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Updated: Feb 28, 2026

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
Published on: July 27, 2018
Using mHealth technologies to improve the identification of behavioral health problems in urban primary care settings
Martha Staeheli1, Robert H Aseltine2, Elizabeth Schilling2
1Program for Recovery and Community Health, Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA.
Introduction:
Behavioral health disorders remain under recognized and under diagnosed among urban primary care patients. Screening patients for such problems is widely recommended, yet is challenging to do in a brief primary care encounter, particularly for this socially and medically complex patient population.
Methods:
In 2013, intervention patients at an urban Connecticut primary clinic were screened for post-traumatic stress disorder, depression, and risky drinking (n = 146) using an electronic tablet-based screening tool. Screening data were compared to electronic health record data from control patients (n = 129) to assess differences in the prevalence of behavioral health problems, rates of follow-up care, and the rate of newly identified cases in the intervention group.
Results:
Results from logistic regressions indicated that both groups had similar rates of disorder at baseline. Patients in the intervention group were five times more likely to be identified with depression (p < 0.05). Post-traumatic stress disorder was virtually unrecognized among controls but was observed in 23% of the intervention group (p < 0.001). The vast majority of behavioral health problems identified in the intervention group were new cases. Follow-up rates were significantly higher in the intervention group relative to controls, but were low overall.
Conclusion:
This tablet-based electronic screening tool identified significantly higher rates of behavioral health disorders than have been previously reported for this patient population. Electronic risk screening using patient-reported outcome measures offers an efficient approach to improving the identification of behavioral health problems and improving rates of follow-up care.
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