Aggregation framework for continuous digital health measures and application to walking activity data

Abstract

Abstract No standardized procedures for aggregating continuous digital health measures exist. Using a modified Delphi approach, we developed a framework to standardize the selection of aggregations that provide clinically meaningful, stable values. This was applied to aggregate seven digital mobility outcomes (DMOs:walking bout (WB) duration, step count, walking speed, stride length, cadence, stride duration, and number of WBs), collected over 7 days, in 2181 individuals with and without mobility disabilities. We defined aggregations as combinations of restrictions (data segments) and summary statistics, and developed a three-part framework. In Part 1, candidate restriction–summary statistic combinations are identified considering technical validity, interpretability, sample size, and distribution. Part 2 assesses within-participant stability with intraclass correlation coefficients (ICCs), and discriminant capacity between healthy and mobility disabilities with the area under the curve (AUC) on as many temporal levels as needed. Part 3 involves expert discussions combining subject-specific experience, guidelines, and data-driven results to select final aggregation(s). Applied to DMOs, we identified candidate aggregations (Part 1), found moderate-to-high daily ICCs (range 0.45–1.00) and varying AUCs (range 0.43–0.90) (Part 2), and finally selected 24 daily- and weekly-aggregated DMOs, classified into walking activity and gait DMOs (Part 3). In summary, reducing digital health data into clinically meaningful, statistically stable values is achievable with standardized procedures.

The authors' abstract, as published at the source. npj Digital Medicine, 2026 · DOI ↗