Observational studies linking physical activity with mortality are susceptible to reverse causation bias from undiagnosed and prevalent diseases. Researchers often attempt to deal with reverse ...causation bias by excluding deaths occurring within the first 1 or 2 years from the analysis, but it is unclear if excluding deaths within this time-frame is sufficient to remove bias.
We examined associations between total and intensity-specific physical activity and sedentary time with all-cause mortality in a prospective cohort of 3542 individuals from the 2003-2006 NHANES cycles. In order to yield measures of association hypothesized as minimally influenced by reverse causation bias the primary analysis excluded individuals with < 5 years of follow-up. Accelerometer-measured physical activity was linked with recently updated vital status from the National Death Index with a median follow-up of 10.8 years.
Hazard ratios (95% confidence intervals) were 0.74 (0.53, 1.04), 0.52 (0.37, 0.73), and 0.61 (0.38, 1.01) for ascending quartiles of total physical activity against the least active reference. Hazard ratios for ascending moderate-to-vigorous physical activity quartiles against the reference were 0.67 (0.47, 1.96), 0.67 (0.47, 0.95), and 0.68 (0.39, 1.18). Associations for light intensity physical activity and sedentary time were smaller in magnitude and all confidence intervals included unity. Total activity and moderate-to-vigorous physical activity hazard ratios from analyses only excluding deaths within the first 2 years were inflated by 13 and 26% relative to analysis restricted to ≥5 years of follow-up.
The pattern of associations suggested total physical activity and moderate-to-vigorous physical activity were associated with lower mortality after more than 10 years of follow-up and excluding the first 5 years of observation time to minimize the impact of reverse causation bias. Excluding deaths within the first 2 years appeared insufficient to minimize the impact of reserve causation bias.
Abstract
Objectives
To compare cardiorespiratory fitness (CRF) expressed as maximal oxygen uptake (VO2max) between patients with long-term JDM and controls and between patients with active and ...inactive disease, as well as to explore exercise limiting factors and associations between CRF and disease variables.
Methods
JDM patients (n = 45) and age- and gender-matched controls (n = 45) performed a cardiopulmonary exercise test (CPET) on a treadmill until exhaustion. Physical activity was measured by accelerometers. Disease activity, damage and muscle strength/function were assessed by validated tools. Clinically inactive disease was defined according to PRINTO criteria.
Results
The mean disease duration was 20.8 (s.d. 11.9) years and 29/45 (64%) patients had inactive disease. A low VO2max was found in 27% of patients vs 4% of controls (P = 0.006). The mean VO2max and maximal ventilation (VEmax) were lower in patients with active and inactive disease compared with controls. Patients with active disease also had lower maximal voluntary ventilation (MVV) compared with controls and lower VEmax and MVV compared with those with inactive disease. Patients with inactive disease had lower physical activity levels compared with controls. VO2max correlated negatively with disease damage in patients with inactive disease and positively with muscle strength/function in patients with active disease.
Conclusion
CRF was lower in JDM patients, both with active and inactive disease, compared with controls after a mean 20 years disease duration. Cardiopulmonary exercise test results suggested different limiting factors contributing to the reduced CRF according to disease activity, including deconditioning in inactive disease and reduced ventilatory capacity in active disease. Further research is needed to verify this.
Purpose
The pandemic of physical inactivity is recognized globally but there is a scarcity of studies employing valid and reliable assessment methods of physical activity (PA) across the lifespan. ...The purpose of this study is to provide a comprehensive description of objectively measured PA, sedentary time, and prevalence of meeting PA recommendations, in a population‐based sample of Norwegian children, adolescents and adults.
Methods
Children and adolescents (6, 9 and 15‐year‐olds) were surveyed in 2011, and adults and older people (20‐85‐year‐olds) were surveyed in 2014/15, including more than 8000 individuals. Anthropometric data were measured in children and adolescents and self‐reported in the adult sample. PA was assessed by ActiGraph accelerometers for seven consecutive days, and PA indices include total PA (counts per minute), intensity‐specific PA, and adherence to PA recommendations.
Results
Six‐year‐olds are 21% and 70% more active than 9‐ and 15‐year‐olds, respectively (P < 0.001). Nine‐year‐olds are 40% more active compared to 15‐year‐olds (P < 0.001). Moving from adolescence (15‐year‐olds) into adulthood (20‐65 years) yields a further reduction in total PA by 18%. Among six‐, nine‐ and 15‐year olds, 90%, 77%, and 48% meet the current PA recommendations, respectively, while adherence among adults and older people are 33% and 31%, respectively. Overweight and obese individuals had lower odds of meeting PA recommendations.
Conclusions
The results from the Norwegian surveillance system indicate a strong association between age and indices of physical activity. The vast majority of Norwegian adults do not meet the PA recommendations and public health action are needed to increase PA in Norway.
Physical activity (PA) monitoring is applied in a growing number of studies within cancer research. However, no consensus exists on how many days PA should be monitored to obtain reliable estimates ...in the cancer population. The objective of the present study was to determine the minimum number of monitoring days required for reliable estimates of different PA intensities in cancer survivors when using a six-days protocol. Furthermore, reliability of monitoring days was assessed stratified on sex, age, cancer type, weight status, and educational level.
Data was obtained from two studies where PA was monitored for seven days using the SenseWear Armband Mini in a total of 984 cancer survivors diagnosed with breast, colorectal or prostate cancer. Participants with ≥22 hours monitor wear-time for six days were included in the reliability analysis (n = 736). The intra-class correlation coefficient (ICC) and the Spearman Brown prophecy formula were used to assess the reliability of different number of monitoring days.
For time in light PA, two monitoring days resulted in reliable estimates (ICC >0.80). Participants with BMI ≥25, low-medium education, colorectal cancer, or age ≥60 years required one additional monitoring day. For moderate and moderate-to-vigorous PA, three monitoring days yielded reliable estimates. Participants with BMI ≥25 or breast cancer required one additional monitoring day. Vigorous PA showed the largest within subject variations and reliable estimates were not obtained for the sample as a whole. However, reliable estimates were obtained for breast cancer survivors (4 days), females, BMI ≥30, and age <60 years (6 days).
Shorter monitoring periods may provide reliable estimates of PA levels in cancer survivors when monitored continuously with a wearable device. This could potentially lower the participant burden and allow for less exclusion of participants not adhering to longer protocols.
There is a scarcity of device measured data on temporal changes in physical activity (PA) in large population-based samples. The purpose of this study is to describe gender and age-group specific ...temporal trends in device measured PA between 2005, 2011 and 2018 by comparing three nationally representative samples of children and adolescents.
Norwegian children and adolescents (6, 9 and 15-year-olds) were invited to participate in 2005 (only 9- and 15-year-olds), 2011 and 2018 through cluster sampling (schools primary sampling units). A combined sample of 9500 individuals participated. Physical activity was assessed by hip worn accelerometers, with PA indices including overall PA (counts per minute), moderate-to-vigorous intensity PA (MVPA), and PA guideline adherence (achieving on average ≥ 60 min/day of moderate-to-vigorous PA). Random-effects linear regressions and logistic regressions adjusted for school-level clusters were used to analyse temporal trends.
In total, 8186 of the participating children and adolescents provided valid PA data. Proportions of sufficiently active 6-year-olds were almost identical in 2011 and 2018; boys 95% (95% CI: 92, 97) and 94% (95%CI: 92, 96) and girls 86% (95% CI: 83, 90) and 86% (95% CI: 82, 90). Proportions of sufficiently active 15-year-olds in 2005 and 2018 were 52% (95% CI: 46, 59) and 55% (95% CI: 48, 62) in boys, and 48% (95% CI: 42, 55) and 44% (95% CI: 37, 51) in girls, respectively, resulting from small differences in min/day of MVPA. Among 9-year-old boys and girls, proportions of sufficiently active declined between 2005 and 2018, from 90% (95% CI: 87, 93) to 84% (95% CI: 80, 87)) and 74% (95% CI: 69, 79) to 68% (95% CI: 64, 72), respectively. This resulted from 9.7 min/day less MVPA in boys (95% CI: - 14.8, - 4.7; p < 0.001) and 3.2 min/day less MVPA (95% CI: - 7.0, 0.7; p = 0.106) in girls.
PA levels have been fairly stable between 2005, 2011 and 2018 in Norwegian youth. However, the declining PA level among 9-year-old boys and the low proportion of 15-year-olds sufficiently active is concerning. To evaluate the effect of, and plan for new, PA promoting strategies, it is important to ensure more frequent, systematic, device-based monitoring of population-levels of PA.
Understanding the associations between health behaviors and which subgroups are at risk of developing health risk behaviors is vital knowledge to develop effective public health interventions to ...reduce the high prevalence of non-communicable diseases (NCDs). The objective of the study was to assess the association between physical activity, diet, tobacco use, and alcohol consumption and sociodemographic determinants (sex and education), and to examine clustering patterns of these health behaviors.
Data was collected from an online self-reported questionnaire from the Norwegian public health survey conducted in 2019. The study sample consisted of 28,047 adults (≥ 18 years old) from Agder county in Southern Norway. Chi-square tests and logistic regression analysis were used to determine the association between sex and education according to physical activity, diet, tobacco use and alcohol consumption. Linear regression was used to examine the association between educational level and number of health risk behaviors, and cluster analysis were performed to determine cluster patterns.
Females were more likely than men to meet the national public health recommendations for diet (p < 0.001), tobacco use (p < 0.01), and alcohol consumption (p < 0.001). High education was associated with meeting the recommendations for each of the four health behaviors and with a lower risk of having three or four health risk behaviors simultaneously. Furthermore, clustering of health risk behaviors was observed in five of the sixteen health behavior patterns.
Our findings show a higher risk of having multiple health risk behaviors for males and individuals with low education, and these subgroup findings could inform public health policy and be target goals in future public health interventions. Clustering patterns were observed in over 30% of the health behavior patterns. More research is needed on the causal relationship between health behaviors and socioeconomic factors, and the association between clustering and health outcomes to design effective interventions in the future.
Levels of physical activity and variation in physical activity and sedentary time by place and person in European children and adolescents are largely unknown. The objective of the study was to ...assess the variations in objectively measured physical activity and sedentary time in children and adolescents across Europe.
Six databases were systematically searched to identify pan-European and national data sets on physical activity and sedentary time assessed by the same accelerometer in children (2 to 9.9 years) and adolescents (≥10 to 18 years). We harmonized individual-level data by reprocessing hip-worn raw accelerometer data files from 30 different studies conducted between 1997 and 2014, representing 47,497 individuals (2-18 years) from 18 different European countries.
Overall, a maximum of 29% (95% CI: 25, 33) of children and 29% (95% CI: 25, 32) of adolescents were categorized as sufficiently physically active. We observed substantial country- and region-specific differences in physical activity and sedentary time, with lower physical activity levels and prevalence estimates in Southern European countries. Boys were more active and less sedentary in all age-categories. The onset of age-related lowering or leveling-off of physical activity and increase in sedentary time seems to become apparent at around 6 to 7 years of age.
Two third of European children and adolescents are not sufficiently active. Our findings suggest substantial gender-, country- and region-specific differences in physical activity. These results should encourage policymakers, governments, and local and national stakeholders to take action to facilitate an increase in the physical activity levels of young people across Europe.
Physical inactivity increases the risk of many adverse health conditions, including the world’s major non-communicable diseases, such as coronary heart disease, type 2 diabetes, and breast and colon ...cancers, shortening life expectancy. There are minimal medical care and personal trainers’ methods to monitor a patient’s actual physical activity types. To improve activity monitoring, we propose an artificial-intelligence-based approach to classify physical movement activity patterns. In more detail, we employ two deep learning (DL) methods, namely a deep feed-forward neural network (DNN) and a deep recurrent neural network (RNN) for this purpose. We evaluate the two models on two physical movement datasets collected from several volunteers who carried tri-axial accelerometer sensors. The first dataset is from the UCI machine learning repository, which contains 14 different activities-of-daily-life (ADL) and is collected from 16 volunteers who carried a single wrist-worn tri-axial accelerometer. The second dataset includes ten other ADLs and is gathered from eight volunteers who placed the sensors on their hips. Our experiment results show that the RNN model provides accurate performance compared to the state-of-the-art methods in classifying the fundamental movement patterns with an overall accuracy of 84.89% and an overall F1-score of 82.56%. The results indicate that our method provides the medical doctors and trainers a promising way to track and understand a patient’s physical activities precisely for better treatment.
The Saltin-Grimby Physical Activity Level Scale (SGPALS) is commonly used to measure physical activity (PA) in population studies, but its validity in adolescents is unknown. This study aimed to ...assess the criterion validity of the SGPALS against accelerometry in a large sample of adolescents. A secondary aim was to examine the validity across strata of sex, body mass index (BMI), parental educational level, study program and self-reported health. The study is based on data from 572 adolescents aged 15-17 years who participated in the Fit Futures Study 2010-11 in Northern Norway. The participants were invited to wear an accelerometer (GT3X) attached to their hip for seven consecutive days. We used Spearman's rho and linear regression models to assess the validity of the SGPALS against the following accelerometry estimates of PA; mean counts/minute (CPM), steps/day, and minutes/day of moderate-to-vigorous physical activity (MVPA). The SGPALS correlated with mean CPM (rho = 0.40, p<0.01), steps/day (rho = 0.35, p<0.01) and MVPA min/day (rho = 0.35, p0.001). Higher scores on SGPALS were associated with a higher CPM, higher number of steps per day and more minutes of MVPA per day, with the following mean differences in PA measurements between the SGPALS ranks: CPM increased by 53 counts (95% CI: 44 to 62), steps/day increased by 925 steps (95% CI: 731 to 1118), and MVPA by 8.4 min/day (95% CI: 6.7 to 10.0). Mean difference between the highest and lowest SGPALS category was 2947 steps/day (6509 vs. 9456 steps/day) and 26.4 min/day MVPA (35.2 minutes vs 61.6 minutes). We found satisfactory ranking validity of SGPALS measured against accelerometry in adolescents, which was fairly stable across strata of sex, BMI, and education. However, the validity of SGPALS in providing information on absolute physical activity levels seem limited.
The purpose of this study was to validate estimated energy intake from a web-based food recall, designed for children and adolescents. We directly compared energy intake to estimates of total energy ...expenditure, calculated from accelerometer outputs, combined with data on weight and sex or resting energy expenditure prediction equations. Children (8-9 years) and adolescents (12-14 years) were recruited through schools in Norway in 2013 (N = 253). Results showed that more than one third (36-37%) were identified as under-reporters of energy. In contrast, only 2-4% were defined as over-reporters of energy. The mean energy intake was under-reported with -1.83 MJ/day for the entire study sample. Increased underestimation was observed for overweight and obese participants, the oldest age group (12-14 years), boys, those with parents/legal guardians with low educational level and those living in non-traditional families. In conclusion, energy intake from the web-based food recall is significantly underestimated compared with total energy expenditure, and should be used with caution in young people.
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