Factors Associated with Appointment Cancellation on a Commercial Digital Mental Health Platform: A Retrospective...

Background: Commercial digital mental health platforms have expanded across the Gulf since 2020, yet cancellation in this delivery model remains understudied. We aimed to identify factors independently associated with session cancellation on a Saudi Arabic-language commercial platform and to determine whether they describe user or provider behaviour. Methods: We conducted a retrospective cohort analysis of 36,544 booked sessions from 21,904 unique users (October 2023-April 2026); the outcome was binary cancellation (cancelled vs. completed). Multivariable logistic regression with cluster-robust standard errors estimated adjusted odds ratios (aORs) for user, provider, and booking characteristics; secondary analyses decomposed cancellation by recorded initiator and modelled age, lead time, and calendar time flexibly. Reporting followed STROBE guidelines. Results: Crude cancellation was 25.6%. Cancellation was strongly patterned by visit type (follow-up aOR 0.26, 95% CI 0.21-0.32), lead time (≥8 days 2.10, 95% CI 1.90-2.33), session type (free 1.19, 95% CI 1.10-1.27), and provider specialty; age reduced odds modestly (aOR 0.90 per decade, p < 0.001) and gender was null (aOR 0.95, p = 0.119). Cause-specific models showed that the lead-time gradient reverses by initiator: at ≥8 days versus same-day, user-initiated cancellation was less likely (aOR 0.62, 95% CI 0.49-0.78), whereas provider-initiated cancellation was far more likely (aOR 4.75, 95% CI 4.23-5.35), and provider-specialty differences were confined almost entirely to provider-initiated cancellation. The very low adjusted odds for emergency sessions (aOR 0.17, 95% CI 0.13-0.23) reflect how emergency bookings are administratively coded on the platform rather than genuine behavioural non-adherence and are regarded as artefactual. Discrimination was moderate (apparent AUC 0.700, development sample). Conclusions: Cancellation reflected operational features of the booking, but its largest correlates-lead time, free-session status, and provider specialty-operate predominantly through provider-side scheduling rather than patient non-adherence.