The use of the prospective NIMH Life Chart Method as a bipolar mood assessment method in research: a systematic review of different methods, outcome measures and interpretations

Dynamic time warp analysis of individual symptom trajectories in individuals with bipolar disorder

BACKGROUND

Manic and depressive mood states in bipolar disorder (BD) may emerge from the non-linear relations between constantly changing mood symptoms exhibited as a complex dynamic system. Dynamic Time Warp (DTW) is an algorithm that may capture symptom interactions from panel data with sparse observations over time.

METHODS

The Young Mania Rating Scale and Quick Inventory of Depressive Symptomatology were repeatedly assessed in 141 individuals with BD, with on average 5.5 assessments per subject every 3-6 months. Dynamic Time Warp calculated the distance between each of the 27 × 27 pairs of standardized symptom scores. The changing profile of standardized symptom scores of BD participants was analyzed in individual subjects, yielding symptom dimensions in aggregated group-level analyses. Using an asymmetric time-window, symptom changes that preceded other symptom changes (i.e., Granger causality) yielded a directed network.

RESULTS

The mean age of the BD participants was 40.1 (SD 13.5) years old, and 60% were female participants. Idiographic symptom networks were highly variable between subjects. Yet, nomothetic analyses showed five symptom dimensions: core (hypo)mania (6 items), dysphoric mania (5 items), lethargy (7 items), somatic/suicidality (6 items), and sleep (3 items). Symptoms of the "Lethargy" dimension showed the highest out-strength, and its changes preceded those of "somatic/suicidality," while changes in "core (hypo)mania" preceded those of "dysphoric mania."

CONCLUSION

Dynamic Time Warp may help to capture meaningful BD symptom interactions from panel data with sparse observations. It may increase insight into the temporal dynamics of symptoms, as those with high out-strength (rather than high in-strength) could be promising targets for intervention.

Efficacy of a Contextualized Measurement of Life Satisfaction: A Pilot Study on the Assessment of Progress in Eating Disorder Therapy

Eating disorders strongly affect psychological distress and its perception. However, most of the existing instruments for assessing life satisfaction rely on a point-estimation method that is biased due to the circumstantial conditions around the time of assessment. The main goal of this study was to apply a different kind of instrument-the Life Satisfaction Chart-that situates the current state of life satisfaction in the context of personal history and describes the life stages through a graph. The assessment was applied to a sample of 29 adolescent women (average age of 17.88) who were enrolled in a clinical program to treat their eating disorders. The results showed that their estimation of their current life satisfaction was almost identical to the estimation provided by a therapist for those who were in therapy phases 1, 2, and 3 (of four), while patients' point-estimation satisfaction showed statistically significant differences when compared with the situated estimations. In therapy phase 4, significant discrepancies were observed between the therapist's perception and the patients' perception, because the therapist focused only on eating disorder recovery, whilst the patients evaluated their lives under almost-normal conditions, taking into account further dimensions. The Life Satisfaction Chart is a new approach to life-satisfaction measurement that showed promising measurement and therapeutical properties.

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.

Predicting recurrence of depression using lifelog data: an explanatory feasibility study with a panel VAR approach

BACKGROUND

Although depression has a high rate of recurrence, no prior studies have established a method that could identify the warning signs of its recurrence.

METHODS

We collected digital data consisting of individual activity records such as location or mobility information (lifelog data) from 89 patients who were on maintenance therapy for depression for a year, using a smartphone application and a wearable device. We assessed depression and its recurrence using both the Kessler Psychological Distress Scale (K6) and the Patient Health Questionnaire-9.

RESULTS

A panel vector autoregressive analysis indicated that long sleep time was a important risk factor for the recurrence of depression. Long sleep predicted the recurrence of depression after 3 weeks.

CONCLUSIONS

The panel vector autoregressive approach can identify the warning signs of depression recurrence; however, the convenient sampling of the present cohort may limit the scope towards drawing a generalised conclusion.

Comparison of clinical and sociodemographic features of bipolar disorder patients with those of social anxiety disorder patients comorbid with bipolar disorder in Turkey

Objectives:

To assess the impact of social anxiety disorder (SAD) comorbidity on the clinical features, illness severity, and response to mood stabilizers in bipolar disorder (BD) patients.

Methods:

This retrospective study included bipolar patients that were treated at the Department of Psychiatry, Haseki Training and Research Hospital, Istanbul, Turkey in 2015, and who provided their informed consents for participation in this study. The study was conducted by assessing patient files retrospectively. Two hundred bipolar patients were assessed using the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, 4th Edition axis-I (SCID-I) in order to detect all possible comorbid psychiatric diagnoses. The sample was split according to the presence of SAD comorbidity and the groups were compared.

Results:

The SAD comorbidity was detected in 17.5% (35/200) of the BD patients. The SAD comorbid bipolar patients were more educated, had earlier onset of BD, lower number of manic episodes, and more severe episodes. There was no difference between groups in terms of total number of episodes, hospitalization, suicidality, being psychotic, treatment response to lithium and anticonvulsants.

Conclusion:

Social anxiety disorder comorbidity may be associated with more severe episodes and early onset of BD. However, SAD comorbidity may not be related to treatment response in bipolar patients.

Electronic self-monitoring of mood using IT platforms in adult patients with bipolar disorder: A systematic review of the validity and evidence

BACKGROUND

Various paper-based mood charting instruments are used in the monitoring of symptoms in bipolar disorder. During recent years an increasing number of electronic self-monitoring tools have been developed. The objectives of this systematic review were 1) to evaluate the validity of electronic self-monitoring tools as a method of evaluating mood compared to clinical rating scales for depression and mania and 2) to investigate the effect of electronic self-monitoring tools on clinically relevant outcomes in bipolar disorder.

METHODS

A systematic review of the scientific literature, reported according to the Preferred Reporting items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines was conducted. MEDLINE, Embase, PsycINFO and The Cochrane Library were searched and supplemented by hand search of reference lists. Databases were searched for 1) studies on electronic self-monitoring tools in patients with bipolar disorder reporting on validity of electronically self-reported mood ratings compared to clinical rating scales for depression and mania and 2) randomized controlled trials (RCT) evaluating electronic mood self-monitoring tools in patients with bipolar disorder.

RESULTS

A total of 13 published articles were included. Seven articles were RCTs and six were longitudinal studies. Electronic self-monitoring of mood was considered valid compared to clinical rating scales for depression in six out of six studies, and in two out of seven studies compared to clinical rating scales for mania. The included RCTs primarily investigated the effect of heterogeneous electronically delivered interventions; none of the RCTs investigated the sole effect of electronic mood self-monitoring tools. Methodological issues with risk of bias at different levels limited the evidence in the majority of studies.

CONCLUSIONS

Electronic self-monitoring of mood in depression appears to be a valid measure of mood in contrast to self-monitoring of mood in mania. There are yet few studies on the effect of electronic self-monitoring of mood in bipolar disorder. The evidence of electronic self-monitoring is limited by methodological issues and by a lack of RCTs. Although the idea of electronic self-monitoring of mood seems appealing, studies using rigorous methodology investigating the beneficial as well as possible harmful effects of electronic self-monitoring are needed.

A Network Approach to Bipolar Symptomatology in Patients with Different Course Types

Objective

The longitudinal mood course is highly variable among patients with bipolar disorder(BD). One of the strongest predictors of the future disease course is the past disease course, implying that the vulnerability for developing a specific pattern of symptoms is rather consistent over time. We therefore investigated whether BD patients with different longitudinal course types have symptom correlation networks with typical characteristics. To this end we used network analysis, a rather novel approach in the field of psychiatry.

Method

Based on two-year monthly life charts, 125 patients with complete 2 year data were categorized into three groups: i.e., a minimally impaired (n = 47), a predominantly depressed (n = 42) and a cycling course (n = 36). Associations between symptoms were defined as the groupwise Spearman’s rank correlation coefficient between each pair of items of the Young Mania Rating Scale (YMRS) and the Quick Inventory of Depressive Symptomatology (QIDS). Weighted symptom networks and centrality measures were compared among the three groups.

Results

The weighted networks significantly differed among the three groups, with manic and depressed symptoms being most strongly interconnected in the cycling group. The symptoms with top centrality that were most interconnected also differed among the course group; central symptoms in the stable group were elevated mood and increased speech, in the depressed group loss of self-esteem and psychomotor slowness, and in the cycling group concentration loss and suicidality.

Conclusion

Symptom networks based on the timepoints with most severe symptoms of bipolar patients with different longitudinal course types are significantly different. The clinical interpretation of this finding and its implications are discussed.

Data quality indicators for daily life chart methodology: prospective self-ratings of bipolar disorder and alcohol use

Background

Self-rating instruments which require a large number of repeated assessments over time are increasingly popular in psychiatry. They are well suited to describing variations in mental states, especially in order to investigate effects of behaviour and events on functioning and mood. For bipolar disorder, the self-rating instrument ‘NIMH daily life chart’ was developed to assess the course of the illness. This instrument has been validated in the customary ways, yet information about data quality (e.g. completeness, consistency, construct validity, reactivity) was lacking. The goal of this study was to develop several data quality indicators computed from data, in order to be able to detect respondents that provide less valid or reliable data.

Methods

During approximately 1 year on average, 137 patients with DSM-IV diagnosed bipolar disorder rated their mood, functioning and number of alcohol units consumed on a daily basis. Three kinds of quality indicators were developed: (1) compliance (i.e. completeness of recording on a daily basis), (2) the association between conceptually related variables—construct validity—and (3) reactivity: any changes in alcohol-drinking behaviour due to the assessments themselves. Relations were measured with Spearman’s rho.

Results

A relation was found between data quality and illness severity: respondents with lower data quality, according to our operationalisations, were more strongly affected by the illness, as expressed in the number of ill days, than respondents with higher data quality.

Conclusion

The more affected patients are by the illness, the lower the data quality to be expected in life chart reports.