Grouping of mood symptoms by time series dynamics.

BACKGROUND: Understanding how symptoms of mood disorders vary over time in relation to each other is potentially valuable for diagnosis and predicting episodes of illness. In this paper, we characterize the degree of similarity of time series of different mood disorder symptoms. METHODS: We collected 32,215 mood disorder symptom questionnaires, administered twice-daily over 18 months to (n = 19) subjects with rapidly cycling bipolar disorder and (n = 20) healthy control subjects, using visual analog scales to rate 11 sets of symptom severity ratings plus a control item. We used Dynamic Time Warping to calculate similarity ratings between all within-subject pairs of severity ratings followed by Exploratory Factor Analysis (EFA) to identify latent factors of symptom time series across all subjects. RESULTS: Two latent factors were identified: one with depression and anxiety; and a second, with concentration, energy, irritability, fatigue, appetite, euphoria/elation and overall mood. Restlessness, racing thoughts, and the control item (daily hours of daylight) did not cluster with any of the others. LIMITATIONS: Limited sample size dictated that we pool bipolar and healthy patients and use an iterative EFA procedure. CONCLUSION: This analysis suggests that, in a pooled sample of individuals with bipolar disorder and in healthy controls, severity ratings of overall depression and overall anxiety vary jointly as one dynamic factor, while some but not all other DSM mood symptoms vary jointly along with overall mood rating as a second dynamic factor. Further investigation may determine if these findings can simplify subjective symptom reporting in mood-monitoring studies.

Mobile and wearable technology for monitoring depressive symptoms in children and adolescents: A scoping review.

BACKGROUND: There has been rapid growth of mobile and wearable tools that may help to overcome challenges in the diagnosis and prediction of Major Depressive Disorder in children and adolescents, tasks that rely on clinical reporting that is inherently based on retrospective recall of symptoms and associated features. This article reviews more objective ways of measuring and monitoring mood within this population. METHODS: A scoping review of peer-reviewed studies examined published research that employs mobile and wearable tools to characterize depression in children and/or adolescents. Our search strategy included the following terms: (1) monitoring or prediction (2) depression (3) mobile apps or wearables and (4) children and youth (including adolescents), and was applied to five databases. RESULTS: Our search produced 829 citations (2008- Feb 2019), of which 30 (journal articles, conference papers and abstracts) were included in the analysis, and 2 reviews included in our discussion. The majority of the evidence involved smartphone apps, with very few studies using actigraphy. Mobile and wearables captured a variety of data including unobtrusive passive analytics, movement and light data, plus physical and mental health data, including depressive symptom monitoring. Most studies also examined feasibility. LIMITATIONS: This review was limited to published research in the English language. The review criteria excluded any apps that were mainly treatment focused, therefore there was not much of a focus on clinical outcomes. CONCLUSIONS: This scoping review yielded a variety of studies with heterogeneous populations, research methods and study objectives, which limited our ability to address our research objectives cohesively. Certain mobile technologies, however, have demonstrated feasibility for tracking depression that could inform models for predicting relapse.

Supporting Homework Compliance in Cognitive Behavioural Therapy: Essential Features of Mobile Apps.

Cognitive behavioral therapy (CBT) is one of the most effective psychotherapy modalities used to treat depression and anxiety disorders. Homework is an integral component of CBT, but homework compliance in CBT remains problematic in real-life practice. The popularization of the mobile phone with app capabilities (smartphone) presents a unique opportunity to enhance CBT homework compliance; however, there are no guidelines for designing mobile phone apps created for this purpose. Existing literature suggests 6 essential features of an optimal mobile app for maximizing CBT homework compliance: (1) therapy congruency, (2) fostering learning, (3) guiding therapy, (4) connection building, (5) emphasis on completion, and (6) population specificity. We expect that a well-designed mobile app incorporating these features should result in improved homework compliance and better outcomes for its users.

Crying, oral contraceptive use and the menstrual cycle.

BACKGROUND: Crying, a complex neurobiological behavior with psychosocial and communication features, has been little studied in relationship to the menstrual cycle. METHODS: In the Mood and Daily Life study (MiDL), a community sample of Canadian women aged 18-43 years, n=76, recorded crying proneness and crying frequency daily for six months along with menstrual cycle phase information. RESULTS: Crying proneness was most likely during the premenstruum, a little less likely during menses and least likely during the mid-cycle phase, with statistically significant differences although the magnitude of these differences were small. By contrast, actual crying did not differ between the three menstrual cycle phases. Oral contraceptive use did not alter the relationship between menstrual cycle phase and either crying variable. A wide range of menstrual cycle phase - crying proneness patterns were seen with visual inspection of the individual women's line graphs. LIMITATIONS: timing of ovulation was not ascertained. Using a three phase menstrual cycle division precluded separate late follicular and early luteal data analysis. The sample size was inadequate for a robust statistical test of actual crying. CONCLUSIONS: reproductive aged women as a group report feeling more like crying premenstrually but may not actually cry more during this menstrual cycle phase. Individual patterns vary substantially. Oral contraceptive use did not affect these relationships. Suggestions for future research are included.

Cognitive Behavior Therapy for Anxious and Depressed Youth: Improving Homework Adherence Through Mobile Technology.

BACKGROUND: Anxiety and mood disorders are the most common mental illnesses, peaking during adolescence and affecting approximately 25% of Canadians aged 14-17 years. If not successfully treated at this age, they often persist into adulthood, exerting a great social and economic toll. Given the long-term impact, finding ways to increase the success and cost-effectiveness of mental health care is a pressing need. Cognitive behavior therapy (CBT) is an evidence-based treatment for mood and anxiety disorders throughout the lifespan. Mental health technologies can be used to make such treatments more successful by delivering them in a format that increases utilization. Young people embrace technologies, and many want to actively manage their mental health. Mobile software apps have the potential to improve youth adherence to CBT and, in turn, improve outcomes of treatment. OBJECTIVE: The purpose of this project is to improve homework adherence in CBT for youth anxiety and/or depression. The objectives are to (1) design and optimize the usability of a mobile app for delivering the homework component of CBT for youth with anxiety and/or depression, (2) assess the app's impact on homework completion, and (3) implement the app in CBT programs. We hypothesize that homework adherence will be greater in the app group than in the no-app group. METHODS: Phase 1: exploratory interviews will be conducted with adolescents and therapists familiar with CBT to obtain views and perspectives on the requirements and features of a usable app and the challenges involved in implementation. The information obtained will guide the design of a prototype. The prototype will be optimized via think-aloud procedures involving an iterative process of evaluation, modification, and re-evaluation, culminating in a fully functional version of the prototype that is ready for optimization in a clinical context. Phase 2: a usability study will be conducted to optimize the prototype in the context of treatment at clinics that provide CBT treatment for youth with anxiety and/or depression. This phase will result in a usable app that is ready to be tested for its effectiveness in increasing homework adherence. Phase 3: a pragmatic clinical trial will be conducted at several clinics to evaluate the impact of the app on homework adherence. Participants in the app group are expected to show greater homework completion than those in the no-app group. RESULTS: Phase 3 will be completed by September 2019. CONCLUSIONS: The app will be a unique adjunct to treatment for adolescents in CBT, focusing on both anxiety and depression, developed in partnership with end users at every stage from design to implementation, customizable for different cognitive profiles, and designed with depression symptom tracking measures for youth made interoperable with electronic medical records.

Sleep quality and the menstrual cycle.

OBJECTIVE: This study aimed to assess the temporal relationship of subjective sleep quality to menstrual cycle phase in a community (non help-seeking) sample of adult women over six months. Previous work has produced contradictory results and often used student samples. PATIENTS/METHODS: This was a cohort study, using daily electronic data collection in the Greater Toronto Area, Ontario, Canada; 76 women aged 18-42 years recruited by random digit telephone dialing, recorded mood, sleep quality, and other health variables on a daily basis for 24 weeks. RESULTS: Using linear mixed models, we assessed the relationship between subjective sleep quality and three menstrual cycle phases (menses, premenstrual and midcycle) over 395 cycles. Premenstrual sleep quality was poorer than during the rest of the cycle, with a mean difference of 1.32 between premenstrual and midcycle reference phase, on a 1-100 quality scale (higher score denotes poorer quality). This difference held when the independent variables of daily exercise and physical health were added to the model; it became non-significant when perceived stress and later, social support were also added to the model. CONCLUSIONS: Sleep quality in adult non-help seeking women is statistically poorer in the premenstruum but the size of the difference is of little clinical significance and was no longer statistically significant with inclusion of the potentially confounding variables, perceived stress and social support.

Use of mental health telemetry to enhance identification and predictive value of early changes during augmentation treatment of major depression.

Standard clinical trial methodology in depression does not allow for careful examination of early changes in symptom intensity. The purpose of this study was to use daily "Mental Health Telemetry" (MHT) to prospectively record change in depressive and anxiety symptoms for depressed patients receiving augmentation treatment, and determine the extent and predictive capacity of early changes. We report results of a 6-week, open-label study of the addition of quetiapine XR (range, 50-300 mg) for adult patients (n = 26) with major depressive disorder who were nonresponsive to antidepressant treatment. In addition to regular study visits, all participants completed daily, wirelessly transmitted self-report ratings of symptoms on a Smartphone. Daily and 3-day moving average mean scores were calculated, and associations between early symptom change and eventual response to treatment were determined. Improvement in depressive and anxiety symptoms was identified as early as day 1 of treatment. Of the total decline in depression severity over 6 weeks, 9% was present at day 1, 28% at day 2, 39% at days 3 and 4, 65% at day 7, and 80% at day 10. Self-report rating of early improvement (≥20%) in depressive symptoms at day 7 significantly predicted responder status at week 6 (P = 0.03). Clinician-rated depressive and anxiety symptoms only became significantly associated with responder status at day 14. In conclusion, very early changes in depressive symptoms were identified using MHT, early changes accounted for most of total change, and MHT-recorded improvement as early as day 7 significantly predicted response to treatment at study end point.

Mood and the menstrual cycle.

BACKGROUND: Premenstrual mood symptoms are considered common in women, but such prevailing attitudes are shaped by social expectations about gender, emotionality and hormonal influences. There are few prospective, community studies of women reporting mood data from all phases of the menstrual cycle (MC). We aimed (i) to analyze daily mood data over 6 months for MC phase cyclicity and (ii) to compare MC phase influences on a woman's daily mood with that attributable to key alternate explanatory variables (physical health, perceived stress and social support). METHOD: A random sample of Canadian women aged 18-40 years collected mood and health data daily over 6 months, using telemetry, producing 395 complete MCs for analysis. RESULTS: Only half the individual mood items showed any MC phase association; these links were either with the menses phase alone or the menses plus the premenstrual phase. With one exception, the association was not solely premenstrual. The menses-follicular-luteal MC division gave similar results. Less than 0.5% of the women's individual periodogram records for each mood item showed MC entrainment. Physical health, perceived stress and social support were much stronger predictors of mood (p < 0.0001 in each case) than MC phase. CONCLUSIONS: The results of this study do not support the widespread idea of specific premenstrual dysphoria in women. Daily physical health status, perceived stress and social support explain daily mood better than MC phase.

Mood assessment via animated characters: a novel instrument to evaluate feelings in young children with anxiety disorders.

We evaluated a novel, computerized feelings assessment instrument (MAAC) in 54 children with anxiety disorders and 35 nonanxious children ages 5 to 11. They rated their feelings relative to 16 feeling animations. Ratings of feelings, order of feeling selection, and correlations with standardized anxiety measures were examined. Positive emotions were rated more highly and visited earlier by nonanxious children. Children with anxiety disorders explored fewer emotions. MAAC ratings on several positive emotions showed inverse correlations with state anxiety. Although needing further evaluation, MAAC may facilitate feelings assessment in young children and may distinguish children with anxiety disorders from nonanxious children.

The effects of irregular sampling and missing data on largest Lyapunov exponents.

Human self-report time series data are typically marked by irregularities in sampling rates arising from the data generation process. The largest Lyapunov exponent lamda1 is an indicator of chaos in time series data. Relatively little has been published to assist the calculation of lamda1's using irregularly sampled data. We report the results of a series of computational experiments on synthetic data sets assessing techniques for handling irregular time series data in the calculation of lamda1 . Regularly sampled data sets were disrupted by data point removal using an empirically motivated data gap distribution of either uniform random or power law form. Missing data segments were patched using segment concatenation, segment filling with average data values, or local interpolation in phase space. We compared results of lamda1 calculations using complete and patched sets. The greatest proportion of missing data possible that will allow an accurate estimate of lamda1 depends on the nature of the underlying system and the patching technique used. Self-similar data patched with segment concatenation was particularly robust. Local interpolation in phase space was successful in many cases, but required potentially impractical quantities of intact data as a primer. Optimally, estimates of lamda1 can readily be recovered with 15%-20% or greater amounts of missing data.

The effects of the irregular sample and missing data in time series analysis.

Human self-report time series data are typically marked by irregularities in sampling rates; furthermore, these irregularities are typically natural outcomes of the data generation process. Relatively little has been published to assist the analysis of irregularly sampled data. We report the results of a series of computational experiments on synthetic data sets designed to assess the utility of techniques for handling irregular time series data. The behavior of a conservative quasiperiodic, a dissipative chaotic, and a self-organized critical dynamics were sampled regularly in time and the regular sampling was disrupted by data point removal or by stochastic shifts in time. Missing data segments were then patched by means of segment concatenation, by segment filling with average data values, or by local interpolation in phase space. We compared results of nonlinear analytical tools such as autocorrelations and correlation dimensions using complete and patched sets, as well as power spectra with Lomb periodograms of the decimated sets. Local interpolation in phase space was particularly successful at preserving key features of the original data, but required potentially impractical quantities of intact data as a primer. While the other patching methods are not limited by the need for intact data, they distort results relative to the intact series. We conclude that irregularly sampled data sets with as much as 15 percent missing data can potentially be re-sampled or repaired for analysis with techniques that assume regular sampling without introducing substantial errors.

Portable mood mapping: the validity and reliability of analog scale displays for mood assessment via hand-held computer.

The long-term natural time course of mood change remains poorly understood, and improved methods that assay multiple mood symptoms quickly and reliably are crucial to further progress. This study describes the reliability and validity of the new visual analog scale (VAS) display method for a recently developed 19-item VAS-based mood questionnaire, the VMQ, administered via hand-held computer (HHC). The effect of the smaller HHC screen size on accuracy and precision of VAS completion was investigated in 28 subjects using 4- and 10-cm paper-based VASs to indicate six specified dates within the year. The influence of digital vs. paper medium was then tested in 39 subjects who completed the same task, using 10-cm paper and 4-cm HHC-based VASs. Test-retest reliability was evaluated in 29 subjects who completed the questionnaire on a HHC twice, 10 min apart. Since the HHC presents VMQ scales with text anchor orientation set randomly, we also considered whether subjects might inadvertently transpose responses on the HHC. We found that reducing VAS size produced no significant loss of response precision or accuracy in subject response. Moreover, there was no significant loss of accuracy or precision between 10-cm paper and 4-cm HHC-based versions of the VAS. HHC-based items also demonstrated excellent test-retest reliability, with excellent values of Cronbach's alpha. The transposition error rate was negligible (0.27%). Our study provides initial evidence that the HHC-based VAS display used in the VMQ is a reliable and valid tool for comprehensive collection of analog mood scale data.