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

Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review

Introduction

Online mental healthcare has gained significant attention due to its effectiveness, accessibility, and scalability in the management of mental health symptoms. Despite these advantages over traditional in-person formats, including higher availability and accessibility, issues with low treatment adherence and high dropout rates persist. Artificial intelligence (AI) technologies could help address these issues, through powerful predictive models, language analysis, and intelligent dialogue with users, however the study of these applications remains underexplored. The following mixed methods review aimed to supplement this gap by synthesizing the available evidence on the applications of AI in online mental healthcare.

Method

We searched the following databases: MEDLINE, CINAHL, PsycINFO, EMBASE, and Cochrane. This review included peer-reviewed randomized controlled trials, observational studies, non-randomized experimental studies, and case studies that were selected using the PRISMA guidelines. Data regarding pre and post-intervention outcomes and AI applications were extracted and analyzed. A mixed-methods approach encompassing meta-analysis and network meta-analysis was used to analyze pre and post-intervention outcomes, including main effects, depression, anxiety, and study dropouts. We applied the Cochrane risk of bias tool and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) to assess the quality of the evidence.

Results

Twenty-nine studies were included revealing a variety of AI applications including triage, psychotherapy delivery, treatment monitoring, therapy engagement support, identification of effective therapy features, and prediction of treatment response, dropout, and adherence. AI-delivered self-guided interventions demonstrated medium to large effects on managing mental health symptoms, with dropout rates comparable to non-AI interventions. The quality of the data was low to very low.

Discussion

The review supported the use of AI in enhancing treatment response, adherence, and improvements in online mental healthcare. Nevertheless, given the low quality of the available evidence, this study highlighted the need for additional robust and high-powered studies in this emerging field.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=443575, identifier CRD42023443575.

MoodSensing: A smartphone app for digital phenotyping and assessment of bipolar disorder

BACKGROUND

Daily life tracking has proven to be of great help in the assessment of patients with bipolar disorder. Although there are many smartphone apps for tracking bipolar disorder, most of them lack academic verification, privacy policy and long-term maintenance.

METHODS

Our developed app, MoodSensing, aims to collect users' digital phenotyping for assessment of bipolar disorder. The data collection was approved by the Institutional Review Board. This study collaborated with professional clinicians to ensure that the app meets both clinical needs and user experience requirements. Based on the collected digital phenotyping, deep learning techniques were applied to forecast participants' weekly HAM-D and YMRS scale scores.

RESULTS

In experiments, the data collected by our app can effectively predict the scale scores, reaching the mean absolute error of 0.84 and 0.22 on the scales. The statistical data also demonstrate the increase in user engagement.

CONCLUSIONS

Our analysis reveals that the developed MoodSensing app can not only provide a good user experience, but also the recorded data have certain discriminability for clinical assessment. Our app also provides relevant policies to protect user privacy, and has been launched in the Apple Store and Google Play Store.

Digital Tools to Facilitate the Detection and Treatment of Bipolar Disorder: Key Developments and Future Directions

Bipolar disorder (BD) impacts over 40 million people around the world, often manifesting in early adulthood and substantially impacting the quality of life and functioning of individuals. Although early interventions are associated with a better prognosis, the early detection of BD is challenging given the high degree of similarity with other psychiatric conditions, including major depressive disorder, which corroborates the high rates of misdiagnosis. Further, BD has a chronic, relapsing course, and the majority of patients will go on to experience mood relapses despite pharmacological treatment. Digital technologies present promising results to augment early detection of symptoms and enhance BD treatment. In this editorial, we will discuss current findings on the use of digital technologies in the field of BD, while debating the challenges associated with their implementation in clinical practice and the future directions.

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).

Digital Therapeutics for Improving Effectiveness of Pharmaceutical Drugs and Biological Products: Preclinical and Clinical Studies Supporting Development of Drug + Digital Combination Therapies for Chronic Diseases

Limitations of pharmaceutical drugs and biologics for chronic diseases (e.g., medication non-adherence, adverse effects, toxicity, or inadequate efficacy) can be mitigated by mobile medical apps, known as digital therapeutics (DTx). Authorization of adjunct DTx by the US Food and Drug Administration and draft guidelines on "prescription drug use-related software" illustrate opportunities to create drug + digital combination therapies, ultimately leading towards drug-device combination products (DTx has a status of medical devices). Digital interventions (mobile, web-based, virtual reality, and video game applications) demonstrate clinically meaningful benefits for people living with Alzheimer's disease, dementia, rheumatoid arthritis, cancer, chronic pain, epilepsy, depression, and anxiety. In the respective animal disease models, preclinical studies on environmental enrichment and other non-pharmacological modalities (physical activity, social interactions, learning, and music) as surrogates for DTx "active ingredients" also show improved outcomes. In this narrative review, we discuss how drug + digital combination therapies can impact translational research, drug discovery and development, generic drug repurposing, and gene therapies. Market-driven incentives to create drug-device combination products are illustrated by Humira® (adalimumab) facing a "patent-cliff" competition with cheaper and more effective biosimilars seamlessly integrated with DTx. In conclusion, pharma and biotech companies, patients, and healthcare professionals will benefit from accelerating integration of digital interventions with pharmacotherapies.

Classifying and clustering mood disorder patients using smartphone data from a feasibility study

Differentiating between bipolar disorder and major depressive disorder can be challenging for clinicians. The diagnostic process might benefit from new ways of monitoring the phenotypes of these disorders. Smartphone data might offer insight in this regard. Today, smartphones collect dense, multimodal data from which behavioral metrics can be derived. Distinct patterns in these metrics have the potential to differentiate the two conditions. To examine the feasibility of smartphone-based phenotyping, two study sites (Mayo Clinic, Johns Hopkins University) recruited patients with bipolar I disorder (BPI), bipolar II disorder (BPII), major depressive disorder (MDD), and undiagnosed controls for a 12-week observational study. On their smartphones, study participants used a digital phenotyping app (mindLAMP) for data collection. While in use, mindLAMP gathered real-time geolocation, accelerometer, and screen-state (on/off) data. mindLAMP was also used for EMA delivery. MindLAMP data was then used as input variables in binary classification, three-group k-nearest neighbors (KNN) classification, and k-means clustering. The best-performing binary classification model was able to classify patients as control or non-control with an AUC of 0.91 (random forest). The model that performed best at classifying patients as having MDD or bipolar I/II had an AUC of 0.62 (logistic regression). The k-means clustering model had a silhouette score of 0.46 and an ARI of 0.27. Results support the potential for digital phenotyping methods to cluster depression, bipolar disorder, and healthy controls. However, due to inconsistencies in accuracy, more data streams are required before these methods can be applied to clinical practice.

Smartphone Apps for Smoking Cessation: Systematic Framework for App Review and Analysis

BACKGROUND

Cigarette smoking is a leading cause of preventable death, and identifying novel treatment approaches to promote smoking cessation is critical for improving public health. With the rise of digital health and mobile apps, these tools offer potential opportunities to address smoking cessation, yet the functionality of these apps and whether they offer scientifically based support for smoking cessation are unknown.

OBJECTIVE

The goal of this research was to use the American Psychiatric Association app evaluation model to evaluate the top-returned apps from Android and Apple app store platforms related to smoking cessation and investigate the common app features available for end users.

METHODS

We conducted a search of both Android and iOS app stores in July 2021 for apps related to the keywords "smoking," "tobacco," "smoke," and "cigarette" to evaluate apps for smoking cessation. Apps were screened for relevance, and trained raters identified and analyzed features, including accessibility (ie, cost), privacy, clinical foundation, and features of the apps, using a systematic framework of 105 objective questions from the American Psychiatric Association app evaluation model. All app rating data were deposited in mindapps, a publicly accessible database that is continuously updated every 6 months given the dynamic nature of apps available in the marketplace. We characterized apps available in July 2021 and November 2022.

RESULTS

We initially identified 389 apps, excluded 161 due to irrelevance and nonfunctioning, and rated 228, including 152 available for Android platforms and 120 available for iOS platforms. Some of the top-returned apps (71/228, 31%) in 2021 were no longer functioning in 2022. Our analysis of rated apps revealed limitations in accessibility and features. While most apps (179/228, 78%) were free to download, over half had costs associated with in-app purchases or full use. Less than 65% (149/228) had a privacy policy addressing the data collected in the app. In terms of intervention features, more than 56% (128/228) of apps allowed the user to set and check in on goals, and more than 46% (106/228) of them provided psychoeducation, although few apps provided evidence-based support for smoking cessation, such as peer support or skill training, including mindfulness and deep breathing, and even fewer provided evidence-based interventions, such as acceptance and commitment therapy or cognitive behavioral therapy. Only 12 apps in 2021 and 11 in 2022 had published studies supporting the feasibility or efficacy for smoking cessation.

CONCLUSIONS

Numerous smoking cessation apps were identified, but analysis revealed limitations, including high rates of irrelevant and nonfunctioning apps, high rates of turnover, and few apps providing evidence-based support for smoking cessation. Thus, it may be challenging for consumers to identify relevant, evidence-based apps to support smoking cessation in the app store, and a comprehensive evaluation system of mental health apps is critically important.

The Minimum Feature Set for Designing Mobile Apps to Support Bipolar Disorder-Affected Patients: Proposal of Essential Functions and Requirements

Research conducted on mobile apps providing mental health services has concluded that patients with mental disorders tend to use such apps to maintain mental health balance technology may help manage and monitor issues like bipolar disorder (BP). This study was conducted in four steps to identify the features of designing a mobile application for BP-affected patients including (1) a literature search, (2) analyzing existing mobile apps to examine their efficiency, (3) interviewing patients affected with BP to discover their needs, and 4) exploring the points of view of experts using a dynamic narrative survey. Literature search and mobile app analysis resulted in 45 features, which were later reduced to 30 after the experts were surveyed about the project. The features included the following: mood monitoring, sleep schedule, energy level evaluation, irritability, speech level, communication, sexual activity, self-confidence level, suicidal thoughts, guilt, concentration level, aggressiveness, anxiety, appetite, smoking or drug abuse, blood pressure, the patient's weight and the side effects of medication, reminders, mood data scales, diagrams or charts of the collected data, referring the collected data to a psychologist, educational information, sending feedbacks to patients using the application, and standard tests for mood assessment. The first phase of analysis should consider an expert and patient view survey, mood and medication tracking, as well as communication with other people in the same situation are the most features to be considered. The present study has identified the necessity of apps intended to manage and monitor bipolar patients to maximize efficiency and minimize relapse and side effects.

Exploring how informed mental health app selection may impact user engagement and satisfaction

The prevalence of mental health app use by people suffering from mental health disorders is rapidly growing. The integration of mental health apps shows promise in increasing the accessibility and quality of treatment. However, a lack of continued engagement is one of the significant challenges of such implementation. In response, the M-health Index and Navigation Database (MIND)- derived from the American Psychiatric Association's app evaluation framework- was created to support patient autonomy and enhance engagement. This study aimed to identify factors influencing engagement with mental health apps and explore how MIND may affect user engagement around selected apps. We conducted a longitudinal online survey over six weeks after participants were instructed to find mental health apps using MIND. The survey included demographic information, technology usage, access to healthcare, app selection information, System Usability Scale, the Digital Working Alliance Inventory, and the General Self-Efficacy Scale questions. Quantitative analysis was performed to analyze the data. A total of 321 surveys were completed (178 at the initial, 90 at the 2-week mark, and 53 at the 6-week mark). The most influential factors when choosing mental health apps included cost (76%), condition supported by the app (59%), and app features offered (51%), while privacy and clinical foundation to support app claims were among the least selected filters. The top ten apps selected by participants were analyzed for engagement. Rates of engagement among the top-ten apps decreased by 43% from the initial to week two and 22% from week two to week six on average. In the context of overall low engagement with mental health apps, implementation of mental health app databases like MIND can play an essential role in maintaining higher engagement and satisfaction. Together, this study offers early data on how educational approaches like MIND may help bolster mental health apps engagement.

Effects of a Smartphone-Based Self-management Intervention for Individuals With Bipolar Disorder on Relapse, Symptom Burden, and Quality of Life: A Randomized Clinical Trial

Importance

Bipolar disorder-specific psychotherapy combined with pharmacotherapy improves relapse risk, symptom burden, and quality of life, but psychotherapy is not easily accessible.

Objective

To determine if a smartphone-based self-management intervention (LiveWell) can assist individuals with bipolar disorder to maintain wellness.

Design, Setting, and Participants

An assessor-blind randomized clinical trial enrolled participants from March 20, 2017, to April 25, 2019, with 48-week follow-up ending on April 10, 2020. Participants were randomly assigned to usual care or usual care plus the smartphone intervention stratified by relapse risk based on initial clinical status (low risk: asymptomatic recovery; high risk: continued symptomatic, prodromal, recovering, symptomatic recovery). Participants with bipolar disorder I were recruited from clinics in the Chicago and Minneapolis-Saint Paul areas. Data were analyzed from June 19, 2020, to May 25, 2022.

Interventions

The smartphone-based self-management intervention consisted of an application (app), coach, and website. Over 16 weeks, participants had a coach visit followed by 6 phone calls, and they completed daily and weekly app check-ins. The app provided adaptive feedback and information for developing a personalized wellness plan, the coach provided support, and the website provided summary data and alerts.

Main Outcomes and Measures

The primary outcome was time to relapse. Secondary outcomes were percentage-time symptomatic, symptom severity, and quality of life.

Results

Of the 205 randomized participants (mean [SD] age, 42 [12] years; 125 female individuals [61%]; 5 Asian [2%], 21 Black [10%], 13 Hispanic or Latino [6%], 7 multiracial [3%], 170 White [83%], 2 unknown race [1%]), 81 (40%) were randomly assigned to usual care, and 124 (60%) were randomly assigned to usual care plus the smartphone intervention. This clinical trial did not detect a reduction in relapse risk for the smartphone intervention (hazard ratio [HR], 0.65; 95% CI, 0.39-1.09; log-rank P = .08). However, decreased relapse was observed for low-risk individuals (HR, 0.32; 95% CI, 0.12-0.88; log-rank P = .02) but not high-risk individuals (HR, 0.86; 95% CI, 0.47-1.57; log-rank P = .62). Reduced manic symptom severity was observed for low-risk individuals (mean [SE] difference, -1.4 [0.4]; P = .001) but not for high-risk individuals (mean [SE] difference, 0 [0.3]; P = .95). The smartphone-based self-management intervention decreased depressive symptom severity (mean [SE] difference, -0.80 [0.34]; P = .02) and improved relational quality of life (mean [SE] difference, 1.03 [0.45]; P = .02) but did not decrease percentage-time symptomatic (mean [SE] difference, -5.6 [4.3]; P = .20).

Conclusions and Relevance

This randomized clinical trial of a smartphone-based self-management intervention did not detect a significant improvement in the primary outcome of time to relapse. However, a significant decrease in relapse risk was observed for individuals in asymptomatic recovery. In addition, the intervention decreased depressive symptom severity and improved relational quality of life. These findings warrant further work to optimize the smartphone intervention and confirm that the intervention decreases relapse risk for individuals in asymptomatic recovery.

Trial Registration

ClinicalTrials.gov Identifier: NCT03088462.

The clinical characterization of the adult patient with bipolar disorder aimed at personalization of management

Bipolar disorder is heterogeneous in phenomenology, illness trajectory, and response to treatment. Despite evidence for the efficacy of multimodal-ity interventions, the majority of persons affected by this disorder do not achieve and sustain full syndromal recovery. It is eagerly anticipated that combining datasets across various information sources (e.g., hierarchical "multi-omic" measures, electronic health records), analyzed using advanced computational methods (e.g., machine learning), will inform future diagnosis and treatment selection. In the interim, identifying clinically meaningful subgroups of persons with the disorder having differential response to specific treatments at point-of-care is an empirical priority. This paper endeavours to synthesize salient domains in the clinical characterization of the adult patient with bipolar disorder, with the overarching aim to improve health outcomes by informing patient management and treatment considerations. Extant data indicate that characterizing select domains in bipolar disorder provides actionable information and guides shared decision making. For example, it is robustly established that the presence of mixed features - especially during depressive episodes - and of physical and psychiatric comorbidities informs illness trajectory, response to treatment, and suicide risk. In addition, early environmental exposures (e.g., sexual and physical abuse, emotional neglect) are highly associated with more complicated illness presentations, inviting the need for developmentally-oriented and integrated treatment approaches. There have been significant advances in validating subtypes of bipolar disorder (e.g., bipolar I vs. II disorder), particularly in regard to pharmacological interventions. As with other severe mental disorders, social functioning, interpersonal/family relationships and internalized stigma are domains highly relevant to relapse risk, health outcomes, and quality of life. The elevated standardized mortality ratio for completed suicide and suicidal behaviour in bipolar disorder invites the need for characterization of this domain in all patients. The framework of this paper is to describe all the above salient domains, providing a synthesis of extant literature and recommendations for decision support tools and clinical metrics that can be implemented at point-of-care.

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.

Assessment of Smartphone Apps for Common Neurologic Conditions (Headache, Insomnia, and Pain): Cross-sectional Study

BACKGROUND

There are thousands of apps for individuals struggling with headache, insomnia, and pain, but it is difficult to establish which of these apps are best suited for patients' specific needs. If clinicians were to have access to a platform that would allow them to make an informed decision on the efficacy and feasibility of smartphone apps for patient care, they would feel confident in prescribing specific apps.

OBJECTIVE

We sought to evaluate the quality of apps for some of the top common, disabling neurologic conditions (headache, insomnia, and pain) based on principles derived from the American Psychiatric Association's (APA) app evaluation model.

METHODS

We used the Mobile Health Index and Navigation database and expanded upon the database's current supported conditions by adding 177 new app entries. Each app was rated for consistency with the APA's app evaluation model, which includes 105 objective questions based on the following 5 major classes of consideration: (1) accessibility, (2) privacy and security, (3) clinical foundation, (4) engagement style, and (5) interoperability. These characteristics were evaluated to gain a broader understanding of the significant features of each app category in comparison against a control group.

RESULTS

Approximately 90% (187/201) of all apps evaluated were free to download, but only 50% (63/201) of headache- and pain-related apps were truly free. Most (87/106, 81%) sleep apps were not truly free to use. The apps had similar limitations with limited privacy, accessibility, and crisis management resources. For example, only 17% (35/201) of the apps were available in Spanish. The apps offered mostly self-help tools with little tailoring; symptom tracking was the most common feature in headache- (32/48, 67%) and pain-related apps (21/47, 45%), whereas mindfulness was the most common feature in sleep-related apps (73/106, 69%).

CONCLUSIONS

Although there are many apps for headache, pain, and insomnia, all 3 types of apps have room for improvement around accessibility and privacy. Pain and headache apps share many common features, whereas insomnia apps offer mostly mindfulness-based resources. Given the many available apps to pick from, clinicians and patients should seek apps that offer the highest-quality features, such as complete privacy, remedial features, and the ability to download the app at no cost. These results suggest that there are many opportunities for the improvement of apps centered on headache, insomnia, and pain.

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.

A Smartphone-Based Self-management Intervention for Individuals With Bipolar Disorder (LiveWell): Empirical and Theoretical Framework, Intervention Design, and Study Protocol for a Randomized Controlled Trial

Background

Bipolar disorder is a severe mental illness with high morbidity and mortality rates. Even with pharmacological treatment, frequent recurrence of episodes, long episode durations, and persistent interepisode symptoms are common and disruptive. Combining psychotherapy with pharmacotherapy improves outcomes; however, many individuals with bipolar disorder do not receive psychotherapy. Mental health technologies can increase access to self-management strategies derived from empirically supported bipolar disorder psychotherapies while also enhancing treatment by delivering real-time assessments, personalized feedback, and provider alerts. In addition, mental health technologies provide a platform for self-report, app use, and behavioral data collection to advance understanding of the longitudinal course of bipolar disorder, which can then be used to support ongoing improvement of treatment.

Objective

A description of the theoretical and empirically supported framework, design, and protocol for a randomized controlled trial (RCT) of LiveWell, a smartphone-based self-management intervention for individuals with bipolar disorder, is provided to facilitate the ability to replicate, improve, implement, and disseminate effective interventions for bipolar disorder. The goal of the trial is to determine the effectiveness of LiveWell for reducing relapse risk and symptom burden as well as improving quality of life (QOL) while simultaneously clarifying behavioral targets involved in staying well and better characterizing the course of bipolar disorder and treatment response.

Methods

The study is a single-blind RCT (n=205; 2:3 ratio of usual care vs usual care plus LiveWell). The primary outcome is the time to relapse. Secondary outcomes are percentage time symptomatic, symptom severity, and QOL. Longitudinal changes in target behaviors proposed to mediate the primary and secondary outcomes will also be determined, and their relationships with the outcomes will be assessed. A database of clinical status, symptom severity, real-time self-report, behavioral sensor, app use, and personalized content will be created to better predict treatment response and relapse risk.

Results

Recruitment and screening began in March 2017 and ended in April 2019. Follow-up ended in April 2020. The results of this study are expected to be published in 2022.

Conclusions

This study will examine whether LiveWell reduces relapse risk and symptom burden and improves QOL for individuals with bipolar disorder by increasing access to empirically supported self-management strategies. The role of selected target behaviors (medication adherence, sleep duration, routine, and management of signs and symptoms) in these outcomes will also be examined. Simultaneously, a database will be created to initiate the development of algorithms to personalize and improve treatment for bipolar disorder. In addition, we hope that this description of the theoretical and empirically supported framework, intervention design, and study protocol for the RCT of LiveWell will facilitate the ability to replicate, improve, implement, and disseminate effective interventions for bipolar and other mental health disorders.

Trial Registration

ClinicalTrials.gov NCT03088462; https://www.clinicaltrials.gov/ct2/show/NCT03088462

International Registered Report Identifier (IRRID)

DERR1-10.2196/30710

Mobile Applications in Mood Disorders and Mental Health: Systematic Search in Apple App Store and Google Play Store and Review of the Literature

OBJECTIVES

The main objective of this work was to explore and characterize the current landscape of mobile applications available to treat mood disorders such as depression, bipolar disorder, and dysthymia.

METHODS

We developed a tool that makes both the Apple App Store and the Google Play Store searchable using keywords and that facilitates the extraction of basic app information of the search results. All app results were filtered using various inclusion and exclusion criteria. We characterized all resultant applications according to their technical details. Furthermore, we searched for scientific publications on each app's website and PubMed, to understand whether any of the apps were supported by any type of scientific evidence on their acceptability, validation, use, effectiveness, etc. Results: Thirty apps were identified that fit the inclusion and exclusion criteria. The literature search yielded 27 publications related to the apps. However, these did not exclusively concern mood disorders. 6 were randomized studies and the rest included a protocol, pilot-, feasibility, case-, or qualitative studies, among others. The majority of studies were conducted on relatively small scales and 9 of the 27 studies did not explicitly study the effects of mobile application use on mental wellbeing.

CONCLUSION

While there exists a wealth of mobile applications aimed at the treatment of mental health disorders, including mood disorders, this study showed that only a handful of these are backed by robust scientific evidence. This result uncovers a need for further clinically oriented and systematic validation and testing of such apps.

Real-world Implementation of a Smartphone-Based Psychoeducation Program for Bipolar Disorder: Observational Ecological Study

BACKGROUND

SIMPLe is an internet-delivered self-management mobile app for bipolar disorder (BD) designed to combine technology with evidence-based interventions and facilitate access to psychoeducational content. The SIMPLe app was launched to the real world to make it available worldwide within the context of BD treatment.

OBJECTIVE

The main aims of this study are as follows: to describe app use, engagement, and retention rates based on server data; to identify patterns of user retention over the first 6-month follow-up of use; and to explore potential factors contributing to discontinuation of app use.

METHODS

This was an observational ecological study in which we pooled available data from a real-world implementation of the SIMPLe app. Participation was open on the project website, and the data-collection sources were a web-based questionnaire on clinical data and treatment history administered at inclusion and at 6 months, subjective data gathered through continuous app use, and the use patterns captured by the app server. Characteristics and engagement of regular users, occasional users, and no users were compared using 2-tailed t tests or analysis of variance or their nonparametric equivalent. Survival analysis and risk functions were applied to regular users' data to examine and compare use and user retention. In addition, a user evaluation analysis was performed based on satisfaction, perceived usefulness, and reasons to discontinue app use.

RESULTS

We included 503 participants with data collected between 2016 and 2018, of whom 77.5% (n=390) used the app. Among the app users, 44.4% (173/390) completed the follow-up assessment, and data from these participants were used in our analyses. Engagement declined gradually over the first 6 months of use. The probability of retention of the regular users after 1 month of app use was 67.4% (263/390; 95% CI 62.7%-72.4%). Age (P=.002), time passed since illness onset (P<.001), and years since diagnosis of BD (P=.048) correlate with retention duration. In addition, participants who had been diagnosed with BD for longer used the app on more days (mean 97.73, SD 69.15 days; P=.002) than those who had had a more recent onset (mean 66.49, SD 66.18 days; P=.002) or those who had been diagnosed more recently (mean 73.45, SD 66 days; P=.01).

CONCLUSIONS

The user retention rate of the app decreased rapidly after each month until reaching only one-third of the users at 6 months. There exists a strong association between age and app engagement of individuals with BD. Other variables such as years lived with BD, diagnosis of an anxiety disorder, and taking antipsychotics seem relevant as well. Understanding these associations can help in the definition of the most suitable user profiles for predicting trends of engagement, optimization of app prescription, and management.

The Promise of Digital Self-Management: A Reflection about the Effects of Patient-Targeted e-Health Tools on Self-Management and Wellbeing

Increasingly, people have direct access to e-Health resources such as health information on the Internet, personal health portals, and wearable self-management applications, which have the potential to reinforce the simultaneously growing focus on self-management and wellbeing. To examine these relationships, we searched using keywords self-management, patient-targeting e-Health tools, and health as wellbeing. Direct access to the health information on the Internet or diagnostic apps on a smartphone can help people to self-manage health issues, but also leads to uncertainty, stress, and avoidance. Uncertainties relate to the quality of information and to use and misuse of information. Most self-management support programs focus on medical management. The relationship between self-management and wellbeing is not straightforward. While the influence of stress and negative social emotions on self-management is recognized as an important cause of the negative spiral, empirical research on this topic is limited to health literacy studies. Evidence on health apps showed positive effects on specific actions and symptoms and potential for increasing awareness and ownership by people. Effects on more complex behaviors such as participation cannot be established. This review discovers relatively unknown and understudied angles and perspectives about the relationship between e-Health, self-management, and wellbeing.

The Life Goals Self-Management Mobile App for Bipolar Disorder: Consumer Feasibility, Usability, and Acceptability Study

Background

Life Goals is an evidence-based self-management intervention that assists individuals with bipolar disorder (BD) by aligning BD symptom coping strategies with their personal goals. The intervention can be availed via in-person and telephonic sessions, and it has been recently developed as an individualized, customizable mobile app.

Objective

We examined the feasibility, usability, and acceptability of the Life Goals self-management app among individuals diagnosed with BD who used the app for up to 6 months.

Methods

A total of 28 individuals with BD used the Life Goals app on their personal smartphone for 6 months. They completed key clinical outcome measurements of functioning, disability, and psychiatric symptoms at baseline, 3 months, and 6 months, in addition to a poststudy survey about usability and satisfaction.

Results

Participants used the app for a median of 25 times (IQR 13-65.75), and for a longer time during the first 3 months of the study. The modules on depression and anxiety were the most frequently used, accounting for 35% and 22% of total usage, respectively. Overall, the study participants found the app useful (15/25, 60%) and easy to use (18/25, 72%), and they reported that the screen displayed the material adequately (22/25, 88%). However, less than half of the participants found the app helpful in managing their health (10/25, 40%) or in making progress on their wellness goals (9/25, 36%). Clinical outcomes showed a trend for improvements in mental and physical health and mania-related well-being.

Conclusions

The Life Goals app showed feasibility of use among individuals with BD. Higher user engagement was observed in the initial 3 months with users interested more frequently in the mood modules than other wellness modules. Participants reported acceptability with the ease of app use and satisfaction with the app user interface, but the app showed low success in encouraging self-management within this small sample. The Life Goals app is a mobile health technology that can provide individuals with serious mental illness with more flexible access to evidence-based treatments.

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.

Voice analyses using smartphone-based data in patients with bipolar disorder, unaffected relatives and healthy control individuals, and during different affective states

BACKGROUND

Voice features have been suggested as objective markers of bipolar disorder (BD).

AIMS

To investigate whether voice features from naturalistic phone calls could discriminate between (1) BD, unaffected first-degree relatives (UR) and healthy control individuals (HC); (2) affective states within BD.

METHODS

Voice features were collected daily during naturalistic phone calls for up to 972 days. A total of 121 patients with BD, 21 UR and 38 HC were included. A total of 107.033 voice data entries were collected [BD (n  = 78.733), UR (n  = 8004), and HC (n  =  20.296)]. Daily, patients evaluated symptoms using a smartphone-based system. Affective states were defined according to these evaluations. Data were analyzed using random forest machine learning algorithms.

RESULTS

Compared to HC, BD was classified with a sensitivity of 0.79 (SD 0.11)/AUC  = 0.76 (SD 0.11) and UR with a sensitivity of 0.53 (SD 0.21)/AUC of 0.72 (SD 0.12). Within BD, compared to euthymia, mania was classified with a specificity of 0.75 (SD 0.16)/AUC  =  0.66 (SD 0.11). Compared to euthymia, depression was classified with a specificity of 0.70 (SD 0.16)/AUC  =  0.66 (SD 0.12). In all models the user dependent models outperformed the user independent models. Models combining increased mood, increased activity and insomnia compared to periods without performed best with a specificity of 0.78 (SD 0.16)/AUC  =  0.67 (SD 0.11).

CONCLUSIONS

Voice features from naturalistic phone calls may represent a supplementary objective marker discriminating BD from HC and a state marker within BD.

Opportunities to Integrate Mobile App-Based Interventions Into Mental Health and Substance Use Disorder Treatment Services in the Wake of COVID-19

The COVID-19 pandemic has heightened concerns about the impact of depression, anxiety, alcohol, and drug use on public health. Mobile apps to address these problems were increasingly popular even before the pandemic, and may help reach people who otherwise have limited treatment access. In this review, we describe pandemic-related substance use and mental health problems, the growing evidence for mobile app efficacy, how health systems can integrate apps into patient care, and future research directions. If equity in access and effective implementation can be addressed, mobile apps are likely to play an important role in mental health and substance use disorder treatment.

Cross cultural and global uses of a digital mental health app: results of focus groups with clinicians, patients and family members in India and the United States

BACKGROUND

Despite significant advancements in healthcare technology, digital health solutions - especially those for serious mental illnesses - continue to fall short of their potential across both clinical practice and efficacy. The utility and impact of medicine, including digital medicine, hinges on relationships, trust, and engagement, particularly in the field of mental health. This paper details results from Phase 1 of a two-part study that seeks to engage people with schizophrenia, their family members, and clinicians in co-designing a digital mental health platform for use across different cultures and contexts in the United States and India.

METHODS

Each site interviewed a mix of clinicians, patients, and their family members in focus groups (n = 20) of two to six participants. Open-ended questions and discussions inquired about their own smartphone use and, after a demonstration of the mindLAMP platform, specific feedback on the app's utility, design, and functionality.

RESULTS

Our results based on thematic analysis indicate three common themes: increased use and interest in technology during coronavirus disease 2019 (COVID-19), concerns over how data are used and shared, and a desire for concurrent human interaction to support app engagement.

CONCLUSION

People with schizophrenia, their family members, and clinicians are open to integrating technology into treatment to better understand their condition and help inform treatment. However, app engagement is dependent on technology that is complementary - not substitutive - of therapeutic care from a clinician.

[Symptom Monitoring and Detection of Early Warning Signs in Bipolar Episodes Via App - Views of Patients and Relatives on e-Health Need]

BACKGROUND

The onset and early warning signs of episodes of bipolar disorder are often realized late by those affected. The earlier an incipient episode is treated, the more prognostically favorable the course will be. Symptom monitoring via smartphone application (app) could be an innovative way to recognize and react to early warning signs more swiftly. The aim of this study was to find out whether patients and their relatives consider technical support through an app to be useful and practical in the early warning sign detection and treatment.

METHODS

In the present study, 51 patients with bipolar disorder and 28 relatives were interviewed. We gathered information on whether participants were able to perceive early warning signs in form of behavioral changes sufficiently and in a timely fashion and also whether they would use an app as treatment support tool.

RESULTS

Although 94.1% of the surveyed patients and 78.6% of their relatives felt that they were well informed about the disease, 13.7% and 35.7%, respectively were not fully satisfied with the current treatment options. Early warning signs of every depressive development were noticed by 25.5% of the patients (relatives 10.7%). Every (hypo)manic development was only noticed by 11.8% of the patients (relatives 7.1%); 88.2% of the patients and 85.7% of the relatives noticed the same symptoms recurrently at the beginning of a depression and 70.6% and 67.9%, respectively, at the beginning of a (hypo)manic episode (in particular changes in physical activity, communication behavior and the sleep-wake rhythm). 84.3% of the patients and 89.3% of the relatives stated that they considered technical support that draws attention to mood and activity changes as useful and that they would use such an app for the treatment.

DISCUSSION

The current options for perceiving early warning signs of a depressive or (hypo)manic episode in bipolar disorder are clinically inadequate. Those affected and their relatives desire innovative, technical support. Early detection of symptoms, which often manifest themselves in changes in behavior or activity patterns, is essentiell for managing the course of bipolar disorder. In the future, smartphone apps could be used for clinical treatment and research through objective, continuous and.

Assessing mental health apps marketplaces with objective metrics from 29,190 data points from 278 apps

OBJECTIVE

Utilizing a standard framework that may help clinicians and patients to identify relevant mental health apps, we sought to gain a comprehensive picture of the space by searching for, downloading, and reviewing 278 mental health apps from both the iOS and Android stores.

METHODS

278 mental health apps from the Apple iOS store and Google Play store were downloaded and reviewed in a standardized manner by trained app raters using a validated framework. Apps were evaluated with this framework comprising 105 questions and covering app origin and accessibility, privacy and security, inputs and outputs, clinical foundation, features and engagement style, and interoperability.

RESULTS

Our results confirm that app stars and downloads-even for the most popular apps by these metrics-did not correlate with more clinically relevant metrics related to privacy/security, effectiveness, and engagement. Most mental health apps offer similar functionality, with 16.5% offering both mood tracking and journaling and 7% offering psychoeducation, deep breathing, mindfulness, journaling, and mood tracking. Only 36.4% of apps were updated with a 100-day window, and 7.5% of apps had not been updated in four years.

CONCLUSION

Current app marketplace metrics commonly used to evaluate apps do not offer an accurate representation of individual apps or a comprehensive overview of the entire space. The majority of apps overlap in terms of features offered, with many domains and other features not well represented. Selecting an appropriate app continues to require personal matching given no clear trends or guidance offered by quantitative metrics alone.