JMIR Aging最新文献

Background: Social isolation and weakened intergenerational ties pose significant threats to the emotional well-being and social support networks of older adults. Although structured intergenerational programs can reduce age-related stereotypes and promote connectedness, their accessibility is often hindered by physical and logistical constraints. The increasing digital literacy among older populations presents new opportunities for technology-based interventions to support meaningful cross-generational engagement.

Objective: This study aimed to design and evaluate a mobile app that fosters intergenerational communication and enhances perceived social support in older adults using a user-centered design framework grounded in the double diamond model.

Methods: The development process followed the 4 phases of the double diamond model. In the discover phase, surveys with older and younger adults identified distinct usability preferences. The define phase synthesized these insights into key design principles. In the develop phase, a prototype was created and iteratively refined through usability testing. Finally, in the deliver phase, a 2-week experimental study involving 39 participants (20 older adults aged 68-82 years and 19 younger adults aged 22-39 years) assessed changes in intergenerational interaction, perceived social support, and user satisfaction.

Results: The app appeared to enhance intergenerational communication and perceived social support, particularly among older participants. Users reported increased comfort and emotional connection in cross-generational conversations. Accessibility features and engaging content were noted as contributing to positive user experiences across age groups.

Conclusions: This study suggests the potential of user-centered digital platforms to promote social well-being among older adults. By addressing the unique needs of multiple generations, such interventions may help foster inclusive digital environments and contribute to age-friendly, connected societies. Despite limitations related to sample size, duration, and cultural context, the study provides preliminary evidence for the potential of co-designed digital tools in supporting intergenerational communication and aging-in-place.

Background: While bidirectional associations among sleep duration, cognitive function, and depression are established, the symptom-level temporal interactions among these factors in China's aging population, which is experiencing unprecedented growth, remain poorly characterized.

Objective: We aim to use a novel temporal network analysis to clarify these dynamics and guide targeted interventions, with a focus on sex-specific dynamic pathways.

Methods: We conducted a longitudinal temporal network analysis on 3136 Chinese adults aged ≥45 years from the China Health and Retirement Longitudinal Study (CHARLS) across 5 waves (2011, 2013, 2015, 2018, and 2020). A graphical vector autoregressive (GVAR) model delineated the interdependencies among sleep duration, cognitive performance (assessed via the Mini-Mental State Examination [MMSE]), and depressive symptoms (evaluated with the 10-item Center for Epidemiologic Studies Depression Scale [CESD-10]). We also examined sex-specific differences in network structures.

Results: The symptom "bothered" was found to predict all other CESD-10 symptoms. There were significant predictive links between sleep and the CESD-10 node (ie, bothered, drained, and depressed), along with sleep and the MMSE functions (ie, numerical ability). Furthermore, sleep duration served as a bridge between depression symptoms and cognitive functions. There were significant differences in longitudinal network structure between sexes. Sex-specific analyses revealed distinct network patterns. Among female participants, the "bothered" node significantly predicted several outcomes over time. In contrast, the temporal network for male participants was sparser, with the "stuck" node in the depression domain being predominantly influenced by other nodes.

Conclusions: Our study revealed that emotional distress, especially the "bothered" symptom, plays a central role in depressive symptoms and cognitive decline. The bridging effect of short sleep duration underscores the potential of interventions targeting both sleep and emotional distress for alleviating depressive symptoms and delaying cognitive deterioration in older adults.

Background: Frailty is a dynamic geriatric syndrome associated with adverse health outcomes, yet its progression can be mitigated through targeted interventions.

Objective: This study aimed to investigate predictors of frailty transitions in Chinese older adults, focusing on physical activity (PA) and physical function.

Methods: Using data from the China Health and Retirement Longitudinal Study (CHARLS), we examined transitions between frailty states (robust, prefrail, and frail) from 2011 (baseline) to 2013 (follow-up) among 1014 participants aged 65 years and older. The following outcomes were assessed, including frailty using the physical frailty phenotype, PA using a modified International Physical Activity Questionnaire, and physical function using the Short Physical Performance Battery (SPPB) and handgrip strength. Ordinal logistic regression models were used to examine the relationship between PA, physical function, and frailty transitions.

Results: Results showed that higher PA levels and better physical function reduced the likelihood of worsening frailty or increased the probability of transitioning to robustness. Key findings from the subgroup include: among robust individuals, greater handgrip strength predicted maintained robustness (average marginal effects [AME]=1.12%; P=.02); in prefrail individuals, higher vigorous PA (AME=21.76%; P=.04) and handgrip strength (AME=0.64%; P=.003) increased transitions to robustness; for frail individuals, increased low-intensity PA (AME =22.48%; P=.04) and higher SPPB walking subscores (AME=27.73%; P=.02) promoted improvement to nonfrailty.

Conclusions: These findings highlight the importance of tailored interventions based on baseline frailty status. Promoting PA and improving physical function, particularly muscle strength and mobility function, may help delay or reverse frailty progression.

Background: Frailty affects over 50% of older adults in long-term care (LTC), and early detection is critical due to its potential reversibility. Wearable sensors enable continuous monitoring of gait and physical activity, and machine learning has shown promise in detecting frailty among community-dwelling older adults. However, its applicability in LTC remains underexplored. Furthermore, dynamic gait outcomes (eg, gait stability and symmetry) may offer more sensitive frailty indicators than traditional measures like gait speed, yet their potential remains largely untapped.

Objective: This study aimed to evaluate whether frailty in LTC facilities could be effectively identified using machine learning models trained on gait and daily physical activity data derived from a single accelerometer.

Methods: This study is a cross-sectional secondary analysis of baseline data from a 2-arm cluster randomized controlled trial. Of the 164 individuals initially enrolled, 51 participants (age: mean 85.0, SD 9.0 years; female: n=24, 47.1%) met the inclusion criteria of completing all assessments required for this study and were included in the final analysis. Frailty status was assessed using the fatigue, resistance, ambulation, incontinence, loss of weight, nutritional approach, and help with dressing (FRAIL-NH) scale. Participants completed a 5-meter walking task while wearing a 3D accelerometer. Following this task, the accelerometer was used to record daily physical activity over approximately 1 week. A total of 34 dynamic and spatial-temporal gait outcomes, 3 physical activity variables, and 6 demographic characteristics were extracted. Five conventional machine learning models were trained to classify frailty status using a leave-one-out cross-validation approach. Model performance was evaluated based on accuracy and the area under the receiver operating characteristic curve. To enhance model interpretability, explainable artificial intelligence techniques were used to identify the most influential predictive outcomes.

Results: The extreme gradient boosting model demonstrated the optimal performance with an accuracy of 86.3% and an area under the curve of 0.92. Explainable artificial intelligence analysis revealed that older adults with frailty exhibited more variable, complex, and asymmetric gait patterns, which were characterized by higher stride length variability, increased sample entropy, and a higher gait symmetry score.

Conclusions: Our findings suggest that dynamic gait outcomes may serve as more sensitive indicators of frailty than spatial-temporal gait outcomes (eg, gait speed) in LTC settings, offering valuable insights for enhancing frailty detection and management.

Background: In long-term care (LTC) for older adults, data on client, employee, and organization levels are collected in various ways, covering quality of care, life, and work. There is, however, a lack of understanding of how to introduce data-informed care in LTC and thus create value from data.

Objective: This study aims to investigate the experiences and perceptions of various stakeholders in LTC regarding data and data-informed care.

Methods: A qualitative study using the World Café cocreation technique was conducted with a diverse group of LTC stakeholders. Four questions were addressed: (1) What thoughts do you have when you hear the term "data" in relation to LTC? (2) What purposes do data have (in the future) in LTC? (3) What knowledge and skills are needed to enable data-informed care? (4) How can data contribute to and improve multidisciplinary learning? Stakeholders' notes and the plenary summary were analyzed using conventional content analysis.

Results: Stakeholders included nurses, members of client councils, data specialists, researchers, and managers (N=20; mean age 50, SD 13 years). Five themes were identified: (1) despite uncertainty, the benefits of using data outweigh the associated risks; (2) the lack of accessibility and uniformity hinders integrating data-informed care; (3) human resources and finance departments pioneer data usage; however, potential lies in clinical decision-making; (4) data-informed care demands individual, collective, and organizational prerequisites; and (5) multidisciplinary collaboration enriches collective knowledge regarding data.

Conclusions: Introducing data-informed care requires enhancing data literacy of health care professionals, establishing clear communication about the role of data within the organization, and introducing new job positions, such as data scientists. Data-informed care was considered a multidisciplinary approach in which data have a supportive role to enhance collective understanding and are considered crucial for improving quality of care. .

Background: Frailty is a common issue among hospitalized older adult patients and is associated with numerous adverse health outcomes. Assessing frailty facilitates better decision-making for treatment plans, patient placement, and discharge planning. Approximately a decade ago, the frailty index based on laboratory tests (FI-Lab) metric was introduced. Although this index has been shown in numerous studies to predict adverse medical outcomes, including mortality, it has not been extensively evaluated among patients hospitalized in internal medicine departments for diverse indications.

Objective: The aim of the study was to investigate the relationship between FI-Lab at admission and all-cause mortality during hospitalization and after discharge in patients aged 65 years and older admitted for diverse clinical indications to internal medicine departments.

Methods: This retrospective cohort study included patients aged 65 years and older hospitalized in the internal medicine departments of a large tertiary hospital. Data included demographic variables, comorbidity, and all-cause mortality. The FI-Lab was calculated based on 16 available blood tests, as well as blood pressure and heart rate measurements. We used Cox proportional hazards regression models to evaluate associations with mortality. Model performance was assessed using the C-index and time-dependent receiver operating characteristic (ROC) curves. Hospitalization data were collected from December 25, 2016, to January 7, 2023.

Results: During the study period, 31,443 patients were hospitalized in internal medicine departments, and FI-Lab was calculable for 31,398 of them. The mean age of the patients was 77.6 (SD 8.2) years, and 52.1% (16,346/31,443) were women. The mean FI-Lab score was 0.38 (SD 0.15). Based on FI-Lab scores, patients were categorized into 4 groups: robust, mildly prefrail, moderately prefrail, and frail. After adjusting for age, sex, and comorbidities, frail and prefrail patients exhibited higher mortality rates than robust patients. For each 0.01 increase in the FI-Lab score (as a continuous variable), adjusted analyses revealed a 5.5% increase in in-hospital mortality, a 2.9% increase in mortality within the first year after hospitalization, and a 1.9% increase in mortality beyond the first year.

Conclusions: The FI-Lab is a readily available and informative metric of frailty in older hospitalized patients. Calculating this index can assist physicians with identifying patients at high risk of mortality and provide meaningful information to support clinical decision-making.

Background: Sarcopenia is associated with cardiovascular diseases (CVDs). However, whether changes in sarcopenia status affect CVD risk remains unclear. In addition, how indoor fuel use impacts the sarcopenia transition process is less well studied.

Objective: This study prospectively examined the association of sarcopenia transitions with CVD risk, while exploring the effect of indoor fuel on these transitions.

Methods: In this prospective observational study, we used data from the China Health and Retirement Longitudinal Study waves 1 to 4 (2011 to 2018). In total, 8739 participants with complete data on sarcopenia and indoor fuel use were included for the indoor fuel use and sarcopenia transition analysis, and 6385 participants without previous CVDs were included for the sarcopenia transition and CVD risk analysis. Sarcopenia transition was defined according to the sarcopenia status at wave 1 (2011) and wave 2 (2013). Incident CVDs included heart diseases, stroke, and composite CVDs. Information on indoor fuel use was obtained at wave 1. Cox proportional hazards models were used to examine the effect of sarcopenia transition on incident CVDs. Logistic regression models were used to investigate the impact of indoor fuel use on these transitions.

Results: During a median of 7.0 years of follow-up, 1233 incident CVDs were documented. Compared to stably normal participants, progressing from a normal state to possible or confirmed sarcopenia brought increased risk of incident CVD (hazard ratio 1.42, 95% CI 1.15-1.77). Conversely, recovering to a normal state was associated with decreased risk (hazard ratio 0.72, 95% CI 0.55-0.95) for baseline participants with possible sarcopenia. In addition, clean fuel use increased the odds of achieving a possible-to-normal transformation (odds ratio 1.32, 95% CI 1.06-1.64), while both solid cooking and heating fuel use were associated with a higher risk of deterioration in sarcopenia status.

Conclusions: An unfavorable transition in sarcopenia status is associated with higher CVD risk, while reversion from possible sarcopenia to a normal state could reduce the risk. Therefore, early intervention for sarcopenia is imperative for CVD prevention, and promoting clean indoor fuel use is recommended.