Medication nonadherence undermines treatment effectiveness in psychiatric care, yet objective, continuous measurement in routine practice is challenging. AI-enabled wearables may offer scalable monitoring but evidence from randomized evaluations remains limited.
We conducted a single-center, prospective, randomized controlled trial of an AI-based, wrist-worn adherence monitoring system in outpatient psychiatric care. Participants (age ≥12 years) were randomized 1:1 to smartwatch intervention or usual care for 4 weeks. Algorithm-derived adherence was calculated as the proportion of detected medication events relative to scheduled doses over prespecified 28-day baseline and post-intervention windows. The primary endpoint was change in adherence (Δ = post - pre). Analyses used complete cases with linear regression (adjusting for baseline adherence, age, sex, and medication covariates) and HC3 standard errors; ANCOVA served as a confirmatory model. Prespecified responder thresholds were Δ ≥10 and ≥20 percentage points (pp). Sensitivity analyses excluded benzodiazepine users.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
TREATMENT
Masking
SINGLE
Enrollment
78
The device was designed to monitor medication-related behaviors in real-world settings (pill taking and, by design, use of eye drops, inhalers, and nasal sprays). A built-in camera remained in sleep mode and recorded brief \~20-second clips only when an electronic tag affixed to the medication container signaled three concurrent conditions: (1) container motion detected by the tag's accelerometer, (2) ambient light detected by the tag's light sensor, and (3) watch-tag proximity within approximately 10-15 cm via BLE ranging. After capturing a clip, the camera returned to sleep. Encrypted videos were transmitted to a secure server and linked to de-identified study IDs. Server-side algorithms then analyzed the full 20-second sequence, covering the continuous hand actions from opening to closing of the container, and returned a binary medication event (medication vs no medication). Participants were instructed to wear the smartwatch for 4 weeks
Wonkwang University Hospital
Iksan, Jeollabuk-do, South Korea
Change in Medication Adherence Rate Measured by Pill Count (Post-Intervention minus Baseline)
Medication adherence was assessed by pill count at baseline and at 4-week follow-up. Adherence rate was calculated as (total prescribed doses - number of returned pills) / total prescribed doses × 100 (%). The primary outcome is the change in adherence rate (Δ = post - pre) between the intervention and control groups.
Time frame: From enrollment to the end of intervention at 28days
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