JMIR Aging最新文献

Background: In our aging population, primary care is under pressure to remain accessible to all. Effective use of digital health care could potentially lower general practitioners' (GPs) workload. Some general practices are already implementing a digital health platform as a primary method to contact their patients. However, it is unknown how older people experience this novel way to communicate with their GP.

Objective: The aim of this study was to study the experiences of patients aged 65 years and older in general practices who use digital health as a primary communication tool. The secondary aims were to identify barriers and facilitators for the use of digital health care and whether a practice focus on digital health influences older patients' choice to enlist.

Methods: We invited all patients aged 65 years and older at 2 general practices in Amsterdam that work with a novel digital health platform. We used purposive sampling to select a heterogeneous group of patients in terms of age, sex, level of education, digital literacy, and experiences with the digital app of their general practice. We conducted 18 semistructured interviews from May through July 2023. All interviews were audio-recorded, transcribed, coded, and thematically analyzed.

Results: We generated three themes: (1) experiences of older people with digital health care in general practice, (2) impact of individual factors on digital health experiences, and (3) reasons for choosing a digitally oriented general practice. Participants reported both positive and negative experiences. The main perceived advantages of the digital health platform were increased accessibility, direct GP contact without an intermediary, and saving time through asynchronous communication. The disadvantages mentioned were log-in difficulties and problems with the automated explanatory questionnaire. Individual factors such as age, digital literacy, and expectations of general practice care seemed to impact people's experiences and could act as barriers or facilitators for using digital health. Reasons for older patients to enlist at a general practice were mainly practical. The digital orientation of the practice hardly played a role in this choice.

Conclusions: Older patients in general practice see benefits to using a digital health platform that offers 2-way chat-based communication between the patient and GP. We found that individual factors such as skills, norms and values, attitudes toward digitalization, and expectations of general practice care impacted older patients' experiences with digital health care. For many older participants, the digital profile of the general practice did not play a role in their choice to enlist. Further improvement of digital health platforms will be necessary to ensure digital health for all in general practice.

Background: Long-term care (LTC) homes face the challenges of increasing care needs of residents and a shortage of health care providers. Literature suggests that artificial intelligence (AI)-enabled robots may solve such challenges and support person-centered care. There is a dearth of literature exploring the perspectives of health care providers, which are crucial to implementing AI-enabled robots.

Objective: This scoping review aims to explore this scant body of literature to answer two questions: (1) what barriers do health care providers perceive in adopting AI-enabled robots in LTC homes? (2) What strategies can be taken to overcome these barriers to the adoption of AI-enabled robots in LTC homes?

Methods: We are a team consisting of 3 researchers, 2 health care providers, 2 research trainees, and 1 older adult partner with diverse disciplines in nursing, social work, engineering, and medicine. Referring to the Joanna Briggs Institute methodology, our team searched databases (CINAHL, MEDLINE, PsycINFO, Web of Science, ProQuest, and Google Scholar) for peer-reviewed and gray literature, screened the literature, and extracted the data. We analyzed the data as a team. We compared our findings with the Person-Centered Practice Framework and Consolidated Framework for Implementation Research to further our understanding of the findings.

Results: This review includes 33 articles that met the inclusion criteria. We identified three barriers to AI-enabled robot adoption: (1) perceived technical complexity and limitation; (2) negative impact, doubted usefulness, and ethical concerns; and (3) resource limitations. Strategies to mitigate these barriers were also explored: (1) accommodate the various needs of residents and health care providers, (2) increase the understanding of the benefits of using robots, (3) review and overcome the safety issues, and (4) boost interest in the use of robots and provide training.

Conclusions: Previous literature suggested using AI-enabled robots to resolve the challenges of increasing care needs and staff shortages in LTC. Yet, our findings show that health care providers might not use robots because of different considerations. The implication is that the voices of health care providers need to be included in using robots.

International registered report identifier (irrid): RR2-doi:10.1136/bmjopen-2023-075278.

Background: During the COVID-19 pandemic, there was a rapid adoption of telehealth care services as a public health strategy to maintain access to essential health care. In Australia, there has been increasing optimism for the expansion of telehealth services. However, little is known about the patterns and determinants of telehealth adoption among older adults, with concerns that an expansion of telehealth services may only be of benefit to those who already have better access to health care.

Objective: Leveraging data collected by The Sax Institute's 45 and Up COVID Insights study between November 2020 and April 2022, the objective of this study was to identify and describe the sociodemographic and health-related determinants of telehealth adoption and use among a cohort of older Australians. We hypothesized that health-related factors would be key determinants of telehealth adoption for Australians aged ≥65 years during the COVID-19 pandemic.

Methods: A repeated cross-sectional design was used. The relationships between telehealth use (classified as low, moderate, or high) and selected sociodemographic and health-related characteristics were assessed using logistic regression techniques. Variable selection and findings were situated within the Technology Acceptance Model, the Unified Theory of Acceptance, and the Use of Technology theoretical frameworks.

Results: Of the 21,830 participants aged ≥65 years, the proportion who indicated adopting telehealth ranged from 50.77% (11,082/21,830) at survey 1 in 2020 to 39.4% (7401/18,782) at survey 5 in 2022. High levels of telehealth use were associated with being female, aged <85 years, living in a major city, cohabiting with others, and being from the most socioeconomically disadvantaged areas (deciles 1-3). Individuals with a disability, chronic disease, multimorbidity, and lower perceived quality of life and those experiencing missed or delayed care were significantly more likely to use telehealth across all levels (P<.001). A temporal association was observed, whereby participants who engaged with telehealth services before or early in the pandemic (as assessed in survey 1) were more likely to continue telehealth use when assessed in survey 5 in 2022 (P<.001).

Conclusions: This research contributes to the broader understanding of telehealth adoption and use among older adults. As telehealth models of care expand, there is an opportunity to tailor these services to the needs of older adults, particularly those living with chronic diseases and multimorbidity, by using targeted strategies that overcome barriers to accessing specialized health care services.

Background: Assistive technology is becoming increasingly accessible and affordable for supporting people with dementia and their care partners living at home, with strong potential for technology-based prompting to assist with initiation and tracking of complex, multistep activities of daily living. However, there is limited direct comparison of different prompt features to guide optimal technology design.

Objective: Across 3 experiments, we investigated the features of tablet-based prompts that best support people with dementia to complete activities of daily living at home, measuring prompt effectiveness and gaining feedback from people with dementia and their care partners about their experiences.

Methods: Across experiments, we developed a specialized iPad app to enable data collection with people with dementia at home over an extended experimental period. In experiment 1, we varied the prompts in a 3 (visual type: text instruction, iconic image, and photographic image) × 3 (audio type: no sound, symbolic sound, and verbal instruction) experimental design using repeated measures across multiple testing sessions involving single-step activities. In experiment 2, we tested the most effective prompt breakdown for complex multistep tasks comparing 3 conditions (1-prompt, 3-prompt, and 7-prompt conditions). In experiment 3, we compared initiation and maintenance alerts that involved either an auditory tone or an auditory tone combined with a verbal instruction. Throughout, we asked people with dementia and their care partners to reflect on the usefulness of prompting technology in their everyday lives and what could be developed to better meet their needs.

Results: First, our results showed that audible verbal instructions were more useful for task completion than either tone-based or visual prompts. Second, a more granular breakdown of tasks was generally more useful and increased independent use, but this varied across individuals. Third, while a voice or text maintenance alert enabled people with dementia to persist with a multistep task for longer when it was more frequent, task initiation still frequently required support from a care partner.

Conclusions: These findings can help inform developers of assistive technology about the design features that promote the usefulness of home prompting systems for people with dementia as well as the preferences and insights of people with dementia and their care partners regarding assistive technology design.

Background: With the aging global population and the rising burden of Alzheimer disease and related dementias (ADRDs), there is a growing focus on identifying mild cognitive impairment (MCI) to enable timely interventions that could potentially slow down the onset of clinical dementia. The production of speech by an individual is a cognitively complex task that engages various cognitive domains. The ease of audio data collection highlights the potential cost-effectiveness and noninvasive nature of using human speech as a tool for cognitive assessment.

Objective: This study aimed to construct a machine learning pipeline that incorporates speaker diarization, feature extraction, feature selection, and classification to identify a set of acoustic features derived from voice recordings that exhibit strong MCI detection capability.

Methods: The study included 100 MCI cases and 100 cognitively normal controls matched for age, sex, and education from the Framingham Heart Study. Participants' spoken responses on neuropsychological tests were recorded, and the recorded audio was processed to identify segments of each participant's voice from recordings that included voices of both testers and participants. A comprehensive set of 6385 acoustic features was then extracted from these voice segments using OpenSMILE and Praat software. Subsequently, a random forest model was constructed to classify cognitive status using the features that exhibited significant differences between the MCI and cognitively normal groups. The MCI detection performance of various audio lengths was further examined.

Results: An optimal subset of 29 features was identified that resulted in an area under the receiver operating characteristic curve of 0.87, with a 95% CI of 0.81-0.94. The most important acoustic feature for MCI classification was the number of filled pauses (importance score=0.09, P=3.10E-08). There was no substantial difference in the performance of the model trained on the acoustic features derived from different lengths of voice recordings.

Conclusions: This study showcases the potential of monitoring changes to nonsemantic and acoustic features of speech as a way of early ADRD detection and motivates future opportunities for using human speech as a measure of brain health.

Background: eHealth literacy is an essential skill for pursuing electronic health information, particularly for older people whose health needs increase with age. South Korea is now at the intersection of a rapidly digitalizing society and an increasingly aged population. eHealth literacy enables older people to maximize the effective use of emerging digital technology for their health and quality of life. Understanding the eHealth literacy of Korean older adults is critical to eliminating the gray digital divide and inequity in health information access.

Objective: This study aims to investigate factors influencing eHealth literacy in older Korean adults and its impact on health outcomes and eHealth use.

Methods: This was a cross-sectional survey. Community-dwelling older adults 65 years and older in 2 urban cities in South Korea were included. eHealth literacy was measured by the eHealth Literacy Scale. Ordinal logistic regression was used to analyze factors associated with eHealth literacy and multivariate ANOVA for the impact of eHealth literacy on health outcomes and eHealth use.

Results: In total, 434 participants were analyzed. A total of 22.3% (97/434) of participants had high eHealth literacy skills. Increasing age, higher monthly income, and time spent on the internet were significantly associated with eHealth literacy (P<.001), and social media users were 3.97 times (adjusted odds ratio 3.97, 95% CI 1.02-15.43; P=.04) more likely to have higher skill. Higher eHealth literacy was associated with better self-perceived health and frequent use of digital technologies for accessing health and care services (P<.001).

Conclusions: Disparity in socioeconomic status and engagement on the internet and social media can result in different levels of eHealth literacy skills, which can have consequential impacts on health outcomes and eHealth use. Tailored eHealth interventions, grounded on the social and digital determinants of eHealth literacy, could facilitate eHealth information access among older adults and foster a digitally inclusive healthy aging community.

Background: As the aging population in the United States continues to increase rapidly, preserving the mobility and independence of older adults becomes increasingly critical for enabling aging in place successfully. While personal vehicular transport remains a popular choice among this demographic due to its provision of independence and control over their lives, age-related changes may heighten the risk of common driving errors and diminish driving abilities.

Objective: This study aims to investigate the driving practices of older adults and their efforts to maintain safe and confident driving habits. Specifically, we sought to identify the factors that positively and negatively influence older adults' driving performance and confidence, as well as the existing efforts put into sustaining their driving abilities.

Methods: We recruited 20 adults aged ≥65 years who remained active drivers during the recruitment from the greater New York area. Then, we conducted semistructured interviews with them to examine their perceptions, needs, and challenges regarding safe and confident driving.

Results: Our findings uncovered a notable disparity between older adults' self-perceived driving skills and the challenges they face, particularly caused by age-related limitations and health conditions such as vision and memory declines and medication routines. Drawing on these findings, we proposed strategies to bridge this gap and empower older adults to drive safely and confidently, including fostering a realistic understanding of their capabilities, encouraging open dialogue regarding their driving, encouraging regular assessments, and increasing awareness of available resources.

Conclusions: This study uncovered a noticeable disparity between the perceived driving competence of older adults and the actual challenges they confront while driving. This divergence underscores a significant need for better support beyond the existing aid available to preserve older adults' driving skills. We hope that our recommendations will offer valuable insights for practitioners and scholars committed to enhancing the overall well-being and quality of life for older adults as they age in their homes.

Background: Digitalization in the German health care system is progressing slowly, even though it offers opportunities for improvement of care. In nursing homes, most of the staff's work is paper based. Following the pandemic, there has been a decrease in the use of telemedicine applications. To ensure long-term implementation, the views of users, in this case nurses, are of interest.

Objective: This cross-sectional study was conducted to describe which digital applications are already being used at inpatient care facilities, the attitude of nurses toward telemedicine, and for which areas the use of telemedicine in the facilities is considered appropriate by the participants.

Methods: All inpatient care facility staff in Schleswig-Holstein were invited to participate in the survey from August 1 to October 31, 2022. The questionnaire consists of 17 determinants that ask about the attitude, use, and possible applications of telemedicine. In addition to a descriptive analysis, the influence of the general attitude toward telemedicine on various determinants was examined using the Fisher exact test for nominal variables and Spearman correlation coefficient for metric variables.

Results: A total of 425 caregivers participated in the survey. Of these respondents, 10.7% (n=41) currently used video consultations, and 76.1% (n=321) of the respondents were in favor of video consultations being practiced in training. Furthermore, 74.8% (n=312) of the respondents would attend a training on telephone medical consultation. Respondents indicated that video consultations have a small added value compared to asynchronous telemedicine (eg, sending photos). However, video consultations were perceived as somewhat less time-consuming than other communication channels. Video consultations are perceived as most useful for clarifying urgent problems. The respondents estimated that one in five paramedic calls at their facilities could be reduced through telemedicine approaches. It was important to the participants that telemedicine is as simple as possible and that there is a high level of data security.

Conclusions: Although many caregivers have a positive attitude toward telemedicine and perceive its advantages, communication channels such as video consultation are still used infrequently in care facilities. To promote the use of telemedicine applications, it is important to emphasize their benefits. The presumed saving of paramedic calls thus represents a benefit, and it is crucial to train caregivers in the use of telemedicine to avoid uncertainties in dealing with the newer technologies. It is important to give them enough time and repetitions of the training.

Background: Markerless motion capture (MMC) uses video cameras or depth sensors for full body tracking and presents a promising approach for objectively and unobtrusively monitoring functional performance within community settings, to aid clinical decision-making in neurodegenerative diseases such as dementia.

Objective: The primary objective of this systematic review was to investigate the application of MMC using full-body tracking, to quantify functional performance in people with dementia, mild cognitive impairment, and Parkinson disease.

Methods: A systematic search of the Embase, MEDLINE, CINAHL, and Scopus databases was conducted between November 2022 and February 2023, which yielded a total of 1595 results. The inclusion criteria were MMC and full-body tracking. A total of 157 studies were included for full-text screening, out of which 26 eligible studies that met the selection criteria were included in the review. .

Results: Primarily, the selected studies focused on gait analysis (n=24), while other functional tasks, such as sit to stand (n=5) and stepping in place (n=1), were also explored. However, activities of daily living were not evaluated in any of the included studies. MMC models varied across the studies, encompassing depth cameras (n=18) versus standard video cameras (n=5) or mobile phone cameras (n=2) with postprocessing using deep learning models. However, only 6 studies conducted rigorous comparisons with established gold-standard motion capture models.

Conclusions: Despite its potential as an effective tool for analyzing movement and posture in individuals with dementia, mild cognitive impairment, and Parkinson disease, further research is required to establish the clinical usefulness of MMC in quantifying mobility and functional performance in the real world.

Background: Digital technologies can assist and optimize health care processes. This is increasingly the case in the musculoskeletal health domain, where digital platforms can be used to support the self-management of musculoskeletal conditions, as well as access to services. However, given a large proportion of the population with musculoskeletal conditions are older adults (aged ≥60 years), it is important to consider the acceptability of such platforms within this demographic.

Objective: This study aims to explore participants' opinions and perceptions on the use of digital platforms for supporting the self-management of musculoskeletal conditions within older adult (aged ≥60 years) populations and to gather their opinions on real examples.

Methods: A total of 2 focus groups (focus group 1: 6/15, 40%; focus group 2: 9/15, 60%) were conducted, in which participants answered questions about their thoughts on using digital health platforms to prevent or manage musculoskeletal conditions. Participants were further presented with 2 example scenarios, which were then discussed. Interviews were audio recorded, transcribed, and analyzed thematically. Participants were aged ≥60 years and with or without current musculoskeletal conditions. Prior experience of using smartphone apps or other digital health platforms for musculoskeletal conditions was not required. Focus groups took place virtually using the Teams (Microsoft Corp) platform.

Results: A total of 6 themes were identified across both focus groups: "experiences of digital health platforms," "preference for human contact," "barriers to accessing clinical services," "individual differences and digital literacy," "trust in technology," and "features and benefits of digital health technologies." Each theme is discussed in detail based on the interview responses. The findings revealed that most participants had some existing experience with digital health platforms for preventing or managing musculoskeletal conditions. Overall, there was a lack of trust in and low expectations of quality for digital platforms for musculoskeletal health within this age group. While there was some concern about the use of digital platforms in place of in-person health consultations, several benefits were also identified.

Conclusions: Results highlighted the need for better communication on the benefits of using digital platforms to support the self-management of musculoskeletal conditions, without the platforms replacing the role of the health care professionals. The concerns about which apps are of suitable quality and trustworthiness lead us to recommend raising public awareness around the role of organizations that verify and assess the quality of digital health platforms.