Assessing sedentary behavior with the GENEActiv: introducing the sedentary sphere

Segregating the Distinct Effects of Sedentary Behavior and Physical Activity on Older Adults' Cardiovascular Structure and Function: Part 1-Linear Regression Analysis Approach

BACKGROUND

Physical behavior [PB, physical activity (PA), and sedentary behavior (SB)] can adjust cardiovascular mortality risk in older adults. The aim of this study was to predict cardiovascular parameters (CVPs) using 21 parameters of PB.

METHODS

Participants [n = 93, 73.8 (6.23) y] wore a thigh-mounted accelerometer for 7 days. Phenotype of the carotid, brachial, and popliteal arteries was conducted using ultrasound.

RESULTS

Sedentary behavior was associated with one of the 19 CVPs. Standing and light-intensity PA was associated with 3 and 1 CVP, respectively. Our prediction model suggested that an hourly increase in light-intensity PA would be negatively associated with popliteal intima-media thickness [0.09 mm (95% confidence interval, 0.15 to 0.03)]. sMVPA [moderate-vigorous PA (MVPA), accumulated in bouts <10 min] was associated with 1 CVP. ₁₀MVPA (MVPA accumulated in bouts ≥10 min) had no associations. W50% had associations with 3 CVP. SB%, alpha, true mean PA bout, daily sum of PA bout time, and total week ₁₀MVPA each were associated with 2 CVP.

CONCLUSIONS

Patterns of PB are more robust predictors of CVP than PB (hours per day). The prediction that popliteal intima-media thickness would be negatively associated with increased standing and light-intensity PA engagement suggests that older adults could obtain health benefits without MVPA engagement.

Assessment of Physical Activity of Hospitalised Older Adults: A Systematic Review

BACKGROUND

The assessment of physical activity levels of hospitalised older people requires accurate and reliable measures. Physical activities that older people in hospital commonly engage in include exercises and walking. Measurement of physical activity levels of older inpatients is essential to evaluate the impact of interventions to improve physical activity levels and to determine associations between physical activity in hospital and other health-related outcome measures.

OBJECTIVE

To determine which measures are used to measure physical activity of older people in hospital, and to describe their properties and applications.

METHOD

A systematic review of four databases: Medline, Embase, CINAHL and AMED was conducted for papers published from 1996 to 2016. Inclusion criteria were participants aged ≥ 65 years and studies which included measures of physical activity in the acute medical inpatient setting. Studies which specifically assessed the activity levels of surgical patients or patients with neurological conditions such as stroke or brain injury were excluded. All study designs were included in the review.

RESULTS

18 studies were included from 127 articles selected for full review. 15 studies used objective measures to measure the physical activity of older inpatients: 11 studies used accelerometers and four used direct systematic observations. Seven accelerometers were identified including the StepWatch Activity Monitor, activPAL, GENEActiv, Kenz Lifecorder EX, Actiwatch-L, Tractivity and AugmenTech Inc. Pittsburgh accelerometer. Three studies used a subjective measure (interviews with nurses and patients) to classify patients into low, intermediate and high mobility groups. The StepWatch Activity Monitor was reported to be most accurate at step-counting in patients with slow gait speed or altered gait. The activPAL was reported to be highly accurate at classifying postures.

CONCLUSION

Physical activity levels of older inpatients can be measured using accelerometers. The accuracy of the accelerometers varies between devices and population-specific validation studies are needed to determine their suitability in measuring physical activity levels of hospitalised older people. Subjective measures are less accurate but can be a practical way of measuring physical activity in a larger group of patients.

Measuring Children's Physical Activity: Compliance Using Skin-Taped Accelerometers

INTRODUCTION

Accelerometer-based physical activity monitoring has become the method of choice in many large-scale physical activity (PA) studies. However, there is an ongoing debate regarding the placement of the device, the determination of device wear time, and how to solve a lack of participant compliance. The aim of this study was to assess the compliance of Axivity AX3 accelerometers taped directly to the skin of 9- to 13-yr-old children.

METHODS

Children in 46 school classes (53.4% girls, age 11.0 ± 1.0 yr, BMI 17.7 ± 2.8 kg·m) across Denmark wore two Axivity AX3 accelerometers, one taped on the thigh (n = 903) and one on the lower back (n = 856), for up to 10 consecutive days. Participants were instructed not to reattach an accelerometer should it fall off. Simple and multiple linear regressions were used to determine associations between accelerometer wear time and age, sex, BMI percentiles, and PA level.

RESULTS

More than 65% had >7 d of uninterrupted, 24-h wear time for the thigh location and 59.5% for the lower back location. From multiple linear regressions, PA levels showed the strongest association with lower wear time (thigh: β = -0.231, R = 0.066; lower back: β = -0.454, R = 0.126). In addition, being a boy, being older (only for lower back), and having higher BMI percentile were associated with lower wear time.

CONCLUSION

Using skin-taped Axivity accelerometers, we obtained 7 d of uninterrupted accelerometer data with 24-h wear time per day with a compliance rate of more than 65%. Thigh placement resulted in higher compliance than lower back placement. Achieving days with 24-h wear time reduces the need for arbitrary decisions regarding wear time validation and most likely improves the validity of daily life PA measurements.

Measuring Activity Performance of Older Adults Using the activPAL: A Rapid Review

Current measures of physical activity and sedentary behaviors such as questionnaires and functional assessments are insufficient to provide comprehensive data on older adults. In response, the use of activity monitors has increased. The purpose of this review was to summarize and assess the quality of observational literature on activity measuring of older adults using the activPAL activity monitor. Seventeen databases and a bibliography, compiled by the activPAL creators, were searched. Articles were included if they were in English, were peer-reviewed, included people 65 years or older, measured activity using the activPAL and reported at least one of the following outcomes: step count, hours upright, hours sitting/lying, hours stepping, or hours standing. The search revealed 404 titles; after exclusions 24 were included in the final review. Of these studies, one examined older adults from residential aged care, six from hospital in-patient clinics, nine from outpatient clinics and eight examined community-dwellers. Mean age ranged from 66.0 to 84.2 years. Not all studies reported similar outcome variables, preventing data pooling. The review found a lack of high quality articles. There may be limitations to using the activPAL among older adults but further research is required to examine its use in this population.

Performance of thigh-mounted triaxial accelerometer algorithms in objective quantification of sedentary behaviour and physical activity in older adults

Accurate monitoring of sedentary behaviour and physical activity is key to investigate their exact role in healthy ageing. To date, accelerometers using cut-off point models are most preferred for this, however, machine learning seems a highly promising future alternative. Hence, the current study compared between cut-off point and machine learning algorithms, for optimal quantification of sedentary behaviour and physical activity intensities in the elderly. Thus, in a heterogeneous sample of forty participants (aged ≥60 years, 50% female) energy expenditure during laboratory-based activities (ranging from sedentary behaviour through to moderate-to-vigorous physical activity) was estimated by indirect calorimetry, whilst wearing triaxial thigh-mounted accelerometers. Three cut-off point algorithms and a Random Forest machine learning model were developed and cross-validated using the collected data. Detailed analyses were performed to check algorithm robustness, and examine and benchmark both overall and participant-specific balanced accuracies. This revealed that the four models can at least be used to confidently monitor sedentary behaviour and moderate-to-vigorous physical activity. Nevertheless, the machine learning algorithm outperformed the cut-off point models by being robust for all individual's physiological and non-physiological characteristics and showing more performance of an acceptable level over the whole range of physical activity intensities. Therefore, we propose that Random Forest machine learning may be optimal for objective assessment of sedentary behaviour and physical activity in older adults using thigh-mounted triaxial accelerometry.

Wrist Accelerometer Cut Points for Classifying Sedentary Behavior in Children

INTRODUCTION

This study aimed to examine the validity and accuracy of wrist accelerometers for classifying sedentary behavior (SB) in children.

METHODS

Fifty-seven children (5-8 and 9-12 yr) completed an ~170-min protocol, including 15 semistructured activities and transitions. Nine ActiGraph (GT3X+) and two GENEActiv wrist cut points were evaluated. Direct observation was the criterion measure. The accuracy of wrist cut points was compared with that achieved by the ActiGraph hip cut point (≤25 counts per 15 s) and the thigh-mounted activPAL3. Analyses included equivalence testing, Bland-Altman procedures, and area under the receiver operating curve (ROC-AUC).

RESULTS

The most accurate ActiGraph wrist cut points (Kim; vector magnitude, ≤3958 counts per 60 s; vertical axis, ≤1756 counts per 60 s) demonstrated good classification accuracy (ROC-AUC = 0.85-0.86) and accurately estimated SB time in 5-8 yr (equivalence P = 0.02; mean bias = 4.1%, limits of agreement = -20.1% to 28.4%) and 9-12 yr (equivalence P < 0.01; -2.5%, -27.9% to 22.9%). The mean bias of SB time estimates from Kim were smaller than ActiGraph hip (5-8 yr: 15.8%, -5.7% to 37.2%; 9-12 yr: 17.8%, -3.9% to 39.5%) and similar to or smaller than activPAL3 (5-8 yr: 12.6%, -39.8% to 14.7%; 9-12 yr: -1.4%, -13.9% to 11.0%), although classification accuracy was similar to ActiGraph hip (ROC-AUC = 0.85) but lower than activPAL3 (ROC-AUC = 0.92-0.97). Mean bias (5-8 yr: 6.5%, -16.1% to 29.1%; 9-12 yr: 10.5%, -13.6% to 34.6%) for the most accurate GENEActiv wrist cut point (Schaefer: ≤0.19 g) was smaller than ActiGraph hip, and activPAL3 in 5-8 yr, but larger than activPAL3 in 9-12 yr. However, SB time estimates from Schaefer were not equivalent to direct observation (equivalence P > 0.05) and classification accuracy (ROC-AUC = 0.79-0.80) was lower than for ActiGraph hip and activPAL3.

CONCLUSION

The most accurate SB ActiGraph (Kim) and GENEActiv (Schaefer) wrist cut points can be applied in children with similar confidence as the ActiGraph hip cut point (≤25 counts per 15 s), although activPAL3 was generally more accurate.

Accelerometry Shows Inpatients With Acute Medical or Surgical Conditions Spend Little Time Upright and Are Highly Sedentary: Systematic Review

BACKGROUND

Physical inactivity and sedentary behaviors have significant and independent effects on health. The use of wearable monitors to measure these constructs in people who are hospitalized with an acute illness is rapidly expanding, but has not been systematically described.

PURPOSE

The purpose of this study was to review the use of accelerometer monitoring with inpatients who are acutely ill, including what activity and sedentary behaviors have been measured and how active or sedentary inpatients are.

DATA SOURCES

Databases used were MEDLINE, EMBASE, CINAHL, and Scopus.

STUDY SELECTION

Quantitative studies of adults with an acute medical or surgical hospital admission, on whom an accelerometer was used to measure a physical activity or sedentary behavior, were selected.

DATA EXTRACTION AND DATA SYNTHESIS

Procedures were completed independently by 2 reviewers, with differences resolved and cross-checked by a third reviewer. Forty-two studies were identified that recruited people who had medical diagnoses (n = 10), stroke (n = 5), critical illness (n = 3), acute exacerbations of lung disease (n = 7), cardiac conditions (n = 7), or who were postsurgery (n = 10). Physical activities or sedentary behaviors were reported in terms of time spent in a particular posture (lying/sitting, standing/stepping), active/inactive, or at a particular activity intensity. Physical activity was also reported as step count, number of episodes or postural transitions, and bouts. Inpatients spent 93% to 98.8% (range) of their hospital stay sedentary, and in most studies completed <1,000 steps/day despite up to 50 postural transitions/day. No study reported sedentary bouts. Many studies controlled for preadmission function as part of the recruitment strategy or analysis or both.

LIMITATIONS

Heterogeneity in monitoring devices (17 models), protocols, and variable definitions limited comparability between studies and clinical groups to descriptive synthesis without meta-analysis.

CONCLUSIONS

Hospitalized patients were highly inactive, especially those with medical admissions, based on time and step parameters. Accelerometer monitoring of sedentary behavior patterns was less reported and warrants further research.

A cluster randomised controlled trial to evaluate the effectiveness and cost-effectiveness of the GoActive intervention to increase physical activity among adolescents aged 13-14 years

INTRODUCTION

Adolescent physical activity promotion is rarely effective, despite adolescence being critical for preventing physical activity decline. Low adolescent physical activity is likely to last into adulthood, increasing health risks. The Get Others Active (GoActive) intervention is evidence-based and was developed iteratively with adolescents and teachers. This intervention aims to increase physical activity through increased peer support, self-efficacy, group cohesion, self-esteem and friendship quality, and is implemented using a tiered-leadership system. We previously established feasibility in one school and conducted a pilot randomised controlled trial (RCT) in three schools.

METHODS AND ANALYSIS

We will conduct a school-based cluster RCT (CRCT) in 16 secondary schools targeting all year 9 students (n=2400). In eight schools, GoActive will run for two terms: weekly facilitation support from a council-funded intervention facilitator will be offered in term 1, with more distant support in term 2. Tutor groups choose two weekly activities, encouraged by older adolescent mentors and weekly peer leaders. Students gain points for trying new activities; points are entered into a between-class competition. Outcomes will be assessed at baseline, interim (week 6), postintervention (week 14-16) and 10-month follow-up (main outcome). The primary outcome will be change from baseline in daily accelerometer-assessed moderate-to-vigorous physical activity. Secondary outcomes include accelerometer-assessed activity intensities on weekdays/weekends; self-reported physical activity and psychosocial outcomes; cost-effectiveness and cost-utility analyses; mixed-methods process evaluation integrating information from focus groups and participation logs/questionnaires.

ETHICS AND DISSEMINATION

Ethical approval for the conduct of the study was gained from the University of Cambridge Psychology Research Ethics Committee. Given the lack of rigorously evaluated interventions, and the inclusion of objective measurement of physical activity, long-term follow-up and testing of causal pathways, the results of a CRCT of the effectiveness and cost-effectiveness of GoActive are expected to add substantially to the limited evidence on adolescent physical activity promotion. Workshops will be held with key stakeholders including students, parents, teachers, school governors and government representatives to discuss plans for wider dissemination of the intervention.

TRIAL REGISTRATION NUMBER

ISRCTN31583496.

Comparison of Sedentary Estimates between activPAL and Hip- and Wrist-Worn ActiGraph

PURPOSE

Sedentary behavior is an emerging independent health risk factor. The accuracy of measuring sedentary time using accelerometers may depend on the wear location. This study in older adults evaluated the accuracy of various hip- and wrist-worn ActiGraph accelerometer cutoff points to define sedentary time using the activPAL as the reference method.

METHODS

Data from 62 adults (mean age, 78.4 yr) of the Aging Research Evaluating Accelerometry study were used. Participants simultaneously wore an activPAL accelerometer on the thigh and ActiGraph accelerometers on the hip, dominant, and nondominant wrist for 7 d in a free-living environment. Using the activPAL as the reference criteria, we compared classification of sedentary time to hip-worn and wrist-worn ActiGraph accelerometers over a range of cutoff points for both 60-s and 15-s epochs.

RESULTS

The optimal cutoff point for the hip vertical axis was <22 counts per minute with an area under the curve (AUC) of 0.85; the optimal hip vector magnitude cutoff point was <174 counts per minute with an AUC of 0.89. For the dominant wrist, the optimal vector magnitude cutoff point to define sedentary time was <2303 counts per minute (AUC, 0.86) and for the nondominant wrist <1853 counts per minute (AUC, 0.86). The optimal 15-s cutoff points resulted in lower agreements compared with activPAL.

CONCLUSIONS

Hip- and wrist-worn ActiGraph data may be used to define sedentary time with a moderate to high accuracy when compared with activPAL. The observed optimal cutoff point for hip vertical axis <22 counts per minute is substantially lower than the standard <100 counts per minute. It is unknown how these optimal cutoff points perform in different populations. Results on an individual basis should therefore be interpreted with caution.

Accuracy of Posture Allocation Algorithms for Thigh- and Waist-Worn Accelerometers

PURPOSE

The objective of this study is to compare the accuracy of the activPAL and ActiGraph GT3X+ (waist and thigh) proprietary postural allocation algorithms and an open-source postural allocation algorithm applied to GENEActiv (thigh) and ActiGraph GT3X+ (thigh) data.

METHODS

Thirty-four adults (≥18 yr) wore the activPAL3, GENEActiv, and ActiGraph GT3X+ on the right thigh and an ActiGraph on the right hip while performing four lying, seven sitting, and five upright activities in the laboratory. Lying and sitting tasks incorporated a range of leg angles (e.g., lying with legs bent and sitting with legs crossed). Each activity was performed for 5 min while being directly observed. The percentage of the time the posture was correctly classified was calculated.

RESULTS

Participants consisted of 14 males and 20 females (mean age, 27.2 ± 5.9 yr; mean body mass index, 23.8 ± 3.7 kg·m). All postural allocation algorithms applied to monitors worn on the thigh correctly classified ≥93% of the time lying, ≥91% of the time sitting, and ≥93% of the time upright. The ActiGraph waist proprietary algorithm correctly classified 72% of the time lying, 58% of the time sitting, and 74% of the time upright. Both the activPAL and ActiGraph thigh proprietary algorithms misclassified sitting on a chair with legs stretched out (58% and 5% classified incorrectly, respectively). The ActiGraph thigh proprietary and open-source algorithm applied to the thigh-worn ActiGraph misclassified participants lying on their back with their legs bent 27% and 9% of the time, respectively.

CONCLUSION

All postural allocation algorithms when applied to devices worn on the thigh were highly accurate in identifying lying, sitting, and upright postures. Given the poor accuracy of the waist algorithm for detecting sitting, caution should be taken if inferring sitting time from a waist-worn device.

Raw Accelerometer Data Analysis with GGIR R-package: Does Accelerometer Brand Matter?

PURPOSE

This study aimed to determine the agreement between outputs from contemporaneous measures of acceleration from wrist-worn GENEActiv and ActiGraph accelerometers when processed using the GGIR open source package.

METHODS

Thirty-four participants wore a GENEActiv and an ActiGraph GT3X+ on their nondominant wrist continuously for 2 d to ensure the capture of one 24-h day and one nocturnal sleep. GENEActiv.bin files and ActiGraph .csv files were analyzed with R-package GGIR version 1.2-0. Key outcome variables were as follows: wear time, average magnitude of dynamic wrist acceleration (Euclidean norm minus one [ENMO]), percentile distribution of accelerations, time spent across acceleration levels in a 40-mg resolution, time in moderate-to-vigorous physical activity (MVPA: total, 10-min bouts), and duration of nocturnal sleep.

RESULTS

There was a high agreement between accelerometer brands for all derived outcomes (wear time, MVPA, and sleep; intraclass correlation coefficient [ICC] > 0.96), ENMO (ICC = 0.99), time spent across acceleration levels (ICC > 0.93), and accelerations ≥50th percentile of the distribution (ICC > 0.82). ENMO (mean ± SD, GENEActiv = 29.9 ± 20.7 mg, ActiGraph = 27.8 ± 21.4 mg) and accelerations between the 5th and the 75th percentile of the distribution measured by the GENEActiv were significantly higher than those measured by the ActiGraph. Correspondingly, the number of minutes recorded between 0 and 40 mg was significantly greater for the ActiGraph (745 min cf. 734 min), and the number of minutes recorded between 40 and 80 mg was significantly greater for the GENEActiv (110 min cf. 105 min).

CONCLUSION

Derived outcomes (wear time, MVPA, and sleep) were similar between brands. Brands compared well for acceleration magnitudes >50-80 mg but not lower magnitudes indicative of sedentary time. Caution is advised when comparing the magnitude of ENMO between brands, but there was a high consistency between brands for the ranking of individuals for activity and sleep outcomes.

Moving Forward with Backward Compatibility: Translating Wrist Accelerometer Data

PURPOSE

This study aimed to provide a means for calibrating raw acceleration data from wrist-worn accelerometers in relation to past estimates of children's moderate-to-vigorous physical activity (MVPA) from a range of cut points applied to hip-worn ActiGraph data.

METHODS

This is a secondary analysis of three studies with concurrent 7-d accelerometer wear at the wrist (GENEActiv) and hip (ActiGraph) in 238 children age 9-12 yr. The time spent above acceleration (ENMO) thresholds of 100, 150, 200, 250, 300, 350, and 400 mg from wrist acceleration data (≤5-s epoch) was calculated for comparison with MVPA estimated from widely used children's hip-worn ActiGraph MVPA cut points (Freedson/Trost, 1100 counts per minute; Pate, 1680 counts per minute; Evenson, 2296 counts per minute; Puyau, 3200 counts per minute) with epochs of ≤5, 15, and 60 s.

RESULTS

The optimal ENMO thresholds for alignment with MVPA estimates from ActiGraph cut points determined from 70% of the sample and cross validated with the remaining 30% were as follows: Freedson/Trost = ENMO 150+ mg, irrespective of ActiGraph epoch (intraclass correlation [ICC] ≥ 0.65); Pate = ENMO 200+ mg, irrespective of ActiGraph epoch (ICC ≥ 0.67); Evenson = ENMO 250+ mg for ≤5- and 15-s epochs (ICC ≥ 0.69) and ENMO 300+ mg for 60-s epochs (ICC = 0.73); Puyau = ENMO 300+ mg for ≤5-s epochs (ICC = 0.73), ENMO 350+ mg for 15-s epochs (ICC = 0.73), and ENMO 400+ mg for 60-s epochs (ICC = 0.65). Agreement was robust with cross-validation ICC = 0.62-0.71 and means within ∣7.8∣% ± 4.9% of MVPA estimates from ActiGraph cut points, except Puyau 60-s epochs (ICC = 0.42).

CONCLUSION

Incremental ENMO thresholds enable children's acceleration data measured at the wrist to be simply and directly compared, at a group level, with past estimates of MVPA from hip-worn ActiGraphs across a range of cut points.

Validity and reliability of accelerometry in identification of lying, sitting, standing or purposeful activity in adult hospital inpatients recovering from acute or critical illness: a systematic review

Objective:

To investigate the validity and reliability of accelerometers to detect lying, sitting and standing postures or purposeful activity in hospitalized adults recovering from acute or critical illness.

Data sources:

CINAHL, MEDLINE, EMBASE, AMED, Cochrane Library, PEDro, PsycINFO and SPORTDiscuss were searched from inception to June 2017. Professional networks and reference lists of relevant articles were also searched. The main selection criteria were hospitalized adults with acute or critical illness and studies investigating the validity or reliability of accelerometers to identify body position or purposeful activity.

Review methods:

Two authors individually assessed study eligibility and independently undertook methodological quality assessment and data extraction from selected articles. A narrative synthesis of the data was undertaken.

Results:

Fifteen studies, collectively enrolling 385 hospitalized participants, were identified. Populations included stroke, the elderly, acute exacerbation of chronic respiratory disease, abdominal surgery and those recovering from critical illness. Correlations of r = 0.36 to 0.98 and levels of agreement of κ = 0.28 to 0.98 were reported for identification of lying, sitting or standing postures. Correlations of r = 0.4 to 0.8 with general activity were found, with r = 0.94 and 0.96 reported for step count. The reliability of accelerometry measurement was investigated in one study evaluating step count quantification (intraclass correlation coefficient (ICC) = 0.99, 95% confidence interval (CI) = 0.99–1.00).

Conclusion:

The validity of accelerometers to determine lying, sitting and standing postures or quantify purposeful activity within hospitalized acute or critically ill populations is variable. The reliability of accelerometry measurement within this setting remains largely unexplored.

A study protocol of a three-group randomized feasibility trial of an online yoga intervention for mothers after stillbirth (The Mindful Health Study)

BACKGROUND

In the USA, stillbirth (in utero fetal death ≥20 weeks gestation) is a major public health issue. Women who experience stillbirth, compared to women with live birth, have a nearly sevenfold increased risk of a positive screen for post-traumatic stress disorder (PTSD) and a fourfold increased risk of depressive symptoms. Because the majority of women who have experienced the death of their baby become pregnant within 12-18 months and the lack of intervention studies conducted within this population, novel approaches targeting physical and mental health, specific to the needs of this population, are critical. Evidence suggests that yoga is efficacious, safe, acceptable, and cost-effective for improving mental health in a variety of populations, including pregnant and postpartum women. To date, there are no known studies examining online-streaming yoga as a strategy to help mothers cope with PTSD symptoms after stillbirth.

METHODS

The present study is a two-phase randomized controlled trial. Phase 1 will involve (1) an iterative design process to develop the online yoga prescription for phase 2 and (2) qualitative interviews to identify cultural barriers to recruitment in non-Caucasian women (i.e., predominately Hispanic and/or African American) who have experienced stillbirth (N = 5). Phase 2 is a three-group randomized feasibility trial with assessments at baseline, and at 12 and 20 weeks post-intervention. Ninety women who have experienced a stillbirth within 6 weeks to 24 months will be randomized into one of the following three arms for 12 weeks: (1) intervention low dose (LD) = 60 min/week online-streaming yoga (n = 30), (2) intervention moderate dose (MD) = 150 min/week online-streaming yoga (n = 30), or (3) stretch and tone control (STC) group = 60 min/week of stretching/toning exercises (n = 30).

DISCUSSION

This study will explore the feasibility and acceptability of a 12-week, home-based, online-streamed yoga intervention, with varying doses among mothers after a stillbirth. If feasible, the findings from this study will inform a full-scale trial to determine the effectiveness of home-based online-streamed yoga to improve PTSD. Long-term, health care providers could use online yoga as a non-pharmaceutical, inexpensive resource for stillbirth aftercare.

TRIAL REGISTRATION

NCT02925481.

Wrist-based cut-points for moderate- and vigorous-intensity physical activity for the Actical accelerometer in adults

Wrist-based accelerometers are increasingly used to assess physical activity (PA) in population-based studies; however, cut-points to translate wrist-based accelerometer counts into PA intensity categories are still needed. The purpose of this study was to determine wrist-based cut-points for moderate- and vigorous-intensity ambulatory PA in adults for the Actical accelerometer. Healthy adults (n = 24) completed a four-phase treadmill exercise protocol (1.9, 3.0, 4.0 and 5.2 mph) while wearing an Actical accelerometer on their wrist. Metabolic equivalent of task (MET) levels were assessed by indirect calorimetry. Receiver operating characteristics (ROC) curves were generated to determine accelerometer counts that maximised sensitivity and specificity for classification of moderate (≥3 METs) and vigorous (>6 METs) ambulatory activity. The area under the ROC curves to discriminate moderate- and vigorous-intensity ambulatory activity were 0.93 (95% confidence interval [CI]: 0.90-0.97; P < 0.001) and 0.96 (95% CI: 0.94-0.99; P < 0.001), respectively. The identified cut-point for moderate-intensity ambulatory activity was 1031 counts per minute, which had a corresponding sensitivity and specificity of 85.6% and 87.5%, respectively. The identified cut-point for vigorous intensity ambulatory activity was 3589 counts per minute, which had a corresponding sensitivity and specificity of 88.0% and 98.7%, respectively. This study established intensity-specific cut-points for wrist-based wear of the Actical accelerometer which are recommended for quantification of moderate- and vigorous-intensity ambulatory activity.

Comparison of linear and non-linear models for predicting energy expenditure from raw accelerometer data

This study had three purposes, all related to evaluating energy expenditure (EE) prediction accuracy from body-worn accelerometers: (1) compare linear regression to linear mixed models, (2) compare linear models to artificial neural network models, and (3) compare accuracy of accelerometers placed on the hip, thigh, and wrists. Forty individuals performed 13 activities in a 90 min semi-structured, laboratory-based protocol. Participants wore accelerometers on the right hip, right thigh, and both wrists and a portable metabolic analyzer (EE criterion). Four EE prediction models were developed for each accelerometer: linear regression, linear mixed, and two ANN models. EE prediction accuracy was assessed using correlations, root mean square error (RMSE), and bias and was compared across models and accelerometers using repeated-measures analysis of variance. For all accelerometer placements, there were no significant differences for correlations or RMSE between linear regression and linear mixed models (correlations: r  =  0.71-0.88, RMSE: 1.11-1.61 METs; p  >  0.05). For the thigh-worn accelerometer, there were no differences in correlations or RMSE between linear and ANN models (ANN-correlations: r  =  0.89, RMSE: 1.07-1.08 METs. Linear models-correlations: r  =  0.88, RMSE: 1.10-1.11 METs; p  >  0.05). Conversely, one ANN had higher correlations and lower RMSE than both linear models for the hip (ANN-correlation: r  =  0.88, RMSE: 1.12 METs. Linear models-correlations: r  =  0.86, RMSE: 1.18-1.19 METs; p  <  0.05), and both ANNs had higher correlations and lower RMSE than both linear models for the wrist-worn accelerometers (ANN-correlations: r  =  0.82-0.84, RMSE: 1.26-1.32 METs. Linear models-correlations: r  =  0.71-0.73, RMSE: 1.55-1.61 METs; p  <  0.01). For studies using wrist-worn accelerometers, machine learning models offer a significant improvement in EE prediction accuracy over linear models. Conversely, linear models showed similar EE prediction accuracy to machine learning models for hip- and thigh-worn accelerometers and may be viable alternative modeling techniques for EE prediction for hip- or thigh-worn accelerometers.

Wrist-Worn Accelerometer-Brand Independent Posture Classification

INTRODUCTION

Access to raw acceleration data should facilitate comparisons between accelerometer outputs regardless of monitor brand.

PURPOSE

To evaluate the accuracy of posture classification using the Sedentary Sphere in data from two widely used wrist-worn triaxial accelerometers.

METHODS

Laboratory: Thirty-four adults wore a GENEActiv and an ActiGraph GT3X+ on their nondominant wrist while performing four lying, seven sitting, and five upright activities. Free-living: The same participants wore both accelerometers on their nondominant wrist and an activPAL3 on their right thigh during waking hours for 2 d.

RESULTS

Laboratory: Using the Sedentary Sphere with 15-s epoch GENEActiv data, sedentary and upright postures were correctly identified 74% and 91% of the time, respectively. Corresponding values for the ActiGraph data were 75% and 90%. Free-living: Total sedentary time was estimated at 534 ± 144, 523 ± 143, and 528 ± 137 min by the activPAL, the Sedentary Sphere with GENEActiv data and with ActiGraph data, respectively. The mean bias, relative to the activPAL, was small with moderate limits of agreement (LoA) for both the GENEActiv (mean bias = -12.5 min, LoA = -117 to 92 min) and ActiGraph (mean bias = -8 min, LoA = -103 to 88 min). Strong intraclass correlations (ICC) were evident for the activPAL with the GENEActiv (0.93, 0.84-0.97 (95% confidence interval) and the ActiGraph (0.94, 0.86-0.97). Agreement between the GENEActiv and ActiGraph posture classifications was very high (ICC = 0.98 (0.94-0.99), mean bias = +3 min, LoA = -58 to 63 min).

CONCLUSIONS

These data support the efficacy of the Sedentary Sphere for classification of posture from a wrist-worn accelerometer in adults. The approach is equally valid with data from both the GENEActiv and ActiGraph accelerometers.

Evaluation of raw acceleration sedentary thresholds in children and adults

The aim was to develop sedentary (sitting/lying) thresholds from hip and wrist worn raw tri-axial acceleration data from the ActiGraph and GENEActiv, and to examine the agreement between free-living time spent below these thresholds with sedentary time estimated by the activPAL. Sixty children and adults wore an ActiGraph and GENEActiv on the hip and wrist while performing six structured activities, before wearing the monitors, in addition to an activPAL, for 24 h. Receiver operating characteristic (ROC) curves were used to determine sedentary thresholds based on activities in the laboratory. Agreement between developed sedentary thresholds during free-living and activPAL were assessed by Bland-Altman plots and by calculating sensitivity and specificity. Using laboratory data and ROC-curves showed similar classification accuracy for wrist and hip thresholds (Area under the curve = 0.84-0.92). Greatest sensitivity (97-98%) and specificity (74-78%) were observed for the wrist thresholds, with no large differences between brands. During free-living, Bland-Altman plots showed large mean individual biases and 95% limits of agreement compared with activPAL, with smallest difference for the ActiGraph wrist threshold in children (+30 min, P = 0.3). Sensitivity and specificity for the developed thresholds during free-living were low for both age groups and for wrist (Sensitivity, 68-88%, Specificity, 46-59%) and hip placements (Sensitivity, 89-97%, Specificity, 26-34%). Laboratory derived sedentary thresholds generally overestimate free-living sedentary time compared with activPAL. Wrist thresholds appear to perform better than hip thresholds for estimating free-living sedentary time in children and adults relative to activPAL, however, specificity for all the developed thresholds are low.

Intensity Thresholds on Raw Acceleration Data: Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD) Approaches

Objectives

(1) To develop and internally-validate Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD) thresholds for separating sedentary behaviours from common light-intensity physical activities using raw acceleration data collected from both hip- and wrist-worn tri-axial accelerometers; and (2) to compare and evaluate the performances between the ENMO and MAD metrics.

Methods

Thirty-three adults [mean age (standard deviation (SD)) = 27.4 (5.9) years; mean BMI (SD) = 23.9 (3.7) kg/m²; 20 females (60.6%)] wore four accelerometers; an ActiGraph GT3X+ and a GENEActiv on the right hip; and an ActiGraph GT3X+ and a GENEActiv on the non-dominant wrist. Under laboratory-conditions, participants performed 16 different activities (11 sedentary behaviours and 5 light-intensity physical activities) for 5 minutes each. ENMO and MAD were computed from the raw acceleration data, and logistic regression and receiver-operating-characteristic (ROC) analyses were implemented to derive thresholds for activity discrimination. Areas under ROC curves (AUROC) were calculated to summarise performances and thresholds were assessed via executing leave-one-out-cross-validations.

Results

For both hip and wrist monitor placements, in comparison to the ActiGraph GT3X+ monitors, the ENMO and MAD values derived from the GENEActiv devices were observed to be slightly higher, particularly for the lower-intensity activities. Monitor-specific hip and wrist ENMO and MAD thresholds showed excellent ability for separating sedentary behaviours from motion-based light-intensity physical activities (in general, AUROCs >0.95), with validation indicating robustness. However, poor classification was experienced when attempting to isolate standing still from sedentary behaviours (in general, AUROCs <0.65). The ENMO and MAD metrics tended to perform similarly across activities and accelerometer brands.

Conclusions

Researchers can utilise these robust monitor-specific hip and wrist ENMO and MAD thresholds, in order to accurately separate sedentary behaviours from common motion-based light-intensity physical activities. However, caution should be taken if isolating sedentary behaviours from standing is of particular interest.

Daily physical activity patterns from hip- and wrist-worn accelerometers

Accelerometer wear location may influence physical activity estimates. This study investigates this relationship through the examination of activity patterns throughout the day. Participants from the aging research evaluating accelerometry (AREA) study (n men  =  37, n women  =  47, mean age (SD)  =  78.9 (5.5) years) were asked to wear accelerometers in a free-living environment for 7 d at three different wear locations; one on each wrist and one on the right hip. During waking hours, wrist-worn accelerometers consistently produced higher median activity counts, about 5  ×  higher, as well as wider variability compared to hip-worn monitors. However, the shape of the accrual pattern curve over the course of the day for the hip and wrist are similar; there is a spike in activity in the morning, with a prolonged tapering of activity level as the day progresses. The similar patterns of hip and wrist activity accrual provide support that each location is capable of estimating total physical activity volume. The examination of activity patterns over time may provide a more detailed way to examine differences in wear location and different subpopulations.

Reporting accelerometer methods in physical activity intervention studies: a systematic review and recommendations for authors

OBJECTIVE

This systematic review assessed the completeness of accelerometer reporting in physical activity (PA) intervention studies and assessed factors related to accelerometer reporting.

DESIGN

The PubMed database was used to identify manuscripts for inclusion. Included studies were PA interventions that used accelerometers, were written in English and were conducted between 1 January 1998 and 31 July 2014. 195 manuscripts from PA interventions that used accelerometers to measure PA were included. Manuscript completeness was scored using 12 questions focused on 3 accelerometer reporting areas: accelerometer information, data processing and interpretation and protocol non-compliance. Variables, including publication year, journal focus and impact factor, and population studied were evaluated to assess trends in reporting completeness.

RESULTS

The number of manuscripts using accelerometers to assess PA in interventions increased from 1 in 2002 to 29 in the first 7 months of 2014. Accelerometer reporting completeness correlated weakly with publication year (r=0.24, p<0.001). Correlations were greater when we assessed improvements over time in reporting data processing in manuscripts published in PA-focused journals (r=0.43, p=0.002) compared to manuscripts published in non-PA-focused journals (r=0.19, p=0.021). Only 7 of 195 (4%) manuscripts reported all components of accelerometer use, and only 132 (68%) reported more than half of the components.

CONCLUSIONS

Accelerometer reporting of PA in intervention studies has been poor and improved only minimally over time. We provide recommendations to improve accelerometer reporting and include a template to standardise reports.

Sedentary Behavior and Cardiovascular Morbidity and Mortality: A Science Advisory From the American Heart Association

Epidemiological evidence is accumulating that indicates greater time spent in sedentary behavior is associated with all-cause and cardiovascular morbidity and mortality in adults such that some countries have disseminated broad guidelines that recommend minimizing sedentary behaviors. Research examining the possible deleterious consequences of excess sedentary behavior is rapidly evolving, with the epidemiology-based literature ahead of potential biological mechanisms that might explain the observed associations. This American Heart Association science advisory reviews the current evidence on sedentary behavior in terms of assessment methods, population prevalence, determinants, associations with cardiovascular disease incidence and mortality, potential underlying mechanisms, and interventions. Recommendations for future research on this emerging cardiovascular health topic are included. Further evidence is required to better inform public health interventions and future quantitative guidelines on sedentary behavior and cardiovascular health outcomes.

Criterion Validity of Competing Accelerometry-Based Activity Monitoring Devices

PURPOSE

The purpose of this study was to examine the comparative and criterion validity of the three activity monitors in relation to a portable metabolic analyzer (Oxycon Mobile (OM)) in adults.

METHODS

A total of 52 adults age 18-40 yr each performed a series of 15 activities for 5 min each, with 1-min resting intervals between different activities. Participants completed the trials while wearing the three activity monitors and while being measured with the OM. Estimates of energy expenditure (EE) were obtained from the ActiGraph (one based on the vertical axis and the other from vector magnitude) as well as from the activPAL (AP) and the Core Armband (CA). The EE estimates were converted into MET(RMR) values by standardizing EE values with each person's resting metabolic rate and then temporarily matched to facilitate minute-by-minute comparisons. Equivalence testing and mean absolute percent errors (MAPE) were used to evaluate the agreement.

RESULTS

MET(RMR) values from the CA were significantly equivalent to those from the OM for the overall group comparison (90% confidence interval (CI), 3.65 and 3.85 MET(RMR)) and vigorous intensity (90% CI, 8.27 and 10.10 MET(RMR)). The CA had the smallest MAPE for moderate (20.7%) and vigorous (14.5%) intensity, but the AP had smaller MAPE for sedentary activities (27.4%) and light (24.7%) intensity activities.

CONCLUSIONS

The CA showed good agreement relative to the OM for the overall group comparison and for moderate and vigorous activities. The AP, in contrast, was the most accurate for sedentary and light activities. The combined use of the CA and AP may yield more accurate estimates of EE than using a single monitor.

Field evaluation of a random forest activity classifier for wrist-worn accelerometer data

OBJECTIVES

Wrist-worn accelerometers are convenient to wear and associated with greater wear-time compliance. Previous work has generally relied on choreographed activity trials to train and test classification models. However, validity in free-living contexts is starting to emerge. Study aims were: (1) train and test a random forest activity classifier for wrist accelerometer data; and (2) determine if models trained on laboratory data perform well under free-living conditions.

DESIGN

Twenty-one participants (mean age=27.6±6.2) completed seven lab-based activity trials and a 24h free-living trial (N=16).

METHODS

Participants wore a GENEActiv monitor on the non-dominant wrist. Classification models recognising four activity classes (sedentary, stationary+, walking, and running) were trained using time and frequency domain features extracted from 10-s non-overlapping windows. Model performance was evaluated using leave-one-out-cross-validation. Models were implemented using the randomForest package within R. Classifier accuracy during the 24h free living trial was evaluated by calculating agreement with concurrently worn activPAL monitors.

RESULTS

Overall classification accuracy for the random forest algorithm was 92.7%. Recognition accuracy for sedentary, stationary+, walking, and running was 80.1%, 95.7%, 91.7%, and 93.7%, respectively for the laboratory protocol. Agreement with the activPAL data (stepping vs. non-stepping) during the 24h free-living trial was excellent and, on average, exceeded 90%. The ICC for stepping time was 0.92 (95% CI=0.75-0.97). However, sensitivity and positive predictive values were modest. Mean bias was 10.3min/d (95% LOA=-46.0 to 25.4min/d).

CONCLUSIONS

The random forest classifier for wrist accelerometer data yielded accurate group-level predictions under controlled conditions, but was less accurate at identifying stepping verse non-stepping behaviour in free living conditions Future studies should conduct more rigorous field-based evaluations using observation as a criterion measure.

Validation and Comparison of Accelerometers Worn on the Hip, Thigh, and Wrists for Measuring Physical Activity and Sedentary Behavior

Background

Recent evidence suggests that physical activity (PA) and sedentary behavior (SB) exert independent effects on health. Therefore, measurement methods that can accurately assess both constructs are needed.

Objective

To compare the accuracy of accelerometers placed on the hip, thigh, and wrists, coupled with machine learning models, for measurement of PA intensity category (SB, light-intensity PA [LPA], and moderate- to vigorous-intensity PA [MVPA]) and breaks in SB.

Methods

Forty young adults (21 female; age 22.0 ± 4.2 years) participated in a 90-minute semi-structured protocol, performing 13 activities (three sedentary, 10 non-sedentary) for 3–10 minutes each. Participants chose activity order, duration, and intensity. Direct observation (DO) was used as a criterion measure of PA intensity category, and transitions from SB to a non-sedentary activity were breaks in SB. Participants wore four accelerometers (right hip, right thigh, and both wrists), and a machine learning model was created for each accelerometer to predict PA intensity category. Sensitivity and specificity for PA intensity category classification were calculated and compared across accelerometers using repeated measures analysis of variance, and the number of breaks in SB was compared using repeated measures analysis of variance.

Results

Sensitivity and specificity values for the thigh-worn accelerometer were higher than for wrist- or hip-worn accelerometers, > 99% for all PA intensity categories. Sensitivity and specificity for the hip-worn accelerometer were 87–95% and 93–97%. The left wrist-worn accelerometer had sensitivities and specificities of > 97% for SB and LPA and 91–95% for MVPA, whereas the right wrist-worn accelerometer had sensitivities and specificities of 93–99% for SB and LPA but 67–84% for MVPA. The thigh-worn accelerometer had high accuracy for breaks in SB; all other accelerometers overestimated breaks in SB.

Conclusion

Coupled with machine learning modeling, the thigh-worn accelerometer should be considered when objectively assessing PA and SB.

Comparison of physical activity assessed using hip- and wrist-worn accelerometers

OBJECTIVES

It is unclear how physical activity estimates differ when assessed using hip- vs wrist-worn accelerometers. The objective of this study was to compare physical activity assessed by hip- and wrist-worn accelerometers in free-living older women.

DESIGN

A cross-sectional study collecting data in free-living environment.

METHODS

Participants were from the Women's Health Study, in which an ancillary study is objectively measuring physical activity using accelerometers (ActiGraph GT3X+). We analyzed data from 94 women (mean (SD) age=71.9 (6.0) years) who wore a hip-worn and wrist-worn accelerometers simultaneously for 7 days.

RESULTS

Using triaxial data (vector magnitude, VM), total activity volume (counts per day) between the two locations was moderately correlated (Spearman's r=0.73). Hip and wrist monitors wear locations identically classified 71% individuals who were at the highest 40% or lowest 40% of their respective distributions. Similar patterns and slightly stronger agreements were observed when examining steps instead of VM counts.

CONCLUSIONS

Accelerometer-assessed physical activity using hip- vs wrist-worn devices was moderately correlated in older, free-living women. However, further research needs to be conducted to examine comparisons of specific activities or physical activity intensity levels.

Comparison of Self-Reported and Accelerometer-Assessed Physical Activity in Older Women

Background

Self-reported physical activity measures continue to be validated against accelerometers; however, the absence of standardized, accelerometer moderate-to-vigorous physical activity (MVPA) definitions has made comparisons across studies difficult. Furthermore, recent accelerometer models assess accelerations in three axes, instead of only the vertical axis, but validation studies have yet to take incorporate triaxial data.

Methods

Participants (n = 10 115) from the Women’s Health Study wore a hip-worn accelerometer (ActiGraph GT3X+) for seven days during waking hours (2011–2014). Women then completed a physical activity questionnaire. We compared self-reported with accelerometer-assessed MVPA, using four established cutpoints for MVPA: three using only vertical axis data (760, 1041 and 1952 counts per minute (cpm)) and one using triaxial data (2690 cpm).

Results

According to self-reported physical activity, 66.6% of women met the US federal physical activity guidelines, engaging in ≥150 minutes per week of MVPA. The percent of women who met guidelines varied widely depending on the accelerometer MVPA definition (760 cpm: 50.0%, 1041 cpm: 33.0%, 1952 cpm: 13.4%, and 2690 cpm: 19.3%).

Conclusions

Triaxial count data do not substantially reduce the difference between self-reported and accelerometer-assessed MVPA.

The emergence of sedentary behaviour physiology and its effects on the cardiometabolic profile in young and older adults

It has recently emerged that sedentary behaviour is independent of a lack of physical activity as individuals can be sufficiently active, based on the recommended physical activity guidelines, but also spend the majority of their waking hours engaging in sedentary behaviour. Individuals who follow this pattern of physical activity and sedentary behaviour are known as 'active couch potatoes'. Sedentary behaviour has been found to have detrimental effects on cardiometabolic markers associated with cardiovascular disease. Since the positive effects of moderate-to-vigorous intensity physical activity do not necessarily negate the deleterious effects of sedentary behaviour on cardiometabolic markers, it is postulated that engaging in light physical activity is an intervention that will successfully reduce levels of sedentary behaviour and may hence improve health markers of quality of life. We propose that such lifestyle changes may be particularly relevant to older populations as these engage in sedentary behaviour for the majority of their waking hours, thereby adding to the negative aging effect on cardiometabolic markers.

The validity of the GENEActiv wrist-worn accelerometer for measuring adult sedentary time in free living

OBJECTIVES

Based on self-reported measures, sedentary time has been associated with chronic disease and mortality. This study examined the validity of the wrist-worn GENEactiv accelerometer for measuring sedentary time (i.e. sitting and lying) by posture classification, during waking hours in free living adults.

DESIGN

Fifty-seven participants (age=18-55 years 52% male) were recruited using convenience sampling from a large metropolitan Australian university.

METHODS

Participants wore a GENEActiv accelerometer on their non-dominant wrist and an activPAL device attached to their right thigh for 24-h (00:00 to 23:59:59). Pearson's Correlation Coefficient was used to examine the convergent validity of the GENEActiv and the activPAL for estimating total sedentary time during waking hours. Agreement was illustrated using Bland and Altman plots, and intra-individual agreement for posture was assessed with the Kappa statistic.

RESULTS

Estimates of average total sedentary time over 24-h were 623 (SD 103) min/day from the GENEActiv, and 626 (SD 123) min/day from the activPAL, with an Intraclass Correlation Coefficient of 0.80 (95% confidence intervals 0.68-0.88). Bland and Altman plots showed slight underestimation of mean total sedentary time for GENEActiv relative to activPAL (mean difference: -3.44min/day), with moderate limits of agreement (-144 to 137min/day). Mean Kappa for posture was 0.53 (SD 0.12), indicating moderate agreement for this sample at the individual level.

CONCLUSIONS

The estimation of sedentary time by posture classification of the wrist-worn GENEActiv accelerometer was comparable to the activPAL. The GENEActiv may provide an alternative, easy to wear device based measure for descriptive estimates of sedentary time in population samples.

Improving wear time compliance with a 24-hour waist-worn accelerometer protocol in the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE)

Background

We compared 24-hour waist-worn accelerometer wear time characteristics of 9–11 year old children in the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE) to similarly aged U.S. children providing waking-hours waist-worn accelerometer data in the 2003–2006 National Health and Nutrition Examination Survey (NHANES).

Methods

Valid cases were defined as having ≥4 days with ≥10 hours of waking wear time in a 24-hour period, including one weekend day. Previously published algorithms for extracting total sleep episode time from 24-hour accelerometer data and for identifying wear time (in both the 24-hour and waking-hours protocols) were applied. The number of valid days obtained and a ratio (percent) of valid cases to the number of participants originally wearing an accelerometer were computed for both ISCOLE and NHANES. Given the two surveys’ discrepant sampling designs, wear time (minutes/day, hours/day) from U.S. ISCOLE was compared to NHANES using a meta-analytic approach. Wear time for the 11 additional countries participating in ISCOLE were graphically compared with NHANES.

Results

491 U.S. ISCOLE children (9.92±0.03 years of age [M±SE]) and 586 NHANES children (10.43 ± 0.04 years of age) were deemed valid cases. The ratio of valid cases to the number of participants originally wearing an accelerometer was 76.7% in U.S. ISCOLE and 62.6% in NHANES. Wear time averaged 1357.0 ± 4.2 minutes per 24-hour day in ISCOLE. Waking wear time was 884.4 ± 2.2 minutes/day for U.S. ISCOLE children and 822.6 ± 4.3 minutes/day in NHANES children (difference = 61.8 minutes/day, p < 0.001). Wear time characteristics were consistently higher in all ISCOLE study sites compared to the NHANES protocol.

Conclusions

A 24-hour waist-worn accelerometry protocol implemented in U.S. children produced 22.6 out of 24 hours of possible wear time, and 61.8 more minutes/day of waking wear time than a similarly implemented and processed waking wear time waist-worn accelerometry protocol. Consistent results were obtained internationally. The 24-hour protocol may produce an important increase in wear time compliance that also provides an opportunity to study the total sleep episode time separate and distinct from physical activity and sedentary time detected during waking-hours.

Trial registration

ClinicalTrials.gov NCT01722500.

Electronic supplementary material

The online version of this article (doi:10.1186/s12966-015-0172-x) contains supplementary material, which is available to authorized users.

Autocalibration of accelerometer data for free-living physical activity assessment using local gravity and temperature: an evaluation on four continents

Wearable acceleration sensors are increasingly used for the assessment of free-living physical activity. Acceleration sensor calibration is a potential source of error. This study aims to describe and evaluate an autocalibration method to minimize calibration error using segments within the free-living records (no extra experiments needed). The autocalibration method entailed the extraction of nonmovement periods in the data, for which the measured vector magnitude should ideally be the gravitational acceleration (1 g); this property was used to derive calibration correction factors using an iterative closest-point fitting process. The reduction in calibration error was evaluated in data from four cohorts: UK (n = 921), Kuwait (n = 120), Cameroon (n = 311), and Brazil (n = 200). Our method significantly reduced calibration error in all cohorts (P < 0.01), ranging from 16.6 to 3.0 mg in the Kuwaiti cohort to 76.7 to 8.0 mg error in the Brazil cohort. Utilizing temperature sensor data resulted in a small nonsignificant additional improvement (P > 0.05). Temperature correction coefficients were highest for the z-axis, e.g., 19.6-mg offset per 5°C. Further, application of the autocalibration method had a significant impact on typical metrics used for describing human physical activity, e.g., in Brazil average wrist acceleration was 0.2 to 51% lower than uncalibrated values depending on metric selection (P < 0.01). The autocalibration method as presented helps reduce the calibration error in wearable acceleration sensor data and improves comparability of physical activity measures across study locations. Temperature ultization seems essential when temperature deviates substantially from the average temperature in the record but not for multiday summary measures.

Evolution of accelerometer methods for physical activity research

The technology and application of current accelerometer-based devices in physical activity (PA) research allow the capture and storage or transmission of large volumes of raw acceleration signal data. These rich data not only provide opportunities to improve PA characterisation, but also bring logistical and analytic challenges. We discuss how researchers and developers from multiple disciplines are responding to the analytic challenges and how advances in data storage, transmission and big data computing will minimise logistical challenges. These new approaches also bring the need for several paradigm shifts for PA researchers, including a shift from count-based approaches and regression calibrations for PA energy expenditure (PAEE) estimation to activity characterisation and EE estimation based on features extracted from raw acceleration signals. Furthermore, a collaborative approach towards analytic methods is proposed to facilitate PA research, which requires a shift away from multiple independent calibration studies. Finally, we make the case for a distinction between PA represented by accelerometer-based devices and PA assessed by self-report.