Active and passive data collection using mHealth to monitor mental wellness: Pilot findings from the Texas resilience against depression study - Scorecard - MDSpire
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Active and passive data collection using mHealth to monitor mental wellness: Pilot findings from the Texas resilience against depression study
Clinical Scorecard: Utilizing mHealth for Active and Passive Data Gathering to Assess Mental Health: Preliminary Results from the Texas Resilience Against Depression Study
At a Glance
Category
Detail
Condition
Key Mechanisms
Digital phenotyping through active and passive data collection via mobile applications.
Target Population
Care Setting
Key Highlights
Lifetime depression rates increased from 20.6% in 2017 to 29% in 2023.
Depression prevalence is higher in individuals with chronic conditions like diabetes (28%) and stroke (18%).
The mHealth application collects both active and passive data to assess mental health.
Guideline-Based Recommendations
Diagnosis
Screening for depression and suicide is recommended for adults, including older adults and pregnant/postpartum women.
Management
Early diagnosis and prompt treatment are essential to improve quality of life.
Monitoring & Follow-up
Continuous data collection on behavioral patterns and mood-related symptoms is encouraged.
Risks
Untreated depression can exacerbate existing medical conditions and increase risk of suicidality.
Patient & Prescribing Data
Adults with Major Depressive Disorder and chronic medical conditions.
Utilization of mHealth for ongoing monitoring and assessment of mental health.
Clinical Best Practices
Incorporate digital phenotyping tools in clinical research to capture real-time data.
Utilize mobile applications for continuous monitoring of mood and behavioral patterns.
