Polygenic Risk Scores and Physical Activity

Purpose Polygenic risk scores (PRS) summarize genome-wide genotype data into a single variable that produces an individual-level risk score for genetic liability. PRS has been used for prediction of chronic diseases and some risk factors. As PRS has been studied less for physical activity (PA), we constructed PRS for PA and studied how much variation in PA can be explained by this PRS in independent population samples. Methods We calculated PRS for self-reported and objectively measured PA using UK Biobank genome-wide association study summary statistics, and analyzed how much of the variation in self-reported (MET-hours per day) and measured (steps and moderate-to-vigorous PA minutes per day) PA could be accounted for by the PRS in the Finnish Twin Cohorts (FTC;N= 759-11,528) and the Northern Finland Birth Cohort 1966 (NFBC1966;N= 3263-4061). Objective measurement of PA was done with wrist-worn accelerometer in UK Biobank and NFBC1966 studies, and with hip-worn accelerometer in the FTC. Results The PRS accounted from 0.07% to 1.44% of the variation (R-2) in the self-reported and objectively measured PA volumes (Pvalue range = 0.023 toPeer reviewe

Temporal patterns of physical activity and sedentary time and their association with health at mid-life:the Northern Finland Birth Cohort 1966 study

Abstract Physical activity reduces mortality and morbidity and improves physical and psychological health. Lately, the detrimental health associations of excess sedentary time have also been acknowledged. It is still unknown how temporal patterns of physical activity and sedentary time are associated with health, as previous studies have mainly focused on summary metrics of these behaviors; for example, the weekly duration of moderate to vigorous physical activity. This study aimed to investigate the associations between the amount and temporal patterns of physical activity and sedentary time and health at mid-life. Physical activity and sedentary time were objectively measured for two weeks using an accelerometer-based activity monitor in the Northern Finland Birth Cohort 1966 46-year follow-up (n=5,621). Participants attended clinical examinations and completed health and behaviour questionnaires. A machine learning method (X-means cluster analysis) was used to identify distinct groups of participants with different patterns of physical activity and sedentary behaviour based on the activity data. A positive, dose-response association was found with perceived health and self-reported leisure time and objectively measured moderate to vigorous physical activity. Higher prolonged sedentary time was associated with better heart rate variability but not with resting heart rate or post-exercise heart rate recovery. Four distinct physical activity clusters (inactive, evening active, moderately active and very active) were recognised. The risk of developing cardiovascular disease was significantly lower in the very active cluster compared to the inactive, and in women also in the moderately active cluster compared to the inactive and evening active clusters. On average, the cardiovascular disease risk was low, indicating good cardiovascular health in the study population. Prolonged sedentary time was associated with cardiac autonomic function, which in this study was not explained by physical activity or cardiorespiratory fitness level. Higher cardiovascular disease risk was found in the activity clusters in which the amount of physical activity was lower and in women took place later in the evening. Results of the study can be used for designing feasible interventions for risk groups with unhealthy physical activity and sedentary behaviour patterns.Tiivistelmä Fyysinen aktiivisuus vähentää sairastavuutta, kuolleisuutta sekä parantaa fyysistä ja psyykkistä terveyttä. Viime aikoina on lisäksi tunnistettu liiallisen paikallaanolon terveyshaitat. Vielä ei tiedetä, miten fyysisen aktiivisuuden ja paikallaanolon ajallinen jakautuminen päivän aikana vaikuttaa terveyteen, koska aiemmat tutkimukset ovat keskittyneet enimmäkseen tiettyihin summamuuttujiin kuten kohtuullisesti kuormittavan liikkumisen määrään viikossa. Työn tarkoituksena oli tutkia fyysisen aktiivisuuden ja paikallaanolon määrän ja ajallisen jakautumisen terveysyhteyksiä keski-iässä. Fyysinen aktiivisuus ja paikallaanolo mitattiin kiihtyvyysanturipohjaisella aktiivisuusmittarilla kahden viikon ajan Pohjois-Suomen vuoden 1966 syntymäkohortin 46-vuotistutkimuksessa (n=5621). Tutkittavat osallistuivat kliinisiin tutkimuksiin ja täyttivät kyselyitä terveydentilastaan ja käyttäytymisestään. Koneoppimismenetelmällä (X-means cluster analysis) tutkittavat luokiteltiin aktiivisuusdatan perusteella ryhmiin, joissa fyysisen aktiivisuuden määrä ja ajallinen jakautuminen päivän aikana poikkesi mahdollisimman paljon ryhmien välillä. Positiivinen annos-vasteyhteys löydettiin koetun terveyden ja itseraportoidun vapaa-ajan liikunnan sekä mitatun kohtuullisesti kuormittavan liikkumisen väliltä. Suurempi pitkittynyt paikallaanoloaika oli yhteydessä parempaan sykevälivaihteluun mutta ei leposykkeeseen tai harjoituksen jälkeiseen sykkeen palautumiseen. Neljä aktiivisuusryhmää tunnistettiin (inaktiiviset, ilta-aktiiviset, kohtuullisen aktiiviset ja erittäin aktiiviset). Sydän- ja verisuonitautien sairastumisriski oli merkitsevästi pienempi erittäin aktiivisessa ryhmässä verrattuna inaktiiviseen ryhmään ja lisäksi naisilla kohtuullisen aktiivisessa ryhmässä verrattuna inaktiiviseen ja ilta-aktiiviseen ryhmään. Sairastumisriski oli keskimäärin matala viitaten hyvään sydänterveyteen tutkimusjoukossa. Pitkillä paikallaanolojaksoilla oli yhteys sydämen autonomiseen säätelyyn, jota tässä työssä ei selittänyt fyysinen aktiivisuus tai aerobinen kunto. Korkeampi sydän- ja verisuonitautien riski löydettiin aktiivisuusryhmistä, joissa fyysisen aktiivisuuden määrä oli vähäisempää ja naisilla painottunut myöhäisempään iltaan. Tutkimuksen tuloksia voidaan hyödyntää interventioiden suunnittelussa riskiryhmille, joiden fyysisen aktiivisuuden ja paikallaanolon piirteet ovat terveydelle haitallisia

Calibration and validation of accelerometer-based activity monitors:a systematic review of machine-learning approaches

Abstract Background: Objective measures using accelerometer-based activity monitors have been extensively used in physical activity (PA) and sedentary behavior (SB) research. To measure PA and SB precisely, the field is shifting towards machine learning-based (ML) approaches for calibration and validation of accelerometer-based activity monitors. Nevertheless, various parameters regarding the use and development of ML-based models, including data type (raw acceleration data versus activity counts), sampling frequency, window size, input features, ML technique, accelerometer placement, and free-living settings, affect the predictive ability of ML-based models. The effects of these parameters on ML-based models have remained elusive, and will be systematically reviewed here. The open challenges were identified and recommendations are made for future studies and directions. Method: We conducted a systematic search of PubMed and Scopus databases to identify studies published before July 2017 that used ML-based techniques for calibration and validation of accelerometer-based activity monitors. Additional articles were manually identified from references in the identified articles. Results: A total of 62 studies were eligible to be included in the review, comprising 48 studies that calibrated and validated ML-based models for predicting the type and intensity of activities, and 22 studies for predicting activity energy expenditure. Conclusions: It appears that various ML-based techniques together with raw acceleration data sampled at 20–30 Hz provide the opportunity of predicting the type and intensity of activities, as well as activity energy expenditure with comparable overall predictive accuracies regardless of accelerometer placement. However, the high predictive accuracy of laboratory-calibrated models is not reproducible in free-living settings, due to transitive and unseen activities together with differences in acceleration signals

Machine-learning models for activity class prediction:a comparative study of feature selection and classification algorithms

Abstract Purpose: Machine-learning (ML) approaches have been repeatedly coupled with raw accelerometry to classify physical activity classes, but the features required to optimize their predictive performance are still unknown. Our aim was to identify appropriate combination of feature subsets and prediction algorithms for activity class prediction from hip-based raw acceleration data. Methods: The hip-based raw acceleration data collected from 27 participants was split into training (70 %) and validation (30 %) subsets. A total of 206 time- (TD) and frequencydomain (FD) features were extracted from 6-second non-overlapping windows of the signal. Feature selection was done using seven filter-based, two wrapper-based, and one embedded algorithm, and classification was performed with artificial neural network (ANN), support vector machine (SVM), and random forest (RF). For every combination between the feature selection method and the classifiers, the most appropriate feature subsets were found and used for model training within the training set. These models were then validated with the left-out validation set. Results: The appropriate number of features for the ANN, SVM, and RF ranged from 20 to 45. Overall, the accuracy of all the three classifiers was higher when trained with feature subsets generated using filter-based methods compared with when they were trained with wrapper-based methods (range: 78.1 %–88 % vs. 66 %–83.5 %). TD features that reflect how signals vary around the mean, how they differ with one another, and how much and how often they change were more frequently selected via the feature selection methods. Conclusions: A subset of TD features from raw accelerometry could be sufficient for ML-based activity classification if properly selected from different axes

Evaluating and enhancing the generalization performance of machine learning models for physical activity intensity prediction from raw acceleration data

Abstract Purpose: To evaluate and enhance the generalization performance of machine learning physical activity intensity prediction models developed with raw acceleration data on populations monitored by different activity monitors. Method: Five datasets from four studies, each containing only hip- or wrist-based raw acceleration data (two hip- and three wrist-based) were extracted. The five datasets were then used to develop and validate artificial neural networks (ANN) in three setups to classify activity intensity categories (sedentary behavior, light, and moderate-to-vigorous). To examine generalizability, the ANN models were developed using within-dataset (leave-one-subject-out) cross-validation, and then cross-tested to other datasets with different accelerometers. To enhance the models’ generalizability, a combination of four of the five datasets was used for training and the fifth dataset for validation. Finally, all the five datasets were merged to develop a single model that is generalizable across the datasets (50% of the subjects from each dataset for training, the remaining for validation). Results: The datasets showed high performance in within-dataset cross-validation (accuracy 71.9–95.4%, Kappa K=0.63–0.94). The performance of the within-dataset validated models decreased when applied to datasets with different accelerometers (41.2–59.9%, K=0.21–0.48). The trained models on merged datasets consisting hip and wrist data predicted the left-out dataset with acceptable performance (65.9–83.7%, K=0.61–0.79). The model trained with all five datasets performed with acceptable performance across the datasets (80.4–90.7%, K=0.68–0.89). Conclusions: Integrating heterogeneous datasets in training sets seems a viable approach for enhancing the generalization performance of the models. Instead, within-dataset validation is not sufficient to understand the models’ performance on other populations with different accelerometers

Correlates of physical activity behavior in adults:a data mining approach

Abstract Purpose: A data mining approach was applied to establish a multilevel hierarchy predicting physical activity (PA) behavior, and to methodologically identify the correlates of PA behavior. Methods: Cross-sectional data from the population-based Northern Finland Birth Cohort 1966 study, collected in the most recent follow-up at age 46, were used to create a hierarchy using the chi-square automatic interaction detection (CHAID) decision tree technique for predicting PA behavior. PA behavior is defined as active or inactive based on machine-learned activity profiles, which were previously created through a multidimensional (clustering) approach on continuous accelerometer-measured activity intensities in one week. The input variables (predictors) used for decision tree fitting consisted of individual, demographical, psychological, behavioral, environmental, and physical factors. Using generalized linear mixed models, we also analyzed how factors emerging from the model were associated with three PA metrics, including daily time (minutes per day) in sedentary (SED), light PA (LPA), and moderate-to-vigorous PA (MVPA), to assure the relative importance of methodologically identified factors. Results: Of the 4582 participants with valid accelerometer data at the latest follow-up, 2701 and 1881 had active and inactive profiles, respectively. We used a total of 168 factors as input variables to classify these two PA behaviors. Out of these 168 factors, the decision tree selected 36 factors of different domains from which 54 subgroups of participants were formed. The emerging factors from the model explained minutes per day in SED, LPA, and/or MVPA, including body fat percentage (SED: B = 26.5, LPA: B = − 16.1, and MVPA: B = − 11.7), normalized heart rate recovery 60 s after exercise (SED: B = -16.1, LPA: B = 9.9, and MVPA: B = 9.6), average weekday total sitting time (SED: B = 34.1, LPA: B = -25.3, and MVPA: B = -5.8), and extravagance score (SED: B = 6.3 and LPA: B = − 3.7). Conclusions: Using data mining, we established a data-driven model composed of 36 different factors of relative importance from empirical data. This model may be used to identify subgroups for multilevel intervention allocation and design. Additionally, this study methodologically discovered an extensive set of factors that can be a basis for additional hypothesis testing in PA correlates research

Physical activity profiles and glucose metabolism:a population‐based cross‐sectional study in older adults

Abstract The aim was to analyze the relationship of accelerometry measured physical activity (PA) and sedentary time (SED) profiles to glucose metabolism in 660 people aged 67‐69 years. In this cross‐sectional study, four different PA profiles were identified (couch potatoes, light movers, sedentary actives, actives) based on moderate to vigorous physical activity (MVPA) and SED. Glucose metabolism was determined by an oral glucose tolerance test. The prevalence of any glucose metabolism disorder was lower in more active PA profiles than in less active profiles (couch potatoes 50%, actives 33%). According to multivariable linear regression, insulin resistance, 120‐min glucose, and insulin values were lower among the actives compared with the couch potatoes (HOMA‐IR: β = −0.239, 95% CI − 0.456 to −0.022, P = .031; 120‐min glucose: β = −0.459, 95% CI − 0.900 to −0.019, P = .041; 120‐min insulin: β = −0.210, 95% CI − 0.372 to −0.049, P = .011). Prevalence of glucose metabolism disorders were lower and insulin sensitivity was better among the actives compared with the couch potatoes. Active lifestyle with daily MVPA and low SED seems to improve glucose metabolism even in older age and should be recommended for older adults

Compositional association of 24-h movement behavior with incident major adverse cardiac events and all-cause mortality

Abstract Cardiovascular disease (CVD) causes a high disease burden. Physical activity (PA) reduces CVD morbidity and mortality. We aimed to determine the relationship between the composition of moderate-to-vigorous PA (MVPA), light PA (LPA), sedentary behavior (SB), and sleep during midlife to the incidence of major adverse cardiac events (MACE) and all-cause mortality at a 7-year follow-up. The study population consisted of Northern Finland Birth Cohort 1966 members who participated in the 46-year follow-up in 2012 and were free of MACE (N = 4147). Time spent in MVPA, LPA, and SB was determined from accelerometer data. Sleep time was self-reported. Hospital visits and deaths were obtained from national registers. Participants were followed until December 31, 2019, or first MACE occurrence (acute myocardial infarction, unstable angina pectoris, stroke, hospitalization due to heart failure, or death due to CVD), death from another cause, or censoring. Cox proportional hazards model was used to estimate hazard ratios of MACE incidence and all-cause mortality. Isotemporal time reallocations were used to demonstrate the dose–response association between time spent in behaviors and outcome. The 24-h time composition was significantly associated with incident MACE and all-cause mortality. More time in MVPA relative to other behaviors was associated with a lower risk of events. Isotemporal time reallocations indicated that the greatest risk reduction occurred when MVPA replaced sleep. Higher MVPA associates with a reduced risk of incident MACE and all-cause mortality after accounting for the 24-h movement composition and confounders. Regular engagement in MVPA should be encouraged in midlife

Comparison and agreement between device-estimated and self-reported sleep periods in adults

Abstract Objectives: Discriminating sleep period from accelerometer data remains a challenge despite many studies have adapted 24-h measurement protocols. We aimed to compare and examine the agreement among device-estimated and self-reported bedtime, wake-up time, and sleep periods in a sample of adults. Materials and methods: Participants (108 adults, 61 females) with an average age of 33.1 (SD 0.4) were asked to wear two wearable devices (Polar Active and Ōura ring) simultaneously and record their bedtime and wake up time using a sleep diary. Sleep periods from Polar Active were detected using an in-lab algorithm, which is openly available. Sleep periods from Ōura ring were generated by commercial Ōura system. Scatter plots, Bland–Altman plots, and intraclass correlation coefficients (ICCs) were used to evaluate the agreement between the methods. Results: Intraclass correlation coefficient values were above 0.81 for bedtimes and wake-up times between the three methods. In the estimation of sleep period, ICCs ranged from 0.67 (Polar Active vs. sleep diary) to 0.76 (Polar Active vs. Ōura ring). Average difference between Polar Active and Ōura ring was −1.8 min for bedtimes and −2.6 min for wake-up times. Corresponding values between Polar Active and sleep diary were −5.4 and −18.9 min, and between Ōura ring and sleep diary −3.6 min and −16.2 min, respectively. Conclusion: Results showed a high agreement between Polar Active activity monitor and Ōura ring for sleep period estimation. There was a moderate agreement between self-report and the two devices in estimating bedtime and wake-up time. These findings suggest that potentially wearable devices can be interchangeably used to detect sleep period, but their accuracy remains limited

Chronotypes and objectively measured physical activity and sedentary time at midlife

Abstract Morning, day, or evening chronotypes differ by the circadian timing of alertness and the preferred timing of sleep. It has been suggested that evening chronotype is associated with low physical activity (PA) and high sedentary time (SED). Our aim was to investigate whether such an association is confirmed by objectively measured PA and SED. In 46‐year follow‐up of the Northern Finland Birth Cohort 1966 study, total PA (MET min/day) and SED (min/day) among 5156 participants were determined using wrist‐worn accelerometers for 14 days. We used the shortened Morningness‐Eveningness Questionnaire to define participants’ chronotypes. As covariates, we used self‐reported physical strenuousness of work, health, and demographics, and clinical measures. We used adjusted general linear models (B coefficients with 95% confidence intervals, CI) to analyze how chronotype was related to total PA or SED. As compared to evening chronotype, men with day and morning chronotypes had higher total PA volumes (adjusted B 75.2, 95% CI [8.1, 142.4], P = .028, and 98.6, [30.2, 167.1], P = .005). Men with day and morning chronotypes had less SED (−35.8, [−53.8, −17.8], P < .0001, and − 38.6, [−56.9, −20.2], P < .0001). Among women, morning chronotype was associated with higher total PA (57.8, [10.5, 105.0], P = .017), whereas no association between chronotype and SED emerged. Evening chronotype was associated with low objectively measured PA in both sexes and with high SED in men, even after adjustments for established potential confounders. Chronotype should be considered in PA promotion.Correction In the abstract, a typo (missing − -sign) was corrected in the sentence : Men with day and morning chronotypes had less SED (−35.8, [−53.8, −17.8], p < 0.0001, and −38.6, [−56.9, −20.2], p < 0.0001)