Physical activity trajectories following gynecological cancer: results from a prospective, longitudinal cohort study

Objectives: The mechanisms by which overweight and physical inactivity lead to hypertension are complex. Leptin, an adipocyte-derived hormone, has been linked with hypertension. We studied the relationship between leptin, physical activity, and new-onset hypertension. Methods: A prospective study design based on data from the 3. and 4. Copenhagen City Heart Study (CCHS). From the 3. CCHS, which was performed in 1991 to 1994, we identified 1111 subjects (744 women and 367) who were normotensive. Based on questionnaire items, the participants were divided into two groups with low (n = 674) and high (n = 437) level of leisure time physical activity, respectively. Between the 3. and the 4. CCHS examination, which was performed in 2001 to 2003, 304 had developed hypertension, defined as systolic blood pressure (SBP) ≥140 mm Hg or diastolic blood pressure (DBP) ≥90 mm Hg or use of antihypertensive medication. Results: In a logistic regression model, including age, sex, body mass index, SBP, DBP, level of physical activity, and leptin, we found a significant interaction between leptin and level of physical activity with new-onset hypertension as outcome variable (P = 0.012). When we entered the interaction variables: effect of leptin with low level of physical activity and with high level of physical activity, respectively, in the original model, leptin only predicted new-onset hypertension in participants with low level of physical activity (odds ratio (95% confidence interval): 1.16 (1.01–1.33) for one unit increase in log-transformed leptin levels, P = 0.038), but not in participants with high level of physical activity (0.88 (0.74–1.05), P = 0.15). If we included other risk factors of hypertension and possible mediators of overweight-related hypertension, such as the triglyceride to HDL cholesterol concentration ratio, fibrinogen, glucose, diagnosis of diabetes, adiponectin, and heart rate, in the model, leptin still predicted new-onset hypertension in participants with a low level of leisure time physical activity (P = 0.040). Conclusion: This study is the first prospective study to report that the hypertensive effect of leptin is modified by leisure time physical activity.

Physical activity in the prevention and treatment of obesity and its comorbidities: evidence report of independent panel to assess the role of physical activity in the treatment of obesity and its comorbidities.

To investigate whether out-of-hospital cardiac arrest (OHCA) survivors had lower levels of self-reported physical activity compared to a non-cardiac arrest control group with myocardial infarction (MI), and to explore if symptoms of anxiety, depression, kinesiophobia (fear of movement), and fatigue were associated with a low level of physical activity. Pre-defined case-control sub-study within the international Targeted Hypothermia versus Targeted Normothermia after Out-of-Hospital Cardiac Arrest (TTM2) trial. Out-of-hospital cardiac arrest survivors at 8 of 61 TTM2 sites in Sweden, Denmark, and the UK were invited. Participants were matched 1:1 to MI controls. Both OHCA survivors and MI controls answered two questions on self-reported physical activity, categorized as a low, moderate, or high level of physical activity, and questionnaires on anxiety and depression symptoms, kinesiophobia, and fatigue 7 months after the cardiac event. Overall, 106 of 184 (58%) eligible OHCA survivors were included and matched to 91 MI controls. In total, 25% of OHCA survivors and 20% of MI controls reported a low level of physical activity, with no significant difference (P = 0.13). Symptoms of kinesiophobia and fatigue were significantly associated with a low level of physical activity in both groups. Out-of-hospital cardiac arrest survivors had significantly more kinesiophobia compared to MI controls (18% vs. 9%, P = 0.04), while levels of anxiety and depression symptoms and fatigue were similar. Out-of-hospital cardiac arrest survivors had similar levels of physical activity compared to matched MI controls. High level of kinesiophobia and fatigue were associated with a low level of physical activity in both groups. ClinicalTrials.gov: NCT03543332.

Objective. The mechanisms by which overweight and physical inactivity lead to hypertension are complex. Leptin, an adipocyte-derived hormone, has been linked with hypertension. We wanted to investigate the relationship between leptin, physical activity and new-onset hypertension. Methods. The study was a prospective cohort study of 744 women and 367 men, who were normotensive in the third Copenhagen City Heart Study (CCHS) examination, performed 1991–94. Based on questionnaire items, the participants were divided into two groups with low (n = 674) and high (n = 437) levels of leisure-time physical activity, respectively. Results. Between the third and the fourth CCHS examination, performed 2001–03, 304 had developed hypertension, defined as systolic blood pressure (SBP) ≥ 140 mmHg or diastolic blood pressure (DBP) ≥ 90 mmHg or use of antihypertensive medication. In a logistic regression model, including age, sex, body mass index, SBP, DBP, level of physical activity and leptin, we found a significant interaction between leptin and level of physical activity with new-onset hypertension as outcome variable (p = 0.012). When we entered the interaction variables, effect of leptin with low level of physical activity and with high level of physical activity, respectively, in the original model, leptin predicted new-onset hypertension in participants with low level of physical activity [odds ratio (95% confidence interval): 1.16 (1.01–1.33) for one unit increase in log-transformed leptin levels, p = 0.038], but not in participants with high level of physical activity [0.88 (0.74–1.05), p = 0.15]. Conclusion. We found that leptin predicted new-onset hypertension but only in participants with low level of physical activity.

The primary objective was to compare health outcomes (balance, functional capacity, lower extremity strength, fear of falling, sleep quality, and quality of life) between older adults with higher and lower levels of physical activity. A secondary objective was to explore whether these health-related factors mediated the association between physical activity level and quality of life. A comparative cross-sectional study involved 88 older adults with higher levels of physical activity (higher-PA group) and 88 with lower levels of physical activity (lower-PA group), who underwent mobility tests including the Timed Up and Go Test (TUGT), 2-Minute Walk Test (2MWT), and 10-Meter Walk Test (10MWT); balance and strength assessments such as the Modified Clinical Test of Sensory Interaction in Balance (mCTSIB) and Five-Time Sit-to-Stand Test (5xSTST); and completed self-reported questionnaires including the Fall Efficacy Scale-International (FES-I), Pittsburgh Sleep Quality Index (PSQI), and World Health Organization Quality of Life - Brief (WHOQOL-BREF) Primary analyses compared outcomes between groups with lower and higher levels of physical activity. Mediation analysis was performed to explore indirect effects of balance, fear of falling, lower extremity strength, and sleep quality on the relationship between PA level and Quality of Life (QoL). A total of 176 participants with a mean ± SD age of 65.90 ± 3.84 years and a mean BMI of 23.08 ± 1.43 kg/m² were included. Higher-PA group demonstrated significantly better performance on TUGT, 2MWT, and 10MWT than the lower-PA group, with the younger higher-PA group showing greater 2MWT scores. They demonstrated significantly better mCTSIB, FES-I, and PSQI scores than the lower-PA group. Participants with higher levels of physical activity showed significantly lower FES-I and 10MWT scores compared to those with lower levels. Additionally, mCTSIB scores were significantly influenced by age, income, and occupation, with higher physical activity levels associated with higher mCTSIB scores. Higher-PA group reported significantly better physical and psychological health and environmental and social relationships in QoL domains. Furthermore, adequate sleep duration was significantly associated with improved sleep quality. Parallel mediation analyses revealed that in the physical health domain, all mediators showed significant partial mediation, with the strongest indirect effect via 10MWT (β = 1.116, p = .003). In the psychological domain, PSQI emerged as the most prominent mediator (β = 1.061, p = .001). For the social relationship domain, 2MWT demonstrated the strongest mediation effect (β = 0.546, p = .006). In the environmental health domain, only PSQI (β = 0.475, p = .002) and FES-I (β = 0.239, p = .047) showed significant partial mediation. While controlling for sociodemographic characteristics in the models, all domains showed significant total effects, with partial mediation observed through psychosocial and physical performance variables; however, none of the indirect effects reached statistical significance. Older adults engaging in higher levels of physical activity demonstrated markedly better mobility, balance, and overall QoL compared to their less active counterpart. Physical activity positively influenced all domains of QoL through multiple functional pathways, with significant partial mediation observed via mobility, balance, sleep quality, and fear of falling, most notably within the physical and psychological domains. However, after adjusting for key sociodemographic covariates, the indirect effects were no longer statistically significant. These results highlight the critical role of functional capacities in translating physical activity into improved well-being among older adults.

AimsTo describe the level of physical activity 6 months after an out-of-hospital cardiac arrest (OHCA) and to explore potential risk factors of a low level of physical activity. MethodsPost-hoc analyses of the international multicentre Targeted Hypothermia versus Targeted Normothermia after Out-of-Hospital Cardiac arrest (TTM2) trial. At 6 months, survivors at 61 sites in Europe, Australia and New Zeeland were invited to a follow-up. The participants answered two questions on self-reported physical activity. Answers were categorized as a low, moderate, or high level of physical activity and further dichotomized into a low versus moderate/high level of physical activity. Potential risk factors for a low level of physical activity were collected and investigated by univariable and multivariable logistic regression. ResultsAt 6 months, 807 of 939 (86%) OHCA survivors answered the two questions of physical activity; 34% reported a low, 44% moderate and 22% high level of physical activity. Obesity (OR = 1.75, 95% CI 1.10–2.77, p = 0.018), mobility problems by EuroQol 5 dimensions 5 levels (OR = 1.73, 95% CI 1.06–2.84, p = 0.029), and cognitive impairment by Symbol Digit Modalities Test (OR = 1.78, 95% CI 1.13–2.82, p = 0.013) were significantly associated with a low level of physical activity in the multivariable analysis. ConclusionOne third of the OHCA survivors reported a low level of physical activity. Obesity, mobility problems, and cognitive impairment were associated with a low level of physical activity. ClinicalTrials.gov IdentifierNCT02908308.

FRI0634-HPR LEVEL OF PHYSICAL ACTIVITY IN ANTIPHOSPHOLIPID SYNDROME AND ITS RELATIONSHIP TO ATHEROSCLEROSIS PROGRESSION – ANALYSIS OF THE SERBIAN COHORT

In 2008, a low level of physical activity (<30 min of moderate/vigorous activity per day) accounted for 9% of the occurrences of death in the world. In addition, it is associated with impaired mobility in the elderly, aged 80 and over. However, due to methodological difficulties, there are few population-based studies carried out on the association between low levels of physical activity and impaired mobility and the risk of death, using an objective method to evaluate physical activity; and to date there are no known studies which have verified this association in Latin America. Objective: To identify the prevalence of low levels of physical activity and its association with impaired mobility and the risk of death in elderly individuals aged 65 and over living in the city of So Paulo in 2010. Methods: This was an exploratory and quantitative population-based study which used the database of the SABE study 2010 and the occurrences of death in 2014; 599 individuals were evaluated in 2010. The level of physical activity was analyzed in two ways: 1) low level of physical activity (<30 minutes of moderate and/or vigorous activity daily) and high level of physical activity (> 30 minutes moderate and/or vigorous activity daily); and 2) the sample was divided into tertiles according to the counts per minute, and grouped into two groups, the elderly in the lowest tertile classified as low level of physical activity and the elderly in the remaining two tertiles as intermediate/high level physical activity. Hierarchical logistic regression was used to: 1) identify the variables associated with low levels of physical activity; 2) analyze the association between low levels of physical activity and impaired mobility; and 3) estimate the risk of death in elderly individuals with low levels of physical activity. The survival curve was analyzed by the Kaplan-Meier method using the log-rank test and the proportional risk was calculated using the Cox's proportional hazards model. Results: The prevalence of elderly individuals with low levels of physical activity was 85.4% and the associated variables, after adjustment, were sex (female), age group (>75 years), multimorbidity (>2 chronic diseases), chronic pain (chronic pain in the previous 3 months) and body mass index (highest mean value). A low level of physical activity remained significantly associated with impaired mobility (OR = 3.49, CI95% 2.00 to 6.13) and risk of death (HR = 2.91, CI95% 1.75 to 4.82), even after adjusting for sociodemographic and clinical variables. Conclusion: The prevalence of low levels of physical activity in elderly individuals living in So Paulo was higher than those found in the Brazilian population, however comes close to other populations that used the same method to assess physical activity. A low level of physical activity (< 30 min moderate/vigorous activity) was associated with sociodemographic (female and age group) and clinical variables (multimorbidity, chronic pain and body mass index). A low level of physical activity (lowest tertile) was associated with impaired mobility and the risk of death in four years. Thus, a low level of physical activity could be used as an appropriate method to identify elderly people who are more likely to present impaired mobility and an increased risk of death.

Background: The impact of retirement on physical activity among older individuals remains ambiguous. This study aims to investigate the influence of retirement on physical activity and delineate the trajectories of physical activity changes during the retirement transition among elderly Chinese residents. Additionally, we endeavor to examine the factors that contribute to each trajectory. Methods: This longitudinal cohort study used data from four surveys of the China Health and Retirement Longitudinal Study and included a sample of 428 individuals who underwent formal retirement and provided information on physical activity. We employed generalized estimating equation to explore the impact of the retirement transition on physical activity among Chinese older adults. Latent class growth analysis was used to identify distinct trajectories of physical activity, and binary logistic regression was performed to identify pre-retirement factors influencing changes in physical activity. Results: Our findings indicate that retirement can lead to a decline in physical activity among older Chinese residents (OR=0.85, 95%CI 0.75~0.97). We identified three distinct trajectories of physical activity during the retirement transition: Trajectory 1 – “sustained low level of physical activity” (7.94%); Trajectory 2 – “middle level of physical activity with gradual decline” (69.16%); Trajectory 3 – “sustained high level of physical activity with significant fluctuations” (22.90%). Furthermore, we discovered that individuals in the “middle level of physical activity and gradual decline” trajectory were more likely to have an annual income exceeding 40,000 yuan (OR=9.69, 95%CI 1.12~83.63), reside in urban areas (OR=2.27, 95%CI 1.14~4.52), and have a fondness for playing Mahjong (OR=2.42, 95%CI 1.18~5.00) compared to those in the “sustained high level of physical activity with significant fluctuations” trajectory. Additionally, having an annual income exceeding 40,000 yuan (OR=19.67, 95%CI 1.30~298.61) predicted membership in the “sustained low level of physical activity” trajectory when compared to the “sustained high level of physical activity with significant fluctuations” trajectory. Conclusion: Retirement represents a substantial milestone in the life course and is associated with notable alterations in physical activity patterns. Among older Chinese residents, the trajectories of physical activity during the retirement transition exhibit diverse paths and are influenced by pre-retirement factors, including annual income, residential location, and hobbies. The findings of this study have important implications for the formulation of policies aimed at promoting healthy aging among individuals approaching retirement age.

BackgroundThe impact of retirement on physical activity among older individuals remains ambiguous. This study aims to investigate the influence of retirement on physical activity and delineate the trajectories of physical activity changes during the retirement transition among elderly Chinese residents. Additionally, we endeavor to examine the factors that contribute to each trajectory.MethodsThis longitudinal cohort study used data from four surveys of the China Health and Retirement Longitudinal Study and included a sample of 428 individuals who underwent formal retirement and provided information on physical activity. We employed generalized estimating equation to explore the impact of the retirement transition on physical activity among Chinese older adults. Latent class growth analysis was used to identify distinct trajectories of physical activity, and binary logistic regression was performed to identify pre-retirement factors influencing changes in physical activity.ResultsOur findings indicate that retirement can lead to a decline in physical activity among older Chinese residents (OR = 0.85, 95%CI 0.75 ~ 0.97). We identified three distinct trajectories of physical activity during the retirement transition: Trajectory 1 – “sustained low level of physical activity” (7.94%); Trajectory 2 – “middle level of physical activity with gradual decline” (69.16%); Trajectory 3 – “sustained high level of physical activity with significant fluctuations” (22.90%). Furthermore, we discovered that individuals in the “middle level of physical activity and gradual decline” trajectory were more likely to have an annual income exceeding 40,000 yuan (OR = 9.69, 95%CI 1.12 ~ 83.63), reside in urban areas (OR = 2.27, 95%CI 1.14 ~ 4.52), and have a fondness for playing Mahjong (OR = 2.42, 95%CI 1.18 ~ 5.00) compared to those in the “sustained high level of physical activity with significant fluctuations” trajectory. Additionally, having an annual income exceeding 40,000 yuan (OR = 19.67, 95%CI 1.30 ~ 298.61) predicted membership in the “sustained low level of physical activity” trajectory when compared to the “sustained high level of physical activity with significant fluctuations” trajectory.ConclusionRetirement represents a substantial milestone in the life course and is associated with notable alterations in physical activity patterns. Among older Chinese residents, the trajectories of physical activity during the retirement transition exhibit diverse paths and are influenced by pre-retirement factors, including annual income, residential location, and hobbies. The findings of this study have important implications for the formulation of policies aimed at promoting healthy aging among individuals approaching retirement age.

To evaluate the quality of life (QoL) of patients with Chagas disease (CD) and the association between QoL domains and several clinical, socioeconomic and lifestyle characteristics of this population. Cross-sectional observational study conducted from March 2014 to March 2017 including a total of 361 outpatients followed at Evandro Chagas National Institute of Infectious Disease, Brazil. QoL was assessed using the Portuguese shorter version of the original WHO Quality of Life questionnaire (WHOQOL-BREF). Information about clinical CD presentation, presence of comorbidities, functional class, previous benznidazole treatment, socioeconomic profile and lifestyle was also obtained. Environment and physical domains presented the worst QoL scores, while the social relationship domain presented the highest score. Multivariate regression analysis demonstrated that variables independently associated with QoL were functional class, sex, clinical presentation of CD, sleep duration, schooling, physical activity level, smoking, income per capita and residents by domicile. The low socioeconomic status and the physical limitations imposed by the disease presented an important impact on the QoL reduction among CD patients, especially on environment and physical domains. Strategies to improve QoL among CD patients should be tailored and consider many different variables to maximise improvements not only of patients' physical but also of their mental health.

Study purpose. The objective of the study was to assess the relationship between quality of life and physical activity level and family well-being.&#x0D; Materials and Methods. The International Physical Activity Questionnaire (IPAQ) was used to assess parental physical activity. The quality of life was assessed with the Short Form (SF-36) Health Survey Questionnaire. The data obtained were processed using cluster and correlation analysis, and descriptive statistics. The study involved 106 young adults (married couples), who were parents of preschool and primary school-aged children.&#x0D; Results. An assessment of parental physical activity showed that 62.3% of the respondents had a low level of physical activity and 37.7% had a moderate level, whereas no individuals with a high level of physical activity were found. Families with children were divided into clusters with the k-means method according to the level of physical activity: Cluster 1 included families with a low level of physical activity; and Cluster 2 included the families with a moderate level of physical activity. The results of the study confirmed the relationship between the quality of life and the level of physical activity and family well-being. It was found that all the quality of life components of the study participants with a moderate level of physical activity are significantly higher (р&lt;0.05) than those of the participants with a low level of physical activity. Correlation analysis of family well-being and quality of life indicators revealed significant relationships (p&lt;0.05) between physical functioning, physical role functioning, vitality, and mental health.&#x0D; Conclusions. The direct statistically significant relationship between family well-being score and quality of life components of the respondents was demonstrated.

To investigate the physical activity levels of lung cancer survivors, analyse the influencing factors, and construct a predictive model for the physical activity levels of lung cancer survivors based on machine learning algorithms. This was a cross-sectional study. Convenience sampling was used to survey lung cancer survivors across 14 hospitals in eastern, central, and western China. Data on demographic, disease-related, health-related, physical, and psychosocial factors were also collected. Descriptive analyses were performed using SPSS 25.0, and predictors were identified through multiple logistic regression analyses. Four machine learning models-random forest, gradient boosting tree, support vector machine, and logistic regression-were developed and evaluated based on the Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC), accuracy, precision, recall, and F1 score. The best model was used to create an online computational tool using Python 3.11 and Flask 3.0.3. This study was conducted and reported in accordance with the TRIPOD guidelines and checklist. Among the 2231 participants, 670 (30%), 1185 (53.1%), and 376 (16.9%) exhibited low, moderate, and high physical activity levels, respectively. Multivariate logistic regression identified 15 independent influencing factors: residential location, geographical region, religious beliefs, histological type, treatment modality, regional lymph node stage, grip strength, 6-min walking distance, globulin, white blood cells, aspartate aminotransferase, blood urea, MDASI score, depression score, and SRAHP score. The random forest model performed best among the four algorithms, achieving AUC-ROC values of 0.86, 0.70, 0.72, and 0.67, respectively, and was used to develop an online predictive tool (URL: http://10.60.32.178:5000). This study developed a machine learning model to predict physical activity levels in lung cancer survivors, with the random forest model demonstrating the highest accuracy and clinical utility. This tool enables the early identification of low-activity survivors, facilitating timely, personalised rehabilitation and health management. The development of a predictive model for physical activity levels in lung cancer survivors can help clinical medical staff identify survivors with relatively low physical activity levels as early as possible. Thus, personalised rehabilitation plans can be formulated to optimise quality of life during their survival period. Physical activity has been used as a nonpharmacological intervention in cancer patient rehabilitation plans. However, a review of past studies has shown that lung cancer survivors generally have low physical activity levels. In this study, we identified the key factors influencing physical activity among lung cancer survivors through a literature review. We constructed a prediction model for their physical activity levels using machine learning algorithms. Clinical medical staff can use this model to identify patients with low physical activity levels early and to develop personalised intervention plans to improve their quality of life during survival. The study adhered to the relevant EQUATOR reporting guidelines, the TRIPOD Checklist for Prediction Model Development and Validation. During the data collection phase, participants were recruited to complete the questionnaires.

Physical activity and health

Obesity is associated with functional disabilities and impairments of quality of life, and many factors affect this relationship. This study aimed at characterizing and identifying the impact of clinical and functional conditions on health-related quality of life (HRQoL) in obese old women. The HRQoL was assessed by the questionnaires Outcomes Study Short Form-36 Health Survey (SF-36) and Impact of Weight on Quality of Life - Lite (IWQOL - Lite), which were applied to 63 women with body mass index ≥30 kg/m2. Regression models were developed for general (SF-36) and specific (IWQOL-Lite) HRQoL. The associated factors investigated were: age, number of medicines, number of diseases, depressive symptoms, body mass index, grip strength, level of physical activity, and functional performance. The old women had a low level of strength and physical activity. Their functional performance was good to moderate, but a third of the sample presented deficit of mobility. The level of physical activity and functional performance had a positive impact on the general HRQoL and number of drugs had a negative one (R2=0.44). Depressive symptoms and body mass index negatively affected the specific HRQoL (R2=0.57). The study concluded that obese old women with depressive symptoms, low levels of physical activity, and functional performance, making use of a great number of drugs, are more vulnerable to experiencing poor HRQoL. All the factors associated with the HRQoL in this study are potentially modifiable with interventions of health prevention and promotion.