Physical frailty trajectories in older stroke survivors: Findings from a national cohort study.

BACKGROUND: 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. AIMS: To identify PF trajectories and relative predictors associated with the PF trajectories in older stroke survivors. DESIGN: This is a secondary analysis of a population-based cohort study in the United States. METHODS: 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. RESULTS: 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). CONCLUSION: PF trajectories in older stroke survivors were heterogeneous and were associated with age, self-rated health status, comorbidities, breathing problems and balance problems. IMPLICATION TO CLINICAL PRACTICE: 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. REPORTING METHOD: The reporting followed the STROBE guideline.

Trajectories and predictors of anxiety and depression among older cancer survivors: a nationally representative cohort study.

PURPOSE: The study aimed to describe the long-term trajectories of anxiety and depression among older cancer survivors and examine sociodemographic and health-related predictors of different trajectories. METHODS: Data were from the National Health and Aging Trends Study. Patient Health Questionnaire-4 was used to assess anxiety and depression. Group-based trajectory model was used to identify the distinct trajectories of anxiety and depression from 2015 to 2021. Design-based multinomial logistic regression was used to examine predictors of different trajectories. All analyses accounted for the complex sample design and survey weights. RESULTS: A total of 1766 older cancer survivors were included representing 8.9 million older cancer survivors. The prevalence of anxiety and depression from 2015 to 2021 ranged from 25.12 to 29.11%. Four trajectories were identified: sustained low-risk (49.0%), deteriorating (24.1%), meliorating (11.0%), and sustained high-risk (16.1%). Potential predictors of high-risk anxiety and depression include older age, female, lower annual income, abnormal BMI, poorer self-rated health, more difficulty in activities of daily living (ADL), and worse cognitive function (P < 0.05). CONCLUSIONS: The anxiety and depression progression patterns are heterogeneous among older cancer survivors. The trajectory affiliations could be predicted by sociodemographic and health-related factors, which have the potential to inform targeted clinical strategies (e.g., improve ADL and ameliorate cognitive function). IMPLICATIONS FOR CANCER SURVIVORS: Anxiety and depression are common among older cancer survivors, and long-term trajectories identified by this study might help realize early-stage identification and individualized interventions for mental disorders.

Association between accelerometry measured patterns of sedentary behaviors and functional status in older adults.

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.

Long-term trajectories of frailty phenotype in older cancer survivors: a nationally representative longitudinal cohort study.

BACKGROUND: Frailty is a dynamic process associated with adverse health outcomes. However, little is known about the long-term trajectories of frailty in older cancer survivors. OBJECTIVES: To describe the trajectories of frailty phenotype over time amongst older cancer survivors and examine the socio-demographic and health-related predictors of different trajectories. DESIGN: Population-based longitudinal cohort study. SETTING: Community-dwelling older adults in the United States. SUBJECTS: 1,763 older adults who were diagnosed with cancer from the National Health and Ageing Trends Study. METHODS: Frailty was assessed by the Fried Frailty Phenotype. The group-based trajectory model was used to identify the trajectories of frailty. Multinomial logistic regression analyses were used to examine the socio-demographic and health-related predictors of different trajectories. RESULTS: Three frailty trajectories were identified; 52.8% of older cancer survivors had a sustained low risk of frailty over time, 25.0% had a low frailty risk at baseline but the risk increased steadily, and 22.3% had a high frailty risk with a slight change in the observed period. Older cancer survivors were at a high-risk frailty trajectory if they were older, female, African American, had lower education status, had lower annual income, were underweight or obese, self-rated poorer health, had more chronic conditions and difficulties with activities of daily living (ADL), and had worse cognitive functions (P < 0.05). CONCLUSIONS: Long-term frailty trajectories in older cancer survivors are heterogeneous. This study helps identify patients at high risk of sustained or deteriorating frailty and has the potential to inform targeted frailty management strategies addressing modifiable factors identified (e.g. body mass index, ADL).

Comorbidities were associated with cancer clinical trial discussion and participation: findings from the Health Information National Trends Survey-Surveillance, Epidemiology, and End Results Program (2021).

OBJECTIVES: Oncology clinical trials are recommended to better reflect real-world cancer patient populations and to increase patient access to new treatments in trials. The influence of comorbidities on trial participation is unclear. This study examined the association of having comorbidities and patients' experiences with clinical trial discussion or actual participation. STUDY DESIGN AND SETTING: We included 958 cancer survivors from Health Information National Trends Survey-Surveillance, Epidemiology, and End Results Program. Trial discussion was defined as whether their medical team discussed cancer clinical trials, and trial participation was defined as whether they participated. Comorbidities included diabetes, hypertension, heart condition, chronic lung disease, and depression/anxiety disorder. Design-based logistic regression results were conducted. RESULTS: Seventy-five percent of patients had one or more comorbidities, commonly having hypertension (56%) and diabetes (26%). Only 15% of participants reported trial discussion and 8% reported trial participation. Having one or more comorbidities was significantly associated with lower rates of trial discussion in univariate analysis (22.9% vs. 12.1%, odds ratio = 0.46, P = 0.001), and such association was pertained in adjusted logistic regression (20.5% vs. 12.8%, adjusted odds ratio = 0.54, P = 0.02). CONCLUSION: Findings suggest patients with comorbidities were underrepresented in cancer clinical trials, implying a potential lack of representativeness among trial participants.

[Latest Findings in Key Research Areas of Precision Nursing for Chronic Diseases in Older Adults].

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.

Symptom Clusters in Adults with Post-COVID-19: A Cross-Sectional Survey.

More than 100 symptoms have been reported for post-coronavirus disease 2019 (COVID-19) and this study aimed to organize self-reported symptoms by identifying symptom clusters. We used a cross-sectional survey with a convenience sample of 491 adults who reported experiencing prolonged symptoms of COVID. A list of 25 symptoms of post-COVID-19 was used to measure the symptoms, and exploratory factor analysis was undertaken to identify symptom clusters for people with symptoms lasting 5 to 8 weeks and 9 weeks or longer. Six symptom clusters were identified for each of the two groups, and five clusters were similar across both groups: respiratory, general viral, smell/taste, cognitive cardiac, and mental health. The >9-week group reported symptoms primarily from two factors: respiratory-muscular and mental health. Post-COVID-19 symptom clusters differ across timeframes. Symptom clusters were useful in establishing coherent patterns of multiple complex symptoms.

Prolonged bouts of sedentary behavior in people with chronic obstructive pulmonary disease and associated factors.

BACKGROUND: Increased sedentary behavior (SB), especially in prolonged bouts, is associated with adverse health outcomes, but little is known about patterns of SB in people with chronic obstructive pulmonary disease (COPD). OBJECTIVES: The purpose of this study was to describe SB and patterns of SB and to examine factors associated with prolonged bouts of SB in inactive community-dwelling adults with COPD. METHODS: This cross-sectional analysis used data from inactive adults with COPD who were enrolled in an exercise program but had not started exercising. Participants were ≥ 50 years old with a diagnosis of COPD, had a forced expiratory volume in one second < 80% predicted, and were inactive. Participants wore an activPAL device for seven days to measure their SB and completed surveys and physical measures. Data were analyzed with multiple regression. RESULTS: The sample included 160 participants with a mean age (± SD) of 69 ± 8, and a mean total sedentary time of 742 ± 150 min/day, with 254 ± 146 min/day in SB bouts ≥ 60 min. DURATION: Time spent in bouts of SB ≥ 60-min. was negatively associated with self-efficacy for overcoming barriers to light physical activity (P<0.05), balance (P<0.05), chair stand test (P<0.05), FEV1% predicted (P<0.05) and positively associated with BMI (P<0.001). CONCLUSIONS: Inactive people with COPD engage in extensive SB, much of it in prolonged bouts. Self-efficacy, balance, and lower body strength are modifiable variables associated with SB and potential targets for future interventions to reduce time in prolonged sedentary behavior.

Use of Digital Health Technology Among Older Adults With Cancer in the United States: Findings From a National Longitudinal Cohort Study (2015-2021).

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.

Substance Use and the Self-Management of Persistent Symptoms of COVID-19.

Background: Understanding the self-management practices of persistent symptoms of SARS-Cov-2 (COVID-19) is critical given the misinformation that has been presented about this disease in the U.S. The purpose of this descriptive study is to assess the self-management of persistent symptoms of COVID-19 with commonly used and misused substances (i.e., alcohol, marijuana and commonly prescribed medications) among adults in the U.S. Methods: The data for this study comes from a cross-sectional survey of U.S. adults that was designed to broadly assess symptom burden, persistent symptom patterns, self-efficacy for symptom management and self-management strategies among people who experienced persistent/Long COVID. Multiple logistic regression analyses were used to assess how symptom length of COVID-19 was associated with the use of several substances to manage these persistent symptoms. Results: The analysis found that adults who had COVID-19 symptoms that persisted for 13 weeks or longer had higher rates of using alcohol (27.3%), marijuana (30.9%) and prescription tranquilizers (21.4%) to manage these symptoms when compared to their adult peers who had COVID-19 symptoms persist for only 4 weeks or less. For instance, the odds of indicating the use of marijuana (AOR = 4.21 95% CI = 1.68,10.5) to manage COVID-19 related symptoms was roughly four times higher for respondents who had COVID-19 symptoms persist for 13 weeks or longer when compared to respondents whose COVID-19 symptoms persisted for only 4 weeks or less. Conclusion: The findings suggest that screening of substance use disorders should be considered among healthcare providers who are treating adults who have persistent symptoms of COVID-19.

Comparison of Pre-Diagnosis Physical Activity and Its Correlates between Lung and Other Cancer Patients: Accelerometer Data from the UK Biobank Prospective Cohort.

Purpose: Physical activity (PA) plays an important role in health outcomes for people with cancer, and pre-diagnosis PA influences PA behaviors after cancer treatment. Less is known about the PA of lung cancer patients, and the strong history of smoking could influence pre-diagnosis levels of PA and place them at risk for health problems. This study aimed to compare pre-diagnosis PA and its correlates in patients with lung cancer and other types of cancer (female breast, colorectal, and prostate cancer) and examine the relationship between pre-diagnosis PA and all-cause mortality. Methods: This study used data from the UK Biobank, which is a national cohort study with accelerometry data. We included 2662 participants and used adjusted linear regressions and survival analyses. Results: Male and female lung cancer groups spent a mean of 78 and 91 min/day in pre-diagnosis moderate to vigorous PA (MVPA), respectively; this is lower than the 3 other types of cancer (p < 0.001). Younger age and faster walking pace had a strong association with PA in all the four types of cancer (p < 0.01). Smoking status had a strong association with PA in the lung cancer group, while obesity had a strong association with PA in female breast, colorectal, and prostate cancer (p < 0.01). Higher levels of pre-diagnosis MVPA (≥1.5 h/day) were associated with a significantly lower all-cause mortality risk. Conclusions: The present study suggests that lung cancer patients are the most inactive population before diagnosis. The identified difference in correlates of PA suggest that cancer-specific approaches are needed in PA research and practices. This study also highlights the importance of high PA for individuals with high cancer risk.

Profiles of sedentary behaviors in the oldest old: findings from the National Health and Aging Trends Study.

BACKGROUND: Sedentary behavior is a significant health risk. Emerging research suggests that mentally active sedentary behaviors (e.g., computer use and reading) are associated with better health than mentally passive sedentary behaviors (e.g., watching TV). However, these relationships are not well established in the literature, and little is known about the oldest old (age ≥ 80). AIMS: The aims of this study were to (1) identify distinct subgroups of oldest old adults based on six domains of sedentary behavior (watching TV, using a computer/tablet, talking to friends or family members, doing hobby or other activities, transportation, and resting/napping); and (2) compare health-related outcomes across identified subgroups, using the National Health and Aging Trends Study (NHATS) dataset. METHODS: Latent profile analysis was used to identify distinct profiles of sedentary behavior. Design-based linear and logistic regressions were used to examine associations between different profiles and health outcomes, accounting for socio-demographic characteristics. RESULTS: A total of 852 participants were included. We identified four profiles and named them based on total sedentary time (ST) and passive/active pattern: "Medium-passive", "High-passive", "Low", "High-mentally active". Compared to the "High-passive" group, "Low" group and "High-mentally active" group were associated with fewer difficulties with activities of daily living, fewer problems limiting activities and higher cognitive function. CONCLUSION: This study, with a national representative sample of the oldest old population, suggests that both total ST and sedentary behavior pattern matter when evaluating health outcomes of being sedentary. Interventions should encourage oldest old adults to reduce ST and especially target mentally passive ST.

Physical activity in surgical lung cancer patients: a systematic review.

PURPOSE: Perioperative exercise could improve health outcomes of surgical lung cancer patients, but less is known about their natural physical activity (PA) behavior before exercise interventions. This review aimed to synthesize evidence on PA, regarding the following: (1) proportion of patients meeting PA guidelines, (2) amount of PA, (3) PA trajectory following surgery, and (4) correlates of PA before or after surgery. METHODS: We conducted a systematic review using PubMed, CINHAL, Scopus, and SPORTDiscus (July 2021). Observational or experimental studies that measure PA of lung cancer patients before/after surgery were included. We assessed methodological quality using the NIH Quality Assessment Tools and extracted data using a standardized form. RESULTS: Seventeen studies (25 articles, N = 1737 participants) published between 2009 and 2021 were included. Fourteen studies had sample sizes less than 100. Thirteen studies were of fair quality and four studies were of good quality. Only 23-28% of patients met PA guideline (150 min/week moderate-vigorous PA) at 6 months-6 years after surgery. Patients took an average of 3822-10,603 daily steps before surgery and 3934-8863 steps at 1-3 months after surgery. Physical activity was lower at 1 day-3 months after surgery, compared with preoperative levels. Perioperative PA was positively associated with exercise capacity, quality of life and reduced postoperative complications. CONCLUSION: This review suggests that PA is low among surgical lung cancer patients, and it may not recover within 3 months following surgery. Physical activity has the potential to improve postoperative outcomes. However, the existing evidence is weak, and future larger longitudinal studies are needed.

Bidirectional association between knee osteoarthritis and depressive symptoms: evidence from a nationwide population-based cohort.

BACKGROUND: Both knee osteoarthritis (KOA) and depressive symptoms (DS) are major public health issues affecting the quality of life. This study aimed to examine the association between KOA and DS. METHODS: Data were gathered from the China Health and Retirement Longitudinal Study in 2011-2015 which surveyed middle-aged to elderly individuals and their spouses in 28 provinces in China. An adjusted Cox proportional hazards regression model was used to estimate hazard ratios (HRs). RESULTS: The analysis for baseline KOA and the subsequent risk of DS was based on 2582 participants without baseline DS. During the follow-up, KOA patients were more likely to have DS than non-KOA participants (adjusted HR = 1.38: 95% CI = 1.23 to 1.83). The analysis for baseline DS and the subsequent risk of KOA was based on 4293 participants without baseline KOA, those with DS were more likely to develop KOA than non-DS participants (adjusted HR = 1.51: 95% CI = 1.26 to 1.81). Subgroup analysis showed sex and age had no significant moderating effect on the KOA-DS association. CONCLUSIONS: Our results provide evidence that the association between KOA and DS is bidirectional. Therefore, primary prevention and management of KOA and DS should consider this relationship.

Trends, composition and distribution of nurse workforce in China: a secondary analysis of national data from 2003 to 2018.

OBJECTIVES: Given the increased ageing population and frequent epidemic challenges, it is vital to have the nurse workforce of sufficient quantity and quality. This study aimed to demonstrate the trends, composition and distribution of nurse workforce in China. DESIGN: Secondary analysis using national public datasets in China from 2003 to 2018. SETTING/PARTICIPANTS: National population, nurse workforce and physician workforce. PRIMARY AND SECONDARY OUTCOME MEASURES: Frequency and proportion were used to demonstrate: (1) the longitudinal growth of nurse workforce; (2) the diversity of nurse workforce in gender, age, work experience and education level; and (3) the distribution of nurse workforce among provinces, rural-urban areas and hospital/community settings. The Gini coefficient and Theil L index were used to measure the inequality trends of nurse workforce. RESULTS: The total number of nurses increased from 1.3 million to 4.1 million and the density increased from 1 to 2.94 per 1000 population over 2003-2018. The nurses to physician ratio changed from 0.65:1 to 1.14:1. The majority of the nurse workforce was female, under 35 years old, with less than 30 years of work experience, with an associate's degree and employed within hospitals. Central and eastern regions had more nurses and there were 5.08 nurses per 1000 population in urban areas while less than two in rural areas in 2018. The Gini coefficient and between-provincial Theil index experienced a consistent decline. Within-province inequality accounted for overall inequality has risen from 52.38% in 2010 to 71.43% in 2018 suggested that the differences of distribution are mainly reflected in urban and rural areas. CONCLUSION: Chinese nurse workforce has been changed significantly in the past 15 years that may be associated with the reformations of policy, nursing education in China. Our study suggests current features in the nurse workforce and can be used to strengthen future health services.

Device-measured sedentary behavior in oldest old adults: A systematic review and meta-analysis.

Sedentary behavior contributes to health decline and frailty in older adults, especially the oldest old. The purpose of this systematic review was to synthesize evidence describing the volume of device-measured sedentary behavior and factors that influence sedentary behavior in community-dwelling adults aged 80 and older. Four electronic databases were searched in August 2018; the search was updated in September 2019 and December 2020. Twenty-one articles representing 16 unique datasets from six countries met inclusion criteria. Various devices and data processing methods were used to measure sedentary behavior; the most common device was the ActiGraph accelerometer. Sedentary time during the waking day ranged from 7.6 to 13.4 h/day. Studies using similar measurement methods (hip-worn ActiGraph with uniaxial cut-point <100 counts per minute) had a weighted mean of 10.6 h/day. Subgroup analyses revealed that male gender and age ≥85 may contribute to increased sedentary behavior. Only seven individual articles examined factors that influence sedentary behavior in the 80 and older age group; older age, male gender, non-Hispanic white race/ethnicity, social disadvantage, and declining cognitive function (in men) were associated with increased sedentary behavior. In conclusion, the oldest old are highly sedentary and little is known about factors that influence their sedentary behavior.