When Users Log Mood After Assessment: Temporal-Lag, Discriminant Validity, and the Simplicity Paradox of Single-Label Ecological Momentary Mood Tracking in a Saudi Arabic-Language Digital Mental Health Cohort—A Diagnostic-Accuracy Study

Abstract

Background: Digital mental health platforms increasingly deploy self-directed single-label mood tracking as an ecological momentary assessment (EMA) tool, often framed as a screening indicator for depression or anxiety. Whether such naturalistic mood logs accurately detect screening-positive PHQ-9 and GAD-7 scores has not been tested at scale against validated Arabic-language reference-standard instruments. Objectives: To evaluate, retrospectively, how accurately naturalistic single-label Arabic mood tracking detects screening-positive scores on the validated Arabic PHQ-9 and Arabic GAD-7 administered on the same platform, and to characterise the convergent-validity signal on continuous scores. Methods: This was a retrospective linked-records study of a Saudi Arabic-language commercial digital mental health platform. From 25,555 adult mood-logging users (aged 18 years or older), we assembled three intersection cohorts: depression (n = 4236), anxiety (n = 2449), and both-scales (n = 1825). Nine Arabic mood labels were categorised a priori as negative, positive, or neutral. Operating characteristics with bootstrap 95% confidence intervals were computed for four primary indices; thirteen comparator specifications and four machine-learning models were compared against the best-performing index. AUC differences were evaluated with a DeLong-equivalent bootstrap test. Reporting followed STARD. Results: The best-performing index (proportion of negative logs) yielded an AUC of 0.624 (95% CI 0.604–0.642) for screening-positive detection of depression and 0.652 (0.629–0.676) for anxiety, both below the conventional 0.80 benchmark for a clinically useful screening test. Youden-optimal sensitivity and specificity were 0.522 (0.506–0.538) and 0.712 (0.679–0.741) for depression and 0.569 (0.547–0.589) and 0.706 (0.670–0.743) for anxiety. Positive likelihood ratios of 1.81 and 1.93 fell below the 2–5 range that is conventionally associated with even small clinically meaningful shifts in post-test probability. Spearman correlations with continuous scores were modest but stable across bootstrap resamples (ρ = 0.232 for depression, 0.268 for anxiety; both p < 0.001). Three structural findings emerged. First, a temporal-sequencing pattern was observed: median lag was +19 days for depression and +25 days for anxiety, with 80.9% and 79.4% of mood logs recorded on or after scale completion, respectively; among users with mood logs recorded before scale completion, AUC was 0.586 (0.554–0.618) for depression and 0.605 (0.569–0.641) for anxiety. Second, genuine discriminant validity was found for the depression-specific composite (Steiger’s Z = 2.82, p = 0.005) but not for the anxiety-specific single label (Z = 0.32, p = 0.75), reflecting a 2.24% anxiety-label prevalence on this platform. Third, we observed a simplicity-paradox pattern in which added composite complexity either failed to raise cross-validated AUC or destroyed discriminant validity. Conclusions: On this platform, single-label Arabic mood tracking shows a modest but statistically reliable retrospective association with screening-positive status on the validated Arabic PHQ-9 and Arabic GAD-7 that does not reach the conventional 0.80 AUC benchmark for a clinically useful screening test; it should not substitute for these validated instruments. It is best treated as a longitudinal engagement and self-monitoring indicator rather than a stand-alone screening tool.

The authors' abstract, as published at the source. Diagnostics, 2026 · DOI ↗