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Background Aims Design Methods Results Conclusion Implication to Clinical Practice Reporting Method Physical frailty (PF) is highly prevalent and associated with undesirable outcomes in stroke survivors aged 65 years or older. However, the long‐term trajectories of PF are understudied in those older stroke survivors.To identify PF trajectories and relative predictors associated with the PF trajectories in older stroke survivors.This is a secondary analysis of a population‐based cohort study in the United States.Six hundred and sixty‐three older stroke survivors from the National Health and Ageing Trends Study from 2015 to 2021 were included. PF was operationally assessed based on the Fried Frailty Phenotype. Trajectories were identified by group‐based trajectory modelling. The associations between sociodemographic characteristics, clinical factors, symptoms, cognitive factors and PF trajectories were examined using the design‐based logistic regression method.Most older stroke survivors were 75 and older (63.32%), female (53.99%), white (80.54%) and partnered (50.64%). Two PF trajectory groups were identified (Group 1: low risk, robust; 49.47%; Group 2: high risk, deteriorating; 50.53%). Individuals were at a higher risk to be assigned to Group 2 if they were 75–84 years (adjusted odds ratio [aOR]: 2.16, 95% CI: 1.23–3.80) or 85+ years (aOR: 2.77, 95% CI: 1.52–5.04), had fair self‐reported health (aOR: 2.78, 95% CI: 1.53–5.07) or poor self‐reported health (aOR: 3.37, 95% CI: 1.51–7.52), had comorbidities (aOR: 8.44, 95% CI: 1.31–54.42), had breathing problems (aOR: 2.18, 95% CI: 1.18–4.02) and had balance problems (aOR: 1.70, 95% CI: 1.06–2.73).PF trajectories in older stroke survivors were heterogeneous and were associated with age, self‐rated health status, comorbidities, breathing problems and balance problems.Early, routine, dynamic screening for stroke‐related physical frailty (PF) and relative predictors might be beneficial for identifying the most vulnerable individuals. Our findings might help develop strategies to manage PF progression.The reporting followed the STROBE guideline. [ABSTRACT FROM AUTHOR]

Background: Older adults are highly sedentary, and too much sedentary behavior (SB) is associated with negative health effects, but little is known about SB patterns and their associations with functional status. Aims: To examine the association between objectively measured sedentary behavior time (SBT) and sedentary behavior fragmentation (SBF) and functional status in older adults using the National Health Aging Trends Study (NHATS) dataset, a nationally representative sample from 2021. Methods: Data from NHATS were analyzed using weighted linear regressions to examine the association between objective measures of SBT (mean hours spent in SB/day during waking hours) and SBF, and six functional variables (difficulties with activities of daily living [ADL], short physical performance battery, hand grip strength, immediate word recall, delayed word recall, and mental health), accounting for sociodemographic, body mass index, and the number of chronic conditions. Results: A total of 738 individuals from the NHATS were included. Higher SBT was associated with greater difficulties with ADL, poorer short physical performance battery and hand grip strength, lower scores in both immediate and delayed word recall, and poorer mental health. Higher SBF was associated with fewer difficulties with ADL, better short physical performance battery and hand grip strength, a higher score in immediate word recall, and better mental health. Discussionand conclusions: Greater fragmentation of SB was associated with better function, and increasing SBF may be a useful strategy for mitigating the effects of SB in older adults, but prospective research is needed to support this approach. [ABSTRACT FROM AUTHOR]

Background: Physical inactivity is a global issue for cancer survivors. Wearable activity trackers are promising to address physical inactivity by providing real-time feedback on physical activity and offering opportunities for self-monitoring and goal setting. Meta-analysis has reported the effects of interventions that incorporate wearable activity trackers on improved physical inactivity and related health outcomes (eg, BMI, anxiety and depression, and self-rated health status). However, wearable activity trackers were often used as an adjunct to physical activity interventions, and the effectiveness of wearable activity trackers alone is unknown., Objective: This study aims to determine the association of wearable activity trackers with physical activity and health outcomes in patients with cancer., Methods: Data from 957 cancer survivors from the Health Information National Trends Survey-Surveillance, Epidemiology, and End Results (HINTS-SEER) were analyzed. The outcome variables examined were time spent in moderate to vigorous physical activity, weekly frequency of strength training, BMI, anxiety and depression levels, and self-assessed health status. The primary independent variable was whether cancer survivors had used wearable activity trackers within the past 12 months. Design-based linear regression for continuous outcome variables and ordinal logistic regression for ordinal outcome variables were conducted to determine the associations after controlling for sociodemographic, cancer-related, and health-related factors. All data analyses accounted for the complex survey design and sample weights., Results: Only 29% of cancer survivors reported wearable activity tracker use. Bivariate analyses showed that younger age (P<.001), higher education (P=.04), higher income (P<.001), and an employed status (P<.001) were significantly associated with wearable activity tracker use. Wearable activity tracker use was significantly associated with higher time spent in moderate to vigorous physical activity (adjusted =37.94, 95% CI 8.38-67.5; P=.01), more frequent strength training per week (adjusted odds ratio [OR] 1.50, 95% CI 1.09-2.06; P=.01), and better self-rated health status (adjusted OR 1.58, 95% CI 1.09-2.29; P=.01), but not with BMI or anxiety and depression., Conclusions: This study suggests that the uptake of wearable activity trackers is low and highlights the digital divide among patients with cancer. This study has confirmed the associations of wearable activity tracker use with physical activity and self-rated health, supporting using wearable activity trackers as a promising tool to facilitate physical activity promotion., (©Weijiao Zhou, Shaomei Shang, Youmin Cho. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 22.10.2024.)

The advent of the era of biomedical big data has helped promote the development of precision nursing. Precision nursing for chronic diseases in older adults is an interdisciplinary research field in which accurate individualized data are utilized to carry out early screening and health management of older adult populations at high risk for chronic diseases and early intervention of diseases, which plays an important role in improving the prognosis of diseases and the health level of the older adult population. Herein, we introduced the concept of precision nursing, and discussed the latest research findings in the key areas of precision nursing for chronic diseases in older adults, including precision symptom management in cancer patients and precision nursing in older patients with multimorbidity. At present, research concerning precise symptom management of cancer patients is mainly focused on prediction modelling for risks of symptoms, longitudinal change trajectories, core symptom identification, etc. Investigations in the precise nursing of cancer patients are conducted in the following areas, risk prediction, the timing of interventions, and intervention targets. Research on precision nursing for multimorbidity is mainly focused on assessment of chronic disease multimorbidity, multimorbidity pattern recognition, and health management of multimorbidity. We also discussed potential opportunities and challenges of precision nursing in the future, in order to provide a scientific basis for the improving the practice and theories of precision nursing. In the future, precision nursing will play an ever more important role in uncovering pathogenic information, the diagnosis and treatment of diseases, the health of the research population, and the promotion of medical research., (Copyright© by Editorial Board of Journal of Sichuan University (Medical Sciences).)

Background: Despite the benefits of digital health technology use, older adults with cancer (ie, aged 65 years) have reported challenges to technology adoption. However, there has been a lack of a good understanding of their digital health technology use patterns and the associated influential factors in the past few years., Objective: This study aimed to examine the trends in and factors associated with digital health technology use among older adults with cancer., Methods: The National Health and Aging Trends Study (NHATS) data set is a national longitudinal cohort study with annual survey waves of Medicare beneficiaries 65 years and older. Participants were community-dwelling older adults who self-reported previous or current cancer diagnoses in each round. The study sample size of each round ranged from 1996 (2015) to 1131 (2021). Digital health technology use was defined as using the internet or online in the last month to order or refill prescriptions, contact medical providers, handle Medicare or other insurance matters, or get information about their health conditions. The association of sociodemographics, clinical factors (self-rated health, chronic conditions, difficulties in activities of daily living, dementia, anxiety, and depression), and physical function (Short Physical Performance Battery and grip strength) with digital health technology use was examined using design-based logistic regression. All statistical analyses accounted for the complex sample design., Results: The prevalence of any digital health technology use increased from 36% in 2015 to 45% in 2019. In 2020-2021, which was amid the COVID-19 pandemic, it ranged from 51% to 52%. In terms of each digital health technology use behavior, in 2015, overall, 28% of older cancer survivors used digital health technology to obtain health information, followed by contacting clinicians (19%), filling prescriptions (14%), and handling insurance (11%). Greater use of digital health technology was associated with younger age, being White, having a college or higher education, having a higher income, having more comorbidities, nondementia, and having a higher gait speed., Conclusions: Digital health technology use in older adults with cancer has gradually increased, particularly during the COVID-19 pandemic. However, socioeconomic and racial disparities have remained in older cancer survivors. Additionally, older adults with cancer may have some unique features associated with digital health technology use; for example, their use of digital health may be increased by their comorbidities (ie, health care needs) and reduced by their frailty., (©Weijiao Zhou, Youmin Cho, Shaomei Shang, Yun Jiang. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 31.05.2023.)