Perceived Usability as a Factor Associated with Clinical Outcomes in Mobile Health Diabetes Management: A Bayesian Mediation and Equity Analysis

Abstract

Background: While mobile health (mHealth) interventions show promise for type 2 diabetes management, mechanisms linking user experience to clinical outcomes remain poorly understood. We hypothesized that perceived usability may mediate associations between patient characteristics and short-term clinical changes, with implications for health equity in digital interventions. Methods: Secondary analysis of the intervention arm from a randomized controlled trial in urban Mexican primary care (ClinicalTrials.gov NCT05924516). Participants used a diabetes self-management mobile application for 90 days. We assessed usability with the validated Computer System Usability Questionnaire (CSUQ; 16 items, 7-point scale) and measured clinical changes in body mass index (BMI), systolic blood pressure (SBP), and HbA1c. Bayesian mediation analysis (literature-informed priors) examined interface quality as a mediator of age-related clinical effects. Item-level analysis identified educational disparities in specific usability dimensions using independent t-tests adjusted for multiple comparisons. Results: Mean overall usability was 5.20/7 (SD = 0.89, 74th percentile). Interface quality mediated 39% of the age–SBP association. Participants experiencing high usability (≥6) versus low usability showed BMI reduction −0.78 vs. −0.21 kg/m2 (Cohen’s d = 0.56) and SBP reduction −7.3 vs. −1.2 mmHg (Cohen’s d = 0.51). No mediation effect was observed for HbA1c change. Users with ≤primary education (41% of sample) scored 1.9 points lower on error messages (3.2 vs. 5.1, p < 0.01) and 1.4 points lower on help documentation (3.6 vs. 5.0, p < 0.03). These disparities persisted after controlling for age and baseline severity. Conclusions: Perceived usability was associated with a potential mechanistic pathway linking user experience to clinical outcomes. Higher usability scores were associated with clinically meaningful improvements in cardiometabolic parameters. Educational disparities in understanding error messages and helping documentation represent modifiable design barriers. Implementing contextual error explanations with visual examples and plain-language help content may enhance both clinical effectiveness and equity in digital diabetes interventions.