥20 g/d

5.2 Accelerometer-measured physical activity

Physical activity was objectively measured with an activity monitor from 5,621

participants from which at least four valid days (I and II) were available from 5,481

(98%) and seven consecutive valid days (III) available from 4,582 (82%)

participants. Compared to those with valid PA data from at least four days, there

were less employed (79% vs. 89%, p<0.05) and more participants reporting “other”

as their employment status (10% vs. 4%, p<0.05) among those participants who

48

had less than four valid days. Those participants without seven consecutive valid

days were more often men (53% vs. 42%, p<0.001), had 0.41 units higher BMI (95%

CI 0.07–0.74, p=0.018) and 0.68 units higher fat percentage (95% CI 0.04–1.31,

p=0.038), consumed 2.32 g/d (95% CI 1.08–3.56, p<0.001) more alcohol, were

more often heavy users of alcohol (11% vs. 8%, p<0.001), and smokers (23% vs.

18%, p<0.05), and less often non-smokers (48% vs. 55%, p<0.05) compared to

participants with 7-day valid PA data (data not shown).

Accelerometer-measured average daily minutes at different activity intensity

levels from participants with at least four valid days are presented in Table 3. Men

had on average more SED, more moderate and very vigorous PA and less light and

vigorous PA compared to women (p<0.001). Men had also higher total volume of

MVPA compared to women (p<0.001).

Table 3. Accelerometer-measured physical activity by intensity levels.

Variable Men (n=2,413) Women (n=3,068)

SED (1–1.99 MET), min/d 644 (95)* 621 (88)

LPA (2–3.49 MET), min/d 266 (70)* 288 (73)

Moderate intensity PA (3.5–4.99 MET), min/d 48 (25)* 28 (15)

Vigorous intensity PA (5–7.99 MET), min/d 18 (13)* 28 (17)

Very vigorous intensity PA (≥8 MET), min/d 12 (13)* 5 (7)

MVPA (≥3.5 MET), min/d 79 (39)* 61 (29)

vMVPA, MET min/d 422 (234)* 329 (177)

Values are mean (SD), SED=sedentary time, LPA=light physical activity, PA=physical activity,

MVPA=moderate to vigorous physical activity, vMVPA=volume of moderate to vigorous physical activity, *Different from women (p<0.001)

5.3 Physical activity and perceived health (I)

Self-reported STs on a normal weekday in different domains, total ST, and leisure

time PA in brisk and light activities and total volume of LTPA are presented in Table

4. Women reported significantly more light PA per week compared to men (p<0.001)

and men reported 60 min more sitting per day than women (p<0.001). Overall, most

of the daily sitting took place at work and at home watching TV or videos for both

genders.

Over half of the study population (53%) perceived their health as good, 13%

as very good, and 30%, 3%, and 1% as fair, poor and very poor, respectively. When

divided into two groups, those with good PH (answer alternatives good/very good)

had 60 min more MVPA and over 100 min more self-reported LTPA compared to

49

those with fair, poor or very poor PH (p<0.001). There was no significant difference

in self-reported ST between the PH groups (455 vs. 459 min/d, in good/very good

and fair/poor/very poor PH groups respectively, p>0.05) (Table 4 in study I).

Table 4. Self-reported leisure time physical activity and sitting time.

Variable Mena Womenb

Self-reported PA

Total PA, min/wk 188 (75–345)* 233 (115–398)

Total PA volume, MET min/wk 713 (248–1350)* 855 (435–1500)

Light PA, min/wk 75 (30–210)* 113 (50–225)

Brisk PA, min/wk 75 (10–188)* 75 (23–188)

Self-reported ST

Total ST, min/d 480 (315–615)* 420 (270–570)

Sitting at work, min/d 210 (60–360)** 210 (60–390)

Sitting at home watching TV or videos, min/d 120 (60–150)* 120 (60–120)

Sitting at home in front of computer, min/d 60 (30–60)* 30 (30–60)

Sitting in a vehicle, min/d 60 (30–90)* 30 (15–60)

Sitting in another place, min/d 0 (0–60)** 0 (0–60)

Values are median (25th–75th percentiles), PA=physical activity, ST=sitting time, aPhysical activity

variables available from 2,878 and sitting time variables from 3,681 men, bPhysical activity variables

available from 3,506 and sitting time variables from 3,054 women, *Different from women (p<0.001), **Different from women (p<0.05)

The association between MVPA and PH (Model 1) and LTPA and PH (Model 2)

was studied through binary logistic regression analysis, Table 5. Both regression

models were statistically significant (p<0.001) in the likelihood ratio test (χ2=704.4

and 882.2 for Models 1 and 2, respectively) and R2 values were 0.212 and 0.262,

respectively. MVPA was positively associated with good PH after adjustment for

gender, BMI, prevalence of diseases, smoking, alcohol consumption, ST and

socioeconomic factors. ORs were greater in the II-IV MVPA quartile (ORs=1.37,

1.49, and 1.66) compared to the lowest quartile. Greater LTPA was also associated

with higher odds of good PH (II-IV quartile ORs=1.72, 2.41, and 4.33). Higher

education and income, lack of diagnosed diseases, and marriage or cohabitation

increased the odds of good PH. In contrast, higher BMI, heavy alcohol consumption

and smoking were associated with lower odds of good PH. There was no

association between ST or gender and PH.

50

Table 5. Odds ratios for good perceived health. Model 1 includes accelerometer-

measured MVPA, Model 2 self-reported LTPA.

Variable Model 1. OR (95% CI)a Model 2. OR (95% CI)a

MVPA quartiles, min/wk

I (7–313) 1

II (314–440) 1.37 (1.13–1.67)

III (441–603) 1.49 (1.21–1.82)

IV (604–2,638) 1.66 (1.35–2.05)

LTPA quartiles, min/wk

I (0–89) 1

II (90–224) 1.72 (1.41–2.09)

III (225–374) 2.41 (1.96–2.96)

IV (375–1,260) 4.33 (3.47–5.40)

ST quartiles, min/d

I (20–300) 1 1

II (301–480) 0.96 (0.78–1.18) 0.97 (0.78–1.20)

III (481–600) 1.04 (0.84–1.23) 1.06 (0.86–1.31)

IV (601–1080) 0.87 (0.71–1.07) 0.93 (0.75–1.14)

Gender

Men 1 1

Women 1.13 (0.97–1.31) 0.96 (0.82–1.11)

Marital status

Married/cohabiting 1 1

Unmarried 0.86 (0.71–1.05) 0.77 (0.63–0.95)

Education

Vocational/college level or no education 1 1

Polytechnic/university degree 1.31 (1.10–1.56) 1.28 (1.08–1.53)

Employment status

Working 1 1

Unemployed 0.54 (0.40–0.74) 0.48 (0.35–0.65)

Other 0.44 (0.32–0.60) 0.39 (0.28–0.53)

Smoking

Non-smoker 1 1

Former smoker 1.00 (0.84–1.18) 0.97 (0.82–1.15)

Current smoker 0.60 (0.50–0.73) 0.64 (0.53–0.78)

Prevalence of diseasesb

One or more 1 1

None 2.16 (1.83–2.55) 2.27 (1.92–2.69)

Heavy alcohol consumptionc

Yes 1 1

No 1.43 (1.12–1.83) 1.31 (1.02–1.69)

BMI, kg/m2 0.90 (0.89–0.91) 0.90 (0.89–0.92)

51

Variable Model 1. OR (95% CI)a Model 2. OR (95% CI)a

Income quartiles, €k/year

I (0–34.5) 1 1

II (34.6–59.5) 1.14 (0.93–1.40) 1.11 (0.90–1.37)

III (59.6–79.9) 1.51 (1.20–1.90) 1.43 (1.13–1.81)

IV (≥80) 2.06 (1.62–2.62) 1.84 (1.44–2.35)

MVPA=moderate to vigorous physical activity, LTPA=leisure time physical activity, aStatistically significant

associations (p<0.05) are in bold, bPrevalence of cardiovascular disease, diabetes mellitus, cancer,

musculoskeletal diseases or mental disorders, cAlcohol consumption in men at least 40 g/d, in women at

least 20 g/d

5.4 Cardiac autonomic function and prolonged sedentary time (II)

Valid cardiac autonomic function, CRF, and PA data was available from 4,150

participants and average levels are presented with laboratory measurements in

Table 6. Women had on average lower levels of BP, LDL cholesterol, and

triglycerides, and higher HDL cholesterol compared to men (p<0.001). No

differences in prolonged SED measured as duration in bouts of at least 30 min and

60 min of SED was found between genders (p>0.05). Higher HRpeak, and HRR60

(p<0.001) were found in women. Women had lower LF/HF ratio and higher rMSSD

than men (p<0.001).

Significant linear associations with total SED and/or sedentary bouts were

found for the variables rMSSD, LF/HF ratio (in women), HRrest, HRR60, and

HRslope. Overall, the correlation coefficients were small (see Table 2 in study II).

Multivariate linear regression analysis was computed separately for men and

women for these variables (Appendix 1, Table 9) adjusting for HRpeak (bpm), fat%,

SBP (mmHg), HDL and LDL cholesterol and triglycerides (mmol/l), alcohol

consumption, smoking, and volume of LPA and MVPA (MET min/d).

More time spent in prolonged sedentary bouts was associated with higher

rMSSD in both genders (β=0.082–0.104, all p<0.001). After adjustments, total SED

and SED30 were not associated with resting HR, post-exercise heart rate recovery

nor its steepness (HRrest, HRRslope and HRR60) (all p>0.05). Longer duration of

SED60 was associated with lower LF/HF ratio in women (β=−0.060, p=0.013).

Better CRF (lower HRpeak) was associated with higher rMSSD, better HR

recovery (HRslope and HRR60) and lower resting HR in men and women, and in

lower LF/HF ratio in women. Lower SBP was associated with higher rMSSD,

lower resting HR, and better HR recovery (HRR60) in men and women. The

volume of PA was not associated with rMSSD. Higher vMVPA was associated with

52

better HR recovery (HRslope and HRR60). After including total SED in the model,

the association between HRrest and vLPA became nonsignificant in men. For other

dependent variables, the beta coefficients of covariates did not change notably after

adding sedentary variables in block 2.

Table 6. Clinical measurements, level of cardiorespiratory fitness, physical activity and

prolonged sedentary time among the participants (n=4,150).

Variable Men (n=1,855) Women (n=2,295)

Clinical measurements

HDL cholesterol, mmol/l 1.4 (0.3)* 1.7 (0.4)

LDL cholesterol, mmol/l 3.8 (0.9)* 3.2 (0.9)

Triglycerides, mmol/l 1.2 (0.9–1.7)* 0.9 (0.7–1.2)

Systolic BP, mmHg 130 (14)* 120 (15)

Diastolic BP, mmHg 86 (10)* 82 (10)

Cardiac autonomic function measurement

HRrest, bpm 71 (12) 72 (10)

rMSSD, ms 20 (13–30)* 24 (16–36)

LF/HF 2.3 (1.3–4.2)* 1.2 (0.7–2.3)

CRF measurement

HRpeak, bpm 146 (16)* 149 (15)

HRR60, bpm 38 (11)* 44 (11)

HRslope, bpm*s−1 −0.95 (0.32)* −1.10 (0.34)

Physical activity

vLPA, MET min/d 704 (190)* 742 (191)

vMVPA, MET min/d 436 (232)* 341 (179)

total SED, min/d 642 (93)* 617 (87)

SED30, min/d 62 (35–106) 64 (38–97)

SED60, min/d 7 (0–20) 9 (0–20)

Values are mean (SD) or median (25th–75th percentiles), HDL=high-density lipoprotein, LDL=low-density

lipoprotein, BP=blood pressure, HRrest=resting heart rate, rMSSD=root mean square of successful

differences in R-R intervals, LF=low frequency power, HF=high frequency power, CRF=cardiorespiratory

fitness, HRpeak=peak heart rate during cardiorespiratory fitness test, HRR60=heart rate recovery 60

seconds after cardiorespiratory fitness test, HRslope=the steepness of heart rate recovery, total

SED=total sedentary time, vLPA=total volume of light physical activity, vMVPA=total volume of moderate

to vigorous physical activity, SED30=accumulated daily time in bouts of at least 30 min of consecutive

MET values in between 1–2 METs, SED60=accumulated daily time in bouts of at least 60 min of

consecutive MET values in between 1–2 METs, *Different from women (p<0.001)

53

5.5 Temporal patterns of physical activity and cardiovascular

disease risk (III)

The final cluster model included four distinct clusters based on the intensity and

temporal patterns of PA and SED; cluster 1 (inactive, n=1,881, 41% of the

participants with valid data), cluster 2 (evening active, n=802, 18%), cluster 3

(moderately active, n=1,297, 28%), and cluster 4 (very active, n=602, 13%). The

characteristics and the levels of PA in the PA clusters are presented in Table 7. There

were significant differences in the amount of daily wear time, SED, LPA and MVPA

between the clusters (p<0.001). The very active cluster had the highest amount of

MVPA (122 min/d) which was over an hour more compared to the inactive cluster

(p<0.05). Substantially lower levels of MVPA obtained in at least 10 min bouts

were found in all the clusters (below 20 min/d) compared to the total MVPA time.

Table 7. Characteristics and PA by intensity levels in the PA clusters (n=4,582).

Variable Cluster Overall

p-value*

Inactive

(n=1,881)

Evening active

(n=802)

Moderately

active

(n=1,297)

Very active

(n=602)

Proportion of men, n (%) 648 (34)a,c 394 (49)d,e 493 (38)f 381 (63) <0.001

BMI, kg/m2 27.2 (5.2)b,c 27.0 (4.9)d 26.2 (4.4) 26.2 (4.1) <0.001

Fat%

Men 24.3 (6.9)b,c 23.6 (7.5)e 22.8 (6.9)f 21.1 (7.0) <0.001

Women 34.8 (8.4)a,b,c 33.1 (8.2)d,e 31.4 (7.9) 29.9 (8.1) <0.001

Visceral fat area, cm2

Men 103.0 (38.5)c 101.9 (43.0)e 98.2 (37.4) 91.4 (36.8) <0.001

Women 115.5 (44.2)b,c 110.2 (42.1)d,e 100.0 (38.5) 96.7 (38.6) <0.001

Education, n (%) <0.001

Vocational/college level

or no education

1172 (67)b,c

497 (68)e 884 (72)f 469 (84)

Polytechnic/university

degree

573 (33) 238 (32)e 339 (28)f 93 (16)

Employment status, n (%) <0.001

Employed 1499 (86)b 627 (86)d 1144 (95)f 494 (89)

Unemployed 117 (6)b 43 (6)d 24 (2)f 34 (6)

Other 133 (8)b 61 (8)d,e 34 (3) 25 (5)

54

Variable Cluster Overall

p-value*

Inactive

(n=1,881)

Evening active

(n=802)

Moderately

active

(n=1,297)

Very active

(n=602)

Smoking, n (%) <0.001

Non-smoker 990 (55)a 359 (48)d,e 737 (59) 317 (56)

Current smoker 335 (19) 171 (22)d,e 195 (16) 81 (14)

Former smoker 458 (26) 224 (30) 307 (25) 171 (30)

Alcohol consumption, g/d 4.1 (1.0–11.2)a 5.6 (1.2–15.1)d 4.1 (1.0–11.2) 4.5 (1.0–14.4) <0.001

Heavy alcohol users, n (%) 138 (8) 69 (9) 89 (7) 44 (8) 0.482

PA variables

SED, min/d 675 (79)a,b,c 637 (95)d,e 625 (76)f 534 (78) <0.001

SED30, min/d 96 (60)a,b,c 67 (53)e 63 (45)f 39 (31) <0.001

Light PA, min/d 242 (59)a,b,c 298 (73)e 298 (63)f 351 (69) <0.001

MVPA, min/d 48 (21)a,b,c 71 (30)d,e 74 (24)f 122 (42) <0.001

MVPA10, min/d 7 (9)b,c 8 (10)d,e 11 (13)f 15 (19) <0.001

Wear time, min/d 966 (57)a,b,c 1006 (64)d 997 (52)f 987 (60) <0.001

Values are mean (SD) or median (25th–75th percentiles) if not otherwise stated, BMI=body mass index,

Fat%=Body fat percentage, PA=physical activity, SED=sedentary time, SED30=sedentary time obtained

in ≥30 min bouts, MVPA=moderate to vigorous physical activity, MVPA10=moderate to vigorous physical

activity obtained in ≥10 min bouts, *Pairwise comparisons were made if overall p-value was significant.

Only significant (p<0.05) pairwise comparison p-values are reported: ainactive compared to evening

active, binactive compared to moderately active, cinactive compared to very active, devening active

compared to moderately active, eevening active compared to very active, fmoderately active compared to

very active

Temporal patterns of average daily PA over the one-week measurement period in

clusters are presented in Fig. 3a, and PA patterns separately during weekdays and

weekend days are presented in Fig. 3b and 3c respectively. Fairly similar temporal

PA patterns were found in inactive and moderately active clusters, but the inactive

cluster had overall lower PA intensity. Most of the activities occurred between 7:00

and 19:00 in these two clusters. In the evening active cluster, PA behaviours had

shifted in time scale, with most of the activities taking place between 12:00 and

21:00. The highest PA intensity level during waking hours was found in the very

active cluster. When weekdays and weekend days were separated, a clearer peak in

activity intensity in mornings during weekdays was found in the moderately active

and inactive clusters. On weekend days, activity patterns shifted in time scale in all

clusters, with higher PA intensities occurring between 10:00 and 21:00 and the

intensity of activities being lower in the evening compared to the morning.

55

Fig. 3. Average metabolic equivalent values every 10 minute of a) all measured days, b)

all measured weekdays, c) all measured weekend days in clusters. (III)

56

A low risk (<10%) of developing CVD in the next 10 years was found in the

majority of the study participants; 74% of the men and 97% of the women. A high

risk (>20%) was found only in 2% of men and 0.1% of women. CVD risk and risk-

related factors are presented separately for men and women in Table 8a and Table

8b respectively. There were significant differences in the CVD risk between

clusters in both genders (p<0.05). In men, the inactive cluster had a higher CVD

risk compared to the very active cluster. The level of HDL cholesterol was lower in

the inactive and moderately active clusters compared to the very active cluster in

men. Also, the prevalence of smokers was higher in the inactive cluster compared

to the very active cluster in men. In women, the inactive cluster had a higher CVD

risk compared to the moderately active and very active clusters, and the evening

active cluster had a higher risk compared to the moderately active cluster. The level

of HDL cholesterol was higher in the very active and moderately active clusters

compared to other clusters and total cholesterol was higher in the inactive and

evening active clusters compared to the moderately active cluster in women.

57

Table 8a. Ten-year cardiovascular disease risk based on Framingham risk model and

risk factors in men (n=1,469).

Variable Cluster Overall p-

value*

Inactive

(n=487)

Evening active

(n=305)

Moderately

active

(n=376)

Very active

(n=301)

CVD risk, % 8.8 (5.4)c 8.5 (4.7) 8.1 (4.1) 7.6 (3.8) 0.028

HDL cholesterol, mmol/l 1.38 (0.34)c 1.42 (0.35) 1.41 (0.34)f 1.48 (0.35) <0.001

Total cholesterol, mmol/l 5.52 (1.01) 5.56 (0.96) 5.47 (0.97) 5.58 (0.99) 0.152

SBP not treated, mmHg 126.5 (14.6) 127.5 (13.8) 127.9 (13.5) 128.1 (12.5) 0.285

SBP treated, mmHg 132.7 (14.1) 129.3 (11.5) 131.2 (12.3) 133.5 (14.9) 0.638

Smoker, n (%) 108 (22)c 74 (24)e 74 (20) 38 (13) 0.002

Diabetic, n (%) 17 (4) 11 (4) 12 (3) 11 (4) 0.987

Values are mean (SD) if not otherwise stated, CVD=cardiovascular disease, HDL=high-density

lipoprotein, SBP=systolic blood pressure, *Pairwise comparisons were made if overall p-value was

significant. Only significant (p<0.05) pairwise comparison p-values are reported: ainactive compared to

evening active, binactive compared to moderately active, cinactive compared to very active, devening

active compared to moderately active, eevening active compared to very active, fmoderately active

compared to very active

Table 8b. Ten-year cardiovascular disease risk based on Framingham risk model and

risk factors in women (n=2,228).

Variable Cluster Overall p-

value*

Inactive

(n=1,005)

Evening active

(n=336)

Moderately

active

(n=705)

Very active

(n=182)

CVD risk, % 3.5 (2.4)b,c 3.6 (2.8)d 3.0 (1.8) 3.1 (1.9) <0.001

HDL cholesterol, mmol/l 1.66 (0.39)b,c 1.67 (0.38)d 1.71 (0.37)f 1.81 (0.41) <0.001

Total cholesterol, mmol/l 5.23 (0.88)b 5.20 (0.83)d 5.06 (0.79) 5.13 (0.82) 0.001

SBP not treated, mmHg 117.9 (15.7) 117.7 (13.7) 117.2 (14.4) 117.5 (13.8) 0.658

SBP treated, mmHg 125.1 (15.3) 125.4 (15.9) 122.5 (14.2) 127.2 (17.3) 0.390

Smoker, n (%) 166 (17) 76 (23)d 98 (14) 26 (14) 0.004

Diabetic, n (%) 39 (3) 17 (4) 20 (3) 1 (1) 0.152

Values are mean (SD) if not otherwise stated, CVD=cardiovascular disease, HDL=high-density

lipoprotein, SBP=systolic blood pressure, *Pairwise comparisons were made if overall p-value was

significant. Only significant (p<0.05) pairwise comparison p-values are reported: ainactive compared to

evening active, binactive compared to moderately active, cinactive compared to very active, devening

active compared to moderately active, eevening active compared to very active, fmoderately active

compared to very active

58

59

6 Discussion

The current study investigated the association between the amount and

accumulation patterns of accelerometer-measured PA and SED and different health

outcomes among middle-aged people. The volume and temporal patterns of PA and

SED showed associations with perceived health, cardiac autonomic function and

cardiometabolic health. The participants with good or very good perceived health

had substantially higher levels of measured MVPA and self-reported LTPA

compared to those with poorer perceived health. Objectively measured prolonged

SED in at least 30-minute bouts was associated with HRV. Four distinct PA clusters

with different temporal patterns and intensity of PA were recognised. These clusters

differed significantly in terms of CVD risk.

We found that self-reported leisure time PA, including light and brisk activities,

was strongly and positively associated with health perception, as well as a dose-

response association, after controlling for lifestyle, sociodemographic and health

factors (study I). This points out the importance of encouraging a range of physical

activities. LTPA often aims for recreation, enjoyment and social interaction and thus

has the potential to improve mood and psychological well-being in addition to the

known physiological health benefits. Also, accelerometer-measured MVPA had a

positive dose-response association with PH, but it was not as strongly associated

with PH compared to self-reported LTPA. This might suggest that the sum of all PA

including leisure time, commuting and occupational PA is beneficial overall for

health, but some aspects of it might attenuate the relationship. For example, work-

related activities can be repetitive and physically demanding, and some previous

studies have reported weaker or a complete lack of association between

occupational PA and health outcomes (Oppert et al., 2006; Sofi et al., 2007). There

have also been previous studies reporting LTPA being more strongly associated

with PH than other PA domains (Abu-Omar & Rütten, 2008; Bogaert et al., 2014;

Lera-López et al., 2017). Self-reported ST in this study was not associated with PH.

This lack of relationship has also been previously reported (Hamer & Stamatakis,

2010; Withall et al., 2014). However, previous studies have reported numerous

other detrimental health associations with self-reported ST such as cancer and CVD

morbidity and mortality, and type 2 diabetes morbidity (Biswas et al., 2015;

Matthews et al., 2012). Thus, the unhealthiness of excessive ST should not be

evaluated only in terms of health perception.

In study II we investigated the association between total and prolonged SED

and cardiac autonomic function which included measures of HRV, resting HR and

60

post-exercise HR recovery. Prolonged SED was assessed as the daily average

duration of uninterrupted sedentary bouts lasting at least 30 and 60 minutes. In the

analyses, we controlled for significant confounders including volume of LPA and

MVPA, BP, cholesterol and triglyceride levels, CRF, body fat percentage, smoking,

and consumption of alcohol. Prolonged sedentary bouts but not total SED were

significantly associated with rMSSD after adjustments. Higher SED60 was

associated with higher rMSSD, describing higher parasympathetic (vagal) activity,

in men and women. Additionally, in women SED30 was also positively associated

with rMSSD.

Only a few previous studies have examined the association between

objectively measured SED and HRV. Hallman et al. (2015) reported higher ST at

work being associated with lower nocturnal rMSSD but no relationship was found

between rMSSD and leisure-related ST among blue-collar workers (n=126).

However, in their recent study conducted with a larger sample (n=490, blue-collar

workers), and after controlling for BMI, age, smoking, and leisure time MVPA, no

significant association was found between rMSSD (or other HRV indexes) and

leisure or occupational ST (Hallman et al., 2019). These findings are in line with

the present study’s finding regarding a lack of association between total SED and

HRV indexes after adjusting for potential confounders. The possibly complex

explanation for the positive association between SED bouts and rMSSD obtained

in this study includes both physiological factors and underlying confounders. In

addition, the correlation between SED and HRV variables were small, warranting

cautiousness in interpretation of the obtained results. The physiological effect of

bodily stress (mental, physical) is decreased HRV due to increased sympathetic

activity and the withdrawal of parasympathetic activity (Kim, H., Cheon, Bai, Lee,

& Koo, 2017; Thayer et al., 2010). Presumably, the amount and type of stressors

occurring during leisure time compared to work differ and partly affect this

discrepancy. Measuring HRV in the laboratory might have induced additional stress

in the participants and affected the HRV measurement results. Possible confounders

not accounted for in this study could include socioeconomical factors such

occupation. In addition to sedentary bouts, better CRF, lower SBP, lower levels of

triglycerides, and, in men, lower fat%, LDL cholesterol, and abstinence of smoking,

and, in women, lower HDL cholesterol were associated with higher rMSSD.

Higher SED60 was associated with a lower LF/HF ratio in women but not in

men. LF/HF ratio has been used to describe sympatho-vagal balance; LF power has

been suggested to be generated by sympathetic nervous system and HF power by

parasympathetic nervous system and, therefore, a low LF/HF ratio would reflect

61

parasympathetic dominance (Pagani et al., 1986). Previously, no differences in LF

and HF frequencies were reported between SED tertiles in hypertensive patients

(Gerage et al., 2015). A recent study reported a small negative association between

LF and leisure ST, but the association became non-significant after adjustments

(Hallman et al., 2019). The LF/HF ratio has been considered to be a rather

controversial measure, as LF power is suggested to reflect sympathetic outflow

only poorly (Billman, 2013). LF/HF ratio has also been critiqued in short-term HRV

recordings because low frequencies in the signal might be insufficiently sampled

(Heathers, 2014). These limitations should be considered when drawing

conclusions on the negative association in women between SED60 and LF/HF ratio

obtained in this study.

After adjustments, we found no significant association between total SED or

SED bouts and resting HR or HRR after the exercise test. CRF was the most

significant predictor of these HR metrics. A higher volume of MVPA was

associated with better HR recovery and, in men, higher vLPA was associated, albeit

rather weakly, with higher resting HR. In our study, the measurement of HRV was

carried out in a controlled environment in the laboratory at same time of day but

exercising during the previous day was not restricted and might have had some

effect on the results of HRV measurements.

In study III we investigated the association between PA clusters and CVD risk.

Four distinct clusters (inactive, evening active, moderately active and very active)

were identified using X-means cluster analysis for accelerometer data from a one-

week measurement period. The intensity levels of PA were significantly different

between the clusters. In addition, there was temporal variation of the occurrence of

PA, for example the evening active cluster were more active in the evenings, went

to bed later and woke up later compared to the three other clusters. There were also

changes in the temporal PA patterns in all clusters when comparing weekdays and

weekends. During weekends, waking up occurred later in all clusters and more

physical activities took place in the morning and noon, compared to weekdays

when higher MET values occurred throughout the day.

Notably, the amount of MVPA was substantially higher in the very active

cluster, being around 2.5-fold compared to the amount of MVPA obtained in the

inactive cluster. When taking account of bouts of only 10 minutes of MVPA, all

clusters had much lower MVPA duration and the differences between the clusters

were modest. In the very active cluster, the amount of SED was lowest, over

100min/d less compared to other clusters, which had smaller differences in SED

between each other. However, in all clusters on average over half of the wear time

62

was spent being sedentary. The proportion of men was highest in the very active

cluster and lowest in the inactive cluster.

To our knowledge, there are no previous studies using X-means cluster analysis

for PA data collected with an accelerometer, but the K-means approach has been

used previously and rather similar differences in the PA intensity and temporal

patterns between clusters were found compared to this study (Fukuoka et al., 2017;

Lee, P. H. et al., 2013). With a study population of inactive women, Fukuoka et al.

(2017) found three different clusters (afternoon engaged, morning engaged, and

unengaged) using raw minute-level MET data from seven days. Similarly, three

clusters were found, when they applied the K-means algorithm to normalised MET

data (only using ≥3 METs). With the latter approach the clusters differed with the

temporal patterns of MVPA; MVPA evening peak, MVPA afternoon peak and MVPA

morning and evening peak. Lee et al. (2013) found two clusters in Chinese adults

(inactive and active) using mean counts per hour from acceleration data from two

weekdays and two weekend days. The main differences between the clusters were

found in the intensity of PA, maybe because the data was averaged over a longer

time period. For example, differences between the clusters in temporal patterns

such as the times the study subjects woke up and went to bed did not stand out.

In our study, we found significant differences in CVD risk, estimated using the

Framingham risk model, between the PA clusters. Significant differences in the

CVD risk model variables were found in HDL cholesterol levels, which were

higher in the more active clusters compared to the less active. In women, total

cholesterol was also higher in the inactive and evening active clusters compared to

the other two clusters. Overall, the mean HDL level was above the minimum

recommendation and the total cholesterol level was on average slightly above the

recommended maximum level based on the current national guidelines in both

genders in all PA clusters (Working group set up by the Finnish Medical Society

Duodecim and the Finnish Cardiac Society, 2017).

Differences in the CVD risks between clusters were modest, in men 1.2%

percentage units between the inactive and very active clusters and in women only

0.6% percentage units between the evening active and moderately active clusters,

and their clinical relevance can be argued. Overall, the CVD risk in the study

population was low, 97% of women and 74% of men had a low risk (<10% risk)

and only 2% of all had a high risk (>20%) of developing CVD over the next 10

years. In comparison, a recent study reported 63% of the study population having

a low risk and 16% having a high risk of developing CVD (or already had CVD)

among Finnish adults (Vasankari et al., 2017). This discrepancy might be due to the

63

wide age range of the participants (18–85 years) in their study. We also excluded

participants who already had CVD (n=36) from the analyses, which might have

lowered the risk levels in our study. The prevalence of CVD increases in men

around 60–65 years of age and in women even later on (Yazdanyar & Newman,

2009). Thus, at the age of 46 years, the 10-year risk estimate will probably not yet

clearly separate those at high risk later on in life.

This study has some limitations. Causal interactions cannot be concluded due

to the cross-sectional study design. Also, the lack of posture recognition when

measuring SED with a wrist-worn monitor is a limitation, as the physiological

consequences of sitting and standing can be different (Katzmarzyk, 2014), but these

behaviours could not have been reliably separated from each other in this study.

Those participants who provided valid PA data from seven consecutive days (III)

seemed to have more preferable body composition, seemed to smoke less and

appeared to consume less alcohol compared to those without valid PA data, which

might have induced some selection bias in the study sample. However, those

participants providing four valid days of PA data seemed to have only minor

differences compared to those without valid PA data. The study population

consisted of individuals born in 1966 in the two northernmost provinces of Finland,

covering approximately half of the country’s land mass. This limits the

generalizability of the results to other countries or age groups. Based on the

obtained results, some of the associations (especially in study II) were, although

statistically significant, rather weak. Further studies are warranted in order to

confirm the current findings.

The strengths of the study include a wide population-based sample with wide

background information available from the participants. The compliance of

wearing the accelerometer was high in all the studies (98% in I and II, and >80%

in III). In studies I and II we controlled for a variety of variables with known

associations with outcome variables. In study II we controlled for the total volume

of PA including all at least light intensity activity as previous studies have mostly

controlled only for MVPA. To the best of our knowledge, no previous study has

utilized the same machine learning method for objectively measured PA data as was

used in study III. All clinical measurements were carried out in a controlled

environment in a laboratory, which guaranteed uniform and high-quality data.

64

65

7 Conclusions

The present study indicated that the volume and temporal patterns of PA and SED

have associations with perceived health, cardiac autonomic function, and

cardiometabolic health in mid-life. Based on the aims of this study, it can be

concluded that:

1. Both self-reported leisure time PA and objectively measured moderate to

vigorous PA were positively associated with perceived health. The self-

reported ST was not associated with health perception.

2. Objectively measured prolonged SED was positively associated with HRV

after controlling for total PA, CRF, and other potential confounders. This

suggests prolonged SED is positively associated with cardiac parasympathetic

activity in mid-life.

3. Four distinct PA clusters were identified based on the objectively measured PA

data, with clear differences in the intensity and occurrence of PA. A lower CVD

risk was found in more active clusters compared to less active clusters.

66

67

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Appendix 1

Table 9. Multivariate linear regression between total sedentary time, accumulated

prolonged sedentary bouts (30 and 60 min) and cardiac autonomic function

variables in men and women, R2 and standardised beta values for each model.

Men R2 β p-value Women R2 β p-value

rMSSD rMSSD

Covariates 0.178 Covariates 0.087

CRF (HRpeak) −0.250 <0.001 CRF (HRpeak) −0.202 <0.001

SBP −0.101 <0.001 SBP −0.095 <0.001

LDL cholesterol −0.065 0.005 HDL cholesterol −0.050 0.024

triglycerides −0.109 <0.001 triglycerides −0.135 <0.001

smoking −0.068 0.002

fat% −0.100 <0.001

SED60 0.082 <0.001 SED30 0.104 <0.001

Covariates + SED60 0.185 SED60 0.094 <0.001

Covariates + SED30 0.098

Covariates + SED60 0.096

HRrest HRrest

Covariates 0.367 Covariates 0.284

CRF (HRpeak) 0.514 <0.001 CRF (HRpeak) 0.526 <0.001

SBP 0.087 <0.001 SBP 0.061 0.001

smoking 0.078 <0.001 triglycerides 0.117 <0.001

triglycerides 0.091 <0.001 fat% −0.096 <0.001

LDL cholesterol 0.041 0.040

fat% 0.053 0.021

vLPA 0.040 0.045

total SED −0.037 0.168 total SED −0.017 0.378

Covariates + total SED 0.368 Covariates + total SED 0.285

HRslope HRslope

Covariates 0.224 Covariates 0.158

CRF (HRpeak) 0.214 <0.001 CRF (HRpeak) 0.171 <0.001

smoking 0.085 <0.001 triglycerides 0.143 <0.001

triglycerides 0.113 <0.001 LDL cholesterol 0.050 0.024

LDL cholesterol 0.095 <0.001 fat% 0.096 <0.001

fat% 0.148 <0.001 vMVPA −0.138 <0.001

alcohol consumption 0.046 0.034

vMVPA −0.111 <0.001

total SED 0.005 0.846 total SED 0.009 0.707

SED30 −0.016 0.492 SED30 −0.025 0.229

Covariates + total SED 0.224 Covariates + total SED 0.159

Covariates + SED30 0.224 Covariates + SED30 0.159

86

Men R2 β p-value Women R2 β p-value

HRR60 HRR60

Covariates 0.182 Covariates 0.118

CRF (HRpeak) −0.151 <0.001 CRF (HRpeak) −0.124 <0.001

SBP −0.081 <0.001 SBP −0.060 0.005

smoking −0.123 <0.001 triglycerides −0.159 <0.001

triglycerides −0.116 <0.001 LDL cholesterol −0.051 0.022

LDL cholesterol −0.094 <0.001 vMVPA 0.154 <0.001

fat% −0.108 <0.001

vMVPA 0.119 <0.001

total SED 0.019 0.460 total SED 0.017 0.463

Covariates + total SED 0.182 Covariates + total SED 0.118

LF/HF

Covariates 0.020

CRF (HRpeak) 0.056 0.024

fat% 0.089 <0.001

vLPA 0.089 <0.001

SED30 −0.037 0.212

SED60 −0.060 0.013

Covariates + SED30 0.021

Covariates + SED60 0.023

rMSSD=root mean square of successive differences in R–R intervals, CRF=cardiorespiratory fitness,

HRpeak=peak heart rate during submaximal exercise test, SBP=systolic blood pressure, LDL=low-

density lipoprotein, HDL=high-density lipoprotein vLPA=total volume of light physical activity,

vMVPA=total volume of moderate to vigorous physical activity, total SED=total time obtained in

between 1–2 METs intensity, SED30=accumulated daily time in bouts of at least 30 min of consecutive

MET values in between 1–2 METs, SED60=accumulated daily time in bouts of at least 60 min of

consecutive MET values in between 1–2 METs, HRslope=steepness of heart rate recovery after

submaximal exercise test, HRrest=resting heart rate, HRR60=heart rate recovery at 60 sec after

submaximal exercise test, LF=low-frequency power, HF=high-frequency power

87

Original publications

I Niemelä, M., Kangas, M., Ahola, R., Auvinen, J., Leinonen, A-M., Tammelin, T., . . . Jämsä, T. (2019). Dose-response relation of self-reported and accelerometer-measured physical activity to perceived health in middle age—the Northern Finland Birth Cohort 1966 Study. BMC Public Health 19: 21. doi: 10.1186/s12889-018-6359-8

II Niemelä, M., Kiviniemi, A., Kangas, M., Farrahi, V., Leinonen, A-M., Ahola R., . . . Jämsä, T. (In press). Prolonged bouts of sedentary time and cardiac autonomic function in midlife. Translational Sports Medicine. doi: 10.1002/tsm2.100

III Niemelä, M., Kangas, M., Farrahi, V., Kiviniemi, A., Leinonen, A-M., Ahola, R., . . . Jämsä, T. (2019). Intensity and temporal patterns of physical activity and cardiovascular disease risk in midlife. Preventive Medicine 124: 33–41. doi: 10.1016/j.ypmed.2019.04.023

Published with CC BY licence (I) and CC BY-NC-ND licence (II and III).

Original publications are not included in the electronic version of the thesis.

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TEMPORAL PATTERNS OF PHYSICAL ACTIVITY AND SEDENTARY TIME AND THEIR ASSOCIATION WITH HEALTH AT MID-LIFETHE NORTHERN FINLAND BIRTH COHORT 1966 STUDY

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