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Background In recent times, digital mental health interventions (DMHIs) have been proven to be efficacious; however, most are available only for English speakers, leaving limited options for non-English languages like Spanish. Research shows that mental health services in one's dominant language show better outcomes. Conversational agents (CAs) offer promise in supporting mental health in non-English populations. This study compared a culturally adapted version of an artificial intelligence (AI)-led mental health app, called Wysa, in Spanish and English. Objectives To compare user engagement patterns on Wysa-Spanish and Wysa-English and to understand expressions of distress and preferred language in both versions of Wysa. Methods We adopted a cross-sectional retrospective exploratory design with mixed methods, analyzing users from 10 Spanish-speaking countries between 1 February and 1 August 2022. A quantitative sample A (n = 2767) was used for descriptive statistics, including user engagement metrics with a Wilcoxon test. A subset qualitative sample B (n = 338) was examined for word count differences based on valence, and a content analysis was conducted to examine idioms of distress. Results Compared to Wysa-English, Wysa-Spanish had more sessions ( P < .001, d = 0.18) and a greater volume of disclosure of distress. In Wysa-Spanish, the average length of a conversation was significantly longer than in Wysa-English ( P < .001, d = 0.44). Users preferred interventions with free text responses (“Thought recording”) in Spanish ( P < .01, d = 0.41), and Spanish messages were significantly longer ( P < .01, d = 0.24). Wysa-Spanish saw more frequent expressions of negative emotions and feelings of self-harm and suicide. Conclusion Given the high engagement within the Spanish version of Wysa, the findings demonstrate the need for culturally adapted DMHIs among non-English populations, emphasizing the importance of considering linguistic and cultural differences in the development of DMHIs to improve accessibility for diverse populations.
Dinesh et al. (Mon,) studied this question.