Although the health benefits of physical activity are well documented, a large proportion of the population fails to meet current guidelines for physical activity. In order to develop evidence-based ...public health policies, surveillance of physical activity prevalence and trends is essential. The main aim of this study was to present updated data on physical activity trends in a Norwegian general population over the last decades. Data were collected from 40 690 individuals (50% men) aged ≥20 years participating in at least one of six surveys of the population-based Tromsø Study between 1979 and 2016. Age-standardized prevalences and trends in leisure-time and occupational physical activity were obtained from three questionnaires used in the different surveys. We observed an increase in the proportion engaging in exercise in leisure-time between 1994-95 and 2001 (p <0.001). Based on a different questionnaire, the age-standardized prevalence of engagement in exercise in leisure-time increased significantly from 16% in 2001 to 23% in 2007-08, and further to 28% in 2015-16 (p <0.001). The proportion who reported exercising approximately every day increased from 19% in 2007-08 to 28% in 2015-16 (p <0.001). The age-standardized prevalence of sedentary occupational activity increased from 53% in 2007-08 to 57% in 2015-16 (p <0.001), which extends the gradual increase from 36% in 1979-80.The present study extends previous findings from the Tromsø Study by demonstrating an increase in the proportion exercising regularly over the last three decades. This increase may partially counteract the gradual increase in the proportion with sedentary occupational activity.
To date, non-wear detection algorithms commonly employ a 30, 60, or even 90 mins interval or window in which acceleration values need to be below a threshold value. A major drawback of such intervals ...is that they need to be long enough to prevent false positives (type I errors), while short enough to prevent false negatives (type II errors), which limits detecting both short and longer episodes of non-wear time. In this paper, we propose a novel non-wear detection algorithm that eliminates the need for an interval. Rather than inspecting acceleration within intervals, we explore acceleration right before and right after an episode of non-wear time. We trained a deep convolutional neural network that was able to infer non-wear time by detecting when the accelerometer was removed and when it was placed back on again. We evaluate our algorithm against several baseline and existing non-wear algorithms, and our algorithm achieves a perfect precision, a recall of 0.9962, and an F1 score of 0.9981, outperforming all evaluated algorithms. Although our algorithm was developed using patterns learned from a hip-worn accelerometer, we propose algorithmic steps that can easily be applied to a wrist-worn accelerometer and a retrained classification model.
Accurate detection of accelerometer non-wear time is crucial for calculating physical activity summary statistics. In this study, we evaluated three epoch-based non-wear algorithms (Hecht, Troiano, ...and Choi) and one raw-based algorithm (Hees). In addition, we performed a sensitivity analysis to provide insight into the relationship between the algorithms' hyperparameters and classification performance, as well as to generate tuned hyperparameter values to better detect episodes of wear and non-wear time. We used machine learning to construct a gold-standard dataset by combining two accelerometers and electrocardiogram recordings. The Hecht and Troiano algorithms achieved poor classification performance, while Choi exhibited moderate performance. Meanwhile, Hees outperformed all epoch-based algorithms. The sensitivity analysis and hyperparameter tuning revealed that all algorithms were able to achieve increased classification performance by employing larger intervals and windows, while more stringently defining artificial movement. These classification gains were associated with the ability to lower the false positives (type I error) and do not necessarily indicate a more accurate detection of the total non-wear time. Moreover, our results indicate that with tuned hyperparameters, epoch-based non-wear algorithms are able to perform just as well as raw-based non-wear algorithms with respect to their ability to correctly detect true wear and non-wear episodes.
The objective was to examine the association of physical activity and resting heart rate (RHR) with hospital-diagnosed atrial fibrillation (AF) in a Norwegian cohort.
This prospective study included ...20 484 adults (50.3% men) who participated in the third Tromsø Study survey in 1986-87. At baseline, physical activity was assessed by a validated questionnaire, and RHR was objectively measured. Participants were followed from baseline through 2010 with respect to incident cases of hospital-diagnosed AF documented on an electrocardiogram. During a mean follow-up period of 20 years (409 045 person-years), 750 participants (70.5% men) were diagnosed with AF. Compared with the low physical activity group, moderately active individuals had a 19% lower risk of any AF adjusted hazard ratio (HR) 0.81, 95% confidence interval (CI) 0.68-0.97, whereas highly active had similar risk of AF. Vigorously active individuals showed a non-significantly higher risk of AF (adjusted HR 1.37, 95% CI 0.77-2.43). Risk of AF increased with decreasing RHR (adjusted HR 0.92, 95% CI 0.86-0.98 for each 10 b.p.m. increase in RHR), and RHR < 50 b.p.m. was a risk factor for AF (P < 0.05).
In this prospective cohort study, leisure time physical activity was associated with AF in a J-shaped pattern. Moderate physical activity was associated with a reduced risk of AF, whereas higher activity levels attenuated the benefits of moderate activity. Low RHR was a risk factor for AF. Our results support the hypothesis that moderate and vigorous physical activity may affect AF risk via different pathophysiological mechanisms.
The European Working Group on Sarcopenia in Older People (EWGSOP2) recommends grip strength and chair stand tests to be used as primary defining measures. It is unclear how either test affects ...prevalence estimates.
This cross-sectional study involved 3498 community-dwelling participants (40-84 years) from the 7th Tromsø Study survey (2015-2016). We used grip strength, five-repetition chair stands, four-meter Walk Speed Test, Timed-Up-and-Go (TUG) and Dual-Energy X-ray Absorptiometry measurements. Data were analyzed using multiple linear regression models and ROC-curves.
Probable and confirmed sarcopenia prevalence was 1.3 and 4.4% based on grip strength and chair stands, respectively. There was very low agreement between grip strength and chair stand cut-offs (κ = 0.07), with only 4.3% of participants defined as having probable sarcopenia overlapping in the two criteria. Participants with grip strength-based sarcopenia had lower mean height, weight, waist circumference, and appendicular lean mass relative to body height (ALM
) than non-sarcopenic participants (all p < 0.001), after adjusting for multiple covariates. Conversely, participants with chair stand-based sarcopenia had similar height, higher weight, waist circumference and body fat% compared to non-sarcopenic participants (all p < 0.05). Area-under-curves (AUCs) for TUG-time were significantly larger when using chair stand instead of grip strength cut-offs (0.86, 95% CI 0.84-0.89 vs. 0.75, 95% CI 0.69-0.83).
Using chair stands instead of grip strength more than doubled probable sarcopenia prevalence across all ages. The two measures defined individuals of contradictory anthropometrics, body composition, and dissimilar physical function to have probable sarcopenia. Researchers should further evaluate the consequences of using different strength measures in the EWGSOP2 definition to classify sarcopenia.
To examine whether leisure time physical activity changes predict subsequent body mass index (BMI) changes, and conversely, whether BMI changes predict subsequent leisure time physical activity ...changes.
This prospective cohort study included adults attending ≥3 consecutive Tromsø Study surveys (time: T1, T2, T3) during 1974-2016 (n = 10779). If participants attended >3 surveys, we used the three most recent surveys. We computed physical activity change (assessed by the Saltin-Grimby Physical Activity Level Scale) from T1 to T2, categorized as Persistently Inactive (n = 992), Persistently Active (n = 7314), Active to Inactive (n = 1167) and Inactive to Active (n = 1306). We computed BMI change from T2 to T3, which regressed on preceding physical activity changes using analyses of covariance. The reverse association (BMI change from T1 to T2 and physical activity change from T2 to T3; n = 4385) was assessed using multinomial regression.
Average BMI increase was 0.86 kg/m
(95% CI: 0.82-0.90) from T2 to T3. With adjustment for sex, birth year, education, smoking and BMI at T2, there was no association between physical activity change from T1 to T2 and BMI change from T2 to T3 (Persistently Inactive: 0.89 kg/m
(95% CI: 0.77-1.00), Persistently Active: 0.85 kg/m
(95% CI: 0.81-0.89), Active to Inactive: 0.90 kg/m
(95% CI: 0.79-1.00), Inactive to Active 0.85 kg/m
(95% CI: 0.75-0.95), p = 0.84). Conversely, increasing BMI was associated with Persistently Inactive (odds ratio (OR): 1.17, 95% CI: 1.08-1.27, p < 0.001) and changing from Active to Inactive (OR: 1.16, 95% CI: 1.07-1.25, p < 0.001) compared with being Persistently Active.
We found no association between leisure time physical activity changes and subsequent BMI changes, whereas BMI change predicted subsequent physical activity change. These findings indicate that BMI change predicts subsequent physical activity change at population level and not vice versa.
Surveillance of physical activity at the population level increases the knowledge on levels and trends of physical activity, which may support public health initiatives to promote physical activity. ...Physical activity assessed by accelerometry is challenged by varying data processing procedures, which influences the outcome. We aimed to describe the levels and prevalence estimates of physical activity, and to examine how triaxial and uniaxial accelerometry data influences these estimates, in a large population-based cohort of Norwegian adults.
This cross-sectional study included 5918 women and men aged 40-84 years who participated in the seventh wave of the Tromsø Study (2015-16). The participants wore an ActiGraph wGT3X-BT accelerometer attached to the hip for 24 hours per day over seven consecutive days. Accelerometry variables were expressed as volume (counts·minute-1 and steps·day-1) and as minutes per day in sedentary, light physical activity and moderate and vigorous physical activity (MVPA).
From triaxial accelerometry data, 22% (95% confidence interval (CI): 21-23%) of the participants fulfilled the current global recommendations for physical activity (≥150 minutes of MVPA per week in ≥10-minute bouts), while 70% (95% CI: 69-71%) accumulated ≥150 minutes of non-bouted MVPA per week. When analysing uniaxial data, 18% fulfilled the current recommendations (i.e. 20% difference compared with triaxial data), and 55% (95% CI: 53-56%) accumulated ≥150 minutes of non-bouted MVPA per week. We observed approximately 100 less minutes of sedentary time and 90 minutes more of light physical activity from triaxial data compared with uniaxial data (p<0.001).
The prevalence estimates of sufficiently active adults and elderly are more than three times higher (22% vs. 70%) when comparing triaxial bouted and non-bouted MVPA. Physical activity estimates are highly dependent on accelerometry data processing criteria and on different definitions of physical activity recommendations, which may influence prevalence estimates and tracking of physical activity patterns over time.
Although commonly associated with cardiovascular disease or other medical conditions, atrial fibrillation may also occur in individuals without any known underlying conditions. This manifestation of ...atrial fibrillation has been linked to extensive and long-term exercise, as prolonged endurance exercise has shown to increase prevalence and risk of atrial fibrillation. In contrast, more modest physical activity is associated with a decreased risk of atrial fibrillation, and current research indicates a J-shaped association between atrial fibrillation and the broad range of physical activity and exercise. This has led to the hypothesis that the mechanisms underlying an increased risk of atrial fibrillation with intensive exercise are different from those underlying a reduced risk with moderate physical activity, possibly linked to distinctive characteristics of the population under study. High volumes of exercise over many years performed by lean, healthy endurance trained athletes may lead to cardiac (patho)physiological alterations involving the autonomic nervous system and remodelling of the heart. The mechanisms underlying a reduced risk of atrial fibrillation with light and moderate physical activity may involve a distinctive pathway, as physical activity can potentially reduce the risk of atrial fibrillation through favourable effects on cardiovascular risk factors.
Physical activity (PA) might influence the risk or progression of chronic pain through pain tolerance. Hence, we aimed to assess whether habitual leisure-time PA level and PA change affects pain ...tolerance longitudinally in the population. Our sample (n = 10,732; 51% women) was gathered from the sixth (Tromsø6, 2007-08) and seventh (Tromsø7, 2015-16) waves of the prospective population-based Tromsø Study, Norway. Level of leisure-time PA (sedentary, light, moderate, or vigorous) was derived from questionnaires; experimental pain tolerance was measured by the cold-pressor test (CPT). We used ordinary, and multiple-adjusted mixed, Tobit regression to assess 1) the effect of longitudinal PA change on CPT tolerance at follow-up, and 2) whether a change in pain tolerance over time varied with level of LTPA. We found that participants with high consistent PA levels over the two surveys (Tromsø6 and Tromsø7) had significantly higher tolerance than those staying sedentary (20.4 s. (95% CI: 13.7, 27.1)). Repeated measurements show that light (6.7 s. (CI 3.4, 10.0)), moderate (CI 14.1 s. (9.9, 18.3)), and vigorous (16.3 s. (CI 6.0, 26.5)) PA groups had higher pain tolerance than sedentary, with non-significant interaction showed slightly falling effects of PA over time. In conclusion, being physically active at either of two time points measured 7-8 years apart was associated with higher pain tolerance compared to being sedentary at both time-points. Pain tolerance increased with higher total activity levels, and more for those who increased their activity level during follow-up. This indicates that not only total PA amount matters but also the direction of change. PA did not significantly moderate pain tolerance change over time, though estimates suggested a slightly falling effect possibly due to ageing. These results support increased PA levels as a possible non-pharmacological pathway towards reducing or preventing chronic pain.
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