Comparison of sleep/wake parameters for self-monitoring bipolar disorder

Longitudinal Digital Mood Charting in Bipolar Disorder: Experiences with ChronoRecord Over 20 Years

INTRODUCTION

Longitudinal study is an essential methodology for understanding disease trajectories, treatment effects, symptom changes, and long-term outcomes of affective disorders. Daily self-charting of mood and other illness-related variables is a commonly recommended intervention. With the widespread acceptance of home computers in the early 2000s, automated tools were developed for patient mood charting, such as ChronoRecord, a software validated by patients with bipolar disorder. The purpose of this study was to summarize the daily mood, sleep, and medication data collected with ChronoRecord, and highlight some of the key research findings. Lessons learned from implementing a computerized tool for patient self-reporting are also discussed.

METHODS

After a brief training session, ChronoRecord software for daily mood charting was installed on a home computer and used by 609 patients with affective disorders.

RESULTS

The mean age of the patients was 40.3±11.8 years, a mean age of onset was 22±11.2 years, and 71.4% were female. Patients were euthymic for 70.8% of days, 15.1% had mild depression, 6.6% had severe depression, 6.6% had hypomania, and 0.8% had mania. Among all mood groups, 22.4% took 1-2 medications, 37.2% took 3-4 medications, 25.7 took 5-6 medications, 11.6% took 7-8 medications, and 3.1% took >8 medications.

CONCLUSION

The daily mood charting tool is a useful tool for increasing patient involvement in their care, providing detailed patient data to the physician, and increasing understanding of the course of illness. Longitudinal data from patient mood charting was helpful in both clinical and research settings.

Dim light melatonin patterns in unaffected offspring of parents with bipolar disorder: A case-control high-risk study

BACKGROUND

Circadian dysregulation has long been thought to be a key component in the pathophysiology of bipolar disorder (BD). However, it remains unclear whether this dysregulation constitutes a risk factor, manifestation, or consequence of BD. This study aimed to compare dim light melatonin secretion patterns between unaffected offspring of parents with BD (OBD) and offspring of control parents (OCP).

METHODS

This case-control study included unaffected OBD (mean age 14.0 years; male 50.0 %) and age- and sex-matched OCP (mean age 13.0 years; male: 43.5 %). Seventeen saliva samples were collected in dim light conditions. Dim light melatonin onset (DLMO), phase angles, and area under the curve (AUC) were calculated.

RESULTS

185 saliva samples from 12 OBD (n = 12) and 741 from OCP (n = 46) were collected. Unaffected OBD had a significant lower nocturnal melatonin level (14.8 ± 4.6 vs. 20.3 ± 11.7 pg/mL) and a smaller melatonin AUC within two hours after DLMO (35.5 ± 11.3 vs. 44.6 ± 18.1 pg/mL) but a significant larger phase angle between DLMO and sleep onset (2.2 ± 1.0 vs. 1.4 ± 1.2 h) than OCP. There was no significant between-group difference in DLMO. The graphic illustrations showed a considerably flattened melatonin secretion in unaffected OBD.

LIMITATIONS

The main limitations include lack of 24-h dim melatonin secretion measurement, large age range of participants, and small sample size.

CONCLUSIONS

These findings suggest that unaffected OBD already presented with circadian rhythm dysregulations. Future investigations are needed to clarify the role of abnormal melatonin secretion in the onset of BD.

Apps and gaps in bipolar disorder: A systematic review on electronic monitoring for episode prediction

BACKGROUND

Long-term clinical monitoring in bipolar disorder (BD) is an important therapeutic tool. The availability of smartphones and wearables has sparked the development of automated applications to remotely monitor patients. This systematic review focus on the current state of electronic (e-) monitoring for episode prediction in BD.

METHODS

We systematically reviewed the literature on e-monitoring for episode prediction in adult BD patients. The systematic review was done according to the guidelines for reporting of systematic reviews and meta-analyses (PRISMA) and was registered in PROSPERO on April 29, 2020 (CRD42020155795). We conducted a search of Web of Science, MEDLINE, EMBASE, and PsycINFO (all 2000-2020) databases. We identified and extracted data from 17 published reports on 15 relevant studies.

RESULTS

Studies were heterogeneous and most had substantial methodological and technical limitations. Models varied widely in their performance. Published metrics were too heterogeneous to lend themselves to a meta-analysis. Four studies reported sensitivity (range: 0.21 - 0.95); and two reported specificity for prediction of mood episodes (range: 0.36 - 0.99). Two studies reported accuracy (range: 0.64 - 0.88) and four reported area under the curve (AUC; range: 0.52-0.95). Overall, models were better in predicting manic or hypomanic episodes, but their performance depended on feature type.

LIMITATIONS

Our conclusions are tempered by the lack of appropriate information impeding our ability to synthesize the available evidence.

CONCLUSIONS

Given the clinical variability in BD, predicting mood episodes remains a challenging task. Emerging e-monitoring technology for episode prediction in BD requires more development before it can be adopted clinically.

Development of an Emotion-Sensitive mHealth Approach for Mood-State Recognition in Bipolar Disorder

Internet- and mobile-based approaches have become increasingly significant to psychological research in the field of bipolar disorders. While research suggests that emotional aspects of bipolar disorders are substantially related to the social and global functioning or the suicidality of patients, these aspects have so far not sufficiently been considered within the context of mobile-based disease management approaches. As a multiprofessional research team, we have developed a new and emotion-sensitive assistance system, which we have adapted to the needs of patients with bipolar disorder. Next to the analysis of self-assessments, third-party assessments, and sensor data, the new assistance system analyzes audio and video data of these patients regarding their emotional content or the presence of emotional cues. In this viewpoint, we describe the theoretical and technological basis of our emotion-sensitive approach and do not present empirical data or a proof of concept. To our knowledge, the new assistance system incorporates the first mobile-based approach to analyze emotional expressions of patients with bipolar disorder. As a next step, the validity and feasibility of our emotion-sensitive approach must be evaluated. In the future, it might benefit diagnostic, prognostic, or even therapeutic purposes and complement existing systems with the help of new and intuitive interaction models.

Highs and lows, ups and downs: Meteorology and mood in bipolar disorder

Seasonal variation of manic and depressive symptoms is a controversial topic in bipolar disorder research. Several studies report seasonal patterns of hospital admissions for depression and mania and variation in symptoms that appear to follow a seasonal pattern, whereas others fail to report such patterns. Differences in research methodologies, data analysis strategies, and temporal resolution of data may partly explain the variation in findings between studies. The current study adds a novel perspective to the literature by investigating specific meteorological factors such as atmospheric pressure, hours of sunshine, relative humidity, and daily maximum and minimum temperatures as more proximal predictors of self-reported daily mood change in people diagnosed with bipolar disorder. The results showed that daily maximum temperature was the only meteorological variable to predict clinically-relevant mood change, with increases in temperature associated with greater odds of a transition into manic mood states. The mediating effects of sleep and activity were also investigated and suggest at least partial influence on the prospective relationship between maximum temperature and mood. Limitations include the small sample size and the fact that the number and valence of social interactions and exposure to natural light were not investigated as potentially important mediators of relationships between meteorological factors and mood. The current data make an important contribution to the literature, serving to clarify the specific meteorological factors that influence mood change in bipolar disorder. From a clinical perspective, greater understanding of seasonal patterns of symptoms in bipolar disorder will help mood episode prophylaxis in vulnerable individuals.

Electronic monitoring of self-reported mood: the return of the subjective?

This narrative review describes recent developments in the use of technology for utilizing the self-monitoring of mood, provides some relevant background, and suggests some promising directions. Subjective experience of mood is one of the valuable sources of information about the state of an integrated mind/brain system. During the past century, psychiatry and psychology moved away from subjectivity, emphasizing external observation, precise measurement, and laboratory techniques. This shift, however, provided only a limited improvement in the understanding of mood disorders, and it appears that self-monitoring of mood has the potential to enrich our knowledge, particularly when combined with the advances in technology. Modern technology, with its ability to transfer information from the individual directly to the researcher via electronic applications, enables us now to study mood regulation more thoroughly. Frequent subjective ratings can be helpful in identifying individualized treatment with effective mood stabilizers and recognizing subtypes of mood disorders. The variability of subjective ratings may also help us estimate the increased risk of recurrence and guide a tailored treatment.

Sleep, residual mood symptoms, and time to relapse in recovered patients with bipolar disorder

BACKGROUND

Sleep disturbance in bipolar disorder (BD) is common during and between mood episodes. In recovered (euthymic at least two months) BD patients, we assessed sleep compared to controls and its relationships with residual mood symptoms and mood episode recurrence.

METHOD

Recovered Stanford University BD Clinic patients diagnosed with the Systematic Treatment Enhancement Program for BD (STEP-BD) Affective Disorders Evaluation and monitored with the STEP-BD Clinical Monitoring Form (CMF) for >1 year and healthy controls completed the Pittsburgh Sleep Quality Index (PSQI). PSQI parameters were compared in BD patients versus controls, and the most robustly differentiating PSQI parameter was assessed in relationship to residual mood symptoms, and time to mood episode recurrence in BD patients.

RESULTS

Eighty nine recovered BD patients compared to 56 healthy controls had significantly worse PSQI global score, more sleep medication use, longer sleep latency, and worse daytime dysfunction. PSQI global score had the greatest BD patient versus control effect size, and among BD patients, correlated significantly with residual mood symptoms and predicted earlier mood episode recurrence, even after covarying for residual mood symptoms.

LIMITATIONS

Use of subjective (PSQI) rather objective (polysomnography) sleep metric. Statistical power limited by small sample size. Potential psychotropic medication confound. Northern California tertiary BD clinic referral sample.

CONCLUSION

Further research is needed to confirm that in recovered BD patients, poor sleep quality correlates with residual mood symptoms, and independently predicts mood episode recurrence. If confirmed, these observations suggest potential mood benefit for focusing on sleep quality in interventions for recovered BD patients.

Applications of time-series analysis to mood fluctuations in bipolar disorder to promote treatment innovation: a case series

Treatment innovation for bipolar disorder has been hampered by a lack of techniques to capture a hallmark symptom: ongoing mood instability. Mood swings persist during remission from acute mood episodes and impair daily functioning. The last significant treatment advance remains Lithium (in the 1970s), which aids only the minority of patients. There is no accepted way to establish proof of concept for a new mood-stabilizing treatment. We suggest that combining insights from mood measurement with applied mathematics may provide a step change: repeated daily mood measurement (depression) over a short time frame (1 month) can create individual bipolar mood instability profiles. A time-series approach allows comparison of mood instability pre- and post-treatment. We test a new imagery-focused cognitive therapy treatment approach (MAPP; Mood Action Psychology Programme) targeting a driver of mood instability, and apply these measurement methods in a non-concurrent multiple baseline design case series of 14 patients with bipolar disorder. Weekly mood monitoring and treatment target data improved for the whole sample combined. Time-series analyses of daily mood data, sampled remotely (mobile phone/Internet) for 28 days pre- and post-treatment, demonstrated improvements in individuals' mood stability for 11 of 14 patients. Thus the findings offer preliminary support for a new imagery-focused treatment approach. They also indicate a step in treatment innovation without the requirement for trials in illness episodes or relapse prevention. Importantly, daily measurement offers a description of mood instability at the individual patient level in a clinically meaningful time frame. This costly, chronic and disabling mental illness demands innovation in both treatment approaches (whether pharmacological or psychological) and measurement tool: this work indicates that daily measurements can be used to detect improvement in individual mood stability for treatment innovation (MAPP).

Differences in sleep disturbances among offspring of parents with and without bipolar disorder: association with conversion to bipolar disorder

OBJECTIVES

Disruptions in sleep and dysregulation in circadian functioning may represent core abnormalities in the pathophysiology of bipolar disorder (BP). However, it is not clear whether these dysfunctions are state or trait markers of BP. This report compared sleep and circadian phenotypes among three groups: offspring of parents with BP diagnosed with BP at intake (BP/OB; n = 47), offspring of parents with BP without BP at intake (non-BP/OB; n = 386), and offspring of matched control parents who did not have BP (controls; n = 301). We also examined the association of baseline sleep parameters with subsequent development of BP among the non-BP/OB group.

METHODS

Pittsburgh Bipolar Offspring Study youth (ages 6-18 years) and their parents completed assessments every two years pertaining to the child's sleep and circadian phenotypes and current psychopathology. Mixed-effects models examined differences in baseline sleep and circadian variables among the three groups.

RESULTS

BP/OB offspring who were in a mood episode differed significantly on sleep parameters from the non-BP/OB and the offspring of controls, such as having inadequate sleep. Mixed logistic regression procedures showed that baseline sleep and circadian variables, such as frequent waking during the night, significantly predicted the development of BP among non-BP/OB over longitudinal follow-up.

CONCLUSIONS

While lifetime diagnostic status accounted for differences among the groups in sleep and circadian disturbances, psychopathology explained the differences even further. Additionally, sleep disturbance may be a prognostic indicator of the development of BP in high-risk youth. Future studies are required to further disentangle whether sleep and circadian disruption are state or trait features of BP.

Nonlinear dynamics of mood regulation in bipolar disorder

OBJECTIVES

We sought to study the underlying dynamic processes involved in mood regulation in subjects with bipolar disorder and healthy control subjects using time-series analysis and to then analyze the relation between anxiety and mood using cross-correlation techniques.

METHODS

We recruited 30 healthy controls and 30 euthymic patients with bipolar disorder. Participants rated their mood, anxiety, and energy levels using a paper-based visual analog scale; and they also recorded their sleep and any life events. Information on these variables was provided over a three-month period on a daily basis, twice per day. We analyzed the data using Box-Jenkins time series analysis to obtain information on the autocorrelation of the series (for mood) and cross-correlation (mood and anxiety series).

RESULTS

Throughout the study, we analyzed 10,170 data points. Self-ratings for mood, anxiety, and energy were normally distributed in both groups. Autocorrelation functions for mood in both groups were governed by the autoregressive integrated moving average (ARIMA) (1,1,0) model, which means that current values in the series were related to one previous point only. We also found a negative cross-correlation between mood and anxiety.

CONCLUSIONS

Mood can be considered a memory stochastic process; it is a flexible, dynamic process that has a 'short memory' both in healthy controls and euthymic patients with bipolar disorder. This process may be quite different in untreated patients or in those acutely ill. Our results suggest that nonlinear measures can be applied to the study of mood disorders.

Significant sleep disturbances in euthymic bipolar patients

OBJECTIVE

A growing amount of data suggests that sleep dysfunction is frequently observed in bipolar disorder (BD) patients even when they do not fulfill the criteria for major mood episodes. Thus, we performed a case-control study assessing sleep status in a group of euthymic BD patients and a group of health controls.

METHODS

A total of 209 subjects (104 health controls and 105 BD patients) were enrolled in the study. The Pittsburgh Sleep Quality Index (PSQI) was used for sleep assessment. Inclusion criteria for the BD group were a diagnosis of BD, following DSM-IV-TR criteria, according to the MINI-plus structured clinical interview. Euthymia was established as a score lower than 7 both in the Hamilton Depression Rating Scale (HDRS) and in the Young Mania Rating Scale (YMRS). Health controls were also interviewed using the MINI-plus and included in this study if they were free of any current or past DSM-IV-TR axis I psychiatric disorder as well the actual use of psychopharmacological medications.

RESULTS

While 21.2 % of the control group displayed poor sleep quality according to the global PSQI-BR score, 82.9 % of the euthymic BD patients had poor sleep quality (p=0.000). PSQI sleep duration subcomponent showed comparable results in the two groups (p=0.535), even though BD patients had significant disruptions in sleep latency (p=0.000) and sleep efficiency (p=0.000) subcomponents.

CONCLUSION

We were able to show that BD patients, even in euthymic phase, exhibit a significantly worse sleep quality as compared with health controls as assessed by PSQI total score and five of its seven subcomponents.

Behavioral treatment of insomnia in bipolar disorder

Sleep disturbance is common in bipolar disorder. Stimulus control and sleep restriction are powerful, clinically useful behavioral interventions for insomnia, typically delivered as part of cognitive-behavioral therapy for insomnia (CBT-I). Both involve short-term sleep deprivation. The potential for manic or hypomanic symptoms to emerge after sleep deprivation in bipolar disorder raises questions about the appropriateness of these methods for treating insomnia. In a series of patients with bipolar disorder who underwent behavioral treatment for insomnia, the authors found that regularizing bedtimes and rise times was often sufficient to bring about improvements in sleep. Two patients in a total group of 15 patients reported mild increases in hypomanic symptoms the week following instruction on stimulus control. Total sleep time did not change for these individuals. Two of five patients who underwent sleep restriction reported mild hypomania that was unrelated to weekly sleep duration. Sleep restriction and stimulus control appear to be safe and efficacious procedures for treating insomnia in patients with bipolar disorder. Practitioners should encourage regularity in bedtimes and rise times as a first step in treatment, and carefully monitor changes in mood and daytime sleepiness throughout the intervention.

Daily variations in sleep-wake patterns and severity of psychopathology: a pilot study in community-dwelling individuals with chronic schizophrenia

The daily co-occurrence of change in sleep characteristics and psychopathology was examined in six individuals with schizophrenia and seven healthy controls using a prospective assessment of rest-activity patterns conducted in the person's home for up to 28 days. The results provide preliminary evidence that a change in sleep-wake timing is followed by a change in symptom severity.