Mental health researchers are increasingly looking towards digital health tools to gather day-to-day lived experiences of people living with mental health conditions, by using apps and wearable devices complementing episodic clinical assessments. One of the key goals of collecting longitudinal real-world multimodal data (RWD) is to help build personalized computational models that may help explain the heterogeneity in clinical outcomes, mechanisms of action, and pathophysiology of mental health disorders across individuals.
However, while the digital health tools and platforms may enable researchers to reach and collect RWD from a large population, we still don’t fully understand scalable mechanisms to engage diverse study participants in longitudinal and decentralized research studies. For example, remote participation may be impacted by a combination of various factors that include, but are not limited to:
● socio-demographic factors (e.g., age, race, ethnicity, education, health literacy, and economic status)
● comfort with technology
● concerns regarding sharing of sensitive and labeled personal data (symptoms and severity) for research purposes.
Other potential factors linked to differential recruitment and longitudinal engagement in fully remote studies may be related to fluctuations in participants’ mental health symptoms and severity during the course of the study.
Not understanding what digital health tools work for whom and for how long could potentially bias the behavioral RWD collection, impacting the generalizability and robustness of inference derived from computational psychiatry models built using the individualized multi-modal datasets. There is a clear unmet need for further research to help understand how potential biases in remote data collection may impact the utility of new real-world data sources (e.g. GPS-derived physical mobility, vocal biomarkers) in developing robust digital bio-signatures of mental health. Ultimately, the use of longitudinal RWD to build precision computational psychiatry models that generate actionable real-world evidence in mental health will require researchers to understand and address participant concerns and challenges for enrolling and long-term engagement in remote mental health studies.
This Research Topic will explore a broad set of themes related to participant enrollment, engagement, and retention in digital mental health research. We are interested in Original Research, Brief Report, Systematic Reviews, Mini-Review, Perspective, and Commentary articles covering the following sub-topics.
● Real-world studies highlighting approaches to reach, enroll, and longitudinally engage the target population.
● Machine learning approaches that help understand patterns of engagement based on socio-demographic factors and/or severity of mental health symptoms.
● Concerns and willingness of people with lived experience of mental health conditions to participate and share their data in remote research studies.
● Digital health tools and frameworks that promote scalable design and deployment of online studies, with a focus on participant engagement.
● Best practices for improving longitudinal retention in digital mental health studies. For example:
- incentive models to keep study participants engaged over time
- return of information
- behavioral economics strategies
- approaches to identify and manage bad actors.
Mental health researchers are increasingly looking towards digital health tools to gather day-to-day lived experiences of people living with mental health conditions, by using apps and wearable devices complementing episodic clinical assessments. One of the key goals of collecting longitudinal real-world multimodal data (RWD) is to help build personalized computational models that may help explain the heterogeneity in clinical outcomes, mechanisms of action, and pathophysiology of mental health disorders across individuals.
However, while the digital health tools and platforms may enable researchers to reach and collect RWD from a large population, we still don’t fully understand scalable mechanisms to engage diverse study participants in longitudinal and decentralized research studies. For example, remote participation may be impacted by a combination of various factors that include, but are not limited to:
● socio-demographic factors (e.g., age, race, ethnicity, education, health literacy, and economic status)
● comfort with technology
● concerns regarding sharing of sensitive and labeled personal data (symptoms and severity) for research purposes.
Other potential factors linked to differential recruitment and longitudinal engagement in fully remote studies may be related to fluctuations in participants’ mental health symptoms and severity during the course of the study.
Not understanding what digital health tools work for whom and for how long could potentially bias the behavioral RWD collection, impacting the generalizability and robustness of inference derived from computational psychiatry models built using the individualized multi-modal datasets. There is a clear unmet need for further research to help understand how potential biases in remote data collection may impact the utility of new real-world data sources (e.g. GPS-derived physical mobility, vocal biomarkers) in developing robust digital bio-signatures of mental health. Ultimately, the use of longitudinal RWD to build precision computational psychiatry models that generate actionable real-world evidence in mental health will require researchers to understand and address participant concerns and challenges for enrolling and long-term engagement in remote mental health studies.
This Research Topic will explore a broad set of themes related to participant enrollment, engagement, and retention in digital mental health research. We are interested in Original Research, Brief Report, Systematic Reviews, Mini-Review, Perspective, and Commentary articles covering the following sub-topics.
● Real-world studies highlighting approaches to reach, enroll, and longitudinally engage the target population.
● Machine learning approaches that help understand patterns of engagement based on socio-demographic factors and/or severity of mental health symptoms.
● Concerns and willingness of people with lived experience of mental health conditions to participate and share their data in remote research studies.
● Digital health tools and frameworks that promote scalable design and deployment of online studies, with a focus on participant engagement.
● Best practices for improving longitudinal retention in digital mental health studies. For example:
- incentive models to keep study participants engaged over time
- return of information
- behavioral economics strategies
- approaches to identify and manage bad actors.