JMIR Publications

ABSTRACT

Background:

Anhedonia is a core symptom of depressive disorders. While group-level cutoff points for the Snaith–Hamilton Pleasure Scale (SHAPS) are established, real-time, individual-level monitoring remains limited. Ecological momentary assessment (EMA) enables high-frequency sampling, and simple moving averages (SMA; 7/14/30 days) offer an interpretable way to smooth daily signals and surface early-warning trends.

Objective:

To develop and validate a cross-device platform that collects EMA-based SHAPS responses and visualizes SMA trends for individual anhedonia monitoring. This Part I protocol focuses on platform design, feasibility, usability, data quality, and initial analytical validity to inform subsequent evaluation.

Methods:

We will conduct a single-arm methodological pilot with clinical and nonclinical cohorts. Participants will complete daily EMA-SHAPS for 30 days. The platform computes SMA (7/14/30) and renders trend/alert panels. Candidate alert logic includes fast–slow MA crossovers and deviations from individualized baselines. Primary outcomes are feasibility (adherence/completion), usability (eg, System Usability Scale), and face validity. Analytical checks include content validity, correspondence between daily SHAPS and SMA windows, and trend stability.

Results:

This protocol specifies the design-validate sequence and the 30-day pilot to evaluate feasibility, usability, and initial analytical validity. Planned outputs for Part I include finalized UI components, adherence/usability metrics, and parameter estimates to finalize alert logic.

Conclusions:

Combining EMA-SHAPS with SMA-based trend visualization offers an interpretable, pragmatic approach to individual-level anhedonia monitoring. The Part I findings will determine feasibility and refine analytic parameters to support a subsequent controlled evaluation.