Effects of Body Mass Index and Tilt Angle on Output of Two Wearable Activity Monitors

Association Between Patient Factors and the Effectiveness of Wearable Trackers at Increasing the Number of Steps per Day Among Adults With Cardiometabolic Conditions: Meta-analysis of Individual Patient Data From Randomized Controlled Trials

BACKGROUND

Current evidence supports the use of wearable trackers by people with cardiometabolic conditions. However, as the health benefits are small and confounded by heterogeneity, there remains uncertainty as to which patient groups are most helped by wearable trackers.

OBJECTIVE

This study examined the effects of wearable trackers in patients with cardiometabolic conditions to identify subgroups of patients who most benefited and to understand interventional differences.

METHODS

We obtained individual participant data from randomized controlled trials of wearable trackers that were conducted before December 2020 and measured steps per day as the primary outcome in participants with cardiometabolic conditions including diabetes, overweight or obesity, and cardiovascular disease. We used statistical models to account for clustering of participants within trials and heterogeneity across trials to estimate mean differences with the 95% CI.

RESULTS

Individual participant data were obtained from 9 of 25 eligible randomized controlled trials, which included 1481 of 3178 (47%) total participants. The wearable trackers revealed that over the median duration of 12 weeks, steps per day increased by 1656 (95% CI 918-2395), a significant change. Greater increases in steps per day from interventions using wearable trackers were observed in men (interaction coefficient -668, 95% CI -1157 to -180), patients in age categories over 50 years (50-59 years: interaction coefficient 1175, 95% CI 377-1973; 60-69 years: interaction coefficient 981, 95% CI 222-1740; 70-90 years: interaction coefficient 1060, 95% CI 200-1920), White patients (interaction coefficient 995, 95% CI 360-1631), and patients with fewer comorbidities (interaction coefficient -517, 95% CI -1188 to -11) compared to women, those aged below 50, non-White patients, and patients with multimorbidity. In terms of interventional differences, only face-to-face delivery of the tracker impacted the effectiveness of the interventions by increasing steps per day.

CONCLUSIONS

In patients with cardiometabolic conditions, interventions using wearable trackers to improve steps per day mostly benefited older White men without multimorbidity.

TRIAL REGISTRATION

PROSPERO CRD42019143012; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=143012.

Objectively measured physical activity and sedentary time in children with overweight, obesity and morbid obesity: a cross-sectional analysis

Background

Limited physical activity (PA) and a high level of sedentary time (ST) are associated with childhood obesity and are a target for intervention. This study aimed to assess objectively measured PA and ST in Dutch children across weight categories, age groups and sex.

Methods

202 children with overweight, obesity and morbid obesity (55% girls, 12 ± 3y of age, BMI z-score + 3.15 ± 0.73), referred to the Centre for Overweight Adolescents and Children’s Healthcare (COACH, Maastricht UMC+) were included. PA (total PA, light PA and moderate to vigorous PA (MVPA)) and ST were measured with the GT3X Actigraph accelerometer. Wear time validation was set to include at least four days, 480 min/day, including one weekend day.

Results

PA levels in children with morbid obesity were higher compared to children with obesity, also after correction for age and sex (corrected difference (B) 118 counts per minute (cpm), p = .006). ST was lower in children with morbid obesity compared to children with obesity (B − 51 min/day, p = .018). Girls performed significantly less MVPA than boys (B − 11 min/day, p < .001) and for each year increase of age, children performed less PA (B − 46 cpm, p < .001) and ST increased (B 18 min/day, p < .001).

Conclusion

PA and ST is different in subgroups of children with overweight, obesity and morbid obesity, depending on sex, age and overweight severity. In particular, children with obesity perform less PA and more ST compared to children with morbid obesity. Future research could explore the preferences and needs for PA and ST in children in the different weight categories.

Trial registration

The trial is registered with Clinicaltrials.govNCT02091544 at March 19, 2014.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-11555-5.

Accelerometry calibration in people with class II-III obesity: Energy expenditure prediction and physical activity intensity identification

BACKGROUND

Almost all accelerometer calibration studies were developed for non-obese people, which hampers an accurate prediction of energy expenditure (EE) and induces a misclassification of sedentary activity (SA) and physical activity intensities (PAI) in class II-III obese people.

RESEARCH QUESTION

The purpose of this study was to develop regression equations to predict EE and cut-points to classify SA and PAI in severe obese people based on several metrics obtained from hip and back accelerometer placement data.

METHODS

43 class II-III obese participants performed a protocol that included sitting and standing positions and walking at several speeds. During the protocol participants wore an accelerometer at hip and back, and respiratory gas exchange was measured by indirect calorimetry. Accelerometer metrics analyzed were: activity counts, mean amplitude deviation and euclidean norm minus one. EE was predicted through linear mixed models while cut-points to classify SA and PAI were obtained applying receiver operating characteristic curves. Leave-one-out cross-validation data was used to calculate Bland-Altman plots, prediction accuracy, Kappa statistic and percent agreement.

RESULTS

All prediction models presented a quadratic equation that had as predictors body mass and one of the accelerometer metrics. Predicted EE indicated a good agreement and a root mean square error below 1.02 kcal min^-1. Global classification agreement from developed cut-points was categorized as almost perfect with a percent agreement above 84 %. Prediction accuracy and classification agreement were similar among accelerometer metrics in each position and between them in hip and back placement.

SIGNIFICANCE

Hip and back accelerometer data collected in severe obese people allow to accurately estimate EE and to correctly classify SA and PAI. These results enable future studies to adopt appropriate regression equations and cut-points developed for class II-III obese people rather than those established for non-obese people.

Triaxial accelerometer output predicts oxygen uptake in adults with Down syndrome

PURPOSE

This study examined if the relationship between the rate of oxygen uptake (V̇O₂) and output from hip-and wrist-worn accelerometers differs between adults with and without DS, and evaluated the accuracy of accelerometer output in estimating V̇O₂.

MATERIALS AND METHODS

Sixteen adults with DS (10 men) and 19 adults without DS (10 men) performed 12 tasks including physical activities and sedentary behaviors. We measured V̇O₂ with portable spirometry and accelerometer output (vector magnitude [VM]) with hip- and wrist-worn accelerometers. We used multi-level regressions to predict V̇O₂ from VM, group, body mass index (BMI), age, height, weight, and sex. We evaluated prediction accuracy with absolute percent error and Bland-Altman plots.

RESULTS

For both hip- and wrist-accelerometers, VM and group significantly predicted V̇O₂ (p ≤ 0.021). When BMI was added, BMI was a significant predictor but group was not. The final models included VM and BMI (p ≤ 0.001; R² = 0.78 and 0.57, for hip and wrist accelerometer model, respectively). Absolute error was greater for the wrist- than the hip-accelerometer model (wrist: 37.9 ± 38.1%; hip: 22.5 ± 27.4%).

CONCLUSIONS

Adults with DS have different V̇O₂ to VM responses, and this appears due to their higher BMI. Predictability of V̇O₂ from accelerometer output is better for hip- than wrist-worn accelerometers.IMPLICATIONS FOR REHABILITATIONOutput from a triaxial accelerometer has high potential in predicting the energy expenditure and classifying the intensity of physical activity and sedentary behavior in adults with Down syndrome.Accuracy of predicting energy expenditure from accelerometer output is better for hip- than wrist-worn triaxial accelerometers.The development of appropriate rehabilitation interventions that include physical activity for improving health and function in adults with Down syndrome requires accurate assessments of physical activity levels.

A Dual-Accelerometer System for Classifying Physical Activity in Children and Adults

INTRODUCTION

Accurately monitoring 24-h movement behaviors is a vital step for progressing the time-use epidemiology field. Past accelerometer-based measurement protocols are either hindered by lack of wear time compliance, or the inability to accurately discern activities and postures. Recent work has indicated that skin-attached dual-accelerometers exhibit excellent 24-h uninterrupted wear time compliance. This study extends this work by validating this system for classifying various physical activities and sedentary behaviors in children and adults.

METHODS

Seventy-five participants (42 children) were equipped with two Axivity AX3 accelerometers; one attached to their thigh, and one to their lower back. Ten activity trials (e.g., sitting, standing, lying, walking, running) were performed while under direct observation in a lab setting. Various time- and frequency-domain features were computed from raw accelerometer data, which were then used to train a random forest machine learning classifier. Model performance was evaluated using leave-one-out cross-validation. The efficacy of the dual-sensor protocol (relative to single sensors) was evaluated by repeating the modeling process with each sensor individually.

RESULTS

Machine learning models were able to differentiate between six distinct activity classes with exceptionally high accuracy in both adults (99.1%) and children (97.3%). When a single thigh or back accelerometer was used, there was a pronounced drop in accuracy for nonambulatory activities (up to a 26.4% decline). When examining the features used for model training, those that took the orientation of both sensors into account concurrently were more important predictors.

CONCLUSIONS

When previous wear time compliance results are taken together with our findings, it represents a promising step forward for monitoring and understanding 24-h time-use behaviors. The next step will be to examine the generalizability of these findings in a free-living setting.

Metabolic Costs of Standing and Walking in Healthy Military-Age Adults: A Meta-regression

INTRODUCTION

The Load Carriage Decision Aid (LCDA) is a U.S. Army planning tool that predicts physiological responses of soldiers during different dismounted troop scenarios. We aimed to develop an equation that calculates standing and walking metabolic rates in healthy military-age adults for the LCDA using a meta-regression.

METHODS

We searched for studies that measured the energetic cost of standing and treadmill walking in healthy men and women via indirect calorimetry. We used mixed effects meta-regression to determine an optimal equation to calculate standing and walking metabolic rates as a function of walking speed (S, m·s). The optimal equation was used to determine the economical speed at which the metabolic cost per distance walked is minimized. The estimation precision of the new LCDA walking equation was compared with that of seven reference predictive equations.

RESULTS

The meta-regression included 48 studies. The optimal equation for calculating normal standing and walking metabolic rates (W·kg) was 1.44 + 1.94S + 0.24S. The economical speed for level walking was 1.39 m·s (~ 3.1 mph). The LCDA walking equation was more precise across all walking speeds (bias ± SD, 0.01 ± 0.33 W·kg) than the reference predictive equations.

CONCLUSION

Practitioners can use the new LCDA walking equation to calculate energy expenditure during standing and walking at speeds <2 m·s in healthy, military-age adults. The LCDA walking equation avoids the errors estimated by other equations at lower and higher walking speeds.

Step Counting: A Review of Measurement Considerations and Health-Related Applications

Step counting has long been used as a method of measuring distance. Starting in the mid-1900s, researchers became interested in using steps per day to quantify ambulatory physical activity. This line of research gained momentum after 1995, with the introduction of reasonably accurate spring-levered pedometers with digital displays. Since 2010, the use of accelerometer-based “activity trackers” by private citizens has skyrocketed. Steps have several advantages as a metric for assessing physical activity: they are intuitive, easy to measure, objective, and they represent a fundamental unit of human ambulatory activity. However, since they measure a human behavior, they have inherent biological variability; this means that measurements must be made over 3–7 days to attain valid and reliable estimates. There are many different kinds of step counters, designed to be worn on various sites on the body; all of these devices have strengths and limitations. In cross-sectional studies, strong associations between steps per day and health variables have been documented. Currently, at least eight prospective, longitudinal studies using accelerometers are being conducted that may help to establish dose–response relationships between steps/day and health outcomes. Longitudinal interventions using step counters have shown that they can help inactive individuals to increase by 2500 steps per day. Step counting is useful for surveillance, and studies have been conducted in a number of countries around the world. Future challenges include the need to establish testing protocols and accuracy standards, and to decide upon the best placement sites. These challenges should be addressed in order to achieve harmonization between studies, and to accurately quantify dose–response relationships.

Assessing Physical Activity as a Core Component in Cardiac Rehabilitation: A POSITION STATEMENT OF THE AMERICAN ASSOCIATION OF CARDIOVASCULAR AND PULMONARY REHABILITATION

Physical inactivity is a well-established major risk factor for cardiovascular disease. As such, physical activity counseling is 1 of the 10 core components of cardiac rehabilitation/secondary prevention programs recommended by the American Heart Association and the American Association of Cardiovascular and Pulmonary Rehabilitation (AACVPR). In addition, the ability to perform a physical activity assessment and report outcomes is 1 of the 10 core competencies of cardiac rehabilitation/secondary prevention professionals published by the AACVPR. Unfortunately, standardized procedures for physical activity assessment of cardiac rehabilitation patients have not been developed and published. Thus, the objective of this AACVPR statement is to provide an overview of physical activity assessment concepts and procedures and to provide a recommended approach for performing a standardized assessment of physical activity in all comprehensive cardiac rehabilitation programs following the core components recommendations.

Using Activity Monitors to Measure Sit-to-Stand Transitions in Overweight/Obese Youth

INTRODUCTION

Reducing sedentary behavior has emerged as an important health intervention strategy. Although hip-worn, and more recently wrist-worn, accelerometers are commonly used for measuring physical activity and sedentary behavior, they may not provide accurate measures of postural changes. The current study examined the validity of commonly used hip- and wrist-worn accelerometer cut points and the thigh-worn activPAL activity monitor for measuring sit-to-stand transitions as compared with direct observation in youth with overweight and obesity.

METHODS

Nine children wore three activity monitors while being directly observed. The monitors included a hip- and wrist-worn ActiGraph and thigh-worn activPAL. The hip-worn ActiGraph was processed with the normal- and low-frequency filters and the inclinometer function. Cut points of ≤25 counts per 15-s epoch for the hip and ≤105 counts per 15-s epoch for the wrist were applied to the vertical axis to identify sit-to-stand transitions. Epoch-level absolute agreement, Bland-Altman plots, mixed-effects linear regression, and intraclass correlation coefficients (ICC) were investigated.

RESULTS

The hip and wrist accelerometer cut points and the hip inclinometer function overestimated the number of hourly sit-to-stand transitions by approximately triple as compared with direct observation. ICC values between the ActiGraph methods and the direct observation were all <0.12. Sit-to-stand transitions assessed from activPAL were within 17% of direct observation; ICC was 0.26.

CONCLUSION

Despite the common use of the 100-count hip-worn accelerometer cut point for assessing sedentary time, these processing decisions should be used with caution for assessing sit-to-stand transitions. Future research should investigate other processing methods for ActiGraph data, and studies investigating postural changes should consider including devices such as activPAL.

Empirically derived cut-points for sedentary behaviour: are we sitting differently?

Sedentary behaviour (SB) is associated with a number of adverse health outcomes. Studies that have used ActiGraph monitors to define sedentary time tend to use a threshold of  <100 counts per minute (cpm) for classifying SB; however, this cut-point was not empirically derived for adults. It is not known whether ActiGraph cut-points for SB differ depending on the context in which it occurs. We aimed to: (1) empirically derive an optimal threshold for classifying SB, using the cpm output from the ActiGraph GT3X+, compared to the sedentary classification from the activPAL3^™; and (2) ascertain whether this varied by day of the week and in working time versus non-working time. A convenience sample of 30 office-based university employees (females (66.67%); age 40.47  ±  10.95 years; BMI 23.93  ±  2.46 kg m^-2) wore the ActiGraph GT3X+ and activPAL3^™ devices simultaneously for seven days. Data were downloaded in 1 min epochs and non-wear time was removed. Generalised estimating equations were used to make minute by minute comparisons of sedentary time from the two devices, using sedentary minutes (when all 60 s were classified as sitting/lying) from the activPAL3^™ as the criterion measure. After data reduction participants provided on average 11 h 27 min of data per day. The derived cut-points from the models were significantly higher on a Saturday (97 cpm) compared to weekdays (60 cpm) and Sunday (57 cpm). Derived cpm for sedentary time during working time were significantly lower compared to non-working time (35 (95%CI 30-41) versus 73 (54-113)). Compared to the 100 cpm and 150 cpm thresholds, the empirically derived cut-points were not significantly different in terms of area-under-the-curve, but had lower mean bias for working and non-working times. Accelerometer cut-points for SB can depend on day and also domain, suggesting that the nature of sitting differs depending on the context in which sedentary time is accrued.

Calibration and Validation of a Wrist- and Hip-Worn Actigraph Accelerometer in 4-Year-Old Children

Introduction

To determine time spent at different physical activity intensities, accelerometers need calibration. The aim of this study was to develop and cross-validate intensity thresholds for the Actigraph GT3X+ accelerometer for wrist and hip placement in four-year-old children.

Methods

In total 30 children (49 months, SD 3.7) were recruited from five preschools in Stockholm. Equipped with an accelerometer on the wrist and another on the hip, children performed three indoor activities and one free-play session while being video recorded. Subsequently, physical activity intensity levels were coded every 5^th second according to the Children’s Activity Rating Scale. Receiver Operating Characteristic (ROC) curves was used to develop wrist and hip intensity thresholds, the upper threshold for sedentary, and lower threshold for moderate-to-vigorous physical activity (MVPA), for the vertical axis (VA) and for the vector magnitude (VM). A leave-one-out method was used to cross-validate the thresholds.

Results

Intensity thresholds for wrist placement were ≤ 178 (VA) and ≤ 328 (VM) for sedentary and ≥ 871 (VA) and ≥ 1393 (VM) counts/5 seconds for MVPA. The corresponding thresholds for hip placement were ≤ 43 (VA) and ≤ 105 (VM) for sedentary and ≥ 290 (VA) and ≥ 512 (VM) for MVPA. The quadratic weighted Kappa was 0.92 (95% CI 0.91–0.93) (VA) and 0.95 (95% CI 0.94–0.96) (VM) for the wrist-worn accelerometer and 0.76 (98% CI 0.74–0.77) and 0.86 (95% CI 0.85–0.87) for the hip-worn.

Conclusion

Using wrist placement and the VM when measuring physical activity with accelerometry in 4-year-old children is recommended.

Evaluation of the ability of three physical activity monitors to predict weight change and estimate energy expenditure

Activity monitors such as the Actical accelerometer, the Sensewear armband, and the Intelligent Device for Energy Expenditure and Activity (IDEEA) are commonly validated against gold standards (e.g., doubly labeled water, or DLW) to determine whether they accurately measure total daily energy expenditure (TEE) or activity energy expenditure (AEE). However, little research has assessed whether these parameters or others (e.g., posture allocation) predict body weight change over time. The aims of this study were to (i) test whether estimated energy expenditure or posture allocation from the devices was associated with weight change during and following a low-calorie diet (LCD) and (ii) compare free-living TEE and AEE predictions from the devices against DLW before weight change. Eighty-seven participants from 2 clinical trials wore 2 of the 3 devices simultaneously for 1 week of a 2-week DLW period. Participants then completed an 8-week LCD and were weighed at the start and end of the LCD and 6 and 12 months after the LCD. More time spent walking at baseline, measured by the IDEEA, significantly predicted greater weight loss during the 8-week LCD. Measures of posture allocation demonstrated medium effect sizes in their relationships with weight change. Bland-Altman analyses indicated that the Sensewear and the IDEEA accurately estimated TEE, and the IDEEA accurately measured AEE. The results suggest that the ability of energy expenditure and posture allocation to predict weight change is limited, and the accuracy of TEE and AEE measurements varies across activity monitoring devices, with multi-sensor monitors demonstrating stronger validity.

Ankle Accelerometry for Assessing Physical Activity Among Adolescent Girls: Threshold Determination, Validity, Reliability, and Feasibility

PURPOSE

Ankle accelerometry allows for 24-hr data collection and improves data volume/integrity versus hip accelerometry. Using Actical ankle accelerometry, the purpose of this study was to (a) develop sensitive/specific thresholds, (b) examine validity/reliability, (c) compare new thresholds with those of the manufacturer, and (d) examine feasibility in a community sample (low-income, urban adolescent girls).

METHOD

Two studies were conducted with 6th- through 7th-grade girls (aged 10-14 years old): First was a laboratory study (n = 24), in which 2 Actical accelerometers were placed on the ankle and worn while measuring energy expenditure (Cosmed K4b2, metabolic equivalents [METs]) during 10 prescribed activities. Analyses included device equivalence reliability (intraclass correlation coefficient [ICC], activity counts of 2 Acticals), criterion-related validity (correlation, activity counts and METs), and calculations of sensitivity, specificity, kappa, and receiver-operating characteristic curves for thresholds. The second was a free-living study (n = 459), in which an Actical was worn for more than 7 days on the ankle (full 24-hr days retained). Analyses included feasibility (frequencies, missing data) and paired t tests (new thresholds vs. those of the manufacturer).

RESULTS

In the laboratory study, the Actical demonstrated reliability (ICC = .92) and validity (r = .81). Thresholds demonstrated sensitivity (91%), specificity (84%), kappa = .73 (p = .043), area under curve range = .81-.97. In the free-living study, 99.6% of participants wore the accelerometer; 84.1% had complete/valid data (mean = 5.7 days). Primary reasons for missing/invalid data included: improper programming/documentation (5.2%), failure to return device (5.0%), and wear-time ≤ 2 days (2.8%). The moderate-to-vigorous physical activity threshold (> 3,200 counts/minute) yielded 37.2 min/day, 2 to 4.5 times lower than that of the manufacturer's software (effect size = 0.74-4.05).

CONCLUSIONS

Validity, reliability, and feasibility evidences support Actical ankle accelerometry to assess physical activity in community studies of adolescent girls. When comparing manufacturers' software versus new thresholds, a major difference was observed.

Self-reported and objectively measured physical activity in adults with systemic lupus erythematosus

OBJECTIVE

Most estimates of physical activity (PA) patterns in systemic lupus erythematosus (SLE) are based on subjective self-report measures prone to error. The aims of this study were to obtain objective measurements of PA using an accelerometer and estimates of energy expenditure based on the self-reported International Physical Activity Questionnaire (IPAQ), and to describe their relationship.

METHODS

The Activity in Lupus To Energize and Renew study, a cross-sectional study of PA, included 129 persons with SLE. Accelerometer measures >7 days included total daily activity counts and minutes of moderate-vigorous PA (MVPA). Each person completed the IPAQ via telephone interview. Spearman's correlations (r) and 95% confidence intervals (95% CIs) assessed associations between accelerometer and IPAQ.

RESULTS

Daily PA means and SDs from accelerometer measures were 502,910 ± 118,755 for total daily activity counts and 40 ± 30 minutes for MVPA. The median (interquartile range) MET minutes/day for IPAQ intensities were: 400 (159-693) for total; 83 (26-184) for walking; and 231 (77-514) for MVPA, and domains were: 0 (0-73) for work; 28 (0-85) for active transportation; 77 (26-231) for domestic and garden; and 57 (0-213) for leisure. Associations between accelerometer measures and IPAQ were 1) total daily count versus IPAQ total: r = 0.21 (95% CI 0.03, 0.37) and 2) MVPA versus IPAQ moderate-vigorous: r = 0.16 (95% CI - 230.02, 0.33).

CONCLUSION

Accelerometer measures and IPAQ energy expenditure estimates were moderately correlated. IPAQ provided descriptive PA data, whereas accelerometers captured all daily activities, which can help assess guideline attainment. The choice of IPAQ versus accelerometer measure should include considering the purpose for which PA is measured.

Psychometric properties of the modified RESIDE physical activity questionnaire among low-income overweight women

OBJECTIVES

This study explored the criterion-related validity and test-retest reliability of the modified RESIDential Environment physical activity questionnaire and whether the instrument's validity varied by body mass index, education, race/ethnicity, or employment status.

DESIGN

Validation study using baseline data collected for randomized trial of a weight loss intervention.

METHODS

Participants recruited from health departments wore an ActiGraph accelerometer and self-reported non-occupational walking, moderate and vigorous physical activity on the modified RESIDential Environment questionnaire. We assessed validity (n=152) using Spearman correlation coefficients, and reliability (n=57) using intraclass correlation coefficients.

RESULTS

When compared to steps, moderate physical activity, and bouts of moderate/vigorous physical activity measured by accelerometer, these questionnaire measures showed fair evidence for validity: recreational walking (Spearman correlation coefficients 0.23-0.36), total walking (Spearman correlation coefficients 0.24-0.37), and total moderate physical activity (Spearman correlation coefficients 0.18-0.36). Correlations for self-reported walking and moderate physical activity were higher among unemployed participants and women with lower body mass indices. Generally no other variability in the validity of the instrument was found. Evidence for reliability of RESIDential Environment measures of recreational walking, total walking, and total moderate physical activity was substantial (intraclass correlation coefficients 0.56-0.68).

CONCLUSIONS

Evidence for questionnaire validity and reliability varied by activity domain and was strongest for walking measures. The questionnaire may capture physical activity less accurately among women with higher body mass indices and employed participants. Capturing occupational activity, specifically walking at work, may improve questionnaire validity.

Physical activity monitoring in extremely obese adolescents from the Teen-LABORATORIES study

BACKGROUND

The accuracy of physical activity (PA) monitors to discriminate between PA, sedentary behavior, and nonwear in extremely obese (EO) adolescents is unknown.

METHODS

Twenty-five subjects (9 male/16 female; age = 16.5 ± 2.0 y; BMI = 51 ± 8 kg/m2) wore 3 activity monitors (StepWatch [SAM], Actical [AC], Actiheart [AH]) during a 400-m walk test (400MWT), 2 standardized PA bouts of varying duration, and 1 sedentary bout.

RESULTS

For the 400MWT, percent error between observed and monitor-recorded steps was 5.5 ± 7.1% and 82.1 ± 38.6% for the SAM and AC steps, respectively (observed vs. SAM steps: -17.2 ± 22.2 steps; observed vs. AC steps: -264.5 ± 124.8 steps). All activity monitors were able to differentiate between PA and sedentary bouts, but only SAM steps and AH heart rate were significantly different between sedentary behavior and nonwear (P < .001). For all monitors, sedentary behavior was characterized by bouts of zero steps/counts punctuated by intermittent activity steps/counts; nonwear was represented almost exclusively by zero steps/counts.

CONCLUSION

Of all monitors tested, the SAM was most accurate in terms of counting steps and differentiating levels of PA and thus, most appropriate for EO adolescents. The ability to accurately characterize PA intensity in EO adolescents critically depends on activity monitor selection.

The use of individual cut points from treadmill walking to assess free-living moderate to vigorous physical activity in obese subjects by accelerometry: is it useful?

Background

Variation in counts between subjects at a given speed or work rate are the most important source of error in physical activity (PA) measurements with accelerometers. The aim of this study was to explore how the use of individual accelerometer cut points (ICPs) affected the analysis of PA field data.

Methods

We performed a treadmill calibration protocol to determine cut points for moderate to vigorous PA (MVPA) (≥3 metabolic equivalents) and assessed free-living PA in 44 severely obese subjects using the Actigraph GT1M accelerometer. We obtained cut points in 42 subjects (11 men, mean (standard deviation) of body mass index (BMI) 39.8 (5.7), age 43.2 (9.2) years), of whom 35 had valid measurement of free-living PA (minutes of MVPA/day). Linear regression was used to analyze associations with the ICPs and time in MVPA/day. MVPA/day was also compared with values derived using a group cut point (GCP).

Results

Resting oxygen consumption (partial r = 0.74, p < .001), work economy (partial r = −0.76, p < .001) and BMI (partial r = 0.52, p = .001) explained 68.4% of the variation in the ICPs (F = 26.7, p < .001). The ICPs explained 79.1% of the variation in the minutes spent in MVPA/day. Moderate to vigorous PA/day derived from the ICPs vs. the GCP varied substantially (R² = 14%, p = .023, coefficient of variation = 45.1%).

Conclusions

The results indicate that the use of ICPs had a strong influence on the PA level. Two thirds of the variation in the ICPs could be explained, however, a certain degree of measurement error will be present. Thus, we are not able to conclude with respect to the most appropriate procedure for analyzing time in MVPA.

Device-based monitoring in physical activity and public health research

Measurement of physical activity is important, given the vital role of this behavior in physical and mental health. Over the past quarter of a century, the use of small, non-invasive, wearable monitors to assess physical activity has become commonplace. This review is divided into three sections. In the first section, a brief history of physical activity monitoring is provided, along with a discussion of the strengths and weaknesses of different devices. In the second section, recent applications of physical activity monitoring in physical activity and public health research are discussed. Wearable monitors are being used to conduct surveillance, and to determine the extent and distribution of physical activity and sedentary behaviors in populations around the world. They have been used to help clarify the dose-response relation between physical activity and health. Wearable monitors that provide feedback to users have also been used in longitudinal interventions to motivate research participants and to assess their compliance with program goals. In the third section, future directions for research in physical activity monitoring are discussed. It is likely that new developments in wearable monitors will lead to greater accuracy and improved ease-of-use.

Translation equations to compare ActiGraph GT3X and Actical accelerometers activity counts

BACKGROUND

This study aimed to develop a translation equation to enable comparison between Actical and ActiGraph GT3X accelerometer counts recorded minute by minute.

METHODS

Five males and five females of variable height, weight, body mass index and age participated in this investigation. Participants simultaneously wore an Actical and an ActiGraph accelerometer for two days. Conversion algorithms and R(2) were calculated day by day for each subject between the omnidirectional Actical and three different ActiGraph (three-dimensional) outputs: 1) vertical direction, 2) combined vector, and 3) a custom vector. Three conversion algorithms suitable for minute/minute conversions were then calculated from the full data set.

RESULTS

The vertical ActiGraph activity counts demonstrated the closest relationship with the Actical, with consistent moderate to strong conversions using the algorithm: y = 0.905x, in the day by day data (R(2) range: 0.514 to 0.989 and average: 0.822) and full data set (R(2) = 0.865).

CONCLUSIONS

The Actical is most sensitive to accelerations in the vertical direction, and does not closely correlate with three-dimensional ActiGraph output. Minute by minute conversions between the Actical and ActiGraph vertical component can be confidently performed between data sets and might allow further synthesis of information between studies.

Treadmill Calibration of the Actigraph GT1M in Young-to-Middle-Aged Obese-to-Severely Obese Subjects

To understand the impact of physical activity (PA) on health, valid accelerometer count cut points must be applied to measure PA. Because cut points may be population specific, we aimed to establish accelerometer cut points for moderate PA (MPA) and vigorous PA (VPA) (defined as ≥3 and ≥6 metabolic equivalents, resp.) in young-to-middle-aged obese-to-severely obese subjects. Data from 42 subjects (11 men; body mass index 39.8 ± 5.7; age 43.2 ± 9.2 years) who performed a treadmill calibration using the Actigraph GT1M, were analyzed using ordinary linear regression (OLR), linear mixed model regression (MIX), and receiver operating characteristics curves (ROC 1; ROC 2). Cut points obtained from the models were quite different (612 to 1646 counts/min for MPA; 3061 to 7220 counts/min for VPA). We argue that the MIX approach, which resulted in cut points of 612 and 4980 counts/min for MPA and VPA, respectively, is the most appropriate method to establish accelerometer cut points in this setting. We conclude that accelerometer cut points are lower in young-to-middle-aged obese-to-severely obese subjects compared to young normal-weight subjects and that care should be taken when analyzing PA level in groups that vary in age and degree of obesity.

Effects of BMI and abdominal volume on the accuracy of step count obtained from a tri-axial accelerometer

Accelerometers are widely accepted as practical wearable devices capable of measuring and assessing physical activity. These devices may, however, be subject to errors which could impact on their ability to acquire an accurate step count. A limited number of studies have examined the effects of body mass index (BMI) on the accuracy of accelerometers functioning as step counters. It has been suggested that BMI may not be the best indicator of adiposity. The aim of the present study was to assess the effects of BMI and abdominal volume on the accuracy of a step count obtained from a tri-axial accelerometer. Accelerometers were placed directly onto the skin at the chest, waist and lower back of 12 participants. Participants then walked on a motorized treadmill at 0.89 m/s and 1.34 m/s. Analysis of the results indicated that BMI and abdominal volume did not affect the accuracy of the step count obtained from accelerometers under any conditions. Walking speed, however, had a significant effect with step count accuracy decreasing at the slower speed.