Accelerometers are widely used to measure sedentary time, physical activity, physical activity energy expenditure (PAEE), and sleep-related behaviors, with the ActiGraph being the most frequently ...used brand by researchers. However, data collection and processing criteria have evolved in a myriad of ways out of the need to answer unique research questions; as a result there is no consensus.
The purpose of this review was to: (1) compile and classify existing studies assessing sedentary time, physical activity, energy expenditure, or sleep using the ActiGraph GT3X/+ through data collection and processing criteria to improve data comparability and (2) review data collection and processing criteria when using GT3X/+ and provide age-specific practical considerations based on the validation/calibration studies identified.
Two independent researchers conducted the search in PubMed and Web of Science. We included all original studies in which the GT3X/+ was used in laboratory, controlled, or free-living conditions published from 1 January 2010 to the 31 December 2015.
The present systematic review provides key information about the following data collection and processing criteria: placement, sampling frequency, filter, epoch length, non-wear-time, what constitutes a valid day and a valid week, cut-points for sedentary time and physical activity intensity classification, and algorithms to estimate PAEE and sleep-related behaviors. The information is organized by age group, since criteria are usually age-specific.
This review will help researchers and practitioners to make better decisions before (i.e., device placement and sampling frequency) and after (i.e., data processing criteria) data collection using the GT3X/+ accelerometer, in order to obtain more valid and comparable data.
CRD42016039991.
Evidence suggests that participation in physical activity may support young people's current and future mental health. Although previous reviews have examined the relationship between physical ...activity and a range of mental health outcomes in children and adolescents, due to the large increase in published studies there is a need for an update and quantitative synthesis of effects.
The objectives of this study were to determine the effect of physical activity interventions on mental health outcomes by conducting a systematic review and meta-analysis, and to systematically synthesize the observational evidence (both longitudinal and cross-sectional studies) regarding the associations between physical activity and sedentary behavior and mental health in preschoolers (2-5 years of age), children (6-11 years of age) and adolescents (12-18 years of age).
A systematic search of the PubMed and Web of Science electronic databases was performed from January 2013 to April 2018, by two independent researchers. Meta-analyses were performed to examine the effect of physical activity on mental health outcomes in randomized controlled trials (RCTs) and non-RCTs (i.e. quasi-experimental studies). A narrative synthesis of observational studies was conducted. Studies were included if they included physical activity or sedentary behavior data and at least one psychological ill-being (i.e. depression, anxiety, stress or negative affect) or psychological well-being (i.e. self-esteem, self-concept, self-efficacy, self-image, positive affect, optimism, happiness and satisfaction with life) outcome in preschoolers, children or adolescents.
A total of 114 original articles met all the eligibility criteria and were included in the review (4 RCTs, 14 non-RCTs, 28 prospective longitudinal studies and 68 cross-sectional studies). Of the 18 intervention studies, 12 (3 RCTs and 9 non-RCTs) were included in the meta-analysis. There was a small but significant overall effect of physical activity on mental health in children and adolescents aged 6-18 years (effect size 0.173, 95% confidence interval 0.106-0.239, p < 0.001, percentage of total variability attributed to between-study heterogeneity I
= 11.3%). When the analyses were performed separately for children and adolescents, the results were significant for adolescents but not for children. Longitudinal and cross-sectional studies demonstrated significant associations between physical activity and lower levels of psychological ill-being (i.e. depression, stress, negative affect, and total psychological distress) and greater psychological well-being (i.e. self-image, satisfaction with life and happiness, and psychological well-being). Furthermore, significant associations were found between greater amounts of sedentary behavior and both increased psychological ill-being (i.e. depression) and lower psychological well-being (i.e. satisfaction with life and happiness) in children and adolescents. Evidence on preschoolers was nearly non-existent.
Findings from the meta-analysis suggest that physical activity interventions can improve adolescents' mental health, but additional studies are needed to confirm the effects of physical activity on children's mental health. Findings from observational studies suggest that promoting physical activity and decreasing sedentary behavior might protect mental health in children and adolescents. PROSPERO Registration Number: CRD42017060373.
The inter-relationship between physical activity, sedentary behaviour and sleep (collectively defined as physical behaviours) is of interest to researchers from different fields. Each of these ...physical behaviours has been investigated in epidemiological studies, yet their codependency and interactions need to be further explored and accounted for in data analysis. Modern accelerometers capture continuous movement through the day, which presents the challenge of how to best use the richness of these data. In recent years, analytical approaches first applied in other scientific fields have been applied to physical behaviour epidemiology (eg, isotemporal substitution models, compositional data analysis, multivariate pattern analysis, functional data analysis and machine learning). A comprehensive description, discussion, and consensus on the strengths and limitations of these analytical approaches will help researchers decide which approach to use in different situations. In this context, a scientific workshop and meeting were held in Granada to discuss: (1) analytical approaches currently used in the scientific literature on physical behaviour, highlighting strengths and limitations, providing practical recommendations on their use and including a decision tree for assisting researchers' decision-making; and (2) current gaps and future research directions around the analysis and use of accelerometer data. Advances in analytical approaches to accelerometer-determined physical behaviours in epidemiological studies are expected to influence the interpretation of current and future evidence, and ultimately impact on future physical behaviour guidelines.
Digital clinical measures based on data collected by wearable devices have seen rapid growth in both clinical trials and healthcare. The widely-used measures based on wearables are epoch-based ...physical activity counts using accelerometer data. Even though activity counts have been the backbone of thousands of clinical and epidemiological studies, there are large variations of the algorithms that compute counts and their associated parameters-many of which have often been kept proprietary by device providers. This lack of transparency has hindered comparability between studies using different devices and limited their broader clinical applicability. ActiGraph devices have been the most-used wearable accelerometer devices for over two decades. Recognizing the importance of data transparency, interpretability and interoperability to both research and clinical use, we here describe the detailed counts algorithms of five generations of ActiGraph devices going back to the first AM7164 model, and publish the current counts algorithm in ActiGraph's ActiLife and CentrePoint software as a standalone Python package for research use. We believe that this material will provide a useful resource for the research community, accelerate digital health science and facilitate clinical applications of wearable accelerometry.
Physical fitness is an important marker of current and future health status, yet the association between physical fitness and indicators of mental health in youth has not been systematically reviewed ...and meta-analyzed.
The aim of this work was to systematically review and meta-analyze the association between physical fitness components (i.e. cardiorespiratory fitness, muscular fitness, speed-agility, flexibility and fitness composite) and mental health indicators (i.e. psychological well-being and psychological ill-being) in preschoolers, children and adolescents.
Systematic review and meta-analysis.
Systematic searches were conducted in PubMed, Web of Science and Scopus from database inception to May 2020.
Studies (cross-sectional, longitudinal and intervention designs) were included if they measured at least one physical fitness component and one mental health indicator in healthy youth (2-18 years).
A total of 58 unique studies (52 cross-sectional, 4 longitudinal and 4 intervention studies) met all eligibility criteria and were included. There was a significant positive overall association between physical fitness and mental health in children and adolescents (pooled r = 0.206, p < 0.001). We found suggestive evidence of moderation by age group, fitness components and socioeconomic status (all p < 0.08). No relevant studies focusing on preschoolers were identified. Evidence based on longitudinal and intervention studies was limited.
We observed a small to medium sized positive association between physical fitness and overall mental health in youth. However, as the majority of studies were cross-sectional, additional longitudinal and intervention studies are needed to provide evidence of causation.
PROSPERO registration number CRD42017080005.
The aims of the present article are to systematically review and meta-analyze the existing evidence on: 1) differences in physical activity (PA), sedentary behavior (SB), cardiorespiratory fitness ...(CRF) and muscular strength (MST) between metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO); and 2) the prognosis of all-cause mortality and cardiovascular disease (CVD) mortality/morbidity in MHO individuals, compared with the best scenario possible, i.e., metabolically healthy normal-weight (MHNW), after adjusting for PA, SB, CRF or MST. Our systematic review identified 67 cross-sectional studies to address aim 1, and 11 longitudinal studies to address aim 2. The major findings and conclusions from the current meta-analysis are: 1) MHO individuals are more active, spend less time in SB, and have a higher level of CRF (yet no differences in MST) than MUO individuals, suggesting that their healthier metabolic profile could be at least partially due to these healthier lifestyle factors and attributes. 2) The meta-analysis of cohort studies which accounted for PA (N = 10 unique cohorts, 100% scored as high-quality) support the notion that MHO individuals have a 24–33% higher risk of all-cause mortality and CVD mortality/morbidity compared to MHNW individuals. This risk was borderline significant/non-significant, independent of the length of the follow-up and lower than that reported in previous meta-analyses in this topic including all type of studies, which could be indicating a modest reduction in the risk estimates as a consequence of accounting for PA. 3) Only one study has examined the role of CRF in the prognosis of MHO individuals. This study suggests that the differences in the risk of all-cause mortality and CVD mortality/morbidity between MHO and MHNW are largely explained by differences in CRF between these two phenotypes.
Accelerometers’ accuracy for sedentary time (ST) and moderate-to-vigorous physical activity (MVPA) classification depends on accelerometer placement, data processing, activities, and sample ...characteristics. As intensities differ by age, this study sought to determine intensity cut-points at various wear locations people more than 70 years old. Data from 59 older adults were used for calibration and from 21 independent participants for cross-validation purposes. Participants wore accelerometers on their hip and wrists while performing activities and having their energy expenditure measured with portable calorimetry. ST and MVPA were defined as ≤1.5 metabolic equivalents (METs) and ≥3 METs (1 MET = 2.8 mL/kg/min), respectively. Receiver operator characteristic (ROC) analyses showed fair-to-good accuracy (area under the curve AUC = 0.62–0.89). ST cut-points were 7 mg (cross-validation: sensitivity = 0.88, specificity = 0.80) and 1 count/5 s (cross-validation: sensitivity = 0.91, specificity = 0.96) for the hip; 18 mg (cross-validation: sensitivity = 0.86, specificity = 0.86) and 102 counts/5 s (cross-validation: sensitivity = 0.91, specificity = 0.92) for the non-dominant wrist; and 22 mg and 175 counts/5 s (not cross-validated) for the dominant wrist. MVPA cut-points were 14 mg (cross-validation: sensitivity = 0.70, specificity = 0.99) and 54 count/5 s (cross-validation: sensitivity = 1.00, specificity = 0.96) for the hip; 60 mg (cross-validation: sensitivity = 0.83, specificity = 0.99) and 182 counts/5 s (cross-validation: sensitivity = 1.00, specificity = 0.89) for the non-dominant wrist; and 64 mg and 268 counts/5 s (not cross-validated) for the dominant wrist. These cut-points can classify ST and MVPA in older adults from hip- and wrist-worn accelerometers.
Background
Exercise holds promise as a non‐pharmacological intervention for the improvement of sleep quality. Therefore, this study investigates the effects of different training modalities on sleep ...quality parameters.
Material & methods
A total of 69 (52.7% women) middle‐aged sedentary adults were randomized to (a) control group, (b) physical activity recommendation from the World Health Organization, (c) high‐intensity interval training (HIIT) and (d) high‐intensity interval training group adding whole‐body electromyostimulation training (HIITEMS). Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI) scale and accelerometers.
Results
All intervention groups showed a lower PSQI global score (all P < .022). HIIT‐EMS group improved all accelerometer parameters, with higher total sleep time and sleep efficiency, and lower wake after sleep onset (all P < .016). No differences were found between groups in any sleep quality parameter.
Conclusion
In conclusion, exercise training induced an improvement in subjective sleep quality in sedentary middleaged adults. Moreover, HIIT‐EMS training showed an improvement in objective sleep quality parameters (total sleep time, sleep efficiency and wake after sleep onset) after 12 weeks of exercise intervention. The changes observed in the HIIT‐EMS group were not statistically different to the other exercise modalities.
The associations of movement behaviours (physical activity PA, sedentary behaviour SB, and sleep) with body composition and physical fitness from pre-school to childhood, as well as the direction of ...the associations, could provide important information for healthy lifestyle promotion in children. This study investigated the longitudinal and bidirectional associations of movement behaviours with body composition and physical fitness measured at 4 and 9 years of age.
This longitudinal study included baseline (n = 315, 4.5 SD = 0.1 years) and follow-up data (n = 231, 9.6 SD = 0.1 years) from the MINISTOP study. Movement behaviours were measured for 7 days using wrist-worn accelerometers, body composition with air-displacement plethysmography, and physical fitness with the ALPHA health-related fitness test battery. Cross-lagged panel models and mediation analyses were performed in combination with compositional data analysis.
We did not observe direct associations of the movement behaviours at 4 years with either body composition or physical fitness at 9 years (all P > 0.05). However, fat mass index at 4 years was negatively associated with vigorous PA (VPA), relative to remaining behaviours (VPA, β = - 0.22, P = 0.002) and light PA (LPA), relative to SB and sleep (β = - 0.19, P = 0.016) at 9 years. VPA (relative to remaining), moderate PA (MPA) (relative to LPA, SB, and sleep), and SB (relative to sleep) tracked from 4 to 9 years (all β ≥ 0.17, all P < 0.002), and these behaviours shared variance with fat mass index (all|β| ≥ 0.19, all P < 0.019), and aerobic, motor, and muscular fitness (all|β| ≥ 0.19, all P < 0.014) at 9 years. Mediation analysis suggested that the tracking of VPA (relative to remaining behaviours) from 4 to 9 years was negatively associated with fat mass index (β ≥ - 0.45, P = 0.012), and positively with aerobic fitness at 9 years (β ≥ 1.64, P = 0.016).
PA and SB tracked from the pre-school years into childhood. Fat mass index at 4 years of age was negatively associated with VPA (relative to remaining behaviours) and LPA (relative to SB and sleep) at 9 years of age. The tracking of VPA was associated with lower fat mass index and higher aerobic fitness at 9 years of age. These findings suggest that higher levels of VPA in pre-school age, if maintained throughout childhood, may support the development of healthy body composition and aerobic fitness levels in later childhood.
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