Current methods for analyzing time-series patient-generated health data to assess treatment response: a scoping...

We aimed to identify and map recent studies using high-frequency, time-series electronic patient-generated health data (ePGHD) to assess treatment response; characterize ePGHD types and collection methods; summarize ePGHD-based definitions of treatment response; and describe analytical approaches used.

We systematically searched 4 databases for articles published between January 2022 and June 2024, supplemented by a forward citation search until June 2025. Peer-reviewed studies were eligible if ePGHD were collected outside clinical settings, and either reported at least weekly (ie, if actively reported by participants) or summarized discretely (eg, daily) if passively collected via wearables/sensors. We screened articles for eligibility independently in duplicate and synthesized extracted data descriptively.

Our search yielded 4030 articles, of which we included 186. Most studies collected ePGHD using mobile applications or webforms (n = 133) over 4-12 weeks (n = 67). Prior to analysis, 132 studies excluded portions or condensed ePGHD into one or more summaries. Among 172 studies estimating treatment response, 98 applied longitudinal methods (eg, mixed-effects models) that accounted for repeated measures while capturing within- and between-subject variations, whereas 74 used cross-sectional approaches. Of 18 prediction modeling studies, 16 employed machine learning techniques, with only 4 explicitly modeling repeated measures. Five studies identified clusters of response trajectories generally without incorporating temporal dependencies (eg, using K-means).

Many studies in this review did not fully leverage the high-frequency, longitudinal nature of ePGHD. Future research should adopt more appropriate and readily available analytic methods to maximize the potential of time-series ePGHD for generating insights into treatment response.