Exploring Young Adults' Attitudes Toward AI-Driven mHealth Apps: Qualitative Study
Bibliographic record
Abstract
Background: Artificial intelligence (AI)-driven mobile health (mHealth) apps are emerging as a promising tool for health management, yet little is known about users' psychological perceptions and attitudes toward these technologies. Understanding these aspects is crucial for both the appropriate design and the effective use of these technologies, ensuring the psychological and physical well-being of potential end users. Objective: This study aimed to investigate the attitudes and perceptions of young adults toward a possible use of AI-driven mHealth apps, focusing on the perceived benefits and potential concerns related to their future adoption. Methods: A qualitative focus group methodology was used. Fifteen participants (12 men, 3 women; mean age 27 years, range: 25-34 years) were recruited. Data were analyzed using thematic analysis to identify key themes influencing engagement with these technologies. Results: Four main themes emerged: "Usability," which emphasized the importance of user-friendly, personalized experiences; "Innovation and Reliability," where participants expressed both enthusiasm and skepticism towards AI's potential; "Affectivity and Interaction with AI," highlighting mixed opinions on the emotional impact of AI interactions; and "Perceived Risks," which focused on concerns regarding data privacy and the need for human supervision. These factors contributed to ambivalent attitudes toward AI-driven mHealth apps, with some participants being open to adoption, while others remained cautious. Conclusions: To foster greater engagement with AI-driven mHealth apps, developers should prioritize usability, trust, emotional support, and privacy issues, considering users' psychological needs and expectations. The findings offer valuable insights for designing more user-oriented mHealth solutions. Further research should explore how perceptions evolve with direct experience and long-term use.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".