JMIR Human Factors最新文献

Background: Chronic pain is among the most common conditions worldwide and requires a multidisciplinary treatment approach. Spinal cord stimulation is a possible treatment option for pain management; however, patients undergoing this intervention require close follow-up, which is not always feasible. eHealth apps offer opportunities for improved patient follow-up, although adherence to these apps tends to decrease over time, with rates dropping to approximately 60%. To improve adherence to remote follow-up, we developed a remote follow-up system consisting of a mobile app for patients, a website for health care professionals, and a remote support center.

Objective: Our objective was to evaluate patient adherence to remote follow-up using a system that includes a mobile app and a remote support center.

Methods: After review of the literature and approval of the design of the follow-up system by a multidisciplinary committee, a team of experts developed a system based on a mobile app, a website for health care professionals, and a remote support center. The system was developed in collaboration with health care professionals and uses validated scales to capture patients' clinical data at each stage of treatment (ie, pretreatment phase, trial phase, and implantation phase). Data were collected prospectively between January 2020 to August 2023, including the number of total surveys sent, surveys completed, SMS text message reminders sent, and reminder calls made.

Results: A total of 64 patients were included (n=40 women, 62.5%) in the study. By the end of the study, 19 (29.7%) patients remained in the pretreatment phase, 8 (12.5%) patients had completed the trial phase, and 37 (57.8%) reached the implantation phase. The mean follow-up period was 15.30 (SD 9.43) months. A total of 1574 surveys were sent, along with 488 SMS text message reminders and 53 reminder calls. The mean adherence rate decreased from 94.53% (SD 20.63%) during the pretreatment phase to 65.68% (SD 23.49%) in the implantation phase, with an overall mean adherence rate of 87.37% (SD 15.37%) for the app. ANOVA showed that adherence was significantly higher in the earlier phases of treatment (P<.001).

Conclusions: Our remote follow-up system, supported by a remote support center improves adherence to follow-up in later phases of treatment, although adherence decreased over time. Further studies are needed to investigate the relationship between adherence to the app and pain management.

Background: The integration of health care and social welfare services together with the consolidation of health care information systems (HISs) and client information systems (CISs) has become a timely topic. Despite this development, there is a scarcity of systematic research on physicians', registered nurses' (RNs) and social welfare professionals' (SWPs) experiences of participating in the development of HISs and CISs.

Objective: This study aimed to examine how physicians, RNs and SWPs experience collaboration with HIS or CIS vendors, and what kinds of end users have participated in HIS or CIS development.

Methods: National cross-sectional usability surveys were conducted in Finland among RNs and SWPs in 2020 and physicians in 2021. Questions concerning participation experiences were analyzed by professional group, working sector, managerial position, and age.

Results: In total, 4683 physicians, 3610 RNs, and 990 SWPs responded to the surveys. In all 3 professional groups, those working in nonmanagerial positions and the youngest respondents participated least in HIS or CIS development, and 76% (n=3528) of physicians, 78% (n=2814) of RNs and 67% (n=664) of SWPs had not participated at all. When comparing the groups, physicians were least aware of feedback processes and least satisfied with vendors' interest in end-user feedback and the manner and speed of HIS development. Those who had dedicated working time for HIS or CIS development were less critical of vendors' interest and responsiveness to development ideas than those who had not participated at all. In all 3 professional groups, the youngest were most dissatisfied with HIS and CIS vendor collaboration.

Conclusions: Experiences of participation in HIS and CIS development were relatively negative across all 3 professional groups, with physicians being the most critical. Dialogue and collaboration between developers and end users-also the youngest ones and frontline workers-need improvement; simply increasing allotted working time is unlikely to produce more positive participation experiences.

Background: Patients with cerebrovascular accident (CVA) should be involved in setting their rehabilitation goals. A personalized prediction of CVA outcomes would allow care professionals to better inform patients and informal caregivers. Several accurate prediction models have been created, but acceptance and proper implementation of the models are prerequisites for model adoption.

Objective: This study aimed to assess the added value of a prediction model for long-term outcomes of rehabilitation after CVA and evaluate how it can best be displayed, implemented, and integrated into the care process.

Methods: We designed a mock-up version, including visualizations, based on our recently developed prediction model. We conducted focus groups with CVA patients and informal caregivers, and separate focus groups with health care professionals (HCPs). Their opinions on the current information management and the model were analyzed using a thematic analysis approach. Lastly, a Measurement Instrument for Determinants of Innovations (MIDI) questionnaire was used to collect insights into the prediction model and visualizations with HCPs.

Results: The analysis of 6 focus groups, with 9 patients, 4 informal caregivers, and 8 HCPs, resulted in 10 themes in 3 categories: evaluation of the current care process (information absorption, expectations of rehabilitation, prediction of outcomes, and decision aid), content of the prediction model (reliability, relevance, and influence on the care process), and accessibility of the model (ease of understanding, model type preference, and moment of use). We extracted recommendations for the prediction model and visualizations. The results of the questionnaire survey (9 responses, 56% response rate) underscored the themes of the focus groups.

Conclusions: There is a need for the use of a prediction model to assess CVA outcomes, as indicated by the general approval of participants in both the focus groups and the questionnaire survey. We recommend that the prediction model be geared toward HCPs, as they can provide the context necessary for patients and informal caregivers. Good reliability and relevance of the prediction model will be essential for its wide adoption.

Background: Telemedicine has been utilized in the care of patients with COVID-19, allowing real-time remote monitoring of vital signs. This technology reduces the risk of transmission while providing high-quality care to both self-quarantined patients with mild symptoms and critically ill patients in hospitals.

Objective: This study aims to investigate the application of telemedicine technology in the care of patients with COVID-19, specifically focusing on usability, effectiveness, and patient outcomes in both home isolation and hospital ward settings.

Methods: The study was conducted between January 2022 and December 2022. More than 800 cases were monitored using the QOCA remote home care system, a telemedicine platform that enables remote monitoring of physiological data-including heart rate, blood pressure, temperature, and oxygen levels-through Internet of Things devices and a 4G-connected tablet. Of these, 27 patients participated in thie study: the QOCA remote home care system was deployed 36 times in the isolation ward and 21 times to those in home isolation. The QOCA remote care system monitored isolated cases through remote care packages and a 4G tablet. Case managers and physicians provided telemedicine appointments and medications. Innovative methods were developed to enhance usage, including online health education, remote care equipment instructions via QR core links, and video consultations for patients without smartphones.

Results: A clinical nurse satisfaction survey revealed that most respondents found the content of the remote care package comprehensive and the interface easy to learn. They expressed a desire to continue using the system. The majority also agreed that using the remote care system and package would reduce their workload and that patients and caregivers could easily learn to use the package. While some respondents expressed concerns about network and Bluetooth connectivity, the majority (24/27, 89%) agreed to include the remote device as part of their routine equipment, with an average score of 84.8 points.

Conclusions: The integration of telemedicine technology improves the quality of care while reducing the workload and exposure of health care workers to viruses.

Background: Only 15% of the nearly 30 million Americans with hearing loss use hearing aids, partly due to high cost, stigma, and limited access to professional hearing care. Hearing impairment in adults can lead to social isolation and depression and is associated with an increased risk of falls. Given the persistent barriers to hearing aid use, the Food and Drug Administration issued a final rule to allow over-the-counter hearing aids to be sold directly to adult consumers with perceived mild to moderate hearing loss at pharmacies, stores, and online retailers without seeing a physician or licensed hearing health care professional.

Objective: We evaluated the safety and usability of an over-the-counter hearing aid prior to Food and Drug Administration approval and market release.

Methods: We first conducted a formative usability test of the device and associated app with 5 intended users to identify outstanding safety and usability issues (testing round 1). Following design modifications, we performed a summative usability test with 15 intended users of the device (testing round 2). We concurrently conducted a test with 21 nonintended users (ie, users with contraindications to use) to ascertain if consumers could determine when they should not use the device, based on the packaging, instructions, and labeling (testing round 3). Participants were asked to complete 2-5 tasks, as if they were using the hearing aid in real life. After each task, participants rated the task difficulty. At the end of each session, participants completed a 10-question knowledge assessment and the System Usability Scale and then participated in debriefing interviews to gather qualitative feedback. All sessions were video recorded and analyzed to identify use errors and design improvement opportunities.

Results: Usability issues were identified in all 3 usability testing rounds. There were minimal safety-related issues with the device. Round 1 testing led to several design modifications which then increased task success in round 2 testing. Participants had the most difficulty with the task of pairing the hearing aids to the cell phone. Participants also had difficulty distinguishing the right and left earbuds. Nonintended users did not always understand device contraindications (eg, tinnitus and severe hearing loss). Overall, test findings informed 9 actionable design modifications (eg, clarifying pairing steps and increasing font size) that improved device usability and safety.

Conclusions: This study evaluated the usability and safety of an over-the-counter hearing aid for adults with mild to moderate hearing loss. Human factors engineering methods identified opportunities to improve the safety and usability of this direct-to-consumer medical device for individuals with perceived mild-moderate hearing loss.

Background: The Swedish health care system is undergoing a transformation. eHealth technologies are increasingly being used. The System Usability Scale is a widely used tool, offering a standardized and reliable measure for assessing the usability of digital health solutions. However, despite the existence of several translations of the System Usability Scale into Swedish, none have undergone psychometric validation. This highlights the urgent need for a validated and standardized Swedish version of the System Usability Scale to ensure accurate and reliable usability evaluations.

Objective: The aim of the study was to translate and psychometrically evaluate a Swedish version of the System Usability Scale.

Methods: The study utilized a 2-phase design. The first phase translated the System Usability Scale into Swedish and the second phase tested the scale's psychometric properties. A total of 62 participants generated a total of 82 measurements. Descriptive statistics were used to visualize participants' characteristics. The psychometric evaluation consisted of data quality, scaling assumptions, and acceptability. Construct validity was evaluated by convergent validity, and reliability was evaluated by internal consistency.

Results: The Swedish version of the System Usability Scale demonstrated high conformity with the original version. The scale showed high internal consistency with a Cronbach α of .852 and corrected item-total correlations ranging from 0.454 to 0.731. The construct validity was supported by a significant positive correlation between the System Usability Scale and domain 5 of the eHealth Literacy Questionnaire (P=.001).

Conclusions: The Swedish version of the System Usability Scale demonstrated satisfactory psychometric properties. It can be recommended for use in a Swedish context. The positive correlation with domain 5 of the eHealth Literacy Questionnaire further supports the construct validity of the Swedish version of the System Usability Scale, affirming its suitability for evaluating digital health solutions. Additional tests of the Swedish version of the System Usability Scale, for example, in the evaluation of more complex eHealth technology, would further validate the scale.

Background: Falls in hospitalized patients are a serious problem, resulting in physical injury, secondary complications, impaired activities of daily living, prolonged hospital stays, and increased medical costs. Establishing a fall prediction scoring system to identify patients most likely to fall can help prevent falls among hospitalized patients.

Objectives: This study aimed to identify predictive factors of falls in acute care hospital patients, develop a scoring system, and evaluate its validity.

Methods: This single-center, retrospective cohort study involved patients aged 20 years or older admitted to Shizuoka General Hospital between April 2019 and September 2020. Demographic data, candidate predictors at admission, and fall occurrence reports were collected from medical records. The outcome was the time from admission to a fall requiring medical resources. Two-thirds of cases were randomly selected as the training set for analysis, and univariable and multivariable Cox regression analyses were used to identify factors affecting fall risk. We scored the fall risk based on the estimated hazard ratios (HRs) and constructed a fall prediction scoring system. The remaining one-third of cases was used as the test set to evaluate the predictive performance of the new scoring system.

Results: A total of 13,725 individuals were included. During the study period, 2.4% (326/13,725) of patients experienced a fall. In the training dataset (n=9150), Cox regression analysis identified sex (male: HR 1.60, 95% CI 1.21-2.13), age (65 to <80 years: HR 2.26, 95% CI 1.48-3.44; ≥80 years: HR 2.50, 95% CI 1.60-3.92 vs 20-<65 years), BMI (18.5 to <25 kg/m²: HR 1.36, 95% CI 0.94-1.97; <18.5 kg/m²: HR 1.57, 95% CI 1.01-2.44 vs ≥25 kg/m²), independence degree of daily living for older adults with disabilities (bedriddenness rank A: HR 1.81, 95% CI 1.26-2.60; rank B: HR 2.03, 95% CI 1.31-3.14; rank C: HR 1.23, 95% CI 0.83-1.83 vs rank J), department (internal medicine: HR 1.23, 95% CI 0.92-1.64; emergency department: HR 1.81, 95% CI 1.26-2.60 vs department of surgery), and history of falls within 1 year (yes: HR 1.66, 95% CI 1.21-2.27) as predictors of falls. Using these factors, we developed a fall prediction scoring system categorizing patients into 3 risk groups: low risk (0-4 points), intermediate risk (5-9 points), and high risk (10-15 points). The c-index indicating predictive performance in the test set (n=4575) was 0.733 (95% CI 0.684-0.782).

Conclusions: We developed a new fall prediction scoring system for patients admitted to acute care hospitals by identifying predictors of falls in Japan. This system may be useful for preventive interventions in patient populations with a high likelihood of falling in acute care settings.

The study explored older adults' perceptions after participating in an online survey about medication decisions, finding that approximately 80% of participants provided positive feedback about the research methodology and their experience.