Comparative Analysis of ActiGraph Step Counting Methods in Adults: A Systematic Literature Review and Meta-Analysis.

PURPOSE: The primary aim of this study was to compare steps per day across ActiGraph models, wear locations, and filtering methods. A secondary aim was to compare ActiGraph steps per day to those estimated by the ankle-worn StepWatch. METHODS: We conducted a systematic literature review to identify studies of adults published before May 12, 2022, that compared free-living steps per day of ActiGraph step counting methods and studies that compared ActiGraph to StepWatch. Random-effects meta-analysis compared ActiGraph models, wear locations, filter mechanisms, and ActiGraph to StepWatch steps per day. A sensitivity analysis of wear location by younger and older age was included. RESULTS: Twelve studies, with 46 comparisons, were identified. When worn on the hip, the AM-7164 recorded 123% of the GT series steps (no low-frequency extension (no LFE) or default filter). However, the AM-7164 recorded 72% of the GT series steps when the LFE was enabled. Independent of the filter used (i.e., LFE, no LFE), ActiGraph GT series monitors captured more steps on the wrist than on the hip, especially among older adults. Enabling the LFE on the GT series monitors consistently recorded more steps, regardless of wear location. When using the default filter (no LFE), ActiGraph recorded fewer steps than StepWatch (ActiGraph on hip 73% and ActiGraph on wrist 97% of StepWatch steps). When LFE was enabled, ActiGraph recorded more steps than StepWatch (ActiGraph on the hip, 132%; ActiGraph on the wrist, 178% of StepWatch steps). CONCLUSIONS: The choice of ActiGraph model, wear location, and filter all impacted steps per day in adults. These can markedly alter the steps recorded compared with a criterion method (StepWatch). This review provides critical insights for comparing studies using different ActiGraph step counting methods.

Sedentary Time and Prescription Medication Use Among US Adults: 2017-2018 National Health and Nutrition Examination Survey.

BACKGROUND: Prescription medication usage has been used as a predictor of disease prevalence and overall health status. Evidence suggests an inverse relationship exists between polypharmacy, which is the use of 5 or more medications, and physical activity participation. However, there is limited evidence examining the relationship between sedentary time and polypharmacy in adults. The aim of this study was to examine the associations between sedentary time and polypharmacy in a large nationally representative sample of US adults. METHODS: Study sample (N = 2879) included nonpregnant adult (≥20 y old) participants from the 2017-2018 National Health and Nutrition Examination Survey. Self-reported minutes per day of sedentary time were converted to hours per day. The dependent variable was polypharmacy (≥5 medications). RESULTS: Analysis revealed that for every hour of sedentary time, there was 4% greater odds of polypharmacy (odds ratio, 1.04; 95% confidence interval, 1.00-1.07, P = .04) after adjusting for age, race/ethnicity, education, waist circumference, and the interaction term between race/ethnicity and education. CONCLUSION: Our findings suggest increased sedentary time is associated with an increased risk of polypharmacy among a large nationally representative sample of US adults.

The role of resistance training in influencing insulin resistance among adults living with obesity/overweight without diabetes: A systematic review and meta-analysis.

The purpose of this study was to systematically examine the independent effect of resistance training (RT) on markers of insulin resistance (IR) (fasting insulin and HOMA-IR) among individuals with overweight/obesity without diabetes. PubMed, SPORTdiscus, SCOPUS, Prospero, and clinicaltrials.gov were searched through December 19, 2022. Article screening was conducted in three phases: title screen (n = 5020), abstract screen (n = 202), and full text screen (n = 73). A total of 27 studies with 402 individual data points were used for the meta-analysis. Comprehensive Meta-Analysis software version 3.0 was used to interpret pre- and post-IR measurements with a random-effects model. Exploratory sub-analyses were conducted on studies for only females, only males, and age (< 40 and ≥ 40 years). RT had a significant effect on fasting insulin (- 1.03, 95 % CI - 1.03, - 0.75 p < 0.001) and HOMA-IR (- 1.05, 95 % CI - 1.33, - 0.76, p < 0.001). Sub-analyses revealed that males had a more pronounced effect compared to females and those < 40 years of age had a more pronounced effect compared to those ≥ 40 years. The results of this meta-analysis illustrate that RT plays an independent role in improving IR among adults with overweight/obesity. RT should continue to be recommended as part of preventive measures among these populations. Future studies examining the effect of RT on IR should consider dose centered on the current U.S. physical activity guidelines.

Step Monitor Accuracy During PostStroke Physical Therapy and Simulated Activities.

Introduction/Purpose: The amount of stepping activity during rehabilitation post-stroke can predict walking outcomes, although the most accurate methods to evaluate stepping activity are uncertain with conflicting findings on available stepping monitors during walking assessments. Rehabilitation sessions also include non-stepping activities and the ability of activity monitors to differentiate these activities from stepping is unclear. The objective of this study was to examine the accuracy of different activity monitors worn by individuals post-stroke with variable walking speeds during clinical physical therapy (PT) and research interventions focused on walking. Methods: In Part I, 28 participants post-stroke wore a StepWatch, ActiGraph with and without a Low Frequency Extension (LFE) filter, and Fitbit on paretic and non-paretic distal shanks at or above the ankle during clinical PT or research interventions with steps simultaneously hand counted. Mean absolute percent errors were compared between limbs and tasks performed. In Part II, 12 healthy adults completed 8 walking and 9 non-walking tasks observed during clinical PT or research. Data were descriptively analyzed and used to assist interpretation of Part I results. Results: Part I results indicate most devices did not demonstrate an optimal limb configuration during research sessions focused on walking, with larger errors during clinical PT on the non-paretic limb. Using the limb that minimized errors for each device, the StepWatch had smaller errors than the ActiGraph and Fitbit (p<0.01), particularly in those who walked < 0.8 m/s. Conversely, errors from the ActiGraph-LFE demonstrated inconsistent differences in step counts between Fitbit and ActiGraph. Part II results indicate that errors observed during different stepping and non-stepping activities were often device-specific, with non-stepping tasks frequently detected as stepping. Conclusions: The StepWatch and ActiGraph-LFE had smaller errors than the Fitbit or ActiGraph, with greater errors in those walking at slower speeds. Inclusion of non-stepping activities affected step counts and should be considered when measuring stepping activity in individuals post-stroke to predict locomotor outcomes following rehabilitation.

Time Trends in Physical Activity Using Wearable Devices: A Systematic Review and Meta-analysis of Studies from 1995 to 2017.

INTRODUCTION: Conflicting evidence exists on whether physical activity (PA) levels of humans have changed over the last quarter-century. The main objective of this study was to determine if there is evidence of time trends in PA, from cross-sectional studies that assessed PA at different time points using wearable devices (e.g., pedometers and accelerometers). A secondary objective was to quantify the rate of change in PA. METHODS: A systematic literature review was conducted of English-language studies indexed in PubMed, SPORTDiscus, and Web of Science (1960-2020) using search terms (time OR temporal OR secular) AND trends AND (steps per day OR pedometer OR accelerometer OR MVPA). Subsequently, a meta-analytic approach was used to aggregate data from multiple studies and to examine specific factors (i.e., sex, age-group, sex and age-group, and PA metric). RESULTS: Based on 16 peer-reviewed scientific studies conducted between 1995 and 2017, levels of ambulatory PA are trending downward in developed countries. Significant declines were seen in both males and females (P < 0.001) as well as in children (P = 0.020), adolescents (P < 0.001), and adults (P = 0.004). The average study duration was 9.4 yr (accelerometer studies, 5.3 yr; pedometer studies, 10.8 yr). For studies that assessed steps, the average change in PA was -1118 steps per day over the course of the study (P < 0.001), and adolescents had the greatest change in PA at -2278 steps per day (P < 0.001). Adolescents also had the steepest rate of change over time, expressed in steps per day per decade. CONCLUSIONS: Evidence from studies conducted in eight developed nations over a 22-yr period indicates that PA levels have declined overall, especially in adolescents. This study emphasizes the need for continued research tracking time trends in PA using wearable devices.

Accelerometer-Measured Physical Activity and Cardiometabolic Risk Factors by Race-Ethnicity: 2003-2006 NHANES.

Previous studies suggest that the magnitude of morbidity/mortality reduction may differ between race-ethnic groups despite equated dose of physical activity (PA). The purpose of this study was to compare the potential racial-ethnic differences in cardiometabolic risk factors (CMRF) across quartiles of accelerometer-derived total activity counts/day (TAC/d) among US adults. The final sample (n=4144) included adults who participated in the 2003-2006 National Health and Nutrition Examination Survey (NHANES). CMRF included fasting glucose (FG), fasting insulin (FI), HOMA-IR, resting systolic (SBP) and diastolic blood pressure (DBP), waist circumference (WC), BMI, CRP, HDL-C, LDL-C, and triglycerides. Race-ethnic groups examined included non-Hispanic white (NHW), non-Hispanic black (NHB), and Mexican American (MA). In the highest quartile, NHW had significantly lower values of HOMA-IR, FI, SBP, BMI, WC, and HDL-C when compared to NHB. Compared to MA in the highest quartile, NHW had significantly lower values of HOMA-IR, FI, BMI, and triglycerides. Significant race-ethnic differences were found for several CMRF, especially among those who were in the top quartile of PA (e.g., the most active adults). It is probable that the protective effect of higher volumes of PA on CMRF is moderated by other non-PA factors distinct to NHB and MA.

Better Together? A Pilot Study of Romantic Partner Influence on Exercise Adherence and Cardiometabolic Risk in African-American Couples.

BACKGROUND: African-Americans (AAs) have higher rates of inactivity, obesity, and cardiometabolic risk compared to other races/ethnicities. Romantic partners can positively influence health habits, yet whether or not couples have to exercise together in order to adopt regular exercise remains unclear. This study examined whether exercising together influences exercise adherence and cardiometabolic risk in AA couples. METHODS: Nine AA romantic couples (age 62.8 ± 7.7 years; body mass index 31.0 ± 4.4 kg/m2; 6105 ± 1689 average steps/day) completed a 12-week walking (≥ 30 min, 3 days/week) plus resistance training (RT; 2 days/week) pilot intervention. Couples were randomized to either exercise together (ET) or separately (ES). Waist and hip circumferences, iDXA-measured body composition, blood pressure, and blood biomarkers (glucose, hemoglobin A1c (HbA1c), total cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides, C-reactive protein, and fibrinogen) were assessed pre- and post-intervention. Independent-sample t tests and generalized linear mixed models, controlling for gender, were used to analyze data. Significance was accepted at P < 0.05. RESULTS: There were no significant group × time interactions for any outcome. However, ET trended toward more walking (86.5 ± 57.7 min/week) than ES (66.1 ± 31.7 min/week). There were also significant overall time effects for waist circumference (P < 0.001), body fat (P = 0.020), fat mass (P = 0.007), gynoid fat (P = 0.041), HbA1c (P = 0.020), and HDL (P = 0.047), where all variables decreased. CONCLUSIONS: Trends showed exercising together may promote walking prescription adherence, although more research is needed in a larger sample. This intervention may also improve cardiometabolic risk factors in this population. These pilot data will inform the current investigators' future exercise intervention research in AA adult dyads.

Video-Recorded Validation of Wearable Step Counters under Free-living Conditions.

PURPOSE: The purpose of this study was to determine the accuracy of 14-step counting methods under free-living conditions. METHODS: Twelve adults (mean ± SD age, 35 ± 13 yr) wore a chest harness that held a GoPro camera pointed down at the feet during all waking hours for 1 d. The GoPro continuously recorded video of all steps taken throughout the day. Simultaneously, participants wore two StepWatch (SW) devices on each ankle (all programmed with different settings), one activPAL on each thigh, four devices at the waist (Fitbit Zip, Yamax Digi-Walker SW-200, New Lifestyles NL-2000, and ActiGraph GT9X (AG)), and two devices on the dominant and nondominant wrists (Fitbit Charge and AG). The GoPro videos were downloaded to a computer and researchers counted steps using a hand tally device, which served as the criterion method. RESULTS: The SW devices recorded between 95.3% and 102.8% of actual steps taken throughout the day (P > 0.05). Eleven step counting methods estimated less than 100% of actual steps; Fitbit Zip, Yamax Digi-Walker SW-200, and AG with the moving average vector magnitude algorithm on both wrists recorded 71% to 91% of steps (P > 0.05), whereas the activPAL, New Lifestyles NL-2000, and AG (without low-frequency extension (no-LFE), moving average vector magnitude) worn on the hip, and Fitbit Charge recorded 69% to 84% of steps (P < 0.05). Five methods estimated more than 100% of actual steps; AG (no-LFE) on both wrists recorded 109% to 122% of steps (P > 0.05), whereas the AG (LFE) on both wrists and the hip recorded 128% to 220% of steps (P < 0.05). CONCLUSIONS: Across all waking hours of 1 d, step counts differ between devices. The SW, regardless of settings, was the most accurate method of counting steps.

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.

StepWatch accuracy during walking, running, and intermittent activities.

INTRODUCTION: The purpose of this study was two-fold: 1) to investigate effects of cadence and sensitivity settings for the StepWatch (SW3) on step count accuracy over a wide range of ambulatory speeds, and 2) to compare the preprogrammed "quick start" settings to modified settings during intermittent lifestyle activities. METHODS: Part 1: Fifteen participants (18-57 years of age) performed two trials of treadmill walking and running at ten speeds ranging from 26.8 to 268mmin-1 while wearing four SW3 devices. During the first trial, the cadence setting was maintained while sensitivity was varied; in the second trial sensitivity was maintained while the cadence setting was varied. Part 2: Fifteen participants performed four intermittent activities and drove an automobile while wearing two SW3 devices, one with preprogrammed settings and the other with the modified settings determined in Part 1. RESULTS: Part 1: The modified settings (cadence setting of 70% of default and sensitivity of 16) provided the greatest step counting accuracy across a wide range of speeds reporting 96.0-104% of actual steps between 53.6 and 268mmin-1. Part 2: The preprogrammed settings tended to have higher accuracy for light household tasks (recording 88% to 94% of actual steps) than the modified settings (recording 82% to 86% of actual steps) which showed a trend towards higher accuracy for tennis (recording 93% vs. 89% of actual steps) (p<0.05). CONCLUSION: The preprogrammed "quick start" StepWatch settings should be used with individuals who do not engage in running and vigorous sports. However, for individuals who engage in running and tennis, use of modified settings may result in improved step counting accuracy.