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Chatbot-Based Mindfulness-Based Stress Reduction Program for University Students With Depressive Symptoms: Intervention Development and Pilot Evaluation. | LitMetric

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Article Abstract

Background: Depression is a growing concern among university students. Chatbots provide flexible, accessible, personalized psychosocial support. Delivering Mindfulness-Based Stress Reduction (MBSR) sessions via chatbots may reduce depressive symptoms in university students.

Aim: This study aims to evaluate the feasibility, acceptability, safety, and preliminary efficacy of a chatbot-based MBSR intervention for university students with depressive symptoms.

Methods: A rule-based MBSR chatbot was developed and evaluated with a single-group pretest-posttest study for university students in Hong Kong ( = 30) reporting depressive symptoms, followed by the collection of their subjective feedback. The intervention lasted eight weeks. The primary clinical outcome was depression levels, with a range of secondary outcomes.

Results: The chatbot-based MBSR program demonstrated satisfying recruitment, retention, and adherence rates. The safety of the program was confirmed by the absence of any adverse events directly related to the intervention, tracked from the onset of the intervention to the completion of data assessment. Significant improvements were observed in both primary and secondary outcomes. Participant feedback highlighted the benefits of the program and its effects on depressive symptoms.

Conclusions: The program has shown feasibility, acceptability, safety, and preliminary efficacy in reducing depressive symptoms among 30 university students in Hong Kong. The intervention should now be evaluated in a randomized controlled trial with follow-up. This study highlights the potential role of chatbot-based interventions in mental health promotion, nursing, and clinical practice and will inform the subsequent development of innovative digital interventions to address mental health challenges faced by university students.

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http://dx.doi.org/10.1177/10783903241302092DOI Listing

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