Background: In a country of 1.3 billion people with cultural and lifestyle diversities, several previous attempts have been made to compile the burden of cardiovascular diseases. 3.2 million people ...die each year due to physical inactivity. Traditionally, studies of diet and CVD risk have focused on individual foods and macronutrients. Yet, food is typically consumed in combination and not in isolation. Objective: To compare the dietary pattern and physical activity among 2 different age groups of people in a village of coastal Karnataka. Methodology: A cross-sectional study conducted in a rural area of coastal Karnataka with sample size of 320 (160 each from 20–30 years & 50–60 years group). The dietary pattern involving daily intake of carbohydrates, fats, proteins and total calories was assessed using the 7-day dietary cycle. Physical activity pattern was assessed using IPAQ study tool. Statistical analysis was done using SPSS; parametric and non-parametric tests were done. p<0.05 was taken as statistically significant. Results: 20 - 30 years age group had a consumption pattern of daily carbohydrates, proteins and fat intake of 24.11 g%, 10.89 g% & 20.41 g% as compared to 32.96 g%, 13 g% and 29.44 g% respectively in the 50 - 60 years age group. Total calorie intake, daily carbohydrate, protein and fat intake was significantly more in the 20-30 years age group as compared to 50-60 years age group. Gram-percentage consumption of carbohydrates, proteins and fats as part of total calories was significantly lesser as compared to 50-60 years age group. 50.6% of the 20-30 years & 13.8% of the 50-60 years age group were physically inactive. Conclusion: The study population followed an unbalanced diet with addition of trans fat and added sugars. Majority were physically inactive and the daily work load has decreased considerably with people spending most of their leisure time by sitting.
Background: Non-communicable diseases pose significant public health problem, and prevention efforts are mainly aimed at addressing their risk factors. Unhealthy lifestyle including physical ...inactivity gets initiated early in life. Hence identifying the factors associated with physical inactivity among youth is essential for developing targeted interventions. Objectives: To estimate the prevalence and factors associated with physical inactivity among youth in Kolar district, Karnataka. Methodology: The present study is a secondary data analysis of a cross- sectional study undertaken in the Kolar district of Karnataka. Information on various risk factors was collected as part of the Kolar Youth Health Survey. Physical inactivity is defined as any those achieving less than 600 MET minutes of physical activity in the previous week and was assessed using WHO tool GPAQ. Univariate and multivariate logistics regression analysis was undertaken to identify the factors associated with physical inactivity. Results: In Kolar, 82.7% of youth had less than recommended level of physical activity (<600 MET minutes per week). Residing in urban area, being female increased the risk of physical inactivity. Individuals involved in occupations like cultivators, agricultural labourer’s and skilled workers had lower risk of physical inactivity. Students too had lower risk. Youth engaged in community volunteering activities had lower risk of physical inactivity. Some of the risk factors or risk conditions like smokeless tobacco use, gambling, anxiety, history of road traffic injury and unintentional injury lowered the risk of physical inactivity. Conclusion: The prevalence of physical inactivity is high among youth in the Kolar district. Some of the sociodemographic factors pertaining to youth increases the risk of physical inactivity and these information needs to be utilized for implementing targeted interventions. However, the association between smokeless tobacco use, gambling, anxiety and injury with physical inactivity needs further exploration.
•Compared to before COVID-19 pandemic, VO₂max decreased by approximately 35.4% in non-physical activity group (NPAG) and around 4.5% in physical activity group (PAG) after COVID-19. Grip strength ...(-46.1%) and sit-ups (-10.1%) decreased in NPAG, whereas they increased by 7.5% and 14.6% in PAG.•While the NPAG experienced increases in body weight (∼6.7%), fat mass (∼33.1%), and waist-to-hip ratio (WHR) (8.4%), along with a decrease in muscle mass (-10.4%), the PAG showed slight changes or improvements in body weight (∼0.9%), fat mass (1.6%), WHR (-1.7%), and muscle mass (∼1.6%).•IL-6 increased by approximately 38.9% in NPAG, while it decreased by around 6.7% in PAG. TNF-α (-38.3.1%) and CRP (33.6%) increased in NPAG, whereas they decreased by approximately 0.5% and 0.05% in PAG, respectively.•Neutrophils, eosinophils, basophils, monocytes, and lymphocytes decreased in NPAG, whereas they increased in PAG. Among lymphocyte subtypes, NK cells decreased by ∼22.3% in NPAG, while they increased by ∼23.91% in PAG. Similarly, CD3+T cells, CD4+T cells, CD8+T cells, and CD19+B cells decreased in NPAG compared to those of PAG.•Adverse effects of physical inactivity are accompanied by elevated cytokine levels and decreased immunocyte counts through deteriorated physical fitness and body composition.•For the older adults, it is advisable to promote physical activity to prevent its decline, thereby ensuring the proper functioning of immune cells.
The prolonged period of COVID-19 has ingrained physical inactivity as a habit, leading to a reluctance to move. This has resulted in a decline in physical fitness and the loss of a healthy body composition. While this trend is particularly noticeable among the older adults, its impact on the immune cell defense system, which is crucial for minimizing viral infections, remains unclear. This study aimed to investigate the physical fitness, body composition, cytokines and immunocytes of older adults who engaged in physical activity (PA) before the COVID-19 pandemic but had to stop it due to the lockdown. A total of 172 older adults aged 61 to 85 years participated in this study: 90 in non-PA group (NPAG, 34 men and 56 women), and 82 in PA group (PAG, 29 men and 53 women). Physical inactivity was 45.13 ± 5.67 weeks in the NPAG and 1.70 ± 0.43 weeks in the PAG. Although there was no significant difference in calorie intake, PA volume showed a significant decrease in NPGA (P < 0.001). VO₂max, strength, and sit-ups decreased in NPAG, whereas they maintained or increased in PAG (Ps < 0.001). NPAG experienced an increase in fat mass (∼33.0%), along with a decrease in muscle mass (∼10.4%), but PAG showed slight increases (∼1.1% vs. ∼1.5%, Ps < 0.001). Interleukin-6 (∼38.9%), tumor necrosis factor-α (∼38.3%), and C-reactive protein (∼33.6%) increased, whereas immunocytes decreased in NPAG (Ps < 0.001). In contrast, those in PAG showed the opposite phenomenon. This study indicates that even during the COVID-19 situation, maintaining active PA in the older adults helps retain beneficial physical fitness and body composition, reduces inflammatory factors, and contributes to preserving or enhancing the function of immunocytes.
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The COVID-19 pandemic and physical activity Woods, Jeffrey A.; Hutchinson, Noah T.; Powers, Scott K. ...
Sports medicine and health science,
06/2020, Letnik:
2, Številka:
2
Journal Article
Recenzirano
Odprti dostop
The SARS-CoV-2-caused COVID-19 pandemic has resulted in a devastating threat to human society in terms of health, economy, and lifestyle. Although the virus usually first invades and infects the lung ...and respiratory track tissue, in extreme cases, almost all major organs in the body are now known to be negatively impacted often leading to severe systemic failure in some people. Unfortunately, there is currently no effective treatment for this disease. Pre-existing pathological conditions or comorbidities such as age are a major reason for premature death and increased morbidity and mortality. The immobilization due to hospitalization and bed rest and the physical inactivity due to sustained quarantine and social distancing can downregulate the ability of organs systems to resist to viral infection and increase the risk of damage to the immune, respiratory, cardiovascular, musculoskeletal systems and the brain. The cellular mechanisms and danger of this “second wave” effect of COVID-19 to the human body, along with the effects of aging, proper nutrition, and regular physical activity, are reviewed in this article.
•Sedentary behaviour had a small positive association with anxiety.•Association remained in studies controlling for sociodemographic and health-related factors.•Associations for sitting time appeared ...stronger than those for screen time.•Associations for anxiety symptoms appeared stronger than those for anxiety disorders.•No evidence of publication bias in the results.
This research synthesis sought to determine the magnitude of the association between sedentary behaviour (sitting time) and anxiety.
A comprehensive literature search of eight electronic databases (and a manual search) identified 13 observational studies that met inclusion criteria (22 effect sizes; total n = 70,425). Pooled mean effects were computed using inverse-variance weighted random effects meta-analysis and moderation by study and population characteristics were tested using random effects meta-regression.
Sedentary behaviour was associated with an increased risk of anxiety for non-adjusted effect sizes (k = 7, OR = 1.33 95% CI: 1.14, 1.55) and effect sizes adjusted for sociodemographic and health-related factors (k = 11, OR = 1.48 95% CI: 1.25, 1.75). There was no evidence of publication bias in the results. The regression models showed that effect sizes were not moderated by age or gender. However, there was some evidence of moderation by study quality and measurement of sedentary behaviour and anxiety. Measures of sitting time showed larger associations than measures of screen time, and measures of anxiety symptoms showed larger associations than measures of anxiety disorders.
The research synthesis provides evidence that sedentary behaviour has a small positive association with anxiety, after controlling for sociodemographic and other health-related factors. Study limitations include low statistical power in meta-regression models and heterogeneity in measures of anxiety and sedentary behaviour. Findings might be of interest to health care professionals developing health care initiatives to reduce risk of anxiety.
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