Developing a culturally tailored digital health intervention for insomnia in Black women

为黑人女性失眠症开发一种符合其文化背景的数字健康干预措施

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Abstract

Insomnia disorder is highly prevalent among Black women. Cognitive-behavioral therapy for insomnia (CBT-I) is considered the optimal treatment, but very little efficacy research has been conducted in minority populations. Culturally tailoring intervention content may increase participant engagement and improve treatment outcomes. We culturally tailored an Internet-delivered CBT-I program (Sleep Healthy Using the Internet; SHUTi) for Black women. First, relevant stakeholders were identified. Semi-structured interviews were conducted after stakeholders completed each of the six SHUTi intervention sessions. Questions focused on improving program relatability and engagement for Black women. Key themes pertinent to peripheral, evidential, and sociocultural strategies for cultural adaptation were identified using thematic content analysis, and adaptation recommendations were developed. A total of 50 interviews, across 9 stakeholders, were conducted. Two overarching themes were identified: (i) there was limited visual African American representation, and (ii) there was a lack of diversity in the environments and lifestyles of the patient vignettes. Respondents provided peripheral, evidential, and sociocultural recommendations for program modifications, emphasizing the importance of race-concordant visual content and didactic content exploring the diverse cultural and social contexts in which insomnia occurs for Black women. As more diverse patients seek evidence-based insomnia treatment, digital health interventions must consider whether it is therapeutically important to address and tailor for cultural differences. Here, stakeholders made clear recommendations for taking cultural contexts into account to improve patient engagement with the program. Further research should work to understand the extent to which culturally tailored interventions are beneficial for health outcomes among minority populations.

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