The feasibility of collecting and linking multimodal data for digital mental health research: A pilot observational study involving digital phenotyping and genetics data (Preprint)
Authors: Beames Joanne R , Dabash Omar , Spoelma Michael J , Shvetcov Artur , Zheng Wu Yi , Slade Aimy , Han Jin , Hoon Leonard , Kupper Joost Funke , Parker Richard , Mitchell Brittany , Martin Nicholas G , Newby Jill M , Whitton Alexis E , Christensen Helen 
Published: February 2025

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|<1.12, Ps>.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.