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BACKGROUND AND AIMS: Physical inactivity, sedentary behaviour (SB), and inadequate sleep are key behavioural risk factors of cardiometabolic diseases. Each behaviour is mainly considered in isolation, despite clear behavioural and biological interdependencies. The aim of this study was to investigate associations of five-part movement compositions with adiposity and cardiometabolic biomarkers. METHODS: Cross-sectional data from six studies (n = 15 253 participants; five countries) from the Prospective Physical Activity, Sitting and Sleep consortium were analysed. Device-measured time spent in sleep, SB, standing, light-intensity physical activity (LIPA), and moderate-vigorous physical activity (MVPA) made up the composition. Outcomes included body mass index (BMI), waist circumference, HDL cholesterol, total:HDL cholesterol ratio, triglycerides, and glycated haemoglobin (HbA1c). Compositional linear regression examined associations between compositions and outcomes, including modelling time reallocation between behaviours. RESULTS: The average daily composition of the sample (age: 53.7 ± 9.7 years; 54.7% female) was 7.7â h sleeping, 10.4â h sedentary, 3.1â h standing, 1.5â h LIPA, and 1.3â h MVPA. A greater MVPA proportion and smaller SB proportion were associated with better outcomes. Reallocating time from SB, standing, LIPA, or sleep into MVPA resulted in better scores across all outcomes. For example, replacing 30â min of SB, sleep, standing, or LIPA with MVPA was associated with -0.63 (95% confidence interval -0.48, -0.79), -0.43 (-0.25, -0.59), -0.40 (-0.25, -0.56), and -0.15 (0.05, -0.34) kg/m2 lower BMI, respectively. Greater relative standing time was beneficial, whereas sleep had a detrimental association when replacing LIPA/MVPA and positive association when replacing SB. The minimal displacement of any behaviour into MVPA for improved cardiometabolic health ranged from 3.8 (HbA1c) to 12.7 (triglycerides) min/day. CONCLUSIONS: Compositional data analyses revealed a distinct hierarchy of behaviours. Moderate-vigorous physical activity demonstrated the strongest, most time-efficient protective associations with cardiometabolic outcomes. Theoretical benefits from reallocating SB into sleep, standing, or LIPA required substantial changes in daily activity.
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Enfermedades Cardiovasculares , Sedestación , Humanos , Femenino , Adulto , Persona de Mediana Edad , Masculino , HDL-Colesterol , Hemoglobina Glucada , Estudios Transversales , Estudios Prospectivos , Ejercicio Físico , Triglicéridos , Sueño , Enfermedades Cardiovasculares/epidemiología , Enfermedades Cardiovasculares/prevención & controlRESUMEN
AIMS/HYPOTHESIS: The aim of this study was to examine the dose-response associations of device-measured physical activity types and postures (sitting and standing time) with cardiometabolic health. METHODS: We conducted an individual participant harmonised meta-analysis of 12,095 adults (mean ± SD age 54.5±9.6 years; female participants 54.8%) from six cohorts with thigh-worn accelerometry data from the Prospective Physical Activity, Sitting and Sleep (ProPASS) Consortium. Associations of daily walking, stair climbing, running, standing and sitting time with a composite cardiometabolic health score (based on standardised z scores) and individual cardiometabolic markers (BMI, waist circumference, triglycerides, HDL-cholesterol, HbA1c and total cholesterol) were examined cross-sectionally using generalised linear modelling and cubic splines. RESULTS: We observed more favourable composite cardiometabolic health (i.e. z score <0) with approximately 64 min/day walking (z score [95% CI] -0.14 [-0.25, -0.02]) and 5 min/day stair climbing (-0.14 [-0.24, -0.03]). We observed an equivalent magnitude of association at 2.6 h/day standing. Any amount of running was associated with better composite cardiometabolic health. We did not observe an upper limit to the magnitude of the dose-response associations for any activity type or standing. There was an inverse dose-response association between sitting time and composite cardiometabolic health that became markedly less favourable when daily durations exceeded 12.1 h/day. Associations for sitting time were no longer significant after excluding participants with prevalent CVD or medication use. The dose-response pattern was generally consistent between activity and posture types and individual cardiometabolic health markers. CONCLUSIONS/INTERPRETATION: In this first activity type-specific analysis of device-based physical activity, ~64 min/day of walking and ~5.0 min/day of stair climbing were associated with a favourable cardiometabolic risk profile. The deleterious associations of sitting time were fully attenuated after exclusion of participants with prevalent CVD and medication use. Our findings on cardiometabolic health and durations of different activities of daily living and posture may guide future interventions involving lifestyle modification.
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Ejercicio Físico , Postura , Sedestación , Caminata , Humanos , Femenino , Ejercicio Físico/fisiología , Persona de Mediana Edad , Masculino , Caminata/fisiología , Postura/fisiología , Sueño/fisiología , Estudios Prospectivos , Acelerometría , Adulto , Biomarcadores/sangre , Anciano , Circunferencia de la Cintura/fisiología , Posición de Pie , HDL-Colesterol/sangre , Estudios Transversales , Triglicéridos/sangre , Índice de Masa Corporal , Enfermedades Cardiovasculares/prevención & control , Enfermedades Cardiovasculares/epidemiología , Conducta Sedentaria , Subida de Escaleras/fisiologíaRESUMEN
BACKGROUND: The more accurate we can assess human physical behaviour in free-living conditions the better we can understand its relationship with health and wellbeing. Thigh-worn accelerometry can be used to identify basic activity types as well as different postures with high accuracy. User-friendly software without the need for specialized programming may support the adoption of this method. This study aims to evaluate the classification accuracy of two novel no-code classification methods, namely SENS motion and ActiPASS. METHODS: A sample of 38 healthy adults (30.8 ± 9.6 years; 53% female) wore the SENS motion accelerometer (12.5 Hz; ±4 g) on their thigh during various physical activities. Participants completed standardized activities with varying intensities in the laboratory. Activities included walking, running, cycling, sitting, standing, and lying down. Subsequently, participants performed unrestricted free-living activities outside of the laboratory while being video-recorded with a chest-mounted camera. Videos were annotated using a predefined labelling scheme and annotations served as a reference for the free-living condition. Classification output from the SENS motion software and ActiPASS software was compared to reference labels. RESULTS: A total of 63.6 h of activity data were analysed. We observed a high level of agreement between the two classification algorithms and their respective references in both conditions. In the free-living condition, Cohen's kappa coefficients were 0.86 for SENS and 0.92 for ActiPASS. The mean balanced accuracy ranged from 0.81 (cycling) to 0.99 (running) for SENS and from 0.92 (walking) to 0.99 (sedentary) for ActiPASS across all activity types. CONCLUSIONS: The study shows that two available no-code classification methods can be used to accurately identify basic physical activity types and postures. Our results highlight the accuracy of both methods based on relatively low sampling frequency data. The classification methods showed differences in performance, with lower sensitivity observed in free-living cycling (SENS) and slow treadmill walking (ActiPASS). Both methods use different sets of activity classes with varying definitions, which may explain the observed differences. Our results support the use of the SENS motion system and both no-code classification methods.
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Acelerometría , Ejercicio Físico , Muslo , Caminata , Humanos , Femenino , Masculino , Adulto , Acelerometría/métodos , Ejercicio Físico/fisiología , Caminata/fisiología , Adulto Joven , Algoritmos , Programas Informáticos , Carrera/fisiología , Ciclismo/fisiología , PosturaRESUMEN
BACKGROUND: Retirement often leads to a more passive lifestyle and may therefore lead to weight gain. This study aims to investigate longitudinal associations between changes in 24-h movement behaviors and BMI and waist circumference in relation to the transition from work to retirement. METHODS: The study population included 213 retiring public sector workers (mean age 63.5 years, standard deviation 1.1) from the Finnish Retirement and Aging study. Before and after retirement participants wore an Axivity accelerometer on their thigh and filled in a daily log for at least four days to measure daily time spent sleeping, in sedentary behavior (SED), light physical activity (LPA) and moderate-to-vigorous physical activity (MVPA). Also, their body mass index (BMI) and waist circumference were measured repeatedly. Compositional linear regression analysis and isotemporal substitution analysis were used to study associations between one-year changes in 24-h movement behaviors and concurrent changes in BMI and waist circumference. RESULTS: An increase in MVPA in relation to sleep, SED and LPA was associated with a decreasing BMI (ß = -0.60, p = 0.04) and waist circumference (ß = -2.14, p = 0.05) over one year from before retirement to after retirement. In contrast, increasing sleep in relation to SED, LPA and MVPA was associated with an increasing BMI (ß = 1.34, p = 0.02). Reallocating 60 min from MVPA to SED or sleep was estimated to increase BMI by on average 0.8-0.9 kg/m2 and waist circumference by 3.0 cm during one year. CONCLUSIONS: During the transition from work to retirement, increasing MVPA was associated with a slight decrease in BMI and waist circumference, whereas increasing sleep was associated with an increasing BMI. Common life transitions, like retirement, should be considered when giving recommendations and guidance for physical activity and sleep.
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Obesidad , Jubilación , Humanos , Persona de Mediana Edad , Obesidad/epidemiología , Obesidad/prevención & control , Ejercicio Físico , Índice de Masa Corporal , Sueño , AcelerometríaRESUMEN
Accelerometers placed on the thigh provide accurate measures of daily physical activity types, postures and sedentary behaviours, over 24 h and across consecutive days. However, the ability to estimate sleep duration or quality from thigh-worn accelerometers is uncertain and has not been evaluated in comparison with the 'gold-standard' measurement of sleep polysomnography. This study aimed to develop an algorithm for sleep estimation using the raw data from a thigh-worn accelerometer and to evaluate it in comparison with polysomnography. The algorithm was developed and optimised on a dataset consisting of 23 single-night polysomnography recordings, collected in a laboratory, from 15 asymptomatic adults. This optimised algorithm was then applied to a separate evaluation dataset, in which, 71 adult males (mean [SD] age 57 [11] years, height 181 [6] cm, weight 82 [13] kg) wore ambulatory polysomnography equipment and a thigh-worn accelerometer, simultaneously, whilst sleeping at home. Compared with polysomnography, the algorithm had a sensitivity of 0.84 and a specificity of 0.55 when estimating sleep periods. Sleep intervals were underestimated by 21 min (130 min, Limits of Agreement Range [LoAR]). Total sleep time was underestimated by 32 min (233 min LoAR). Our results evaluate the performance of a new algorithm for estimating sleep and outline the limitations. Based on these results, we conclude that a single device can provide estimates of the sleep interval and total sleep time with sufficient accuracy for the measurement of daily physical activity, sedentary behaviour, and sleep, on a group level in free-living settings.
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Sueño , Muslo , Masculino , Adulto , Humanos , Persona de Mediana Edad , Polisomnografía/métodos , Reproducibilidad de los Resultados , Algoritmos , Acelerometría , Actigrafía/métodosRESUMEN
The present study aims to describe accelerometer-assessed physical activity (PA) patterns and fulfillment of PA recommendations in a large sample of middle-aged men and women, and to study differences between subgroups of socio-demographic, socio-economic, and lifestyle-related variables. A total of 27 890 (92.5% of total participants, 52% women, aged 50-64 years) middle-aged men and women with at least four days of valid hip-worn accelerometer data (Actigraph GT3X+, wGT3X+ and wGT3X-BT) from the Swedish CArdioPulmonary bioImage Study, SCAPIS, were included. In total, 54.5% of daily wear time was spent sedentary, 39.1% in low, 5.4% in moderate, and only 0.1% in vigorous PA. Male sex, higher education, low financial strain, born in Sweden, and sedentary/light working situation were related to higher sedentary time, but also higher levels of vigorous PA. High BMI and having multiple chronic diseases associated strongly with higher sedentary time and less time in all three PA intensities. All-year physically active commuters had an overall more active PA pattern. The proportion fulfilling current PA recommendations varied substantially (1.4% to 92.2%) depending on data handling procedures and definition used. Twenty-eight percent was defined as having an "at-risk" behavior, which included both high sedentary time and low vigorous PA. In this large population-based sample, a majority of time was spent sedentary and only a fraction in vigorous PA, with clinically important variations between subgroups. This study provides important reference material and emphasizes the importance of a comprehensive assessment of all aspects of the individual PA pattern in future research and clinical practice.
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Ejercicio Físico , Conducta Sedentaria , Acelerometría , Adulto , Estudios de Cohortes , Femenino , Humanos , Masculino , Persona de Mediana Edad , Actividad MotoraRESUMEN
BACKGROUND: The ActiPASS software was developed from the open-source Acti4 activity classification algorithm for thigh-worn accelerometry. However, the original algorithm has not been validated in children or compared with a child-specific set of algorithm thresholds. This study aims to evaluate the accuracy of ActiPASS in classifying activity types in children using 2 published sets of Acti4 thresholds. METHODS: Laboratory and free-living data from 2 previous studies were used. The laboratory condition included 41 school-aged children (11.0 [4.8] y; 46.5% male), and the free-living condition included 15 children (10.0 [2.6] y; 66.6% male). Participants wore a single accelerometer on the dominant thigh, and annotated video recordings were used as a reference. Postures and activity types were classified with ActiPASS using the original adult thresholds and a child-specific set of thresholds. RESULTS: Using the original adult thresholds, the mean balanced accuracy (95% CI) for the laboratory condition ranged from 0.62 (0.56-0.67) for lying to 0.97 (0.94-0.99) for running. For the free-living condition, accuracy ranged from 0.61 (0.48-0.75) for lying to 0.96 (0.92-0.99) for cycling. Mean balanced accuracy for overall sedentary behavior (sitting and lying) was ≥0.97 (0.95-0.99) across all thresholds and conditions. No meaningful differences were found between the 2 sets of thresholds, except for superior balanced accuracy of the adult thresholds for walking under laboratory conditions. CONCLUSIONS: The results indicate that ActiPASS can accurately classify different basic types of physical activity and sedentary behavior in children using thigh-worn accelerometer data.
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INTRODUCTION: Physical activity, sedentary behavior, and sleep, that is, 24-h movement behaviors, often change in the transition from work to retirement, which may affect cardiometabolic health. This study investigates the longitudinal associations between changes in 24-h movement behaviors and cardiometabolic biomarkers during the retirement transition. METHODS: Retiring public sector workers ( n = 212; mean (SD) age, 63.5 (1.1) yr) from the Finnish Retirement and Aging study used a thigh-worn Axivity accelerometer and filled out a diary to obtain data on daily time spent in sedentary behavior (SED), light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA), and sleep before and after retirement (1 yr in-between). Cardiometabolic biomarkers, including LDL-cholesterol, HDL-cholesterol, total/HDL-cholesterol ratio, triglycerides, C-reactive protein, fasting glucose, and insulin, were measured. Associations between changes in 24-h movement behaviors and cardiometabolic biomarkers were analyzed using compositional robust regression and isotemporal substitution analysis. RESULTS: Increasing LPA in relation to remaining behaviors was associated with an increase in HDL-cholesterol and decrease in total/HDL-cholesterol ratio ( P < 0.05 for both). For instance, reallocation of 30 min from sleep/SED to LPA was associated with an increase in HDL-cholesterol by 0.02 mmol·L -1 . Moreover, increasing MVPA in relation to remaining behaviors was associated with a decrease in triglycerides ( P = 0.02). Reallocation of 30 min from SED/sleep to MVPA was associated with 0.07-0.08 mmol·L -1 decrease in triglycerides. Findings related to LDL-cholesterol, C-reactive protein, fasting glucose, and insulin were less conclusive. CONCLUSIONS: During the transition from work to retirement, increasing physical activity at the expense of passive behaviors was associated with a better lipid profile. Our findings suggest that life transitions like retirement could be utilized more as an optimal time window for promoting physical activity and health.
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Biomarcadores , HDL-Colesterol , Ejercicio Físico , Insulina , Jubilación , Conducta Sedentaria , Sueño , Humanos , Persona de Mediana Edad , Masculino , Ejercicio Físico/fisiología , Biomarcadores/sangre , Sueño/fisiología , Femenino , Estudios Longitudinales , HDL-Colesterol/sangre , Insulina/sangre , Glucemia/metabolismo , Proteína C-Reactiva/análisis , Proteína C-Reactiva/metabolismo , Triglicéridos/sangre , Finlandia , Acelerometría , LDL-Colesterol/sangre , Factores de Riesgo CardiometabólicoRESUMEN
Risk assessments of hand-intensive and repetitive work are commonly done using observational methods, and it is important that the methods are reliable and valid. However, comparisons of the reliability and validity of methods are hampered by differences in studies, e.g., regarding the background and competence of the observers, the complexity of the observed work tasks and the statistical methodology. The purpose of the present study was to evaluate six risk assessment methods, concerning inter- and intra-observer reliability and concurrent validity, using the same methodological design and statistical parameters in the analyses. Twelve experienced ergonomists were recruited to perform risk assessments of ten video-recorded work tasks twice, and consensus assessments for the concurrent validity were carried out by three experts. All methods' total-risk linearly weighted kappa values for inter-observer reliability (when all tasks were set to the same duration) were lower than 0.5 (0.15-0.45). Moreover, the concurrent validity values were in the same range with regards to total-risk linearly weighted kappa (0.31-0.54). Although these levels are often considered as being fair to substantial, they denote agreements lower than 50% when the expected agreement by chance has been compensated for. Hence, the risk of misclassification is substantial. The intra-observer reliability was only somewhat higher (0.16-0.58). Regarding the methods ART (Assessment of repetitive tasks of the upper limbs) and HARM (Hand Arm Risk Assessment Method), it is worth noting that the work task duration has a high impact in the risk level calculation, which needs to be taken into account in studies of reliability. This study indicates that when experienced ergonomists use systematic methods, the reliability is low. As seen in other studies, especially assessments of hand/wrist postures were difficult to rate. In light of these results, complementing observational risk assessments with technical methods should be considered, especially when evaluating the effects of ergonomic interventions.
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Mano , Extremidad Superior , Reproducibilidad de los Resultados , Medición de Riesgo/métodos , Ergonomía/métodosRESUMEN
Sickness absence from work has a large adverse impact on both individuals and societies in Sweden and the costs for sickness absence were calculated to 64.6 billion Swedish kronor (approx. 5.6 billion in Euros) in 2020. Although high cardiorespiratory fitness may protect against potential adverse effects of high physical workload, research on the moderating effect of respiratory fitness in the relation between having an occupation with high physical workload and sickness absence is scarce. To study the moderating effect of cardiorespiratory fitness in the association between occupation and psychiatric, musculoskeletal, and cardiorespiratory diagnoses. Data was retrieved from the HPI Health Profile Institute database (1988-2020) and Included 77,366 participants (mean age 41.8 years, 52.5% women) from the Swedish workforce. The sample was chosen based on occupational groups with a generally low education level and differences in physical workload. Hurdle models were used to account for incident sickness absence and the rate of sickness absence days. There were differences in sickness absence between occupational groups for musculoskeletal and cardiorespiratory diagnoses, but not for psychiatric diagnoses. In general, the association between occupation and musculoskeletal and cardiorespiratory diagnoses was moderated by cardiorespiratory fitness in most occupational groups with higher physical workload, whereas no moderating effect was observed for psychiatric diagnoses. The study results encourage community and workplace interventions to both consider variation in physical workload and to maintain and/or improve cardiorespiratory fitness for a lower risk of sickness absence, especially in occupations with high physical workload.
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Capacidad Cardiovascular , Humanos , Femenino , Adulto , Masculino , Carga de Trabajo , Ocupaciones , Ejercicio Físico , Lugar de Trabajo , Aptitud FísicaRESUMEN
BACKGROUND: Device-based measurements of physical behavior, using the current methods, place a large burden on participants. The Motus system could reduce this burden by removing the necessity for in-person meetings, replacing diaries written on paper with digital diaries, and increasing the automation of feedback generation. OBJECTIVE: This study aims to describe the development of the Motus system and evaluate its potential to reduce participant burden in a two-phase usability evaluation. METHODS: Motus was developed around (1) a thigh-worn accelerometer with Bluetooth data transfer; (2) a smartphone app containing an attachment guide, a digital diary, and facilitating automated data transfer; (3) a cloud infrastructure for data storage; (4) an analysis software to generate feedback for participants; and (5) a web-based app for administrators. We recruited 19 adults with a mean age of 45 (SD 11; range 27-63) years, of which 11 were female, to assist in the two-phase evaluation of Motus. A total of 7 participants evaluated the usability of mockups for a smartphone app in phase 1. Participants interacted with the app while thinking aloud, and any issues raised were classified as critical, serious, or minor by observers. This information was used to create an improved and functional smartphone app for evaluation in phase 2. A total of 12 participants completed a 7-day free-living measurement with Motus in phase 2. On day 1, participants attempted 20 system-related tasks under observation, including registration on the study web page, reading the information letter, downloading and navigating the smartphone app, attaching an accelerometer on the thigh, and completing a diary entry for both work and sleep hours. Task completion success and any issues encountered were noted by the observer. On completion of the 7-day measurement, participants provided a rating from 0 to 100 on the System Usability Scale and participated in a semistructured interview aimed at understanding their experience in more detail. RESULTS: The task completion rate for the 20 tasks was 100% for 13 tasks, >80% for 4 tasks, and <50% for 3 tasks. The average rating of system usability was 86 on a 0-100 scale. Thematic analysis indicated that participants perceived the system as easy to use and remember, and subjectively pleasing overall. Participants with shift work reported difficulty with entering sleep hours, and 66% (8/12) of the participants experienced slow data transfer between the app and the cloud infrastructure. Finally, a few participants desired a greater degree of detail in the generated feedback. CONCLUSIONS: Our two-phase usability evaluation indicated that the overall usability of the Motus system is high in free-living. Issues around the system's slow data transfer, participants with atypical work shifts, and the degree of automation and detail of generated feedback should be addressed in future iterations of the Motus system. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/35697.
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BACKGROUND: There is increasing recognition of the need for more comprehensive surveillance data, including information on physical activity of all intensities, sedentary behavior, and sleep. However, meeting this need poses significant challenges for current surveillance systems, which are mainly reliant on self-report. OBJECTIVE: The primary objective of this project is to develop and evaluate the feasibility of a sensor-based system for use in the surveillance of physical activity, sedentary behavior, and sleep (SurPASS) at a national level in Denmark. METHODS: The SurPASS project involves an international, multidisciplinary team of researchers collaborating with an industrial partner. The SurPASS system consists of (1) a thigh-worn accelerometer with Bluetooth connectivity, (2) a smartphone app, (3) an integrated back end, facilitating the automated upload, analysis, storage, and provision of individualized feedback in a manner compliant with European Union regulations on data privacy, and (4) an administrator web interface (web application) to monitor progress. The system development and evaluation will be performed in 3 phases. These phases will include gathering user input and specifications (phase 1), the iterative development, evaluation, and refinement of the system (phase 2), and the feasibility evaluation (phase 3). RESULTS: The project started in September 2020 and completed phase 2 in February 2022. Phase 3 began in March 2022 and results will be made available in 2023. CONCLUSIONS: If feasible, the SurPASS system could be a catalyst toward large-scale, sensor-based surveillance of physical activity, sedentary behavior, and sleep. It could also be adapted for cohort and interventional research, thus contributing to the generation of evidence for both interventions and public health policies and recommendations. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/35697.