Acceptance of Using Artificial Intelligence and Digital Technology for Mental Health Interventions: The Development and Initial Validation of the UTAUT‐AI‐DMHI
Bibliographic record
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".