JMIR Human Factors最新文献

Background: Health-related quality of life (HRQoL) reflects an individual's perception of their physical and mental health over time. Despite numerous studies linking physical activity to improved HRQoL, most rely on self-reported data, limiting the accuracy and generalizability of findings. This study leverages objective accelerometer data to explore the association between physical activity and HRQoL in Korean adults.

Objective: The objective of this study is to analyze the relationship between objectively measured physical activity using accelerometers and HRQoL among Korean adults, aiming to inform targeted interventions for enhancing HRQoL through physical activity.

Methods: This observational study included 1298 participants aged 19-64 years from the Korea National Health and Nutrition Examination Survey (KNHANES) VI, who wore an accelerometer for 7 consecutive days. HRQoL was assessed using the EQ-5D questionnaire, and physical activity was quantified as moderate-to-vigorous physical activity accelerometer-total (MVPA-AT) and accelerometer-bout (MVPA-AB). Data were analyzed using logistic regression to determine the odds ratio (ORs) for low HRQoL, adjusting for socioeconomic variables and mental health factors.

Results: Participants with higher HRQoL were younger, more likely to be male, single, highly educated, employed in white-collar jobs, and had higher household incomes. They also reported less stress and better subjective health status. The high HRQoL group had significantly more participants meeting MVPA-AB ≥600 metabolic equivalents (P<.01). Logistic regression showed that participants meeting MVPA-AB ≥600 metabolic equivalents had higher odds of high HRQoL (OR 1.55, 95% CI 1.11-2.17). Adjusted models showed consistent results, although the association weakened when adjusting for mental health factors (OR 1.45, 95% CI 1.01-2.09).

Conclusions: The study demonstrates a significant association between HRQoL and moderate to vigorous physical activity sustained for at least 10 minutes, as measured by accelerometer. These findings support promoting physical activity, particularly sustained moderate to vigorous activity, to enhance HRQoL. Further interventional studies focusing on specific physical activity domains such as occupational, leisure-time, and commuting activities are warranted.

Background: Digital technologies have impacted health care delivery globally, and are increasingly being deployed in clinical practice. However, there is limited research on patients' expectations of doctors' clinical competencies when using digital health care technologies (DHTs) in medical care. Understanding these expectations can reveal competency gaps, enhance patient confidence, and contribute to digital innovation initiatives.

Objective: This study explores patients' perceptions of doctors' use of DHTs in clinical care. Using Singapore as a case study, it examines patients' expectations regarding doctors' communication, diagnosis, and treatment skills when using telemedicine, health apps, wearable devices, electronic health records, and artificial intelligence.

Methods: Findings were drawn from individual semistructured interviews with patients from outpatient clinics. Participants were recruited using purposive sampling. Data were analyzed qualitatively using thematic analysis.

Results: Twenty-five participants from different backgrounds and with various chronic conditions participated in the study. They expected doctors to be adept in handling medical data from apps and wearable devices. For telemedicine, participants expected a level of assessment of their medical conditions akin to in-person consultations. In addition, they valued doctors recognizing when a physical examination was necessary. Interestingly, eye contact was appreciated but deemed nonessential by participants across all age bands when electronic health records were used, as they valued the doctor's efficiency more than eye contact. Nonetheless, participants emphasized the need for empathy throughout the clinical encounter regardless of DHT use. Furthermore, younger participants had a greater expectation for DHT use among doctors compared to older ones, who preferred DHTs as a complement rather than a replacement for clinical skills. The former expected doctors to be knowledgeable about the algorithms, principles, and purposes of DHTs such as artificial intelligence technologies to better assist them in diagnosis and treatment.

Conclusions: By identifying patients' expectations of doctors amid increasing health care digitalization, this study highlights that while basic clinical skills remain crucial in the digital age, the role of clinicians needs to evolve with the introduction of DHTs. It has also provided insights into how DHTs can be integrated effectively into clinical settings, aligning with patients' expectations and preferences. Overall, the findings offer a framework for high-income countries to harness DHTs in enhancing health care delivery in the digital era.

Background: This study examined the social well-being of single older adults through the companionship of a social robot, LOVOT (Love+Robot; Groove X). It is designed as a companion for older adults, providing love and affection through verbal and physical interaction. We investigated older adults' perceptions of the technology and how they benefitted from interacting with LOVOT, to guide the future development of social robots.

Objective: This study aimed to use a phenomenological research design to understand the participants' experiences of companionship provided by the social robot. Our research focused on (1) examining the social well-being of single older adults through the companionship of social robots and (2) understanding the perceptions of single older adults when interacting with social robots. Given the prevalence of technology use to support aging, understanding single older adults' social well-being and their perceptions of social robots is essential to guide future research on and design of social robots.

Methods: A total of 5 single women, aged 60 to 75 years, participated in the study. The participants interacted independently with the robot for a week in their own homes and then participated in a poststudy interview to share their experiences.

Results: In total, 4 main themes emerged from the participants' interactions with LOVOT, such as caring for a social robot, comforting presence of the social robot, meaningful connections with the social robot, and preference for LOVOT over pets.

Conclusions: The results indicate that single older adults can obtain psychosocial support by interacting with LOVOT. LOVOT is easily accepted as a companion and makes single older adults feel like they have a greater sense of purpose and someone to connect with. This study suggests that social robots can provide companionship to older adults who live alone. Social robots can help alleviate loneliness by allowing single older adults to form social connections with robots as companions. These findings are particularly important given the rapid aging of the population and the increasing number of single-person households in Singapore.

Background: Our prototype smoking cessation chatbot, Quin, provides evidence-based, personalized support delivered via a smartphone app to help people quit smoking. We developed Quin using a multiphase program of co-design research, part of which included focus group evaluation of Quin among stakeholders prior to clinical testing.

Objective: This study aimed to gather and compare feedback on the user experience of the Quin prototype from end users and smoking cessation professionals (SCPs) via a beta testing process to inform ongoing chatbot iterations and refinements.

Methods: Following active and passive recruitment, we conducted web-based focus groups with SCPs and end users from Queensland, Australia. Participants tested the app for 1-2 weeks prior to focus group discussion and could also log conversation feedback within the app. Focus groups of SCPs were completed first to review the breadth and accuracy of information, and feedback was prioritized and implemented as major updates using Agile processes prior to end user focus groups. We categorized logged in-app feedback using content analysis and thematically analyzed focus group transcripts.

Results: In total, 6 focus groups were completed between August 2022 and June 2023; 3 for SCPs (n=9 participants) and 3 for end users (n=7 participants). Four SCPs had previously smoked, and most end users currently smoked cigarettes (n=5), and 2 had quit smoking. The mean duration of focus groups was 58 (SD 10.9; range 46-74) minutes. We identified four major themes from focus group feedback: (1) conversation design, (2) functionality, (3) relationality and anthropomorphism, and (4) role as a smoking cessation support tool. In response to SCPs' feedback, we made two major updates to Quin between cohorts: (1) improvements to conversation flow and (2) addition of the "Moments of Crisis" conversation tree. Participant feedback also informed 17 recommendations for future smoking cessation chatbot developments.

Conclusions: Feedback from end users and SCPs highlighted the importance of chatbot functionality, as this underpinned Quin's conversation design and relationality. The ready accessibility of accurate cessation information and impartial support that Quin provided was recognized as a key benefit for end users, the latter of which contributed to a feeling of accountability to the chatbot. Findings will inform the ongoing development of a mature prototype for clinical testing.

Background: 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.

Objective: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Background: 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.

Objective: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Background: 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.

Objective: This study aims to develop and validate a patient-physician hybrid recommendation (PPHR) model with response metrics for better triage performance.

Methods: 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.

Results: 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%.

Conclusions: The PPHR model uses semantic features and response metrics to enable patients to accurately find the physician who best suits their needs.