JMIR Aging最新文献

Background: In Quebec, the shortage of nurses during night shifts compromises the safety and quality of resident care, particularly in small residential and long-term care centers ("Centres d'hébergement et de soins de longue durée"; CHSLDs) located in rural areas. The need to ensure the continuous presence of nurses 24 hours a day in CHSLDs has become more pressing, forcing some facilities to implement exceptional measures such as on-call telephone services to ensure access to a nurse. In light of these challenging circumstances, the Direction nationale des soins et des services infirmiers of Quebec's Ministère de la Santé et des Services sociaux has rolled out a teleconsultation pilot project.

Objective: This study aims to explore nurses' and nursing assistants' experience of integrating teleconsultation during night shifts in rural CHSLDs with ≤50 residents.

Methods: The 6-month pilot project was rolled out sequentially in 3 rural CHSLDs located in 2 administrative regions of Quebec between July 2022 and March 2023. A total of 18 semistructured interviews were conducted with 9 nurses and nursing assistants between February and July 2023.

Results: Participants' experiences revealed that teleconsultation provided significant added value by improving clinical, administrative, and organizational practices. Some practices remained unchanged, indicating stable workflows. Workflow optimization through an expanded scope of practice ensured efficient and safe continuity of care. Enhanced collaboration between nurses and nursing assistants led to improved care coordination and communication. The leadership played a significant role in clarifying professionals' roles and in supporting effective adaptation to teleconsultation.

Conclusions: This pilot project represents a significant step forward in improving care for CHSLD residents in Quebec. Teleconsultation not only makes it possible to overcome recruitment challenges and ensure the continuous presence of nurses during night shifts but also optimizes professional practices while ensuring the safety and quality of care provided to residents.

Background: We live in a digital age where social media has become an essential part of people's lives. It is also one of the leading platforms responsible for spreading health-related falsehoods. This study explores who adults of different age groups perceive as responsible for reducing health-related falsehoods on social media.

Objective: Despite growing concern over older adults' exposure to false health information on social media, little research examines their beliefs on how to address the problem. This study examines how the age of US adults is associated with their reported experiences with health-related falsehoods on social media and their beliefs about who should be tasked with reducing such falsehoods.

Methods: This study is a secondary analysis of data from the 2022 Health Information National Trends Survey, a nationally representative survey of US adults (18 years and older). Multivariable logistic regressions estimated how a respondent's age was associated with their self-reported social media use, their difficulty to detect health-related falsehoods on social media, their discussion of health information found on social media with medical providers, and their beliefs regarding who should be responsible for reducing health-related falsehoods on social media. Regression estimates were adjusted for respondents' sociodemographic and health characteristics.

Results: Daily social media use decreased with respondents' age. Respondents aged 50-64 years (b=0.515, P=.01) and 65-74 years (b=0.697, P=.002) were more likely than respondents aged 18-34 years to report they strongly agree that it is difficult for them to detect health-related falsehoods on social media. Compared to younger adults, older adults (65-74 years: b=0.818, P=.002; 75 years and older: b=1.058, P<.001) were more likely to believe medical providers should be responsible for reducing online falsehoods.

Conclusions: In addition to ongoing efforts by social media platforms to detect and remove falsehoods, the findings suggest medical providers should be tasked with discrediting health-related falsehoods on social media for older adults. However, time during the clinical visit is limited. Future research is needed to discover new approaches and tools tailored to older adults to assist with filtering and discrediting health-related falsehoods on social media.

Background: Transcatheter aortic valve replacement (TAVR) is a commonly used treatment for severe aortic stenosis. As degenerative aortic stenosis is primarily a disease afflicting older adults, a frailty assessment is essential to patient selection and optimal periprocedural outcomes.

Objective: This study aimed to enhance frailty assessments of TAVR candidates by integrating real-world structured and unstructured data.

Methods: This study analyzed data from 14,000 patients between January 2018 and December 2019 to assess frailty in TAVR patients at the University of Florida. Frailty was identified using the Fried criteria, which includes weight loss, exhaustion, walking speed, grip strength, and physical activity. Latent Dirichlet allocation for topic modeling and Extreme Gradient Boosting for frailty prediction were applied to unstructured clinical notes and structured electronic health record (EHR) data. We also used least absolute shrinkage and selection operator regression for feature selection. Model performance was rigorously evaluated using nested cross-validation, ensuring the generalizability of the findings.

Results: Model performance was significantly improved by combining unstructured clinical notes with structured EHR data, achieving an area under the receiver operating characteristic curve of 0.82 (SD 0.07), which surpassed the EHR-only model's area under the receiver operating characteristic curve of 0.64 (SD 0.08). The Shapley Additive Explanations analysis found that congestive heart failure management, back problems, and atrial fibrillation were the top frailty predictors. Additionally, the latent Dirichlet allocation topic modeling identified 7 key topics, highlighting the role of specific medical treatments in predicting frailty.

Conclusions: Integrating unstructured clinical notes and structured EHR data led to a notable enhancement in predicting frailty. This method shows great potential for standardizing frailty assessments using real-world data and improving patient selection for TAVR.

Background: Patients with knee osteoarthritis (KOA) often present lower extremity motor dysfunction. However, traditional radiography is a static assessment and cannot achieve long-term dynamic functional monitoring. Plantar pressure signals have demonstrated potential applications in the diagnosis and rehabilitation monitoring of KOA.

Objective: Through wearable gait analysis technology, we aim to obtain abundant gait information based on machine learning techniques to develop a simple, rapid, effective, and patient-friendly functional assessment model for the KOA rehabilitation process to provide long-term remote monitoring, which is conducive to reducing the burden of social health care system.

Methods: This cross-sectional study enrolled patients diagnosed with KOA who were able to walk independently for 2 minutes. Participants were given clinically recommended functional tests, including the 40-m fast-paced walk test (40mFPWT) and timed up-and-go test (TUGT). We used a smart shoe system to gather gait pressure data from patients with KOA. The multidimensional gait features extracted from the data and physical characteristics were used to establish the KOA functional feature database for the plantar pressure measurement system. 40mFPWT and TUGT regression prediction models were trained using a series of mature machine learning algorithms. Furthermore, model stacking and average ensemble learning methods were adopted to further improve the generalization performance of the model. Mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE) were used as regression performance metrics to evaluate the results of different models.

Results: A total of 92 patients with KOA were included, exhibiting varying degrees of severity as evaluated by the Kellgren and Lawrence classification. A total of 380 gait features and 4 physical characteristics were extracted to form the feature database. Effective stepwise feature selection determined optimal feature subsets of 11 variables for the 40mFPWT and 10 variables for the TUGT. Among all models, the weighted average ensemble model using 4 tree-based models had the best generalization performance in the test set, with an MAE of 2.686 seconds, MAPE of 9.602%, and RMSE of 3.316 seconds for the prediction of the 40mFPWT and an MAE of 1.280 seconds, MAPE of 12.389%, and RMSE of 1.905 seconds for the prediction of the TUGT.

Conclusions: This wearable plantar pressure feature technique can objectively quantify indicators that reflect functional status and is promising as a new tool for long-term remote functional monitoring of patients with KOA. Future work is needed to further explore and investigate the relationship between gait characteristics and functional status with more functional tests and in larger sample cohorts.

Background: Population aging affects society, with a profound impact on daily activities for those of a low socioeconomic status and with motor impairments. Social assistive robots (SARs) and monitoring technologies can improve older adults' well-being by assisting with and monitoring home activities.

Objective: This study explored the opinions and needs of older adults, including those with motor difficulties and of a low socioeconomic status, regarding SARs and monitoring technologies at home to promote daily activities and reduce sedentary behaviors.

Methods: A mixed methods approach was used, with 31 older adults divided into 3 groups: those of a low socioeconomic status, those with motor difficulties, and healthy individuals. Focus groups were conducted, and they were analyzed using thematic analysis. Perceived mental and physical well-being were assessed using the 12-Item Short Form Health Survey, and attitudes toward robots were evaluated using the Multidimensional Robot Attitude Scale.

Results: The results identified 14 themes in four key areas: (1) technology use for supporting daily activities and reducing sedentary behaviors, (2) perceived barriers, (3) suggestions and preferences, and (4) actual home technology use. Lower perceived physical well-being was associated with higher levels of familiarity, interest, perceived utility, and control related to SARs. Lower perceived psychological well-being was linked to a more negative attitude, increased concerns about environmental fit, and a preference for less variety. Notably, older adults from the low-socioeconomic status group perceived less control over SARs, whereas older adults with motor difficulties expressed higher perceived utility compared to other groups, as well as higher familiarity and interest compared to the low-socioeconomic status group.

Conclusions: Participants indicated that SARs and monitoring technologies could help reduce sedentary behaviors by assisting in the management of daily activities. The results are discussed in the context of these outcomes and the implementation of SARs and monitoring technologies at home. This study highlights the importance of considering the functional and socioeconomic characteristics of older adults as future users of SARs and monitoring technologies to promote widespread adoption and improve well-being within this population.

Background: Subjective cognitive concerns (SCC) may be among the earliest clinical symptoms of dementia. There is growing interest in applying mobile app-based cognitive assessment to remotely screen for cognitive status in preclinical dementia, but the relationship between SCC and relevant mobile assessment metrics is uncertain.

Objective: We characterized the relationship between SCC and adherence, satisfaction, and performance on mobile-app assessments in cognitively unimpaired older adults.

Methods: Participants (N=122, Mage=69.85, Meducation=16.52, %female=66.7, %White=86.2) completed 8 assessment days using Mobile Monitoring of Cognitive Change (M2C2), an app-based testing platform, with brief daily sessions within morning, afternoon, and evening time windows (24 total testing sessions). M2C2 includes digital working memory, processing speed, and episodic memory tasks. Participants provided feedback about their satisfaction and motivation related to M2C2 upon study completion. SCC was assessed using the Cognitive Function Instrument (CFI). Regression analyses evaluated the association between SCC and adherence, satisfaction, and performance on M2C2, controlling for age, sex, depression, and loneliness. Linear-mixed effects models evaluated whether SCC predicted M2C2 subtest performance over the 8-day testing period, controlling for covariates.

Results: SCC was not associated with app satisfaction or protocol motivation, but it was significantly associated with lower rates of protocol adherence (ß=-0.197, p=.037, 95% CI -0.647, -0.021). Higher SCC endorsement significantly predicted worse overall episodic memory performance (ß=-0.200, p = .020, 95% CI -0.020, -0.002), but not working memory or processing speed. There was a main effect of SCC on working memory performance at day 1 (Est=-1.047, SE=0.47, p=0.031) and a significant interaction between SCC and working memory over the 8-day period (Est=0.048, SE=0.02, p=0.031), such that SCC was associated with initially worse, then progressively better working memory performance.

Conclusions: SCCs are associated with worse overall memory performance on mobile-app assessments, patterns of cognitive inefficiency (variable working memory) and mildly diminished adherence across an 8-day assessment period. Findings suggest that mobile app assessments may be sensitive to subtle cognitive changes, with important implications for early detection and treatment for individuals at risk for dementia.

Clinicaltrial:

Background: Older adults adopt and use eHealth systems to build autonomy, competence, and relatedness and engage in healthy behaviors. The motivational technology model posits that technology features, such as those on websites, smart displays, and mobile phones, must allow for navigability, interactivity, and customizability, which spur feelings of self-determination and intrinsic motivation. We studied ElderTree, an online system for older adults that provides on-demand videos of healthy living content, self-monitoring, and weekly researcher-hosted video meetings.

Objective: We aimed to understand the theoretical crossover between the motivational technology model and self-determination theory using features of ElderTree to understand the usability of the technology and how it may support older adults' autonomy, competence, and relatedness.

Methods: Drawing participants from a randomized controlled trial of a mobile health app for older adults with multiple chronic conditions, we conducted qualitative interviews with 22 older adults about their use of the app; the interviews were coded using qualitative thematic analysis.

Results: Older adults did find that features within ElderTree such as content available on demand, good navigation, and weekly researcher-led video calls supported feelings of autonomy, competence, and relatedness, respectively. Individual differences such as a background using computers also influenced participants' experiences with the smart displays.

Conclusions: Participants confirmed the features that increased internal motivation, such as interactivity correlating with feelings of relatedness, but they also found other ways to support autonomous health behavior change beyond narrow views of navigability, interactivity, and customization.