Data Collection for Mental Health Studies Through Digital Platforms: Requirements and Design of a Prototype

Testing unit root non-stationarity in the presence of missing data in univariate time series of mobile health studies

The use of digital devices to collect data in mobile health studies introduces a novel application of time series methods, with the constraint of potential data missing at random or missing not at random (MNAR). In time-series analysis, testing for stationarity is an important preliminary step to inform appropriate subsequent analyses. The Dickey-Fuller test evaluates the null hypothesis of unit root non-stationarity, under no missing data. Beyond recommendations under data missing completely at random for complete case analysis or last observation carry forward imputation, researchers have not extended unit root non-stationarity testing to more complex missing data mechanisms. Multiple imputation with chained equations, Kalman smoothing imputation, and linear interpolation have also been used for time-series data, however such methods impose constraints on the autocorrelation structure and impact unit root testing. We propose maximum likelihood estimation and multiple imputation using state space model approaches to adapt the augmented Dickey-Fuller test to a context with missing data. We further develop sensitivity analyses to examine the impact of MNAR data. We evaluate the performance of existing and proposed methods across missing mechanisms in extensive simulations and in their application to a multi-year smartphone study of bipolar patients.

Relationship between daily rated depression symptom severity and the retrospective self-report on PHQ-9: A prospective ecological momentary assessment study on 80 psychiatric outpatients

BACKGROUND

Depression-related negative bias in emotional processing and memory may bias accuracy of recall of temporally distal symptoms. We tested the hypothesis that when responding to the Patient Health Questionnaire (PHQ-9) the responses reflect more accurately temporally proximal than distal mood states.

METHODS

Currently, depressed psychiatric outpatients (N = 80) with depression confirmed in semi-structured interviews had the Aware application installed on their smartphones for ecological momentary assessment (EMA). The severity of "low mood", "hopelessness", "low energy", "anhedonia", and "wish to die" was assessed on a Likert scale five times daily during a 12-day period, and thereafter, the PHQ-9 questionnaire was completed. We used auto- and cross-correlation analyses and linear mixed-effects multilevel models (LMM) to investigate the effect of time lag on the association between EMA of depression symptoms and the PHQ-9.

RESULTS

Autocorrelations of the EMA of depressive symptom severity at two subsequent days were strong (r varying from 0.7 to 0.9; p < 0.001). "Low mood" was the least and "wish to die" the most temporally stable symptom. The correlations between EMA of depressive symptoms and total scores of the PHQ-9 were temporally stable (r from 0.3 to 0.6; p < 0.001). No effect of assessment time on the association between EMA data and the PHQ-9 emerged in the LMM.

LIMITATIONS

Altogether 11.5 % of observations were missing.

CONCLUSIONS

Despite fluctuations in severity of some of the depressive symptoms, patients with depression accurately recollect their most dominant symptoms, without a significant recall bias favouring the most recent days, when responding to the PHQ-9.

The PPD-ACT app in Canada: feasibility and a latent class analysis of participants with postpartum depression recruited to a psychiatric genetics study using a mobile application

BACKGROUND

Postpartum depression (PPD) and postpartum psychosis (PPP) are linked to negative consequences for women and families. Virtual applications present a solution to the challenge of recruiting large samples for genetic PPD/PPP research. This study aimed to evaluate the feasibility of a protocol for enrolling Canadian women with PPD and PPP to a large international psychiatric genetics study using a mobile application (PPD-ACT), and identify clinically distinct subtypes of PPD in the recruited sample.

METHODS

From April 2017-June 2019, Canadian women provided phenotypic data through the PPD-ACT app. Requests for a genetic sample were made from those with a current or past PPD episode based on an Edinburgh Postnatal Depression Scale (EPDS) score > 12 with onset in pregnancy or 0-3 months postpartum, and from those self-reporting lifetime PPP. Latent class analysis (LCA) was used to identify clinically distinct PPD subgroups based on participant responses to the EPDS scale.

RESULTS

We identified 797 PPD cases, 404 of whom submitted DNA. There were 109 PPP cases, with 66 submitting DNA. PPD cases (86.7% White, mean 4.7 +/- 7.0 years since their episode) came from across Canadian provinces/territories. LCA identified two PPD classes clinically distinct by symptom severity: [1] moderate-severity (mean EPDS = 18.5+/- 2.5; 8.6% with suicidality), and [2] severe (mean EPDS = 24.5+/- 2.1; 52.8% with suicidality).

CONCLUSIONS

A mobile application rapidly collected data from individuals with moderate and severe symptoms of PPD, an advantage for genetics where specificity is optimal, as well as from women with a history of PPP, supporting future work using this approach.

Public Attitudes to Digital Health Research Repositories: Cross-sectional International Survey

Background

Digital health research repositories propose sharing longitudinal streams of health records and personal sensing data between multiple projects and researchers. Motivated by the prospect of personalizing patient care (precision medicine), these initiatives demand broad public acceptance and large numbers of data contributors, both of which are challenging.

Objective

This study investigates public attitudes toward possibly contributing to digital health research repositories to identify factors for their acceptance and to inform future developments.

Methods

A cross-sectional online survey was conducted from March 2020 to December 2020. Because of the funded project scope and a multicenter collaboration, study recruitment targeted young adults in Denmark and Brazil, allowing an analysis of the differences between 2 very contrasting national contexts. Through closed-ended questions, the survey examined participants’ willingness to share different data types, data access preferences, reasons for concern, and motivations to contribute. The survey also collected information about participants’ demographics, level of interest in health topics, previous participation in health research, awareness of examples of existing research data repositories, and current attitudes about digital health research repositories. Data analysis consisted of descriptive frequency measures and statistical inferences (bivariate associations and logistic regressions).

Results

The sample comprises 1017 respondents living in Brazil (1017/1600, 63.56%) and 583 in Denmark (583/1600, 36.44%). The demographics do not differ substantially between participants of these countries. The majority is aged between 18 and 27 years (933/1600, 58.31%), is highly educated (992/1600, 62.00%), uses smartphones (1562/1600, 97.63%), and is in good health (1407/1600, 87.94%). The analysis shows a vast majority were very motivated by helping future patients (1366/1600, 85.38%) and researchers (1253/1600, 78.31%), yet very concerned about unethical projects (1219/1600, 76.19%), profit making without consent (1096/1600, 68.50%), and cyberattacks (1055/1600, 65.94%). Participants’ willingness to share data is lower when sharing personal sensing data, such as the content of calls and texts (1206/1600, 75.38%), in contrast to more traditional health research information. Only 13.44% (215/1600) find it desirable to grant data access to private companies, and most would like to stay informed about which projects use their data (1334/1600, 83.38%) and control future data access (1181/1600, 73.81%). Findings indicate that favorable attitudes toward digital health research repositories are related to a personal interest in health topics (odds ratio [OR] 1.49, 95% CI 1.10-2.02; P=.01), previous participation in health research studies (OR 1.70, 95% CI 1.24-2.35; P=.001), and awareness of examples of research repositories (OR 2.78, 95% CI 1.83-4.38; P<.001).

Conclusions

This study reveals essential factors for acceptance and willingness to share personal data with digital health research repositories. Implications include the importance of being more transparent about the goals and beneficiaries of research projects using and re-using data from repositories, providing participants with greater autonomy for choosing who gets access to which parts of their data, and raising public awareness of the benefits of data sharing for research. In addition, future developments should engage with and reduce risks for those unwilling to participate.

An Iterative and Collaborative End-to-End Methodology Applied to Digital Mental Health

Artificial intelligence (AI) algorithms together with advances in data storage have recently made it possible to better characterize, predict, prevent, and treat a range of psychiatric illnesses. Amid the rapidly growing number of biological devices and the exponential accumulation of data in the mental health sector, the upcoming years are facing a need to homogenize research and development processes in academia as well as in the private sector and to centralize data into federalizing platforms. This has become even more important in light of the current global pandemic. Here, we propose an end-to-end methodology that optimizes and homogenizes digital research processes. Each step of the process is elaborated from project conception to knowledge extraction, with a focus on data analysis. The methodology is based on iterative processes, thus allowing an adaptation to the rate at which digital technologies evolve. The methodology also advocates for interdisciplinary (from mathematics to psychology) and intersectoral (from academia to the industry) collaborations to merge the gap between fundamental and applied research. We also pinpoint the ethical challenges and technical and human biases (from data recorded to the end user) associated with digital mental health. In conclusion, our work provides guidelines for upcoming digital mental health studies, which will accompany the translation of fundamental mental health research to digital technologies.

Requirements and Operational Guidelines for Secure and Sustainable Digital Phenotyping: Design and Development Study

Background

Digital phenotyping, the measurement of human behavioral phenotypes using personal devices, is rapidly gaining popularity. Novel initiatives, ranging from software prototypes to user-ready research platforms, are innovating the field of biomedical research and health care apps. One example is the BEHAPP project, which offers a fully managed digital phenotyping platform as a service. The innovative potential of digital phenotyping strategies resides among others in their capacity to objectively capture measurable and quantitative components of human behavior, such as diurnal rhythm, movement patterns, and communication, in a real-world setting. The rapid development of this field underscores the importance of reliability and safety of the platforms on which these novel tools are operated. Large-scale studies and regulated research spaces (eg, the pharmaceutical industry) have strict requirements for the software-based solutions they use. Security and sustainability are key to ensuring continuity and trust. However, the majority of behavioral monitoring initiatives have not originated primarily in these regulated research spaces, which may be why these components have been somewhat overlooked, impeding the further development and implementation of such platforms in a secure and sustainable way.

Objective

This study aims to provide a primer on the requirements and operational guidelines for the development and operation of a secure behavioral monitoring platform.

Methods

We draw from disciplines such as privacy law, information, and computer science to identify a set of requirements and operational guidelines focused on security and sustainability. Taken together, the requirements and guidelines form the foundation of the design and implementation of the BEHAPP behavioral monitoring platform.

Results

We present the base BEHAPP data collection and analysis flow and explain how the various concepts from security and sustainability are addressed in the design.

Conclusions

Digital phenotyping initiatives are steadily maturing. This study helps the field and surrounding stakeholders to reflect upon and progress toward secure and sustainable operation of digital phenotyping–driven research.

The spatiotemporal movement of patients in and out of a psychiatric hospital: an observational GPS study

BACKGROUND

Movement is a basic component of health. Little is known about the spatiotemporal movement of patients with mental disorders. The aim of this study was to determine how spatiotemporal movement of patients related to their symptoms and wellbeing.

METHOD

A total of 106 patients (inpatients (n = 69) and outpatients (n = 37)) treated for a wide range of mental disorders (transdiagnostic sample) carried a GPS-enabled smartphone for one week at the beginning of treatment. Algorithms were applied to establish spatiotemporal clusters and subsequently related to known characteristics of these groups (i.e., at the hospital, at home). Symptomatology, Wellbeing, and Psychological flexibility were also assessed.

RESULTS

Spatiotemporal patterns of inpatients and outpatients showed differences consistent with predictions (e.g., outpatients showed higher active areas). These patterns were largely unassociated with symptoms (except for agoraphobic symptoms). Greater movement and variety of movement were more predictive of wellbeing, however, in both inpatients and outpatients.

CONCLUSION

Measuring spatiotemporal patterns is feasible, predictive of wellbeing, and may be a marker of patient functioning. Ethical issues of collecting GPS data are discussed.

i-MANN: A Web-Based System for Data Management of Mental Health Research in India

BACKGROUND

National Mental Health Program (NMHP) was launched by the government with an aim to improve mental health of the society through precise and focused interventions and policies. In order to provide reliable data and evidence for NMHP, there is a strong requirement of a comprehensive system for integrative collection, storage, and analysis of data generated by this program.

METHODS

Data collection tools, questionnaires, instruments, and scales provided by the National Coordinating Unit were digitized using the District Health Information Software 2 (DHIS2) framework (version 2.30). The rules for data validation and automated scoring were implemented as per the scales. The developed system (i-MANN, ICMR-Mental Health Assessment National Network) is based on modular architecture with role-based access to data input forms and dashboards.

RESULTS

The data are stored on a centralized server at ICMR. i-MANN captures data on basic and advanced demographic details followed by category specific forms from 15 multicentric ICMR-funded projects. Data collection module is divided into 12 categories containing 93 scales/instruments with built-in validation rules, scoring patterns, and indicators. As of August 2020, the system contains 17,690 records.

CONCLUSIONS

i-MANN is the first web-based, modular, robust, and extendable system for collection, integration, management, and analysis of data on mental health in India.

RADAR-Base: Open Source Mobile Health Platform for Collecting, Monitoring, and Analyzing Data Using Sensors, Wearables, and Mobile Devices

BACKGROUND

With a wide range of use cases in both research and clinical domains, collecting continuous mobile health (mHealth) streaming data from multiple sources in a secure, highly scalable, and extensible platform is of high interest to the open source mHealth community. The European Union Innovative Medicines Initiative Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) program is an exemplary project with the requirements to support the collection of high-resolution data at scale; as such, the Remote Assessment of Disease and Relapse (RADAR)-base platform is designed to meet these needs and additionally facilitate a new generation of mHealth projects in this nascent field.

OBJECTIVE

Wide-bandwidth networks, smartphone penetrance, and wearable sensors offer new possibilities for collecting near-real-time high-resolution datasets from large numbers of participants. The aim of this study was to build a platform that would cater for large-scale data collection for remote monitoring initiatives. Key criteria are around scalability, extensibility, security, and privacy.

METHODS

RADAR-base is developed as a modular application; the backend is built on a backbone of the highly successful Confluent/Apache Kafka framework for streaming data. To facilitate scaling and ease of deployment, we use Docker containers to package the components of the platform. RADAR-base provides 2 main mobile apps for data collection, a Passive App and an Active App. Other third-Party Apps and sensors are easily integrated into the platform. Management user interfaces to support data collection and enrolment are also provided.

RESULTS

General principles of the platform components and design of RADAR-base are presented here, with examples of the types of data currently being collected from devices used in RADAR-CNS projects: Multiple Sclerosis, Epilepsy, and Depression cohorts.

CONCLUSIONS

RADAR-base is a fully functional, remote data collection platform built around Confluent/Apache Kafka and provides off-the-shelf components for projects interested in collecting mHealth datasets at scale.

Smartphone-Based Tracking of Sleep in Depression, Anxiety, and Psychotic Disorders

PURPOSE OF REVIEW

Sleep is an important feature in mental illness. Smartphones can be used to assess and monitor sleep, yet there is little prior application of this approach in depressive, anxiety, or psychotic disorders. We review uses of smartphones and wearable devices for sleep research in patients with these conditions.

RECENT FINDINGS

To date, most studies consist of pilot evaluations demonstrating feasibility and acceptability of monitoring sleep using smartphones and wearable devices among individuals with psychiatric disorders. Promising findings show early associations between behaviors and sleep parameters and agreement between clinic-based assessments, active smartphone data capture, and passively collected data. Few studies report improvement in sleep or mental health outcomes. Success of smartphone-based sleep assessments and interventions requires emphasis on promoting long-term adherence, exploring possibilities of adaptive and personalized systems to predict risk/relapse, and determining impact of sleep monitoring on improving patients' quality of life and clinically meaningful outcomes.

IoT-Based Services and Applications for Mental Health in the Literature

Internet of Things (IoT) has emerged as a new paradigm today, connecting a variety of physical and virtual elements integrated with electronic components, sensors, actuators and software to collect and exchange data. IoT is gaining increasing attention as a priority research topic in the Health sector in general and in specific areas such as Mental Health. The main objective of this paper is to show a review of the existing research works in the literature, referring to the main IoT services and applications in Mental Health diseases. The scientific databases used to carry out the review are Google Scholar, IEEE Xplore, PubMed, Science Direct, and Web of Science, taking into account as date of publication the last 10 years, from 2008 to the present. Several search criteria were established such as "IoT OR Internet of Things AND (Application OR Service) AND Mental Health" selecting the most interesting articles. A total of 51 articles were found on IoT-based services and applications in Mental Health, of which 14 have been identified as relevant works in mental health. Many of the publications (more than 60%) found show the applications developed for monitoring patients with mental disorders through sensors and networked devices. The inclusion of the new IoT technology in Health brings many benefits in terms of monitoring, welfare interventions and providing alert and information services. In pathologies such as Mental Health is a vital factor to improve the patient life quality and effectiveness of the medical service.

Quantitative account of social interactions in a mental health care ecosystem: cooperation, trust and collective action

Mental disorders have an enormous impact in our society, both in personal terms and in the economic costs associated with their treatment. In order to scale up services and bring down costs, administrations are starting to promote social interactions as key to care provision. We analyze quantitatively the importance of communities for effective mental health care, considering all community members involved. By means of citizen science practices, we have designed a suite of games that allow to probe into different behavioral traits of the role groups of the ecosystem. The evidence reinforces the idea of community social capital, with caregivers and professionals playing a leading role. Yet, the cost of collective action is mainly supported by individuals with a mental condition - which unveils their vulnerability. The results are in general agreement with previous findings but, since we broaden the perspective of previous studies, we are also able to find marked differences in the social behavior of certain groups of mental disorders. We finally point to the conditions under which cooperation among members of the ecosystem is better sustained, suggesting how virtuous cycles of inclusion and participation can be promoted in a 'care in the community' framework.

Toward Impactful Collaborations on Computing and Mental Health

We describe an initiative to bring mental health researchers, computer scientists, human-computer interaction researchers, and other communities together to address the challenges of the global mental ill health epidemic. Two face-to-face events and one special issue of the Journal of Medical Internet Research were organized. The works presented in these events and publication reflect key state-of-the-art research in this interdisciplinary collaboration. We summarize the special issue articles and contextualize them to present a picture of the most recent research. In addition, we describe a series of collaborative activities held during the second symposium and where the community identified 5 challenges and their possible solutions.