· web view), and this is particularly evident in the mental health difficulty bipolar disorder...

Sleep appraisal Delphi study 1

The development of a theoretically derived measure exploring extreme appraisals of sleep in

Bipolar Disorder:

A Delphi study with professionals

*Blind Review*

RUNNING HEAD: Sleep appraisal Delphi study

Correspondence concerning this article should be addressed to:

*Blind Review*

The authors declare no conflicts of interest with respect to this publication.

This research received no specific grant from any funding agency, commercial or not-for-profit

sectors.

The authors have abided by the Ethical Principles of Psychologists and Code of Conduct as set out

by the APA.

When consulted, the university research ethics committee did not require ethical approval since this

study only asked professionals non-sensitive questions deemed strictly within their professional

competence. Additionally, personal identifiable data was not collected from the participants.

2

Abstract

Background: Sleep and mood are known to be linked and this is particularly evident in people with

a diagnosis of bipolar disorder (BD). It has been proposed that psychological interventions

improving sleep can be a pathway for improving mood. In order to develop an appropriate

psychological sleep intervention, the common cognitive processes maintaining the range of sleep

disturbances need to be investigated.

Aim: This study aimed to explore and identify expert consensus on positive and negative sleep

appraisals in the context of low and high mood states, using the Integrative Cognitive Model as a

theoretical guide.

Method: A Delphi approach was utilised to allow clinical and research professionals, with

experience in the field of BD, to be anonymously consulted about their views on sleep appraisals.

These experts were invited to participate in up to 3 rounds of producing and rating statements that

represented positive and negative sleep appraisals.

Results: A total of 38 statements were developed and rated, resulting in a final list of 19 statements

that were rated as “Essential” or “Important” by >80% of the participants. These statements

represent the full range of extreme sleep appraisals this study had set out to explore, confirming the

importance of better understanding and identifying positive and negative sleep cognitions in the

context of high and low mood.

Conclusion: The statements reviewed in this study will be used to inform the development of a

sleep cognition measure that may be useful in cognitive therapy addressing sleep disturbances

experienced along the bipolar spectrum.

Keywords: bipolar disorder; mood swings; insomnia; hypersomnia; circadian; cognitive appraisals;

reduced need for sleep; cognitive therapy


Exploring extreme appraisals of sleep in Bipolar Disorder:

A Delphi study with professionals

Mood and sleep are recognised to have a bidirectional relationship (Alvaro, Roberts, &

Harris, 2013; Kahn, Sheppes, & Sadeh, 2013), and this is particularly evident in the mental health

difficulty bipolar disorder (BD) (Abreu & Braganca, 2015; Plante & Winkelman, 2008). BD is a

mood disorder characterised by at least one episode of elevated or irritable mood and/or episodes of

depressed mood. Changes to sleeping patterns are reported as a possible symptom during both

depressed mood states and elevated mood states (Ritter et al., 2015; Rosa et al., 2013). During

depression a person might experience difficulties with falling and staying asleep (insomnia), or the

person might sleep more than usual (hypersomnia). During elevated or irritable mood a person

might experience feeling rested after only 3 hours of sleep, referred to as reduced need for sleep

(American Psychiatric Association, 2013).

These clinically significant changes in sleeping patterns are not only a symptom during low

or elevated mood, but are also known to occur before a mood episode happens (Correll et al., 2007;

Gruber et al., 2011) and between mood episodes (Rosa et al., 2013). It has also been shown to be a

risk factor for BD (Ritter et al., 2015), as evidenced in research that has looked at familial risk

groups (Duffy, Alda, Hajek, Sherry, & Grof, 2010; Ng et al., 2015; Ritter et al., 2015), groups who

score high on measures that indicate a tendency toward high mood (Ankers & Jones, 2009; Ng et

al., 2015) and those who meet bipolar at-risk criteria (Castro et al., 2015).

Both sleep and mood related difficulties are known to have significant negative impacts on a

person’s quality of life (Roth & Ancoli-Israel, 1999). The impact is also felt on society due to

occupational and health related costs for the person (Altshuler et al., 2006; Boland et al., 2015;

Murray & Lopez, 1997), such as taking extended time off from work (Das Gupta & Guest, 2002).

For these reasons, it is important that research is done in order to better understand and inform

4

intervention options. Due to the increasing recognition that sleep and mood are bidirectional, as

discussed above, it is important that interventions incorporate support for both difficulties.

Pharmacological approaches are a common intervention for supporting those with BD and sleep

disturbances. However, it is important to consider psychological interventions as this would enable

more treatment choice and is in line with current NICE guidelines (National Institute for Health and

Care Excellence [NICE], 2014). Additionally, psychological interventions have a range of positive

advantages over pharmacological approaches. Harvey, Kaplan, and Soehner (2015) explain the

disadvantages of medication include adverse drug interactions with mood stabilising medication,

the risk of daytime residual effects, and possible dependence on or abuse of the medication (Levin

& Hennessy, 2004). It should be noted that psychological interventions can have unintended side

effects due to factors such as clients’ expectations to being met and limits in therapist competence

(Curran et al., 2019). In order to improve potential adverse effects of psychological intervention,

robust research needs to be conducted and disseminated.

A psychological intervention that incorporates sleep support in the field of BD is the

behavioural psychotherapy intervention Interpersonal and Social Rhythm Therapy. This therapy

focuses on the importance of maintaining a regular social rhythm (Frank, Swartz, & Kupfer, 2000).

Although this intervention has shown reduced recurrence of mood episodes when delivered

following a mood episode (Frank et al., 2005), it emphasises a biological approach that mood

difficulties are due to circadian rhythm irregularities. However, sleep disturbances are not explained

by biological processes alone. For this reason, it is important a psychological intervention is

informed by an approach that considers the common biological and cognitive behavioural processes

(Harvey, Watkins, Mansell, & Shafran, 2004). Cognitive sleep research has primarily focused on

the sleep disturbance insomnia, and has identified cognitive processes that maintain this

disturbance. This is detailed in the Cognitive Model of Insomnia and includes negative cognitive

beliefs a person endorses about difficulties with sleeping (Harvey, 2002). Identifying these

cognitive beliefs informs Cognitive Therapy (CT) intervention, which then targets these beliefs


(Edinger & Wohlgemuth, 2001). CT has been shown to be a useful intervention for people with

insomnia (Morin, Vallieres, & Ivers, 2007). Additionally, CT for insomnia has been shown to have

positive outcomes on both mood and sleep for people with a diagnosis of depression (Manber et al.,

2008) and BD (Harvey et al., 2015).

In order to identify these cognitive processes, a sleep cognition measure is required. A

recently conducted scoping review aiming to identify the range of sleep cognition measures for

sleep duration disturbances highlighted that there is currently no available measure developed

specifically for use with hypersomnia or reduced need for sleep (Pearson, Mansell, Turner, &

Parker, 2018). The review instead highlighted that sleep cognitions in insomnia have been widely

researched. However, the cognitive processes proposed to maintain insomnia might not account for

these other sleep disturbances. There is a need for a cognitive model that accounts for the range of

sleep disturbances and can guide CT intervention for people with fluctuating sleep duration

disturbances. Without an available cognitive model in the sleep literature, the area of BD research is

a useful guide since BD is a psychiatric disorder that accounts for mood and sleep fluctuations.

Healy and Williams (1989) first proposed that circadian rhythm disruption, such as sleep

disturbances caused by stressors, in conjunction with cognitive distortions play a significant role in

BD vulnerability. Jones (2001) further explained this relationship by proposing a model guided by

Power and Dalgleish (1997) & Jones (1979) that explains it is one’s internal attribution of circadian

change that facilitates and maintains BD symptoms. Building on these theoretical models in BD, the

Integrative Cognitive Model (ICM) (Mansell, Morrison, Reid, Lowens, & Tai, 2007) and explains

that multiple, extreme positive and negative appraisals about mood play an important role in driving

mood up into an elevated state and down into a negative state. For example, heightened mood could

be appraised positively as a sign of great success or negatively such as an impending breakdown

(Dodd, Mansell, Bentall, & Tai, 2011). These appraisals contribute to the person engaging in

either ascent (Mansell & Lam, 2003) or descent behaviours in order to control their internal state,

6

causing mood to fluctuate as different appraisals enter the person’s awareness (Mansell, 2006). In a

recent systematic review of 31 studies, multiple lines of evidence have suggested that these extreme

appraisals are associated with mood difficulties across the BD spectrum in both clinical and non-

clinical groups (Kelly, Dodd, & Mansell, 2017). It is this conflict of multiple, extreme appraisals

that could also play a role in the changes in sleep disruption across the shifting mood states. For

example, a person might be driven to reduce sleep to a few hours per night when in an activated

mood state, so they can ‘do as much as possible’ following a period in which they were low in

mood and perhaps sleeping much more. However, they may also appraise sleeping much less per

night as a risk for relapsing into mania and being admitted into hospital. Therefore, periods of only

a few hours’ sleep to make up for lost time may be followed by several days of sleeping much

longer than usual in an attempt to stave off a mood episode.

Building on the previous research in the insomnia literature, the aim of this Delphi study

was to explore and identify expert consensus on the range of positive and negative appraisals a

person might endorse about their sleep in the context of the different sleep disturbances experienced

in BD, in line with the theoretical ICM. These appraisals can then inform the development of a

measure that will offer a unique and novel identification of cognitions that may be maintaining a

more complex range of sleep changes and disturbances found across the BD spectrum. This

potential measure has the opportunity for providing a more comprehensive identification of sleep

related cognitions that can better inform and suit the needs of a person engaging in CT for sleep and

mood difficulties. This study complements parallel work streams in a research program for

psychometrically testing this sleep cognition measure, reviewing it with service users, and

incorporating it in studies to test the role of sleep within the ICM framework.

1. Method

1.1. Delphi Method


The Delphi method is an anonymous consensus method that sets out to determine how much

experts (those who are highly knowledgeable about the domain of interest (Boateng, Neilands,

Frongillo, Melgar-Quinonez, & Young, 2018)) agree on a particular topic or issue. As explained by

Hasson, Keeney, and McKenna (2000) and Jones and Hunter (1995), this method follows a series of

rounds in which the identified experts contribute independent suggestions and recommendations on

the topic or issue. These suggestions and recommendations are then developed into relevant

headings or statements, which the experts are then invited to rank their level of agreement with.

Additional rounds are completed to ensure that consensus is reached by the entire participant group.

For these additional rounds, information from the previous round is supplied, such as the rate of

agreement among the group. This allows the individual participant an opportunity to change their

ranking based on the information of how the group ranked the statement. For the purposes of this

study, the authors followed the approach used by Law and Morrison (2014) and Morrison and

Barratt (2010) in which Round 1 is a preliminary phase for a smaller group of experts who are

invited to help refine the statements for consensus with a larger participant group in later rounds.

Conducting this study with experts who have a range of both clinical and research experience

working with those who have BD enables a wide range of experiences to be taken into account with

this clinical group. In addition, both the use of experts and the Delphi methodology are

recommended in the development and evaluation of scales (Boateng et al., 2018).

1.2. Participants.

For the development and refinement of statements in Round 1, published academics and/or

research and training professionals were recruited who had been identified in the literature as

having made a significant contribution to the understanding of BD and in some instances also in the

field of sleep research and/or in the ICM. Each potential participant was sent an invitation e-mail

introducing the team conducting this study and a brief introduction about the study and what it

involved. A secure online link for SelectSurvey was included in the e-mail for the participant to

8

take part anonymously in the study. The online link included the participant information sheet and

statements for the participant to provide feedback on along with the opportunity for suggesting new

statements.

For the remaining rounds in consensus rating, the panel of experts was widened to include

more participants who had worked clinically with people who experienced mood and sleep

difficulties. The inclusion criteria for these additional rounds were:

Minimum training level of either qualified occupational therapist, qualified social worker,

research assistant, grade 6 nurse, CBT therapist, trainee clinical psychologist, or junior

doctor.

At least 1 years’ experience working with people who experience significant low and

elevated mood difficulties (e.g. bipolar disorder or bipolar at-risk) AND who have reported

sleep difficulties (e.g. initiating, maintaining, or not needing sleep).

For these rounds, the same experts invited for Round 1 were also invited to take part.

Additionally, the invitation asked the participant to inform colleagues who might be suitable to take

part based on the inclusion criteria. Participants were also invited who worked in mental health

services across the northwest of England, where the authors of this study have close links with

relevant services. Only the participants who took part in Round 2 were invited to take part in the

final Round 3. All participants completed the study through the SelectSurvey platform

anonymously. A separate SelectSurvey link was available for the participant to leave their e-mail

address for being invited to the additional rounds. This meant the data provided by the participants

was anonymous to the researcher. Participants were given 4 weeks to complete each round, and

were sent reminder e-mails when 2 and 1 week(s) remained. It was made clear that at any time an

invitee or participant wanted to withdraw from taking part they could request to be removed from

the e-mail invitation list.

1.3. Procedure.


When consulted, the university research ethics committee did not require ethical approval

since this study only asked professionals non-sensitive questions deemed strictly within their

professional competence. Additionally, personal identifiable data was not collected from the

participants.

Before Round 1, an initial group of statements representing extreme positive and negative

appraisals were developed by the research team based on the theory of the ICM. This was

completed by reviewing the literature and commonly used self-report measures for sleep disruptive

cognitions, including the Dysfunctional Beliefs and Attitudes about Sleep Scale (Morin, Stone,

Trinkle, Mercer, & Remsberg, 1993). Additionally, one of the authors (L.P.) reviewed an online

BD Forum (www.psychforums.com) in September 2016 for themes about sleep from a service user

perspective that were reported in the messages on the forum. This is a widely used psychology and

mental health forum with over 100,000 members, and enables discussion about personal

experiences on a wide range of mental health topics. In the BD forum, the keywords “sleep”,

“insomnia”, and hypersomnia” were used in the search bar to locate relevant postings for review

that dated back to 2005. The themes obtained included difficulties around falling asleep and impact

on mood, sleep as an avoidance of emotions, lack of sleep allowing motivation and creativity, and

the positive impact of sleep on mood and stability.

In line with the theory of the ICM, the authors wrote the statements as accounting for the

following 4 domains: positive appraisals of sleeping less than usual, positive appraisals of sleeping

more than usual, negative appraisals of sleeping less than usual, and negative appraisals of sleeping

more than usual. The research team agreed the statements should reflect extreme positive and

negative appraisals of sleep, whilst also accounting for the change in sleep duration that is

represented by the sleep disturbances that are characteristic of BD, e.g. reduced need for sleep or

insomnia is sleeping less than usual, whilst hypersomnia is sleeping more than usual. The result was

31 statements that were developed by the research team as a starting point for Round 1.

http://www.psychforums.com/

10

1.3.1. Round 1.

The participants for Round 1 were instructed to review the developed statements and rate

how essential the statement was for inclusion on the new self-report measure. This was completed

using a 5 point scale (1 = essential, 2 = important, 3 = do not know/depends, 4 = unimportant, 5 =

should not be included). The participants were also invited to provide feedback on each of the

statements with reasons for their choice of rating and any additional comments such as word

changes. Finally, there was an option at the end for the participant to provide additional comments,

statements or other suggestions for the measure based on their clinical and research expertise.

The authors reviewed the consensus rating and feedback and suggestions from Round 1, and

amended the statements accordingly. Statements that received a high consensus rating of being

either essential or important with minimal feedback or comments were kept the same. Statements

that had received a low consensus rating and had significant comments or suggestions were

replaced with amended versions. Comments and suggestions for new statements that were not on

the original list were discussed in the research team and written by the authors as new statements.

This resulted in 13 statements remaining the same, 15 statements amended, 3 statements removed,

and 10 new statements added.

1.3.2. Round 2.

The revised list of 38 statements was then put out for Round 2 with the wider group of

participants for consensus rating only. Following the method employed by (Langlands, Jorm,

Kelly, & Kitchener, 2008; Law & Morrison, 2014; Morrison & Barratt, 2010), the following cut-off

points were used by the research team to determine items for inclusion, exclusion, and re-rating:

Statements rated by 80% or more of the participants as essential or important will be

included in the self-report measure.


Statements rated by 70-79% of the participants as essential or important will be re-rated in a

further round for a further consensus check.

Any statements that did not meet at least 70% rating of essential or important will be

excluded.

The participants were asked to read each statement and rate how relevant the statement was

for being included in a sleep measure for use with those who have mood swings. The participants

rated each statement using the same 5-point scale used in Round 1. This resulted in the inclusion of

16 items, 17 items discarded, and 5 items for re-rating.

1.3.3. Round 3.

In Round 3, the participants from Round 2 who had left their contact details for taking part

in additional rounds were invited to take part anonymously via an online link with SelectSurvey.

Participants were asked to re-rate only those items that 70-79% of respondents had rated as essential

or important during Round 2 (n=5). To help inform the participants’ decision for re-rating, the

percentage from Round 2 was included with each of the statements. It was explained that any

statements from this round that met 79% or less consensus for essential or important would be

discarded. Of these 5 statements, 3 were retained and 2 discarded. Table 1 outlines the phases of

this Delphi study.

12

Table 1: Delphi Study Outline

Phase 1: Initial development of items Define the construct

Literature review

Review online BD forum for sleep themes

Initial list of 31 items developed by the research team

Phase 2: Delphi method; Preliminary phase Round 1: Development and refinement of items

Experts in the field of BD invited

Experts rated items for importance of including in the measure, offered wording changes and gave suggestions for new items.

Research team reviewed experts feedback and revised statements, resulting in 38 items.

Phase 3: Delphi method consensus rounds Round 2: Wider group of experts invited to take part in consensus rounds.

Participants rated items for importance (1 = “essential” to 5 = “should not be included”).

Research team used cut-off points to determine what items reached high consensus (rated as “essential” or “important” by > 80% of the participants) and what items were discarded (items that did not meet rating of “essential” or “important” by more than 70% of the participants).

Round 3: Participants who took part in Round 2 were invited to take part in Round 3 to re-rate the 5 items that were rated by 70-79% of the participants as “essential” or “important.”

2. Results

2.1 Demographics

For Round 1, the research team invited 24 experts identified in the literature, 12 of whom

responded to take part (50% response rate). However, there were 10 participants who completed

the round in full and so only their responses were used for analysis. For Round 2, 25 participants

took part and for Round 3, 18 of the 25 participants took part (72% response rate). Table 2 provides

an overview of the demographic information for all 3 rounds. Participants reported their current

professional role (noted in Table 2) but also shared what previous relevant career history they had


with BD and this included having been a researcher, an assistant psychologist, or having worked in

different clinical settings.

Table 2: Participant Characteristics

Round 1

(n = 10)

Round 2

(n = 25)

Round 3

(n = 18)Gender

Male

Female

6 (60%)

4 (40%)

9 (35%)

16 (64%)

9 (50%)

9 (50%)Age

Mean Age 41 35 34

Current ProfessionConsultant Clinical Psychologist

Clinical Psychologist

Trainee Clinical Psychologist

Psychological Therapist

Assistant Psychologist

Research Assistant

Lecturer / Research

Professor

Assistant Practitioner

Mental Health Nurse

2 (20%)

5 (50%)

0

1 (10%)

0

0

1 (10%)

1 (10%)

0

0

1 (4%)

11 (44%)

1 (4%)

2 (8%)

2 (8%)

3 (12%)

1 (4%)

2 (8%)

1 (4%)

1 (4%)

1 (6%)

10 (56%)

1 (6%)

1 (6%)

1 (6%)

3 (17%)

1 (6%)

0

0

0

2.2 Ratings of Importance Results

A total of 19 statements were retained in the final statement list after being rated as

important or essential by > 80% of participants. Figure 1 summarises the number of items included,

re-rated, and excluded at each round of the study.

14

Figure 1: Number of items included, rerated, and excluded at each round of the study

Table 3: High Consensus Items

Item Stage Included

Percentage Agreement

Appraisal Domain

The only way I can stop myself thinking is to sleep more 2 80% + / ↑

Round 1Reviewed by n=10

31 statements

Items remaining the same

13 statements

Items amended

15 statements

Items removed

3 statements

Items added

10 statements


38 statements

Items kept

16 statements

Items for re-rating

5 statements

Items discarded

17 statements


5 statements

Items kept

3 statements

Items discarded

2 statements


than normal.I sleep more to escape from the real world. 2 100% + / ↑

I have to sleep more to keep my mood stable. 2 80% + / ↑

Sleep prevents me from completing my many projects. 3 83% - / ↑

When I sleep more than normal I feel so useless 2 84% - / ↑

Sleeping too much is a sign that I am becoming depressed. 2 96% - / ↑

If I only need a few hours of sleep, it is a sign that I'm on top of things.

3 83% + / ↓

I stay up very late or all night to bring on my elevated mood.

2 80% + / ↓

It is necessary for me to stay up very late or all night to get all of my work done.

2 92% + / ↓

I stay up very late or all night because of all my good ideas. 2 80% + / ↓

I stay up very late or all night because this is when I feel most creative.

2 88% + / ↓

I stay up very late or all night when I have elevated mood, because this is when I feel my best.

3 89% + / ↓

The less I sleep, the more likely it is I get everything done. 2 88% + / ↓

If I do not get enough sleep each night, I become extremely anxious.

2 88% - / ↓

If I do not get enough sleep each night, I won't be able to function at all the next day.

2 96% - / ↓

If I do not get enough sleep each night I will not be able to get done what needs to be done the next day.

2 84% - / ↓

If I do not get enough sleep each night, my moods become uncontrollable.

2 92% - / ↓

If I do not get enough sleep, I will feel it in my body. 2 88% - / ↓

If I do not get enough sleep at night, this will have a terrible impact on me the next day.

2 96% - / ↓

The final 19 statements are shown below in Table 3. The two statements that did not meet at

least 70% for a further re-rating both reached only 67% consensus for essential or important by the

participant group. The three statements that were included each reached above 80% consensus. For

16

this reason, a further round of re-rating was not required. Also depicted in Table 2 are the

statements included by each round, the percentage of agreement, and the domain the statement

assesses for: positive appraisals of sleeping more than usual (n = 3), negative appraisals of sleeping

more than usual (n = 3), positive appraisals of sleeping less than usual (n = 7), and negative

appraisals of sleeping less than usual (n = 6).

There were 6 items that reached extremely high consensus in Round 2 (> 90%). These

statements have been highlighted in grey in Table 2 and are from the following domains: positive

appraisals of sleeping more than usual (n = 1), negative appraisals of sleeping more than usual (n =

1), positive appraisals of sleeping less than usual (n = 1), and negative appraisals of sleeping less

than usual (n = 3).

3. Discussion

The aim of this Delphi study was to explore and identify expert consensus on positive and

negative sleep appraisals using the ICM as the theoretical framework for development of items. The

results suggest that professionals working in the field of BD recognise that those who have sleep

and mood difficulties endorse a range of extreme sleep appraisals. A total of 38 statements were

developed and rated, resulting in high consensus for half of the statements. These final 19

statements all represent the range of extreme positive and negative appraisals of sleep that are in

line with the theory of the ICM the authors had set out to investigate. This range covers four

domains: positive appraisals of sleeping less than usual, positive appraisals of sleeping more than

usual, negative appraisals of sleeping less than usual, and negative appraisals of sleeping more than

usual.

With the focus in the literature and past research on cognitions about sleep having been

focused on negative beliefs regarding insomnia (e.g. CT for insomnia), it is of particular interest

that the domain that reached the most number (n = 7) of high consensus for statements was the

domain assessing for positive appraisals of sleeping less than usual. This domain represents sleep


appraisals in the context of reduced need for sleep. As highlighted by the recent scoping review

conducted (Pearson et al., 2018), there have not been measures developed that assess for this

domain. However, this Delphi study has shown that experts and clinicians in the field of BD

recognise this to be at least as important compared to the more widely recognised and researched

negative appraisals of sleeping less than usual (e.g. insomnia).

3.1 Clinical Implications

The extreme sleep appraisals identified in this Delphi study can inform the development of a

new sleep cognition measure that will offer a unique and novel identification of sleep cognitions

that may be maintaining a more complex range of sleep changes and disturbances found across the

BD spectrum. This potential measure has the opportunity for providing a more comprehensive

assessment that can better inform and suit the needs of a person with BD engaging in CT for sleep

disturbances.

3.2 Strengths and Limitations

This study had several strengths. First, the strength of a Delphi consensus method enables

decision making to be shared amongst a group of equally level participants anonymously (Jones &

Hunter, 1995). This prevents the risk of anyone dominating the consensus process, since everyone

independently provides their response in the rounds (Keeney, Hasson, & McKenna, 2006; Rowe &

Wright, 2001). Specific to this study, the recommendations and the consensus of the experts went

beyond what has been reported in the literature regarding the additional domains of positive and

negative sleep appraisals in the context of sleeping more or less than usual. Second, the response

rate for Round 3 was 72%, which meets the recommended rate of at least 70% (Hasson et al., 2000).

Third, service user input was included to help identify the range of positive and negative sleep

appraisals people with BD might endorse. This was completed through reviewing postings on an

online forum. Forums have been reported to provide a comfortable space for people to

anonymously discuss personal issues (Campbell et al., 2001) whilst also enabling participants to

18

join in the discussions at their convenience (Hsiung, 2000) . It could be argued that this allows a

person to give an honest explanation of their difficulties at times when they are experiencing

symptoms. Fourth, the appraisals identified and explored in this study were derived using the ICM

as the theoretical framework. These appraisals will inform the development of a sleep cognition

measure which will be assessed on content validity. Content validity is the degree to which the

measure adequately reflects the construct in question. Assessment of construct validity for a

measure includes rating if the measure’s origin of construct was defined by a theoretical model

(Terwee et al., 2018), which this study has clearly stated.

There are several limitations to this study. First, it is increasingly recognised that service

users should be involved in questionnaire development in order to truly capture their perspectives

and to ensure the questionnaire is understandable (Terwee et al., 2018). Although the research team

utilised an online BD forum for sleep themes, it is unknown what number of service users had a

clinical history of elevated or depressed mood across the BD spectrum. To ensure the measure is

understandable and relevant for those who experience sleep fluctuations common across the BD

spectrum, a service user review will be conducted. This will be an opportunity for service users to

rate the measure for content validity and compare it to a commonly used, validated measure of

insomnia specific cognitions. Additionally, future research could include qualitative interviews with

those who have BD in order to further develop and refine the statements. A second limitation was

the response rate was less than 70% for Round 1 (when 24 people had been invited) and thus did

not meet the recommended 70% rate (Hasson et al., 2000). Second, there were relatively low

numbers of participants taking part in each round. One reason for this may be because invitations to

take part in the study were mainly conducted by e-mail, whereas previous research has found that

conducting the first round in person has led to a higher response rate in subsequent rounds

(McKenna, 1994). Additionally, because there are low numbers of experts taking part in the study it

may be that the range of appraisals has not been fully identified. However, there was consensus for


all four domains this study set out to explore which does give validity to the range having been

represented.

4. Conclusion

To the authors’ knowledge, this is the first study to have sought the expert opinion and

consensus on extreme positive and negative sleep appraisals in the context of low and high mood

states. A high level of consensus was reached for a range of sleep appraisals that cover each of the

four domains this study set out to explore and identify. These findings confirm shared agreement

amongst professionals in the field of BD that extreme positive and negative sleep appraisals are

important to understand and identify across the range of mood states. The statements reviewed in

this study will be used to inform the development of a sleep cognition measure that will be tested

for use with CT addressing fluctuating sleep disturbances experienced across the BD spectrum.

5. Acknowledgments

The authors would like to thank all the participants for their time and contributions to this

research.

6. Reference List

Abreu, T., & Braganca, M. (2015). The bipolarity of light and dark: A review on Bipolar Disorder

and circadian cycles. J Affect Disord, 185, 219-229. doi: 10.1016/j.jad.2015.07.017

Altshuler, L. L., Post, R. M., Black, D. O., Keck, P. E., Jr., Nolen, W. A., Frye, M. A., . . . Mintz, J.

(2006). Subsyndromal depressive symptoms are associated with functional impairment in

patients with bipolar disorder: results of a large, multisite study. J Clin Psychiatry, 67(10),

1551-1560.

Alvaro, P. K., Roberts, R. M., & Harris, J. K. (2013). A systematic review assessing bidirectionality

between sleep disturbances, anxiety, and depression. Sleep, 36(7), 1059-1068. doi:

10.5665/sleep.2810

20

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders

(5th ed.). Arlington, VA: American Psychiatric Publishing.

Ankers, D., & Jones, S. H. (2009). Objective assessment of circadian activity and sleep patterns in

individuals at behavioural risk of hypomania. J Clin Psychol, 65(10), 1071-1086. doi:

10.1002/jclp.20608

Boateng, G. O., Neilands, T. B., Frongillo, E. A., Melgar-Quinonez, H. R., & Young, S. L. (2018).

Best Practices for Developing and Validating Scales for Health, Social, and Behavioral

Research: A Primer. Front Public Health, 6, 149. doi: 10.3389/fpubh.2018.00149

Boland, E. M., Stange, J. P., Molz Adams, A., LaBelle, D. R., Ong, M. L., Hamilton, J. L., . . .

Alloy, L. B. (2015). Associations between sleep disturbance, cognitive functioning and work

disability in Bipolar Disorder. Psychiatry Res, 230(2), 567-574. doi:

10.1016/j.psychres.2015.09.051

Campbell, M. C., Meier, A., Carr, C., Enga, Z., James, A. S., Reedy, J., & Zheng, B. (2001). Health

behavior changes after colon cancer: a comparison of findings from face-to-face and on-line

focus groups. Fam Community Health, 24(3), 88-103.

Castro, J., Zanini, M., Goncalves, B. d. S. B., Coelho, F. M., Bressan, R., Bittencourt, L., . . . Tufik,

S. (2015). Circadian rest-activity rhythm in individuals at risk for psychosis and bipolar

disorder. Schizophr Res, 168(1-2), 50-55. doi: 10.1016/j.schres.2015.07.024

Correll, C. U., Penzner, J. B., Lencz, T., Auther, A., Smith, C. W., Malhotra, A. K., . . . Cornblatt,

B. A. (2007). Early identification and high-risk strategies for bipolar disorder. Bipolar

Disorders, 9(4), 324-338. doi: 10.1111/j.1399-5618.2007.00487.x

Curran, J., Parry, G. D., Hardy, G. E., Darling, J., Mason, A. M., & Chambers, E. (2019). How

Does Therapy Harm? A Model of Adverse Process Using Task Analysis in the Meta-

Synthesis of Service Users' Experience. Front Psychol, 10, 347. doi:

10.3389/fpsyg.2019.00347


Das Gupta, R., & Guest, J. F. (2002). Annual cost of bipolar disorder to UK society. Br J

Psychiatry, 180, 227-233.

Dodd, A. L., Mansell, W., Bentall, R. P., & Tai, S. (2011). Do extreme beliefs about internal states

predict mood swings in an analogue sample? Cognitive Therapy and Research, 35(6), 497-

504. doi: 10.1007/s10608-010-9342-y

Duffy, A., Alda, M., Hajek, T., Sherry, S. B., & Grof, P. (2010). Early stages in the development of

bipolar disorder. J Affect Disord, 121(1-2), 127-135. doi: 10.1016/j.jad.2009.05.022

Edinger, J. D., & Wohlgemuth, W. K. (2001). Psychometric comparisons of the standard and

abbreviated DBAS-10 versions of the dysfunctional beliefs and attitudes about sleep

questionnaire. Sleep Medicine, 2, 493-500.

Frank, E., Kupfer, D. J., Thase, M. E., Mallinger, A. G., Swartz, H. A., Fagiolini, A. M., . . . Monk,

T. (2005). Two-year outcomes for interpersonal and social rhythm therapy in individuals

with bipolar I disorder. Arch Gen Psychiatry, 62(9), 996-1004. doi:

10.1001/archpsyc.62.9.996

Frank, E., Swartz, H. A., & Kupfer, D. J. (2000). Interpersonal and social rhythm therapy:

managing the chaos of bipolar disorder. Biol Psychiatry, 48(6), 593-604. doi:

10.1016/s0006-3223(00)00969-0

Gruber, J., Miklowitz, D. J., Harvey, A. G., Frank, E., Kupfer, D., Thase, M. E., . . . Ketter, T. A.

(2011). Sleep matters: sleep functioning and course of illness in bipolar disorder. J Affect

Disord, 134(1-3), 416-420. doi: 10.1016/j.jad.2011.05.016

Harvey, A. G. (2002). A cognitive model of insomnia. Behav Res Ther, 40(8), 869-893.

Harvey, A. G., Kaplan, K. A., & Soehner, A. M. (2015). Interventions for sleep disturbance in

bipolar disorder. Sleep Med Clin, 10(1), 101-105. doi: 10.1016/j.jsmc.2014.11.005

Harvey, A. G., Watkins, E., Mansell, W., & Shafran, R. (2004). Cognitive behavioural processes

across psychological disorders: A transdiagnostic approach to research and treatment.

Oxford: Oxford University Press.

22

Hasson, F., Keeney, S., & McKenna, H. (2000). Research guidelines for the Delphi survey

technique. J Adv Nurs, 32(4), 1008-1015.

Healy, D., & Williams, J. M. (1989). Moods, misattributions and mania: an interaction of biological

and psychological factors in the pathogenesis of mania. Psychiatr Dev, 7(1), 49-70.

Hsiung, R. C. (2000). The best of both worlds: An online self-help group hosted by a mental health

professional. CyberPsychology & Behavior, 3(6), 935-950. doi: 10.1089/109493100452200

Jones, E. E. (1979). The rocky road from acts to dispositions. American Psychologist, 34(2), 107-

117. doi: 10.1037/0003-066x.34.2.107

Jones, J., & Hunter, D. (1995). Consensus methods for medical and health services research. BMJ,

311, 376-380.

Jones, S. (2001). Circadian rhythms, multilevel models of emotion and bipolar disorder--an initial

step towards integration? Clin Psychol Rev, 21(8), 1193-1209.

Kahn, M., Sheppes, G., & Sadeh, A. (2013). Sleep and emotions: bidirectional links and underlying

mechanisms. Int J Psychophysiol, 89(2), 218-228. doi: 10.1016/j.ijpsycho.2013.05.010

Keeney, S., Hasson, F., & McKenna, H. (2006). Consulting the oracle: ten lessons from using the

Delphi technique in nursing research. J Adv Nurs, 53(2), 205-212. doi: 10.1111/j.1365-

2648.2006.03716.x

Kelly, R. E., Dodd, A. L., & Mansell, W. (2017). "When my moods drive upward there is nothing I

can do about it": A review of extreme appraisals of internal states and the bipolar spectrum.

Front Psychol, 8, 1235. doi: 10.3389/fpsyg.2017.01235

Langlands, R. L., Jorm, A. F., Kelly, C. M., & Kitchener, B. A. (2008). First aid recommendations

for psychosis: using the Delphi method to gain consensus between mental health consumers,

carers, and clinicians. Schizophr Bull, 34(3), 435-443. doi: 10.1093/schbul/sbm099

Law, H., & Morrison, A. P. (2014). Recovery in psychosis: a Delphi study with experts by

experience. Schizophr Bull, 40(6), 1347-1355. doi: 10.1093/schbul/sbu047


Levin, F. R., & Hennessy, G. (2004). Bipolar disorder and substance abuse. Biol Psychiatry, 56(10),

738-748. doi: 10.1016/j.biopsych.2004.05.008

Manber, R., Edinger, J. D., Gress, J. L., San Pedro-Salcedo, M. G., Kuo, T. F., & Kalista, T. (2008).

Cognitive behavioral therapy for insomnia enhances depression outcome in patients with

comorbid major depressive disorder and insomnia. Sleep, 31(4), 489-495.

Mansell, W. (2006). The Hypomanic Attitudes and Positive Predictions Inventory (HAPPI): A pilot

study to select cognitions that are elevated in individuals with bipolar disorder compared to

non-clinical controls. Behav Cogn Psychother, 34(04), 467. doi:

10.1017/s1352465806003109

Mansell, W., & Lam, D. (2003). Conceptualizing a cycle of ascent into mania: a case report. Behav

Cogn Psychother, 31(3), 363-367. doi: 10.1017/s1352465803003102

Mansell, W., Morrison, A. P., Reid, G., Lowens, I., & Tai, S. (2007). The Interpretation of, and

Responses to, Changes in Internal States: An Integrative Cognitive Model of Mood Swings

and Bipolar Disorders. Behav Cogn Psychother, 35(05). doi: 10.1017/s1352465807003827

McKenna, H. P. (1994). The Delphi technique: a worthwhile research approach for nursing? J Adv

Nurs, 19(6), 1221-1225. doi: https://doi.org/10.1111/j.1365-2648.1994.tb01207.x

Morin, C. M., Stone, J., Trinkle, D., Mercer, J., & Remsberg, S. (1993). Dysfunctional beliefs and

attitudes about sleep among older adults with and without insomnia complaints. Psychology

and Aging, 8(3), 463-467.

Morin, C. M., Vallieres, A., & Ivers, H. (2007). Dysfunctional Beliefs and Attitudes about Sleep

(DBAS): validation of a brief version (DBAS-16). Sleep, 30(11), 1547-1554.

Morrison, A. P., & Barratt, S. (2010). What are the components of CBT for psychosis? A Delphi

study. Schizophr Bull, 36(1), 136-142. doi: 10.1093/schbul/sbp118

Murray, C. J. L., & Lopez, A. D. (1997). Global mortality, disability, and the contribution of risk

factors: Global Burden of Disease Study. The Lancet, 349(9063), 1436-1442. doi:

10.1016/s0140-6736(96)07495-8

24

National Institute for Health and Care Excellence [NICE]. (2014). Bipolar disorder: assessment and

management. NICE Clinical Guideline 185. Available at:

https://www.nice.org.uk/guidance/cg185/chapter/1-recommendations (Updated Edition).

Ng, T. H., Chung, K. F., Ho, F. Y., Yeung, W. F., Yung, K. P., & Lam, T. H. (2015). Sleep-wake

disturbance in interepisode bipolar disorder and high-risk individuals: a systematic review

and meta-analysis. Sleep Med Rev, 20, 46-58. doi: 10.1016/j.smrv.2014.06.006

Pearson, L., Mansell, W., Turner, E., & Parker, S. (2018). Beyond insomnia: A scoping review of

self-report sleep cognition questionnaires for sleep duration disturbances. Unpublished

manuscript.

Plante, D. T., & Winkelman, J. W. (2008). Sleep disturbance in bipolar disorder: therapeutic

implications. Am J Psychiatry, 165(7), 830-843. doi: 10.1176/appi.ajp.2008.08010077

Power, M. J., & Dalgleish, T. (1997). Cognition and emotion: from order to disorder. Hove:

Psychology Press.

Ritter, P. S., Hofler, M., Wittchen, H. U., Lieb, R., Bauer, M., Pfennig, A., & Beesdo-Baum, K.

(2015). Disturbed sleep as risk factor for the subsequent onset of bipolar disorder--Data

from a 10-year prospective-longitudinal study among adolescents and young adults. J

Psychiatr Res, 68, 76-82. doi: 10.1016/j.jpsychires.2015.06.005

Rosa, A. R., Comes, M., Torrent, C., Sole, B., Reinares, M., Pachiarotti, I., . . . Vieta, E. (2013).

Biological rhythm disturbance in remitted bipolar patients. Int J Bipolar Disord, 1, 1-6. doi:

10.1186/2194-7511-1-6

Roth, T., & Ancoli-Israel, S. (1999). Daytime consequences and correlates of insomnia in the

United States: results of the 1991 National Sleep Foundation Survey. II. Sleep, 22(Supl. 2),

S354-S358.

Rowe, G., & Wright, G. (2001). Expert opinions in forecasting: the role of the Delphi technique. 30,

125-144. doi: 10.1007/978-0-306-47630-3_7

http://www.nice.org.uk/guidance/cg185/chapter/1-recommendations


Terwee, C. B., Prinsen, C. A. C., Chiarotto, A., Westerman, M. J., Patrick, D. L., Alonso, J., . . .

Mokkink, L. B. (2018). COSMIN methodology for evaluating the content validity of

patient-reported outcome measures: a Delphi study. Qual Life Res, 27(5), 1159-1170. doi:

10.1007/s11136-018-1829-0

· web view), and this is particularly evident in the mental health difficulty bipolar disorder...

Documents