Engagement and Intersectionality in Digital Self-Management Interventions for Asthma and Chronic Obstructive...

Asthma and COPD are long-term respiratory conditions that require active self-management to improve quality of life and reduce health care burdens. Digital health interventions (DHIs) are increasingly used to support behavior change, symptom monitoring, and medication adherence, offering new opportunities for personalized care and real-time feedback. Understanding patient engagement with digital tools is essential for optimizing intervention design, improving clinical outcomes, and addressing potential inequalities in access and effectiveness. This review is informed by a novel conceptual foundation combining the Analyzing and Measuring Usage and Engagement Data (AMUsED) framework (for the analysis of digital engagement) and layered vulnerabilities (an intersectional approach). Together, these frameworks enable a more nuanced examination of how engagement is shaped by user behavior and structural factors.

This study aims to evaluate how diverse patient groups engage with digital self-management interventions for asthma and COPD by examining the reporting of demographic characteristics, outcome measures, and usage data. The review also explores how these data types are combined in analysis, how authors interpret results, and the extent to which current reporting practices support equitable and meaningful evaluation of digital interventions.

A 2-phase study selection process was applied. First, empirical studies were systematically identified if they reported demographic characteristics, clinical outcome measures, and usage data. Second, reported usage measures were reviewed to identify measures that were meaningful across interventions, defined as numerically comparable measures without subjective user input. Descriptive thematic analysis was conducted to map key concepts across studies, and patient and public involvement sessions were used to contextualize findings and inform interpretation of the results.

Twenty-seven studies met the inclusion criteria. Four comparable usage measures were identified, with studies reporting a mean of 2.15 (SD 0.74) usage measures. Thirteen (48.1%) studies reported 2 or more types of outcome measures (disease-specific self-reported, physiological, or other self-reported). Age, sex, and disease severity were reported in all studies, but characteristics linked to health inequalities were underreported; for example, 8 (29.6%) studies reported ethnicity and 2 (7.4%) reported socioeconomic status. Seventeen (62.9%) studies did not combine demographic, outcome, and usage data in analysis. Thematic analysis identified three cross-cutting issues: (1) limited characterization of engagement patterns, (2) dominance of single-trait demographic analysis, and (3) inconsistent conceptualization of health care support.

This review is the first to integrate the AMUsED framework with layered vulnerabilities and to map how demographic, outcome, and usage data are reported and combined in digital self-management research. By identifying structural gaps in reporting and analysis, the review provides recommendations for more equitable and analytically rigorous digital health research. Strengthening reporting practices, particularly through richer usage data and intersectional analyses, will support clinicians, developers, and policymakers in tailoring digital self-management tools to diverse patient populations and improving real-world effectiveness.