Data Missingness in Digital Phenotyping: Implications for Clinical Inference and Decision-Making

Abstract Background Digital phenotyping, the use of personal digital devices to capture and categorize real-world behavioral and physiological data, holds great potential for complementing traditional clinical assessments. However, missing data remains a critical challenge in this field, especially in longitudinal studies where missingness might obscure clinically relevant insights. Objective This paper examines the impact of data missingness on digital phenotyping clinical research, proposes a framework for reporting and accounting for data missingness, and explores its implications for clinical inference and decision-making. Methods We analyzed digital phenotyping data from a study involving 85 patients with chronic musculoskeletal pain, focusing on active (PROMIS-29 survey responses) and passive (accelerometer and GPS measures) data collected via the Beiwe Research Platform. We assessed data completeness and missingness at different timescales (day, hour, and minute levels), examined the relationship between data missingness and accelerometer measures and imputed GPS summary statistics, and studied the stability of regression models across varying levels of data missingness. We further investigated the association between functional status and day-level data missingness in PROMIS-29 subscores. Results Data completeness showed substantial variability across timescales. Accelerometer-based cadence and imputed GPS-based home time and number of significant locations were generally robust to varying levels of data missingness. However, the stability of regression models was affected at higher thresholds (40% for cadence and 60% for home time). We also identified patterns wherein data missingness was associated with functional status. Conclusion Data missingness in clinical digital phenotyping studies impacts individual- and group-level analyses. Given these results, we recommend that studies account for and report data at multiple timescales (we recommend day, hour, and minute-level where possible), depending on the clinical goals of data collection. We propose a modified framework for categorizing missingness mechanisms in digital phenotyping, emphasizing the need for clinically relevant reporting and interpretation of missing data. Our framework highlights the importance of integrating clinical with statistical expertise, specifically to ensure that imputing missing data does not obscure but helps capture clinically meaningful changes in functional status.