Low movement, deep-learned sitting patterns, and sedentary behavior in the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE)

BACKGROUND/OBJECTIVES

Sedentary behavior (SB) has both movement and postural components, but most SB research has only assessed low movement, especially in children. The purpose of this study was to compare estimates and health associations of SB when derived from a standard accelerometer cut-point, a novel sitting detection technique (CNN Hip Accelerometer Posture for Children; CHAP-Child), and both combined.

METHODS

Data were from the International Study of Childhood Obesity, Lifestyle, and the Environment (ISCOLE). Participants were 6103 children (mean ± SD age 10.4 ± 0.56 years) from 12 countries who wore an ActiGraph GT3X+ accelerometer on the right hip for approximately one week. We calculated SB time, mean SB bout duration, and SB breaks using a cut-point (SBmovement), CHAP-Child (SBposture), and both methods combined (SBcombined). Mixed effects regression was used to test associations of SB variables with pediatric obesity variables (waist circumference, body fat percentage, and body mass index z-score).

RESULTS

After adjusting for MVPA, SBposture showed several significant obesity associations favoring lower mean SB bout duration (b = 0.251-0.449; all p < 0.001) and higher SB breaks (b = -0.005--0.052; all p < 0.001). Lower total SB was unexpectedly related to greater obesity (b = -0.077--0.649; p from <0.001-0.02). For mean SB bout duration and SB breaks, more associations were observed for SBposture (n = 5) than for SBmovement (n = 3) or SBcombined (n = 1), and tended to have larger magnitude as well.

CONCLUSIONS

Using traditional measures of low movement as a surrogate for SB may lead to underestimated or undetected adverse associations between SB and obesity. CHAP-Child allows assessment of sitting posture using hip-worn accelerometers. Ongoing work is needed to understand how low movement and posture are related to one another, as well as their potential health implications.

Greater accelerometer-measured physical activity is associated with better cognition and cerebrovascular health in older adults

OBJECTIVES

Physical activity (PA) may help maintain brain structure and function in aging. Since the intensity of PA needed to effect cognition and cerebrovascular health remains unknown, we examined associations between PA and cognition, regional white matter hyperintensities (WMH), and regional cerebral blood flow (CBF) in older adults.

METHOD

Forty-three older adults without cognitive impairment underwent magnetic resonance imaging (MRI) and comprehensive neuropsychological assessment. Waist-worn accelerometers objectively measured PA for approximately one week.

RESULTS

Higher time spent in moderate to vigorous PA (MVPA) was uniquely associated with better memory and executive functioning after adjusting for all light PA. Higher MVPA was also uniquely associated with lower frontal WMH volume although the finding was no longer significant after additionally adjusting for age and accelerometer wear time. MVPA was not associated with CBF. Higher time spent in all light PA was uniquely associated with higher CBF but not with cognitive performance or WMH volume.

CONCLUSIONS

Engaging in PA may be beneficial for cerebrovascular health, and MVPA in particular may help preserve memory and executive function in otherwise cognitively healthy older adults. There may be differential effects of engaging in lighter PA and MVPA on MRI markers of cerebrovascular health although this needs to be confirmed in future studies with larger samples. Future randomized controlled trials that increase PA are needed to elucidate cause-effect associations between PA and cerebrovascular health.

Association of perceived and objective neighborhood walkability with accelerometer-measured physical activity and sedentary time in the Maastricht Study

BACKGROUND

We investigated the association of neighborhood walkability with accelerometer-measured physical activity (PA) and sedentary behavior (SB) and examined whether objective and subjective measures of walkability resulted in similar findings.

METHODS

PA and SB from the first 7689 Maastricht Study participants ages 40-75 from 2010 to 2017 were measured using accelerometers for 7 days. Mean daily step count, light-intensity PA, moderate- to vigorous- intensity PA (MVPA), and SB were calculated. Objective walkability was measured by the 7-component Dutch Walkability Index within 500 m Euclidean buffers around residential addresses of participants. Subjective walkability was obtained from the Abbreviated Neighborhood Environment Walkability Scale. Linear regression models analyzed the associations of walkability with PA and SB, controlling for potential confounders.

RESULTS

Objective walkability was negatively associated with light intensity PA in the most walkable quartile (b = -14.58, 95% CI = -20.94, -8.23). Compared to participants living in the least walkable neighborhoods, those in the most walkable quartile had statistically significantly higher SB levels (b = 11.64, 95% CI = 4.95, 18.32). For subjective walkability, mean daily step count was significantly higher in the most walkable quartile (b = 509.60, 95% CI = 243.38, 775.81). Higher subjective walkability was positively associated with MVPA (b = 4.40, 95% CI = 2.56, 6.23).

CONCLUSION

Living in a neighborhood with higher objective walkability was associated with lower levels of PA and higher SB levels while higher subjective walkability was associated with higher levels of PA. These results show discordant findings and thus, the effect of walkability on participant PA and SB within our sample is to be determined.

Sleep, physical activity, and psychological outcomes in children and adolescents with pediatric onset multiple sclerosis

BACKGROUND

Sleep, physical activity (PA) and sedentary behavior (SED) have bidirectional associations with mental health in children. The relationships among sleep, PA, SED, with depressive and fatigue symptoms have not been investigated in Pediatric Onset Multiple Sclerosis (POMS) but are needed to inform sleep and PA behavior change interventions.

OBJECTIVES

(1) To describe sleep quality including: sleep efficiency, latency, total sleep time, number of awakenings, time in bed, and wake after sleep onset using actigraphy in children and adolescents ages 11 to 18 diagnosed with POMS, and to compare these sleep metrics to those of an age- and sex-matched non-MS group (2) To examine the relationship between time spent in sedentary, light (LIPA), moderate and vigorous PA (MVPA), sleep quality, with depression, fatigue, and quality of life in children and adolescents with POMS and an age and sex matched non-MS group.

METHODS

A cross-sectional study recruited children and adolescents with POMS ages 11 to 18 years followed at a tertiary pediatric hospital (Toronto, Canada) and an age and sex matched non-MS group from the general population. Participants were consented prior to initiation of study procedures. Participants wore an Actiwatch monitor and GT3X accelerometer and completed standardized questionnaires validated to capture data on sleep disturbances, depression, fatigue, and quality of life. Objective sleep data were collected using an Actiwatch including sleep efficiency, total sleep time, number of awakenings, wake after sleep onset (WASO), and sleep latency. A GT3X accelerometer was used to collect PA data including time spent in SED, light (LPA), and moderate to vigorous (MVPA) PA. Correlational analyses and tests of difference were used to compare the groups.

RESULTS

25 POMS (21F; 16.6 years ±1.1 yrs., median Expanded Disability Status Scale (EDSS) =1.5, IQR=1) and 25 Non-MS (22 F; 16±1.3 yrs.) took part. POMS had higher BMI (T= -5.1, P<0.001) compared to Non-MS. No differences in sleep efficiency (MS mean = 87%, vs. 88%) sleep time (MS Mean = 7.3 hrs. vs. 7.4 hrs.,), WASO (MS mean=37 mins. vs. 36 mins), latency (MS mean=15 mins vs. 11 mins), SED (MS mean =763 mins. vs. 730 mins) or PA (MS, mean LPA = 68 mins. vs 60 mins; MS mean MVPA = 12.7 mins. vs. 12.4 mins). Within POMS, higher sleep efficiency was associated with more SED (SR= 0.4, p = 0.05), while higher sleep efficiency was associated with less SED in Non-MS (SR = -0.7, p< 0.0). In children with POMS, less sleep time, shorter sleep onset latency and more WASO was associated with more SED (SR range = -0.45 to -0.58, P< 0.01). Higher sleep efficiency was associated with less fatigue. Less WASO was associated with lower depression, lower fatigue (SR = 0.67, p<0.01) and better quality of life (SR= -0.6, p<0.01). Greater LPA was associated with lower sleep onset latency (-0.45, p<0.05).

CONCLUSIONS

Children with POMS did not differ in Actiwatch monitored sleep quality metrics. However, within the POMS group sleep quality was associated with better fatigue, depression and QOL. Further, total sleep time, WASO and latency associated with time spent SED and LPA, which independently associate with mental health outcome. Longitudinal work should determine the temporal associations between WASO, sleep latency, sleep time, PA, and mental health outcomes and whether reallocation of specific sleep or PA behaviors (time to sleep, total sleep time, sedentary to MVPA) result in improved depression fatigue, or quality of life in children and adolescents with POMS.

Accelerometric Classification of Resting and Postural Tremor Amplitude

Clinical rating scales for tremors have significant limitations due to low resolution, high rater dependency, and lack of applicability in outpatient settings. Reliable, quantitative approaches for assessing tremor severity are warranted, especially evaluating treatment effects, e.g., of deep brain stimulation (DBS). We aimed to investigate how different accelerometry metrics can objectively classify tremor amplitude of Essential Tremor (ET) and tremor in Parkinson's Disease (PD). We assessed 860 resting and postural tremor trials in 16 patients with ET and 25 patients with PD under different DBS settings. Clinical ratings were compared to different metrics, based on either spectral components in the tremorband or pure acceleration, derived from simultaneous triaxial accelerometry captured at the index finger and wrist. Nonlinear regression was applied to a training dataset to determine the relationship between accelerometry and clinical ratings, which was then evaluated in a holdout dataset. All of the investigated accelerometry metrics could predict clinical tremor ratings with a high concordance (>70%) and substantial interrater reliability (Cohen's weighted Kappa > 0.7) in out-of-sample data. Finger-worn accelerometry performed slightly better than wrist-worn accelerometry. We conclude that triaxial accelerometry reliably quantifies resting and postural tremor amplitude in ET and PD patients. A full release of our dataset and software allows for implementation, development, training, and validation of novel methods.

A Scoping Review of the Validity and Reliability of Smartphone Accelerometers When Collecting Kinematic Gait Data

The aim of this scoping review is to evaluate and summarize the existing literature that considers the validity and/or reliability of smartphone accelerometer applications when compared to 'gold standard' kinematic data collection (for example, motion capture). An electronic keyword search was performed on three databases to identify appropriate research. This research was then examined for details of measures and methodology and general study characteristics to identify related themes. No restrictions were placed on the date of publication, type of smartphone, or participant demographics. In total, 21 papers were reviewed to synthesize themes and approaches used and to identify future research priorities. The validity and reliability of smartphone-based accelerometry data have been assessed against motion capture, pressure walkways, and IMUs as 'gold standard' technology and they have been found to be accurate and reliable. This suggests that smartphone accelerometers can provide a cheap and accurate alternative to gather kinematic data, which can be used in ecologically valid environments to potentially increase diversity in research participation. However, some studies suggest that body placement may affect the accuracy of the result, and that position data correlate better than actual acceleration values, which should be considered in any future implementation of smartphone technology. Future research comparing different capture frequencies and resulting noise, and different walking surfaces, would be useful.

Predicting a Fall Based on Gait Anomaly Detection: A Comparative Study of Wrist-Worn Three-Axis and Mobile Phone-Based Accelerometer Sensors

Falls by the elderly pose considerable health hazards, leading not only to physical harm but a number of other related problems. A timely alert about a deteriorating gait, as an indication of an impending fall, can assist in fall prevention. In this investigation, a comprehensive comparative analysis was conducted between a commercially available mobile phone system and two wristband systems: one commercially available and another representing a novel approach. Each system was equipped with a singular three-axis accelerometer. The walk suggestive of a potential fall was induced by special glasses worn by the participants. The same standard machine-learning techniques were employed for the classification with all three systems based on a single three-axis accelerometer, yielding a best average accuracy of 86%, a specificity of 88%, and a sensitivity of 86% via the support vector machine (SVM) method using a wristband. A smartphone, on the other hand, achieved a best average accuracy of 73% also with an SVM using only a three-axis accelerometer sensor. The significance analysis of the mean accuracy, sensitivity, and specificity between the innovative wristband and the smartphone yielded a p-value of 0.000. Furthermore, the study applied unsupervised and semi-supervised learning methods, incorporating principal component analysis and t-distributed stochastic neighbor embedding. To sum up, both wristbands demonstrated the usability of wearable sensors in the early detection and mitigation of falls in the elderly, outperforming the smartphone.

Accelerometry applications and methods to assess standing balance in older adults and mobility-limited patient populations: a narrative review

Accelerometers provide an opportunity to expand standing balance assessments outside of the laboratory. The purpose of this narrative review is to show that accelerometers are accurate, objective, and accessible tools for balance assessment. Accelerometry has been validated against current gold standard technology, such as optical motion capture systems and force plates. Many studies have been conducted to show how accelerometers can be useful for clinical examinations. Recent studies have begun to apply classification algorithms to accelerometry balance measures to discriminate populations at risk for falls. In addition to healthy older adults, accelerometry can monitor balance in patient populations such as Parkinson's disease, multiple sclerosis, and traumatic brain injury. The lack of software packages or easy-to-use applications have hindered the shift into the clinical space. Lack of consensus on outcome metrics has also slowed the clinical adoption of accelerometer-based balance assessments. Future studies should focus on metrics that are most helpful to evaluate balance in specific populations and protocols that are clinically efficacious.

Day-to-day variability in accelerometer-measured physical activity in mid-aged Australian adults

PURPOSE

The aim was to use accelerometer data to describe day-to-day variability in physical activity in a single week, according to sociodemographic variables, in mid-aged Australian adults.

METHODS

Data were from participants in the How Areas in Brisbane Influence HealTh and AcTivity (HABITAT) study who took part in a 2014 sub-study (N = 612; Mean age 60.6 [SD 6.9; range 48-73]). Participants wore a triaxial accelerometer (ActiGraph wGT3X-BT) on their non-dominant wrist for seven days, and data were expressed as acceleration in gravitational equivalent units (1 mg = 0.001 g). These were, used to estimate daily acceleration (during waking hours) and daily time spent in moderate-vigorous physical activity (MVPA, defined as ≥ 100mg). Coefficient of variation (calculated as [standard deviation/mean of acceleration and MVPA across the seven measurement days] * 100%) was used to describe day-to-day variability.

RESULTS

Average values for both acceleration (24.1-24.8 mg/day) and MVPA (75.9-79.7 mins/day) were consistent across days of the week, suggesting little day-to-day variability (at the group level). However, over seven days, average individual day-to-day variability in acceleration was 18.8% (SD 9.3%; range 3.4-87.7%) and in MVPA was 35.4% (SD 15.6%; range 7.3-124.6%), indicating considerable day-to-day variability in some participants. While blue collar workers had the highest average acceleration (28.6 mg/day) and MVPA (102.5 mins/day), their day-to-day variability was low (18.3% for acceleration and 31.9% for MVPA). In contrast, variability in acceleration was highest in men, those in professional occupations and those with high income; and variability in MVPA was higher in men than in women.

CONCLUSION

Results show group-level estimates of average acceleration and MVPA in a single week conceal considerable day-to-day variation in how mid-age Australians accumulate their acceleration and MVPA on a daily basis. Overall, there was no clear relationship between overall volume of activity and variability. Future studies with larger sample sizes and longitudinal data are needed to build on the findings from this study and increase the generalisability of these findings to other population groups.

Criterion validity of wrist accelerometry for assessing energy intake via the intake-balance technique

BACKGROUND

Intake-balance assessments measure energy intake (EI) by summing energy expenditure (EE) with concurrent change in energy storage (ΔES). Prior work has not examined the validity of such calculations when EE is estimated via open-source techniques for research-grade accelerometry devices. The purpose of this study was to test the criterion validity of accelerometry-based intake-balance methods for a wrist-worn ActiGraph device.

METHODS

Healthy adults (n = 24) completed two 14-day measurement periods while wearing an ActiGraph accelerometer on the non-dominant wrist. During each period, criterion values of EI were determined based on ΔES measured by dual X-ray absorptiometry and EE measured by doubly labeled water. A total of 11 prediction methods were tested, 8 derived from the accelerometer and 3 from non-accelerometry methods (e.g., diet recall; included for comparison). Group-level validity was assessed through mean bias, while individual-level validity was assessed through mean absolute error, mean absolute percentage error, and Bland-Altman analysis.

RESULTS

Mean bias for the three best accelerometry-based methods ranged from -167 to 124 kcal/day, versus -104 to 134 kcal/day for the non-accelerometry-based methods. The same three accelerometry-based methods had mean absolute error of 323-362 kcal/day and mean absolute percentage error of 18.1-19.3%, versus 353-464 kcal/day and 19.5-24.4% for the non-accelerometry-based methods. All 11 methods demonstrated systematic bias in the Bland-Altman analysis.

CONCLUSIONS

Accelerometry-based intake-balance methods have promise for advancing EI assessment, but ongoing refinement is necessary. We provide an R package to facilitate implementation and refinement of accelerometry-based methods in future research (see paulhibbing.com/IntakeBalance).

PIPTO: Precise Inertial-Based Pipeline for Threshold-Based Fall Detection Using Three-Axis Accelerometers

After traffic-related incidents, falls are the second cause of human death, presenting the highest percentage among the elderly. Aiming to address this problem, the research community has developed methods built upon different sensors, such as wearable, ambiance, or hybrid, and various techniques, such as those that are machine learning- and heuristic based. Concerning the models used in the former case, they classify the input data between fall and no fall, and specific data dimensions are required. Yet, when algorithms that adopt heuristic techniques, mainly using thresholds, are combined with the previous models, they reduce the computational cost. To this end, this article presents a pipeline for detecting falls through a threshold-based technique over the data provided by a three-axis accelerometer. This way, we propose a low-complexity system that can be adopted from any acceleration sensor that receives information at different frequencies. Moreover, the input lengths can differ, while we achieve to detect multiple falls in a time series of sum vector magnitudes, providing the specific time range of the fall. As evaluated on several datasets, our pipeline reaches high performance results at 90.40% and 91.56% sensitivity on MMsys and KFall, respectively, while the generated specificity is 93.96% and 85.90%. Lastly, aiming to facilitate the research community, our framework, entitled PIPTO (drawing inspiration from the Greek verb "πι´πτω", signifying "to fall"), is open sourced in Python and C.

Association of Sit-to-Stand Capacity and Free-Living Performance Using Thigh-Worn Accelerometers among 60- to 90-Yr-Old Adults

PURPOSE

Five times sit-to-stand (STS) test is commonly used as a clinical assessment of lower-extremity functional ability, but its association with free-living performance has not been studied. Therefore, we investigated the association between laboratory-based STS capacity and free-living STS performance using accelerometry. The results were stratified according to age and functional ability groups.

METHODS

This cross-sectional study included 497 participants (63% women) 60-90 yr old from three independent studies. A thigh-worn triaxial accelerometer was used to estimate angular velocity in maximal laboratory-based STS capacity and in free-living STS transitions over 3-7 d of continuous monitoring. Functional ability was assessed with short physical performance battery.

RESULTS

Laboratory-based STS capacity was moderately associated with the free-living mean and maximal STS performance ( r = 0.52-0.65, P < 0.01). Angular velocity was lower in older compared with younger and in low- versus high-functioning groups, in both capacity and free-living STS variables (all P < 0.05). Overall, angular velocity was higher in capacity compared with free-living STS performance. The STS reserve (test capacity - free-living maximal performance) was larger in younger and in high-functioning groups compared with older and low-functioning groups (all P < 0.05).

CONCLUSIONS

Laboratory-based STS capacity and free-living performance were found to be associated. However, capacity and performance are not interchangeable but rather provide complementary information. Older and low-functioning individuals seemed to perform free-living STS movements at a higher percentage of their maximal capacity compared with younger and high-functioning individuals. Therefore, we postulate that low capacity may limit free-living performance.

Observational longitudinal association between waking movement behaviours and psychological distress among adolescents using isotemporal analysis: DADOS study

This study aimed to examine the impact of reallocating time spent in waking movement behaviours at baseline on indicators of psychological distress at 24-month follow-up using isotemporal substitution regression models among a sample of Spanish adolescents. The DADOS (Deporte, ADOlescencia y Salud) study is a 3-year longitudinal observational research project carried out between years 2015-2017. The analyses included 197 adolescents (91 girls) aged 13.9 ± 0.3 years at baseline. Waking movement behaviours were assessed by a wrist-worn GENEActiv triaxial accelerometer and expressed as minutes/day of light physical activity (LPA), moderate-vigorous physical activity (MVPA) and time spent in sedentary behaviour (SB). The Behaviour Assessment System for Children and Adolescents (level 3 for adolescents) was used to assess psychological distress indicators (i.e., anxiety, social stress, and risk of depression). Results showed significant associations only for girls. The substitution of 10 min/day of SB or LPA at baseline with 10 min/day of MVPA was associated with lower levels of anxiety (both p ≤ 0.01) and social stress (both p < 0.05) at follow-up. The substitution of 10 min/day of SB with 10 min/day of LPA was associated with higher levels of anxiety at follow-up (p = 0.01). These findings highlight the need of specific physical activity recommendations for mental health paying special attention to sex-differences.

Validation of an accelerometer system for measuring physical activity and sedentary behavior in healthy children and adolescents

The study aims to assess the concurrent validity of the SENS motion® accelerometer system for device-based measurement of physical activity and sedentary behavior in healthy children and adolescents. Thirty-six healthy children and adolescents (mean ± standard deviation (SD) age, 10.2 ± 2.3 years) were fitted with three SENS sensors while performing standardized activities including walking, fast walking, sitting/lying, and arm movements. Data from the sensors were compared with video observations (reference criteria). The agreement between SENS motion® and observation was analyzed using Student's t-test and illustrated in Bland-Altman plots. The concurrent validity was further evaluated using intraclass correlation coefficient (ICC) and was expressed as standard error of measurement (SEM) and minimal detectable change (MDC). Strong agreement was found between SENS and observation for walking time, sedentary time, and lying time. In contrast, moderate agreement was observed for number of steps, sitting time, and time with and without arm movement. ICC2.1 values were overall moderate to excellent (0.5-0.94), with correspondingly low SEM% for walking time, sedentary time, lying time, and time with arm movement (2-9%). An acceptable SEM% level was reached for both steps and sitting time (11% and 12%). For fast walking time, the results showed a weak agreement between the measurement methods, and the ICC value was poor.

CONCLUSION

SENS motion® seems valid for detecting physical activity and sedentary behavior in healthy children and adolescents with strong agreement and moderate to excellent ICC values. Furthermore, the explorative results on arm movements seem promising.

WHAT IS KNOWN

• Inactivity and sedentary behavior follow an increasing trend among children and adolescents. • SENS motion® seems to be valid for measuring physical activity and sedentary behavior in adults and elderly patients.

WHAT IS NEW

• SENS motion® seems valid with strong agreement between video observations and SENS measurement, and ICC values are moderate to excellent when measuring physical activity and sedentary behavior in healthy children and adolescents. • SENS motion® seems promising for detection of arm movements.

Assessment of Training Load, Sleep, Injuries, and Operational Physical Performance During Basic Military Qualification

INTRODUCTION

Optimizing training load (TL) and sleep is essential to maximize physical performance and prevent musculoskeletal injuries (MSKIs) for Canadian forces recruits during the 10-week basic military qualification (BMQ) course. The purpose of this study was to assess the TL, sleep duration, the occurrence of MSKIs during the BMQ, and the operation fitness performance during the BMQ.

MATERIALS AND METHODS

Forty Canadian recruits, eight females and 32 males, (age 24 ± 5 years; height 176.4 ± 10.4 cm), were monitored with an accelerometer (GENEActiv) on their wrist between weeks 1 and 9 to evaluate the TL and sleep duration. During weeks 2 and 10, the recruits completed an operational fitness evaluation. Injury surveillance was performed over 10 weeks.

RESULTS

TL intensity was significantly different (P = 0.0001) from week to week. The weekly average total time of moderate and vigorous physical activity was 189.7 ± 48.1 min and 44.7 ± 15.2 min, respectively. The average sleep duration was 5.4 ± 0.4 h per night and decreased to 4.2 h ± 0.4 during field exercises. A significant difference in sleep duration was observed between recruits with and without a MSKI. The recruits accumulated a total of 95 days under medical restrictions with an average of 3.8 consecutive days. The VO2peak estimated from the Fitness for Operational Requirements of Canadian Armed Forces Employment job-based simulation test significantly improved from weeks 2 to 10 (pre, 47.1 ± 6.3; post: 50.2 ± 5.8; P = 0.001).

CONCLUSIONS

TL is of high magnitude and varies from week to week. The reported mean sleep duration per week may perhaps negatively impact the occurrence of MSKI. No significant improvement was detected in operational fitness by the end of the BMQ.

Global Accelerometer-derived Physical Activity Levels from Preschoolers to Adolescents: A Multilevel Meta-analysis and Meta-regression

BACKGROUND

Global level physical activity surveillance studies were primarily based on self-report data that could generate inaccurate results.

PURPOSE

To investigate global accelerometer-measured daily moderate to vigorous physical activity (MVPA) changes from preschool age to adolescence as well as gender differences in MVPA while adjusting for the geographic regions and major MVPA cut points.

METHODS

A comprehensive search was conducted through August 2020 that includes 30 databases such as Academic Search Ultimate, Child Development & Adolescent Studies, Education Full Text, ERIC, General Science, PsycINFO, ScienceDirect, and SPORTDiscuss. We included both cross-sectional and longitudinal MVPA tracking studies with daily MVPA being measured by waist-worn accelerometers and determined by either Freedson 3 METs, 4 METs, or Evenson cut points for preschoolers, children, and adolescents.

RESULTS

Researchers analyzed 84 studies reporting on 124 effect sizes with a total of 57,587 participants. The combined data showed significant MVPA differences among various continents of participants (p < .001) or cut points (p < .05-.001) for both preschoolers, children, and adolescents. Globally, when continents and cut points were controlled, individuals' daily MVPA time decreased every year by an average of 7.88, 10.37, and 6.68 min from preschool age to adolescence, preschool age to children, and children to adolescence, respectively. When cut points and continents were controlled, boys had significantly higher daily MVPA than girls for all three age groups (p < .001).

CONCLUSIONS

Globally, individuals' daily MVPA starts to decline dramatically as early as the beginning of preschool age. Early intervention is needed to counteract the high decline rate in MVPA.

Unsupervised Noise Reductions for Gravitational Reference Sensors or Accelerometers Based on the Noise2Noise Method

Onboard electrostatic suspension inertial sensors are important applications for gravity satellites and space gravitational-wave detection missions, and it is important to suppress noise in the measurement signal. Due to the complex coupling between the working space environment and the satellite platform, the process of noise generation is extremely complex, and traditional noise modeling and subtraction methods have certain limitations. With the development of deep learning, applying it to high-precision inertial sensors to improve the signal-to-noise ratio is a practically meaningful task. Since there is a single noise sample and unknown true value in the measured data in orbit, odd-even sub-samplers and periodic sub-samplers are designed to process general signals and periodic signals, and adds reconstruction layers consisting of fully connected layers to the model. Experimental analysis and comparison are conducted based on simulation data, GRACE-FO acceleration data, and Taiji-1 acceleration data. The results show that the deep learning method is superior to traditional data smoothing processing solutions.

Sedentary and physical activity time differs between self-reported ATLS-2 physical activity questionnaire and accelerometer measurements in adolescents and young adults in the United Arab Emirates

BACKGROUND

Most young adults and adolescents in the United Arab Emirates (UAE) do not meet the established internationally recommended physical activity levels per day. The Arab Teen Lifestyle Study (ATLS) physical activity questionnaire has been recommended for measuring self-reported physical activity of Arab adolescents and young adults (aged 14 years to mid-twenties). The first version of the ATLS has been validated with accelerometers and pedometers (r ≤ 0.30). The revised version of the questionnaire (ATLS-2, 2021) needs further validation. The aim of this study was to validate the self-reported subjective sedentary and physical activity time of the ATLS-2 (revised version) physical activity questionnaire with that of Fibion accelerometer-measured data.

METHODS

In this cross-sectional study, 131 healthy adolescents and young adults (aged 20.47 ± 2.16 [mean ± SD] years (range 14-25 years), body mass index 23.09 ± 4.45 (kg/m2) completed the ATLS-2 and wore the Fibion accelerometer for a maximum of 7 days. Participants (n = 131; 81% non-UAE Arabs (n = 106), 13% Asians (n = 17) and 6% Emiratis (n = 8)) with valid ATLS-2 data without missing scores and Fibion data of minimum 10 h/day for at least 3 weekdays and 1 weekend day were analyzed. Concurrent validity between the two methods was assessed by the Spearman rho correlation and Bland-Altman plots.

RESULTS

The questionnaire underestimated sedentary and physical activity time compared to the accelerometer data. Only negligible to weak correlations (r ≤ 0.12; p > 0.05) were found for sitting, walking, cycling, moderate intensity activity, high intensity activity and total activity time. In addition, a proportional/systematic bias was evident in the plots for all but two (walking and moderate intensity activity time) of the outcome measures of interest.

CONCLUSIONS

Overall, self-reported ATLS-2 sedentary and physical activity time had low correlation and agreement with objective Fibion accelerometer measurements in adolescents and young adults in the UAE. Therefore, sedentary and physical activity assessment for these groups should not be limited to self-reported measures.

Association of Accelerometer-Measured Physical Activity and Sedentary Behavior With Incident Cardiovascular Disease, Myocardial Infarction, and Ischemic Stroke: The Women's Health Study

Background Few studies have investigated associations of acclerometer-based assessments of physical activity (PA) and sedentary behavior (SB) with incidence of cardiovascular disease (CVD) and its components. This prospective cohort study assessed the associations of accelerometer-measured PA and SB with total CVD, myocardial infarction, and ischemic stroke (IS). Methods and Results The authors included 16 031 women aged 62 years and older, free of CVD, with adherent accelerometer wear (≥10 hours/day for ≥4 days) from the Women's Health Study (mean age, 71.4 years [SD, 5.6 years]). Hip-worn ActiGraph GT3X+ accelerometers measured total volume of PA (total average daily vector magnitude), minutes per day of high-light PA and moderate to vigorous PA (MVPA), and SB. Women reported diagnoses of CVD, which were adjudicated using medical records and death certificates. Hazard ratios (HRs) were estimated for each exposure, and 95% CIs using Cox proportional hazards models were adjusted for accelerometer wear time, age, self-reported general health, postmenopausal hormone therapy, smoking status, and alcohol use. The hypothetical effect of replacing 10 minutes/day of SB or high-light PA with MVPA on CVD incidence was assessed using adjusted isotemporal substitution Cox models. Over a mean of 7.1 years (SD, 1.6 years) of follow-up, 482 total CVD cases, 107 myocardial infarction cases, and 181 IS cases were diagnosed. Compared with the lowest quartiles of total average daily vector magnitude and MVPA (≤60 minutes), women who were in the highest quartiles (>120 minutes of MVPA) had a 43% (95% CI, 24%-58%) and 38% (95% CI, 18%-54%) lower hazard of total CVD, respectively. Estimates were similar for total average daily vector magnitude and MVPA with IS, but PA was not associated with myocardial infarction overall. High-light PA was not associated with any CVD outcomes. Women who spent <7.4 hours sedentary per day had a 33% (95% CI, 11%-49%) lower hazard of total CVD compared with those who spent ≥9.5 hours sedentary. Replacing 10 minutes of SB with MVPA was associated with a 4% lower incidence of total CVD (HR, 0.96 [95% CI, 0.93-0.99]). Conclusions Accelerometer-assessed total PA and MVPA were inversely associated with total CVD and IS incidence, and SB was directly associated with total CVD; high-light PA was not related to CVD.

A Case for Unifying Accelerometry-Derived Movement Behaviors and Tests of Exercise Capacity for the Assessment of Relative Physical Activity Intensity

Albert Einstein taught us that "everything is relative." People's experience of physical activity (PA) is no different, with "relativism" particularly pertinent to the perception of intensity. Markers of absolute and relative intensities of PA have different but complimentary utilities, with absolute intensity considered best for PA guideline adherence and relative intensity for personalized exercise prescription. Under the paradigm of exercise and PA as medicine, our Technical Note proposes a method of synchronizing accelerometry with the incremental shuttle walking test to facilitate description of the intensity of the free-living PA profile in absolute and relative terms. Our approach is able to generate and distinguish "can do" or "cannot do" (based on exercise capacity) and "does do" or "does not do" (based on relative intensity PA) classifications in a chronic respiratory disease population, facilitating the selection of potential appropriate individually tailored interventions. By synchronizing direct assessments of exercise capacity and PA, clearer insights into the intensity of PA performed during everyday life can be gleaned. We believe the next steps are as follows: (1) to determine the feasibility and effectiveness of using relative and absolute intensities in combination to personalize the approach, (2) to determine its sensitivity to change following interventions (eg, exercise-based rehabilitation), and (3) to explore the use of this approach in healthier populations and in other long-term conditions.

Machine Learning on Prediction of Relative Physical Activity Intensity Using Medical Radar Sensor and 3D Accelerometer

BACKGROUND

One of the most critical topics in sports safety today is the reduction in injury risks through controlled fatigue using non-invasive athlete monitoring. Due to the risk of injuries, it is prohibited to use accelerometer-based smart trackers, activity measurement bracelets, and smart watches for recording health parameters during performance sports activities. This study analyzes the synergy feasibility of medical radar sensors and tri-axial acceleration sensor data to predict physical activity key performance indexes in performance sports by using machine learning (ML). The novelty of this method is that it uses a 24 GHz Doppler radar sensor to detect vital signs such as the heartbeat and breathing without touching the person and to predict the intensity of physical activity, combined with the acceleration data from 3D accelerometers.

METHODS

This study is based on the data collected from professional athletes and freely available datasets created for research purposes. A combination of sensor data management was used: a medical radar sensor with no-contact remote sensing to measure the heart rate (HR) and 3D acceleration to measure the velocity of the activity. Various advanced ML methods and models were employed on the top of sensors to analyze the vital parameters and predict the health activity key performance indexes. three-axial acceleration, heart rate data, age, as well as activity level variances.

RESULTS

The ML models recognized the physical activity intensity and estimated the energy expenditure on a realistic level. Leave-one-out (LOO) cross-validation (CV), as well as out-of-sample testing (OST) methods, have been used to evaluate the level of accuracy in activity intensity prediction. The energy expenditure prediction with three-axial accelerometer sensors by using linear regression provided 97-99% accuracy on selected sports (cycling, running, and soccer). The ML-based RPE results using medical radar sensors on a time-series heart rate (HR) dataset varied between 90 and 96% accuracy. The expected level of accuracy was examined with different models. The average accuracy for all the models (RPE and METs) and setups was higher than 90%.

CONCLUSIONS

The ML models that classify the rating of the perceived exertion and the metabolic equivalent of tasks perform consistently.

Validation of Oura ring energy expenditure and steps in laboratory and free-living

BACKGROUND

Commercial activity trackers are increasingly used in research and compared with research-based accelerometers are often less intrusive, cheaper, with improved storage and battery capacity, although typically less validated. The present study aimed to determine the validity of Oura Ring step-count and energy expenditure (EE) in both laboratory and free-living.

METHODS

Oura Ring EE was compared against indirect calorimetry in the laboratory, followed by a 14-day free-living study with 32 participants wearing an Oura Ring and reference monitors (three accelerometers positioned at hip, thigh, and wrist, and pedometer) to evaluate Oura EE variables and step count.

RESULTS

Strong correlations were shown for Oura versus indirect calorimetry in the laboratory (r = 0.93), and versus reference monitors for all variables in free-living (r ≥ 0.76). Significant (p < 0.05) mean differences for Oura versus reference methods were found for laboratory measured sitting (- 0.12 ± 0.28 MET), standing (- 0.27 ± 0.33 MET), fast walk (- 0.82 ± 1.92 MET) and very fast run (- 3.49 ± 3.94 MET), and for free-living step-count (2124 ± 4256 steps) and EE variables (MET: - 0.34-0.26; TEE: 362-494 kcal; AEE: - 487-259 kcal). In the laboratory, Oura tended to underestimate EE with increasing discrepancy as intensity increased. The combined activities and slow running in the laboratory, and all MET placements, TEE hip and wrist, and step count in free-living had acceptable measurement errors (< 10% MAPE), whereas the remaining free-living variables showed close to (≤13.2%) acceptable limits.

CONCLUSION

This is the first study investigating the validity of Oura Ring EE against gold standard methods. Oura successfully identified major changes between activities and/or intensities but was less responsive to detailed deviations within activities. In free-living, Oura step-count and EE variables tightly correlated with reference monitors, though with systemic over- or underestimations indicating somewhat low intra-individual validity of the ring versus the reference monitors. However, the correlations between the devices were high, suggesting that the Oura can detect differences at group-level for active and total energy expenditure, as well as step count.

How many days are needed? Measurement reliability of wearable device data to assess physical activity

INTRODUCTION/PURPOSE

Physical activity studies often utilize wearable devices to measure participants' habitual activity levels by averaging values across several valid observation days. These studies face competing demands-available resources and the burden to study participants must be balanced with the goal to obtain reliable measurements of a person's longer-term average. Information about the number of valid observation days required to reliably measure targeted metrics of habitual activity is required to inform study design.

METHODS

To date, the number of days required to achieve a desired level of aggregate long-term reliability (typically 0.80) has often been estimated by applying the Spearman-Brown Prophecy formula to short-term test-retest reliability data from studies with single, relatively brief observation windows. Our work, in contrast, utilizes a resampling-based approach to quantify the long-term test-retest reliability of aggregate measures of activity in a cohort of 79 participants who were asked to wear a FitBit Flex every day for approximately one year.

RESULTS

The conventional approach can produce reliability estimates that substantially overestimate the actual test-retest reliability. Six or more valid days of observation for each participant appear necessary to obtain 0.80 reliability for the average amount of time spent in light physical activity; 8 and 10 valid days are needed for sedentary time and moderate/vigorous activity respectively.

CONCLUSION

Protocols that result in 7-10 valid observation days for each participant may be needed to obtain reliable measurements of key physical activity metrics.

Validation of an Activity Type Recognition Model Classifying Daily Physical Behavior in Older Adults: The HAR70+ Model

Activity monitoring combined with machine learning (ML) methods can contribute to detailed knowledge about daily physical behavior in older adults. The current study (1) evaluated the performance of an existing activity type recognition ML model (HARTH), based on data from healthy young adults, for classifying daily physical behavior in fit-to-frail older adults, (2) compared the performance with a ML model (HAR70+) that included training data from older adults, and (3) evaluated the ML models on older adults with and without walking aids. Eighteen older adults aged 70-95 years who ranged widely in physical function, including usage of walking aids, were equipped with a chest-mounted camera and two accelerometers during a semi-structured free-living protocol. Labeled accelerometer data from video analysis was used as ground truth for the classification of walking, standing, sitting, and lying identified by the ML models. Overall accuracy was high for both the HARTH model (91%) and the HAR70+ model (94%). The performance was lower for those using walking aids in both models, however, the overall accuracy improved from 87% to 93% in the HAR70+ model. The validated HAR70+ model contributes to more accurate classification of daily physical behavior in older adults that is essential for future research.

Generalizability and performance of methods to detect non-wear with free-living accelerometer recordings

Wearable physical activity sensors are widely used in research and practice as they provide objective measures of human behavior at a low cost. An important challenge for accurate assessment of physical activity behavior in free-living is the detection non-wear. Traditionally, heuristic algorithms that rely on specific interval lengths have been employed to detect non-wear time; however, machine learned models are emerging. We explore the potential of detecting non-wear using decision trees that combine raw acceleration and skin temperature, and we investigate the generalizability of our models, traditional heuristic algorithms, and recently developed machine learned models by external validation. The Decision tree models were trained using one week of data from thigh- and hip-worn accelerometers from 64 children. External validation was performed using data from wrist-worn accelerometers of 42 adolescents. For non-wear episodes longer than 60 min, the heuristic algorithms performed the best with F1-scores above 0.96. However, regarding episodes shorter than 60 min, the best performing method was the decision tree model including the six most important predictors with F1 scores above 0.74 for all sensor locations. We conclude that for classifying non-wear time, researchers should carefully select an appropriate method and we encourage the use of external validation when reporting on machine learned non-wear models.