Effectiveness of a Smartphone App With a Wearable Activity Tracker in Preventing the Recurrence of Mood Disorders: Prospective Case-Control Study

A digital phenotyping dataset for impending panic symptoms: a prospective longitudinal study

This study investigated the utilization of digital phenotypes and machine learning algorithms to predict impending panic symptoms in patients with mood and anxiety disorders. A cohort of 43 patients was monitored over a two-year period, with data collected from smartphone applications and wearable devices. This research aimed to differentiate between the day before panic (DBP) and stable days without symptoms. With RandomForest, GradientBoost, and XGBoost classifiers, the study analyzed 3,969 data points, including 254 DBP events. The XGBoost model demonstrated performance with a ROC-AUC score of 0.905, while a simplified model using only the top 10 variables maintained an ROC-AUC of 0.903. Key predictors of panic events included evaluated Childhood Trauma Questionnaire scores, increased step counts, and higher anxiety levels. These findings indicate the potential of machine learning algorithms leveraging digital phenotypes to predict panic symptoms, thereby supporting the development of proactive and personalized digital therapies and providing insights into real-life indicators that may exacerbate panic symptoms in this population.

Accurately predicting mood episodes in mood disorder patients using wearable sleep and circadian rhythm features

Wearable devices enable passive collection of sleep, heart rate, and step-count data, offering potential for mood episode prediction in mood disorder patients. However, current models often require various data types, limiting real-world application. Here, we develop models that predict future episodes using only sleep-wake data, easily gathered through smartphones and wearables when trained on an individual's sleep-wake history and past mood episodes. Using mathematical modeling to longitudinal data from 168 patients (587 days average clinical follow-up, 267 days wearable data), we derived 36 sleep and circadian rhythm features. These features enabled accurate next-day predictions for depressive, manic, and hypomanic episodes (AUCs: 0.80, 0.98, 0.95). Notably, daily circadian phase shifts were the most significant predictors: delays linked to depressive episodes, advances to manic episodes. This prospective observational cohort study (ClinicalTrials.gov: NCT03088657, 2017-3-23) shows sleep-wake data, combined with prior mood episode history, can effectively predict mood episodes, enhancing mood disorder management.

The application of artificial intelligence in health communication development: A scoping review

This scoping review explores the integration of Artificial Intelligence (AI) with communication, behavioral, and social theories to enhance health behavior interventions. A systematic search of articles published through February 2024, following PRISMA guidelines, identified 28 relevant studies from 13,723 screened. These studies, conducted across various countries, addressed health issues such as smoking cessation, musculoskeletal injuries, diabetes, chronic diseases and mental health using AI-driven tools like chatbots and apps. Despite AI's potential, a gap exists in aligning technical advancements with theoretical frameworks. The proposed AI Impact Communications Model (AI-ICM) aims to bridge this gap, offering a road map for future research and practice.

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

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Relationship between nonexercise activity and mood in patients with eating disorders

INTRODUCTION

Many patients with eating disorders (EDs) engage in excessive and compulsive physical activity (pathological exercise, PE) to regulate negative mood or to "burn calories." PE can lead to negative health consequences. Non-exercise activity (NEA) bears the potential to serve as intervention target to counteract PE and problematic eating behaviors since it has been associated with positive mood effects. However, to date, there is no investigation on whether the positive link between NEA and mood seen in the healthy translates to patients with ED.

MATERIAL AND METHODS

To study potential associations of NEA and mood in ED, we subjected 29 ED-patients and 35 healthy controls (HCs) to an ambulatory assessment study across 7 days. We measured NEA via accelerometers and repeatedly assessed mood on electronic smartphone diaries via a mixed sampling strategy based on events, activity and time. Within- and between-subject effects of NEA on mood, PE as moderator, and the temporal course of effects were analyzed via multilevel modeling.

RESULTS

NEA increased valence (β = 2.12, p < 0.001) and energetic arousal (β = 4.02, p < 0.001) but showed no significant effect on calmness. The effects of NEA on energetic arousal where significantly stronger for HCs (βHC = 6.26, p < 0.001) than for EDs (βED = 4.02, p < 0.001; βinteraction = 2.24, p = 0.0135). Effects of NEA were robust across most timeframes of NEA and significantly moderated by PE, that is, Lower PE levels exhibited stronger NEA effects on energetic arousal.

CONCLUSION

Patients with ED and HC show an affective benefit from NEA, partly depending on the level of PE. If replicated in experimental daily life studies, this evidence may pave the way towards expedient NEA interventions to cope with negative mood. Interventions could be especially promising if delivered as Just-in-time adaptive interventions (JITAIs) and should be tailored according to the PE level.

A comprehensive survey of the clinical trial Landscape on digital therapeutics

Background

Digital therapeutics (DTx) is an emerging and groundbreaking medical intervention that utilizes health software to treat or alleviate various diseases, disorders, conditions, or injuries. Although the potential of digital therapy is enormous, it is still in its nascent stage and faces multiple challenges and obstacles. The purpose of this study is to provide an overview of all DTx-related clinical trials in ClinicalTrials.gov and to promote the advancement of DTx.

Methods

Two reviewers and one expert evaluated data from all DTx clinical trials on ClinicalTrials.gov as of August 8, 2023. Trials utilizing digital therapeutics independently or in combination with traditional approaches were included. Incomplete trials and those lacking an evidence-based foundation were excluded. Basic information about product launches and primary outcome measures was extracted and analyzed.

Results

A total of 280 eligible trials were categorized into treating a disease (141, 50.4 %), managing a disease (120, 42.9 %), and improving a health function (19, 6.8 %). The focus was primarily on mental and behavioral disorders, neurological disorders, and endocrine, nutritional, and metabolic disorders. The number of trials has been increasing annually, yet trial design and conduct remain inconsistent. Randomized controlled trials (RCTs) accounted for 67.5 % of completed trials, and 36 trials (12.9 %) involved products already approved for marketing.

Conclusions

The growth in clinical studies on DTx underscores their potential in healthcare. However, challenges persist in standardization, regulation, and clinical efficacy. There is a need for a harmonized global classification of digital therapeutics and standardized clinical trial protocols to ensure efficacy and improve healthcare services.

Daily Self-Monitoring and Feedback of Circadian Rhythm Measures in Major Depression and Bipolar Disorder Using Wearable Devices and Smartphones-The Circadian Rhythm for Mood (CRM®) Trial Protocol: A Randomized Sham Controlled Double-Blind Trial

The circadian rhythm for mood (CRM) is a digital therapeutic, which aims to prevent mood episode and improve clinical course in patients with major mood disorders. Developed on the circadian rhythm hypothesis of mood disorder, CRM predicts the impending risk of mood episode with its built-in algorithm, utilizing wearable devices data and daily self-reports, and provides personalized feedback. In a pilot study of the CRM, the users experienced less frequent and shorter duration of mood episodes than the non-users. To investigate the efficacy of the upgraded CRM, a double-blind, randomized, sham-controlled, parallel-group trial is designed. Patients aged between 19 and 70, diagnosed with bipolar I disorder, bipolar II disorder, or major depressive disorder, in a euthymic state for more than two months, can participate. During this 12-month trial, participants are assessed for episode recurrence every three months, and the efficacy of the CRM as a potential digital therapeutic is evaluated. Trial registration: ClinicalTrials.gov Identifier: NCT05400785.

The validity and reliability of the Chinese version of the biological rhythms interview of assessment in neuropsychiatry in the community: a large Chinese college student population

Objective

To test the psychometric properties of the Chinese version of the biological rhythms interview of assessment in neuropsychiatry (C-BRIAN) in a group of young adults with and without depressive symptoms.

Methods

Three hundred and seventy-eight university students were recruited as participants. Based on the scores from Center for Epidemiological Survey Depression Scale (CES-D), students were divided into the depressed group and healthy group. Explorative factor analysis was applied to assess the construct validity of the C-BRIAN. The Pittsburgh Sleep Quality Index (PSQI) and CES-D were compared with the C-BRIAN to test the convergent validity. The internal consistency of the C-BRIAN was also examined.

Results

Three factors were extracted (activities, eating patterns, and sleep factors) explaining 63.9% of the total variance. The internal consistencies were very good with a coefficient of 0.94 (overall) and 0.89-0.91 for three factors. The domains of activities, eating patterns, and sleep were moderately correlated with PSQI (r=0.579) and CES-D (r=0.559) (ps<0.01).

Conclusion

Our findings suggest that C-BRIAN has good validity and reliability which can be used to assess the biological rhythm in the young adult population with depressive symptoms. C-BRIAN would be a reliable tool to detect depressive symptoms for timely prevention and intervention in the community.

Exploring depressive symptom trajectories in COVID-19 patients with clinically mild condition in South Korea using remote patient monitoring: longitudinal data analysis

Background

During the height of the COVID-19 pandemic, the Korean government temporarily allowed full scale telehealth care for safety and usability. However, limited studies have evaluated the impact of telehealth by analyzing the physical and/or mental health data of patients with COVID-19 diagnosis collected through telehealth targeting Korean population.

Objective

This study aimed to identify subgroup of depressive symptom trajectories in patients with clinically mild COVID-19 using collected longitudinal data from a telehealth-based contactless clinical trial.

Methods

A total of 199 patients with COVID-19 were accrued for contactless clinical trial using telehealth from March 23 to July 20, 2022. Depressive symptoms were measured using the patient health questionnaire-9 on the start day of quarantine, on the final day of quarantine, and 1 month after release from quarantine. Additionally, acute COVID-19 symptoms were assessed every day during quarantine. This study used a latent class mixed model to differentiate subgroups of depressive symptom trajectories and a logistic regression model with Firth's correction to identify associations between acute COVID-19 symptoms and the subgroups.

Results

Two latent classes were identified: class 1 with declining linearity at a slow rate and class 2 with increasing linearity. Among COVID-19 symptoms, fever, chest pain, and brain fog 1 month after release from quarantine showed strong associations with class 2 (fever: OR, 19.43, 95% CI, 2.30-165.42; chest pain: OR, 6.55, 95% CI, 1.15-34.61; brain fog: OR, 7.03, 95% CI 2.57-20.95). Sleeping difficulty and gastrointestinal symptoms were also associated with class 2 (gastrointestinal symptoms: OR, 4.76, 95% CI, 1.71-14.21; sleeping difficulty: OR, 3.12, 95% CI, 1.71-14.21).

Conclusion

These findings emphasize the need for the early detection of depressive symptoms in patients in the acute phase of COVID-19 using telemedicine. Active intervention, including digital therapeutics, may help patients with aggravated depressive symptoms.

Telepsychiatry in the Treatment of Major Depressive Disorders

This chapter explores the transformative role of telepsychiatry in managing major depressive disorders (MDD). Traversing geographical barriers and reducing stigma, this innovative branch of telemedicine leverages digital platforms to deliver effective psychiatric care. We investigate the evolution of telepsychiatry, examining its diverse interventions such as videoconferencing-based psychotherapy, medication management, and mobile applications. While offering significant advantages like increased accessibility, cost-effectiveness, and improved patient engagement, challenges in telepsychiatry include technological barriers, privacy concerns, ethical and legal considerations, and digital literacy gaps. Looking forward, emerging technologies like virtual reality, artificial intelligence, and precision medicine hold immense potential to personalize and enhance treatment effectiveness. Recognizing its limitations and advocating for equitable access, this chapter underscores telepsychiatry's power to revolutionize MDD treatment, making quality mental healthcare a reality for all.

Practical application of digital therapeutics in people with mood disorders

Digital therapeutics (DTx) offer evidence-based digitally-delivered high quality standards applications and/or softwares in the prevention, management and treatment of several medical conditions, including mood disorders. Nowadays, there are only three DTx officially approved by the Food and Drug Administration for mental conditions and there are still very few DTx developed in the context of mood disorders. The current comprehensive overview aims at providing a summary of currently published studies on DTx clinical applications in major depressive disorder (MDD), depressive symptomatology and bipolar disorder (BD), by using PubMed/MEDLINE and Scopus databases. Fifteen studies have been selected (10 on DTx in depressive symptomatology and/or MDD; 4 on BD; 1 on MDD and BD). Literature on DTx in mood disorders is still lacking, being mostly constituted by feasibility and acceptability rather than efficacy/effectiveness outcomes, particularly in BD. More studies focused on MDD compared to BD. Most DTx on MDD have been developed based on cognitive behaviour therapy interventions while on BD are based on psychoeducation. All studies assessing symptom severity improvement pre- vs. postinterventions demonstrated a significant postintervention improvement. Therefore, despite the preliminary encouraging results of studies here retrieved, their methodology is still too heterogeneous to allow comparisons and the generalizability of their findings. Further studies are warranted, in more larger samples involving multiple sites, including measures of both specific symptom effects as well as acceptability, feasibility and effectiveness in the real-world settings.

The New Emerging Treatment Choice for Major Depressive Disorders: Digital Therapeutics

The chapter provides an in-depth analysis of digital therapeutics (DTx) as a revolutionary approach to managing major depressive disorder (MDD). It discusses the evolution and definition of DTx, their application across various medical fields, regulatory considerations, and their benefits and limitations. This chapter extensively covers DTx for MDD, including smartphone applications, virtual reality interventions, cognitive-behavioral therapy (CBT) platforms, artificial intelligence (AI) and chatbot therapies, biofeedback, wearable technologies, and serious games. It evaluates the effectiveness of these digital interventions, comparing them with traditional treatments and examining patient perspectives, compliance, and engagement. The integration of DTx into clinical practice is also explored, along with the challenges and barriers to their adoption, such as technological limitations, data privacy concerns, ethical considerations, reimbursement issues, and the need for improved digital literacy. This chapter concludes by looking at the future direction of DTx in mental healthcare, emphasizing the need for personalized treatment plans, integration with emerging modalities, and the expansion of access to these innovative solutions globally.

Digital Phenotyping for Monitoring Mental Disorders: Systematic Review

BACKGROUND

The COVID-19 pandemic has increased the impact and spread of mental illness and made health services difficult to access; therefore, there is a need for remote, pervasive forms of mental health monitoring. Digital phenotyping is a new approach that uses measures extracted from spontaneous interactions with smartphones (eg, screen touches or movements) or other digital devices as markers of mental status.

OBJECTIVE

This review aimed to evaluate the feasibility of using digital phenotyping for predicting relapse or exacerbation of symptoms in patients with mental disorders through a systematic review of the scientific literature.

METHODS

Our research was carried out using 2 bibliographic databases (PubMed and Scopus) by searching articles published up to January 2023. By following the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) guidelines, we started from an initial pool of 1150 scientific papers and screened and extracted a final sample of 29 papers, including studies concerning clinical populations in the field of mental health, which were aimed at predicting relapse or exacerbation of symptoms. The systematic review has been registered on the web registry Open Science Framework.

RESULTS

We divided the results into 4 groups according to mental disorder: schizophrenia (9/29, 31%), mood disorders (15/29, 52%), anxiety disorders (4/29, 14%), and substance use disorder (1/29, 3%). The results for the first 3 groups showed that several features (ie, mobility, location, phone use, call log, heart rate, sleep, head movements, facial and vocal characteristics, sociability, social rhythms, conversations, number of steps, screen on or screen off status, SMS text message logs, peripheral skin temperature, electrodermal activity, light exposure, and physical activity), extracted from data collected via the smartphone and wearable wristbands, can be used to create digital phenotypes that could support gold-standard assessment and could be used to predict relapse or symptom exacerbations.

CONCLUSIONS

Thus, as the data were consistent for almost all the mental disorders considered (mood disorders, anxiety disorders, and schizophrenia), the feasibility of this approach was confirmed. In the future, a new model of health care management using digital devices should be integrated with the digital phenotyping approach and tailored mobile interventions (managing crises during relapse or exacerbation).

Using wearable activity trackers for research in the global south: Lessons learned from adolescent psychotherapy research in Kenya

Wearable activity trackers have emerged as valuable tools for health research, providing high-resolution data on measures such as physical activity. While most research on these devices has been conducted in high-income countries, there is growing interest in their use in the global south. This perspective discusses the challenges faced and strategies employed when using wearable activity trackers to test the effects of a school-based intervention for depression and anxiety among Kenyan youth. Lessons learned include the importance of validating data output, establishing an internal procedure for international procurement, providing on-site support for participants, designating a full-time team member for wearable activity tracker operation, and issuing a paper-based information sheet to participants. The insights shared in this perspective serve as guidance for researchers undertaking studies with wearables in similar settings, contributing to the evidence base for mental health interventions targeting youth in the global south. Despite the challenges to set up, deploy and extract data from wearable activity trackers, we believe that wearables are a relatively economical approach to provide insight into the daily lives of research participants, and recommend their use to other researchers.

Real-time monitoring of increases in restlessness to assess idiographic risk of recurrence of depressive symptoms

BACKGROUND

This confirmatory study aimed to examine whether we can foresee recurrence of depressive symptoms using personalized modeling of rises in restlessness.

METHODS

Participants were formerly depressed patients (N = 41) in remission who (gradually) discontinued antidepressants. Participants completed five smartphone-based Ecological Momentary Assessments (EMA) a day, for a period of 4 months, yielding a total of 21 180 observations. Statistical Process Control by means of Exponentially Weighted Moving Average (EWMA) control charts was used to detect rises in the EMA item 'I feel restless', for each individual separately.

RESULTS

An increase in restlessness was detected in 68.3% of the participants with recurring depressive symptoms, and in 26.3% of those who stayed in remission (Fisher's exact test p = 0.01, sensitivity was 68.3%, specificity was 73.7%). In the participants with a recurrence and an increase in restlessness, this increase could be detected in the prodromal phase of depression in 93.3% of the cases and at least a month before the onset of the core symptoms of depression in 66.7% of the cases.

CONCLUSIONS

Restlessness is a common prodromal symptom of depression. The sensitivity and specificity of the EWMA charts was at least as good as prognostic models based on cross-sectional patient characteristics. An advantage of the current idiographic method is that the EWMA charts provide real-time personalized insight in a within-person increase in early signs of depression, which is key to alert the right patient at the right time.

Potential effectiveness of digital therapeutics specialized in executive functions as adjunctive treatment for clinical symptoms of attention-deficit/hyperactivity disorder: a feasibility study

Introduction

The role of digital therapeutics (DTx) in the effective management of attention deficit/hyperactivity disorder (ADHD) is beginning to gain clinical attention. Therefore, it is essential to verify their potential efficacy.

Method

We aimed to investigate the improvement in the clinical symptoms of ADHD by using DTx AimDT01 (NUROW) (AIMMED Co., Ltd., Seoul, Korea) specialized in executive functions. NUROW, which consists of Go/No-go Task- and N-Back/Updating-based training modules and a personalized adaptive algorithm system that adjusts the difficulty level according to the user's performance, was implemented on 30 Korean children with ADHD aged 6 to 12 years. The children were instructed to use the DTx for 15 min daily for 4 weeks. The Comprehensive attention test (CAT) and Childhood Behavior Checklist (CBCL) were used to assess the children at baseline and endpoint. In contrast, the ADHD-Rating Scale (ARS) and PsyToolkit were used weekly and followed up at 1 month, for any sustained effect. Repeated measures ANOVA was used to identify differences between the participants during visits, while t-tests and Wilcoxon signed-rank tests were used to identify changes before and after the DTx.

Results

We included 27 participants with ADHD in this analysis. The ARS inattention (F = 4.080, p = 0.010), hyperactivity (F = 5.998. p < 0.001), and sum (F = 5.902, p < 0.001) significantly improved. After applying NUROW, internalized (t = -3.557, p = 0.001, 95% CI = -3.682--0.985), other (Z = -3.434, p = 0.001, effect size = -0.661), and sum scores (t = -3.081, p = 0.005, 95% CI = -10.126--2.022) were significantly changed in the CBCL. The overall effect was confirmed in the ARS sustained effect analysis even after 1 month of discontinuing the DTx intervention.

Discussion

According to caregivers, the findings indicate that DTx holds potential effect as an adjunctive treatment in children with ADHD, especially in subjective clinical symptoms. Future studies will require detailed development and application targeting specific clinical domains using DTx with sufficient sample sizes.Clinical trial registration: KCT0007579.

Mental Health Problems During COVID-19 and Attitudes Toward Digital Therapeutics

OBJECTIVE

We aimed to elucidate public mental health problems and associated factors during the coronavirus disease-2019 (COVID-19). Furthermore, we evaluated people's attitudes toward digital therapeutics during the pandemic.

METHODS

Data was collected online from participants, aged between 20-50 without any history of mental illness, from June 1st to June 30th 2021. The survey consisted of questions regarding demographics, changes during pandemic and attitude towards digital therapeutics, and mental health measures.

RESULTS

Among the total of 445 participants, 49.2% reported significant level of stress and 13.5% and 7.0% met the screening criteria for major depressive disorder and generalized anxiety disorder, respectively. Significant predictive factors for mental health problems were-younger age group, female sex, currently being treated for medical or surgical disease, change in the amount of time spent on mobile device or computer after pandemic, change in household income, and change in work environment due to pandemic. Furthermore, 35.1% of participants, considered psychiatric consultation, at least slightly, but were hesitant to receive it due to the fear of contacting COVID-19 at the clinics. Instead, 54.4% of them preferred using digital therapeutics as an alternative to visiting offline clinics.

CONCLUSION

We demonstrated that COVID-19 increased mental health problems along with access problems and identified their predictive factors. Digital therapeutics emerged as a viable solution to mental health problems and it was well-received by those in need of psychiatric consultation. Therefore, development and implementation of digital therapeutics should be considered to improve the mental health of people.

Artificial intelligence and machine learning in mobile apps for mental health: A scoping review

Mental health conditions can have significant negative impacts on wellbeing and healthcare systems. Despite their high prevalence worldwide, there is still insufficient recognition and accessible treatments. Many mobile apps are available to the general population that aim to support mental health needs; however, there is limited evidence of their effectiveness. Mobile apps for mental health are beginning to incorporate artificial intelligence and there is a need for an overview of the state of the literature on these apps. The purpose of this scoping review is to provide an overview of the current research landscape and knowledge gaps regarding the use of artificial intelligence in mobile health apps for mental health. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) and Population, Intervention, Comparator, Outcome, and Study types (PICOS) frameworks were used to structure the review and the search. PubMed was systematically searched for randomised controlled trials and cohort studies published in English since 2014 that evaluate artificial intelligence- or machine learning-enabled mobile apps for mental health support. Two reviewers collaboratively screened references (MMI and EM), selected studies for inclusion based on the eligibility criteria and extracted the data (MMI and CL), which were synthesised in a descriptive analysis. 1,022 studies were identified in the initial search and 4 were included in the final review. The mobile apps investigated incorporated different artificial intelligence and machine learning techniques for a variety of purposes (risk prediction, classification, and personalisation) and aimed to address a wide range of mental health needs (depression, stress, and suicide risk). The studies' characteristics also varied in terms of methods, sample size, and study duration. Overall, the studies demonstrated the feasibility of using artificial intelligence to support mental health apps, but the early stages of the research and weaknesses in the study designs highlight the need for more research into artificial intelligence- and machine learning-enabled mental health apps and stronger evidence of their effectiveness. This research is essential and urgent, considering the easy availability of these apps to a large population.

Digital tools for the assessment of pharmacological treatment for depressive disorder: State of the art

Depression is an invalidating disorder, marked by phenotypic heterogeneity. Clinical assessments for treatment adjustments and data-collection for pharmacological research often rely on subjective representations of functioning. Better phenotyping through digital applications may add unseen information and facilitate disentangling the clinical characteristics and impact of depression and its pharmacological treatment in everyday life. Researchers, physicians, and patients benefit from well-understood digital phenotyping approaches to assess the treatment efficacy and side-effects. This review discusses the current possibilities and pitfalls of wearables and technology for the assessment of the pharmacological treatment of depression. Their applications in the whole spectrum of treatment for depression, including diagnosis, treatment of an episode, and monitoring of relapse risk and prevention are discussed. Multiple aspects are to be considered, including concerns that come with collecting sensitive data and health recordings. Also, privacy and trust are addressed. Available applications range from questionnaire-like apps to objective assessment of behavioural patterns and promises in handling suicidality. Nonetheless, interpretation and integration of this high-resolution information with other phenotyping levels, remains challenging. This review provides a state-of-the-art description of wearables and technology in digital phenotyping for monitoring pharmacological treatment in depression, focusing on the challenges and opportunities of its application in clinical trials and research.

Design and Methods of a Prospective Smartphone App-Based Study for Digital Phenotyping of Mood and Anxiety Symptoms Mixed With Centralized and Decentralized Research Form: The Search Your Mind (S.Y.M., ) Project

In this study, the Search Your Mind (S.Y.M., ) project aimed to collect prospective digital phenotypic data centered on mood and anxiety symptoms across psychiatric disorders through a smartphone application (app) platform while using both centralized and decentralized research designs: the centralized research design is a hybrid of a general prospective observational study and a digital platform-based study, and it includes face-to-face research such as informed written consent, clinical evaluation, and blood sampling. It also includes digital phenotypic assessment through an application-based platform using wearable devices. Meanwhile, the decentralized research design is a non-face-to-face study in which anonymous participants agree to electronic informed consent forms on the app. It also exclusively uses an application-based platform to acquire individualized digital phenotypic data. We expect to collect clinical, biological, and digital phenotypic data centered on mood and anxiety symptoms, and we propose a possible model of centralized and decentralized research design.

Using Artificial Intelligence to Enhance Ongoing Psychological Interventions for Emotional Problems in Real- or Close to Real-Time: A Systematic Review

Emotional disorders are the most common mental disorders globally. Psychological treatments have been found to be useful for a significant number of cases, but up to 40% of patients do not respond to psychotherapy as expected. Artificial intelligence (AI) methods might enhance psychotherapy by providing therapists and patients with real- or close to real-time recommendations according to the patient's response to treatment. The goal of this investigation is to systematically review the evidence on the use of AI-based methods to enhance outcomes in psychological interventions in real-time or close to real-time. The search included studies indexed in the electronic databases Scopus, Pubmed, Web of Science, and Cochrane Library. The terms used for the electronic search included variations of the words "psychotherapy", "artificial intelligence", and "emotional disorders". From the 85 full texts assessed, only 10 studies met our eligibility criteria. In these, the most frequently used AI technique was conversational AI agents, which are chatbots based on software that can be accessed online with a computer or a smartphone. Overall, the reviewed investigations indicated significant positive consequences of using AI to enhance psychotherapy and reduce clinical symptomatology. Additionally, most studies reported high satisfaction, engagement, and retention rates when implementing AI to enhance psychotherapy in real- or close to real-time. Despite the potential of AI to make interventions more flexible and tailored to patients' needs, more methodologically robust studies are needed.

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

BACKGROUND

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

METHODS

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

RESULTS

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

LIMITATIONS

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

CONCLUSIONS

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

LIMITATIONS

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

CONCLUSIONS

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

Assessing the Mental Health of Emerging Adults Through a Mental Health App: Protocol for a Prospective Pilot Study

BACKGROUND

Individuals can experience different manifestations of the same psychological disorder. This underscores the need for a personalized model approach in the study of psychopathology. Emerging adulthood is a developmental phase wherein individuals are especially vulnerable to psychopathology. Given their exposure to repeated stressors and disruptions in routine, the emerging adult population is worthy of investigation.

OBJECTIVE

In our prospective study, we aim to conduct multimodal assessments to determine the feasibility of an individualized approach for understanding the contextual factors of changes in daily affect, sleep, physiology, and activity. In other words, we aim to use event mining to predict changes in mental health.

METHODS

We expect to have a final sample size of 20 participants. Recruited participants will be monitored for a period of time (ie, between 3 and 12 months). Participants will download the Personicle app on their smartphone to track their activities (eg, home events and cycling). They will also be given wearable sensor devices (ie, devices that monitor sleep, physiology, and physical activity), which are to be worn continuously. Participants will be asked to report on their daily moods and provide open-ended text responses on a weekly basis. Participants will be given a battery of questionnaires every 3 months.

RESULTS

Our study has been approved by an institutional review board. The study is currently in the data collection phase. Due to the COVID-19 pandemic, the study was adjusted to allow for remote data collection and COVID-19-related stress assessments.

CONCLUSIONS

Our study will help advance research on individualized approaches to understanding health and well-being through multimodal systems. Our study will also demonstrate the benefit of using individualized approaches to study interrelations among stress, social relationships, technology, and mental health.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/25775.

Psychological Screening and Tracking of Athletes and Digital Mental Health Solutions in a Hybrid Model of Care: Mini Review

BACKGROUND

There is a persistent need for mental ill-health prevention and intervention among at-risk and vulnerable subpopulations. Major disruptions to life, such as the COVID-19 pandemic, present an opportunity for a better understanding of the experience of stressors and vulnerability. Faster and better ways of psychological screening and tracking are more generally required in response to the increased demand upon mental health care services. The argument that mental and physical health should be considered together as part of a biopsychosocial approach is garnering acceptance in elite athlete literature. However, the sporting population are unique in that there is an existing stigma of mental health, an underrecognition of mental ill-health, and engagement difficulties that have hindered research, prevention, and intervention efforts.

OBJECTIVE

The aims of this paper are to summarize and evaluate the literature on athletes' increased vulnerability to mental ill-health and digital mental health solutions as a complement to prevention and intervention, and to show relationships between athlete mental health problems and resilience as well as digital mental health screening and tracking, and faster and better treatment algorithms.

METHODS

This mini review shapes literature in the fields of athlete mental health and digital mental health by summarizing and evaluating journal and review articles drawn from PubMed Central and the Directory of Open Access Journals.

RESULTS

Consensus statements and systematic reviews indicated that elite athletes have comparable rates of mental ill-health prevalence to the general population. However, peculiar subgroups require disentangling. Innovative expansion of data collection and analytics is required to respond to engagement issues and advance research and treatment programs in the process. Digital platforms, machine learning, deep learning, and artificial intelligence are useful for mental health screening and tracking in various subpopulations. It is necessary to determine appropriate conditions for algorithms for use in recommendations. Partnered with real-time automation and machine learning models, valid and reliable behavior sensing, digital mental health screening, and tracking tools have the potential to drive a consolidated, measurable, and balanced risk assessment and management strategy for the prevention and intervention of the sequelae of mental ill-health.

CONCLUSIONS

Athletes are an at-risk subpopulation for mental health problems. However, a subgroup of high-level athletes displayed a resilience that helped them to positively adjust after a period of overwhelming stress. Further consideration of stress and adjustments in brief screening tools is recommended to validate this finding. There is an unrealized potential for broadening the scope of mental health, especially symptom and disorder interpretation. Digital platforms for psychological screening and tracking have been widely used among general populations, but there is yet to be an eminent athlete version. Sports in combination with mental health education should address the barriers to help-seeking by increasing awareness, from mental ill-health to positive functioning. A hybrid model of care is recommended, combining traditional face-to-face approaches along with innovative and evaluated digital technologies, that may be used in prevention and early intervention strategies.