JMIR Aging最新文献

Background: With rapid digitalization, technologies are increasingly integrated as part of our everyday lives and are becoming increasingly essential for individuals to participate in society. Technology presents opportunities to support healthy aging. Examples include digital health monitoring and opportunities to maintain social connectedness through online platforms. However, the processes in which older adults select and integrate technologies into their daily lives have not been well examined.

Objectives: This study uses the Selection, Optimization, and Compensation (SOC) model to understand how older adults integrate technology into their everyday lives to live well. The two key research questions are as follows: (1) How do older adults describe their technology use and their choices, analyzed with respect to SOC processes? (2) How do older adults perceive that technology is a part of living well?

Methods: A descriptive qualitative study was conducted. Purposive sampling was used to recruit older adults who were aged 55 years and older, were living in the community, spoke and understood English, and resided in Australia. Five in-person focus groups comprising 20 older adults were conducted. Data were analyzed using hybrid inductive and deductive reflexive thematic analyses, based on the SOC model.

Results: All participants resided in Brisbane, Queensland. Older adults selected technology intentionally to enhance different aspects of their daily lives. Perceived "involuntary" selection of technology could lead to feelings of resentment or frustration. Optimization strategies included self-monitoring, integrating technology into daily routines, digital literacy and proficiency, and problem-solving skills. Compensatory strategies included choosing alternative technology that suited participants' abilities or skills and seeking support through informal or formal avenues.

Conclusions: These findings provide important considerations for technology developers to design technology in collaboration with older adults to ensure that they align with needs and preferences. Digital literacy is not sufficient to empower older adults to use technology; when empowering older adults to use technology, service providers should also consider facilitating other intrinsic and extrinsic resources and skills.

Background: Self-management is critical for older adults with type 2 diabetes mellitus (T2DM); however, its practice remains suboptimal. Social media has become an accessible and effective stimulus source for the public, which has the potential to promote health behaviors, but its effect on the self-management of older adults with T2DM remains unknown.

Objective: We aimed to investigate the relationship between social media exposure, specifically time exposure and content exposure, and the self-management of older adults with T2DM.

Methods: In this cross-sectional study, we enrolled 257 older adults with T2DM who used short-form video apps from community health care centers. We assessed subjective and objective time and content exposure. We transformed text-based content exposure into diabetes-related content exposure encompassing irrelevant, harmful, hypobeneficial, and hyperbeneficial categories using Q-methodology. Self-management was assessed through a validated questionnaire. We used restricted cubic splines and linear regression models to model the relationships between time exposure and content exposure and self-management, respectively.

Results: Of 257 older adults with T2DM, the median age was 69 (IQR 65-72) years, 53.3% (n=137) were women, the mean sum score of self-management was 35.7 (SD 10.4), the median subjective time exposure was 120 (IQR 60-120) minutes, and 61.1% (n=157) of them were exposed to hyperbeneficial content. There was an approximate L-shaped dextrorotatory relationship between time exposure and self-management, with a decline in self-management when time exposure surpassed 139.8 minutes daily. Exposure to hyperbeneficial content was positively associated with the overall self-management (B=3.46, 95% CI 0.71-6.21). For participants exposed for more than 139.8 minutes daily, this positive association remained robust (B=7.27, 95% CI 1.54-13.00). In subdimensional analyses, hyperbeneficial content exposure was positively associated with general diet (B=1.51, 95% CI 0.54-2.49) and blood glucose testing (B=1.31, 95% CI 0.25-2.38).

Conclusions: Social media exposure presented a double-edged sword for self-management of older adults with T2DM. Self-management declined when the daily time spent on social media exceeded 139.8 minutes. However, exposure to hyperbeneficial content was associated with better self-management of individuals, regardless of excessive time spent on social media. Future longitudinal and experimental studies that validate the multifaceted association between social media exposure and health behaviors are needed. If confirmed, these findings would support the implementation of media prescription programs by health care providers in communities.

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. .