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

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 Oura 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 Oura ring were generated by commercial Oura 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. Oura ring). Average difference between Polar Active and Oura 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 Oura ring and sleep diary -3.6 min and -16.2 min, respectively. CONCLUSION: Results showed a high agreement between Polar Active activity monitor and Oura 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.Key MessagesEstimation of sleep period from different devices could be comparable.Difference between sleep periods from monitors and sleep diary are under 20 min.Device-based estimation of sleep period is encouraged in population-based studies.

Residential history and changes in perceived health-The Northern Finland Birth Cohort 1966 study.

Neighbourhood socioeconomic status and physical access to amenities and greenness are factors that have been associated with mental, physical and perceived health. However, associations between long-time exposure to these circumstances and changes in perceived health in the middle-age population have remained a relatively underexamined area. This study aimed to examine the association between residential environmental history and changes in perceived health in the Northern Finland Birth Cohort 1966 (N = 5973) population encompassing the two latest data collections at 31 and 46 years of age. Longitudinal time-varying geographical data on the residential environment's economic dependency ratio, population density, distance to local services and presence of green areas were derived from various spatial registers and linked to cohort members' exact residential history. According to a multivariable logistic regression analysis, having a residential history in municipalities with higher-than-average (poor) economic dependency ratios was associated with higher odds of poor perceived health changing to good. Among men, living farther than 2 km away from local services was associated with a higher risk of change from good perceived health to poor, and living farther than 300 m away from green areas was associated with a lower risk of change from good perceived health to poor. The residential environments's urban/rural context may be one factor contributing to the findings. The results point to the importance of considering local residential area characteristics and residence duration in certain areas as potential determinants of health. Finally, having long-term residential history in areas with poor access to services and amenities has the potential to undermine health during one's lifetime.

Cardiovascular disease risk and all-cause mortality associated with accelerometer-measured physical activity and sedentary time ‒ a prospective population-based study in older adults.

BACKGROUND: Low levels of physical activity (PA) and high sedentary time (ST) are common in older adults and lack of PA is a risk factor for cardiovascular disease (CVD). Knowledge about associations with accelerometer-measured PA, ST and CVD risk in older adults is insufficient. This study examines the associations of accelerometer-measured PA and ST with cardiovascular risk measured using the Framingham risk score (FRS) and all-cause mortality in older adults. METHODS: A population-based sample of 660 (277 men, 383 women) older people (mean age 68.9) participated in the Oulu45 cohort study from 2013‒2015. PA and ST were measured with wrist-worn accelerometers at baseline for two weeks. Ten-year CVD risk (%) was estimated with FRS. The data for all-cause mortality were identified from the Digital and Population Data Services Agency, Finland after an average of 6.2 years follow-up. The associations between moderate to vigorous physical activity (MVPA), light physical activity (LPA), ST and FRS were analyzed using the multivariable linear regression analysis. Associations between LPA, ST and mortality were analyzed using the Cox proportional-hazard regression models. RESULTS: Each 10 min increase in MVPA (ß = -0.779, 95% CI -1.186 to -0.371, p < 0.001) and LPA (ß = -0.293, 95% CI -0.448 to -0.138, p < 0.001) was negatively associated with FRS while a 10 min increase in ST (ß = 0.290, 95% CI 0.158 to 0.421, p < 0.001) was positively associated with FRS. After adjustment for waist circumference, only ST was significantly associated with FRS. Each 10 min increase in LPA was associated with 6.5% lower all-cause mortality risk (HR = 0.935, 95% CI 0.884 to 0.990, p = 0.020) and each 10 min increase in ST with 5.6% increased mortality risk (HR = 1.056, 95% CI 1.007 to 1.108, p = 0.025). CONCLUSION: A higher amount of daily physical activity, at any intensity level, and avoidance of sedentary time are associated with reduced cardiovascular disease risk in older people. Higher time spent in light physical activity and lower sedentary time are associated with lower all-cause mortality.

More Physical Activity, More Work Engagement? A Northern Finland Birth Cohort 1966 Study.

OBJECTIVE: To examine the role of physical activity (PA) and sedentary behavior (SED) for work engagement. METHODS: We used data from Northern Finland Birth Cohort 1966 Study ( n = 3046 to 4356) to analyze self-reported weekly leisure-time physical activity (LTPA), daily leisure-time sitting time (LTST) and work engagement. PA and SED 24-hour were also measured with accelerometer for 14 days. The data were analyzed using linear regression analyses. RESULTS: High self-reported LTPA and sports participation were associated with higher work engagement and its subdimensions. High self-reported ST was associated with lower work engagement, vigor, and absorption. Accelerometer-measured light PA was associated with higher work engagement and vigor, and accelerometermeasured steps were linked to higher vigor. Accelerometer-measured SED was associated with lower work engagement, vigor, and dedication. CONCLUSIONS: Self-reported and accelerometer-measured PA and SED may play a role in people's work engagement.

Cross-Sectional Associations of Sedentary Behavior and Sitting with Serum Lipid Biomarkers in Midlife.

INTRODUCTION: Physical inactivity, excessive total time spent in sedentary behavior (SB) and prolonged sedentary bouts have been proposed to be risk factors for chronic disease morbidity and mortality worldwide. However, which patterns and postures of SB have the most negative impacts on health outcomes is still unclear. This population-based study aimed to investigate the independent associations of the patterns of accelerometer-based overall SB and sitting with serum lipid biomarkers at different moderate- to vigorous-intensity physical activity (MVPA) levels. METHODS: Physical activity and SB were measured in a birth cohort sample ( N = 3272) at 46 yr using a triaxial hip-worn accelerometer in free-living conditions for 14 d. Raw acceleration data were classified into SB and PA using a machine learning-based model, and the bouts of overall SB and sitting were identified from the classified data. The participants also answered health-related questionnaires and participated in clinical examinations. Associations of overall SB (lying and sitting) and sitting patterns with serum lipid biomarkers were investigated using linear regression. RESULTS: The overall SB patterns were more consistently associated with serum lipid biomarkers than the sitting patterns after adjustments. Among the participants with the least and the most MVPA, high total time spent in SB and SB bouts of 15-29.99 and ≥30 min were associated with impaired lipid metabolism. Among those with moderate amount of MVPA, higher time spent in SB and SB bouts of 15-29.99 min was unfavorably associated with serum lipid biomarkers. CONCLUSIONS: The associations between SB patterns and serum lipid biomarkers were dependent on MVPA level, which should be considered when planning evidence-based interventions to decrease SB in midlife.

Association between accelerometer-measured physical activity, glucose metabolism, and waist circumference in older adults.

AIMS: To examine the association of physical activity (PA) and sedentary time (ST) with glucose metabolism according to waist circumference (WC) in older people. METHODS: A population-based sample of 702 individuals (aged 67-70 years) wore wrist-worn accelerometers for two weeks and underwent an oral glucose tolerance test. The associations between moderate-to-vigorous (MVPA) and light (LPA) PA, ST, and glucose metabolism across the tertiles of WC were analysed using general linear regression. RESULTS: Among highest WC tertile, LPA negatively associated with fasting insulin (ß =  - 0.047, 95% CI - 0.082 to - 0.012), HOMA-IR (ß =  - 0.098, 95% CI - 0.184 to - 0.012), and HOMA-ß (ß =  - 3.367, CI - 6.570 to - 0.783). ST associated with 120 min glucose (ß = 0.140, CI 0.021 to 0.260). Among lowest WC tertile, MVPA negatively associated with 30 min insulin (ß =  - 0.086, 95% CI - 0.168 to - 0.004) and 120 min insulin (ß =  - 0.160, 95% CI - 0.257 to - 0.063) and positively associated with Matsuda index (ß = 0.076, 95% CI 0.014 to 0.139). Light PA negatively associated with 120 min insulin (ß =  - 0.054, 95% CI - 0.104 to - 0.005). CONCLUSION: With the limitation of the cross-sectional study, reducing ST and increasing LPA may be beneficial for glucose metabolism among abdominally obese older adults. Lean older adults could benefit more from increasing MVPA.

Parental Factors Related to Physical Activity among Adolescent Men Living in Built and Natural Environment: A Population-Based MOPO Study.

Introduction: Physical inactivity is a global concern, especially among adolescent men. Little research has been done on the association between parental factors and young adults' physical activity in the context of residential environment. We aimed to reveal what parental factors are associated with physical activity among adolescent men living in built and natural environments. Methods: A population-based sample of 1,904 men (mean age = 17.9, SD = 0.7 years) completed a questionnaire regarding physical activity, parental factors, and lifestyle in Northern Finland in 2012 and 2013. Geographical information system methods and dominant land-use type were used to define the residential environment in a 1-kilometer radius buffer zone surrounding each participant's home address. If the residential area included more artificial surfaces, it was defined as a built environment, and areas including more nature were defined as natural environments. Results: According to multivariable analyses, a mother's physical activity (OR = 1.9; 95% CI: 1.3-2.8) was positively associated with the physical activity of adolescent men living in built environments, and the father's physical activity was positively associated with the physical activity of adolescent men living in natural environments (2.8; 1.7-4.8). Self-rated health (built 5.9 [4.0-8.7]; natural 5.2 [3.0-9.0]) was positively associated with physical activity level. Those with symptoms of depression were more likely to be physically inactive (built 0.5 [0.3-0.8]; natural 0.3 [0.1-0.6]). Adolescent men were equally physically active regardless of the living environment. Conclusions: The level of physical activity of parents, self-rated health, and depressive symptoms should be considered when designing physical activity promotions for adolescent men according to their residential environments.

Accumulation patterns of sedentary time and breaks and their association with cardiometabolic health markers in adults.

Breaking up sedentary time with physical activity (PA) could modify the detrimental cardiometabolic health effects of sedentary time. Our aim was to identify profiles according to distinct accumulation patterns of sedentary time and breaks in adults, and to investigate how these profiles are associated with cardiometabolic outcomes. Participants (n = 4439) of the Northern Finland Birth Cohort 1966 at age 46 years wore a hip-worn accelerometer for 7 consecutive days during waking hours. Uninterrupted ≥1-min sedentary bouts were identified, and non-sedentary bouts in between two consecutive sedentary bouts were considered as sedentary breaks. K-means clustering was performed with 65 variables characterizing how sedentary time was accumulated and interrupted. Linear regression was used to determine the association of accumulation patterns with cardiometabolic health markers. Four distinct groups were formed as follows: "Couch potatoes" (n = 1222), "Prolonged sitters" (n = 1179), "Shortened sitters" (n = 1529), and "Breakers" (n = 509). Couch potatoes had the highest level of sedentariness and the shortest sedentary breaks. Prolonged sitters, accumulating sedentary time in bouts of ≥15-30 min, had no differences in cardiometabolic outcomes compared with Couch potatoes. Shortened sitters accumulated sedentary time in bouts lasting <15 min and performed more light-intensity PA in their sedentary breaks, and Breakers performed more light-intensity and moderate-to-vigorous PA. These latter two profiles had lower levels of adiposity, blood lipids, and insulin sensitivity, compared with Couch potatoes (1.1-25.0% lower values depending on the cardiometabolic health outcome, group, and adjustments for potential confounders). Avoiding uninterrupted sedentary time with any active behavior from light-intensity upwards could be beneficial for cardiometabolic health in adults.

Compositional Associations of Sleep and Activities within the 24-h Cycle with Cardiometabolic Health Markers in Adults.

PURPOSE: This study aimed to examine how compositions of 24-h time use and time reallocations between movement behaviors are associated with cardiometabolic health in a population-based sample of middle-age Finnish adults. METHODS: Participants were 3443 adults 46 yr of age from the Northern Finland Birth Cohort 1966 study. Participants wore a hip-worn accelerometer for 14 d from which time spent in sedentary behavior (SB), light-intensity physical activity (LPA), and moderate- to vigorous-intensity physical activity (MVPA) were determined. These data were combined with self-reported sleep to obtain the 24-h time-use composition. Cardiometabolic outcomes included adiposity markers, blood lipid levels, and markers of glucose control and insulin sensitivity. Multivariable-adjusted regression analysis, using a compositional data analysis approach based on isometric log-ratio transformation, was used to examine associations between movement behaviors with cardiometabolic outcomes. RESULTS: More daily time in MVPA and LPA, relative to other movement behaviors, was consistently favorably associated with all cardiometabolic outcomes. For example, relative to time spent in other behaviors, 30 min·d-1 more MVPA and LPA were both associated with lower 2-h post-glucose load insulin level (-11.8% and -2.7%, respectively). Relative to other movement behaviors, more daily time in SB was adversely associated with adiposity measures, lipid levels, and markers of insulin sensitivity, and more daily time asleep was adversely associated with adiposity measures, blood lipid, fasting plasma glucose, and 2-h insulin. For example, 60 min·d-1 more SB and sleep relative to the remaining behaviors were both associated with higher 2-h insulin (3.5% and 5.7%, respectively). CONCLUSIONS: Altering daily movement behavior compositions to incorporate more MVPA at the expense of any other movement behavior, or more LPA at the expense of SB or sleep, could help to improve cardiometabolic health in midadulthood.

Satellite Imaging-Based Residential Greenness and Accelerometry Measured Physical Activity at Midlife-Population-Based Northern Finland Birth Cohort 1966 Study.

BACKGROUND: Recently, the importance of light physical activity (LPA) for health has been emphasized, and residential greenness has been positively linked to the level of LPA and a variety of positive health outcomes. However, people spend less time in green environments because of urbanization and modern sedentary leisure activities. AIMS: In this population-based study, we investigated the association between objectively measured residential greenness and accelerometry measured physical activity (PA), with a special interest in LPA and gender differences. METHODS: The study was based on the Northern Finland Birth Cohort 1966 (5433 members). Participants filled in a postal questionnaire and underwent clinical examinations and wore a continuous measurement of PA with wrist-worn Polar Active Activity Monitor accelerometers for two weeks. The volume of PA (metabolic equivalent of task or MET) was used to describe the participant's total daily activity (light: 2-3.49 MET; moderate: 3.5-4.99 MET; vigorous: 5-7.99 MET; very vigorous: ≥8 MET). A geographic information system (GIS) was used to assess the features of each individual's residential environment. The normalized difference vegetation index (NDVI) was used for the objective quantification of residential greenness. Multiple linear regression and a generalized additive model (GAM) were used to analyze the association between residential greenness and the amount of PA at different intensity levels. RESULTS: Residential greenness (NDVI) was independently associated with LPA (unadjusted ß = 174; CI = 140, 209) and moderate physical activity (MPA) (unadjusted ß = 75; CI = 48, 101). In the adjusted model, residential greenness was positively and significantly associated with LPA (adjusted ß = 70; CI = 26, 114). In men, residential greenness was positively and significantly associated with LPA (unadjusted ß = 224; CI = 173, 275), MPA (unadjusted ß = 75; CI = 48, 101), and moderate to vigorous physical activity (MVPA) (unadjusted ß = 89; CI = 25, 152). In women, residential greenness was positively related to LPA (unadjusted ß = 142; CI = 96, 188) and inversely associated with MPA (unadjusted ß = -22; CI = -36, -8), vigorous/very vigorous physical activity (VPA/VVPA) (unadjusted ß = -49; CI = -84, -14), and MVPA (unadjusted ß = -71; CI = -113, -29). In the final adjusted models, residential greenness was significantly associated only with the amount of LPA in men (adjusted ß = 140; CI = 75, 204). CONCLUSIONS: Residential greenness was positively associated with LPA in both genders, but the association remained significant after adjustments only in men. Residential greenness may provide a supportive environment for promoting LPA.

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

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.

Association between chronic diseases and falls among a sample of older people in Finland.

BACKGROUND: Falls are a major problem for older people and recurrent fallers are especially prone to severe consequences due to falls. This study investigated the association between chronic conditions and falls. METHODS: Responses from 872 older persons (age 65-98) to a health questionnaire were used in the analyses. Characteristics and disease prevalence between recurrent fallers, one-time fallers and non-fallers were compared. A hierarchical clustering method was applied to find combinations of chronic conditions that were associated with recent recurrent falling. RESULTS: The results showed that recurrent fallers had a higher number of diseases (median 4, interquartile range, IQR = 2.0-5.0) compared to non-fallers (median 2, IQR = 1.0-3.0). Eight clusters were formed based on the data. The participants in the low chronic disease cluster were younger, more physically active, not frail, and had fewer geriatric conditions. Multiple chronic disease cluster participants were older, less physically active, overweight (body mass index, BMI > 30), at risk of malnutrition, and had more geriatric conditions. Significantly increased risk of recurrent falls relative to the low chronic cluster was found for respondents in the osteoporosis cluster and multiple chronic disease cluster (OR = 5.65, 95% confidence interval CI: 1.23-25.85, p = 0.026, and OR = 13.42, 95% CI: 2.47-72.96, p = 0.002, respectively). None of the clusters were associated with increased risk of one-time falling. CONCLUSIONS: The results implicate that the number of chronic diseases is related with risk of recurrent falling. Furthermore, the results implicate the potential of identifying certain combinations of chronic diseases that increase fall risk by analyzing health record data, although further studies are needed with a larger population sample.

Chronotypes and objectively measured physical activity and sedentary time at midlife.

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.

A Persuasive mHealth Behavioral Change Intervention for Promoting Physical Activity in the Workplace: Feasibility Randomized Controlled Trial.

BACKGROUND: Employees in an office setting are more likely to remain physically inactive. Physical inactivity has become one of the major barriers to overcoming the risk factors for anxiety, depression, coronary heart disease, certain cancers, and type 2 diabetes. Currently, there is a gap in mobile health (mHealth) apps to promote physical activity (PA) for workers in the workplace. Studies on behavior change theories have concluded that health apps generally lack the use of theoretical constructs. OBJECTIVE: The objective of this study was to study the feasibility of a persuasive app aimed at encouraging PA among employees and to understand the motivational aspects behind the implementation of mHealth apps among office workers. METHODS: A 4-week study using a mixed methods (quantitative and qualitative) design was conducted with office-based employees in cities in 4 countries: Oulu, Finland; Carlow, Ireland; London, United Kingdom; and Dhaka, Bangladesh. Of the 220 invited participants (experimental group, n=115; control group, n=105), 84 participated (experimental group, n=56; control group, n=28), consisting of working-age volunteers working in an office setting. Participants used 2 different interventions: The experimental group used an mHealth app for PA motivation, and the control group used a paper diary. The purpose was to motivate employees to engage in healthier behavior regarding the promotion of PA in the workplace. A user-centered design process was followed to design, develop, and evaluate the mHealth app, incorporating self-determination theory (SDT) and using game elements. The paper diary had no specific theory-driven approach, design technique, nor game elements. RESULTS: Compliance with app usage remained relatively low, with 27 participants (experimental group, n=20; control group, n=7) completing the study. The results support the original hypothesis that the mHealth app would help increase PA (ie, promoting daily walking in the workplace) in comparison to a paper diary (P=.033). The mHealth app supported 2 of the basic SDT psychological needs, namely autonomy (P=.004) and competence (P=.014), but not the needs of relatedness (P=.535). CONCLUSIONS: The SDT-based mHealth application motivated employees to increase their PA in the workplace. However, compliance with app usage remained low. Future research should further develop the app based on user feedback and test it in a larger sample.

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) in the self-reported and objectively measured PA volumes (P value range = 0.023 to <0.0001) in the FTC and NFBC1966. For both self-reported and objectively measured PA, individuals in the highest PRS deciles had significantly (11%-28%) higher PA volumes compared with the lowest PRS deciles (P value range = 0.017 to <0.0001). CONCLUSIONS: PA is a multifactorial phenotype, and the PRS constructed based on UK Biobank results accounted for statistically significant but overall small proportion of the variation in PA in the Finnish cohorts. Using identical methods to assess PA and including less common and rare variants in the construction of PRS may increase the proportion of PA explained by the PRS.

Evaluating and Enhancing the Generalization Performance of Machine Learning Models for Physical Activity Intensity Prediction From Raw Acceleration Data.

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.

Nocturnal finger skin temperature in menstrual cycle tracking: ambulatory pilot study using a wearable Oura ring.

BACKGROUND: Body temperature is a common method in menstrual cycle phase tracking because of its biphasic form. In ambulatory studies, different skin temperatures have proven to follow a similar pattern. The aim of this pilot study was to assess the applicability of nocturnal finger skin temperature based on a wearable Oura ring to monitor menstrual cycle and predict menstruations and ovulations in real life. METHODS: Volunteer women (n = 22) wore the Oura ring, measured ovulation through urine tests, and kept diaries on menstruations at an average of 114.7 days (SD 20.6), of which oral temperature was measured immediately after wake-up at an average of 1.9 cycles (SD 1.2). Skin and oral temperatures were compared by assessing daily values using repeated measures correlation and phase mean values and differences between phases using dependent t-test. Developed algorithms using skin temperature were tested to predict the start of menstruation and ovulation. The performance of algorithms was assessed with sensitivity and positive predictive values (true positive defined with different windows around the reported day). RESULTS: Nocturnal skin temperatures and oral temperatures differed between follicular and luteal phases with higher temperatures in the luteal phase, with a difference of 0.30 °C (SD 0.12) for skin and 0.23 °C (SD 0.09) for oral temperature (p < 0.001). Correlation between skin and oral temperatures was found using daily temperatures (r = 0.563, p < 0.001) and differences between phases (r = 0.589, p = 0.004). Menstruations were detected with a sensitivity of 71.9-86.5% in window lengths of ±2 to ±4 days. Ovulations were detected with the best-performing algorithm with a sensitivity of 83.3% in fertile window from - 3 to + 2 days around the verified ovulation. Positive predictive values had similar percentages to those of sensitivities. The mean offset for estimations were 0.4 days (SD 1.8) for menstruations and 0.6 days (SD 1.5) for ovulations with the best-performing algorithm. CONCLUSIONS: Nocturnal skin temperature based on wearable ring showed potential for menstrual cycle monitoring in real life conditions.

Intensity and temporal patterns of physical activity and cardiovascular disease risk in midlife.

Physical activity (PA) and sedentary time (SED) are associated with the risk of cardiovascular disease (CVD), but the temporal patterns of these behaviors most beneficial for cardiovascular health remain unknown. We aimed to identify the intensity and temporal patterns of PA and SED measured continuously by an accelerometer and their relationship with CVD risk. At the age of 46 years, 4582 members (1916 men; 2666 women) of the Northern Finland Birth Cohort 1966 study underwent continuous measurement of PA with Polar Active (Polar Electro, Finland) accelerometers for one week. X-means clustering was applied based on 10 min average MET (metabolic equivalent) values during the measurement period. Ten-year risk of CVD was estimated using the Framingham risk model. Most of the participants had low risk for CVD. Four distinct PA clusters were identified that were well differentiable by the intensity and temporal patterns of activity (inactive, evening active, moderately active, very active). A significant difference in 10-year CVD risk across the clusters was found in men (p = 0.028) and women (p < 0.001). Higher levels of HDL cholesterol were found in more active clusters compared to less active clusters (p < 0.001) in both genders. In women total cholesterol was lower in the moderately active cluster compared to the inactive and evening active clusters (p = 0.001). Four distinct PA clusters were recognized based on accelerometer data and X-means clustering. A significant difference in CVD risk across the clusters was found in both genders. These results can be used in developing and promoting CVD prevention strategies.

Dose-response relation of self-reported and accelerometer-measured physical activity to perceived health in middle age-the Northern Finland Birth Cohort 1966 Study.

BACKGROUND: Regular physical activity (PA) promotes health and decreases mortality. The positive relationship between PA and perceived health (PH) is well known. However, previous research in the field has often used self-reported PA measures. The aim of this population-based NFBC1966 birth cohort study was to assess the relationship between both self-reported and objectively measured PA and PH in midlife. METHODS: A sample group of 6384 participants (2878 men, 3506 women, response rate 62%) aged 46 completed a questionnaire on PH and health behaviors, including items on weekly leisure time physical activity (LTPA) and daily sitting time (ST). PH was dichotomized as good (very good or good) and other (fair, poor, or very poor). PA was measured with a wrist-worn Polar Active (Polar Electro, Finland) accelerometer for 14 days (n = 5481, 98%) and expressed as daily average time spent in moderate to vigorous intensity PA (MVPA). Odds ratios (OR) and 95% confidence intervals for good PH were calculated using binary logistic regression and adjusted for relevant demographic, socioeconomic, and health characteristics, and ST. RESULTS: The level of PA was positively associated with PH after adjustments with covariates and ST. There was a dose-response relationship across the PA quartiles according to the adjusted multivariable models. Self-reported LTPA was more strongly associated with good PH (OR from 1.72 to 4.33 compared to lowest PA quartile) than objectively measured PA (OR from 1.37 to 1.66 compared to lowest PA quartile). CONCLUSIONS: In this large population-based birth cohort study, we for the first time show a positive dose-response relationship of both self-reported and objectively measured PA to PH, the relationship being stronger for self-reported LTPA. Despite the cross-sectional design of this study, the results from this large sample suggest that both self-reported and objectively measured physical activity are strongly associated with PH, which is a predictor of morbidity and mortality, and regular PA should be encouraged in midlife.

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

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.