JMIR Human Factors最新文献

Background: Dementia increases the risk of individuals getting lost due to cognitive decline, impacting daily functioning and heightening caregiver worry. Traditional search methods are often time-consuming and stressful, whereas GPS-based technologies face limitations such as battery dependency. A crowdsourcing Internet of Things (IoT) technology using energy-efficient Bluetooth Low Energy (BLE) offers a potential solution to locate missing individuals with dementia more effectively by harnessing the power of the crowd and fostering a caring and inclusive community.

Objective: This study aimed to evaluate the effectiveness of a BLE-based privacy-preserving crowdsourcing IoT system consisting of a BLE tag and an Android and iOS app in improving lost-related behavior and psychological well-being by facilitating searches, after-care arrangements, and reducing caregiver worry, as well as to assess its usability among caregivers of individuals with dementia in Hong Kong.

Methods: A single-arm, prospective observational study was conducted from November 2020 to October 2023. Caregivers (N=1034) of individuals with dementia used a staff-assisted crowdsourcing IoT technology comprising a BLE tag, mobile app sensor, and location cloud server. Outcomes included search strategies, post-getting lost care arrangements, caregiver worry and distress (10-point scale), and usability (modified Quebec User Evaluation of Satisfaction with Assistive Technology 2.0 survey). Data were collected at 6- and 12-month follow-ups and analyzed using generalized estimating equations and linear mixed models.

Results: Of the 1034 participants, 143 (13.82%) reported lost episodes, with 51 (35.7%) using BLE tags for searches. Worry about future lost episodes decreased significantly over time (P=.008), especially among BLE tag users (P=.04). There was an association between BLE tag use and adoption of proactive search strategies (eg, going out to search: adjusted odds ratio 2.78, 95% CI 1.33-5.82; P=.007) and preventative measures (eg, IoT devices or CCTV: adjusted odds ratio 2.92, 95% CI 1.61-5.29; P<.001). Usability satisfaction was high for design and data security, whereas approximately half of the participants (309/707, 43.7%) were satisfied with accuracy.

Conclusions: The BLE crowdsourcing system may reduce caregiver worry and encourage proactive search behaviors, although accuracy depends on broader community adoption. Integration into dementia care plans could enhance safety and autonomy. Further research with a randomized controlled trial design is needed to confirm these findings.

Background: Although research has found online peer support forums to be helpful for those with mental health conditions, no studies have explored the experiences of those who use forums for support with postpartum psychosis (PP) specifically.

Objective: This study aimed to understand the lived experiences of using online forums for PP, and how this form of support differs from professional and other informal support.

Methods: This study used a qualitative approach, including semistructured interviews with 8 participants. Recruitment took place via an online forum run by a charity called Action on Postpartum Psychosis. Transcripts were analyzed using interpretative phenomenological analysis.

Results: Four themes were developed in line with participants' experiences (1) from isolation to connection: validation, growth, and hope from shared experiences; (2) complementing not replacing: filling the gaps in support; (3) impacts of privacy, representation, and readiness to share on engagement; and (4) relational experiences within peer support: altruism, boundaries, and comparison. All participants believed forums were helpful to their well-being and recovery; however, some also reported difficulties with engagement, comparison, and regulating their own use. Findings suggest that forums may benefit from being designed in a way that protects users and their identities, for example, via trigger warnings and setting boundaries.

Conclusions: Peer online forums offer a unique and potentially effective addition to existing support provided by professionals and personal connections. Professionals should signpost people experiencing PP to forums, but should also understand the support that may be needed in terms of monitoring use and ensuring that appropriate boundaries are put into place.

Background: Making optimal use of mobile health technologies requires the validation of digital biomarkers, which in turn demands high levels of participant adherence and retention. However, current remote digital health studies have high attrition rates and low participant adherence, which may introduce bias and limit the generalizability of their findings.

Objective: This study aimed to identify longitudinal indicators of participant retention and adherence, which may serve to develop strategies to improve data collection in digital health studies and improve understanding of how study cohorts are shaped by participant withdrawal and nonadherence.

Methods: We performed secondary analyses on the Brighten study, which consisted of 2 remote, smartphone-based randomized controlled trials evaluating mobile apps for depression treatment, enrolling 2193 participants in total. Participants were asked, after baseline assessment, to complete 7 digital questionnaires regularly. We assessed adherence to digital questionnaires, engagement (postbaseline participation), and retention rates (the proportion of participants who continued completing questionnaires over time) as outcomes. We investigated the relationship between these outcomes and both static measures (eg, demographics and average questionnaire scores) and dynamic measures (eg, changes in questionnaire scores over time).

Results: The study included 2201 participants, of whom 1093 completed at least 1 nonbaseline questionnaire, with a median completion rate of 37.6% (IQR 15.5%-67.9%). We found significantly higher adherence rates in participants who were less depressed on average over the course of the study (t752=-5.63; P<.001) and in those who perceived clinical improvement (t744=3.78; P=.001). There were significant demographic differences in adherence and engagement, including differences by gender, race, education, income, and income satisfaction. Participants who were more depressed at baseline were more likely to withdraw before completing any nonbaseline questionnaire (t1917=-2.53; P=.01). However, participants who showed improvement in depressive symptoms during the study showed better adherence (Mann-Whitney U=127,084; P<.001) and retention (hazard ratio 0.78, 95% CI 0.67-0.91; P=.002), despite showing greater depressive symptoms at baseline.

Conclusions: We show that participants' clinical trajectory of depressive symptoms, as well as their perception of improvement, are important indicators of engagement, adherence, and retention. Expanding knowledge regarding these longitudinal indicators may improve interpretation of outcomes and help build strategies to improve retention and adherence in future clinical trials.

Background: Trust in artificial intelligence (AI) remains a critical barrier to the adoption of AI in mental health care. This study explores the formation of trust in an AI mental health model and its human-computer interface among clinicians at a web-based mental health clinic in the Region of Southern Denmark with national coverage.

Objective: This study aims to explore clinicians' perspectives on how trust is built in the context of an AI-supported mental health screening model and to identify the factors that influence this process.

Methods: This was a qualitative case study using semistructured interviews with clinicians involved in the pilot of a mental health AI model. Thematic analysis was used to identify key factors contributing to trust formation.

Results: Clinicians' initial attitudes toward AI were shaped by prior positive experiences with AI and their perception of AI's potential to reduce cognitive load in routine screening. Trust development followed a sequential pattern resembling a "trust journey": (1) sense-making, (2) risk appraisal, and (3) conditional decision to rely. Trust formation was supported by the explainability of the model, particularly through (1) visualization of confidence and uncertainty through violin plots, aligning with the clinicians' expectations of decision ambiguity; (2) feature attribution for and against predictions, which mirrored clinical reasoning; and (3) use of pseudo-sumscores in the AI model, which increased interpretability by presenting explanations in familiar clinical formats. Trust was contextually bounded to low-risk clinical scenarios, such as preinterview patient screening, and contingent on safety protocols (eg, suicide risk flagging). The use of both structured and unstructured patient data was seen as key to expanding trust into more complex clinical contexts. Participants also expressed a need for ongoing evaluation data to reinforce and maintain trust.

Conclusions: Clinicians' trust in AI tools is contextually and sequentially constructed, influenced by both model performance and alignment with clinical reasoning. Interpretability features were essential in establishing intrinsic trust, particularly when presented in ways that resonate with clinical norms. For broader acceptance and responsible deployment, trust must be supported by rigorous evaluation data and the inclusion of clinically relevant data types in model design.

Background: Technology is rapidly reshaping conventional hospital environments into smart spaces, enhancing care, improving clinical workflows, and reducing workloads. However, successful implementation depends not only on the effectiveness of the technology but also on organizational readiness for change.

Objective: This study aimed to identify the key enablers and barriers to readiness for change for a smart hospital ward initiative.

Methods: We conducted a qualitative study to gauge organizational readiness for change for a smart ward initiative. Using purposive sampling, we captured diverse views from clinicians, IT staff, operational support staff, and health care redesign staff. Data were coded deductively under 3 key domains in Weiner's theory of organizational readiness: change efficacy, change commitment, and contextual factors. Subthemes were derived inductively under each domain.

Results: We interviewed 19 participants, including clinicians and support staff. Six subthemes emerged: (1) perceived valence and feasibility; (2) transparency and trust in management; (3) shared understanding and readiness to act; (4) resources, training, and staff capability; (5) innovation culture; and (6) past experiences. Participants viewed the initiative as valuable and were motivated to change, citing that the institution's innovation culture was a key enabler. However, there were key barriers, including unclear timelines, inconsistent training, limited resources, and a lack of infrastructure to support innovation. Concerns about overreliance on technology were also prominent, with staff wary of its impact on clinical judgment and system reliability.

Conclusions: Enabling readiness for the smart ward initiative requires transparent communication of timelines and project awareness, particularly for ground staff, the development of training frameworks, and adequate prioritization of innovation. Alleviating commonly reported technology concerns, such as overreliance, loss of human touch, and system reliability, will also be key to adoption and sustainability.