Smartphone data as an electronic biomarker of illness activity in bipolar disorder

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

BACKGROUND

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

METHODS

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

DISCUSSION

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

TRIAL REGISTRATION

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

Using digital phenotyping to classify bipolar disorder and unipolar disorder - exploratory findings using machine learning models

The aims were to investigate 1) differences in smartphone-based data on phone usage between bipolar disorder (BD) and unipolar disorder (UD) and 2) by using machine learning models, the sensitivity, specificity, and AUC of the combined smartphone data in classifying BD and UD. Daily smartphone-based self-assessments of mood and same-time passively collected smartphone data on smartphone usage was available for six months. A total of 64 patients with BD and 74 patients with UD were included. Patients with BD during euthymic states compared with UD in euthymic states had a lower number of incoming phone calls/ day (B: -0.70, 95%CI: -1.37; -0.70, p=0.040). Patients with BD during depressive states had a lower number of incoming and outgoing phone calls/ day as compared with patients with UD in depressive states. In classification by using machine learning models, 1) overall (regardless of the affective state), patients with BD were classified with an AUC of 0.84, which reduced to 0.48 when using a leave-one-patient-out crossvalidation (LOOCV) approach; similarly 2) during a depressive state, patients with BD were classified with an AUC of 0.86, which reduced to 0.42 with LOOCV; 3) during a euthymic state, patients with BD were classified with an AUC of 0.87, which reduced to 0.46 with LOOCV. While digital phenotyping shows promise in differentiating between patients with BD and UD, it highlights the challenge of generalizing to unseen individuals. It should serve as an complement to comprehensive clinical evaluation by clinicians.

Remote sensing mental health: A systematic review of factors essential to clinical translation from validation research

Background

Mental illness remains a major global health challenge largely due to the absence of definitive biomarkers applicable to diagnostics and care processes. Although remote sensing technologies, embedded in devices such as smartphones and wearables, offer a promising avenue for improved mental health assessments, their clinical integration has been slow.

Objective

This scoping review, following preferred reporting items for systematic reviews and meta-analyses guidelines, explores validation studies of remote sensing in clinical mental health populations, aiming to identify critical factors for clinical translation.

Methods

Comprehensive searches were conducted in six databases. The analysis, using narrative synthesis, examined clinical and socio-demographic characteristics of the populations studied, sensing purposes, temporal considerations and reference mental health assessments used for validation.

Results

The narrative synthesis of 50 included studies indicates that ten different sensor types have been studied for tracking and diagnosing mental illnesses, primarily focusing on physical activity and sleep patterns. There were many variations in the sensor methodologies used that may affect data quality and participant burden. Observation durations, and thus data resolution, varied by patient diagnosis. Currently, reference assessments predominantly rely on deficit focussed self-reports, and socio-demographic information is underreported, therefore representativeness of the general population is uncertain.

Conclusion

To fully harness the potential of remote sensing in mental health, issues such as reliance on self-reported assessments, and lack of socio-demographic context pertaining to generalizability need to be addressed. Striking a balance between resolution, data quality, and participant burden whilst clearly reporting limitations, will ensure effective technology use. The scant reporting on participants' socio-demographic data suggests a knowledge gap in understanding the effectiveness of passive sensing techniques in disadvantaged populations.

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

INTRODUCTION

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

METHODS AND ANALYSIS

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

ETHICS AND DISSEMINATION

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

TRIAL REGISTRATION

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

Irritability in bipolar disorder and unipolar disorder measured daily using smartphone-based data: An exploratory post hoc study

OBJECTIVE

To investigate (i) the proportions of time with irritability and (ii) the association between irritability and affective symptoms and functioning, stress, and quality of life in patients with bipolar disorder (BD) and unipolar depressive disorder (UD).

METHODS

A total of 316 patients with BD and 58 patients with UD provided self-reported once-a-day data on irritability and other affective symptoms using smartphones for a total of 64,129 days with observations. Questionnaires on perceived stress and quality of life and clinical evaluations of functioning were collected multiple times during the study.

RESULTS

During a depressive state, patients with UD spent a significantly higher proportion of time with presence of irritability (83.10%) as compared with patients with BD (70.27%) (p = 0.045). Irritability was associated with lower mood, activity level and sleep duration and with increased stress and anxiety level, in both patient groups (p-values<0.008). Increased irritability was associated with impaired functioning and increased perceived stress (p-values<0.024). In addition, in patients with UD, increased irritability was associated with decreased quality of life (p = 0.002). The results were not altered when adjusting for psychopharmacological treatments.

CONCLUSIONS

Irritability is an important part of the symptomatology in affective disorders. Clinicians could have focus on symptoms of irritability in both patients with BD and UD during their course of illness. Future studies investigating treatment effects on irritability would be interesting.

Digital self-report instruments for repeated measurement of mental health in the general adult population: a protocol for a systematic review

INTRODUCTION

Digital technologies present tremendous opportunities for enabling long-term measurement of mental health in the general population. Emerging studies have established preliminary efficacy of collecting self-report data digitally. However, a key challenge when developing a new self-report instrument is navigating the abundance of existing instruments to select relevant constructs for measurements. This review is a precursor to developing a novel future integrated digital instrument for repeated measurements. We interrogate the literature as the first step towards optimal measurement of the multifaceted mental health concept, in the context of digital repeated measurement. This review aims to identify (1) digital self-report instruments administered repeatedly to measure the mental health of the general adult population; (2) their structure and format; (3) their psychometric properties; (4) their usage in empirical studies; and (5) the constructs these instruments were designed to measure (as characterised in the original publication), and the constructs the instruments have been used to measure in the identified empirical studies.

METHODS AND ANALYSIS

Five major electronic databases will be searched. Studies administering mental health instruments (in English) repeatedly to community dwellers in the general adult population are eligible. A reviewer will preliminarily screen for eligible studies. Then, two reviewers will independently screen the full text of the eligible articles and extract data. Both reviewers will resolve any disagreement through discussion or with a third reviewer. After the data extraction, a reviewer will manually search for the structure, format, psychometric properties and the original constructs these instruments were developed to measure. This review will synthesise the results in a narrative approach. The reporting in this review will be guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist.

ETHICS AND DISSEMINATION

Ethical approval is not required as no data will be collected. Findings of the systematic review will be disseminated through peer-reviewed publications and conference presentations.

PROSPERO REGISTRATION NUMBER

CRD42022306547.

The Use of Passive Smartphone Data to Monitor Anxiety and Depression Among College Students in Real-World Settings: Protocol for a Systematic Review

BACKGROUND

College students are particularly at risk of depression and anxiety. These disorders have a serious impact on public health and affect patients' daily lives. The potential for using smartphones to monitor these mental conditions, providing passively collected physiological and behavioral data, has been reported among the general population. However, research on the use of passive smartphone data to monitor anxiety and depression among specific populations of college students has never been reviewed.

OBJECTIVE

This review's objectives are (1) to provide an overview of the use of passive smartphone data to monitor depression and anxiety among college students, given their specific type of smartphone use and living setting, and (2) to evaluate the different methods used to assess those smartphone data, including their strengths and limitations.

METHODS

This review will follow the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Two independent investigators will review English-language, full-text, peer-reviewed papers extracted from PubMed and Web of Science that measure passive smartphone data and levels of depression or anxiety among college students. A preliminary search was conducted in February 2022 as a proof of concept.

RESULTS

Our preliminary search identified 115 original articles, 8 of which met our eligibility criteria. Our planned full study will include an article selection flowchart, tables, and figures representing the main information extracted on the use of passive smartphone data to monitor anxiety and depression among college students.

CONCLUSIONS

The planned review will summarize the published research on using passive smartphone data to monitor anxiety and depression among college students. The review aims to better understand whether and how passive smartphone data are associated with indicators of depression and anxiety among college students. This could be valuable in order to provide a digital solution for monitoring mental health issues in this specific population by enabling easier identification and follow-up of the patients.

TRIAL REGISTRATION

PROSPERO CRD42022316263; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=316263.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/38785.

Interventions for helping people recognise early signs of recurrence in bipolar disorder

This is a protocol for a Cochrane Review (intervention). The objectives are as follows:

To evaluate the effectiveness of EWS plus TAU or EWS plus psychological therapy versus TAU alone or psychological treatment (without EWS) independently on time to recurrence of any bipolar episode and hospitalisation, and other clinically relevant outcome measures. 

To evaluate the effectiveness of intermittent medication used on recognition of EWS without continued mood‐stabilising medication versus TAU involving continued mood‐stabilising medication on time to recurrence of any bipolar episodes.

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

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

Developing “MinDag” – an app to capture symptom variation and illness mechanisms in bipolar disorder

Introduction

The illness course of bipolar disorder (BD) is highly heterogeneous with substantial variation between individuals with the same BD subtype and within individuals over time. This heterogeneity is not well-delineated and hampers the development of more targeted treatment. Furthermore, although lifestyle-related behaviors are believed to play a role in the illness course, such mechanisms are poorly understood. To address some of these knowledge gaps, we aimed to develop an app for collection of multi-dimensional longitudinal data on BD-relevant symptoms and lifestyle-related behaviors.

Methods

An app named MinDag was developed at the Norwegian Center for Mental Disorders Research in Oslo, Norway. The app was designed to tap into selected areas: mood, sleep, functioning/activities (social, occupational, physical exercise, leisure), substance use, emotional reactivity, and psychotic experiences. Ethical, security and usability issues were highly prioritized throughout the development and for the final app solution. We conducted beta- and pilot testing to eliminate technical problems and enhance usability and acceptability.

Results

The final version of MinDag comprises six modules; three which are presented for the user once daily (the Sleep module in the morning and the Mood and Functoning/Activities modules in the evening) and three which are presented once weekly (Substance Use, Emotional Reactivity, and Psychotic Experiences modules). In general, MinDag was well received in both in the beta-testing and the pilot study, and the participants provided valuable feedback that was taken into account in the final development. MinDag is now in use as part of the research protocol at the NORMENT center and in a specialized treatment unit for BD at Oslo University Hospital in Norway.

Discussion

We believe that MinDag will generate unique longitudinal data well suited for capturing the heterogeneity of BD and clarifying important unresolved issues such as how life-style related behavior may influence BD symptoms. Also, the experiences and knowledge derived from the development of MinDag may contribute to improving the security, acceptability, and benefit of digital tools in mental health.

Smartphone Ownership, Smartphone Utilization, and Interest in Using Mental Health Apps to Address Substance Use Disorders: Literature Review and Cross-sectional Survey Study Across Two Sites

Background

In recent years, there has been increasing interest in implementing digital technologies to diagnose, monitor, and intervene in substance use disorders. Smartphones are now a vehicle for facilitating telepsychiatry visits, measuring health metrics, and communicating with health care professionals. In light of the COVID-19 pandemic and the movement toward web-based and hybrid clinic visits and meetings, it has become especially salient to assess phone ownership among individuals with substance use disorders and their comfort in navigating phone functionality and using phones for mental health purposes.

Objective

The aims of this study were to summarize the current literature around smartphone ownership, smartphone utilization, and the acceptability of using smartphones for mental health purposes and assess these variables across two disparate substance use treatment sites.

Methods

We performed a focused literature review via a search of two academic databases (PubMed and Google Scholar) for publications since 2007 on the topics of smartphone ownership, smartphone utilization, and the acceptability of using mobile apps for mental health purposes among the substance use population. Additionally, we conducted a cross-sectional survey study that included 51 participants across two sites in New England—an inpatient detoxification unit that predominantly treats patients with alcohol use disorder and an outpatient methadone maintenance treatment clinic.

Results

Prior studies indicated that mobile phone ownership among the substance use population between 2013 and 2019 ranged from 83% to 94%, while smartphone ownership ranged from 57% to 94%. The results from our study across the two sites indicated 96% (49/51) mobile phone ownership and 92% (47/51) smartphone ownership among the substance use population. Although most (43/49, 88%) patients across both sites reported currently using apps on their phone, a minority (19/48, 40%) reported previously using any apps for mental health purposes. More than half of the participants reported feeling at least neutrally comfortable with a mental health app gathering information regarding appointment reminders (32/48, 67%), medication reminders (33/48, 69%), and symptom surveys (26/45, 58%). Most patients were concerned about privacy (34/51, 67%) and felt uncomfortable with an app gathering location (29/47, 62%) and social (27/47, 57%) information for health care purposes.

Conclusions

The majority of respondents reported owning a mobile phone (49/51, 96%) and smartphone (47/51, 92%), consistent with prior studies. Many respondents felt comfortable with mental health apps gathering most forms of personal information and with communicating with their clinician about their mental health. The differential results from the two sites, namely greater concerns about the cost of mental health apps among the methadone maintenance treatment cohort and less experience with downloading apps among the older inpatient detoxification cohort, may indicate that clinicians should tailor technological interventions based on local demographics and practice sites and that there is likely not a one-size-fits-all digital psychiatry solution.

Differences in mobility patterns according to machine learning models in patients with bipolar disorder and patients with unipolar disorder

BACKGROUND

It is essential to differentiate bipolar disorder (BD) from unipolar disorder (UD) as the course of illness and treatment guidelines differ between the two disorders. Measurements of activity and mobility could assist in this discrimination.

AIMS

1) To investigate differences in smartphone-based location data between BD and UD, and 2) to investigate the sensitivity, specificity, and AUC of combined location data in classifying BD and UD.

METHODS

Patients with BD and UD completed smartphone-based self-assessments of mood for six months, along with same-time passively collected smartphone data on location reflecting mobility patterns, routine and location entropy (chaos). A total of 65 patients with BD and 75 patients with UD were included.

RESULTS

A total of 2594 (patients with BD) and 2088 (patients with UD) observations of smartphone-based location data were available. During a depressive state, compared with patients with UD, patients with BD had statistically significantly lower mobility (e.g., total duration of moves per day (e^B 0.74, 95% CI 0.57; 0.97, p = 0.027)). In classification models during a depressive state, patients with BD versus patients with UD, there was a sensitivity of 0.70 (SD 0.07), a specificity of 0.77 (SD 0.07), and an AUC of 0.79 (SD 0.03).

LIMITATIONS

The relative low symptom severity in the present study may have contributed to the magnitude of the AUC.

CONCLUSION

Mobility patterns derived from mobile location data is a promising digital diagnostic marker in discriminating between patients with BD and UD.

Associations among smartphone app-based measurements of mood, sleep and activity in bipolar disorder

The recent popularization of smart technology presents new opportunities for continual, digital-monitoring of patient status. In this project, we used a smartphone app to track the mood, sleep, and activity levels of 159 outpatients with bipolar disorder (BD). The participants were asked to report their daily wake/sleep time and emotional status in the app, while daily activity data were automatically collected via GPS. We performed repeated-measures correlation analysis to examine possible correlations between the readouts. Mood, sleep and activity levels all showed intra-variable correlations with readings on the next day, in the next week, and in the next month. Furthermore, mood and sleep at the reference time were positively correlated with activity in subsequent weeks or months, and activity was positively correlated with mood and sleep in the same time ranges. Thus, our results were in line with previous studies, showing that mood, sleep, and activity levels are interdependent in patients with BD. With the association between mood on future activity level was most significant, and the correlations between each readout and the others were dependent on time frame. Our findings suggest our smartphone app has potential to provide an informative and reliable means for real-time tracking of BD status.

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

Background

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

Objective

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

Methods

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

Results

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

Conclusions

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

The feasibility and acceptability of mobile application-based assessment of suicidality using self-report components of a novel tool, the Suicide Ideation and Behavior Assessment Tool (SIBAT)

The aim of this study was to assess the validity of a mobile application-based self-report questionnaire in the assessment of suicidality. We developed a program for the administration of self-report components of the Suicide Ideation and Behavior Assessment Tool (SIBAT). We invited university students and trainees enrolled in a study of addictions to complete this component of the SIBAT using the program on their mobile devices or personal computer. 196 participants completed all required modules of the SIBAT, with 97 using their mobile device and 99 using their personal computer. Rates of completed questionnaires between the two groups were compared, as were the responses to the items and the total scores. There was a significant difference between proportions of scale completion in both groups, with a greater number of participants who used a personal computer to complete the scale not responding to all questions compared to participants who used a mobile device to complete the scale. Data collected via mobile device showed good concurrent validity with data collected via personal computer. A trend toward greater disclosure of suicidality was observed in the mobile device group however, replication of these findings using larger sample sizes is needed.

Digital Biomarkers in Living Labs for Vulnerable and Susceptible Individuals: An Integrative Literature Review

PURPOSE

The study aimed to identify which digital biomarkers are collected and which specific devices are used according to vulnerable and susceptible individual characteristics in a living-lab setting.

MATERIALS AND METHODS

A literature search, screening, and appraisal process was implemented using the Web of Science, Pubmed, and Embase databases. The search query included a combination of terms related to "digital biomarkers," "devices that collect digital biomarkers," and "vulnerable and susceptible groups." After the screening and appraisal process, a total of 37 relevant articles were obtained.

RESULTS

In elderly people, the main digital biomarkers measured were values related to physical activity. Most of the studies used sensors. The articles targeting children aimed to predict diseases, and most of them used devices that are simple and can induce some interest, such as wearable device-based smart toys. In those who were disabled, digital biomarkers that measured location-based movement for the purpose of diagnosing disabilities were widely used, and most were measured by easy-to-use devices that did not require detailed explanations. In the disadvantaged, digital biomarkers related to health promotion were measured, and various wearable devices, such as smart bands and headbands were used depending on the purpose and target.

CONCLUSION

As the digital biomarkers and devices that collect them vary depending on the characteristics of study subjects, researchers should pay attention not only to the purpose of the study but also the characteristics of study subjects when collecting and analyzing digital biomarkers from living labs.

Breaking the Data Value-Privacy Paradox in Mobile Mental Health Systems Through User-Centered Privacy Protection: A Web-Based Survey Study

BACKGROUND

Mobile mental health systems (MMHS) have been increasingly developed and deployed in support of monitoring, management, and intervention with regard to patients with mental disorders. However, many of these systems rely on patient data collected by smartphones or other wearable devices to infer patients' mental status, which raises privacy concerns. Such a value-privacy paradox poses significant challenges to patients' adoption and use of MMHS; yet, there has been limited understanding of it.

OBJECTIVE

To address the significant literature gap, this research aims to investigate both the antecedents of patients' privacy concerns and the effects of privacy concerns on their continuous usage intention with regard to MMHS.

METHODS

Using a web-based survey, this research collected data from 170 participants with MMHS experience recruited from online mental health communities and a university community. The data analyses used both repeated analysis of variance and partial least squares regression.

RESULTS

The results showed that data type (P=.003), data stage (P<.001), privacy victimization experience (P=.01), and privacy awareness (P=.08) have positive effects on privacy concerns. Specifically, users report higher privacy concerns for social interaction data (P=.007) and self-reported data (P=.001) than for biometrics data; privacy concerns are higher for data transmission (P=.01) and data sharing (P<.001) than for data collection. Our results also reveal that privacy concerns have an effect on attitude toward privacy protection (P=.001), which in turn affects continuous usage intention with regard to MMHS.

CONCLUSIONS

This study contributes to the literature by deepening our understanding of the data value-privacy paradox in MMHS research. The findings offer practical guidelines for breaking the paradox through the design of user-centered and privacy-preserving MMHS.

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

BACKGROUND

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

METHODS

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

RESULTS

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

LIMITATIONS

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

CONCLUSIONS

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

LIMITATIONS

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

CONCLUSIONS

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

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

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

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.

Digital Phenotyping in Child and Adolescent Psychiatry: A Perspective

Digital phenotyping (DP) provides opportunities to study child and adolescent psychiatry from a novel perspective. DP combines objective data obtained from digital sensors with participant-generated "active data," in order to understand better an individual's behavior and environmental interactions. Although this new method has led to advances in adult psychiatry, its use in child psychiatry has been more limited. This review aims to demonstrate potential benefits of DP methodology and passive data collection by reviewing studies specifically in child and adolescent psychiatry. Twenty-six studies were identified that collected passive data from four different categories: accelerometer/actigraph data, physiological data, GPS data, and step count. Study topics ranged from the associations between manic symptomology and cardiac parameters to the role of daily emotions, sleep, and social interactions in treatment for pediatric anxiety. Reviewed studies highlighted the diverse ways in which objective data can augment naturalistic self-report methods in child and adolescent psychiatry to allow for more objective, ecologically valid, and temporally resolved conclusions. Though limitations exist-including a lack of participant adherence and device failure and misuse-DP technology may represent a new and effective method for understanding pediatric cognition, behavior, disease etiology, and treatment efficacy.

Technical feasibility and adherence of the Remote Monitoring Application in Psychiatry (ReMAP) for the assessment of affective symptoms

BACKGROUND

Smartphone-based monitoring constitutes a cost-effective instrument to assess and predict affective symptom trajectories. Large-scale transdiagnostic studies utilizing this methodology are yet lacking in psychiatric research. Thus, we introduce the Remote Monitoring Application in Psychiatry (ReMAP) and evaluate its feasibility and adherence in a large transdiagnostic sample.

METHODS

The ReMAP app was distributed among n = 997 healthy control participants and psychiatric patients, including affective, anxiety, and psychotic disorders. Passive sensor data (acceleration, geolocation, walking distance, steps), optional standardized self-reports on mood and sleep, and voice samples were assessed. Feasibility and adherence were evaluated based on frequency of transferred data, and participation duration. Preliminary results are presented while data collection is ongoing.

RESULTS

Retention rates of 90.25% for the minimum study duration of two weeks and 33.09% for one year were achieved (median participation 135 days, IQR=111). Participants transferred an average of 51.83 passive events per day. An average of 34.59 self-report events were transferred per user, with a considerable range across participants (0-552 events). Clinical and non-clinical subgroups did not differ in participation duration or rate of data transfer. The mean rate of days with passive data was higher and less heterogeneous in iOS (91.85%, SD=21.25) as compared to Android users (63.04%, SD=35.09).

LIMITATIONS

Subjective user experience was not assessed limiting conclusions about app acceptance.

CONCLUSIONS

ReMAP is a technically feasible tool to assess affective symptoms with high temporal resolution in large-scale transdiagnostic samples with good adherence. Future studies should account for differences between operating systems.

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

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

Development and Evaluation of Acceptability and Feasibility of a Web-Based Intervention for Patients With Bipolar Disorder in Iran: Implementation Study

Background

Psychoeducation for bipolar disorder has a significant impact on symptoms and treatment adherence. In Iran, as a low-resource setting, infrastructural barriers, such as inadequate mental health professionals, difficulties in transportation, and costs of care, may hinder optimum delivery of this evidence-based intervention to patients.

Objective

This study sought to explore the acceptability and feasibility of a web-based intervention for bipolar patients in Iran.

Methods

A website has been developed as a platform for providing psychoeducational content about bipolar disorder. Patients were chosen via a convenient sampling method in 2018-2019. The main component of the intervention included streaming 7 weekly video clips after attending a single in-person meeting, as well as a medication self-monitoring application. Information was collected about the feasibility and acceptability of the intervention.

Results

We invited 45 patients from the day center and the outpatient clinic of Roozbeh psychiatric hospital and some private clinics in Tehran. Of the 23 patients (51%) who attended the first in-person session and provided informed consent, 14 patients dropped out during the study. While 9 patients completed the course (attended 4 or more online sessions), only 5 watched all the video sessions. The rate of adherence to the intervention and frequency of exposure to the website were much higher for those recruited from the private and outpatient clinics.

Conclusions

This web-based intervention can be feasible and acceptable only for a subgroup of patients with specific educational status and socioeconomic level.

Data Missing Not at Random in Mobile Health Research: Assessment of the Problem and a Case for Sensitivity Analyses

Background

Missing data are common in mobile health (mHealth) research. There has been little systematic investigation of how missingness is handled statistically in mHealth randomized controlled trials (RCTs). Although some missing data patterns (ie, missing at random [MAR]) may be adequately addressed using modern missing data methods such as multiple imputation and maximum likelihood techniques, these methods do not address bias when data are missing not at random (MNAR). It is typically not possible to determine whether the missing data are MAR. However, higher attrition in active (ie, intervention) versus passive (ie, waitlist or no treatment) conditions in mHealth RCTs raise a strong likelihood of MNAR, such as if active participants who benefit less from the intervention are more likely to drop out.

Objective

This study aims to systematically evaluate differential attrition and methods used for handling missingness in a sample of mHealth RCTs comparing active and passive control conditions. We also aim to illustrate a modern model-based sensitivity analysis and a simpler fixed-value replacement approach that can be used to evaluate the influence of MNAR.

Methods

We reanalyzed attrition rates and predictors of differential attrition in a sample of 36 mHealth RCTs drawn from a recent meta-analysis of smartphone-based mental health interventions. We systematically evaluated the design features related to missingness and its handling. Data from a recent mHealth RCT were used to illustrate 2 sensitivity analysis approaches (pattern-mixture model and fixed-value replacement approach).

Results

Attrition in active conditions was, on average, roughly twice that of passive controls. Differential attrition was higher in larger studies and was associated with the use of MAR-based multiple imputation or maximum likelihood methods. Half of the studies (18/36, 50%) used these modern missing data techniques. None of the 36 mHealth RCTs reviewed conducted a sensitivity analysis to evaluate the possible consequences of data MNAR. A pattern-mixture model and fixed-value replacement sensitivity analysis approaches were introduced. Results from a recent mHealth RCT were shown to be robust to missing data, reflecting worse outcomes in missing versus nonmissing scores in some but not all scenarios. A review of such scenarios helps to qualify the observations of significant treatment effects.

Conclusions

MNAR data because of differential attrition are likely in mHealth RCTs using passive controls. Sensitivity analyses are recommended to allow researchers to assess the potential impact of MNAR on trial results.

Patient electronic communication data in clinical care: what is known and what is needed

The novel coronavirus (COVID-19) and physical distancing guidelines around the world have resulted in unprecedented changes to normal routine and increased smartphone use to maintain social relationships and support. Reports of depressive and anxiety symptom are on the rise, contributing to suffering among people-especially adolescents and young adults-with pre-existing mental health conditions. Psychiatric care has shifted primarily to telehealth limiting the important patient nonverbal communication that has been part of in-person clinical sessions. Supplementing clinical care with patient electronic communication (EC) data may provide valuable information and influence treatment decision making. Research in the impact of patient EC data on managing psychiatric symptoms is in its infancy. This review aims to identify how patient EC has been used in clinical care and its benefits in psychiatry and research. We discuss smartphone applications used to gather different types of EC data, how data have been integrated into clinical care, and implications for clinical care and research.

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Long-Term IoT-Based Maternal Monitoring: System Design and Evaluation

Pregnancy is a unique time when many mothers gain awareness of their lifestyle and its impacts on the fetus. High-quality care during pregnancy is needed to identify possible complications early and ensure the mother’s and her unborn baby’s health and well-being. Different studies have thus far proposed maternal health monitoring systems. However, they are designed for a specific health problem or are limited to questionnaires and short-term data collection methods. Moreover, the requirements and challenges have not been evaluated in long-term studies. Maternal health necessitates a comprehensive framework enabling continuous monitoring of pregnant women. In this paper, we present an Internet-of-Things (IoT)-based system to provide ubiquitous maternal health monitoring during pregnancy and postpartum. The system consists of various data collectors to track the mother’s condition, including stress, sleep, and physical activity. We carried out the full system implementation and conducted a real human subject study on pregnant women in Southwestern Finland. We then evaluated the system’s feasibility, energy efficiency, and data reliability. Our results show that the implemented system is feasible in terms of system usage during nine months. We also indicate the smartwatch, used in our study, has acceptable energy efficiency in long-term monitoring and is able to collect reliable photoplethysmography data. Finally, we discuss the integration of the presented system with the current healthcare system.

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

OBJECTIVE

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

AIMS

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

METHODS

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

RESULTS

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

CONCLUSION

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

Scoping Review of Healthcare Literature on Mobile, Wearable, and Textile Sensing Technology for Continuous Monitoring

Remote monitoring of health can reduce frequent hospitalisations, diminishing the burden on the healthcare system and cost to the community. Patient monitoring helps identify symptoms associated with diseases or disease-driven disorders, which makes it an essential element of medical diagnoses, clinical interventions, and rehabilitation treatments for severe medical conditions. This monitoring can be expensive and time-consuming and provide an incomplete picture of the state of the patient. In the last decade, there has been a significant increase in the adoption of mobile and wearable devices, along with the introduction of smart textile solutions that offer the possibility of continuous monitoring. These alternatives fuel a technology shift in healthcare, one that involves the continuous tracking and monitoring of individuals. This scoping review examines how mobile, wearable, and textile sensing technology have been permeating healthcare by offering alternate solutions to challenging issues, such as personalised prescriptions or home-based secondary prevention. To do so, we have selected 222 healthcare literature articles published from 2007 to 2019 and reviewed them following the PRISMA process under the schema of a scoping review framework. Overall, our findings show a recent increase in research on mobile sensing technology to address patient monitoring, reflected by 128 articles published in journals and 19 articles in conference proceedings between 2014 and 2019, which represents 57.65% and 8.55% respectively of all included articles.

Smartphone-Based Self-Reports of Depressive Symptoms Using the Remote Monitoring Application in Psychiatry (ReMAP): Interformat Validation Study

BACKGROUND

Smartphone-based symptom monitoring has gained increased attention in psychiatric research as a cost-efficient tool for prospective and ecologically valid assessments based on participants' self-reports. However, a meaningful interpretation of smartphone-based assessments requires knowledge about their psychometric properties, especially their validity.

OBJECTIVE

The goal of this study is to systematically investigate the validity of smartphone-administered assessments of self-reported affective symptoms using the Remote Monitoring Application in Psychiatry (ReMAP).

METHODS

The ReMAP app was distributed to 173 adult participants of ongoing, longitudinal psychiatric phenotyping studies, including healthy control participants, as well as patients with affective disorders and anxiety disorders; the mean age of the sample was 30.14 years (SD 11.92). The Beck Depression Inventory (BDI) and single-item mood and sleep information were assessed via the ReMAP app and validated with non-smartphone-based BDI scores and clinician-rated depression severity using the Hamilton Depression Rating Scale (HDRS).

RESULTS

We found overall high comparability between smartphone-based and non-smartphone-based BDI scores (intraclass correlation coefficient=0.921; P<.001). Smartphone-based BDI scores further correlated with non-smartphone-based HDRS ratings of depression severity in a subsample (r=0.783; P<.001; n=51). Higher agreement between smartphone-based and non-smartphone-based assessments was found among affective disorder patients as compared to healthy controls and anxiety disorder patients. Highly comparable agreement between delivery formats was found across age and gender groups. Similarly, smartphone-based single-item self-ratings of mood correlated with BDI sum scores (r=-0.538; P<.001; n=168), while smartphone-based single-item sleep duration correlated with the sleep item of the BDI (r=-0.310; P<.001; n=166).

CONCLUSIONS

These findings demonstrate that smartphone-based monitoring of depressive symptoms via the ReMAP app provides valid assessments of depressive symptomatology and, therefore, represents a useful tool for prospective digital phenotyping in affective disorder patients in clinical and research applications.

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

Background

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

Objective

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

Methods

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

Results

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

Conclusions

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

Automatically Generated Smartphone Data in Young Patients With Newly Diagnosed Bipolar Disorder and Healthy Controls

Background: Smartphones may facilitate continuous and fine-grained monitoring of behavioral activities via automatically generated data and could prove to be especially valuable in monitoring illness activity in young patients with bipolar disorder (BD), who often present with rapid changes in mood and related symptoms. The present pilot study in young patients with newly diagnosed BD and healthy controls (HC) aimed to (1) validate automatically generated smartphone data reflecting physical and social activity and phone usage against validated clinical rating scales and questionnaires; (2) investigate differences in automatically generated smartphone data between young patients with newly diagnosed BD and HC; and (3) investigate associations between automatically generated smartphone data and smartphone-based self-monitored mood and activity in young patients with newly diagnosed BD. Methods: A total of 40 young patients with newly diagnosed BD and 21 HC aged 15-25 years provided daily automatically generated smartphone data for 3-779 days [median (IQR) = 140 (11.5-268.5)], in addition to daily smartphone-based self-monitoring of activity and mood. All participants were assessed with clinical rating scales. Results: (1) The number of outgoing phone calls was positively associated with scores on the Young Mania Rating Scale and subitems concerning activity and speech. The number of missed calls (p = 0.015) and the number of outgoing text messages (p = 0.017) were positively associated with the level of psychomotor agitation according to the Hamilton Depression Rating scale subitem 9. (2) Young patients with newly diagnosed BD had a higher number of incoming calls compared with HC (BD: mean = 1.419, 95% CI: 1.162, 1.677; HC: mean = 0.972, 95% CI: 0.637, 1.308; p = 0.043) and lower self-monitored mood and activity (p's < 0.001). (3) Smartphone-based self-monitored mood and activity were positively associated with step counts and the number of outgoing calls, respectively (p's < 0.001). Conclusion: Automatically generated data on physical and social activity and phone usage seem to reflect symptoms. These data differ between young patients with newly diagnosed BD and HC and reflect changes in illness activity in young patients with BD. Automatically generated smartphone-based data could be a useful clinical tool in diagnosing and monitoring illness activity in young patients with BD.

Digital Communication Biomarkers of Mood and Diagnosis in Borderline Personality Disorder, Bipolar Disorder, and Healthy Control Populations

Background: Remote monitoring and digital phenotyping harbor potential to aid clinical diagnosis, predict episode course and recognize early signs of mental health crises. Digital communication metrics, such as phone call and short message service (SMS) use may represent novel biomarkers of mood and diagnosis in Bipolar Disorder (BD) and Borderline Personality Disorder (BPD). Materials and Methods: BD (n = 17), BPD (n = 17) and Healthy Control (HC, n = 21) participants used a smartphone application which monitored phone calls and SMS messaging, alongside self-reported mood. Linear mixed-effects regression models were used to assess the association between digital communications and mood symptoms, mood state, trait-impulsivity, diagnosis and the interaction effect between mood and diagnosis. Results: Transdiagnostically, self-rated manic symptoms and manic state were positively associated with total and outgoing call frequency and cumulative total, incoming and outgoing call duration. Manic symptoms were also associated with total and outgoing SMS frequency. Transdiagnostic depressive symptoms were associated with increased mean incoming call duration. For the different diagnostic groups, BD was associated with increased total call frequency and BPD with increased total and outgoing SMS frequency and length compared to HC. Depression in BD, but not BPD, was associated with decreased total and outgoing call frequency, mean total and outgoing call duration and total and outgoing SMS frequency. Finally, trait-impulsivity was positively associated with total call frequency, total and outgoing SMS frequency and cumulative total and outgoing SMS length. Conclusion: These results identify a general increase in phone call and SMS communications associated with self-reported manic symptoms and a diagnosis-moderated decrease in communications associated with depression in BD, but not BPD, participants. These findings may inform the development of clinical tools to aid diagnosis and remote symptom monitoring, as well as informing understanding of differential psychopathologies in BD and BPD.

Daily mobility patterns in patients with bipolar disorder and healthy individuals

BACKGROUND

Alterations in energy and activity in bipolar disorder (BD) differ between affective states and compared with healthy control individuals (HC). Measurements of activity could discriminate between BD and HC and in the monitoring of affective states within BD. The aims were to investigate differences in 1) passively collected smartphone-based location data (location data) between BD and HC, and 2) location data in BD between affective states.

METHODS

Daily, patients with BD and HC completed smartphone-based self-assessments of mood for up to nine months. Location data reflecting mobility patterns, routine and location entropy was collected daily. A total of 46 patients with BD and 31 HC providing daily data was included.

RESULTS

A total of 4,859 observations of smartphone-based self-assessments of mood and mobility patterns were available from patients with BD and 1,747 observations from HC. Patients with BD had lower location entropy compared with HC (B= -0.14, 95% CI= -0.24; -0.034, p=0.009). Patients with BD during a depressive state were less mobile compared with a euthymic state. Patients with BD during an affective state had lower location entropy compared with a euthymic state (p<0.0001). The AUC of combined location data was rather high in classifying patients with BD compared with HC (AUC: 0.83).

LIMITATIONS

Individuals willing to use smartphones for daily self-monitoring may represent a more motivated group.

CONCLUSION

Alterations in location data reflecting mobility patterns may be a promising measure of illness and illness activity in patients with BD and may be used to monitor the effects of treatments.

Mood, activity, and sleep measured via daily smartphone-based self-monitoring in young patients with newly diagnosed bipolar disorder, their unaffected relatives and healthy control individuals

Diagnostic evaluations and early interventions of patients with bipolar disorder (BD) rely on clinical evaluations. Smartphones have been proposed to facilitate continuous and fine-grained self-monitoring of symptoms. The present study aimed to (1) validate daily smartphone-based self-monitored mood, activity, and sleep, against validated questionnaires and clinical ratings in young patients with newly diagnosed BD, unaffected relatives (UR), and healthy controls persons (HC); (2) investigate differences in daily smartphone-based self-monitored mood, activity, and sleep in young patients with newly diagnosed BD, UR, and HC; (3) investigate associations between self-monitored mood and self-monitored activity and sleep, respectively, in young patients with newly diagnosed BD. 105 young patients with newly diagnosed BD, 24 UR and 77 HC self-monitored 2 to 1077 days (median [IQR] = 65 [17.5-112.5]). There was a statistically significantly negative association between the mood item on Hamilton Depression Rating Scale (HAMD) and smartphone-based self-monitored mood (B = - 0.76, 95% CI - 0.91; - 0.63, p < 0.001) and between psychomotor item on HAMD and self-monitored activity (B = - 0.44, 95% CI - 0.63; - 0.25, p < 0.001). Smartphone-based self-monitored mood differed between young patients with newly diagnosed BD and HC (p < 0.001), and between UR and HC (p = 0.008) and was positively associated with smartphone-based self-reported activity (p < 0.001) and sleep duration (p < 0.001). The findings support the potential of smartphone-based self-monitoring of mood and activity as part of a biomarker for young patients with BD and UR. Smartphone-based self-monitored mood is better to discriminate between young patients with newly diagnosed BD and HC, and between UR and HC, compared with smartphone-based activity and sleep.Trial registration clinicaltrials.gov NCT0288826.

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

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

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

Background

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

Methods

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

Outcomes

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

Smartphone-based activity measurements in patients with newly diagnosed bipolar disorder, unaffected relatives and control individuals

BACKGROUND

In DSM-5 activity is a core criterion for diagnosing hypomania and mania. However, there are no guidelines for quantifying changes in activity. The objectives of the study were (1) to investigate daily smartphone-based self-reported and automatically-generated activity, respectively, against validated measurements of activity; (2) to validate daily smartphone-based self-reported activity and automatically-generated activity against each other; (3) to investigate differences in daily self-reported and automatically-generated smartphone-based activity between patients with bipolar disorder (BD), unaffected relatives (UR) and healthy control individuals (HC).

METHODS

A total of 203 patients with BD, 54 UR, and 109 HC were included. On a smartphone-based app, the participants daily reported their activity level on a scale from -3 to + 3. Additionally, participants owning an android smartphone provided automatically-generated data, including step counts, screen on/off logs, and call- and text-logs. Smartphone-based activity was validated against an activity questionnaire the International Physical Activity Questionnaire (IPAQ) and activity items on observer-based rating scales of depression using the Hamilton Depression Rating scale (HAMD), mania using Young Mania Rating scale (YMRS) and functioning using the Functional Assessment Short Test (FAST). In these analyses, we calculated averages of smartphone-based activity measurements reported in the period corresponding to the days assessed by the questionnaires and rating scales.

RESULTS

(1) Smartphone-based self-reported activity was a valid measure according to scores on the IPAQ and activity items on the HAMD and YMRS, and was associated with FAST scores, whereas the majority of automatically-generated smartphone-based activity measurements were not. (2) Daily smartphone-based self-reported and automatically-generated activity correlated with each other with nearly all measurements. (3) Patients with BD had decreased daily self-reported activity compared with HC. Patients with BD had decreased physical (number of steps) and social activity (more missed calls) but a longer call duration compared with HC. UR also had decreased physical activity compared with HC but did not differ on daily self-reported activity or social activity.

CONCLUSION

Daily self-reported activity measured via smartphone represents overall activity and correlates with measurements of automatically generated smartphone-based activity. Detecting activity levels using smartphones may be clinically helpful in diagnosis and illness monitoring in patients with bipolar disorder. Trial registration clinicaltrials.gov NCT02888262.

Digital Phenotyping to Enhance Substance Use Treatment During the COVID-19 Pandemic

Due to the COVID-19 pandemic, many clinical addiction treatment programs have been required to transition to telephonic or virtual visits. Novel solutions are needed to enhance substance use treatment during a time when many patients are disconnected from clinical care and social support. Digital phenotyping, which leverages the unique functionality of smartphone sensors (GPS, social behavior, and typing patterns), can buttress clinical treatment in a remote, scalable fashion. Specifically, digital phenotyping has the potential to improve relapse prediction and intervention, relapse detection, and overdose intervention. Digital phenotyping may enhance relapse prediction through coupling machine learning algorithms with the enormous amount of collected behavioral data. Activity-based analysis in real time can potentially be used to prevent relapse by warning substance users when they approach locational triggers such as bars or liquor stores. Wearable devices detect when a person has relapsed to substances through measuring physiological changes such as electrodermal activity and locomotion. Despite the initial promise of this approach, privacy, security, and barriers to access are important issues to address.

A Smartphone App to Monitor Mood Symptoms in Bipolar Disorder: Development and Usability Study

BACKGROUND

There is considerable scientific interest in finding new and innovative ways to capture rapid fluctuations in functioning within individuals with bipolar disorder (BD), a severe, recurrent mental disorder associated with frequent shifts in symptoms and functioning. The use of smartphones can provide valid and real-world tools for use in measurement-based care and could be used to inform more personalized treatment options for this group, which can improve standard of care.

OBJECTIVE

We examined the feasibility and usability of a smartphone to capture daily fluctuations in mood within BD and to relate daily self-rated mood to smartphone use behaviors indicative of psychomotor activity or symptoms of the illness.

METHODS

Participants were 26 individuals with BD and 12 healthy control individuals who were recruited from the Prechter Longitudinal Study of BD. All were given a smartphone with a custom-built app and prompted twice a day to complete questions of mood for 28 days. The app automatically and unobtrusively collected phone usage data. A poststudy satisfaction survey was also completed.

RESULTS

Our sample showed a very high adherence rate to the daily momentary assessments (91% of the 58 prompts completed). Multivariate mixed effect models showed that an increase in rapid thoughts over time was associated with a decrease in outgoing text messages (β=-.02; P=.04), and an increase in impulsivity self-ratings was related to a decrease in total call duration (β=-.29; P=.02). Participants generally reported positive experiences using the smartphone and completing daily prompts.

CONCLUSIONS

Use of mobile technology shows promise as a way to collect important clinical information that can be used to inform treatment decision making and monitor outcomes in a manner that is not overly burdensome to the patient or providers, highlighting its potential use in measurement-based care.

Distant mood monitoring for depressive and bipolar disorders: a systematic review

BACKGROUND

Broadening our knowledge of the longitudinal course of mood symptoms is cardinal to providing effective long-term treatments. Research indicates that patients with mental illness are willing to engage in the use of telemonitoring and mobile technology to assess and monitor their mood states. However, without the provision of distant support, adverse outcomes and events may be difficult to prevent and manage through self-monitoring. Understanding patient perspectives is important to achieving the best balance of self-monitoring, patient empowerment, and distant supporter involvement.

METHODS

This systematic review synthesises quantitative and qualitative evidence of the effectiveness and feasibility of daily/weekly/monthly remote mood monitoring that includes distant support in participants with mood disorders. Inclusion criteria comprised mood monitoring of mood disorder patients as main intervention, study design, method of monitoring, and presence of psychotherapy and psychoeducation. Effectiveness was defined by the change in depression and/or mania scores. Feasibility was determined on participant feedback and completion/attrition rates. Studies were assessed for quality using the Mixed Methods Appraisal Tool version 2018.

RESULTS

Nine studies of acceptable quality met the inclusion criteria. Distant mood monitoring was effective in improving depression scores but not mania scores. Feasibility, as measured through compliance and completion rates and participant feedback, varied.

CONCLUSION

Distant mood monitoring with support may be a useful, acceptable, and feasible intervention for diverse groups of patients in terms of age and ethnicity. Further, it may be effective in improving symptoms of depression, increasing treatment adherence, and facilitating the prevention and management of adverse outcomes. As a task-shifting intervention, distant mood monitoring may help to alleviate the burden on mental health providers in developing countries.

Daily estimates of clinical severity of symptoms in bipolar disorder from smartphone-based self-assessments

Currently, the golden standard for assessing the severity of depressive and manic symptoms in patients with bipolar disorder (BD) is clinical evaluations using validated rating scales such as the Hamilton Depression Rating Scale 17-items (HDRS) and the Young Mania Rating Scale (YMRS). Frequent automatic estimation of symptom severity could potentially help support monitoring of illness activity and allow for early treatment intervention between outpatient visits. The present study aimed (1) to assess the feasibility of producing daily estimates of clinical rating scores based on smartphone-based self-assessments of symptoms collected from a group of patients with BD; (2) to demonstrate how these estimates can be utilized to compute individual daily risk of relapse scores. Based on a total of 280 clinical ratings collected from 84 patients with BD along with daily smartphone-based self-assessments, we applied a hierarchical Bayesian modelling approach capable of providing individual estimates while learning characteristics of the patient population. The proposed method was compared to common baseline methods. The model concerning depression severity achieved a mean predicted R2 of 0.57 (SD = 0.10) and RMSE of 3.85 (SD = 0.47) on the HDRS, while the model concerning mania severity achieved a mean predicted R2 of 0.16 (SD = 0.25) and RMSE of 3.68 (SD = 0.54) on the YMRS. In both cases, smartphone-based self-reported mood was the most important predictor variable. The present study shows that daily smartphone-based self-assessments can be utilized to automatically estimate clinical ratings of severity of depression and mania in patients with BD and assist in identifying individuals with high risk of relapse.

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

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

An Examination of Psychomotor Disturbance in Current and Remitted MDD: An RDoC Study

Major depressive disorder (MDD) is a serious public health problem that has, at best, modest treatment response—potentially due to its heterogeneous clinical presentation. One way to parse the heterogeneity is to investigate the role of particular features of MDD, an endeavor that can also help identify novel and focal targets for treatment and prevention efforts. Our R01 focuses on the feature of psychomotor disturbance (e.g., psychomotor agitation (PmA) and retardation (PmR)), a particularly pernicious feature of MDD, that has not been examined extensively in MDD. Aim 1 is comparing three groups of individuals—those with current MDD (n = 100), remitted MDD (n = 100), and controls (n = 50)—on multiple measures of PmR and PmA (assessed both in the lab and in the subjects’ natural environment). Aim 2 is examining the structural (diffusion MRI) and functional (resting state fMRI) connectivity of motor circuitry of the three groups as well as the relation between motor circuitry and the proposed indicators of PmR and PmA. Aim 3 is following up with subjects three times over 18 months to evaluate whether motor symptoms change in tandem with overall depressive symptoms and functioning over time and/or whether baseline PmR/PmA predicts course of depression and functioning. Aim 3 is particularly clinically significant. Finding that motor functioning and overall depression severity co-vary over time, or that motor variables predict subsequent change in overall depression severity, would support the potential clinical utility of these novel, reliable, and easily administered motor assessments.