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
Black Dog Institute, University of New South Wales, Sydney, Australia.
Department of Neurosciences, Center for Contextual Psychiatry, KU Leuven, Leuven, Belgium.
JMIR Form Res. 2025 Jun 23;9:e71377. doi: 10.2196/71377.
Digital phenotyping-the use of digital data to measure and understand behavior and internal states-shows promise for advancing predictive analytics in mental health, particularly when combined with other data sources. However, linking digital phenotyping data with sources of highly sensitive clinical or genetic data remains rare, primarily due to technical, ethical, and procedural challenges. Understanding the feasibility of collecting and linking these data types is a critical first step toward developing novel multimodal datasets.
The Mobigene Pilot Study examines the feasibility of collecting smartphone-based digital phenotyping and mental health data and linking it to genetic data from an existing cohort of adults with a history of depression (ie, the Australian Genetics of Depression Study). This paper aims to describe (1) rates of study uptake and adherence; (2) levels of adherence and engagement with daily mood assessments; (3) willingness to take part in similar research; and (4) whether feasibility indicators varied according to mental health symptoms.
Participants aged 18-30 years with genetic data from the Australian Genetics of Depression Study were invited to participate in a two-week digital phenotyping study. They completed a baseline mental health survey and then downloaded the MindGRID digital phenotyping app. Active data from cognitive, voice, and typing tasks were collected once per day on days 1 and 11. Daily momentary assessments of self-reported mood were collected on days 2-10 (once per day for 9 days). Passive data (eg, from GPS, accelerometers) were collected throughout the two-week period. A second mental health survey was then completed after two weeks. To measure feasibility, we examined metrics of study uptake (eg, consent) and adherence (eg, proportion of completed momentary assessments), and willingness to participate in similar future research. Pearson correlations and t tests explored the relationship between feasibility indicators and mental health symptoms.
Of 174 consenting and eligible participants, 153 (87.9%) completed the baseline mental health survey and 126 (72.4%) provided data enabling linkage of genetic, self-report, and digital data. After removal of duplicates, we found that 100 (57.5%) of these identified as unique participants and 69 (39.7%) provided complete post-study data. A small proportion of participants dropped out prior to completing the baseline survey (21/174, 12.1%) or during app-based data collection (31/174, 17.8%). Participants completed an average of 5.30 (SD 2.76) daily mood assessments. All 69 (100%) participants who completed the post-study surveys expressed willingness to participate in similar studies in the future. There was no significant association between feasibility indicators and current mental health symptoms.
It is feasible to collect and link multimodal datasets involving digital phenotyping, clinical, and genetic data, although there are some methodological and technical challenges. We provide recommendations for future research related to data collection platforms and compliance.
数字表型分析——利用数字数据来测量和理解行为及内部状态——在推进心理健康预测分析方面显示出前景，特别是与其他数据源结合时。然而，将数字表型分析数据与高度敏感的临床或基因数据来源相链接仍然很少见，主要是由于技术、伦理和程序方面的挑战。了解收集和链接这些数据类型的可行性是开发新型多模态数据集的关键第一步。
Mobigene试点研究考察了收集基于智能手机的数字表型分析和心理健康数据并将其与来自现有抑郁症病史成年人群体（即澳大利亚抑郁症遗传学研究）的基因数据相链接的可行性。本文旨在描述：（1）研究参与率和依从性；（2）日常情绪评估的依从性和参与度水平；（3）参与类似研究的意愿；以及（4）可行性指标是否因心理健康症状而异。
邀请年龄在18 - 30岁且拥有澳大利亚抑郁症遗传学研究基因数据的参与者参加为期两周的数字表型分析研究。他们完成了一项基线心理健康调查，然后下载了MindGRID数字表型分析应用程序。在第1天和第11天，每天收集一次来自认知、语音和打字任务的主动数据。在第2 - 10天（共9天，每天一次）收集自我报告情绪的日常即时评估数据。在整个两周期间收集被动数据（例如，来自全球定位系统、加速度计的数据）。两周后完成第二次心理健康调查以测量可行性，我们检查了研究参与度（例如，同意参与）和依从性（例如，完成即时评估的比例）指标，以及参与未来类似研究的意愿。Pearson相关性分析和t检验探索了可行性指标与心理健康症状之间的关系。
在174名同意参与且符合条件的参与者中，153名（87.9%）完成了基线心理健康调查，126名（72.4%）提供了能够实现基因、自我报告和数字数据相链接的数据。去除重复数据后，我们发现其中100名（57.5%）被确定为唯一参与者，69名（39.7%）提供了完整的研究后数据。一小部分参与者在完成基线调查之前（21/174，12.1%）或基于应用程序的数据收集期间（31/174，17.8%）退出。参与者平均完成了5.30次（标准差2.76）日常情绪评估。所有69名（100%）完成研究后调查的参与者都表示愿意在未来参与类似研究。可行性指标与当前心理健康症状之间没有显著关联。
收集和链接涉及数字表型分析、临床和基因数据的多模态数据集是可行的，尽管存在一些方法学和技术挑战。我们为未来与数据收集平台和合规性相关的研究提供了建议。
