Background: Digital phenotyping (DP), the process of using data from digital devices, like smartphones and wearable technology to understand and monitor people's behaviour, health, and daily activities, has shown significant promise in mental health care within high-income countries (HICs). However, its application in lower and middle-income countries (LMICs) is limited, particularly among impoverished populations such as slum residents. Objective: This study investigates the awareness, knowledge, acceptance, and implementation of DP, including willingness to share data, and concerns regarding privacy and data security, among residents of Dhaka's Korail slum, one of Bangladesh's largest and most densely populated informal settlements. Understanding awareness, acceptance, and privacy concerns surrounding DP in these settings is critical for its effective implementation. Methods: We conducted eight focus group discussions (FGDs) with 38 participants (79% female, mean age 37 ± 13.7 years). Participants included 20 individuals diagnosed with serious mental disorders (SMDs) and 18 caregivers. The FGDs also included a section explaining what DP is. Results: Smartphone ownership was reported by 45% of participants, while 92% had access to a smartphone through family members. There was a general lack of awareness about DP among the participants. Initially, 92% (35/38) of participants had no prior knowledge of DP, but after receiving an explanation, they acknowledged its potential applications and benefits. Participants recognized the utility of DP for health monitoring, particularly in managing mental health conditions. Participants expressed willingness to share certain types of data, particularly phone usage and location data, provided that content-level information remained private. Despite these perceived benefits, significant concerns about privacy and data security emerged. Participants expressed fears about the potential misuse of their personal information, with some feeling resigned to the idea of already being constantly monitored. Trust in DP tools emerged as a critical factor for adoption, highlighting the need for transparent data protection policies and user control over data sharing. Additionally, participants emphasized the importance of adapting DP tools to local contexts, including cultural considerations and technological literacy. Conclusions: While DP presents a promising avenue for mental health support in underserved urban populations, its adoption in LMIC slum settings requires targeted educational initiatives, robust privacy safeguards, and community involvement to ensure trust and #usability. DP tools should be adapted to fit the cultural context of the target population, possibly involving modifications to the types of data collected or the way data is interpreted. In conclusion, while DP holds potential to improve mental health care in underserved communities, addressing barriers related to awareness, privacy, culture and #usability is crucial. Focusing on educational initiatives, robust data protection, cultural adaptation, user-friendly design, and community engagement, DP can become a valuable tool in bridging the mental health care gap in LMICs.

Background: Digital phenotyping, the process of using digital data to measure and understand behaviour and internal states, shows promise for predictive analytics in mental health when combined with other forms of data. However, linking digital phenotyping data to other datasets, particularly those that involve highly sensitive clinical and genetic data, is uncommon due to technical, ethical, and procedural difficulties. Understanding the #feasibility of collecting and linking this data is the first step to create novel multimodal datasets. Objective: The Mobigene Pilot Study explores the #feasibility of collecting and linking new data, primarily smartphone-collected digital phenotyping and clinical data, to genetic data from an existing cohort of adults with a history of depression (Australian Genetics of Depression Study; AGDS). This paper aims to: (1) describe rates of study uptake (e.g., number of consenting and eligible participants, number/proportion whose data could be linked) and adherence (e.g., number/proportion who completed baseline/post-surveys, number/proportion who dropped out); (2) describe levels of adherence and engagement with daily diaries; (3) identify openness to take part in similar research; and (4) determine whether these #feasibility indicators differ by current mental health symptoms. Methods: Participants aged 18-30 with genetic data from the AGDS were invited to take part in a two-week study. Participants completed a baseline demographic and mental health survey and then downloaded the Mind GRID app for digital phenotyping. Active data from cognitive, voice and typing tasks were collected once per day on days 1 and 11; daily diaries assessing self-reported mood were collected on days 2-10 (once/day for 9-days). Passive data (e.g., from Global Positioning Systems, accelerometers) were collected throughout the study. A post-survey was then completed. To measure #feasibility, we computed descriptive statistics to explore study uptake and adherence, daily diary adherence and engagement, and openness for future research. Correlations and t-tests explored the relationship between #feasibility indicators and mental health. Results: Out of 174 consenting and eligible participants, 153 completed the baseline survey (153/174, 87.9%) and 126 provided data that enabled linkage of genetic, self-report, and digital data (126/174, 72.4%). There were 100 unique participants after duplicate removal (100/174, 57.5%) and 69 provided complete data at post (69/174, 39.7%). Dropout occurred prior to completing the baseline survey (23/174, 13.2%) and during app data collection (31/174, 17.8%). Participants completed an average of 5.30 (SD=2.76) daily diaries. All participants who completed post surveys (69/69, 100%) expressed willingness to participate in similar future studies. #feasibility indicators were not related to current mental health symptoms (e.g., |ts|.27). Conclusions: It is feasible to collect and link multimodal datasets involving digital phenotyping, clinical, and genetic data. The next phase involves exploring links between digital phenotyping markers and clinical/genetic correlates to improve detection and prediction of mental health problems.

Despite significant innovation in the mental health app space, low app usage has constrained their impact. In this webinar, Jane Mikkelson, Bridget Dwyer, and Dr. John Torous will discuss how digital phenotyping can personalize mobile app recommendations to improve user engagement. They will also talk about the crucial role of habit formation in sustaining app use and how the real-world applications of this research can enhance mental health support. The highlighted paper in this webinar: https://formative.jmir.org/2024/1/e62725 The webinar was held on February 28 Panelist of this webinar: Bridget Dwyer Jane Mikkelson Moderated by: John Torous, MD, MBI, Cofounder, Society of Digital Psychiatry Find out more about JMIR Mental Health: https://mental.jmir.org Find out more about the Society for Digital Psychiatry (SODP): https://www.sodpsych.org ### About JMIR Publications JMIR Publications is a leading, born-digital, open access publisher of 30+ academic journals and other innovative scientific communication products that focus on the intersection of health, and technology. Its flagship journal, the Journal of Medical Internet Research, is the leading digital health journal globally in content breadth and visibility, and is the largest journal in the medical informatics field. To learn more about JMIR Publications, please visit https://www.JMIRPublications.com or connect with us via: YouTube - / jmirpublications Facebook - / jmedinternetres Twitter - / jmirpub LinkedIn - / jmir-publications Instagram - / jmirpub Head Office - 130 Queens Quay East, Unit 1100 Toronto, ON, M5A 0P6 Canada Media Contact - Communications@JMIR.org The content of this communication is licensed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/..., which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, published by JMIR Publications, is properly cited.

Background: Mood disorders are among the most common #MentalHealth conditions worldwide. Wearables and consumer-grade personal #Digital devices create #Digital traces that can be collected, processed, and analyzed, offering a unique opportunity to…

Background: Insomnia is a prevalent sleep disorder affecting millions worldwide, with significant impacts on daily functioning and quality of life. While traditionally assessed through subjective measures such as the Insomnia Severity Index (ISI), the…