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

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.

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.

Identifying Unmet Needs in Major Depressive Disorder Using a Computer-Assisted Alternative to Conventional Thematic Analysis: Qualitative Interview Study With Psychiatrists

BACKGROUND

The development of digital health tools that are clinically relevant requires a deep understanding of the unmet needs of stakeholders, such as clinicians and patients. One way to reveal unforeseen stakeholder needs is through qualitative research, including stakeholder interviews. However, conventional qualitative data analytical approaches are time-consuming and resource-intensive, rendering them untenable in many industry settings where digital tools are conceived of and developed. Thus, a more time-efficient process for identifying clinically relevant target needs for digital tool development is needed.

OBJECTIVE

The objective of this study was to address the need for an accessible, simple, and time-efficient alternative to conventional thematic analysis of qualitative research data through text analysis of semistructured interview transcripts. In addition, we sought to identify important themes across expert psychiatrist advisor interview transcripts to efficiently reveal areas for the development of digital tools that target unmet clinical needs.

METHODS

We conducted 10 (1-hour-long) semistructured interviews with US-based psychiatrists treating major depressive disorder. The interviews were conducted using an interview guide that comprised open-ended questions predesigned to (1) understand the clinicians' experience of the care management process and (2) understand the clinicians' perceptions of the patients' experience of the care management process. We then implemented a hybrid analytical approach that combines computer-assisted text analyses with deductive analyses as an alternative to conventional qualitative thematic analysis to identify word combination frequencies, content categories, and broad themes characterizing unmet needs in the care management process.

RESULTS

Using this hybrid computer-assisted analytical approach, we were able to identify several key areas that are of interest to clinicians in the context of major depressive disorder and would be appropriate targets for digital tool development.

CONCLUSIONS

A hybrid approach to qualitative research combining computer-assisted techniques with deductive techniques provides a time-efficient approach to identifying unmet needs, targets, and relevant themes to inform digital tool development. This can increase the likelihood that useful and practical tools are built and implemented to ultimately improve health outcomes for patients.

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.

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.

User perspectives on long-term remote active electronic self-monitoring of mood symptoms in bipolar spectrum disorders

BACKGROUND

User feedback is crucial in the development of electronic self-monitoring tools for bipolar spectrum disorders (BSD). Previous studies have examined user experiences in small samples self-monitoring over relatively short time periods. We aimed to explore the experiences of a large sample of individuals with BSD engaged in long-term remote active electronic self-monitoring.

METHODS

An online survey, containing closed and open questions, was sent to participants with BSD enrolled on the Bipolar Disorder Research Network (BDRN) True Colours mood-monitoring system. Questions related to experiences of using True Colours, including viewing mood graphs, and sharing data with healthcare professionals (HCPs) and/or family/friends.

RESULTS

Response rate was 62.7 % (n = 362). 88.4 % reported finding using True Colours helpful. Commonly reported benefits were having a visual record of mood changes, patterns/triggers and identifying early warning signs. Limitations included questions not being comprehensive or revealing anything new. One third had shared their graphs, with 89.9 % finding it helpful to share with HCPs and 78.7 % helpful to share with family/friends. Perceived benefits included aiding communication and limitations included lack of interest/understanding from others.

LIMITATIONS

Responder bias may be present. Findings may not be generalisable to all research cohorts.

CONCLUSIONS

The majority of participants valued long-term self-monitoring. Personalisation and ease of use were important. A potential challenge is continued use when mood is long-term stable, highlighting the need for measures to be sensitive to small changes. Sharing self-monitoring data with HCPs may enhance communication of the lived experience of those with BSD. Future research should examine HCPs' perspectives.

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.

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.

Digital phenotyping in psychiatry: A scoping review

BACKGROUND

Digital phenotyping has been defined as the moment-by-moment assessment of an illness state through digital means, promising objective, quantifiable data on psychiatric patients' conditions, and could potentially improve diagnosis and management of mental illness. As it is a rapidly growing field, it is to be expected that new literature is being published frequently.

OBJECTIVE

We conducted this scoping review to assess the current state of literature on digital phenotyping and offer some discussion on the current trends and future direction of this area of research.

METHODS

We searched four databases, PubMed, Ovid MEDLINE, PsycINFO and Web of Science, from inception to August 25th, 2021. We included studies written in English that 1) investigated or applied their findings to diagnose psychiatric disorders and 2) utilized passive sensing for management or diagnosis. Protocols were excluded. A narrative synthesis approach was used, due to the heterogeneity and variability in outcomes and outcome types reported.

RESULTS

Of 10506 unique records identified, we included a total of 107 articles. The number of published studies has increased over tenfold from 2 in 2014 to 28 in 2020, illustrating the field's rapid growth. However, a significant proportion of these (49% of all studies and 87% of primary studies) were proof of concept, pilot or correlational studies examining digital phenotyping's potential. Most (62%) of the primary studies published evaluated individuals with depression (21%), BD (18%) and SZ (23%) (Appendix 1).

CONCLUSION

There is promise shown in certain domains of data and their clinical relevance, which have yet to be fully elucidated. A consensus has yet to be reached on the best methods of data collection and processing, and more multidisciplinary collaboration between physicians and other fields is needed to unlock the full potential of digital phenotyping and allow for statistically powerful clinical trials to prove clinical utility.

Data Visualization for Chronic Neurological and Mental Health Condition Self-management: Systematic Review of User Perspectives

BACKGROUND

Remote measurement technologies (RMT) such as mobile health devices and apps are increasingly used by those living with chronic neurological and mental health conditions. RMT enables real-world data collection and regular feedback, providing users with insights about their own conditions. Data visualizations are an integral part of RMT, although little is known about visualization design preferences from the perspectives of those living with chronic conditions.

OBJECTIVE

The aim of this review was to explore the experiences and preferences of individuals with chronic neurological and mental health conditions on data visualizations derived from RMT to manage health.

METHODS

In this systematic review, we searched peer-reviewed literature and conference proceedings (PubMed, IEEE Xplore, EMBASE, Web of Science, Association for Computing Machinery Computer-Human Interface proceedings, and the Cochrane Library) for original papers published between January 2007 and September 2021 that reported perspectives on data visualization of people living with chronic neurological and mental health conditions. Two reviewers independently screened each abstract and full-text article, with disagreements resolved through discussion. Studies were critically appraised, and extracted data underwent thematic synthesis.

RESULTS

We identified 35 eligible publications from 31 studies representing 12 conditions. Coded data coalesced into 3 themes: desire for data visualization, impact of visualizations on condition management, and visualization design considerations. Data visualizations were viewed as an integral part of users' experiences with RMT, impacting satisfaction and engagement. However, user preferences were diverse and often conflicting both between and within conditions.

CONCLUSIONS

When used effectively, data visualizations are valuable, engaging components of RMT. They can provide structure and insight, allowing individuals to manage their own health more effectively. However, visualizations are not "one-size-fits-all," and it is important to engage with potential users during visualization design to understand when, how, and with whom the visualizations will be used to manage health.

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

A Smartphone-Based Self-management Intervention for Individuals With Bipolar Disorder (LiveWell): Protocol Development for an Expert System to Provide Adaptive User Feedback

BACKGROUND

Bipolar disorder is a severe mental illness that results in significant morbidity and mortality. While pharmacotherapy is the primary treatment, adjunctive psychotherapy can improve outcomes. However, access to therapy is limited. Smartphones and other technologies can increase access to therapeutic strategies that enhance self-management while simultaneously augmenting care by providing adaptive delivery of content to users as well as alerts to providers to facilitate clinical care communication. Unfortunately, while adaptive interventions are being developed and tested to improve care, information describing the components of adaptive interventions is often not published in sufficient detail to facilitate replication and improvement of these interventions.

OBJECTIVE

To contribute to and support the improvement and dissemination of technology-based mental health interventions, we provide a detailed description of the expert system for adaptively delivering content and facilitating clinical care communication for LiveWell, a smartphone-based self-management intervention for individuals with bipolar disorder.

METHODS

Information from empirically supported psychotherapies for bipolar disorder, health psychology behavior change theories, and chronic disease self-management models was combined with user-centered design data and psychiatrist feedback to guide the development of the expert system.

RESULTS

Decision points determining the timing of intervention option adaptation were selected to occur daily and weekly based on self-report data for medication adherence, sleep duration, routine, and wellness levels. These data were selected for use as the tailoring variables determining which intervention options to deliver when and to whom. Decision rules linking delivery of options and tailoring variable thresholds were developed based on existing literature regarding bipolar disorder clinical status and psychiatrist feedback. To address the need for treatment adaptation with varying clinical statuses, decision rules for a clinical status state machine were developed using self-reported wellness rating data. Clinical status from this state machine was incorporated into hierarchal decision tables that select content for delivery to users and alerts to providers. The majority of the adaptive content addresses sleep duration, medication adherence, managing signs and symptoms, building and utilizing support, and keeping a regular routine, as well as determinants underlying engagement in these target behaviors as follows: attitudes and perceptions, knowledge, support, evaluation, and planning. However, when problems with early warning signs, symptoms, and transitions to more acute clinical states are detected, the decision rules shift the adaptive content to focus on managing signs and symptoms, and engaging with psychiatric providers.

CONCLUSIONS

Adaptive mental health technologies have the potential to enhance the self-management of mental health disorders. The need for individuals with bipolar disorder to engage in the management of multiple target behaviors and to address changes in clinical status highlights the importance of detailed reporting of adaptive intervention components to allow replication and improvement of adaptive mental health technologies for complex mental health problems.

A digital self-report survey of mood for bipolar disorder

OBJECTIVES

Bipolar disorder (BP) is commonly researched in digital settings. As a result, standardized digital tools are needed to measure mood. We sought to validate a new survey that is brief, validated in digital form, and able to separately measure manic and depressive severity.

METHODS

We introduce a 6-item digital survey, called digiBP, for measuring mood in BP. It has three depressive items (depressed mood, fidgeting, fatigue), two manic items (increased energy, rapid speech), and one mixed item (irritability); and recovers two scores (m and d) to measure manic and depressive severity. In a secondary analysis of individuals with BP who monitored their symptoms over 6 weeks (n = 43), we perform a series of analyses to validate the digiBP survey internally, externally, and as a longitudinal measure.

RESULTS

We first verify a conceptual model for the survey in which items load onto two factors ("manic" and "depressive"). We then show weekly averages of m and d scores from digiBP can explain significant variation in weekly scores from the Young Mania Rating Scale (R²  = 0.47) and SIGH-D (R²  = 0.58). Lastly, we examine the utility of the survey as a longitudinal measure by predicting an individual's future m and d scores from their past m and d scores.

CONCLUSIONS

While further validation is warranted in larger, diverse populations, these validation analyses should encourage researchers to consider digiBP for their next digital study of BP.

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.

Behavioural and Self-Reported Data Collected from Smartphones in the Assessment of Depressive and Manic Symptoms for Bipolar Disorder Patients: Prospective Observational Study

BACKGROUND

Smartphones allow for real-time monitoring of patients' behavioural activities in a naturalistic setting. These data are suggested as markers of mental state in bipolar disorder (BD).

OBJECTIVE

We assess the relations between data collected from smartphones and the clinically rated depressive and manic symptoms together with the corresponding affective states in BD.

METHODS

BDmon - a dedicated mobile app was developed and installed on the patients' smartphones to automatically collect statistics about phone calls and text messages, as well as self-assessment of sleep and patient's mood. The final sample for the numerical analyses consisted of 51 eligible patients who participated in at least two psychiatric assessments and used the BDmon app (mean participation time: 208 days ± SD of 132 days). In total, 196 psychiatric assessments were performed using the Hamilton Depression Rating Scale (HDRS) and Young Mania Rating Scale (YMRS). Generalized linear mixed-effects models were applied to quantify the strength of the relation between the daily statistics about behavioural data collected automatically from smartphones and the affective symptoms and mood states in BD.

RESULTS

Objective behavioural data collected from smartphones and their relation to BD states were as follows: (1) depressed patients tended to make phone calls less frequently than in euthymia (β=-0.064, P=.01); (2) the number of incoming answered calls was lower in depression as compared to euthymia (β=-0.15, P=.01) and, at the same time, missed incoming calls were more frequent and increased as depressive symptoms intensified (β=4.431, P<.001; β=4.861, P<.001, respectively); (3) the fraction of outgoing calls was higher in manic states (β=2.73, P=.03); (4) the fraction of missed calls was higher in manic/mixed states as compared to euthymia (β=3.53, P=.01) and positively correlated to the severity of symptoms (β=2.991, P=.02); (5) variability of duration of outgoing calls was higher in manic/mixed states (β=1.22·10-3, P=.045) and positively correlated to the severity of symptoms (β=1.72·10-3, P=.02); (6) the number and length of sent text messages was higher in manic/mixed states as compared to euthymia (β=0.031, P=.01; β=0.015, P=.01, respectively) and positively correlated to the severity of manic symptoms (β=0.116, P<.001; β=0.022, P<.001). We also observed that self-assessment of mood was lower in depressive (β=-1.452, P<.001). and higher in manic states (β=0.509, P<.001).

CONCLUSIONS

Smartphone-based behavioural parameters are valid markers in assessing the severity of affective symptoms and discriminating between mood states. This opens a way toward early detection of worsening of the mental state and thereby increases the patient's chance of improving the course of the illness.

[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.

A Smartphone-Based Self-management Intervention for Bipolar Disorder (LiveWell): User-Centered Development Approach

BACKGROUND

Bipolar disorder is a serious mental illness that results in significant morbidity and mortality. Pharmacotherapy is the primary treatment for bipolar disorder; however, adjunctive psychotherapy can help individuals use self-management strategies to improve outcomes. Yet access to this therapy is limited. Smartphones and other technologies have the potential to increase access to therapeutic strategies that enhance self-management while simultaneously providing real-time user feedback and provider alerts to augment care.

OBJECTIVE

This paper describes the user-centered development of LiveWell, a smartphone-based self-management intervention for bipolar disorder, to contribute to and support the ongoing improvement and dissemination of technology-based mental health interventions.

METHODS

Individuals with bipolar disorder first participated in a field trial of a simple smartphone app for self-monitoring of behavioral targets. To develop a complete technology-based intervention for bipolar disorder, this field trial was followed by design sessions, usability testing, and a pilot study of a smartphone-based self-management intervention for bipolar disorder. Throughout all phases of development, intervention revisions were made based on user feedback.

RESULTS

The core of the LiveWell intervention consists of a daily self-monitoring tool, the Daily Check-in. This self-monitoring tool underwent multiple revisions during the user-centered development process. Daily Check-in mood and thought rating scales were collapsed into a single wellness rating scale to accommodate user development of personalized scale anchors. These anchors are meant to assist users in identifying early warning signs and symptoms of impending episodes to take action based on personalized plans. When users identified personal anchors for the wellness scale, the anchors most commonly reflected behavioral signs and symptoms (40%), followed by cognitive (25%), mood (15%), physical (10%), and motivational (7%) signs and symptoms. Changes to the Daily Check-in were also made to help users distinguish between getting adequate sleep and keeping a regular routine. At the end of the pilot study, users reported that the Daily Check-in made them more aware of early warning signs and symptoms and how much they were sleeping. Users also reported that they liked personalizing their anchors and plans and felt this process was useful. Users experienced some difficulties with developing, tracking, and achieving target goals. Users also did not consistently follow up with app recommendations to contact providers when Daily Check-in data suggested they needed additional assistance. As a result, the human support roles for the technology were expanded beyond app use support to include support for self-management and clinical care communication. The development of these human support roles was aided by feedback on the technology's usability from the users and the coaches who provided the human support.

CONCLUSIONS

User input guided the development of intervention content, technology, and coaching support for LiveWell. Users valued the provision of monitoring tools and the ability to personalize plans for staying well, supporting the role of monitoring and personalization as important features of digital mental health technologies. Users also valued human support of the technology in the form of a coach, and user difficulties with aspects of self-management and care-provider communication led to an expansion of the coach's support roles. Obtaining feedback from both users and coaches played an important role in the development of both the LiveWell technology and human support. Attention to all stakeholders involved in the use of mental health technologies is essential for optimizing intervention development.

Monitoring Sleep Changes via a Smartphone App in Bipolar Disorder: Practical Issues and Validation of a Potential Diagnostic Tool

Background: Sleep disturbances are common early warning signs of an episode of bipolar disorder, and early recognition can favorably impact the illness course. Symptom monitoring via a smartphone app is an inexpensive and feasible method to detect an early indication of changes such as sleep. The study aims were (1) to assess the acceptance of apps and (2) to validate sleeping times measured by the smartphone app UP!. Methods:UP! was used by 22 individuals with bipolar disorder and 23 controls. Participants recorded their time of falling asleep and waking-up using UP! for 3 weeks. Results were compared to a validated accelerometer and the Pittsburgh Sleep Quality Index. Additionally, participants were interviewed regarding early warning signs and their feedback for apps as monitoring tools in bipolar disorder (NCT03275714). Results: With UP!, our study did not find strong reservations concerning data protection or continual smartphone usage. Correlation analysis demonstrates UP! to be a valid tool for measuring falling asleep and waking-up times. Discussion: Individuals with bipolar disorder assessed the measurement of sleep disturbances as an early warning sign with a smartphone as positive. The detection of early signs could change an individual's behavior and strengthen self-management. The study showed that UP! can be used to measure changes in sleep durations accurately. Further investigation of smartphone apps' impact to measure other early signs could significantly contribute to clinical treatment and research in the future through objective, continuous, and individual data collection.

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

BACKGROUND

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

RESULTS

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

CONCLUSIONS

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

Development of an Emotion-Sensitive mHealth Approach for Mood-State Recognition in Bipolar Disorder

Internet- and mobile-based approaches have become increasingly significant to psychological research in the field of bipolar disorders. While research suggests that emotional aspects of bipolar disorders are substantially related to the social and global functioning or the suicidality of patients, these aspects have so far not sufficiently been considered within the context of mobile-based disease management approaches. As a multiprofessional research team, we have developed a new and emotion-sensitive assistance system, which we have adapted to the needs of patients with bipolar disorder. Next to the analysis of self-assessments, third-party assessments, and sensor data, the new assistance system analyzes audio and video data of these patients regarding their emotional content or the presence of emotional cues. In this viewpoint, we describe the theoretical and technological basis of our emotion-sensitive approach and do not present empirical data or a proof of concept. To our knowledge, the new assistance system incorporates the first mobile-based approach to analyze emotional expressions of patients with bipolar disorder. As a next step, the validity and feasibility of our emotion-sensitive approach must be evaluated. In the future, it might benefit diagnostic, prognostic, or even therapeutic purposes and complement existing systems with the help of new and intuitive interaction models.

Digital solutions for shaping mood and behavior among individuals with mood disorders

Mood disorders present on-going challenges to the medical field, with difficulties ranging from establishing effective treatments to understanding complexities of one's mood. One solution is the use of mobile apps and wearables for measuring physiological symptoms and real-time mood in order to shape mood and behavior. Current digital research is focused on increasing engagement in monitoring mood, uncovering mood dynamics, predicting mood, and providing digital microinterventions. This review discusses the importance and risks of user engagement, as well as barriers to improving it. Research on mood dynamics highlights the possibility to reveal data-driven computational phenotypes that could guide treatment. Mobile apps are being used to track voice patterns, GPS, and phone usage for predicting mood and treatment response. Future directions include utilizing mobile apps to deliver and evaluate microinterventions. To continue these advances, standardized reporting and study designs should be considered to improve digital solutions for mood disorders.

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

A comparative study of engagement in mobile and wearable health monitoring for bipolar disorder

OBJECTIVES

Self-monitoring is recommended for individuals with bipolar disorder, with numerous technological solutions available. This study aimed to identify basic components of these solutions that increase engagement with self-monitoring.

METHODS

Participants with bipolar disorder (n = 47) monitored their symptoms with a Fitbit and a smartphone app and were randomly assigned to either review or not review recorded symptoms weekly. We tested whether individuals would better adhere to and prefer monitoring with passive monitoring with an activity tracker compared to active monitoring with a smartphone app and whether individuals would better adhere to self-monitoring if their recorded symptoms were reviewed with an interviewer.

RESULTS

Monitoring with a smartphone app achieved similar adherence and preference to Fitbit (P > .85). Linear mixed effects modeling found adherence decreased significantly more over the study for the Fitbit (12% more, P < .001) even though more participants reported they would use the Fitbit over a year compared to the app (72.3% vs 46.8%). Reviewing symptoms weekly did not improve adherence, but most participants reported they would prefer to review symptoms with a clinician (74.5%) and on monthly basis (57.5%) compared to alternatives. Participants endorsed sleep as the most important symptom to monitor, forgetfulness as the largest barrier to self-monitoring, and raising self-awareness as the best reason for self-monitoring.

CONCLUSIONS

We recommend a combined strategy of wearable and mobile monitoring that includes reminders, targets raising self-awareness, and tracks sleep. A clinician may want to review symptoms on a monthly basis.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03358238.

Review and Implementation of Self-Help and Automated Tools in Mental Health Care

Self-help and automated technologies can be useful for behavioral and mental health education and interventions. These technologies include interactive media, online courses, artificial intelligence-powered chatbots, voice assistants, and video games. Self-help media can include books, videos, audible media like podcasts, blog and print articles, and self-contained Internet sites. Social media, online courses, and mass-market mobile apps also can include such media. These technologies serve to decrease geospatial, temporal, and financial barriers. This article describes different self-help and automated technologies, how to implement such technologies in existing clinical services, and how to implement according to patient needs.

Young adults' perceptions of using wearables, social media and other technologies to detect worsening mental health: A qualitative study

Background

Technological interventions may help support and improve mental health. However young peoples’ perspectives on using different technologies to detect deteriorating mental health in those already diagnosed with a mental health condition is lacking. The study aim was to explore the perspectives of young patients on the feasibility and acceptability of using wearables, social media and technologies to detect mental health deterioration.

Methods

The study was co-produced with young adults with past mental health difficulties. Semi-structured interviews were conducted with young adults with a severe mental health condition in a private room at a community mental health site. Data was triangulated by comparing codes and ideas across the two co-researchers and two researchers over two virtual meetings. Themes were finalised and presented in a thematic map.

Results

Sixteen participants were interviewed (81% female). There were four main themes: dealing with mental health symptoms, signs of mental health deterioration, technology concerns and technological applications to identify worsening mental health. Wearables and mobile apps were considered acceptable and feasible to detect mental health deterioration in real-time if they could measure changes in sleep patterns, mood or activity levels as signs of deterioration. Getting help earlier was deemed essential particularly in reference to dissatisfaction with the current non-technological mental health services. However, patients identified issues to consider before implementation including practicality, safeguarding and patient preference.

Conclusion

Wearables and mobile apps could be viable technological options to help detect deterioration in young people in order to intervene early and avoid delay in accessing mental health services. However, immediate action following detection is required for the patient to trust and use the intervention.

Patients' views of wearable devices and AI in healthcare: findings from the ComPaRe e-cohort

Wearable biometric monitoring devices (BMDs) and artificial intelligence (AI) enable the remote measurement and analysis of patient data in real time. These technologies have generated a lot of "hype," but their real-world effectiveness will depend on patients' uptake. Our objective was to describe patients' perceptions of the use of BMDs and AI in healthcare. We recruited adult patients with chronic conditions in France from the "Community of Patients for Research" (ComPaRe). Participants (1) answered quantitative and open-ended questions about the potential benefits and dangers of using of these new technologies and (2) participated in a case-vignette experiment to assess their readiness for using BMDs and AI in healthcare. Vignettes covered the use of AI to screen for skin cancer, remote monitoring of chronic conditions to predict exacerbations, smart clothes to guide physical therapy, and AI chatbots to answer emergency calls. A total of 1183 patients (51% response rate) were enrolled between May and June 2018. Overall, 20% considered that the benefits of technology (e.g., improving the reactivity in care and reducing the burden of treatment) greatly outweighed the dangers. Only 3% of participants felt that negative aspects (inadequate replacement of human intelligence, risks of hacking and misuse of private patient data) greatly outweighed potential benefits. We found that 35% of patients would refuse to integrate at least one existing or soon-to-be available intervention using BMDs and AI-based tools in their care. Accounting for patients' perspectives will help make the most of technology without impairing the human aspects of care, generating a burden or intruding on patients' lives.

Online help-seeking prior to diagnosis: Can web-based resources reduce the duration of untreated mood disorders in young people?

OBJECTIVE

Mood and anxiety disorders typically begin in adolescence or early adulthood, but those at the age of highest risk are among those least likely to access mental health services. However, they may be more likely than other demographic groups to seek help online. The goal of the present study was to investigate the online help- and information-seeking activity of young people newly diagnosed with mood and anxiety disorders in order to better understand how digital resources might serve this population.

METHOD

Participants, aged 15 to 35, with a diagnosis of a mood or anxiety disorder were eligible if they had received their first mental health diagnosis within 24 months. Participants were interviewed with the Pathways to Care Questionnaire, which inquires about online activity prior to one's first interaction with mental healthcare providers.

RESULTS

Forty people participated (depression n = 30, bipolar disorder n = 5, generalized anxiety disorder n = 5); average age 21 years (SD=3.2), 60% female. Eighty-one percent reported seeking help and/or information about their symptoms online. The gap between symptom onset and in-person help seeking was 91.90 weeks (SD=133.7). Most participants (85%) reported they would be open to communicating with a mental health professional online.

CONCLUSION

A majority of young people experiencing clinically-significant symptoms seek help online. However, the gap between symptom onset and treatment initiation remains unacceptably long. Better strategies are needed to translate young people's interest in online resources into meaningful care, whether through web-based services or facilitated pathways to traditional treatment.

Supporting Self-Management in Bipolar Disorder: Mixed-Methods Knowledge Translation Study

BACKGROUND

Self-management is increasingly recognized as an important method through which individuals with bipolar disorder (BD) may cope with symptoms and improve quality of life. Digital health technologies have strong potential as a method to support the application of evidence-informed self-management strategies in BD. Little is known, however, about how to most effectively maximize user engagement with digital platforms.

OBJECTIVE

The aims of this study were (1) to create an innovative Web-based Bipolar Wellness Centre, (2) to conduct a mixed-methods (ie, quantitative and qualitative) evaluation to assess the impact of different sorts of engagement (ie, knowledge translation [KT]), and (3) to support engagement with the self-management information in the Bipolar Wellness Centre.

METHODS

The project was implemented in 2 phases. In phase 1, community-based participatory research and user-centered design methods were used to develop a website (Bipolar Wellness Centre) housing evidence-informed tools and strategies for self-management of BD. In phase 2, a mixed-methods evaluation was conducted to explore the potential impact of 4 KT strategies (Web-based webinars, Web-based videos, Web-based one-to-one Living Library peer support, and in-person workshops). Quantitative assessments occurred at 2 time points-preintervention and 3 weeks postintervention. Purposive sampling was used to recruit a subsample of participants for the qualitative interviews, ensuring each KT modality was represented, and interviews occurred approximately 3 weeks postintervention.

RESULTS

A total of 94 participants were included in the quantitative analysis. Responses to evaluative questions about engagement were broadly positive. When averaged across the 4 KT strategies, significant improvements were observed on the Bipolar Recovery Questionnaire (F_1,77=5.887; P=.02) and Quality of Life in Bipolar Disorder (F_1,77=8.212; P=.005). Nonsignificant improvements in positive affect and negative affect were also observed. The sole difference that emerged between KT strategies related to the Chronic Disease Self-Efficacy measure, which decreased after participation in the webinar and video arms but increased after the Living Library and workshop arms. A subsample of 43 participants was included in the qualitative analyses, with the majority of participants describing positive experiences with the 4 KT strategies; peer contact was emphasized as a benefit across all strategies. Infrequent negative experiences were reported in relation to the webinar and video strategies, and included technical difficulties, the academic tone of webinars, and feeling unable to relate to the actor in the videos.

CONCLUSIONS

This study adds incremental evidence to a growing literature that suggests digital health technologies can provide effective support for self-management for people with BD. The finding that KT strategies could differentially impact chronic disease self-efficacy (hypothesized as being a product of differences in degree of peer contact) warrants further exploration. Implications of the findings for the development of evidence-informed apps for BD are discussed in this paper.

Early Psychosis Service User Views on Digital Technology: Qualitative Analysis

BACKGROUND

Digital technology has the potential to improve outcomes for people with psychosis. However, to date, research has largely ignored service user views on digital health interventions (DHIs).

OBJECTIVE

The objective of our study was to explore early psychosis service users' subjective views on DHIs.

METHODS

Framework analysis was undertaken with data obtained from 21 semistructured interviews with people registered with early intervention for psychosis services. Robust measures were used to develop a stable framework, including member checking, triangulation, independent verification of themes, and consensus meetings.

RESULTS

The following 4 themes were established a priori: acceptability of technology in psychosis and mental health; technology increasing access to and augmenting mental health support; barriers to adopting DHIs; and concerns about management of data protection, privacy, risk, and security of information. The following 2 themes were generated a posteriori: blending DHIs with face-to-face treatment and empowerment, control, and choice. DHIs were also viewed as potentially destigmatizing, overcoming barriers faced in traditional service settings, facilitating communication, and empowering service users to take active control of their health care.

CONCLUSIONS

In the first study of its kind, early psychosis service users' were largely positive about the potential use of DHIs supporting and managing mental health. Overall, service users felt that DHIs were a progressive, modern, and relevant platform for health care delivery. Concerns were expressed around privacy and data security and practical barriers inherent within DHIs, all of which require further attention. Future research should explore whether findings transfer to other service user groups, other technology delivery formats, and across a range of treatment modalities.