Acceptance of Using Artificial Intelligence and Digital Technology for Mental Health Interventions: The Development and Initial Validation of the UTAUT‐AI‐DMHI

心理干预 心理学 技术接受与使用的统一理论 心理健康 应用心理学 验证性因素分析 干预(咨询) 临床心理学 样品(材料) 结构效度 可靠性(半导体) 心理测量学 医学教育 结构方程建模 社会心理学 期望理论 计算机科学 医学 精神科 机器学习 功率(物理) 色谱法 化学 物理 量子力学
作者
Vera Békés,Beáta Bőthe,Katie Aafjes‐van Doorn
出处
期刊:Clinical Psychology & Psychotherapy [Wiley]
卷期号:32 (3): e70085-e70085
标识
DOI:10.1002/cpp.70085
摘要

ABSTRACT Digital health technologies are being increasingly integrated into mental healthcare. This means that patients have different treatment options, and clinicians need to consider different ways of supporting their patients too. The adoption of Digital Mental Health Intervention (DMHI) technologies will be influenced by patients' and clinicians' attitudes towards these technologies. The Unified Theory of Acceptance and Use of Technology (UTAUT) is the most commonly used model to examine acceptance of technologies in professional settings, which identifies determinants of behavioural intention to use technologies, such as artificial intelligence (AI). We aimed to develop and validate the UTAUT‐AI‐DMHI measure to assess acceptance various types of digital and AI‐based mental health interventions. We assessed the UTAUT‐AI‐DMHI's psychometric properties in three interventions: teletherapy via videoconferencing, AI chatbot and AI virtual therapist interventions in two samples. Sample 1 included n = 528 patients, n = 155 clinicians and n = 432 participants belonging to both groups; Sample 2 was used to corroborate the results and included a representative US community sample of n = 536. Our results demonstrated adequate construct validity and reliability of the UTAUT factors. In line with previous UTAUT literature, confirmatory factor analysis revealed that the final 17‐item (plus one item assessing Behavioural Intention) scale consisted of seven factors: ease of use, social influence, convenience, human connection, perceived privacy risk, hedonic motivation and therapy quality expectations. All factors were positively associated with general attitudes towards AI and intention to use the intervention in the future in each of the three DMHI formats. This implies that the UTAUT‐AI‐DMHI self‐report scale can be applied to assess acceptance of various kinds of digital and AI‐based mental health interventions. Further, the UTAUT‐AI‐DMHI can be administered as a self‐report scale for patients, clinicians and the general public and thus allows for a direct comparison of acceptance of different intervention formats.

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