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BACKGROUND: Wearable tracking devices and mobile health technology are increasingly used in an effort to enhance clinical care and the delivery of personalized medical treatment. Postpartum depression is the most frequently diagnosed complication of childbirth; however, significant gaps in screening and treatment remain. OBJECTIVE: This study aims to investigate the clinical utility, predictive ability, and acceptability of using ecological momentary assessment to collect daily mood, sleep, and activity data through the use of an Apple Watch and mobile app among women with postpartum depression. METHODS: This was a pilot study consisting of 3 in-person research visits over the course of a 6-week enrollment period. Questionnaires to assess depression, anxiety, and maternal functioning were periodically collected, along with daily self-reported symptoms and passively collected physiological data via an Apple Watch. Feedback was collected from study participants and the study clinician to determine the utility and acceptability of daily tracking. Logistic regression was used to determine whether mood scores in the 2 weeks before a visit predicted scores at follow-up. Compliance with daily assessments was also measured. RESULTS: Of the 26 women enrolled, 23 (88%) completed the 6-week study period. On average, the participants completed 67% (34.4/51.5 days) of all active daily assessments and 74% (38/51.5 days) of all passive measures. Furthermore, all 23 participants completed the 3 required visits with the research team. Predictive correlations were found between self-reported mood and Edinburgh Postnatal Depression Scale score at follow-up, self-reported anxiety and EDPS, and sleep quality and Edinburgh Postnatal Depression Scale. CONCLUSIONS: Using ecological momentary assessment to track daily symptoms of postpartum depression using a wearable device was largely endorsed as acceptable and clinically useful by participants and the study clinician and could be an innovative solution to increase care access during the COVID-19 pandemic.
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OBJECTIVE: The authors sought to increase the rate of cardiometabolic monitoring for patients receiving antipsychotic drugs in an academic outpatient psychiatric clinic serving people with serious mental illness. METHODS: Using a prospective quasi-experimental, interrupted time-series design with data from the electronic health record (EHR), the authors determined metabolic monitoring rates before, during, and after implementation of prespecified quality improvement (QI) measures between August 2016 and July 2017. QI measures included a combination of provider, patient, and staff education; systematic barrier reduction; and an EHR-based reminder system. RESULTS: After 1 year of QI implementation, the rate of metabolic monitoring had increased from 33% to 49% (p<0.01) for the primary outcome measure (hemoglobin A1C and lipid panel). This increased monitoring rate was sustained for 27 months beyond the end of the QI intervention. More than 75% of providers did not find the QI reminders burdensome. CONCLUSIONS: Significant improvement in the rate of metabolic monitoring for people taking antipsychotic drugs can be achieved with little added burden on providers. Future research needs to assess the full range of patient, provider, and system barriers that prevent cardiometabolic monitoring for all individuals receiving antipsychotic drugs.
