JMIR Human Factors最新文献

{"title":"Implementation and Evaluation of a Gait Training Assistant for the Use of Crutches: Usability Study.","authors":"Milan Anton Wolf, Leon Sauerwald, Felix Kosmalla, Florian Daiber, Antonio Krüger, Stefan Landgraeber","doi":"10.2196/51898","DOIUrl":"10.2196/51898","url":null,"abstract":"<p><strong>Background: </strong>Surgical procedures on the lower extremities often require weight-bearing on crutches as part of the rehabilitation process. Orthopedic elective procedures enable patients to learn the correct use of crutches in a controlled preoperative setting. Digital assistance systems can safely circumvent a shortage of skilled staff and any contact restrictions that may be necessary.</p><p><strong>Objective: </strong>The usability of a newly developed gait training assistant (GTA) for the use of crutches will be evaluated. An intervention group trained to use crutches by the digital trainer will be compared with a control group trained to use crutches conventionally by a physiotherapist.</p><p><strong>Methods: </strong>As part of the development and implementation of a novel GTA, 14 patients learned to walk with crutches by completing specific exercises while receiving live feedback. Their movements were detected by a depth sensor and evaluated in real time. Specific parameters (step length, synchronous movement, crutch angle, and crutch distance to the feet) were compared with a control group (n=14) trained to use crutches by physiotherapists. The intervention group was also assessed by a physiotherapist. At the end of the study, the patients completed questionnaires to evaluate the usability of the system (Brooke's System Usability Scale score) and patient satisfaction.</p><p><strong>Results: </strong>All patients trained by the novel GTA were able to use crutches correctly. The intervention group showed significantly better values for crutch angle (mean -6.3°, SD 3.5° vs mean -12.4°, SD 4.5°; P<.001) and crutch position (mean 3.3, SD 5.1 cm vs mean -8.5, SD 4.9 cm; P=.02). Both groups reported that they felt confident in the use of crutches, were able to follow the instructions, and enjoyed the training. Even though the majority (12/14, 86%) preferred physical therapy over a purely digital approach, most participants enjoyed using the system (13/14, 93%) and were interested in trying out other digital assistants (11/14, 79%). The usability of the GTA was rated above average by the majority (9/14, 64%) of the patients.</p><p><strong>Conclusions: </strong>The newly designed GTA is a safe method of teaching the use of crutches and is statistically superior to training by a physiotherapist. Even if patients prefer interaction with a physiotherapist over a purely digital approach, digital devices provide a safe and motivating opportunity to learn the essential locomotor skills for rehabilitation.</p>","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"11 ","pages":"e51898"},"PeriodicalIF":2.6,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11364940/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141989108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Building a Client Resource and Communication Platform for Community-Based Organizations to Address Health and Social Needs: Co-Design Study.","authors":"Courtney Lyles, Beth Berrean, Ana Buenaventura, Svetlana Milter, Dayana Daniel Hernandez, Urmimala Sarkar, Christian Gutierrez, Nynikka Palmer, William Brown Iii","doi":"10.2196/53939","DOIUrl":"10.2196/53939","url":null,"abstract":"<p><strong>Background: </strong>Connecting individuals to existing community resources is critical to addressing social needs and improving population health. While there is much ongoing informatics work embedding social needs screening and referrals into health care systems and their electronic health records, there has been less focus on the digital ecosystem and needs of community-based organizations (CBOs) providing or connecting individuals to these resources.</p><p><strong>Objective: </strong>We used human-centered design to develop a digital platform for CBOs, focused on identification of health and social resources and communication with their clients.</p><p><strong>Methods: </strong>Centered in the Develop phase of the design process, we conducted in-depth interviews in 2 phases with community-based organizational leadership and staff to create and iterate on the platform. We elicited and mapped participant feedback to theory-informed domains from the Technology Acceptance Model, such as Usefulness and Ease of Use, to build the final product and summarized all major design decisions as the platform development proceeded.</p><p><strong>Results: </strong>Overall, we completed 22 interviews with 18 community-based organizational leadership and staff in 2 consecutive Develop phases. After coding of the interview transcripts, there were 4 major themes related to usability, relevance, and external factors impacting use. Specifically, CBOs expressed an interest in a customer relationship management software to manage their client interactions and communications, and they needed specific additional features to address the scope of their everyday work, namely (1) digital and SMS text messaging communication with clients and (2) easy ways to identify relevant community resources based on diverse client needs and various program eligibility criteria. Finally, clear implementation needs emerged, such as digital training and support for staff using new platforms. The final platform, titled \"Mapping to Enhance the Vitality of Engaged Neighborhoods (MAVEN),\" was completed in the Salesforce environment in 2022, and it included features and functions directly mapped to the design process.</p><p><strong>Conclusions: </strong>Engaging community organizations in user-centered design of a health and social resource platform was essential to tapping into their deep expertise in serving local communities and neighborhoods. Design methods informed by behavioral theory can be similarly employed in other informatics research. Moving forward, much more work will be necessary to support the implementation of platforms specific to CBOs' needs, especially given the resources, training, and customization needed in these settings.</p>","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"11 ","pages":"e53939"},"PeriodicalIF":2.6,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11342060/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142000883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Handheld Tool for the Rapid Morphological Identification of Mosquito Species (VectorCam) for Community-Based Malaria Vector Surveillance: Summative Usability Study.","authors":"Saisamhitha Dasari, Bhavya Gopinath, Carter James Gaulke, Sunny Mahendra Patel, Khalil K Merali, Aravind Sunil Kumar, Soumyadipta Acharya","doi":"10.2196/56605","DOIUrl":"10.2196/56605","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Malaria impacts nearly 250 million individuals annually. Specifically, Uganda has one of the highest burdens, with 13 million cases and nearly 20,000 deaths. Controlling the spread of malaria relies on vector surveillance, a system where collected mosquitos are analyzed for vector species' density in rural areas to plan interventions accordingly. However, this relies on trained entomologists known as vector control officers (VCOs) who identify species via microscopy. The global shortage of entomologists and this time-intensive process cause significant reporting delays. VectorCam is a low-cost artificial intelligence-based tool that identifies a mosquito's species, sex, and abdomen status with a picture and sends these results electronically from surveillance sites to decision makers, thereby deskilling the process to village health teams (VHTs).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study evaluates the usability of the VectorCam system among VHTs by assessing its efficiency, effectiveness, and satisfaction.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The VectorCam system has imaging hardware and a phone app designed to identify mosquito species. Two users are needed: (1) an imager to capture images of mosquitos using the app and (2) a loader to load and unload mosquitos from the hardware. Critical success tasks for both roles were identified, which VCOs used to train and certify VHTs. In the first testing phase (phase 1), a VCO and a VHT were paired to assume the role of an imager or a loader. Afterward, they swapped. In phase 2, two VHTs were paired, mimicking real use. The time taken to image each mosquito, critical errors, and System Usability Scale (SUS) scores were recorded for each participant.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Overall, 14 male and 6 female VHT members aged 20 to 70 years were recruited, of which 12 (60%) participants had smartphone use experience. The average throughput values for phases 1 and 2 for the imager were 70 (SD 30.3) seconds and 56.1 (SD 22.9) seconds per mosquito, respectively, indicating a decrease in the length of time for imaging a tray of mosquitos. The loader's average throughput values for phases 1 and 2 were 50.0 and 55.7 seconds per mosquito, respectively, indicating a slight increase in time. In terms of effectiveness, the imager had 8% (6/80) critical errors and the loader had 13% (10/80) critical errors in phase 1. In phase 2, the imager (for VHT pairs) had 14% (11/80) critical errors and the loader (for VHT pairs) had 12% (19/160) critical errors. The average SUS score of the system was 70.25, indicating positive usability. A Kruskal-Wallis analysis demonstrated no significant difference in SUS (H value) scores between genders or users with and without smartphone use experience.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;VectorCam is a usable system for deskilling the in-field identification of mosquito specimens in rural Uganda. Upcoming design updates will address the concerns of user","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"11 ","pages":"e56605"},"PeriodicalIF":2.6,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11364941/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141989107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel Approach to Personalized Physician Recommendations Using Semantic Features and Response Metrics: Model Evaluation Study.","authors":"Yingbin Zheng, Yunping Cai, Yiwei Yan, Sai Chen, Kai Gong","doi":"10.2196/57670","DOIUrl":"10.2196/57670","url":null,"abstract":"<p><strong>Background: </strong>The rapid growth of web-based medical services has highlighted the significance of smart triage systems in helping patients find the most appropriate physicians. However, traditional triage methods often rely on department recommendations and are insufficient to accurately match patients' textual questions with physicians' specialties. Therefore, there is an urgent need to develop algorithms for recommending physicians.</p><p><strong>Objective: </strong>This study aims to develop and validate a patient-physician hybrid recommendation (PPHR) model with response metrics for better triage performance.</p><p><strong>Methods: </strong>A total of 646,383 web-based medical consultation records from the Internet Hospital of the First Affiliated Hospital of Xiamen University were collected. Semantic features representing patients and physicians were developed to identify the set of most similar questions and semantically expand the pool of recommended physician candidates, respectively. The physicians' response rate feature was designed to improve candidate rankings. These 3 characteristics combine to create the PPHR model. Overall, 5 physicians participated in the evaluation of the efficiency of the PPHR model through multiple metrics and questionnaires as well as the performance of Sentence Bidirectional Encoder Representations from Transformers and Doc2Vec in text embedding.</p><p><strong>Results: </strong>The PPHR model reaches the best recommendation performance when the number of recommended physicians is 14. At this point, the model has an F₁-score of 76.25%, a proportion of high-quality services of 41.05%, and a rating of 3.90. After removing physicians' characteristics and response rates from the PPHR model, the F₁-score decreased by 12.05%, the proportion of high-quality services fell by 10.87%, the average hit ratio dropped by 1.06%, and the rating declined by 11.43%. According to whether those 5 physicians were recommended by the PPHR model, Sentence Bidirectional Encoder Representations from Transformers achieved an average hit ratio of 88.6%, while Doc2Vec achieved an average hit ratio of 53.4%.</p><p><strong>Conclusions: </strong>The PPHR model uses semantic features and response metrics to enable patients to accurately find the physician who best suits their needs.</p>","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"11 ","pages":"e57670"},"PeriodicalIF":2.6,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11362707/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141984286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of Hearing Help Seeking to Design a Recommendation Module of an mHealth Hearing App: Intensive Longitudinal Study of Feature Importance Assessment.","authors":"Giulia Angonese, Mareike Buhl, Inka Kuhlmann, Birger Kollmeier, Andrea Hildebrandt","doi":"10.2196/52310","DOIUrl":"10.2196/52310","url":null,"abstract":"<p><strong>Background: </strong>Mobile health (mHealth) solutions can improve the quality, accessibility, and equity of health services, fostering early rehabilitation. For individuals with hearing loss, mHealth apps might be designed to support the decision-making processes in auditory diagnostics and provide treatment recommendations to the user (eg, hearing aid need). For some individuals, such an mHealth app might be the first contact with a hearing diagnostic service and should motivate users with hearing loss to seek professional help in a targeted manner. However, personalizing treatment recommendations is only possible by knowing the individual's profile regarding the outcome of interest.</p><p><strong>Objective: </strong>This study aims to characterize individuals who are more or less prone to seeking professional help after the repeated use of an app-based hearing test. The goal was to derive relevant hearing-related traits and personality characteristics for personalized treatment recommendations for users of mHealth hearing solutions.</p><p><strong>Methods: </strong>In total, 185 (n=106, 57.3% female) nonaided older individuals (mean age 63.8, SD 6.6 y) with subjective hearing loss participated in a mobile study. We collected cross-sectional and longitudinal data on a comprehensive set of 83 hearing-related and psychological measures among those previously found to predict hearing help seeking. Readiness to seek help was assessed as the outcome variable at study end and after 2 months. Participants were classified into help seekers and nonseekers using several supervised machine learning algorithms (random forest, naïve Bayes, and support vector machine). The most relevant features for prediction were identified using feature importance analysis.</p><p><strong>Results: </strong>The algorithms correctly predicted action to seek help at study end in 65.9% (122/185) to 70.3% (130/185) of cases, reaching 74.8% (98/131) classification accuracy at follow-up. Among the most important features for classification beyond hearing performance were the perceived consequences of hearing loss in daily life, attitude toward hearing aids, motivation to seek help, physical health, sensory sensitivity personality trait, neuroticism, and income.</p><p><strong>Conclusions: </strong>This study contributes to the identification of individual characteristics that predict help seeking in older individuals with self-reported hearing loss. Suggestions are made for their implementation in an individual-profiling algorithm and for deriving targeted recommendations in mHealth hearing apps.</p>","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"11 ","pages":"e52310"},"PeriodicalIF":2.6,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11347899/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141917597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the Construct Validity of the Charité Alarm Fatigue Questionnaire using Confirmatory Factor Analysis.","authors":"Maximilian Markus Wunderlich, Henning Krampe, Kristina Fuest, Dominik Leicht, Moriz Benedikt Probst, Julian Runge, Sebastian Schmid, Claudia Spies, Björn Weiß, Felix Balzer, Akira-Sebastian Poncette","doi":"10.2196/57658","DOIUrl":"10.2196/57658","url":null,"abstract":"<p><strong>Background: </strong>The Charité Alarm Fatigue Questionnaire (CAFQa) is a 9-item questionnaire that aims to standardize how alarm fatigue in nurses and physicians is measured. We previously hypothesized that it has 2 correlated scales, one on the psychosomatic effects of alarm fatigue and the other on staff's coping strategies in working with alarms.</p><p><strong>Objective: </strong>We aimed to validate the hypothesized structure of the CAFQa and thus underpin the instrument's construct validity.</p><p><strong>Methods: </strong>We conducted 2 independent studies with nurses and physicians from intensive care units in Germany (study 1: n=265; study 2: n=1212). Responses to the questionnaire were analyzed using confirmatory factor analysis with the unweighted least-squares algorithm based on polychoric covariances. Convergent validity was assessed by participants' estimation of their own alarm fatigue and exposure to false alarms as a percentage.</p><p><strong>Results: </strong>In both studies, the χ2 test reached statistical significance (study 1: χ226=44.9; P=.01; study 2: χ226=92.4; P<.001). Other fit indices suggested a good model fit (in both studies: root mean square error of approximation <0.05, standardized root mean squared residual <0.08, relative noncentrality index >0.95, Tucker-Lewis index >0.95, and comparative fit index >0.995). Participants' mean scores correlated moderately with self-reported alarm fatigue (study 1: r=0.45; study 2: r=0.53) and weakly with self-perceived exposure to false alarms (study 1: r=0.3; study 2: r=0.33).</p><p><strong>Conclusions: </strong>The questionnaire measures the construct of alarm fatigue as proposed in our previous study. Researchers and clinicians can rely on the CAFQa to measure the alarm fatigue of nurses and physicians.</p>","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"11 ","pages":"e57658"},"PeriodicalIF":2.6,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11539858/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141907881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of an Adapted Health Action Process Approach Model to Predict Engagement With a Digital Mental Health Website: Cross-Sectional Study.","authors":"Julien Rouvere, Brittany E Blanchard, Morgan Johnson, Isabell Griffith Fillipo, Brittany Mosser, Meghan Romanelli, Theresa Nguyen, Kevin Rushton, John Marion, Tim Althoff, Patricia A Areán, Michael D Pullmann","doi":"10.2196/57082","DOIUrl":"10.2196/57082","url":null,"abstract":"<p><strong>Background: </strong>Digital Mental Health (DMH) tools are an effective, readily accessible, and affordable form of mental health support. However, sustained engagement with DMH is suboptimal, with limited research on DMH engagement. The Health Action Process Approach (HAPA) is an empirically supported theory of health behavior adoption and maintenance. Whether this model also explains DMH tool engagement remains unknown.</p><p><strong>Objective: </strong>This study examined whether an adapted HAPA model predicted engagement with DMH via a self-guided website.</p><p><strong>Methods: </strong>Visitors to the Mental Health America (MHA) website were invited to complete a brief survey measuring HAPA constructs. This cross-sectional study tested the adapted HAPA model with data collected using voluntary response sampling from 16,078 sessions (15,619 unique IP addresses from United States residents) on the MHA website from October 2021 through February 2022. Model fit was examined via structural equation modeling in predicting two engagement outcomes: (1) choice to engage with DMH (ie, spending 3 or more seconds on an MHA page, excluding screening pages) and (2) level of engagement (ie, time spent on MHA pages and number of pages visited, both excluding screening pages).</p><p><strong>Results: </strong>Participants chose to engage with the MHA website in 94.3% (15,161/16,078) of the sessions. Perceived need (β=.66; P<.001), outcome expectancies (β=.49; P<.001), self-efficacy (β=.44; P<.001), and perceived risk (β=.17-.18; P<.001) significantly predicted intention, and intention (β=.77; P<.001) significantly predicted planning. Planning was not significantly associated with choice to engage (β=.03; P=.18). Within participants who chose to engage, the association between planning with level of engagement was statistically significant (β=.12; P<.001). Model fit indices for both engagement outcomes were poor, with the adapted HAPA model accounting for only 0.1% and 1.4% of the variance in choice to engage and level of engagement, respectively.</p><p><strong>Conclusions: </strong>Our data suggest that the HAPA model did not predict engagement with DMH via a self-guided website. More research is needed to identify appropriate theoretical frameworks and practical strategies (eg, digital design) to optimize DMH tool engagement.</p>","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"11 ","pages":"e57082"},"PeriodicalIF":2.6,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11339574/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141903120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of a Digital Care Pathway for Multiple Sclerosis: Observational Study.","authors":"Märt Vesinurm, Anna Maunula, Päivi Olli, Paul Lillrank, Petra Ijäs, Paulus Torkki, Laura Mäkitie, Sini M Laakso","doi":"10.2196/51872","DOIUrl":"10.2196/51872","url":null,"abstract":"<p><strong>Background: </strong>Helsinki University Hospital has developed a digital care pathway (DCP) for people with multiple sclerosis (MS) to improve the care quality. DCP was designed for especially newly diagnosed patients to support adaptation to a chronic disease.</p><p><strong>Objective: </strong>This study investigated the MS DCP user behavior and its impact on patient education-mediated changes in health care use, patient-perceived impact of MS on psychological and physical functional health, and patient satisfaction.</p><p><strong>Methods: </strong>We collected data from the service launch in March 2020 until the end of 2022 (observation period). The number of users, user logins, and their timing and messages sent were collected. The association of the DCP on health care use was studied in a case-control setting in which patients were allowed to freely select whether they wanted to use the service (DCP group n=63) or not (control group n=112). The number of physical and remote appointments either to a doctor, nurse, or other services were considered in addition to emergency department visits and inpatient days. The follow-up time was 1 year (study period). Furthermore, a subgroup of 36 patients was recruited to fill out surveys on net promoter score (NPS) at 3, 6, and 12 months, and their physical and psychological functional health (Multiple Sclerosis Impact Scale) at 0, 3, 6, and 12 months.</p><p><strong>Results: </strong>During the observation period, a total of 225 patients had the option to use the service, out of whom 79.1% (178/225) logged into the service. On average, a user of the DCP sent 6.8 messages and logged on 7.4 times, with 72.29% (1182/1635) of logins taking place within 1 year of initiating the service. In case-control cohorts, no statistically significant differences between the groups were found for physical doctors' appointments, remote doctors' contacts, physical nurse appointments, remote nurse contacts, emergency department visits, or inpatient days. However, the MS DCP was associated with a 2.05 (SD 0.48) visit increase in other services, within 1 year from diagnosis. In the prospective DCP-cohort, no clinically significant change was observed in the physical functional health between the 0 and 12-month marks, but psychological functional health was improved between 3 and 6 months. Patient satisfaction improved from the NPS index of 21 (favorable) at the 3-month mark to the NPS index of 63 (excellent) at the 12-month mark.</p><p><strong>Conclusions: </strong>The MS DCP has been used by a majority of the people with MS as a complementary service to regular operations, and we find high satisfaction with the service. Psychological health was enhanced during the use of MS DCP. Our results indicate that DCPs hold great promise for managing chronic conditions such as MS. Future studies should explore the potential of DCPs in different health care settings and patient subgroups.</p>","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"11 ","pages":"e51872"},"PeriodicalIF":2.6,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11339567/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141903121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patients' Attitudes Toward the Use of Artificial Intelligence as a Diagnostic Tool in Radiology in Saudi Arabia: Cross-Sectional Study.","authors":"Leena R Baghdadi, Arwa A Mobeirek, Dania R Alhudaithi, Fatimah A Albenmousa, Leen S Alhadlaq, Maisa S Alaql, Sarah A Alhamlan","doi":"10.2196/53108","DOIUrl":"10.2196/53108","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (AI) is widely used in various medical fields, including diagnostic radiology as a tool for greater efficiency, precision, and accuracy. The integration of AI as a radiological diagnostic tool has the potential to mitigate delays in diagnosis, which could, in turn, impact patients' prognosis and treatment outcomes. The literature shows conflicting results regarding patients' attitudes to AI as a diagnostic tool. To the best of our knowledge, no similar study has been conducted in Saudi Arabia.</p><p><strong>Objective: </strong>The objectives of this study are to examine patients' attitudes toward the use of AI as a tool in diagnostic radiology at King Khalid University Hospital, Saudi Arabia. Additionally, we sought to explore potential associations between patients' attitudes and various sociodemographic factors.</p><p><strong>Methods: </strong>This descriptive-analytical cross-sectional study was conducted in a tertiary care hospital. Data were collected from patients scheduled for radiological imaging through a validated self-administered questionnaire. The main outcome was to measure patients' attitudes to the use of AI in radiology by calculating mean scores of 5 factors, distrust and accountability (factor 1), procedural knowledge (factor 2), personal interaction and communication (factor 3), efficiency (factor 4), and methods of providing information to patients (factor 5). Data were analyzed using the student t test, one-way analysis of variance followed by post hoc and multivariable analysis.</p><p><strong>Results: </strong>A total of 382 participants (n=273, 71.5% women and n=109, 28.5% men) completed the surveys and were included in the analysis. The mean age of the respondents was 39.51 (SD 13.26) years. Participants favored physicians over AI for procedural knowledge, personal interaction, and being informed. However, the participants demonstrated a neutral attitude for distrust and accountability and for efficiency. Marital status was found to be associated with distrust and accountability, procedural knowledge, and personal interaction. Associations were also found between self-reported health status and being informed and between the field of specialization and distrust and accountability.</p><p><strong>Conclusions: </strong>Patients were keen to understand the work of AI in radiology but favored personal interaction with a radiologist. Patients were impartial toward AI replacing radiologists and the efficiency of AI, which should be a consideration in future policy development and integration. Future research involving multicenter studies in different regions of Saudi Arabia is required.</p>","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"11 ","pages":"e53108"},"PeriodicalIF":2.6,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11339559/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141903122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Older Adults' Acceptance of a Virtual Reality Group Intervention in Nursing Homes: Pre-Post Study Under Naturalistic Conditions.","authors":"Yijun Li, Irina Shiyanov, Beate Muschalla","doi":"10.2196/56278","DOIUrl":"10.2196/56278","url":null,"abstract":"<p><strong>Background: </strong>Virtual reality (VR) group activities can act as interventions against inactivity and lack of meaningful activities in nursing homes. The acceptance of VR among older adults has been explored from different perspectives. However, research on the impact of older adults' individual characteristics on the acceptance of VR group activities in nursing homes is necessary.</p><p><strong>Objective: </strong>This study investigates the impact of individual characteristics (eg, psychosocial capacities) on VR acceptance among older adults in nursing homes, as well as this group's perceptions of VR after participating in a VR intervention.</p><p><strong>Methods: </strong>In this pre-post study conducted in nursing homes, we applied a VR group intervention with 113 older adult participants. These participants were categorized into two groups based on their naturalistic choice to join the intervention: a higher VR acceptance group (n=90) and a lower VR acceptance group (n=23). We compared the two groups with respect to their sociodemographic characteristics, psychosocial capacities, and attitudes toward new technologies. Additionally, we examined the participants' perceptions of VR.</p><p><strong>Results: </strong>The results show that those with lower acceptance of VR initially reported higher capacities in organizing daily activities and stronger interpersonal relationships compared to older adults with higher VR acceptance. The VR group activity might hold limited significance for the latter group, but it offers the chance to activate older adults with lower proactivity. Openness to new technology was associated with a favorable perception of VR. After the VR intervention, the acceptance of VR remained high.</p><p><strong>Conclusions: </strong>This study investigates the acceptance of VR group events as meaningful activities for older adults in nursing homes under naturalistic conditions. The results indicate that the VR group intervention effectively addressed low proactivity and interpersonal relationship issues among older adults in nursing homes. Older adults should be encouraged to experience VR if the opportunity to participate is offered, potentially facilitated by caregivers or trusted individuals.</p>","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"11 ","pages":"e56278"},"PeriodicalIF":2.6,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11468973/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142381858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}