Evidence of weekly cyclicity in mood and functional impairmentin those with a bipolar disorder

Judith Proudfoot a, Alexis E. Whitton a,n, Gordon Parker a, Vijaya Manicavasagar a,Jennifer Nicholas a, Meg Smith b

a School of Psychiatry, University of NSW and Black Dog Institute, Hospital Road, Randwick, Sydney, NSW 2031, Australiab School of Social Sciences, University of Western Sydney, Penrith South DC, NSW, Australia

a r t i c l e i n f o

Article history:Received 29 May 2013Received in revised form21 April 2014Accepted 29 April 2014

Keywords:Mood monitoringWeekly cycleDepressionManiaHypomaniaMedication complianceFunctional impairment

a b s t r a c t

A key characteristic of bipolar disorder is fluctuation in mood symptoms and functional capacity, yetassessment of bipolar symptomatology often relies heavily on interval measurement that is unable tocapture the full range of daily symptom variability and severity. The current study provides a detailedanalysis of the variability in mood symptoms, functional impairment and medication compliance in alarge sample of individuals newly diagnosed with bipolar disorder. Individuals diagnosed with bipolardisorder in the previous 12 months (n¼192) rated their mood, functional impairment, medicationcompliance and symptom triggers daily over 10 consecutive weeks. High mood, low mood andfunctional impairment were found to vary on a weekly cycle, independently of medication compliance.Low mood and functional impairment were worse on weekdays, particularly Mondays and Tuesdays,whereas mood was most elevated on Saturdays. Work-related stressors were the most commonsymptom triggers on weekdays, whereas sleep-related problems and positive social events were themost common triggers on weekends. This study provides evidence that individuals newly diagnosedwith bipolar disorder experience fluctuations in mood and functioning that vary according to a weeklycycle. This finding has implications for the assessment and treatment of patients, and for future research.

& 2014 Elsevier Ireland Ltd. All rights reserved.

1. Introduction

The bipolar disorders are chronic conditions with internationallifetime prevalence rates quantified by Merikangas et al. (2011) as0.6% for bipolar I and 0.4% for bipolar II. While having stronggenetic origins (Nurnberger and Gershon, 1992), mood symptomsare significantly linked with psychosocial triggers (for reviews seeJohnson and Roberts (1995), Proudfoot et al. ( 2010)).

Studies of bipolar symptomatology often rely on cross-sectionalself-report measures and fixed-interval weekly to monthly assess-ments by clinicians (e.g., Simon et al., 2007). However, bipolardisorder involves multiple systems, such that symptom severity,length and frequency of mood episodes, pattern of polarity andtime spent in recovery between episodes differ substantially bothwithin and between individuals (Müller-Oerlinghausen et al.,2002; Judd et al., 2003), and mood symptoms can vary substan-tially on a daily basis (Gottschalk et al., 1995). Reliance on standardinterval measures does not capture the full extent of mood

variability, or the way in which symptoms fluctuate in accordancewith environmental factors on a day-to-day basis. Indeed, dis-crepancies between weekly clinician ratings of symptoms andpatient daily self-ratings are not uncommon. For example, Denicoffet al. (1997) found that weekly clinician ratings captured only31.4% of days of depression and 14.1% of days of mania recorded bypatients completing daily self-ratings.

Assessment of finer-grained patterns in mood variability can beachieved by daily monitoring of mood and functioning. Paper-based mood charts such as the National Institute of Health's LifeChart Method (Leverich and Post, 1996; Denicoff et al., 1997) andthe STEP-BD Mood Chart (Sachs et al., 2003) provide a means forcapturing the frequency, duration, polarity and intensity of moodsymptoms, and are also helpful for identifying triggers.

The use of mood charts as outcome measures has revealedadditional information that has been missed with cross-sectionalmeasures. A modified version of the Life Chart Method – thePatient Mood Chart (PMC; Parker et al., 2007) – was used tomeasure mood swings in bipolar patients over the course of a9-month antidepressant trial. Cross sectional measures showedthat the severity of depression and hypomania decreased whenpatients were taking the active drug relative to the placebo,

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Psychiatry Research

http://dx.doi.org/10.1016/j.psychres.2014.04.0470165-1781/& 2014 Elsevier Ireland Ltd. All rights reserved.

n Corresponding author. Tel.: þ61 2 9385 3526; fax: þ61 2 9382 8207.E-mail address: alexis.e.whitton@gmail.com (A.E. Whitton).

Please cite this article as: Proudfoot, J., et al., Evidence of weekly cyclicity in mood and functional impairment in those with abipolar disorder. Psychiatry Research (2014), http://dx.doi.org/10.1016/j.psychres.2014.04.047i

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however the data captured by the PMC revealed that the activedrug also resulted in a reduction in the number of depressionepisodes, the longest episode of hypomania and the number ofdays impaired, revealing a more detailed picture of treatmentoutcome than the cross-sectional measures. The use of daily moodcharts as an assessment tool therefore adds a valuable componentto treatment studies of bipolar disorder.

Despite the potential benefits, however, few studies haveexamined patterns of mood variation in individuals with bipolardisorder using daily mood charts. Furthermore, existing studieshave typically employed case study designs (Jenner et al., 1967),small samples (n¼7) of individuals with rapid cycling bipolardisorder (Gottschalk et al., 1995), or have focused only on therelationship between mood and triggers of bipolar symptoms –

such as sleep disorder (Bauer et al., 2006) or phases of themenstrual cycle (Shivakumar et al., 2008). No research to datehas examined daily patterns of mood and functioning in a largesample of individuals with bipolar disorder, nor the environmentaltriggers associated with mood shifts in people newly diagnosedwith the condition. This is a significant gap in the literature, asidentifying patterns to mood symptoms can significantly reducethe impact of the illness (Berk et al., 2010), particularly byidentifying targets for wellbeing plans. Therefore, the aim of thecurrent study was to undertake a detailed analysis of moodsymptoms, functional capacity, medication compliance and envir-onmental triggers in a large sample of individuals newly diag-nosed with bipolar disorder, using daily monitoring.

2. Method

2.1. Design

The current exploratory study was conducted within the context of a broaderrandomised controlled trial (RCT) examining the effectiveness of an onlinepsychoeducation program for individuals newly diagnosed with bipolar disorder.A total of 419 participants were randomly assigned to receive an online psychoe-ducation program, an online program with adjunctive peer support or to a waitlistcontrol group. Although no group differences were found at post-intervention orfollow-up, those receiving peer support showed higher adherence to the programand slightly lower depression and impairment levels at a 6-month follow-up. Fulldetails of the RCT are available in Proudfoot et al. (2012). In the present study, RCTparticipants who completed at least 75% of their mood charts over 10 weeks(n¼192) were included for analysis. All procedures were approved by the UNSWAustralia Human Research Ethics Committee.

2.2. Participants

Participants were recruited through the Black Dog Institute Mood DisordersClinic, the Black Dog Institute website, various mental health organizations andpractitioners, as well as advertisements placed in the print media. Inclusion criteriawere: aged 18–75 years, diagnosed with bipolar disorder by a mental healthprofessional in the past 12 months, currently being treated for bipolar disorder by amental health professional, able to read and write in English, and living in Australia.Diagnosis was further confirmed using the Mood Swings Questionnaire (MSQ-27;Parker et al., 2006), a 27-item self-report scale with individuals scoring 22 or abovehaving a high probability of bipolar disorder. Using this cut-off score, the MSQ-27has been shown to have 80.9% sensitivity and 98.2% specificity in differentiatingindividuals with bipolar disorder from those with unipolar depression (Parker et al.,2006). Participants were excluded if they were not currently under the regular careof a mental health professional.

2.3. Measures

2.3.1. Patient Mood ChartThe Patient Mood Chart (PMC; Parker et al., 2007) is a paper-based mood chart

on which participants record their daily mood, functional impairment andmedication compliance. Three categories of mood state are monitored: ‘OK’(indicating euthymia), ‘low’ (depression) and ‘high’ (hypomania/mania). A scoreof zero is used to indicate euthymia (i.e., feeling “average” or “normal”), while ‘low’

and ‘high’ moods are rated for intensity from 1 (mild) to 3 (severe), with severedefined as “the worst you have ever been for any episode”. Participants had the

option of recording both high and low mood in the one day. Functional impairmentis rated from 0 (no functional impairment) to 3 (severe impairment) according to howseverely their mood symptoms had impacted on functional capacity on that day.Medication compliance is also rated on a scale of 0–3, where 0 indicates that nomedication was prescribed, 1 indicates that no medication was taken, 2 indicatesthat some medication was taken, and 3 indicates that all medication was taken.Participants are also given space in the PMC to record factors that they believe mayhave precipitated their mood symptoms each day. These include people, places,events or activities that were deemed by the participant to have “triggered” theirmood symptoms in some way. In each of the three RCT conditions participantswere asked to complete the PMC daily for 10 consecutive weeks.

2.4. Statistical analyses

Univariate Analyses of Variance (ANOVA) were used to test for group differ-ences in mean lows, highs, functional impairment and medication compliance.Analyses revealed no group differences in the PMC data (all Ps40.05), so the datawere aggregated for further analysis of the whole sample. Average daily highs,lows, functional impairment and medication compliance across the 10-week periodwere plotted and visually inspected for trends in the data. In addition, symptomtriggers were categorized into 20 themes by the second author (see Appendix A forthe full list of categories and examples). These themes were not determined apriori, but rather, emerged from the participant's description of triggers. A randomsample of 10% of the triggers was also thematically categorized by the first authorto ensure rating reliability. The frequency with which each trigger categoryoccurred was then totaled for each day of the week and expressed as a percentageof all triggers listed for that day of the week.

3. Results

Clinical characteristics of the participants included in thecurrent analysis relative to the remainder of the RCT sample areshown in Table 1. On inspection of symptom variability across the10-week period, low mood in particular appeared to show cyclicalvariation across the separate weeks of the intervention, with themost severe lows occurring early in the week and the least severeoccurring on or close to the weekend. Post-hoc analyses weretherefore conducted to test for weekly cyclicity in PMC variables.Mean scores for highs, lows, functioning limitation and medicationcompliance for each day of the week (i.e., Monday-Sunday) over

Table 1Baseline clinical characteristics of the current sample relative to the remainder ofthe RCT participants.

Variable Currentsample

Remainder ofRCT sample

Inferential statistics

M S.D. M S.D. d.f. F eta p

Depressiona 6.38 2.14 6.60 2.00 356 0.98 0.00 0.32Anxietya 7.01 2.15 6.95 2.17 352 0.08 0.00 0.78

Self Esteemb 27.24 5.21 27.35 5.68 356 0.04 0.00 0.84Work and Social Adj.c 22.76 9.05 24.52 7.85 358 3.88 0.01 0.05Life Satisfactiond 14.42 6.93 14.21 6.81 360 0.08 0.00 0.78

Health Locus of Controle

Internal 23.24 4.93 23.20 4.66 359 0.01 0.00 0.93Powerful Others 17.81 5.60 16.60 5.36 357 4.56 0.01 0.03Chance 18.14 5.25 17.70 5.02 359 0.66 0.00 0.42

Controlf 3.09 2.35 3.36 2.23 357 1.30 0.00 0.26Understandingf 4.94 2.52 4.74 2.57 358 0.52 0.00 0.47Stigmag 5.92 3.65 6.48 3.01 358 2.56 0.01 0.11

a Goldberg Anxiety and Depression Scale (Goldberg et al., 1988).b Rosenberg Self-Esteem Scale (Rosenberg, 1965).c Work and Social Adjustment Scale (Mundt et al., 2002).d Satisfaction With Life Scale (Diener et al., 1985).e Multidimensional Health Locus Of Control (Wallston et al., 1978).f Brief Illness Perception Questionnaire (Broadbent et al., 2006).g Participants rated 0 (Strongly Disagree) to 10 (Strongly Agree) the extent to

which they agreed with the statement “I do not tell people I have bipolar disorder,as they will think negatively of me”.

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Please cite this article as: Proudfoot, J., et al., Evidence of weekly cyclicity in mood and functional impairment in those with abipolar disorder. Psychiatry Research (2014), http://dx.doi.org/10.1016/j.psychres.2014.04.047i

the 10-week period were computed and a repeated-measuresANOVA, with day of the week (Monday-Sunday) as the within-subjects variable, quantified whether fluctuations in PMC variablesvaried systematically across the week.

3.1. Mood, functional limitation and medication compliance acrossthe week

Daily highs, lows, functional impairment and medication com-pliance in the current sample, averaged for each day of the weekover the 10-week period, are shown in Fig. 1. There was asignificant main effect for day of the week for lows F(6, 1146)¼6.99, ηρ2¼0.04, Po0.001, highs F(6, 1146)¼2.50, ηρ2¼0.01, Po0.05and functional limitation F(6, 1146)¼6.73, ηρ2¼0.03, Po0.001 butnot for medication compliance (P40.05). These analyses sug-gested that cyclical variations in mood and functioning were notrelated to variations in medication compliance.

Mood on Mondays and Tuesdays was significantly lower than onThursdays, Fridays and Saturdays (all Pso0.05), withWednesdays andSundays falling in between. The most intense lows occurred onTuesdays (M¼0.83) and the least intense lows on Saturdays(M¼0.72). In contrast, highs were greatest on Saturdays (M¼0.30),and significantly more so than on Wednesdays (M¼0.25), when theywere the least intense (Po0.05). Similar to lows, the most severefunctional limitation occurred on Mondays (closely followed by Tues-days), with significantly poorer functioning than on Thursdays(Po0.001), and slightly, though not significantly poorer than onFridays (P¼0.06) and Saturdays (P¼0.08). There were no differencesin medication compliance across days of the week (all Ps40.05).

In sum, low mood and functional limitation were most intenseearly in the week (i.e., Mondays and Tuesdays), while highs weremost severe at the beginning of the weekend (i.e., Saturdays).

To examine whether the mean intensity of highs, lows, func-tional limitation and medication compliance improved over thecourse of the RCT, mean ratings for the baseline week, the post-intervention week, the 3-month follow-up week and the 6-monthfollow-up week were computed. Repeated measures ANOVAs withthe within-subjects variable of time (baseline, post-intervention,3-month follow-up, and 6-month follow-up) were then conductedfor each of the four variables.

There was a significant main effect of time for all four variables.For both lows and functional limitation, there were significantimprovements in post-intervention, 3-month follow-up and 6-month follow-up ratings compared to baseline (all Pso0.01),however post, 3-month and 6-month follow-up ratings did notdiffer (all Ps40.05). Analyses of mean intensity ratings of highsshowed that the degree of post-intervention highs was signifi-cantly reduced compared to baseline (Po0.05), although baseline,3-month follow-up and 6-month follow-up highs did not differ (allPs40.05). Finally, analyses of medication compliance across thefour time periods revealed significantly better compliance at post-intervention and 3-month follow-up compared to baseline (bothPso0.05), although compliance at baseline and 6-month follow-up did not differ (P40.05).

To examine whether the weekly variation in symptoms differedfor participants who were likely to follow a structured workweek(e.g., those who were employed or studying) compared to thosewho were less likely to follow a structured workweek (e.g., thosewho were unemployed or retired), we conducted repeated mea-sures ANOVAs with the between-subject factor of workweek(structured vs. unstructured) and the within-subject factor of day(Monday to Sunday) for each of the PMC variables. There were nosignificant Workweek�Day interactions (all Ps40.05), nor werethere any significant main effects of workweek (all Ps40.05).

3.2. Symptom triggers across the week

The most common triggers reported by participants are shownin Fig. 2, along with the frequency with which they occurred oneach day of the week. Negative work-related triggers (e.g., workstress or conflict) were the most frequently cited on weekdays,while sleep-related triggers (e.g., insomnia, sleep deprivation oroversleeping) were most common on Saturdays and Sundays,closely followed by positive social triggers (e.g., spending timewith friends) on Saturdays. The same seven triggers were generallyreported as most salient across all days of the week, except foroverstimulation/chaos, which emerged as a slightly more salienttrigger than partner-related stressors on Tuesdays and Saturdays,and was also more salient than positive social events on Mondays.

Fig. 1. Mean severity of highs, lows, functioning limitation and medication compliance, recorded daily and averaged across the 10-week intervention period, by day of theweek. Scores are rated 0–3 for all variables. Higher scores on highs and lows represent more intense mood symptoms. Higher scores on functioning limitation representgreater functional impairment, and higher scores on medication compliance represent better medication compliance.

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Please cite this article as: Proudfoot, J., et al., Evidence of weekly cyclicity in mood and functional impairment in those with abipolar disorder. Psychiatry Research (2014), http://dx.doi.org/10.1016/j.psychres.2014.04.047i

3.3. Current sample Vs. remainder of RCT sample

The current analyses were limited to data from participantswho completed at least 75% of their mood charts over the courseof the RCT. This sub-sample was compared to the rest of the RCTparticipants to determine whether the current findings could begeneralized to the patient population, and did not simply reflectthe mood patterns of those who were less unwell. Baseline clinicalcharacteristics of the two sub-samples are shown in Table 1. Theonly significant difference between the groups was observed forthe Multidimensional Health Locus of Control, where those in thecurrent sample scored significantly higher on the Powerful Otherssubscale compared to the remainder of the RCT sample (Po0.05).

4. Discussion

The current study aimed to provide a detailed analysis of dailymood symptoms, functional limitation, medication complianceand symptom triggers over 10 consecutive weeks in a sample ofindividuals newly diagnosed with bipolar disorder. In addition toshowing significant improvement in lows, highs, functional limita-tion and medication compliance across the intervention period,results revealed a weekly cycle in mood symptoms and functionallimitation, which to our knowledge has not been reportedpreviously.

Our results show that low mood and functional limitation areworse on weekdays, particularly Mondays and Tuesdays, whilemood is most elevated on Saturdays. Importantly, we establishedthat these patterns occurred independently of any weekly varia-tion in medication compliance. Work-related stressors were themost common triggers on weekdays, whereas sleep-related pro-blems and positive social events were the most common triggerson the weekend. Taken together, these data suggest that variationsin mood and functioning may be closely tied to psychosocialtriggers that tend to occur on a weekly cycle. Previous researchinto mood variability in bipolar disorder has identified a linkbetween bipolar mood symptoms and stressful life events(Johnson et al., 2008), as well as diurnal variation in moodsymptoms (Feldman-Naim et al., 1997). Our results extend thesefindings by showing that mood and functioning in bipolar disorder

also fluctuates in accordance with a weekly cycle that appears tobe independent of an individual's workweek structure and med-ication compliance.

This finding has implications for the assessment and treatmentof bipolar disorder. Firstly, while clinical interviews typicallyinclude an assessment of symptom variation associated withsignificant life events, they would benefit from assessing weeklyvariation in symptoms so that more subtle yet regularly occurringpsychosocial triggers of symptoms can be identified and addressedin psychoeducation and wellbeing plans. Secondly, our data hasrevealed the unique finding that work-related stressors are mostclosely tied to low mood in individuals with bipolar disorder.Given that low mood in bipolar disorder constitutes the greatestburden of the disease (Judd et al., 2003), psychosocial interven-tions aimed at dealing with work-related stressors may beparticularly useful for targeting low mood in this population. Forexample, employers of individuals with bipolar disorder as well asthe individuals themselves may benefit from structuring the workday in a manner that counteracts low mood, by integratingopportunities for mastery, increasing physical activity and ensur-ing that adequate workplace support is available (Lauber andBowen, 2010). Third, given that mood and functioning vary on aweekly cycle, studies involving assessments of treatment responsein individuals with bipolar disorder should control for the day ofthe week at which the assessment was conducted.

A comparison of the clinical characteristics of those who wereincluded in the current analysis (i.e., those who completed at least75% of their mood charts during the RCT) and the remaining RCTsample revealed minimal differences between the two groups,indicating that the current findings do not simply reflect a patternof mood variation in individuals with milder symptomatology. Thedifference observed on the Powerful Others subscale of the HealthLocus of Control Scale is likely to be associated with increasedmood chart compliance rather than differences on the actualmood chart variables, given that an increased perception thatpowerful individuals (e.g., physicians) determine one's health islikely to increase compliance with medical assessment tools, suchas a mood chart.

Some limitations must be kept in mind when interpreting thecurrent data. First, this was an exploratory study. Future researchis required to examine the weekly cycle of mood and functioning,

Fig. 2. The seven most common triggers listed according to day of the week over the 10-week period.

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Please cite this article as: Proudfoot, J., et al., Evidence of weekly cyclicity in mood and functional impairment in those with abipolar disorder. Psychiatry Research (2014), http://dx.doi.org/10.1016/j.psychres.2014.04.047i

and to gauge the severity of the weekly variation in people withbipolar disorder relative to the general population, as similarpatterns of mood variation across the week have been observedin healthy samples (e.g., Ryan et al., 2010). It therefore needs to beestablished whether mood symptoms vary on a weekly cycle to alesser, greater or equal extent in individuals with bipolar disordercompared to the general population. Future studies should alsoexamine whether weekly mood variation in individuals withbipolar disorder is affected more profoundly by environmentaltriggers than the general population. Second, it is possible thatsome participants completed part or all of their mood chartsretrospectively, prior to returning them. Although a paper-basedmood chart was used in the current study so that participantscould record their mood without having to access a computer, thegrowing popularity of portable handheld devices makes it possiblefor electronic mood monitoring programs, such as the electronicLife-Chart Form (Schärer et al., 2002) or the myCompass program(Proudfoot et al., 2013) to be used in future studies. Such programsenhance convenience and adherence and provide automatedrecording of the exact date and time the monitoring is carried out.

To conclude, the present study provides evidence that indivi-duals newly diagnosed with bipolar disorder experience fluctua-tions in mood and functioning that vary according to a weeklycycle. This finding has implications both for the assessment andtreatment of patients, as well as for future research.

Appendix A

A.1. Trigger categories with examples

Category Examples

Work (positive) Achievement, creative pursuitsWork (negative) Work-related stress or conflictFamily (positive) Spending time with familyFamily (negative) Family conflict or problemsSocial (positive) Spending time with friendsSocial (negative) Conflict with friends,

lonelinessPartner (positive) Spending time with partnerPartner (negative) Conflict with partnerSleep Insomnia, oversleepingPhysical health (positive) ExercisePhysical health (negative) Illness, injuryMental health Psychiatrist appointmentsMedication Side effects, missed dosesDaily hassles Day-to-day stressorsSignificant life events(stressful)

Bereavement, exams

Significant life events(exciting)

Birthdays, holidays

Money Financial problemsSubstances Coffee, sugar, recreational

drugsOverstimulation/chaos Concerts, shopping, crowdsCyclical factors Weather, menstruation

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Please cite this article as: Proudfoot, J., et al., Evidence of weekly cyclicity in mood and functional impairment in those with abipolar disorder. Psychiatry Research (2014), http://dx.doi.org/10.1016/j.psychres.2014.04.047i