Retrospective cohort study demonstrates significant quality of life gains in men treated for AGA digitally, indicating care model effectiveness.
Background Androgenetic alopecia (AGA) is a common chronic condition significantly impacting quality of life (QoL), but few studies have explored treatment outcomes in real‐world digital health settings. Aims The objective of this study is to evaluate the effects of AGA treatment delivered through a digital healthcare platform on patient‐reported QoL outcomes over 6 months, and identify factors associated with the magnitude of improvement. Patients/Methods We undertook a before‐and‐after cohort study using prospectively collected data from men enrolled on a digital AGA service in Brazil. QoL was assessed using a 16‐item hair‐specific questionnaire at baseline and 6 months. Multivariable linear regression explored associations between QoL change and variables including baseline QoL score, age, adherence (defined as continuous prescription renewals within the programme), and medication potency. Results The mean total QoL score improved from 89.07 to 76.78, yielding a mean change of −12.29 points (95% CI −14.44 to −10.14; p < 0.001; lower scores denote better QoL). Adherence was the strongest predictor of improvement: participants who maintained full engagement with the programme achieved a mean QoL gain roughly double that of those with interrupted engagement ( p < 0.001). Lower baseline QoL and older age were also associated with greater improvements ( p = 0.040), whereas medication potency was not. Conclusion Treatment of AGA via a fully digital care model was associated with statistically significant improvements in hair‐loss‐related QoL over 6 months. The magnitude of improvement was greater among participants with sustained programme engagement. These findings support the potential value of digital care models for AGA, while recognizing that causal inference is limited by the observational design and absence of an in‐person comparator.
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Bersch-Ferreira et al. (2026) studied this question.
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