JMIR Human Factors最新文献

[This corrects the article DOI: 10.2196/32174.].

Background: Patients with long-term conditions, such as stroke, require regular follow-up visits to health care professionals to identify changes in symptoms. The digital previsit tool Strokehälsa (Strokehealth) has been designed to encourage individuals with stroke to reflect on stroke-related health concerns before a follow-up visit, thereby potentially enhancing their engagement during the visit. Strokehealth has previously been evaluated using a patient satisfaction survey (part 1), but there remains a need to further explore patients' perceptions and needs to optimize its functionality before broader implementation.

Objective: The overall aim was to attain deeper insights into patients' views and experiences of using the digital previsit tool Strokehealth before a follow-up visit. A secondary aim was to identify potential improvements to the tool based on these insights.

Methods: For this qualitative study, patients who had used Strokehealth version 1.0 before a follow-up visit were recruited through the previous survey between November 2020 and June 2021. Individual semistructured interviews were conducted, and data were analyzed using reflexive thematic analysis. Subsequent workshops were held with people with firsthand experiences of stroke, other stakeholders (including health professionals and researchers), and a web consultant to finalize decisions regarding adjustments to be implemented in Strokehealth version 2.0.

Results: Interviews were conducted with 33 participants (23 men and 10 women), with a median age of 67 (IQR 55-76) years. Analysis of the data regarding participants' experiences of using Strokehealth revealed three overarching themes: (1) a supporting tool for preparing dialogue and identifying needs, (2) how Strokehealth is introduced and communicated affects perceived usability, and (3) the wording and structure of Strokehealth influences the response process. The findings captured various aspects of receiving and using the digital previsit tool, highlighting its simplicity and purpose. Overall, Strokehealth was well received and contributed to a sense of being well cared for. Participants generally not only found Strokehealth easy to use but also shared suggestions on how to better address stroke-related issues, such as mental fatigue or pain. Examples of changes that have been implemented in Strokehealth version 2.0, based on participant feedback, include improved explanatory texts and expanded opportunities for free text.

Conclusions: The findings indicate that the freely available digital previsit tool Strokehealth was generally well received by patients with stroke who were scheduled for follow-up visits in outpatient settings.

Trial registration: Researchweb 275135; https://www.researchweb.org/is/vgr/project/275135.

Background: Mental disorders are the leading cause of disability in young people (aged 12-30 years), and their incidence constitutes a major health crisis. Primary youth mental health services are struggling to keep up due to overwhelming demand, the complexity and severity of young people presenting for care, and a shortage of qualified mental health professionals (MHPs). Artificial intelligence (AI) tools have the potential to facilitate necessary improvements to diagnosis, triage, and care planning for young people with emerging mental disorders.

Objective: The objective of the present scoping research was to examine beliefs and attitudes underlying MHP acceptance of AI tools in youth mental health services.

Methods: In total, 57 MHPs (mean age 35.35, SD = 9.50 years, 72% female (n = 39)) with experience working with youth populations (age 12-30) took part in study 1 that involved completing a web-based survey about the acceptability of using AI in early intervention services. During study 2, 15 MHPs also participated in 1-hour semistructured Zoom interviews. Attitudes toward the use of 2 novel AI prototypes (both of which provide recommendations for care coordination based on previously published data analyses) in youth mental health were explored. Quantitative data were interpreted using descriptive statistics, and qualitative analysis followed the thematic analysis approach.

Results: MHPs were more likely to agree than disagree that AI will improve youth mental health care overall (eg, n=37, 64% participants somewhat or strongly agree that the field of mental health will improve with AI). Despite voicing concerns regarding data security and privacy, MHPs also acknowledged a need for AI to improve the "signal-to-noise ratio" in services and address delays to care for those with severe and complex problems. Such problems were seen as pervasive across the youth mental health system and emphasize the serious costs of delaying the development and implementation of novel tools. All participating MHPs discussed the potential negative impacts of not adopting novel tools.

Conclusions: MHP acceptance and uptake of novel AI tools in youth mental health services will be driven by a more complex cost-benefit analysis of both adopting and not adopting, rather than solely on their design. The costs of delay are clear, and so researchers and MHPs have a shared imperative to develop useful and meaningful clinical tools and to work jointly on integrating them into practice. Limitations of our sample (including low sample size limiting generalizability) notwithstanding, these findings should inform the future design and implementation of such tools.

Background: Incorporating serious games and advancements in information and communication technologies into physical rehabilitation can substantially enhance the process, provide unique benefits, and improve its effectiveness and efficiency. While recent literature highlights various game-based interventions for physical rehabilitation, there is a lack of comprehensive guidance on how to design and develop systems that effectively address the actual needs of therapists, practitioners, and individuals with physical disabilities.

Objective: The objective of this study was to explore the intentions, needs, and desires of therapists and other practitioners, as well as to examine the factors and determinants influencing the effectiveness and efficacy of game-based physical rehabilitation, since therapists and other health care practitioners play crucial roles in both patient recovery and the establishment of an effective game-based therapy.

Methods: A design science approach was adopted to achieve this research objective. A focus group of 27 participants was conducted to gather feedback, identify user needs, and understand the requirements for game-based physical rehabilitation. The participants first tested commercially available games and then evaluated mock-ups of the proposed game prototypes.

Results: This study provides essential design insights and guidelines for designers and researchers, focusing on the practical needs and requirements of game-based physical rehabilitation systems.

Conclusions: As proof of concept, these guidelines will be used in the next phase of our research, which involves designing and developing a game-based physical rehabilitation system.

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.

Background: There is an increased focus on involving members of the public in health research. These types of groups, such as "health consumer groups," bring different expertise to inform the design of a research study. There is a growing general concern about older adults' acceptance and use of technologies. This becomes critical when it involves health care services.

Objective: To understand the use of social robots among older adults, it is prudent to gauge stakeholders' perspectives on optimal research design. In line with the philosophy of the "triple helix model," researchers sought the expertise and guidance of a health consumer group.

Methods: Researchers recruited an expert health consumer group for this study. This included 5 participants from an 8-member panel. Semistructured interviews were conducted. Each interviewee was introduced to visual stimuli of assistive technologies, older adults, and social robots. Subsequently, they were asked for their perspectives on what they viewed and to provide guidance on how to best design upcoming research on these phenomena.

Results: Key themes were derived from the interview transcripts with the health consumer group members. Findings include panel members' advice and guidance on explaining the research aims to technology-averse older adults, approaching data collection from this demographic, and, finally, their perceptions of the appearance of social robots.

Conclusions: The advice and guidance of this expert health consumer, in tandem with researchers and industry partners, substantially aid in advancing research efforts toward social robot use among technology-averse older adults in Australia. This research provides vital information, including how best to approach data collection about social robots from this demographic.