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

The experience of self-monitoring using the PolarUs bipolar disorder self-management app: a qualitative report of impacts and unmet needs

BACKGROUND

Self-monitoring is key to detecting and preventing mood episodes in bipolar-disorder (BD). An increasing number of smartphone apps have been developed to facilitate this aspect of self-management; however, the feasibility and efficacy of these interventions is heterogenous. To improve understanding of the subjective experience of app-based self-monitoring interventions for BD, the present study describes a qualitative investigation of the perspectives of individuals participating in an evaluation of a novel self-management app.

METHODS

Twenty-five individuals with BD were given access to PolarUs, an app-based self-management intervention, and were later questioned about perceptions of and engagement with this tool. Thematic analysis was used to identify important aspects of the experience of self-monitoring as part of an app-based intervention.

RESULTS

Four themes describing experiences of self-monitoring were generated. These included increased self-awareness, the use of self-monitoring to guide self-management, positive and negative emotional responses to self-monitoring, and unmet needs for self-monitoring apps. Three subthemes describing unmet needs were identified, including the provision of proactive coping suggestions, support reviewing data, and tracking additional symptoms, behaviours and life areas.

CONCLUSIONS

The present study highlights the importance of taking the subjective experience of users into account during the development, evaluation, and implementation of app-based monitoring interventions in BD. Implications for the use of passively collected data and personalisation of app delivery are discussed.

Smartphone monitoring and digital phenotyping apps for schizophrenia: A review of the academic literature

BACKGROUND

Monitoring-based applications are increasingly administered in mental health research to detect relapses and track symptoms by using digital phenotyping. This review systematically examines unique datasets generated from unique apps for schizophrenia-spectrum disorders, including identifying patterns in study design, sample size, duration, comparison groups, device usage, incentives, and eligibility criteria.

METHODS

In January 2025, we conducted a systematic review with a narrative/qualitative synthesis of research for schizophrenia-related apps and coded them for demographics, eligibility, outcomes and experiences, engagement and features, and app availability. We focused specifically on apps related to monitoring schizophrenia and psychosis symptoms in patients.

RESULTS

The academic literature search yielded 3902 articles, of which 54 were included. Across these, 27 unique monitoring apps related to schizophrenia and psychosis were featured. The average study sample size was N = 78, and the average study duration was 26 weeks. The use of smartphone sensor data and digital phenotyping was common: GPS (18 of 27 apps), accelerometer (10 of 27 apps), screentime (5 of 27 apps), or phone logs (7 of 27 apps). However, nearly all apps supported self-report measures, the majority (21/27) in a survey format. Twenty-six percent of studies (14/54) focused on relapse prevention, but many were secondary analyses. There were only two apps that had replication studies.

CONCLUSION

This review identifies a shift towards scalable digital phenotyping and relapse monitoring in mental health using apps. It underscores the necessity for standardized methodologies and longitudinal studies to evaluate the validity of these results. These findings inform future research directions, emphasizing the potential for personalized digital mental health solutions and early intervention strategies.

Refining smart healthcare care for mental health and substance use disorders: A patient-centred, evidence-based approach

In this article, we comment on the article by Zhang et al, which explores the familiarity, awareness, and usage of smart medical care and its correlation with mental health and personality traits. The use of intelligent healthcare technologies in treating mental disorders and substance use disorders shows significant promise, but involves certain challenges, such as limited access, low technological literacy, and privacy concerns. These barriers disproportionately affect deprived populations and individuals with severe mental health conditions. We highlight the positive impact of smart healthcare solutions, such as telemedicine and wearable technologies, on patient engagement, remote monitoring, and treatment adherence. To overcome these challenges, we propose strategies, such as improving user-friendliness, ensuring equitable access to digital interventions, enhancing cybersecurity, and integrating smart healthcare into clinical workflows. Training healthcare providers and developing policies to ensure the ethical use of patient data are essential. When implemented thoughtfully, smart healthcare technologies can revolutionize mental health and substance use disorder treatment, improve patient outcomes, and reduce healthcare inequities.

Bipolar II disorder: a state-of-the-art review

Bipolar II disorder (BD-II) is currently identified by both the DSM-5 and ICD-11 as a distinct subtype of bipolar disorder, defined by at least one depressive episode and at least one hypomanic episode, with no history of mania. Despite its prevalence and impact, the literature on BD-II remains relatively sparse. This paper provides a comprehensive overview of the available research and current debate on the disorder, including its diagnostic criteria, clinical presentations, comorbidities, epidemiology, risk factors, and treatment strategies. Patients with BD-II often present with recurrent depressive episodes, which outnumber hypomanic episodes by a ratio of 39:1. The condition is therefore often misdiagnosed as major depressive disorder and treated with antidepressant monotherapy, which may worsen its prognosis. The recognition of BD-II is further complicated by the overlap of its symptoms with other disorders, in particular borderline personality disorder. Although BD-II is often perceived as a less severe form of bipolar disorder, evidence suggests significant functional and cognitive impairment, accompanied by an elevated risk of suicidal behavior, including a rate of completed suicide at least equivalent to that observed in bipolar I disorder (BD-I). Psychiatric comorbidities, in particular anxiety and substance use disorders, are common. The disorder is associated with a high prevalence of numerous physical comorbidities, with a particularly high risk of comorbid cardiovascular diseases. Various genetic and environmental risk factors have been identified. Inflammation, circadian rhythm dysregulation and mitochondrial dysfunction are being studied as potential pathophysiological mechanisms. Current treatment guidelines, often extrapolated from BD-I and depression research, may not fully address the unique aspects of BD-II. Nevertheless, substantial evidence supports the value of some pharmacological treatments - primarily mood stabilizers and atypical antipsychotics - augmented by psychoeducation, cognitive behavioral or interpersonal and social rhythm therapy, and lifestyle interventions. Further research on BD-II should be a priority, in order to refine diagnostic criteria, identify potentially modifiable risk factors, and develop targeted interventions.

A Brief Video-Based Intervention to Improve Digital Health Literacy for Individuals With Bipolar Disorder: Intervention Development and Results of a Single-Arm Quantitative Pilot Study

BACKGROUND

Smartphone apps can improve access to bipolar disorder (BD) care by delivering elements of effective psychological interventions, thereby promoting quality of life and reducing relapse risk and mood instability in BD. While many people with BD are interested in using publicly available mental health smartphone apps, without guidance, they risk selecting apps that are unsafe or ineffective.

OBJECTIVE

This study aimed to co-design a brief educational video on identifying appropriate mental health apps and to evaluate the acceptability and impact of this video among individuals with BD.

METHODS

Individuals with lived experience of BD, including 2 peer researchers and members of 2 advisory groups (n=4 and n=7), were consulted to develop a video with information on selecting safe, effective, and engaging mental health apps for BD. Video acceptability and impact on self-reported digital health literacy (including both general eHealth literacy and more specific mobile health literacy) were evaluated via a web-based survey, including both a validated measure and complementary items developed by the research team.

RESULTS

In total, 42 individuals with BD completed the evaluation survey (n=29, 69% women, mean age 38.6, SD 12.0 years). Digital health literacy, measured using the self-report eHealth Literacy Scale, significantly improved after viewing the video (pre: mean 32.40, SD 4.87 and post: mean 33.57, SD 4.67; t41=-3.236; P=.002; d=-0.50). Feedback supported the acceptability of the video content and format. Self-report items developed by the study team to assess mobile health literacy showed that individuals felt better able to determine which apps would protect their data (P=.004) and to ask their health care provider for support in choosing apps (P<.001) after watching the video.

CONCLUSIONS

This study found preliminary evidence that an educational video can help people with BD improve their ability to identify, apply, and evaluate the quality of digital health resources. The video and a supplementary web-based educational module are freely available for implementation in health care settings and have the potential to be a cost-effective and accessible resource for clinicians to support patients with BD to navigate the public app marketplace in support of their self-management goals.

Acoustic features from speech as markers of depressive and manic symptoms in bipolar disorder: A prospective study

INTRODUCTION

Voice features could be a sensitive marker of affective state in bipolar disorder (BD). Smartphone apps offer an excellent opportunity to collect voice data in the natural setting and become a useful tool in phase prediction in BD.

AIMS OF THE STUDY

We investigate the relations between the symptoms of BD, evaluated by psychiatrists, and patients' voice characteristics. A smartphone app extracted acoustic parameters from the daily phone calls of n = 51 patients. We show how the prosodic, spectral, and voice quality features correlate with clinically assessed affective states and explore their usefulness in predicting the BD phase.

METHODS

A smartphone app (BDmon) was developed to collect the voice signal and extract its physical features. BD patients used the application on average for 208 days. Psychiatrists assessed the severity of BD symptoms using the Hamilton depression rating scale -17 and the Young Mania rating scale. We analyze the relations between acoustic features of speech and patients' mental states using linear generalized mixed-effect models.

RESULTS

The prosodic, spectral, and voice quality parameters, are valid markers in assessing the severity of manic and depressive symptoms. The accuracy of the predictive generalized mixed-effect model is 70.9%-71.4%. Significant differences in the effect sizes and directions are observed between female and male subgroups. The greater the severity of mania in males, the louder (β = 1.6) and higher the tone of voice (β = 0.71), more clearly (β = 1.35), and more sharply they speak (β = 0.95), and their conversations are longer (β = 1.64). For females, the observations are either exactly the opposite-the greater the severity of mania, the quieter (β = -0.27) and lower the tone of voice (β = -0.21) and less clearly (β = -0.25) they speak - or no correlations are found (length of speech). On the other hand, the greater the severity of bipolar depression in males, the quieter (β = -1.07) and less clearly they speak (β = -1.00). In females, no distinct correlations between the severity of depressive symptoms and the change in voice parameters are found.

CONCLUSIONS

Speech analysis provides physiological markers of affective symptoms in BD and acoustic features extracted from speech are effective in predicting BD phases. This could personalize monitoring and care for BD patients, helping to decide whether a specialist should be consulted.

Smartphone apps for mental health: systematic review of the literature and five recommendations for clinical translation

OBJECTIVES

Providing adequate access to mental health services is a global challenge. Smartphone apps offer a potentially cost-effective, available and accessible solution for monitoring, supporting and treating mental health conditions. This systematic review describes and evaluates the usage of smartphone apps across a wide range of mental health disorders in terms of clinical effectiveness, feasibility and acceptability.

DESIGN

This is a systematic review of studies examining treatment, self-monitoring and multipurpose smartphone apps for mental health disorders.

DATA SOURCES

Studies were identified through a comprehensive search of the Ovid and PubMed databases. Articles published up to 14 January 2024 were included based on predefined criteria.

ELIGIBILITY CRITERIA

We included randomised controlled trials that compared mental health apps (single- or multipurpose) with treatment-as-usual or no treatment for clinical populations with mental health disorders. Studies were excluded if they focused on web-based interventions, combined apps with non-TAU treatments or targeted physical health apps.

DATA EXTRACTION AND SYNTHESIS

Two independent reviewers screened and selected studies, with a third reviewer resolving inconsistencies. Extracted data included study details, participant characteristics, app information and outcome measures related to effectiveness, feasibility and acceptability. A risk-of-bias assessment for each study was conducted.

RESULTS

Out of 4153 non-duplicate articles screened, 31 studies met full-text eligibility criteria. These included 6 studies on treatment apps, 4 on self-monitoring apps and 21 on multipurpose apps for a range of mental health disorders. Fifteen were identified as having between some and high concern on the risk-of-bias assessment. While smartphone apps were generally effective and acceptable, their feasibility appeared to decline over time.

CONCLUSIONS

Smartphone apps are promising tools for mental healthcare, demonstrating effectiveness and acceptability. However, challenges such as reduced feasibility over time, potential biases and underrepresented demographics require further research. This review proposes five recommendations for improving clinical translation in future studies.

Systematic review and meta-analysis of adverse events in clinical trials of mental health apps

Mental health apps are efficacious, yet they may pose risks in some. This review (CRD42024506486) examined adverse events (AEs) from mental health apps. We searched (May 2024) the Medline, PsycINFO, Web of Science, and ProQuest databases to identify clinical trials of mental health apps. The risk of bias was assessed using the Cochrane Risk of Bias tool. Only 55 of 171 identified clinical trials reported AEs. AEs were more likely to be reported in trials sampling schizophrenia and delivering apps with symptom monitoring technology. The meta-analytic deterioration rate from 13 app conditions was 6.7% (95% CI = 4.3, 10.1, I2 = 75%). Deterioration rates did not differ between app and control groups (OR = 0.79, 95% CI = 0.62-1.01, I2 = 0%). Reporting of AEs was heterogeneous, in terms of assessments used, events recorded, and detail provided. Overall, few clinical trials of mental health apps report AEs. Those that do often provide insufficient information to properly judge risks related to app use.

Effects of low-dose aspirin in bipolar disorder: study protocol for a randomised controlled trial (the A-Bipolar RCT)

INTRODUCTION

Accumulating data support the association between increased inflammation and bipolar disorder (BD), and preliminary data suggest that augmentation with low-dose aspirin (LDA) may protect against the onset and deterioration of BD via anti-inflammatory pathways. The A-bipolar randomised controlled trial (RCT) aims to investigate whether adding LDA to standard treatment improves day-to-day mood instability (MI) in BD.

METHODS AND ANALYSIS

A two-arm, triple-blind, parallel-group, superiority RCT including 250 patients with newly diagnosed BD treated at the Copenhagen Affective Disorder Clinic, Denmark. Participants are randomised 1:1 to either 150 mg of acetylsalicylic acid daily (LDA) or a placebo for six months in addition to their regular treatment. Mood instability, calculated from daily smartphone-based mood evaluations, is the primary outcome measure due to its internal validity as a real-life measure for patients and external validity as it reflects patients' illness severity and functioning. Analyses will be conducted as intention-to-treat analyses using a linear mixed model including time (categorical) and the time-treatment interaction as fixed effects and with an unstructured covariance pattern to account for repeated measurements on each study participant. The trial is Good Clinical Practice monitored.

ETHICS AND DISSEMINATION

The Danish Research Ethics Committee (H-21014515) and the data agency, Capital Region of Copenhagen (P-2021-576) approved the trial. Results will be published in peer-reviewed journals.

TRIAL REGISTRATION NUMBER

NCT05035316.

Self-help interventions for the prevention of relapse in mood disorder: a systematic review and meta-analysis

INTRODUCTION

Self-help interventions may offer a scalable adjunct to traditional care, but their effectiveness in relapse prevention is not well-established. Objectives: This review aimed to assess their effectiveness in preventing relapses among individuals with mood disorders.

METHODS

We systematically reviewed the pertinent trial literature in Web of Science, EMBASE, PubMed, PsycINFO, and Cochrane databases until May 2024. Randomized controlled trials that examined the self-help interventions among individuals diagnosed with major depressive disorder (MDD) or bipolar disorder (BD) were included. The random-effects model computed the pooled risk ratios of relapse, with subgroup analyses and meta-regression analyses to explore heterogeneity sources.

RESULTS

Fifteen papers and 16 comparisons of randomized trials involving 2735 patients with mood disorders were eligible for this meta-analysis. Adjunct self-help interventions had a small but significant effect on reducing the relapse rates of major depressive disorder (pooled risk ratio: 0.78, 95% confidence interval (CI): 0.66-0.92, P = 0.0032, NNT = 11), and were marginally better in bipolar disorder (pooled risk ratio: 0.62, 95% CI: 0.40-0.97, P = .0344, NNT = 12), as compared to treatment as usual (TAU). No subgroup difference was found based on intervention components, settings, delivery method, or guidance levels. The average dropout rate for self-help interventions (18.9%) did not significantly differ from TAU dropout rates. The examination of treatment adherence was highly variable, precluding definitive conclusions.

CONCLUSIONS

Self-help interventions demonstrate a modest preventative effect on relapse in mood disorders, despite low to very low certainty. Future research is essential to identify which elements of self-help interventions are most effective.

Study protocol: group-based psychoeducation for relatives of patients with bipolar disorder-a large scale real-world randomized controlled parallel group trial, the R-bipolar RCT

BACKGROUND

Relatives of patients with bipolar disorder (BD) often experience emotional burden with stress and depressive symptoms that again increase the likelihood of destabilization and relapses in the patient. The effects of group-based psychoeducation have not been investigated in large-scale real-world settings. We are currently conducting a large-scale real-world randomized controlled parallel group trial (RCT) to test whether group-based psychoeducation for 200 relatives to patients with BD improves mood instability and other critical outcomes in relatives and the corresponding patients with BD.

METHODS

The trial is designed as a two-arm, parallel-group randomized trial with a balanced randomization 1:1 to either group-based psychoeducation or a waiting list for approximately 4 months and subsequent group-based psychoeducation. The primary outcome measure is mood instability calculated based on daily smartphone-based mood self-assessments. Other relevant outcomes are measured, including patients' reported outcomes, assessing self-assessed burden, self-efficacy, and knowledge about BD.

DISCUSSION

This protocol describes our currently ongoing randomized controlled trial (RCT) that aims at investigating group-based psychoeducation as an intervention for relatives of individuals diagnosed with bipolar disorder (BD). The study is the first large-scale real-world RCT to focus on a relatively short intervention of psychoeducation (6 sessions of 2 h each) in a large group of relatives (approximately 30 participants per group). With this focus, we wish to test an intervention that is feasible to implement in real-life psychiatric settings with limited budgets and time. It is also the first study to use mood instability in relatives as the primary outcome measure and to investigate whether mood instability and other affective symptoms in patients and relatives covary. It could be considered as limitations, that the trial is not blinded and does not include long-term follow-up.

TRIAL REGISTRATION

ClinicalTrials.gov NCT06176001. Registered on 2023-12-19. The study is approved by the data agency (P-2021-809). The project was allowed to be initiated without permission from the Scientific Ethical Committees for the Capital Region, because it according to section 1, paragraph 4 of the Committee Act was not defined as a health scientific intervention study (case number 21063013).

Mobile health for mental health support: a survey of attitudes and concerns among mental health professionals in Poland over the period 2020-2023

Introduction

Mobile health (mHealth) has emerged as a dynamic sector supported by technological advances and the COVID-19 pandemic and have become increasingly applied in the field of mental health.

Aim

The aim of this study was to assess the attitudes, expectations, and concerns of mental health professionals, including psychiatrists, psychologists, and psychotherapists, towards mHealth, in particular mobile health self-management tools and telepsychiatry in Poland.

Material and methods

This was a survey conducted between 2020 and 2023. A questionnaire was administered to 148 mental health professionals, covering aspects such as telepsychiatry, mobile mental health tools, and digital devices.

Results

The majority of professionals expressed readiness to use telepsychiatry, with a peak in interest during the COVID-19 pandemic, followed by a gradual decline from 2022. Concerns about telepsychiatry were reported by a quarter of respondents, mainly related to difficulties in correctly assessing the patient's condition, and technical issues. Mobile health tools were positively viewed by professionals, with 86% believing they could support patients in managing mental health and 74% declaring they would recommend patients to use them. Nevertheless, 29% expressed concerns about the effectiveness and data security of such tools. Notably, the study highlighted a growing readiness among mental health professionals to use new digital technologies, reaching 84% in 2023.

Conclusion

These findings emphasize the importance of addressing concerns and designing evidence-based mHealth solutions to ensure long-term acceptance and effectiveness in mental healthcare. Additionally, the study highlights the need for ongoing regulatory efforts to safeguard patient data and privacy in the evolving digital health landscape.

Additive effects of adjunctive app-based interventions for mental disorders - A systematic review and meta-analysis of randomised controlled trials

Background

It is uncertain whether app-based interventions add value to existing mental health care.

Objective

To examine the incremental effects of app-based interventions when used as adjunct to mental health interventions.

Methods

We searched PubMed, PsycINFO, Scopus, Web of Science, and Cochrane Library databases on September 15th, 2023, for randomised controlled trials (RCTs) on mental health interventions with an adjunct app-based intervention compared to the same intervention-only arm for adults with mental disorders or respective clinically relevant symptomatology. We conducted meta-analyses on symptoms of different mental disorders at postintervention. PROSPERO, CRD42018098545.

Results

We identified 46 RCTs (4869 participants). Thirty-two adjunctive app-based interventions passively or actively monitored symptoms and behaviour, and in 13 interventions, the monitored data were sent to a therapist. We found additive effects on symptoms of depression (g = 0.17; 95 % CI 0.02 to 0.33; k = 7 comparisons), anxiety (g = 0.80; 95 % CI 0.06 to 1.54; k = 3), mania (g = 0.2; 95 % CI 0.02 to 0.38; k = 4), smoking cessation (g = 0.43; 95 % CI 0.29 to 0.58; k = 10), and alcohol use (g = 0.23; 95 % CI 0.08 to 0.39; k = 7). No significant effects were found on symptoms of depression within a bipolar disorder (g = -0.07; 95 % CI -0.37 to 0.23, k = 4) and eating disorders (g = -0.02; 95 % CI -0.44 to 0.4, k = 3). Studies on depression, mania, smoking, and alcohol use had a low heterogeneity between the trials. For other mental disorders, only single studies were identified. Only ten studies had a low risk of bias, and 25 studies reported insufficient statistical power.

Discussion

App-based interventions may be used to enhance mental health interventions to further reduce symptoms of depression, anxiety, mania, smoking, and alcohol use. However, the effects were small, except for anxiety, and limited due to study quality. Further high-quality research with larger sample sizes is warranted to better understand how app-based interventions can be most effectively combined with established interventions to improve outcomes.

KIOS: A smartphone app for self-monitoring for patients with bipolar disorder

OBJECTIVES

This study examined the use of a self-monitoring/self-management smartphone application (app) for patients with bipolar disorder. The app was specifically designed with patient-centered computational software system based on concepts from nonlinear systems (chaos) theory.

METHODS

This was a randomized, active comparator study of use of the KIOS app compared to an existing free app that has high utilization rates known as eMoods, over 52 weeks, and performed in three academic centers. Patients were evaluated monthly utilizing the Bipolar Inventory of Symptoms Schedule (BISS). The primary outcome measure was the persistence of using the app over the year of the study.

RESULTS

Patients assigned to KIOS persisted in the study longer than those assigned to eMoods; 57 patients (87.70%) in the KIOS group versus 42 (73.69%) in the eMoods group completed the study (p = 0.03). By 52 weeks, significantly more of KIOS group (84.4%) versus eMoods group (54%) entered data into their programs (χ2  = 14.2, df = 1, p = 0.0002). Patient satisfaction for KIOS was greater (F = 5.21, df = 1, 108, p = 0.025) with a standardized effect size (Cohen's d) of 0.41. There was no difference in clinical outcome at the end of the study between the two groups.

CONCLUSIONS

This is the first randomized comparison study comparing two apps for the self-monitoring/self-management of bipolar disorder. The study revealed greater patient satisfaction and greater adherence to a patient-centered software program (KIOS) than a monitoring program that does not provide feedback (eMoods).

Prevalence, attitudes and concerns toward telepsychiatry and mobile health self-management tools among patients with mental disorders during and after the COVID-19 pandemic: a nationwide survey in Poland from 2020 to 2023

Introduction

Mobile Health (mHealth) is a rapidly growing field of medicine that has the potential to significantly change everyday clinical practice, including in psychiatry. The COVID-19 pandemic and technological developments have accelerated the adoption of telepsychiatry and mobile solutions, but patient perceptions and expectations of mHealth remain a key factor in its implementation.

Aim

The aim of this study was to assess (1) the prevalence, (2) attitudes, preferences and (3) concerns about mobile mental health, including telepsychiatry and self-management tools, among patients with mental disorders over the period 2020-2023, i.e., at the onset, peak and after the expiration of the COVID-19 pandemic.

Materials and methods

A semi-structured survey was administrated to 354 patients with mental disorders in Poland. The questions were categorized into three section, addressing prevalence, attitudes, and concerns about telepsychiatry and mobile health self-management tools. The survey was conducted continuously from May 2020 to the end of May 2023.

Result

As many as 95.7% of patients with mental disorders used mobile devices at least once a week. Over the course of 3 years (from 2020 to 2023), there was a significant increase in the readiness of patients to embrace new technologies, with the percentage rising from 20% to 40%. In particular, a remarkable growth in patient preferences for telepsychiatry was observed, with a significant increase from 47% in 2020 to a substantial 96% in 2023. Similarly, mHealth self-management tools were of high interest to patients. In 2020, 62% of patients like the idea of using mobile apps and other mobile health tools to support the care and treatment process. This percentage also increased during the pandemic, reaching 66% in 2023. At the same time, the percentage of patients who have concerns about using m-health solutions has gradually decreased, reaching 35% and 28% in 2023 for telepsychiatry and for the reliability and safety of m-health self-management tools, respectively.

Conclusion

This study highlights the growing acceptance of modern technologies in psychiatric care, with patients showing increased readiness to use telepsychiatry and mobile health self-management tools, in particular mobile applications, after the COVID-19 pandemic. This was triggered by the pandemic, but continues despite its expiry. In the face of patient readiness, the key issue now is to ensure the safety and efficacy of these tools, along with providing clear guidelines for clinicians. It is also necessary to draw the attention of health systems to the widespread implementation of these technologies to improve the care of patients with mental disorders.

Circadian Reinforcement Therapy in Combination With Electronic Self-Monitoring to Facilitate a Safe Postdischarge Period for Patients With Major Depression: Randomized Controlled Trial

BACKGROUND

Patients with major depression exhibit circadian disturbance of sleep and mood, and when they are discharged from inpatient wards, this disturbance poses a risk of relapse. We developed a circadian reinforcement therapy (CRT) intervention to facilitate the transition from the inpatient ward to the home for these patients. CRT focuses on increasing the zeitgeber strength for the circadian clock through social contact, physical activity, diet, daylight exposure, and sleep timing.

OBJECTIVE

In this study, we aimed to prevent the worsening of depression after discharge by using CRT, supported by an electronic self-monitoring system, to advance and stabilize sleep and improve mood. The primary outcome, which was assessed by a blinded rater, was the change in the Hamilton Depression Rating Scale scores from baseline to the end point.

METHODS

Participants were contacted while in the inpatient ward and randomized 1:1 to the CRT or the treatment-as-usual (TAU) group. For 4 weeks, participants in both groups electronically self-monitored their daily mood, physical activity, sleep, and medication using the Monsenso Daybuilder (MDB) system. The MDB allowed investigators and participants to simultaneously view a graphical display of registrations. An investigator phoned all participants weekly to coinspect data entry. In the CRT group, participants were additionally phoned between the scheduled calls if specific predefined trigger points for mood and sleep were observed during the daily inspection. Participants in the CRT group were provided with specialized CRT psychoeducation sessions immediately after inclusion, focusing on increasing the zeitgeber input to the circadian system; a PowerPoint presentation was presented; paper-based informative materials and leaflets were reviewed with the participants; and the CRT principles were used during all telephone consultations. In the TAU group, phone calls focused on data entry in the MDB system. When discharged, all patients were treated at a specialized affective disorders service.

RESULTS

Overall, 103 participants were included. Participants in the CRT group had a significantly larger reduction in Hamilton Depression Scale score (P=.04) than those in the TAU group. The self-monitored MDB data showed significantly improved evening mood (P=.02) and sleep quality (P=.04), earlier sleep onset (P=.009), and longer sleep duration (P=.005) in the CRT group than in the TAU group. The day-to-day variability of the daily and evening mood, sleep offset, sleep onset, and sleep quality were significantly lower in the CRT group (all P<.001) than in the TAU group. The user evaluation was positive for the CRT method and the MDB system.

CONCLUSIONS

We found significantly lower depression levels and improved sleep quality in the CRT group than in the TAU group. We also found significantly lower day-to-day variability in daily sleep, mood parameters, and activity parameters in the CRT group than in the TAU group. The delivery of the CRT intervention should be further refined and tested.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02679768; https://clinicaltrials.gov/study/NCT02679768.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1186/s12888-019-2101-z.

The effect of smartphone-based monitoring and treatment including clinical feedback versus smartphone-based monitoring without clinical feedback in bipolar disorder: the SmartBipolar trial-a study protocol for a randomized controlled parallel-group trial

INTRODUCTION

A substantial proportion of patients with bipolar disorder experience daily subsyndromal mood swings, and the term "mood instability" reflecting the variability in mood seems associated with poor prognostic factors, including impaired functioning, and increased risk of hospitalization and relapse. During the last decade, we have developed and tested a smartphone-based system for monitoring bipolar disorder. The present SmartBipolar randomized controlled trial (RCT) aims to investigate whether (1) daily smartphone-based outpatient monitoring and treatment including clinical feedback versus (2) daily smartphone-based monitoring without clinical feedback or (3) daily smartphone-based mood monitoring only improves mood instability and other clinically relevant patient-related outcomes in patients with bipolar disorder.

METHODS AND ANALYSIS

The SmartBipolar trial is a pragmatic randomized controlled parallel-group trial. Patients with bipolar disorder are invited to participate as part of their specialized outpatient treatment for patients with bipolar disorder in Mental Health Services in the Capital Region of Denmark. The included patients will be randomized to (1) daily smartphone-based monitoring and treatment including a clinical feedback loop (intervention group) or (2) daily smartphone-based monitoring without a clinical feedback loop (control group) or (3) daily smartphone-based mood monitoring only (control group). All patients receive specialized outpatient treatment for bipolar disorder in the Mental Health Services in the Capital Region of Denmark. The trial started in March 2021 and has currently included 150 patients. The outcomes are (1) mood instability (primary), (2) quality of life, self-rated depressive symptoms, self-rated manic symptoms, perceived stress, satisfaction with care, cumulated number and duration of psychiatric hospitalizations, and medication (secondary), and (3) smartphone-based measures per month of stress, anxiety, irritability, activity, and sleep as well as the percentage of days with presence of mixed mood, days with adherence to medication and adherence to smartphone-based self-monitoring. A total of 201 patients with bipolar disorder will be included in the SmartBipolar trial.

ETHICS AND DISSEMINATION

The SmartBipolar trial is funded by the Capital Region of Denmark and the Independent Research Fund Denmark. Ethical approval has been obtained from the Regional Ethical Committee in The Capital Region of Denmark (H-19067248) as well as data permission (journal number: P-2019-809). The results will be published in peer-reviewed academic journals, presented at scientific meetings, and disseminated to patients' organizations and media outlets.

TRIAL REGISTRATION

Trial registration number: NCT04230421. Date March 1, 2021. Version 1.

Understanding Mental Health Clinicians' Perceptions and Concerns Regarding Using Passive Patient-Generated Health Data for Clinical Decision-Making: Qualitative Semistructured Interview Study

BACKGROUND

Digital health-tracking tools are changing mental health care by giving patients the ability to collect passively measured patient-generated health data (PGHD; ie, data collected from connected devices with little to no patient effort). Although there are existing clinical guidelines for how mental health clinicians should use more traditional, active forms of PGHD for clinical decision-making, there is less clarity on how passive PGHD can be used.

OBJECTIVE

We conducted a qualitative study to understand mental health clinicians' perceptions and concerns regarding the use of technology-enabled, passively collected PGHD for clinical decision-making. Our interviews sought to understand participants' current experiences with and visions for using passive PGHD.

METHODS

Mental health clinicians providing outpatient services were recruited to participate in semistructured interviews. Interview recordings were deidentified, transcribed, and qualitatively coded to identify overarching themes.

RESULTS

Overall, 12 mental health clinicians (n=11, 92% psychiatrists and n=1, 8% clinical psychologist) were interviewed. We identified 4 overarching themes. First, passive PGHD are patient driven-we found that current passive PGHD use was patient driven, not clinician driven; participating clinicians only considered passive PGHD for clinical decision-making when patients brought passive data to clinical encounters. The second theme was active versus passive data as subjective versus objective data-participants viewed the contrast between active and passive PGHD as a contrast between interpretive data on patients' mental health and objective information on behavior. Participants believed that prioritizing passive over self-reported, active PGHD would reduce opportunities for patients to reflect upon their mental health, reducing treatment engagement and raising questions about how passive data can best complement active data for clinical decision-making. Third, passive PGHD must be delivered at appropriate times for action-participants were concerned with the real-time nature of passive PGHD; they believed that it would be infeasible to use passive PGHD for real-time patient monitoring outside clinical encounters and more feasible to use passive PGHD during clinical encounters when clinicians can make treatment decisions. The fourth theme was protecting patient privacy-participating clinicians wanted to protect patient privacy within passive PGHD-sharing programs and discussed opportunities to refine data sharing consent to improve transparency surrounding passive PGHD collection and use.

CONCLUSIONS

Although passive PGHD has the potential to enable more contextualized measurement, this study highlights the need for building and disseminating an evidence base describing how and when passive measures should be used for clinical decision-making. This evidence base should clarify how to use passive data alongside more traditional forms of active PGHD, when clinicians should view passive PGHD to make treatment decisions, and how to protect patient privacy within passive data-sharing programs. Clear evidence would more effectively support the uptake and effective use of these novel tools for both patients and their clinicians.

Identifying psychiatric diagnosis from missing mood data through the use of log-signature features

The availability of mobile technologies has enabled the efficient collection of prospective longitudinal, ecologically valid self-reported clinical questionnaires from people with psychiatric diagnoses. These data streams have potential for improving the efficiency and accuracy of psychiatric diagnosis as well predicting future mood states enabling earlier intervention. However, missing responses are common in such datasets and there is little consensus as to how these should be dealt with in practice. In this study, the missing-response-incorporated log-signature method achieves roughly 74.8% correct diagnosis, with f1 scores for three diagnostic groups 66% (bipolar disorder), 83% (healthy control) and 75% (borderline personality disorder) respectively. This was superior to the naive model which excluded missing data and advanced models which implemented different imputation approaches, namely, k-nearest neighbours (KNN), probabilistic principal components analysis (PPCA) and random forest-based multiple imputation by chained equations (rfMICE). The log-signature method provided an effective approach to the analysis of prospectively collected mood data where missing data was common and should be considered as an approach in other similar datasets. Because of treating missing responses as a signal, its superiority also highlights that missing data conveys valuable clinical information.

Mobile phone-based interventions for mental health show promise of effectiveness, but what does the evidence tell us about what needs to come next?

The current manuscript is a commentary on "Mobile phone-based interventions for mental health: A systematic meta-review of 14 meta-analyses of randomized controlled trials". Although embedded within a nuanced discussion, one of the primary conclusions readers have taken from the meta-analysis was "we failed to find convincing evidence in support of any mobile phone-based intervention on any outcome", which seems to contradict the entirety of the evidence presented when taken out of context of the methods applied. In evaluating whether the area produced "convincing evidence of efficacy," the authors used a standard that appeared destined to fail. Specifically, the authors required "no evidence of publication bias", which is a standard that would be unlikely to be found in any area of psychology or medicine. Second, the authors required low to moderate heterogeneity in effect sizes when comparing interventions with fundamentally different and entirely dissimilar target mechanisms. However absent these 2 untenable criteria, the authors actually found highly suggestive evidence of efficacy (N > 1,000, p < .000001) in (1) anxiety; (2) depression; (3) smoking cessation; (4) stress; and (5) quality of life. Perhaps the appropriate conclusions would be that existing syntheses of data testing smartphone intervention suggests that these interventions are promising, but additional work is needed to separate what types of interventions and mechanisms are more promising. Evidence syntheses will be useful as the field matures, but such syntheses should focus on smartphone treatments that are created equal (i.e., similar intent, features, goals, and linkages in a continuum of care model) or use standards for evidence that promote rigorous evaluation while allowing identification of resources that can help those in need.

Monitoring and treatment in patients with bipolar disorder using smartphones-New perspectives for improved quality in patient care

The rapid international growth in access to and capabilities of mobile and wireless technologies (mHealth) presents a feasible route towards augmenting traditional mental health care. The interest in mHealth science in psychiatry has been further heightened by the acknowledged potential for these tools to improve individual risk prediction and diagnostic precision, as well as improved treatment options. We have conducted research within smartphone-based monitoring and treatment in patients with bipolar disorder through the last decade. We conclude that the technological capabilities of smartphones are already changing mental health care and is accompanied by an early but promising evidence base. However, further efforts towards strengthening the evidence and implementation must be addressed for digital mental health technologies to truly improve mental health research and treatment in the future.

Digital Mental Health Interventions for Depression: Scoping Review of User Engagement

BACKGROUND

While many digital mental health interventions (DMHIs) have been found to be efficacious, patient engagement with DMHIs has increasingly emerged as a concern for implementation in real-world clinical settings. To address engagement, we must first understand what standard engagement levels are in the context of randomized controlled trials (RCTs) and how these compare with other treatments.

OBJECTIVE

This scoping review aims to examine the state of reporting on intervention engagement in RCTs of mobile app-based interventions intended to treat symptoms of depression. We sought to identify what engagement metrics are and are not routinely reported as well as what the metrics that are reported reflect about standard engagement levels.

METHODS

We conducted a systematic search of 7 databases to identify studies meeting our eligibility criteria, namely, RCTs that evaluated use of a mobile app-based intervention in adults, for which depressive symptoms were a primary outcome of interest. We then extracted 2 kinds of information from each article: intervention details and indices of DMHI engagement. A 5-element framework of minimum necessary DMHI engagement reporting was derived by our team and guided our data extraction. This framework included (1) recommended app use as communicated to participants at enrollment and, when reported, app adherence criteria; (2) rate of intervention uptake among those assigned to the intervention; (3) level of app use metrics reported, specifically number of uses and time spent using the app; (4) duration of app use metrics (ie, weekly use patterns); and (5) number of intervention completers.

RESULTS

Database searching yielded 2083 unique records. Of these, 22 studies were eligible for inclusion. Only 64% (14/22) of studies included in this review specified rate of intervention uptake. Level of use metrics was only reported in 59% (13/22) of the studies reviewed. Approximately one-quarter of the studies (5/22, 23%) reported duration of use metrics. Only half (11/22, 50%) of the studies reported the number of participants who completed the app-based components of the intervention as intended or other metrics related to completion. Findings in those studies reporting metrics related to intervention completion indicated that between 14.4% and 93.0% of participants randomized to a DMHI condition completed the intervention as intended or according to a specified adherence criteria.

CONCLUSIONS

Findings suggest that engagement was underreported and widely varied. It was not uncommon to see completion rates at or below 50% (11/22) of those participants randomized to a treatment condition or to simply see completion rates not reported at all. This variability in reporting suggests a failure to establish sufficient reporting standards and limits the conclusions that can be drawn about level of engagement with DMHIs. Based on these findings, the 5-element framework applied in this review may be useful as a minimum necessary standard for DMHI engagement reporting.

Smartphone-based interventions in bipolar disorder: Systematic review and meta-analyses of efficacy. A position paper from the International Society for Bipolar Disorders (ISBD) Big Data Task Force

BACKGROUND

The clinical effects of smartphone-based interventions for bipolar disorder (BD) have yet to be established.

OBJECTIVES

To examine the efficacy of smartphone-based interventions in BD and how the included studies reported user-engagement indicators.

METHODS

We conducted a systematic search on January 24, 2022, in PubMed, Scopus, Embase, APA PsycINFO, and Web of Science. We used random-effects meta-analysis to calculate the standardized difference (Hedges' g) in pre-post change scores between smartphone intervention and control conditions. The study was pre-registered with PROSPERO (CRD42021226668).

RESULTS

The literature search identified 6034 studies. Thirteen articles fulfilled the selection criteria. We included seven RCTs and performed meta-analyses comparing the pre-post change in depressive and (hypo)manic symptom severity, functioning, quality of life, and perceived stress between smartphone interventions and control conditions. There was significant heterogeneity among studies and no meta-analysis reached statistical significance. Results were also inconclusive regarding affective relapses and psychiatric readmissions. All studies reported positive user-engagement indicators.

CONCLUSION

We did not find evidence to support that smartphone interventions may reduce the severity of depressive or manic symptoms in BD. The high heterogeneity of studies supports the need for expert consensus to establish ideally how studies should be designed and the use of more sensitive outcomes, such as affective relapses and psychiatric hospitalizations, as well as the quantification of mood instability. The ISBD Big Data Task Force provides preliminary recommendations to reduce the heterogeneity and achieve more valid evidence in the field.

A systematic review of engagement reporting in remote measurement studies for health symptom tracking

Remote Measurement Technologies (RMTs) could revolutionise management of chronic health conditions by providing real-time symptom tracking. However, the promise of RMTs relies on user engagement, which at present is variably reported in the field. This review aimed to synthesise the RMT literature to identify how and to what extent engagement is defined, measured, and reported, and to present recommendations for the standardisation of future work. Seven databases (Embase, MEDLINE and PsycINFO (via Ovid), PubMed, IEEE Xplore, Web of Science, and Cochrane Central Register of Controlled Trials) were searched in July 2020 for papers using RMT apps for symptom monitoring in adults with a health condition, prompting users to track at least three times during the study period. Data were synthesised using critical interpretive synthesis. A total of 76 papers met the inclusion criteria. Sixty five percent of papers did not include a definition of engagement. Thirty five percent included both a definition and measurement of engagement. Four synthetic constructs were developed for measuring engagement: (i) engagement with the research protocol, (ii) objective RMT engagement, (iii) subjective RMT engagement, and (iv) interactions between objective and subjective RMT engagement. The field is currently impeded by incoherent measures and a lack of consideration for engagement definitions. A process for implementing the reporting of engagement in study design is presented, alongside a framework for definition and measurement options available. Future work should consider engagement with RMTs as distinct from the wider eHealth literature, and measure objective versus subjective RMT engagement.Registration: This review has been registered on PROSPERO [CRD42020192652].

Pilot Testing in the Wild: Feasibility, Acceptability, Usage Patterns, and Efficacy of an Integrated Web and Smartphone Platform for Bipolar II Disorder

BACKGROUND

Bipolar II disorder (BD-II) is associated with significant burden, disability, and mortality; however, there continues to be a dearth of evidence-based psychological interventions for this condition. Technology-mediated interventions incorporating self-management have untapped potential to help meet this need as an adjunct to usual clinical care.

OBJECTIVE

The objective of this pilot study is to assess the feasibility, acceptability, and clinical utility of a novel intervention for BD-II (Tailored Recovery-oriented Intervention for Bipolar II Experiences; TRIBE), in which mindfulness-based psychological content is delivered via an integrated web and smartphone platform. The focus of the study is evaluation of the dynamic use patterns emerging from ecological momentary assessment and intervention to assist the real-world application of mindfulness skills learned from web-delivered modules.

METHODS

An open trial design using pretest and posttest assessments with nested qualitative evaluation was used. Individuals (aged 18-65 years) with a diagnosis of BD-II were recruited worldwide and invited to use a prototype of the TRIBE intervention over a 3-week period. Data were collected via web-based questionnaires and phone interviews at baseline and 3-week follow-up.

RESULTS

A total of 25 participants completed baseline and follow-up assessments. Adherence rates (daily app use) were 65.6% across the 3-week study, with up to 88% (22/25) of participants using the app synergistically alongside the web-based program. Despite technical challenges with the prototype intervention (from user, hardware, and software standpoints), acceptability was adequate, and most participants rated the intervention positively in terms of concept (companion app with website: 19/25, 76%), content (19/25, 76%), and credibility and utility in supporting their management of bipolar disorder (17/25, 68%). Evaluation using behavioral archetypes identified important use pathways and a provisional model to inform platform refinement. As hypothesized, depression scores significantly decreased after the intervention (Montgomery-Asberg Depression Rating Scale baseline mean 8.60, SD 6.86, vs follow-up mean 6.16, SD 5.11; t24=2.63; P=.01; Cohen d=0.53, 95% CI 0.52-4.36).

CONCLUSIONS

Our findings suggest that TRIBE is feasible and represents an appropriate and acceptable self-management program for patients with BD-II. Preliminary efficacy results are promising and support full development of TRIBE informed by the present behavioral archetype analysis. Modifications suggested by the pilot study include increasing the duration of the intervention and increasing technical support.

A Mixed-Methods Analysis of Mobile ACT Responses From Two Cohorts

Background

Mobile transdiagnostic therapies offer a solution to the challenges of limited access to psychological care. However, it is unclear if individuals can actively synthesize and adopt concepts and skills via an app without clinician support.

Aims

The present study measured comprehension of and engagement with a mobile acceptance and commitment therapy (ACT) intervention in two independent cohorts. Authors hypothesized that participants would recognize that behaviors can be flexible in form and function and respond in an ACT process-aligned manner.

Methods

Mixed-methods analyses were performed on open-ended responses collected from initial participants (n = 49) in two parallel micro-randomized trials with: 1) first-generation college students (FGCSs) (n = 25) from a four-year public research university and 2) individuals diagnosed with bipolar disorder (BP) (n = 24). Twice each day over six weeks, participants responded to questions about mood and behavior, after which they had a 50-50 chance of receiving an ACT-based intervention. Participants identified current behavior and categorized behavior as values-based or avoidant. Interventions were selected randomly from 84 possible prompts, each targeting one ACT process: engagement with values, openness to internal experiences, or self-awareness. Participants were randomly assigned to either exploratory (10 FGCS, 9 BP) or confirmatory (15 FGCS, 15 BP) groups for analyses. Responses from the exploratory group were used to inductively derive a qualitative coding system. This system was used to code responses in the confirmatory group. Coded confirmatory data were used for final analyses.

Results

Over 50% of participants in both cohorts submitted a non-blank response 100% of the time. For over 50% of participants, intervention responses aligned with the target ACT process for at least 96% of the time (FGCS) and 91% of the time (BP), and current behavior was labeled as values-based 70% (FGCS) and 85% (BP) of the time. Participants labeled similar behaviors flexibly as either values-based or avoidant in different contexts. Dominant themes were needs-based behaviors, interpersonal and family relationships, education, and time as a cost.

Conclusions

Both cohorts were engaged with the app, as demonstrated by responses that aligned with ACT processes. This suggests that participants had some level of understanding that behavior can be flexible in form and function.

Evaluating the quality, safety, and functionality of commonly used smartphone apps for bipolar disorder mood and sleep self-management

Background

Individuals with bipolar disorder (BD) are increasingly turning to smartphone applications (apps) for health information and self-management support. While reviews have raised concerns regarding the effectiveness and safety of publicly available apps for BD, apps surveyed may not reflect what individuals with BD are using. The present study had two aims: first, to characterize the use of health apps to support mood and sleep amongst people with BD, and second, to evaluate the quality, safety and functionality of the most commonly used self-management apps.

Methods

A web-based survey was conducted to explore which apps people with BD reported using to support self-management of mood and sleep. The characteristics of the most commonly nominated apps were described using a standardized framework, including their privacy policy, clinical foundations, and functionality.

Results

Respondents (n = 919) were 77.9% female with a mean age of 36.9 years. 41.6% of participants (n = 382) reported using a self-management app to support mood or sleep. 110 unique apps were nominated in relation to mood, and 104 unique apps nominated in relation to sleep; however, most apps were only mentioned once. The nine most frequently nominated apps related to mood and sleep were subject to further evaluation. All reviewed apps offered a privacy policy, however user control over data was limited and the complexity of privacy policies was high. Only one app was developed for BD populations. Half of reviewed apps had published peer-reviewed evidence to support their claims of efficacy, but little research was specific to BD.

Conclusion

Findings illustrate the potential of smartphone apps to increase the reach of psychosocial interventions amongst people with BD. Apps were largely created by commercial developers and designed for the general population, highlighting a gap in the development and dissemination of evidence-informed apps for BD. There may be risks in using generic health apps for BD self-management; clinicians should enquire about patients’ app use to foster conversations about their particular benefits and limitations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40345-022-00256-6.

Process and Outcome Evaluations of Smartphone Apps for Bipolar Disorder: Scoping Review

BACKGROUND

Mental health apps (MHAs) provide opportunities for accessible, immediate, and innovative approaches to better understand and support the treatment of mental health disorders, especially those with a high burden, such as bipolar disorder (BD). Many MHAs have been developed, but few have had their effectiveness evaluated.

OBJECTIVE

This systematic scoping review explores current process and outcome measures of MHAs for BD with the aim to provide a comprehensive overview of current research. This will identify the best practice for evaluating MHAs for BD and inform future studies.

METHODS

A systematic literature search of the health science databases PsycINFO, MEDLINE, Embase, EBSCO, Scopus, and Web of Science was undertaken up to January 2021 (with no start date) to narratively assess how studies had evaluated MHAs for BD.

RESULTS

Of 4051 original search results, 12 articles were included. These 12 studies included 435 participants, and of these, 343 had BD type I or II. Moreover, 11 of the 12 studies provided the ages (mean 37 years) of the participants. One study did not report age data. The male to female ratio of the 343 participants was 137:206. The most widely employed validated outcome measure was the Young Mania Rating Scale, being used 8 times. The Hamilton Depression Rating Scale-17/Hamilton Depression Rating Scale was used thrice; the Altman Self-Rating Mania Scale, Quick Inventory of Depressive Symptomatology, and Functional Assessment Staging Test were used twice; and the Coping Inventory for Stressful Situations, EuroQoL 5-Dimension Health Questionnaire, Generalized Anxiety Disorder Scale-7, Inventory of Depressive Symptomatology, Mindfulness Attention Awareness Scale, Major Depression Index, Morisky-Green 8-item, Perceived Stress Scale, and World Health Organization Quality of Life-BREF were used once. Self-report measures were captured in 9 different studies, 6 of which used MONARCA. Mood and energy levels were the most commonly used self-report measures, being used 4 times each. Furthermore, 11 of the 12 studies discussed the various confounding factors and barriers to the use of MHAs for BD.

CONCLUSIONS

Reported low adherence rates, usability challenges, and privacy concerns act as barriers to the use of MHAs for BD. Moreover, as MHA evaluation is itself developing, guidance for clinicians in how to aid patient choices in mobile health needs to develop. These obstacles could be ameliorated by incorporating co-production and co-design using participatory patient approaches during the development and evaluation stages of MHAs for BD. Further, including qualitative aspects in trials that examine patient experience of both mental ill health and the MHA itself could result in a more patient-friendly fit-for-purpose MHA for BD.

Effectiveness of mobile applications for patients with severe mental illness: A meta-analysis of randomized controlled trials

BACKGROUND

A systematic review and meta-analysis was conducted to evaluate the effectiveness of mobile applications used by patients diagnosed with mental disorders.

METHODS

An electronic literature search in five databases including PubMed, Embase, the Cochrane Library, CINAHL, and PsychInfo was conducted. The keywords used were "mental disorder," "mental illness," "mobile phone," "smartphone," "mHealth," "application," and "app". The search was restricted to randomized controlled trials (RCTs) written in English and Korean.

RESULTS

Fourteen RCTs, involving 1307 patients diagnosed with depression, schizophrenia, and bipolar disorder were included in the analysis. The included studies were published between 2012 and 2020 and used mobile applications. The risk of bias tool was used to assess methodological quality and the overall risk of bias of the included studies was moderate. The pooled data favored mobile application interventions in reducing the disease-related symptoms of depression (standardized mean difference [SMD] = -0.255, 95% CI: -0.370 to -0.141), mania symptoms (SMD = -0.279, 95% CI: -0.456 to -0.102), and positive (SMD = -0.205, 95% CI: -0.388 to -0.022) and negative psychotic symptoms (SMD = -0.406, 95% CI: -0.791 to -0.020). In subgroup analysis, the incorporation of feedback, notification, and data tracking features in the mobile application intervention produced better outcomes.

CONCLUSION

This review provided evidence that mobile applications could well-assist patients diagnosed with mental disorders. Greater benefits could be achieved by well-designed interventions incorporating strategies with thoughtful consideration of the disease characteristics. Mobile applications present the potential to be effective supplements to clinical treatment.

Estimating longitudinal depressive symptoms from smartphone data in a transdiagnostic cohort

BACKGROUND

Passive measures collected using smartphones have been suggested to represent efficient proxies for depression severity, but the performance of such measures across diagnoses has not been studied.

METHODS

We enrolled a cohort of 45 individuals (11 with major depressive disorder, 11 with bipolar disorder, 11 with schizophrenia or schizoaffective disorder, and 12 individuals with no axis I psychiatric disorder). During the 8-week study period, participants were evaluated with a rater-administered Montgomery-Åsberg Depression Rating Scale (MADRS) biweekly, completed self-report PHQ-8 measures weekly on their smartphone, and consented to collection of smartphone-based GPS and accelerometer data in order to learn about their behaviors. We utilized linear mixed models to predict depression severity on the basis of phone-based PHQ-8 and passive measures.

RESULTS

Among the 45 individuals, 38 (84%) completed the 8-week study. The average root-mean-squared error (RMSE) in predicting the MADRS score (scale 0-60) was 4.72 using passive data alone, 4.27 using self-report measures alone, and 4.30 using both.

CONCLUSIONS

While passive measures did not improve MADRS score prediction in our cross-disorder study, they may capture behavioral phenotypes that cannot be measured objectively, granularly, or over long-term via self-report.

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

BACKGROUND

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

METHODS

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

RESULTS

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

LIMITATIONS

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

CONCLUSIONS

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

Portable technologies for digital phenotyping of bipolar disorder: A systematic review

BACKGROUND

Bias-prone psychiatric interviews remain the mainstay of bipolar disorder (BD) assessment. The development of digital phenotyping promises to improve BD management. We present a systematic review of the evidence about the use of portable digital devices for the identification of BD, BD types and BD mood states and for symptom assessment.

METHODS

We searched Web of Knowledge^SM, Scopus ®, IEEE Xplore, and ACM Digital Library databases (until 5/1/2021) for articles evaluating the use of portable/wearable digital devices, such as smartphone apps, wearable sensors, audio and/or visual recordings, and multimodal tools. The protocol is registered in PROSPERO (CRD42020200086).

RESULTS

We included 62 studies (2325 BD; 724 healthy controls, HC): 27 using smartphone apps, either for recording self-assessments (n = 10) or for passively gathering metadata (n = 7) or both (n = 10); 15 using wearable sensors for physiological parameters; 17 analysing audio and/or video recordings; 3 using multiple technologies. Two thirds of the included studies applied artificial intelligence (AI)-based approaches. They achieved fair to excellent classification performances.

LIMITATIONS

The included studies had small sample sizes and marked heterogeneity. Evidence of overfitting emerged, limiting generalizability. The absence of clear guidelines about reporting classification performances, with no shared standard metrics, makes results hardly interpretable and comparable.

CONCLUSIONS

New technologies offer a noteworthy opportunity to BD digital phenotyping with objectivity and high granularity. AI-based models could deliver important support in clinical decision-making. Further research and cooperation between different stakeholders are needed for addressing methodological, ethical and socio-economic considerations.

mHealth technology to assess, monitor and treat daily functioning difficulties in people with severe mental illness: A systematic review

Severe mental illness (SMI) is associated with poor daily functioning; however available interventions currently under-deliver on their recovery prospect. Mobile digital health (mHealth) interventions are increasingly being developed and evaluated, and have the potential to support recovery. This review evaluates the use of mHealth technology to assess, monitor and reduce functioning difficulties in people with SMI. Studies were systematically searched on multiple databases. Study quality was assessed and double-rated independently. Findings were organised using a narrative synthesis and results were summarised according to the mHealth device purpose, i.e., assessment and monitoring or intervention. Thirty-eight studies comprised of 2262 participants met the inclusion criteria. Smartphones were the most popular mHealth device; personal digital assistants, wearables and tablets were also used. mHealth was widely found to be acceptable and feasible, with preliminary findings suggesting it can support functional recovery by augmenting an intervention, simplifying the assessment, increasing monitoring frequency and/or providing more detailed information. Considerations for overcoming barriers to implementation, recommendations for future research to establish effectiveness, personalisation and specification of mHealth devices and methodologies are discussed. The value of mHealth for remote delivery of recovery based interventions is also considered.

The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality

As the COVID-19 pandemic has largely increased the utilization of telehealth, mobile mental health technologies - such as smartphone apps, vir-tual reality, chatbots, and social media - have also gained attention. These digital health technologies offer the potential of accessible and scalable interventions that can augment traditional care. In this paper, we provide a comprehensive update on the overall field of digital psychiatry, covering three areas. First, we outline the relevance of recent technological advances to mental health research and care, by detailing how smartphones, social media, artificial intelligence and virtual reality present new opportunities for "digital phenotyping" and remote intervention. Second, we review the current evidence for the use of these new technological approaches across different mental health contexts, covering their emerging efficacy in self-management of psychological well-being and early intervention, along with more nascent research supporting their use in clinical management of long-term psychiatric conditions - including major depression; anxiety, bipolar and psychotic disorders; and eating and substance use disorders - as well as in child and adolescent mental health care. Third, we discuss the most pressing challenges and opportunities towards real-world implementation, using the Integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework to explain how the innovations themselves, the recipients of these innovations, and the context surrounding innovations all must be considered to facilitate their adoption and use in mental health care systems. We conclude that the new technological capabilities of smartphones, artificial intelligence, social media and virtual reality are already changing mental health care in unforeseen and exciting ways, each accompanied by an early but promising evidence base. We point out that further efforts towards strengthening implementation are needed, and detail the key issues at the patient, provider and policy levels which must now be addressed for digital health technologies to truly improve mental health research and treatment in the future.

Prevention of Suicidal Relapses in Adolescents With a Smartphone Application: Bayesian Network Analysis of a Preclinical Trial Using In Silico Patient Simulations

Background

Recently, artificial intelligence technologies and machine learning methods have offered attractive prospects to design and manage crisis response processes, especially in suicide crisis management. In other domains, most algorithms are based on big data to help diagnose and suggest rational treatment options in medicine. But data in psychiatry are related to behavior and clinical evaluation. They are more heterogeneous, less objective, and incomplete compared to other fields of medicine. Consequently, the use of psychiatric clinical data may lead to less accurate and sometimes impossible-to-build algorithms and provide inefficient digital tools. In this case, the Bayesian network (BN) might be helpful and accurate when constructed from expert knowledge. Medical Companion is a government-funded smartphone application based on repeated questions posed to the subject and algorithm-matched advice to prevent relapse of suicide attempts within several months.

Objective

Our paper aims to present our development of a BN algorithm as a medical device in accordance with the American Psychiatric Association digital healthcare guidelines and to provide results from a preclinical phase.

Methods

The experts are psychiatrists working in university hospitals who are experienced and trained in managing suicidal crises. As recommended when building a BN, we divided the process into 2 tasks. Task 1 is structure determination, representing the qualitative part of the BN. The factors were chosen for their known and demonstrated link with suicidal risk in the literature (clinical, behavioral, and psychometrics) and therapeutic accuracy (advice). Task 2 is parameter elicitation, with the conditional probabilities corresponding to the quantitative part. The 4-step simulation (use case) process allowed us to ensure that the advice was adapted to the clinical states of patients and the context.

Results

For task 1, in this formative part, we defined clinical questions related to the mental state of the patients, and we proposed specific factors related to the questions. Subsequently, we suggested specific advice related to the patient’s state. We obtained a structure for the BN with a graphical representation of causal relations between variables. For task 2, several runs of simulations confirmed the a priori model of experts regarding mental state, refining the precision of our model. Moreover, we noticed that the advice had the same distribution as the previous state and was clinically relevant. After 2 rounds of simulation, the experts found the exact match.

Conclusions

BN is an efficient methodology to build an algorithm for a digital assistant dedicated to suicidal crisis management. Digital psychiatry is an emerging field, but it needs validation and testing before being used with patients. Similar to psychotropics, any medical device requires a phase II (preclinical) trial. With this method, we propose another step to respond to the American Psychiatric Association guidelines.

Trial Registration

ClinicalTrials.gov NCT03975881; https://clinicaltrials.gov/ct2/show/NCT03975881

Validity of the Aktibipo Self-rating Questionnaire for the Digital Self-assessment of Mood and Relapse Detection in Patients With Bipolar Disorder: Instrument Validation Study

BACKGROUND

Self-reported mood is a valuable clinical data source regarding disease state and course in patients with mood disorders. However, validated, quick, and scalable digital self-report measures that can also detect relapse are still not available for clinical care.

OBJECTIVE

In this study, we aim to validate the newly developed ASERT (Aktibipo Self-rating) questionnaire-a 10-item, mobile app-based, self-report mood questionnaire consisting of 4 depression, 4 mania, and 2 nonspecific symptom items, each with 5 possible answers. The validation data set is a subset of the ongoing observational longitudinal AKTIBIPO400 study for the long-term monitoring of mood and activity (via actigraphy) in patients with bipolar disorder (BD). Patients with confirmed BD are included and monitored with weekly ASERT questionnaires and monthly clinical scales (Montgomery-Åsberg Depression Rating Scale [MADRS] and Young Mania Rating Scale [YMRS]).

METHODS

The content validity of the ASERT questionnaire was assessed using principal component analysis, and the Cronbach α was used to assess the internal consistency of each factor. The convergent validity of the depressive or manic items of the ASERT questionnaire with the MADRS and YMRS, respectively, was assessed using a linear mixed-effects model and linear correlation analyses. In addition, we investigated the capability of the ASERT questionnaire to distinguish relapse (YMRS≥15 and MADRS≥15) from a nonrelapse (interepisode) state (YMRS<15 and MADRS<15) using a logistic mixed-effects model.

RESULTS

A total of 99 patients with BD were included in this study (follow-up: mean 754 days, SD 266) and completed an average of 78.1% (SD 18.3%) of the requested ASERT assessments (completion time for the 10 ASERT questions: median 24.0 seconds) across all patients in this study. The ASERT depression items were highly associated with MADRS total scores (P<.001; bootstrap). Similarly, ASERT mania items were highly associated with YMRS total scores (P<.001; bootstrap). Furthermore, the logistic mixed-effects regression model for scale-based relapse detection showed high detection accuracy in a repeated holdout validation for both depression (accuracy=85%; sensitivity=69.9%; specificity=88.4%; area under the receiver operating characteristic curve=0.880) and mania (accuracy=87.5%; sensitivity=64.9%; specificity=89.9%; area under the receiver operating characteristic curve=0.844).

CONCLUSIONS

The ASERT questionnaire is a quick and acceptable mood monitoring tool that is administered via a smartphone app. The questionnaire has a good capability to detect the worsening of clinical symptoms in a long-term monitoring scenario.

Evaluation of a Mobile Application for Heart Failure Telemonitoring

Adequate adherence to treatment is indispensable in preventing adverse consequences in heart failure patients. Such adherence can be managed through heart failure clinics and various methods of follow-up. In recent years, the use of telemonitoring has shown promising benefits in supporting clinicians' follow-up, as well as contributing to patients' self-care. This article presents the development and evaluation of a telemonitoring application for heart failure, through a Web-based interface for clinicians and a mobile application for patients. The application was evaluated through a 6-month pilot observational descriptive study in 20 outpatients with reduced ejection fraction and two nurses, in the context of a heart failure clinic. A technological acceptance questionnaire was applied to all patients and nurses at the end of the study period. In use, the application generated 64 real-time alerts for early decision-making to prevent complications, and 91% of patients did not present hospital readmissions. Such results, along with high user acceptance, show potential utility of the application as an effective complementary strategy for follow-up of patients with heart failure.

Using apps for bipolar disorder - An online survey of healthcare provider perspectives and practices

BACKGROUND

Smartphone apps have recognized potential for improving access to evidence-based care in the treatment of bipolar disorder (BD). Healthcare providers are well-positioned to play a role in guiding patients to access safe, evidence-supported, and trustworthy apps. However, little is known about whether and how clinicians use apps with people with BD: understanding practices and attitudes of healthcare providers is essential to support the implementation of mHealth interventions in a real-world context.

METHODS

A web-based survey was used to explore clinicians' attitudes towards, and use of apps when working with people with BD. Descriptive statistics were used to summarize quantitative findings. Free text responses were investigated using qualitative content analysis.

RESULTS

Eighty healthcare providers completed the survey. Approximately half of the respondents reported discussing or recommending apps in clinical practice with BD populations. Recommended apps were most commonly related to mood, sleep, and exercise. Barriers to discussing apps included a lack of healthcare provider knowledge/confidence, concerns about patients' ability to access apps, and beliefs that patients lacked interest in apps.

CONCLUSION

Although research suggests that people with BD are interested in using apps, uptake of such technology among clinicians is more limited. A lack of clinician knowledge regarding apps, combined with concerns about the digital divide and patient interest, may account for this relatively limited integration of apps into the management of BD. These findings emphasise the importance of considering the information needs of healthcare providers when planning dissemination strategies for app-based interventions for BD.

Reducing the rate of psychiatric re-admissions in bipolar disorder using smartphones-The RADMIS trial

OBJECTIVES

The MONARCA I and II trials were negative but suggested that smartphone-based monitoring may increase quality of life and reduce perceived stress in bipolar disorder (BD). The present trial was the first to investigate the effect of smartphone-based monitoring on the rate and duration of readmissions in BD.

METHODS

This was a randomized controlled single-blind parallel-group trial. Patients with BD (ICD-10) discharged from hospitalization in the Mental Health Services, Capital Region of Denmark were randomized 1:1 to daily smartphone-based monitoring including a feedback loop (+ standard treatment) or to standard treatment for 6 months. Primary outcomes: the rate and duration of psychiatric readmissions.

RESULTS

We included 98 patients with BD. In ITT analyses, there was no statistically significant difference in rates (hazard rate: 1.05, 95% CI: 0.54; 1.91, p = 0.88) or duration of readmission between the two groups (B: 3.67, 95% CI: -4.77; 12.11, p = 0.39). There was no difference in scores on the Hamilton Depression Rating Scale (B = -0.11, 95% CI: -2.50; 2.29, p = 0.93). The intervention group had higher scores on the Young Mania Rating Scale (B: 1.89, 95% CI: 0.0078; 3.78, p = 0.050). The intervention group reported lower levels of perceived stress (B: -7.18, 95% CI: -13.50; -0.86, p = 0.026) and lower levels of rumination (B: -6.09, 95% CI: -11.19; -1.00, p = 0.019).

CONCLUSIONS

Smartphone-based monitoring did not reduce rate and duration of readmissions. There was no difference in levels of depressive symptoms. The intervention group had higher levels of manic symptoms, but lower perceived stress and rumination compared with the control group.

The effect of smartphone-based monitoring and treatment on the rate and duration of psychiatric readmission in patients with unipolar depressive disorder: The RADMIS randomized controlled trial

BACKGROUND

Patients with unipolar depressive disorder are frequently hospitalized, and the period following discharge is a high-risk-period. Smartphone-based treatments are receiving increasing attention among researchers, clinicians, and patients. We aimed to investigate whether a smartphone-based monitoring and treatment system reduces the rate and duration of readmissions, more than standard treatment, in patients with unipolar depressive disorder following hospitalization.

METHODS

We conducted a pragmatic, investigator-blinded, randomized controlled trial. The intervention group received a smartphone-based monitoring and treatment system in addition to standard treatment. The system allowed patients to self-monitor symptoms and access psycho-educative information and cognitive modules. The patients were allocated a study-nurse who, based on the monitoring data, guided and supported them. The control group received standard treatment. The trial lasted six months, with outcome assessments at 0, 3, and 6 months.

RESULTS

We included 120 patients with unipolar depressive disorder (ICD-10). Intention-to-treat analyses showed no statistically significant differences in time to readmission (Log-Rank p=0.9) or duration of readmissions (B=-16.41,95%CI:-47.32;25.5,p=0.3) (Primary outcomes). There were no differences in clinically rated depressive symptoms (p=0.6) or functioning (p=0.1) (secondary outcomes). The intervention group had higher levels of recovery (B=7,29, 95%CI:0.82;13,75,p=0.028) and a tendency towards higher quality of life (p=0.07), wellbeing (p=0,09) satisfaction with treatment (p=0.05) and behavioral activation (p=0.08) compared with the control group (tertiary outcomes).

LIMITATIONS

Patients and study-nurses were unblinded to allocation.

CONCLUSIONS

We found no effect of the intervention on primary or secondary outcomes. In tertiary outcomes, patients in the intervention group reported higher levels of recovery compared to the control group.

Associations between the cortisol awakening response and patient-evaluated stress and mood instability in patients with bipolar disorder: an exploratory study

OBJECTIVE

The Cortisol Awakening Response (CAR) measured as the transient increase in cortisol levels following morning awakening appears to be a distinct feature of the HPA axis. Patients with bipolar disorder (BD) experience daily stress, mood instability (MI) and studies have shown disrupted HPA-axis dynamics.

AIMS

to evaluate (1) patient-evaluated stress against the CAR, (2) associations between the CAR and mood symptoms, and (3) the effect of smartphone-based treatment on the CAR.

METHODS

Patients with BD (n = 67) were randomized to the use of daily smartphone-based monitoring (the intervention group) or to the control group for six months. Clinically rated symptoms according to the Hamilton Depression Rating Scale 17-items (HDRS), the Young Mania Rating Scale (YMRS), patient-evaluated perceived stress using Cohen's Perceived Stress Scale (PSS) and salivary awakening cortisol samples used for measuring the CAR were collected at baseline, after three and six months. In the intervention group, smartphone-based data on stress and MI were rated daily during the entire study period.

RESULTS

Smartphone-based patient-evaluated stress (B: 134.14, 95% CI: 1.35; 266.92, p = 0.048) and MI (B: 430.23, 95% CI: 52.41; 808.04, p = 0.026) mapped onto increased CAR. No statistically significant associations between the CAR and patient-evaluated PSS or the HDRS and the YMRS, respectively were found. There was no statistically significant effect of smartphone-based treatment on the CAR.

CONCLUSION

Our data, of preliminary character, found smartphone-based patient-evaluations of stress and mood instability as read outs that reflect CAR dynamics. Smartphone-supported clinical care did not in itself appear to disturb CAR dynamics.

Can Smartphone Apps Assist People with Serious Mental Illness in Taking Medications as Prescribed?

Adherence research commonly happens in a silo, focused on a particular disease state or type of therapy. Learning from outside disciplines can bring new insights and ideas. This article presents adherence research as related to people with a diagnosis of a serious mental illness (SMI) and medication adherence through smartphone applications (apps). Individuals with SMI have high rates of not taking medication, increasing risks of relapse and hospitalization. Advances in technology may be advantageous in promoting taking medication. Smartphones apps have been designed for people with SMI. Further research is needed to evaluate their efficacy on improving rates of taking medication.

Mobile App-Based Self-Report Questionnaires for the Assessment and Monitoring of Bipolar Disorder: Systematic Review

Background

Bipolar disorder is a chronic, progressive illness characterized by recurrent episodes of mania and depression. Self-report scales have historically played a significant role in the monitoring of bipolar symptoms. However, these tools rely on episodic memory, which can be unreliable and do not allow the clinician to monitor brief episodic symptoms or the course of symptoms over shorter periods of time. Mobile app–based questionnaires have been suggested as a tool to improve monitoring of patients with bipolar disorder.

Objective

This paper aims to determine the feasibility and validity of mobile app–based self-report questionnaires.

Methods

We performed a systematic review of the literature according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The PubMed, PsycInfo, Web of Science, Ovid MEDLINE, and EMBASE databases were searched for papers published in English that assessed adherence to and the validity of mobile app–based self-report questionnaires. Relevant studies published from database creation to May 22, 2020, were identified, and results examining the validity of and rates of adherence to app-based self-report questionnaires are reported.

Results

A total of 13 records were identified for inclusion in this review. Of these studies, 4 assessed the concurrent validity of mobile app–based self-report tools, with the majority of findings indicating significant associations between data collected using these tools and the Young Mania Rating Scale, Hamilton Depression Rating Scale-17, or Montgomery-Åsberg Depression Rating Scale (P<.001 to P=.24). Three studies comparing the variability or range of symptoms between patients with bipolar disorder and healthy controls suggested that these data are capable of differentiating between known groups. Two studies demonstrated statistically significant associations between data collected via mobile app–based self-report tools and instruments assessing other clinically important factors. Adherence rates varied across the studies examined. However, good adherence rates (>70%) were observed in all but 1 study using a once-daily assessment. There was a wide range of adherence rates observed in studies using twice-daily assessments (42%-95%).

Conclusions

These findings suggest that mobile app–based self-report tools are valid in the assessment of symptoms of mania and depression in euthymic patients with bipolar disorder. Data collected using these tools appear to differ between patients with bipolar disorder and healthy controls and are significantly associated with other clinically important measures. It is unclear at this time whether these tools can be used to detect acute episodes of mania or depression in patients with bipolar disorder. Adherence data indicate that patients with bipolar disorder show good adherence to self-report assessments administered daily for the duration of the study periods evaluated.