Daily electronic self-monitoring in bipolar disorder using smartphones - the MONARCA I trial: a randomized, placebo-controlled, single-blind, parallel group trial

Screening accuracy of a 14-day smartphone ambulatory assessment of depression symptoms and mood dynamics in a general population sample: Comparison with the PHQ-9 depression screening

Introduction

Major depression affects over 300 million people worldwide, but cases are often detected late or remain undetected. This increases the risk of symptom deterioration and chronification. Consequently, there is a high demand for low threshold but clinically sound approaches to depression detection. Recent studies show a great willingness among users of mobile health apps to assess daily depression symptoms. In this pilot study, we present a provisional validation of the depression screening app Moodpath. The app offers a 14-day ambulatory assessment (AA) of depression symptoms based on the ICD-10 criteria as well as ecologically momentary mood ratings that allow the study of short-term mood dynamics.

Materials and methods

N = 113 Moodpath users were selected through consecutive sampling and filled out the Patient Health Questionnaire (PHQ-9) after completing 14 days of AA with 3 question blocks (morning, midday, and evening) per day. The psychometric properties (sensitivity, specificity, accuracy) of the ambulatory Moodpath screening were assessed based on the retrospective PHQ-9 screening result. In addition, several indicators of mood dynamics (e.g. average, inertia, instability), were calculated and investigated for their individual and incremental predictive value using regression models.

Results

We found a strong linear relationship between the PHQ-9 score and the AA Moodpath depression score (r = .76, p < .001). The app-based screening demonstrated a high sensitivity (.879) and acceptable specificity (.745). Different indicators of mood dynamics covered substantial amounts of PHQ-9 variance, depending on the number of days with mood data that were included in the analyses.

Discussion

AA and PHQ-9 shared a large proportion of variance but may not measure exactly the same construct. This may be due to the differences in the underlying diagnostic systems or due to differences in momentary and retrospective assessments. Further validation through structured clinical interviews is indicated. The results suggest that ambulatory assessed mood indicators are a promising addition to multimodal depression screening tools. Improving app-based AA screenings requires adapted screening algorithms and corresponding methods for the analysis of dynamic processes over time.

Mood and Activity Measured Using Smartphones in Unipolar Depressive Disorder

Background: Smartphones comprise a promising tool for symptom monitoring in patients with unipolar depressive disorder (UD) collected as either patient-reportings or possibly as automatically generated smartphone data. However, only limited research has been conducted in clinical populations. We investigated the association between smartphone-collected monitoring data and validated psychiatric ratings and questionnaires in a well-characterized clinical sample of patients diagnosed with UD. Methods: Smartphone data, clinical ratings, and questionnaires from patients with UD were collected 6 months following discharge from psychiatric hospitalization as part of a randomized controlled study. Smartphone data were collected daily, and clinical ratings (i.e., Hamilton Depression Rating Scale 17-item) were conducted three times during the study. We investigated associations between (1) smartphone-based patient-reported mood and activity and clinical ratings and questionnaires; (2) automatically generated smartphone data resembling physical activity, social activity, and phone usage and clinical ratings; and (3) automatically generated smartphone data and same-day smartphone-based patient-reported mood and activity. Results: A total of 74 patients provided 11,368 days of smartphone data, 196 ratings, and 147 questionnaires. We found that: (1) patient-reported mood and activity were associated with clinical ratings and questionnaires (p < 0.001), so that higher symptom scores were associated with lower patient-reported mood and activity, (2) Out of 30 investigated associations on automatically generated data and clinical ratings of depression, only four showed statistical significance. Further, lower psychosocial functioning was associated with fewer daily steps (p = 0.036) and increased number of incoming (p = 0.032), outgoing (p = 0.015) and missed calls (p = 0.007), and longer phone calls (p = 0.012); (3) Out of 20 investigated associations between automatically generated data and daily patient-reported mood and activity, 12 showed statistical significance. For example, lower patient-reported activity was associated with fewer daily steps, shorter distance traveled, increased incoming and missed calls, and increased screen-time. Conclusion: Smartphone-based self-monitoring is feasible and associated with clinical ratings in UD. Some automatically generated data on behavior may reflect clinical features and psychosocial functioning, but these should be more clearly identified in future studies, potentially combining patient-reported and smartphone-generated data.

Digital health data-driven approaches to understand human behavior

Advances in digital technologies and data analytics have created unparalleled opportunities to assess and modify health behavior and thus accelerate the ability of science to understand and contribute to improved health behavior and health outcomes. Digital health data capture the richness and granularity of individuals' behavior, the confluence of factors that impact behavior in the moment, and the within-individual evolution of behavior over time. These data may contribute to discovery science by revealing digital markers of health/risk behavior as well as translational science by informing personalized and timely models of intervention delivery. And they may help inform diagnostic classification of clinically problematic behavior and the clinical trajectories of diagnosable disorders over time. This manuscript provides a review of the state of the science of digital health data-driven approaches to understanding human behavior. It reviews methods of digital health assessment and sources of digital health data. It provides a synthesis of the scientific literature evaluating how digitally derived empirical data can inform our understanding of health behavior, with a particular focus on understanding the assessment, diagnosis and clinical trajectories of psychiatric disorders. And, it concludes with a discussion of future directions and timely opportunities in this line of research and its clinical application.

Real-time mobile monitoring of bipolar disorder: a review of evidence and future directions

Rapidly accumulating data from mobile assessments are facilitating our ability to track patterns of emotions, behaviors, biologic rhythms, and their contextual influences in real time. These approaches have been widely applied to study the core features, traits, changes in states, and the impact of treatments in bipolar disorder (BD). This paper reviews recent evidence on the application of both passive and active mobile technologies to gain insight into the role of the circadian system and patterns of sleep and motor activity in people with BD. Findings of more than two dozen studies converge in demonstrating a broad range of sleep disturbances, particularly longer duration and variability of sleep patterns, lower average and greater variability of motor activity, and a shift to later peak activity and sleep midpoint, indicative of greater evening orientation among people with BD. The strong associations across the domains tapped by real-time monitoring suggest that future research should shift focus on sleep, physical/motor activity, or circadian patterns to identify common biologic pathways that influence their interrelations. The development of novel data-driven functional analytic tools has enabled the derivation of individualized multilevel dynamic representations of rhythms of multiple homeostatic regulatory systems. These multimodal tools can inform clinical research through identifying heterogeneity of the manifestations of BD and provide more objective indices of treatment response in real-world settings. Collaborative efforts with common protocols for the application of multimodal sensor technology will facilitate our ability to gain deeper insight into mechanisms and multisystem dynamics, as well as environmental, physiologic, and genetic correlates of BD.

Digital health developments and drawbacks: a review and analysis of top-returned apps for bipolar disorder

BACKGROUND

Although a growing body of literature highlights the potential benefit of smartphone-based mobile apps to aid in self-management and treatment of bipolar disorder, it is unclear whether such evidence-based apps are readily available and accessible to a user of the app store.

RESULTS

Using our systematic framework for the evaluation of mental health apps, we analyzed the accessibility, privacy, clinical foundation, features, and interoperability of the top-returned 100 apps for bipolar disorder. Only 56% of the apps mentioned bipolar disorder specifically in their title, description, or content. Only one app's efficacy was supported in a peer-reviewed study, and 32 apps lacked privacy policies. The most common features provided were mood tracking, journaling, and psychoeducation.

CONCLUSIONS

Our analysis reveals substantial limitations in the current digital environment for individuals seeking an evidence-based, clinically usable app for bipolar disorder. Although there have been academic advances in development of digital interventions for bipolar disorder, this work has yet to be translated to the publicly available app marketplace. This unmet need of digital mood management underscores the need for a comprehensive evaluation system of mental health apps, which we have endeavored to provide through our framework and accompanying database (apps.digitalpsych.org).

Repeated Digitized Assessment of Risk and Symptom Profiles During Inpatient Treatment of Affective Disorder: Observational Study

BACKGROUND

Predictive models have revealed promising results for the individual prognosis of treatment response and relapse risk as well as for differential diagnosis in affective disorders. Yet, in order to translate personalized predictive modeling from research contexts to psychiatric clinical routine, standardized collection of information of sufficient detail and temporal resolution in day-to-day clinical care is needed. Digital collection of self-report measures by patients is a time- and cost-efficient approach to gain such data throughout treatment.

OBJECTIVE

The objective of this study was to investigate whether patients with severe affective disorders were willing and able to participate in such efforts, whether the feasibility of such systems might vary depending on individual patient characteristics, and if digitally acquired assessments were of sufficient diagnostic validity.

METHODS

We implemented a system for longitudinal digital collection of risk and symptom profiles based on repeated self-reports via tablet computers throughout inpatient treatment of affective disorders at the Department of Psychiatry at the University of Münster. Tablet-handling competency and the speed of data entry were assessed. Depression severity was additionally assessed by a clinical interviewer at baseline and before discharge.

RESULTS

Of 364 affective disorder patients who were approached, 242 (66.5%) participated in the study; 88.8% of participants (215/242) were diagnosed with major depressive disorder, and 27 (11.2%) had bipolar disorder. During the duration of inpatient treatment, 79% of expected assessments were completed, with an average of 4 completed assessments per participant; 4 participants (4/242, 1.6%) dropped out of the study prematurely. During data entry, 89.3% of participants (216/242) did not require additional support. Needing support with tablet handling and slower data entry pace were predicted by older age, whereas depression severity at baseline did not influence these measures. Patient self-reporting of depression severity showed high agreement with standardized external assessments by a clinical interviewer.

CONCLUSIONS

Our results indicate that digital collection of self-report measures is a feasible, accessible, and valid method for longitudinal data collection in psychiatric routine, which will eventually facilitate the identification of individual risk and resilience factors for affective disorders and pave the way toward personalized psychiatric care.

Effects of smartphone-based interventions and monitoring on bipolar disorder: A systematic review and meta-analysis

BACKGROUND

Recently, there has been a range of studies about smartphone-based interventions and monitoring for reducing symptoms of bipolar disorder (BD). However, their efficacy for BD remains unclear.

AIM

To compare the effect of smartphone-based interventions and monitoring with control methods in treating patients with BD.

METHODS

A systematic literature search was performed on PubMed, Embase, Clinical trials, psycINFO, Web of Science, and Cochrane Library. Randomized clinical trials (RCTs) or single-group trials in which smartphone-based interventions and monitoring were compared with control methods or baseline in patients with symptoms of BD were included. Data were synthesized using a random-effects or a fixed-effects model to analyze the effects of psychological interventions and monitoring delivered via smartphone on psychiatric symptoms in patients with BD. The primary outcome measures were set for mania and depression symptoms. Subgroups were created to explore which aspects of smartphone interventions are relevant to the greater or lesser efficacy of treating symptoms.

RESULTS

We identified ten articles, including seven RCTs (985 participants) and three single-group trials (169 participants). Analysis of the between-group study showed that smartphone-based interventions were effective in reducing manic [g = -0.19, 95% confidence interval (CI): -0.33 to -0.04, P = 0.01] and depressive (g = -0.28, 95%CI: -0.55 to -0.01, P < 0.05) symptoms. In within-group analysis, smartphone-based interventions significantly reduced manic (g = 0.17, 95%CI: 0.04 to 0.30, P < 0.01) and depressive (g = 0.48, 95%CI: 0.18 to 0.78) symptoms compared to the baseline. Nevertheless, smartphone-based monitoring systems significantly reduced manic (g = 0.27, 95%CI: 0.02 to 0.51, P < 0.05) but not depressive symptoms. Subgroup analysis indicated that the interventions with psychoeducation had positive effects on depressive (g = -0.62, 95%CI: -0.81 to -0.43, P < 0.01) and manic (g = -0.24, 95%CI: -0.43 to -0.06, P = 0.01) symptoms compared to the controlled conditions, while the interventions without psychoeducation did not (P > 0.05). The contacts between therapists and patients that contributed to the implementation of psychological therapy reduced depression symptoms (g = -0.47, 95%CI: -0.75 to -0.18, P = 0.01).

CONCLUSION

Smartphone-based interventions and monitoring have a significant positive impact on depressive and manic symptoms of BD patients in between-group and within-group analysis.

Digital phenotyping: towards replicable findings with comprehensive assessments and integrative models in bipolar disorders

Background

Digital phenotyping promises to unobtrusively obtaining a continuous and objective input of symptomatology from patients’ daily lives. The prime example are bipolar disorders, as smartphone parameters directly reflect bipolar symptomatology. Empirical studies, however, have yielded inconsistent findings. We believe that three main shortcomings have to be addressed to fully leverage the potential of digital phenotyping: short assessment periods, rare outcome assessments, and an extreme fragmentation of parameters without an integrative analytical strategy.

Methods

To demonstrate how to overcome these shortcomings, we conducted frequent (biweekly) dimensional and categorical expert ratings and daily self-ratings over an extensive assessment period (12 months) in 29 patients with bipolar disorder. Digital phenotypes were monitored continuously. As an integrative analytical strategy, we used structural equation modelling to build latent psychopathological outcomes (mania, depression) and latent digital phenotype predictors (sleep, activity, communicativeness).

Outcomes

Combining gold-standard categorical expert ratings with dimensional self and expert ratings resulted in two latent outcomes (mania and depression) with statistically meaningful factor loadings that dynamically varied over 299 days. Latent digital phenotypes of sleep and activity were associated with same-day latent manic psychopathology, suggesting that psychopathological alterations in bipolar disorders relate to domains (latent variables of sleep and activity) and not only to specific behaviors (such as the number of declined incoming calls). The identification of latent psychopathological outcomes that dimensionally vary on a daily basis will enable to empirically determine which combination of digital phenotypes at which days prior to an upcoming episode are viable as digital prodromal predictors.

Patient-evaluated cognitive function measured with smartphones and the association with objective cognitive function, perceived stress, quality of life and function capacity in patients with bipolar disorder

Background

Cognitive impairments in patients with bipolar disorder (BD) have been associated with reduced functioning. Aims: To investigate the association between (1) patient-evaluated cognitive function measured daily using smartphones and stress, quality of life and functioning, respectively, and (2) patient-evaluated cognitive function and objectively measured cognitive function with neuropsychological tests.

Methods

Data from two randomized controlled trials were combined. Patients with BD (N = 117) and healthy controls (HC) (N = 40) evaluated their cognitive function daily for six to nine months using a smartphone. Patients completed the objective cognition screening tool, the Screen for Cognitive Impairment in Psychiatry and were rated with the Functional Assessment Short Test. Raters were blinded to smartphone data. Participants completed the Perceived Stress Scale and the WHO Quality of Life questionnaires. Data was collected at multiple time points per participant. p-values below 0.0023 were considered statistically significant.

Results

Patient-evaluated cognitive function was statistically significant associated with perceived stress, quality of life and functioning, respectively (all p-values < 0.0001). There was no association between patient-evaluated cognitive function and objectively measured cognitive function (B:0.0009, 95% CI 0.0017; 0.016, p = 0.015). Patients exhibited cognitive impairments in subjectively evaluated cognitive function in comparison with HC despite being in full or partly remission (B: − 0.36, 95% CI − 0.039; − 0.032, p < 0.0001).

Conclusion

The present association between patient-evaluated cognitive function on smartphones and perceived stress, quality of life and functional capacity suggests that smartphones can provide a valid tool to assess disability in remitted BD. Smartphone-based ratings of cognition could not provide insights into objective cognitive function.

Big data in severe mental illness: the role of electronic monitoring tools and metabolomics

There is an increasing interest in the development of effective early detection and intervention strategies in severe mental illness (SMI). Ideally, these efforts should lead to the delineation of accurate staging models of SMI enabling personalized interventions. It is plausible that big data approaches will be instrumental in describing the developmental trajectories of SMI by facilitating the incorporation of data from multiple sources, including those pertaining to the biological make-up of affected subjects. In this review, we first aimed to offer a perspective on how big data are helping the delineation of personalized approaches in SMI, and, second, to offer a quantitative synthesis of big data approaches in metabolomics of SMI. We finally described future directions of this research area.

Digital Phenotyping in Bipolar Disorder: Which Integration with Clinical Endophenotypes and Biomarkers?

Bipolar disorder (BD) is a complex neurobiological disorder characterized by a pathologic mood swing. Digital phenotyping, defined as the 'moment-by-moment quantification of the individual-level human phenotype in its own environment', represents a new approach aimed at measuring the human behavior and may theoretically enhance clinicians' capability in early identification, diagnosis, and management of any mental health conditions, including BD. Moreover, a digital phenotyping approach may easily introduce and allow clinicians to perform a more personalized and patient-tailored diagnostic and therapeutic approach, in line with the framework of precision psychiatry. The aim of the present paper is to investigate the role of digital phenotyping in BD. Despite scarce literature published so far, extremely heterogeneous methodological strategies, and limitations, digital phenotyping may represent a grounding research and clinical field in BD, by owning the potentialities to quickly identify, diagnose, longitudinally monitor, and evaluating clinical response and remission to psychotropic drugs. Finally, digital phenotyping might potentially constitute a possible predictive marker for mood disorders.

Short-term effect of a smartphone application on the mental health of university students: A pilot study using a user-centered design self-monitoring application for mental health

BACKGROUND

Despite the widespread recognition of the importance of mental health in young people, only a small proportion of young people with a mental disorder, including university students, receive mental health care.

OBJECTIVE

We developed a smartphone application (Mental App) for the university students and examined the effects of the app on their mental health.

METHODS

The app was designed according to a questionnaire survey conducted before this study. The Mental App was installed on the students' smartphone and the psychological tests (the Link Stigma Scale, the Center for Epidemiologic Studies Depression Scale, and the 12-item General Health Questionnaire) were performed on the same day. After using the App for two weeks, the students completed a questionnaire survey and underwent the same psychological tests. We compared the results between the app user and non-user group.

RESULTS

A total of 68 students participated, of which 57 students completed the study (app user group, n = 28; control group, n = 29). The mean number of days spent using the app was 5.66 ± 3.16 (mean ± SD). The mean total screen time of the app was 9:03 ± 06:41(min:sec). The mean number of total actions (screen taps or swipes) was 161.91 ± 107.34. There were no significant between-group differences in the ΔLink Stigma Scale score (-0.11 ± 4.28 vs. -0.59 ± 3.30, p = 0.496) or the ΔCenter for Epidemiologic Studies Depression Scale score (-4.39 ± 7.13 vs. -2.07 ± 8.78, p = 0.143). There was a significant between-group difference in the ΔGeneral Health Questionnaire score (-2.21± 2.23 vs. -0.17 ± 2.69, p = 0.007).

CONCLUSIONS

This non-randomized controlled pilot study indicates that the app we developed, may provide effective mental health care for university students, even in the short-term. Trial registration: UMIN000040332.

Promises and risks of web-based interventions in the treatment of depression

Major depression (MD) is a highly prevalent and severe disorder with many patients having no access to efficient treatments such as pharmaco- and psychotherapy. Web-based interventions promise to be a method to provide resource-efficient and widespread access to psychotherapeutic support. Meta-analyses summarizing studies that use face-to-face psychotherapy as a comparator provide evidence for equivalent antidepressant efficacy. Web-based interventions seem to be particularly efficacious when they are accompanied by some form of professional guidance. However, they are also associated with a variety of possible risks (eg, suicidal crises can be overlooked) and unwanted effects (eg, increase in rumination and somatization due to self-monitoring) that are so far under-studied. Although some naturalistic studies yield smaller effect sizes than randomized controlled trials (RCTs), well-designed interventions with adequate guidance have been shown to be successfully integrable into routine care.


Daily Self-Monitoring of Symptoms and Skills Learning in Patients With Borderline Personality Disorder Through a Mobile Phone App: Protocol for a Pragmatic Randomized Controlled Trial

Background

Patient self-monitoring via mobile phones during psychotherapy can enhance and provide an overview of psychotherapeutic progress by graphically displaying current and previous symptom scores, providing feedback to the patient, delivering psychoeducative material, and providing timely data to the therapist or treatment team.

Objective

This study will aim to assess the effects of using a mobile phone to self-monitor symptoms and acquire coping skills instead of using pen and paper during psychotherapy in patients with borderline personality disorder (BPD). Dialectical behavior therapy will be performed to treat BPD. The primary outcome is the mean time needed to learn coping skills directed at emotion regulation; the secondary outcome is changes in the BPD symptom score as measured by the Zanarini Rating Scale for Borderline Personality Disorder.

Methods

This study is a pragmatic, multicenter randomized controlled trial. Participants were recruited through five public general psychiatric outpatient treatment facilities in Denmark. Patients are randomly assigned, on a 1:1 basis, to either the mobile phone condition (using the Monsenso mDiary mobile app) or pen-and-paper condition. Patients will complete several self-report questionnaires on symptom severity; assessments by trained raters on BPD severity will be performed as well. Survival analysis with a shared frailty model will be used to assess the primary outcome.

Results

Recruitment began in June 2017 and was completed in February 2019 after 80 participants were recruited. The study ended in February 2020. It is expected that the benefits of mobile phone–based self-report compared to the pen-and-paper method will be demonstrated for skill learning speed and registration compliance. To our knowledge, this is the first trial exploring the impact of cloud-based mobile registration in BPD treatment.

Conclusions

This trial will report on the effectiveness of mobile phone–based self-monitoring during psychiatric treatment. It has the potential to contribute to evidence-based clinical practice since apps are already in use clinically.

Trial Registration

ClinicalTrials.gov NCT03191565; https://clinicaltrials.gov/ct2/show/NCT03191565

International Registered Report Identifier (IRRID)

DERR1-10.2196/17737

Automatically generated smartphone data and subjective stress in healthy individuals - a pilot study

Background: Most people will also experience symptoms of stress at some point. Smartphone use has increased during the last decade and may be a new way of monitoring stress. Thus, it is of interest to investigate whether automatically generated smartphone data reflecting smartphone use is associated with subjective stress in healthy individuals.Aims: to investigate whether automatically generated smartphone data (e.g. the number of outgoing sms/day) was associated with (1) smartphone-based subjectively reported perceived stress, (2) perceived stress (Cohen's Perceived Stress Scale (PSS)) (3) functioning (Functioning Assessment Short Test (FAST)) and (4) non-clinical depressive symptoms (Hamilton Depression Rating Scale 17-items (HDRS)).Methods: A cohort of 40 healthy blood donors used an app for daily self-assessment of stress for 16 weeks. At baseline participants filled out the PSS and were clinically evaluated using the FAST and the HDRS. The PSS assessment was repeated at the end of the study. Associations were estimated with linear mixed effect regression and linear regression models.Results: There were no statistically significant associations between automatically generated smartphone data and perceived stress, functioning or severity of depressive symptoms, respectively (e.g. the number of outgoing text messages/day and self-assessed stress (B = 0.30, 95% CI: -0.40; 0.99, p = .40).Conclusions: Participants presented with low levels of stress during the study. Automatically generated smartphone data was not able to catch potential subjective stress among healthy individuals in the present study. Due to the small sample and low levels of stress the results should be interpreted with caution.

A Web-Based Adaptation of the Quality of Life in Bipolar Disorder Questionnaire: Psychometric Evaluation Study

BACKGROUND

Quality of life (QoL) is considered a key treatment outcome in bipolar disorder (BD) across research, clinical, and self-management contexts. Web-based assessment of patient-reported outcomes offer numerous pragmatic benefits but require validation to ensure measurement equivalency. A web-based version of the Quality of Life in Bipolar Disorder (QoL.BD) questionnaire was developed (QoL Tool).

OBJECTIVE

This study aimed to evaluate the psychometric properties of a web-based QoL self-report questionnaire for BD (QoL Tool). Key aims were to (1) characterize the QoL of the sample using the QoL Tool, (2) evaluate the internal consistency of the web-based measure, and (3) determine whether the factor structure of the original version of the QoL.BD instrument was replicated in the web-based instrument.

METHODS

Community-based participatory research methods were used to inform the development of a web-based adaptation of the QoL.BD instrument. Individuals with BD who registered for an account with the QoL Tool were able to opt in to sharing their data for research purposes. The distribution of scores and internal consistency estimates, as indicated by Cronbach alpha, were inspected. An exploratory factor analysis using maximum likelihood and oblique rotation was conducted. Inspection of the scree plot, eigenvalues, and minimum average partial correlation were used to determine the optimal factor structure to extract.

RESULTS

A total of 498 people with BD (349/498, 70.1% female; mean age 39.64, SD 12.54 years; 181/498, 36.3% BD type I; 195/498, 39.2% BD type II) consented to sharing their QoL Tool data for the present study. Mean scores across the 14 QoL Tool domains were, in general, significantly lower than that of the original QoL.BD validation sample. Reliability estimates for QoL Tool domains were comparable with that observed for the QoL.BD instrument (Cronbach alpha=.70-.93). Exploratory factor analysis supported the extraction of an 11-factor model, with item loadings consistent with the factor structure suggested by the original study. Findings for the sleep and physical domains differed from the original study, with this analysis suggesting one shared latent construct.

CONCLUSIONS

The psychometric properties of the web-based QoL Tool are largely concordant with the original pen-and-paper QoL.BD, although some minor differences in the structure of the sleep and physical domains were observed. Despite this small variation from the factor structure identified in the QoL.BD instrument, the latent factor structure of the QoL Tool largely reproduced the original findings and theoretical structure of QoL areas relevant to people with BD. These findings underscore the research and clinical utility of this instrument, but further comparison of the psychometric properties of the QoL Tool relative to the QoL.BD instrument is warranted. Future adaptations of the QoL Tool, including the production of an app-based version of the QoL Tool, are also discussed.

The effect of smartphone-based monitoring on illness activity in bipolar disorder: the MONARCA II randomized controlled single-blinded trial

BACKGROUND

Recently, the MONARCA I randomized controlled trial (RCT) was the first to investigate the effect of smartphone-based monitoring in bipolar disorder (BD). Findings suggested that smartphone-based monitoring sustained depressive but reduced manic symptoms. The present RCT investigated the effect of a new smartphone-based system on the severity of depressive and manic symptoms in BD.

METHODS

Randomized controlled single-blind parallel-group trial. Patients with BD, previously treated at The Copenhagen Clinic for Affective Disorder, Denmark and currently treated at community psychiatric centres, private psychiatrists or GPs were randomized to the use of a smartphone-based system or to standard treatment for 9 months. Primary outcomes: differences in depressive and manic symptoms between the groups.

RESULTS

A total of 129 patients with BD (ICD-10) were included. Intention-to-treat analyses showed no statistically significant effect of smartphone-based monitoring on depressive (B = 0.61, 95% CI -0.77 to 2.00, p = 0.38) and manic (B = -0.25, 95% CI -1.1 to 0.59, p = 0.56) symptoms. The intervention group reported higher quality of life and lower perceived stress compared with the control group. In sub-analyses, the intervention group had higher risk of depressive episodes, but lower risk of manic episodes compared with the control group.

CONCLUSIONS

There was no effect of smartphone-based monitoring. In patient-reported outcomes, patients in the intervention group reported improved quality of life and reduced perceived stress. Patients in the intervention group had higher risk of depressive episodes and reduced risk of manic episodes. Despite the widespread use and excitement of electronic monitoring, few studies have investigated possible effects. Further studies are needed.

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.

mHealth in affective disorders: hype or hope? A focused narrative review

The constant growth and widespread availability of mobile technologies (i.e. smartphones and wearables) over the last decades have been a subject of intense interest and research in the affective disorders (AD) field. The potential of mHealth for collecting a new kind of passive and active information while providing cost-effective and tailored interventions have raised many hopes. However, until now, despite some encouraging results, research in the field has not been translated to reach real-world clinical settings or to develop additional evidence-based mHealth tools for people suffering from AD. Meanwhile, commercial untested apps and wearables are already being increasingly used and adopted by patients for the self-management of their illnesses. Hence, there is a latent need and demand from service users to integrate mHealth in their care, which the field cannot yet fulfil. In this article, through a focused narrative review, we discuss the evidence available for the use, validity and efficacy of mHealth tools in AD. Challenges in the academic field hampering the advancement of these technologies and its implementation into clinical practice are discussed. Lastly, we propose a framework to overcome these issues, which may facilitate mHealth solutions reaching service users.

Hypomania/Mania by DSM-5 definition based on daily smartphone-based patient-reported assessments

INTRODUCTION

The DSM-5 has introduced elevated/irritable mood and increased activity/ energy as equal and necessary criterion A symptoms for a diagnosis of (hypo)mania. The impact of these changes is poorly elucidated. The aim of the study was to investigate differences in the prevalence of elevated/irritable mood with and without co-occurring increased activity, and the associations between these, in patients with an ICD-10 and DSM-IV diagnosis of BD, using real life daily smartphone-based patient-reported measures of mood, irritability and activity.

METHODS

Data from two RCTs investigating the effect of smartphone-based treatment in patients with BD were combined. Patients with BD (N = 117) evaluated mood, irritability and activity level daily for six to nine months via a smartphone-based system. Analyses in this study are exploratory post hoc analyses based on previously published data.

RESULTS

During the follow-up period, patients reported elevated mood 8.0% of the time, irritability 28.4% of the time and increased activity 20.6% of the time. Co-occurring elevated/irritable mood and activity were prevalent 0.12% of the time for four consecutive days (duration criteria for a hypomanic episode) compared to 24% of the time with elevated/irritable mood without co-occurring increased activity. In linear mixed effect models accommodating for inter-individual and intra-individual variation, there was a statistically significant positive association between mood and activity (B: 0.14, 95% CI: 0.046; 0.24, p = 0.004). There was no association between irritability and activity (p = 0.23).

CONCLUSION

Based on real life daily assessments, the prevalence of (hypo)manic episodes is substantial reduced as a result of the introduction of DSM-5 and with potentially clinical consequences.

Dropout rates in clinical trials of smartphone apps for depressive symptoms: A systematic review and meta-analysis

BACKGROUND

Low engagement and attrition from app interventions is an increasingly recognized challenge for interpreting and translating the findings from digital health research. Focusing on randomized controlled trials (RCTs) of smartphone apps for depressive symptoms, we aimed to establish overall dropout rates, and how this differed between different types of apps.

METHODS

A systematic review of RCTs of apps targeting depressive symptoms in adults was conducted in May 2019. Random-effects meta-analysis were applied to calculate the pooled dropout rates in intervention and control conditions. Trim-and-fill analyses were used to adjust estimates after accounting for publication bias.

RESULTS

The systematic search retrieved 2,326 results. 18 independent studies were eligible for inclusion, using data from 3,336 participants randomized to either smartphone interventions for depression (n = 1,786) or control conditions (n = 1,550). The pooled dropout rate was 26.2%. This increased to 47.8% when adjusting for publication bias. Study retention rates did not differ between depression vs. placebo apps, clinically-diagnosed vs. self-reported depression, paid vs. unpaid assessments, CBT vs. non-CBT apps, or mindfulness vs. non-mindfulness app studies. Dropout rates were higher in studies with large samples, but lower in studies offering human feedback and in-app mood monitoring.

DISCUSSION

High dropout rates present a threat to the validity of RCTs of mental health apps. Strategies to improve retention may include providing human feedback, and enabling in-app mood monitoring. However, it critical to consider bias when interpreting results of apps for depressive symptoms, especially given the strong indication of publication bias, and the higher attrition in larger studies.

Validity and characteristics of patient-evaluated adherence to medication via smartphones in patients with bipolar disorder: exploratory reanalyses on pooled data from the MONARCA I and II trials

BACKGROUND

Non-adherence to medication is associated with increased risk of relapse in patients with bipolar disorder (BD).

OBJECTIVES

To (1) validate patient-evaluated adherence to medication measured via smartphones against validated adherence questionnaire; and (2) investigate characteristics for adherence to medication measured via smartphones.

METHODS

Patients with BD (n=117) evaluated adherence to medication daily for 6-9 months via smartphones. The Medication Adherence Rating Scale (MARS) and the Rogers' Empowerment questionnaires were filled out. The 17-item Hamilton Depression Rating Scale, the Young Mania Rating Scale and the Functional Assessment Short Test were clinically rated. Data were collected multiple times per patient. The present study represents exploratory pooled reanalyses of data collected as part of two randomised controlled trials.

FINDINGS

During the study 90.50% of the days were evaluated as 'medication taken', 6.91% as 'medication taken with changes' and 2.59% as 'medication not taken'. Adherence to medication measured via smartphones was valid compared with the MARS (B: -0.049, 95% CI -0.095 to -0.003, p=0.033). Younger age and longer illness duration were significant predictors for non-adherence to medication (model concerning age: B: 0.0039, 95% CI 0.00019 to 0.0076, p=0.040). Decreased affective symptoms measured with smartphone-based patient-reported mood and clinical ratings as well as decreased empowerment were associated with non-adherence.

CONCLUSIONS

Smartphone-based monitoring of adherence to medication was valid compared with validated adherence questionnaire. Younger age and longer illness duration were predictors for non-adherence. Increased empowerment was associated with adherence.

CLINICAL IMPLICATIONS

Using smartphones for empowerment of adherence using patient-reported measures may be helpful in everyday clinical settings.

TRIAL REGISTRATION NUMBER

NCT01446406 and NCT02221336.

Smartphones in mental health: a critical review of background issues, current status and future concerns

There has been increasing interest in the use of smartphone applications (apps) and other consumer technology in mental health care for a number of years. However, the vision of data from apps seamlessly returned to, and integrated in, the electronic medical record (EMR) to assist both psychiatrists and patients has not been widely achieved, due in part to complex issues involved in the use of smartphone and other consumer technology in psychiatry. These issues include consumer technology usage, clinical utility, commercialization, and evolving consumer technology. Technological, legal and commercial issues, as well as medical issues, will determine the role of consumer technology in psychiatry. Recommendations for a more productive direction for the use of consumer technology in psychiatry are provided.

Insights from user reviews to improve mental health apps

Mental health applications hold great promise as interventions for addressing common mental issues. Although many people with mental health issues use mobile app interventions, their adherence level remains low. Low engagement affects the effectiveness of mobile interventions. However, there is still a dearth of research to explain the reasons for low engagement. User experience and usability are two factors that determine the adoption and usage of apps. Analyzing user reviews of mobile apps for mental health issues reveals user experience and what features users liked and disliked in the apps and hence informs future app design and refinements. This research aims to analyze user reviews of publicly available mental health applications to uncover their strengths, weaknesses, and gaps, hence revealing why users are likely to cease using these applications. We mined reviews of 106 mental health apps retrieved from Apple's App Store and Google Play and employed thematic analysis on 13,549 reviews. The review analysis shows that users placed more emphasis on the user interface and the user-friendliness of the app. Users also appreciated apps that present them with a variety of options, functionalities, and content that they can choose. Again, apps that offer adaptive functionalities that allow users to adapt some app features also received high ratings. In contrast, poor usability emerged as the most common reason for abandoning mental health apps. Other pitfalls include lack of a content variety, lack of personalization, lack of customer service and trust, and security and privacy issues.

Experiences of a Web-Based Quality of Life Self-Monitoring Tool for Individuals With Bipolar Disorder: A Qualitative Exploration

BACKGROUND

Self-monitoring of symptoms is a cornerstone of psychological interventions in bipolar disorder (BD), but individuals with lived experience also value tracking holistic outcomes, such as quality of life (QoL). Importantly, self-monitoring is not always experienced positively by people with BD and may have lower than expected rates of engagement. Therefore, before progressing into QoL tracking tools, it is important to explore user perspectives to identify possible risks and benefits, optimal methods to support engagement, and possible avenues to integrate QoL self-monitoring practices into clinical work.

OBJECTIVE

This study aimed to conduct a qualitative exploration of how individuals with BD engaged with a Web-based version of a BD-specific QoL self-monitoring instrument, the QoL tool.

METHODS

A total of 43 individuals with BD engaged with a self-management intervention with an optional Web-based QoL self-assessment tool as part of an overarching mixed method study. Individuals were later interviewed about personal experiences of engagement with the intervention, including experiences of gauging their own QoL. A thematic analysis was used to identify salient aspects of the experience of QoL self-monitoring in BD.

RESULTS

In total, 4 categories describing people's experiences of QoL self-monitoring were identified: (1) breadth of QoL monitoring, (2) highlighting the positive, (3) connecting self-monitoring to action, and (4) self-directed patterns of use.

CONCLUSIONS

The findings of this research generate novel insights into ways in which individuals with BD experience the Web-based QoL self-assessment tool. The value of tracking the breadth of domains was an overarching aspect, facilitating the identification of both areas of strength and life domains in need of intervention. Importantly, monitoring QoL appeared to have an inherently therapeutic quality, through validating flourishing areas and reinforcing self-management efforts. This contrasts the evidence suggesting that symptom tracking may be distressing because of its focus on negative experiences and positions QoL as a valuable adjunctive target of observation in BD. Flexibility and personalization of use of the QoL tool were key to engagement, informing considerations for health care providers wishing to support self-monitoring and future research into Web- or mobile phone-based apps.

A Methodologic Systematic Review of Mobile Health Behavior Change Randomized Trials

CONTEXT

Mobile health helps providers offer accessible, affordable, tailored behavior change interventions. However, research assessing mobile health interventions may feature methodologic shortcomings and poor reporting. This review aims to summarize the characteristics, methods, and intervention reporting of RCTs evaluating mobile health behavior change interventions.

EVIDENCE ACQUISITION

This was a methodologic systematic review of RCTs assessing mobile health behavior change interventions published in PubMed from January 1, 2014 to January 1, 2018, in journals with the upper half of Impact Factors (Clarivate Analytics). Three reviewers independently extracted sample characteristics. Primary outcomes were classified as patient-important or not using definitions from the literature. Any non-patient-important outcomes were then reclassified by a panel of 3 patients. Intervention reporting was assessed by the mobile health Evidence Reporting and Assessment checklist. Data were analyzed in December 2018.

EVIDENCE SYNTHESIS

Most of the 231 included RCTs assessed text messaging (51%) or smartphone app (28%) interventions aiming to change nutrition and physical activity (36%) or treatment adherence (25%). Only 8% of RCTs had a patient-important primary outcome, follow-up of ≥6 months, and intent-to-treat analysis. Most primary outcomes were behavioral measures (60%). Follow-up was <3 months in 29% of RCTs. Regarding reporting, 12 of the 16 checklist items were reported in less than half of RCTs (e.g., usability/content testing, 32%; data security, 13%).

CONCLUSIONS

Reports of RCTs assessing mobile health behavior change interventions lack information that would be useful for providers, including reporting of long-term intervention impact on patient-important primary outcomes and information needed for intervention replicability.

[Ambulatory monitoring and digital phenotyping in the diagnostics and treatment of bipolar disorders]

BACKGROUND

Reliable and valid diagnostics and treatment of bipolar disorders and affective episodes are subject to extensive, especially methodological limitations in the clinical practice.

OBJECTIVE

The use of smartphones and mobile sensor technology for improvement in diagnostics and treatment of bipolar disorders.

METHODS

Critical discussion of current research on the use of ambulatory monitoring and digital phenotyping with bipolar disorders.

RESULTS

In many studies the observation periods were too short and the sensors applied were too inaccurate to enable reliable and valid detection of behavioral changes in the context of affective episodes.

CONCLUSION

The clarification and operationalization of psychopathological constructs to allow for the measurement of objectively observable and ascertainable behavioral changes during depressive and (hypo)manic states are essential for the successful application of modern mobile technologies in the diagnostics and treatment of bipolar disorders.

Is there an app for that? A cluster randomised controlled trial of a mobile app-based mental health intervention

Demand for the use of mobile apps in mental health interventions has grown in recent years, particularly among adolescents who experience elevated levels of distress. However, there is a scarcity of evidence for the effectiveness of these tools within this population. The aim of this study was to test the effectiveness of CopeSmart, a mental health mobile app, using a multicentre cluster randomised controlled trial design. Participants were 15-18-years-olds (N = 560) recruited from 10 schools randomly assigned to an intervention or control condition. Intervention participants used the app over a 4-week period. Multi-level modelling analyses revealed no significant changes in the intervention group from pre-test to post-test, when compared to the control group, in terms of emotional distress, well-being, emotional self-awareness or coping strategies. Findings suggest that a 4-week app-based intervention may not be enough to elicit intra-personal changes in mental health outcomes in a general adolescent population.

Methodological Challenges in Randomized Controlled Trials on Smartphone-Based Treatment in Psychiatry: Systematic Review

BACKGROUND

Smartphone-based technology is developing at high speed, and many apps offer potential new ways of monitoring and treating a range of psychiatric disorders and symptoms. However, the effects of most available apps have not been scientifically investigated. Within medicine, randomized controlled trials (RCTs) are the standard method for providing the evidence of effects. However, their rigidity and long time frame may contrast with the field of information technology research. Therefore, a systematic review of methodological challenges in designing and conducting RCTs within mobile health is needed.

OBJECTIVE

This systematic review aimed to (1) identify and describe RCTs investigating the effect of smartphone-based treatment in adult patients with a psychiatric diagnosis, (2) discuss methodological challenges in designing and conducting individual trials, and (3) suggest recommendations for future trials.

METHODS

A systematic search in English was conducted in PubMed, PsycINFO, and EMBASE up to August 12, 2019. The search terms were (1) psychiatric disorders in broad term and for specific disorders AND (2) smartphone or app AND (3) RCT. The Consolidated Standards of Reporting Trials electronic health guidelines were used as a template for data extraction. The focus was on trial design, method, and reporting. Only trials having sufficient information on diagnosis and acceptable diagnostic procedures, having a smartphone as a central part of treatment, and using an RCT design were included.

RESULTS

A total of 27 trials comprising 3312 patients within a range of psychiatric diagnoses were included. Among them, 2 trials were concerning drug or alcohol abuse, 3 psychosis, 10 affective disorders, 9 anxiety and posttraumatic stress disorder, 1 eating disorder, and 1 attention-deficit/hyperactivity disorder. In addition, 1 trial used a cross-diagnostic design, 7 trials included patients with a clinical diagnosis that was subsequently assessed and validated by the researchers, and 11 trials had a sample size above 100. Generally, large between-trial heterogeneity and multiple approaches to patient recruitment, diagnostic procedures, trial design, comparator, outcome measures, and analyses were identified. Only 5 trials published a trial protocol. Furthermore, 1 trial provided information regarding technological updates, and only 18 trials reported on the conflicts of interest. No trial addressed the ethical aspects of using smartphones in treatment.

CONCLUSIONS

This first systematic review of the methodological challenges in designing and conducting RCTs investigating smartphone-based treatment in psychiatric patients suggests an increasing number of trials but with a lower quality compared with classic medical RCTs. Heterogeneity and methodological issues in individual trials limit the evidence. Methodological recommendations are presented.

The efficacy of app-supported smartphone interventions for mental health problems: a meta-analysis of randomized controlled trials

Although impressive progress has been made toward developing empirically-supported psychological treatments, the reality remains that a significant proportion of people with mental health problems do not receive these treatments. Finding ways to reduce this treatment gap is crucial. Since app-supported smartphone interventions are touted as a possible solution, access to up-to-date guidance around the evidence base and clinical utility of these interventions is needed. We conducted a meta-analysis of 66 randomized controlled trials of app-supported smartphone interventions for mental health problems. Smartphone interventions significantly outperformed control conditions in improving depressive (g=0.28, n=54) and generalized anxiety (g=0.30, n=39) symptoms, stress levels (g=0.35, n=27), quality of life (g=0.35, n=43), general psychiatric distress (g=0.40, n=12), social anxiety symptoms (g=0.58, n=6), and positive affect (g=0.44, n=6), with most effects being robust even after adjusting for various possible biasing factors (type of control condition, risk of bias rating). Smartphone interventions conferred no significant benefit over control conditions on panic symptoms (g=-0.05, n=3), post-traumatic stress symptoms (g=0.18, n=4), and negative affect (g=-0.08, n=5). Studies that delivered a cognitive behavior therapy (CBT)-based app and offered professional guidance and reminders to engage produced larger effects on multiple outcomes. Smartphone interventions did not differ significantly from active interventions (face-to-face, computerized treatment), although the number of studies was low (n≤13). The efficacy of app-supported smartphone interventions for common mental health problems was thus confirmed. Although mental health apps are not intended to replace professional clinical services, the present findings highlight the potential of apps to serve as a cost-effective, easily accessible, and low intensity intervention for those who cannot receive standard psychological treatment.

Electronic Ecological Momentary Assessment (EMA) in youth with bipolar disorder: Demographic and clinical predictors of electronic EMA adherence

BACKGROUND

Ecological momentary assessment (EMA) is increasingly used to characterize patients' daily lives, monitor mood, and test efficacy of treatment interventions. However, few studies have examined patient characteristics impacting adherence with EMA protocols, and to our knowledge, no such study has been conducted in youth with bipolar disorder (BD).

METHODS

As part of a larger observational study, 14- to 21-year-olds diagnosed with BD, and who were between episodes of illness (n = 39, 19.0 ± 2.05 Mean ± Standard Deviation years old, 74.4% female) and psychiatrically healthy controls (n = 47, 18.3 ± 2.40 years old, 66.0% female) completed baseline diagnostic and symptom severity interviews, and were instructed to complete diary assessments of mood, sleep, and behavior electronically three times per day for 21 consecutive days (i.e., in total 5418 (or 63 per person) diary entries). Multiple regression was used to examine effects of BD participants' demographic and clinical characteristics on diary completion rates.

RESULTS

53.8 ± 9.3 diary entries per person were actually completed. Adherence rates were high (87.5% of healthy controls and 80.4% of adolescents with BD), but were still significantly poorer in youth with BD. Adequate adherence (≥80%) rates were also significantly poorer in youth with BD relative to healthy controls (56.4% versus 83.0%). Among youth with BD, more lifetime suicide attempts and higher current mood elevation symptom severity predicted significantly poorer adherence.

LIMITATIONS

Limited sample size/generalizability.

CONCLUSIONS

Findings highlight the importance of considering the impact of patient characteristics on adherence with EMA protocols among youth with severe mental illness.

The Validity of Daily Self-Assessed Perceived Stress Measured Using Smartphones in Healthy Individuals: Cohort Study

Background

Smartphones may offer a new and easy tool to assess stress, but the validity has never been investigated.

Objective

This study aimed to investigate (1) the validity of smartphone-based self-assessed stress compared with Cohen Perceived Stress Scale (PSS) and (2) whether smartphone-based self-assessed stress correlates with neuroticism (Eysenck Personality Questionnaire-Neuroticism, EPQ-N), psychosocial functioning (Functioning Assessment Short Test, FAST), and prior stressful life events (Kendler Questionnaire for Stressful Life Events, SLE).

Methods

A cohort of 40 healthy blood donors with no history of personal or first-generation family history of psychiatric illness and who used an Android smartphone were instructed to self-assess their stress level daily (on a scale from 0 to 2; beta values reflect this scale) for 4 months. At baseline, participants were assessed with the FAST rater-blinded and filled out the EPQ, the PSS, and the SLE. The PSS assessment was repeated after 4 months.

Results

In linear mixed-effect regression and linear regression models, there were statistically significant positive correlations between self-assessed stress and the PSS (beta=.0167; 95% CI 0.0070-0.0026; P=.001), the EPQ-N (beta=.0174; 95% CI 0.0023-0.0325; P=.02), and the FAST (beta=.0329; 95% CI 0.0036-0.0622; P=.03). No correlation was found between smartphone-based self-assessed stress and the SLE.

Conclusions

Daily smartphone-based self-assessed stress seems to be a valid measure of perceived stress. Our study contains a modest sample of 40 healthy participants and adds knowledge to a new but growing field of research. Smartphone-based self-assessed stress is a promising tool for measuring stress in real time in future studies of stress and stress-related behavior.

Evidence-Based Psychotherapies for Bipolar Disorder

Bipolar disorder is a recurrent psychiatric disorder marked by waxing and waning affective symptoms and impairment in functioning. Some of the morbidity and mortality associated with the illness may be reduced with evidence-based psychotherapies (EBPs) along with pharmacotherapy. To enhance clinicians' understanding of which therapy modalities have evidence supporting their use, the authors conducted a systematic literature review to identify randomized controlled trials (RCTs) of psychotherapy for adults with bipolar disorder. A strong evidence base exists for psychoeducation, cognitive-behavioral therapy, family-focused therapy, interpersonal and social rhythm therapy, and peer-support programs. Promising modalities include functional remediation, mindfulness-based cognitive therapy, illness management and recovery, and technology-assisted strategies. RCTs demonstrate a consistent advantage of these psychotherapies plus pharmacotherapy, compared with the use of pharmacotherapy alone. Adjunctive EBPs hasten time to remission, delay time to recurrence, and improve functional outcomes. EBPs play an important role in helping individuals develop skills needed to manage the persistent and lifelong psychosocial, neurocognitive, vocational, and interpersonal consequences of bipolar disorder. Continued efforts to improve the effectiveness of EBPs for adults with bipolar disorder are warranted.

Reporting guidelines on remotely collected electronic mood data in mood disorder (eMOOD)-recommendations

Prospective monitoring of mood was started by Kraepelin who made and recorded frequent observations of his patients. During the last decade, the number of research studies using remotely collected electronic mood data has increased markedly. However, standardized measures and methods to collect, analyze and report electronic mood data are lacking. To get better understanding of the nature, correlates and implications of mood and mood instability, and to standardize this process, we propose guidelines for reporting of electronic mood data (eMOOD). This paper provides an overview of remotely collected electronic mood data in mood disorders and discusses why standardized reporting is necessary to evaluate and inform mood research in Psychiatry. Adherence to these guidelines will improve interpretation, reproducibility and future meta-analyses of mood monitoring in mood disorder research.

Psychosocial Interventions for Bipolar II Disorder

Bipolar II disorder causes significant suffering among patients and their families, some of which may be alleviated by psychotherapy alone or as an adjunct to pharmacotherapy. Psychotherapies may be more effective if modified to meet the specific needs of patients with bipolar II disorder.

Technological Interventions for Medication Adherence in Adult Mental Health and Substance Use Disorders: A Systematic Review

BACKGROUND

Medication adherence is critical to the effectiveness of psychopharmacologic therapy. Psychiatric disorders present special adherence considerations, notably an altered capacity for decision making and the increased street value of controlled substances. A wide range of interventions designed to improve adherence in mental health and substance use disorders have been studied; recently, many have incorporated information technology (eg, mobile phone apps, electronic pill dispensers, and telehealth). Many intervention components have been studied across different disorders. Furthermore, many interventions incorporate multiple components, making it difficult to evaluate the effect of individual components in isolation.

OBJECTIVE

The aim of this study was to conduct a systematic scoping review to develop a literature-driven, transdiagnostic taxonomic framework of technology-based medication adherence intervention and measurement components used in mental health and substance use disorders.

METHODS

This review was conducted based on a published protocol (PROSPERO: CRD42018067902) in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses systematic review guidelines. We searched 7 electronic databases: MEDLINE, EMBASE, PsycINFO, the Cochrane Central Register of Controlled Trials, Web of Science, Engineering Village, and ClinicalTrials.gov from January 2000 to September 2018. Overall, 2 reviewers independently conducted title and abstract screens, full-text screens, and data extraction. We included all studies that evaluate populations or individuals with a mental health or substance use disorder and contain at least 1 technology-delivered component (eg, website, mobile phone app, biosensor, or algorithm) designed to improve medication adherence or the measurement thereof. Given the wide variety of studied interventions, populations, and outcomes, we did not conduct a risk of bias assessment or quantitative meta-analysis. We developed a taxonomic framework for intervention classification and applied it to multicomponent interventions across mental health disorders.

RESULTS

The initial search identified 21,749 results; after screening, 127 included studies remained (Cohen kappa: 0.8, 95% CI 0.72-0.87). Major intervention component categories include reminders, support messages, social support engagement, care team contact capabilities, data feedback, psychoeducation, adherence-based psychotherapy, remote care delivery, secure medication storage, and contingency management. Adherence measurement components include self-reports, remote direct visualization, fully automated computer vision algorithms, biosensors, smart pill bottles, ingestible sensors, pill counts, and utilization measures. Intervention modalities include short messaging service, mobile phone apps, websites, and interactive voice response. We provide graphical representations of intervention component categories and an element-wise breakdown of multicomponent interventions.

CONCLUSIONS

Many technology-based medication adherence and monitoring interventions have been studied across psychiatric disease contexts. Interventions that are useful in one psychiatric disorder may be useful in other disorders, and further research is necessary to elucidate the specific effects of individual intervention components. Our framework is directly developed from the substance use disorder and mental health treatment literature and allows for transdiagnostic comparisons and an organized conceptual mapping of interventions.

Differences in mood instability in patients with bipolar disorder type I and II: a smartphone-based study

BACKGROUND

Mood instability in bipolar disorder is associated with a risk of relapse. This study investigated differences in mood instability between patients with bipolar disorder type I and type II, which previously has been sparingly investigated.

METHODS

Patients with bipolar disorder type I (n = 53) and type II (n = 31) used a daily smartphone-based self-monitoring system for 9 months. Data in the present reflect 15.975 observations of daily collected smartphone-based data on patient-evaluated mood.

RESULTS

In models adjusted for age, gender, illness duration and psychopharmacological treatment, patients with bipolar disorder type II experienced more mood instability during depression compared with patients with bipolar disorder type I (B: 0.27, 95% CI 0.007; 0.53, p = 0.044), but lower intensity of manic symptoms. Patients with bipolar disorder type II did not experience lower mean mood or higher intensity of depressive symptoms compared with patients with bipolar disorder type I.

CONCLUSIONS

Compared to bipolar disorder type I, patients with bipolar disorder type II had higher mood instability for depression. Clinically it is of importance to identify these inter-episodic symptoms. Future studies investigating the effect of treatment on mood instability measures are warranted. Trial registration NCT02221336.

Smartphone-based self-monitoring in bipolar disorder: evaluation of usability and feasibility of two systems

Background

The aims of the present multicenter pilot study were to examine the feasibility and usability of two different smartphone-based monitoring systems (the Pulso system and the Trilogis-Monsenso system) from two IT companies in patients with bipolar disorder, developed and selected to be testes as a part of a European Union funded Pre-Commercial Procurement (the NYMPHA-MD project).

Methods

Patients with bipolar disorder (ICD-10), > 18 years of age during a remitted, partial remitted or mild to moderate depressive state (HDRS-17 < 25) from Italy, Spain and Denmark were included. Patients were randomized 1:1 to the use of one of two smartphone-based monitoring systems. The randomization was stratified according to study location (Italy, Spain, Denmark) and all patients were followed for a 4 weeks study period. Usability and feasibility were evaluated using the Computer System Usability Questionnaire, and the Usefulness, Satisfaction, and Ease of use Questionnaire.

Results

A total of 60 patients aged 18–69 years with bipolar disorder (ICD-10) recruited from Italy, Spain, Denmark were included—59 patients completed the study. In Denmark, the patients evaluated the Trilogis-Monsenso system with a statistically significant higher usability compared with the Pulso system. In Italy and Spain, the patients evaluated no statistically significant difference between the two systems in any of the categories, except for the usefulness category favoring the Trilogis-Monsenso system (z = 2.68, p < 0.01).

Conclusions

Both monitoring systems showed acceptable usability and feasibility. There were differences in patient-based evaluations of the two monitoring systems related to the country of the study. Studies investigating the usability and feasibility during longer follow-up periods could perhaps reveal different findings. Future randomized controlled trials investigating the possible positive and negative effects of smartphone-based monitoring systems are needed.

Effectiveness of smartphone-based ambulatory assessment (SBAA-BD) including a predicting system for upcoming episodes in the long-term treatment of patients with bipolar disorders: study protocol for a randomized controlled single-blind trial

BACKGROUND

The detection of early warning signs is essential in the long-term treatment of bipolar disorders. However, in bipolar patients' daily life and outpatient treatment the assessment of upcoming state changes faces several difficulties. In this trial, we examine the effectiveness of a smartphone based automated feedback about ambulatory assessed early warning signs in prolonging states of euthymia and therefore preventing hospitalization. This study aims to assess, whether patients experience longer episodes of euthymia, when their treating psychiatrists receive automated feedback about changes in communication and activity. With this additional information an intervention at an earlier stage in the development of mania or depression could be facilitated. We expect that the amount of time will be longer between affective episodes in the intervention group.

METHODS/DESIGN

The current study is designed as a randomized, multi-center, observer-blind, active-control, parallel group trial within a nationwide research project on the topic of innovative methods for diagnostics, prevention and interventions of bipolar disorders. One hundred and twenty patients with bipolar disorder will be randomly assigned to (1) the experimental group with included automated feedback or (2) the control group without feedback. During the intervention phase, the psychopathologic state of all participants is assessed every four weeks over 18 months. Kaplan-Meier estimators will be used for estimating the survival functions, a Log-Rank test will be used to formally compare time to a new episode across treatment groups. An intention-to-treat analysis will include data from all randomized patients.

DISCUSSION

This article describes the design of a clinical trial investigating the effectiveness of a smartphone-based feedback loop. This feedback loop is meant to elicit early interventions at the detection of warning signs for the prevention of affective episodes in bipolar patients. This approach will hopefully improve the chances of a timely intervention helping patients to keep a balanced mood for longer periods of time. In detail, if our hypothesis can be confirmed, clinical practice treating psychiatrists will be enabled to react quickly when changes are automatically detected. Therefore, outpatients would receive an even more individually tailored treatment concerning time and frequency of doctor's appointments.

TRIAL REGISTRATION

ClinicalTrials.gov : NCT02782910 : Title: "Smartphone-based Ambulatory Assessment of Early Warning Signs (BipoLife_A3)". Registered May 25 2016. Protocol Amendment Number: 03. Issue Date: 26 March 2018. Author(s): ES.

Disease management apps and technical assistance systems for bipolar disorder: Investigating the patients´ point of view

BACKGROUND

Smartphone-based disease management has become increasingly interesting for research in the field of bipolar disorders. This article investigates the attitudes of persons affected by this disorder towards the appropriation of mobile apps or assistance systems for the management of their disease.

METHODS

We conducted two separate studies. Study 1 was an online survey with 88 participants. In study 2 we consulted 15 participants during a semi-structured interview. All the participants had formerly been diagnosed with bipolar disorder.

RESULTS

More than half of the participants of study 1 and most participants of study 2 agreed with the use of an app or assistance system for self-ratings, third party ratings and an objective symptom monitoring. Potential interventions that were popular in both groups included a regular feedback, the visualization of monitored data and advice in crises.

LIMITATIONS

With study 1 we were not able to ensure correct diagnoses or to interact in a flexible way. In Study 2 those issues were resolved, but the small number of participants raises the question of a possible generalisability of the results. Furthermore, for both studies a selection bias could not be excluded.

CONCLUSIONS

Our results indicate positive attitudes of bipolar patients towards disease management apps and assistance systems. Even new and innovative features such as partner apps or the analysis of facial expressions in video data were appreciated and daily interactions were favoured. However, the variety of answers calls for flexible systems which allow activating or deactivating certain features.

An update on adjunctive treatment options for bipolar disorder

OBJECTIVES

Bipolar disorder is a complex illness often requiring combinations of therapies to successfully treat symptoms. In recent years, there have been significant advancements in a number of therapies for bipolar disorder. It is therefore timely to provide an overview of current adjunctive therapeutic options to help treating clinicians to inform their patients and work towards optimal outcomes.

METHODS

Publications were identified from PubMed searches on bipolar disorder and pharmacotherapy, nutraceuticals, hormone therapy, psychoeducation, interpersonal and social rhythm therapy, cognitive remediation, mindfulness, e-Health and brain stimulation techniques. Relevant articles in these areas were selected for further review. This paper provides a narrative review of adjunctive treatment options and is not a systematic review of the literature.

RESULTS

A number of pharmacotherapeutic, psychological and neuromodulation treatment options are available. These have varying efficacy but all have shown benefit to people with bipolar disorder. Due to the complex nature of treating the disorder, combination treatments are often required. Adjunctive treatments to traditional pharmacological and psychological therapies are proving useful in closing the gap between initial symptom remission and full functional recovery.

CONCLUSIONS

Given that response to monotherapy is often inadequate, combination regimens for bipolar disorder are typical. Correspondingly, psychiatric research is working towards a better understanding of the disorder's underlying biology. Therefore, treatment options are changing and adjunctive therapies are being increasingly recognized as providing significant tools to improve patient outcomes. Towards this end, this paper provides an overview of novel treatments that may improve clinical outcomes for people with bipolar disorder.

e-PTSD: an overview on how new technologies can improve prediction and assessment of Posttraumatic Stress Disorder (PTSD)

Background: New technologies may profoundly change our way of understanding psychiatric disorders including posttraumatic stress disorder (PTSD). Imaging and biomarkers, along with technological and medical informatics developments, might provide an answer regarding at-risk patient's identification. Recent advances in the concept of 'digital phenotype', which refers to the capture of characteristics of a psychiatric disorder by computerized measurement tools, is one paradigmatic example. Objective: The impact of the new technologies on health professionals practice in PTSD care remains to be determined. The recent evolutions could disrupt the clinical practices and practitioners in their beliefs, ethics and representations, going as far as questioning their professional culture. In the present paper, we conducted an extensive search to highlight the articles which reflect the potential of these new technologies. Method: We conducted an overview by querying PubMed database with the terms [PTSD] [Posttraumatic stress disorder] AND [Computer] OR [Computerized] OR [Mobile] OR [Automatic] OR [Automated] OR [Machine learning] OR [Sensor] OR [Heart rate variability] OR [HRV] OR [actigraphy] OR [actimetry] OR [digital] OR [motion] OR [temperature] OR [virtual reality]. Results: We summarized the synthesized literature in two categories: prediction and assessment (including diagnostic, screening and monitoring). Two independent reviewers screened, extracted data and quality appraised the sources. Results were synthesized narratively. Conclusions: This overview shows that many studies are underway allowing researchers to start building a PTSD digital phenotype using passive data obtained by biometric sensors. Active data obtained from Ecological Momentary Assessment (EMA) could allow clinicians to assess PTSD patients. The place of connected objects, Artificial Intelligence and remote monitoring of patients with psychiatric pathology remains to be defined. These tools must be explained and adapted to the different profiles of physicians and patients. The involvement of patients, caregivers and health professionals is essential to the design and evaluation of these new tools.

Smartphone-based objective monitoring in bipolar disorder: status and considerations

In 2001, the WHO stated that: "The use of mobile and wireless technologies to support the achievement of health objectives (mHealth) has the potential to transform the face of health service delivery across the globe". Within mental health, interventions and monitoring systems for depression, anxiety, substance abuse, eating disorder, schizophrenia and bipolar disorder have been developed and used. The present paper presents the status and findings from studies using automatically generated objective smartphone data in the monitoring of bipolar disorder, and addresses considerations on the current literature and methodological as well as clinical aspects to consider in the future studies.

A systematic review of the psychometric properties, usability and clinical impacts of mobile mood-monitoring applications in young people

BACKGROUND

Mobile mood-monitoring applications are increasingly used by mental health providers, widely advocated within research, and a potentially effective method to engage young people. However, little is known about their efficacy and usability in young populations.

METHOD

A systematic review addressing three research questions focused on young people: (1) what are the psychometric properties of mobile mood-monitoring applications; (2) what is their usability; and (3) what are their positive and negative clinical impacts? Findings were synthesised narratively, study quality assessed and compared with evidence from adult studies.

RESULTS

We reviewed 25 articles. Studies on the psychometric properties of mobile mood-monitoring applications were sparse, but indicate questionable to excellent internal consistency, moderate concurrent validity and good usability. Participation rates ranged from 30% to 99% across studies, and appeared to be affected by methodological factors (e.g. payments) and individual characteristics (e.g. IQ score). Mobile mood-monitoring applications are positively perceived by youth, may reduce depressive symptoms by increasing emotional awareness, and could aid in the detection of mental health and substance use problems. There was very limited evidence on potential negative impacts.

CONCLUSIONS

Evidence for the use of mood-monitoring applications in youth is promising but limited due to a lack of high-quality studies. Future work should explicate the effects of mobile mood-monitoring applications on effective self-regulation, clinical outcomes across disorders and young people's engagement with mental health services. Potential negative impacts in this population should also be investigated, as the adult literature suggests that application use could potentially increase negativity and depression symptoms.

e-Addictology: An Overview of New Technologies for Assessing and Intervening in Addictive Behaviors

BACKGROUND

New technologies can profoundly change the way we understand psychiatric pathologies and addictive disorders. New concepts are emerging with the development of more accurate means of collecting live data, computerized questionnaires, and the use of passive data. Digital phenotyping, a paradigmatic example, refers to the use of computerized measurement tools to capture the characteristics of different psychiatric disorders. Similarly, machine learning-a form of artificial intelligence-can improve the classification of patients based on patterns that clinicians have not always considered in the past. Remote or automated interventions (web-based or smartphone-based apps), as well as virtual reality and neurofeedback, are already available or under development.

OBJECTIVE

These recent changes have the potential to disrupt practices, as well as practitioners' beliefs, ethics and representations, and may even call into question their professional culture. However, the impact of new technologies on health professionals' practice in addictive disorder care has yet to be determined. In the present paper, we therefore present an overview of new technology in the field of addiction medicine.

METHOD

Using the keywords [e-health], [m-health], [computer], [mobile], [smartphone], [wearable], [digital], [machine learning], [ecological momentary assessment], [biofeedback] and [virtual reality], we searched the PubMed database for the most representative articles in the field of assessment and interventions in substance use disorders.

RESULTS

We screened 595 abstracts and analyzed 92 articles, dividing them into seven categories: e-health program and web-based interventions, machine learning, computerized adaptive testing, wearable devices and digital phenotyping, ecological momentary assessment, biofeedback, and virtual reality.

CONCLUSION

This overview shows that new technologies can improve assessment and interventions in the field of addictive disorders. The precise role of connected devices, artificial intelligence and remote monitoring remains to be defined. If they are to be used effectively, these tools must be explained and adapted to the different profiles of physicians and patients. The involvement of patients, caregivers and other health professionals is essential to their design and assessment.