Effects of filter choice in GT3X accelerometer assessments of free-living activity

Relationships between pain cognitions and physical function in a sample of racially diverse, sedentary individuals with chronic pain

BACKGROUND

Pain from musculoskeletal pain conditions is often persistent, bothersome, and negatively impacts physical function. Individuals with musculoskeletal pain report difficulty with walking and regular activities. For some, this may be related to overly negative pain cognitions, such as pain catastrophizing and kinesiophobia. In a geographically and racially diverse sample, we examined relationships between pain catastrophizing, kinesiophobia, and multimodal physical function (i.e., self-report, performance-based, objective).

METHODS

Participants were sedentary adults with ≥3 months of chronic musculoskeletal pain. Participants completed self-report measures of pain catastrophizing (Pain Catastrophizing Scale), kinesiophobia (Tampa Scale of Kinesiophobia), and physical function (World Health Organization Disability Assessment Scale 2.0). Performance-based physical function was assessed in-clinic with the Six-Minute Walk Test (6MWT). Physical function was objectively measured with ≥4 days of ActiGraph wear outside the clinic. We conducted descriptive, correlation, and linear regression statistics in SPSS.

RESULTS

Higher levels of pain catastrophizing (β = 0.42) and kinesiophobia (β = 0.25) were significantly associated with worse self-reported physical function. Neither pain catastrophizing nor kinesiophobia were related to performance-based or objectively measured physical function. The direction and significance of relationships between pain catastrophizing, kinesiophobia, and physical function measures were consistent in unadjusted and adjusted regression models.

CONCLUSIONS

Pain catastrophizing and kinesiophobia are associated with an individual's perceived physical functioning. Behavioral interventions designed to enhance physical function may benefit from including cognitive restructuring to challenge catastrophic thoughts about pain, as well as thoughts about injuring oneself or worsening pain with movement. More work is needed to understand why neither pain catastrophizing nor kinesiophobia were significantly associated with performance-based or objective assessment of physical function. It is possible that other pain-related cognitions, for example self-efficacy for pain control, or variables (e.g., in vivo pain catastrophizing, mood, stress, sleep) assessed closer in time to performance-based or objective measures of physical function are more relevant.

Can step count be used to identify older adults with high sedentary time and low moderate-to-vigorous physical activity?

INTRODUCTION

Combined high sedentary time (ST) and low moderate-to-vigorous physical activity (MVPA) has been associated with adverse cardiovascular events. However, accurately assessing ST and MVPA in older adults is challenging in clinical practice.

PURPOSE

To investigate whether step count can identify older adults with unhealthier movement behavior (high ST/low MVPA) and poorer cardiometabolic profile.

METHODS

Cross-sectional study (n = 258; 66 ± 5 years). Step count, ST, and MVPA were assessed by hip accelerometry during 7 days. The cardiometabolic profile was assessed using a continuous metabolic syndrome score (cMetS), including blood pressure, HDL-cholesterol, triglycerides, fasting glucose, and waist circumference. Receiving operating curve analysis was used to test the performance of step count in identifying older adults with unhealthier movement behavior (highest tertile of ST/lowest tertile of MVPA). Healthier movement behavior was defined as lowest tertile of ST/highest tertile of MVPA, with neutral representing the remaining combinations of ST/MVPA.

RESULTS

A total of 40 participants (15.5%) were identified with unhealthier movement behavior (ST ≥ 11.4 h/day and MVPA ≤ 10 min/day). They spent ~73% and 0.4% of waking hours in ST and MVPA, respectively. Step count identified those with unhealthier movement behavior (area under the curve 0.892, 0.850-0.934; cutoff: ≤5263 steps/day; sensitivity/specificity: 83%/81%). This group showed a higher cMetS compared with neutral (β = .25, p = .028) and healthier movement behavior groups (β = .41, p = .008).

CONCLUSION

Daily step count appears to be a practical, simple metric for identifying community-dwelling older adults with concomitant high ST and low MVPA, indicative of unhealthier movement behavior, who have a poorer cardiometabolic profile.

Effects of ActiGraph's filter, epoch length and non-wearing time algorithm on step counts in people with COPD

The influence of the ActiGraph® processing criteria on estimating step counts in chronic obstructive pulmonary disease (COPD) remains uncertain. This study aimed to assess the influence of filters, epoch lengths and non-wearing time (NWT) algorithms on steps/day in people with COPD. ActiGraph GT3X+ was worn on the waist for seven days. Steps were detected using different filters (normal and low-frequency extension [LFE]), epoch lengths (15s and 60s), and NWT algorithms (Choi and Troiano). Linear mixed-effects model was applied to assess the effects of filter, epoch length, NWT algorithm on steps/day. Lin's concordance correlation and Bland-Altman were used to measure agreement. A total of 136 people with COPD (107 male; 69 ± 8 years; FEV1 51 ± 17% predicted) were included. Significant differences were found between filters (p < 0.001), but not between epoch lengths or NWT algorithms. The LFE increased, on average, approximately 7500 steps/day compared to the normal filter (p < 0.001). Agreement was poor (<0.3) and proportional bias was significant when comparing steps/day computed with different filters, regardless of the epoch length and NWT algorithm. Filter choice but not epoch lengths or NWT algorithms seem to impact measurement of steps/day. Future studies are needed to recommend the most accurate technique for measuring steps/day in people with COPD.

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.

Associations of air pollution mixtures with ambulatory blood pressure: The MobiliSense sensor-based study

Air pollution is acknowledged as a determinant of blood pressure (BP), supporting the hypothesis that air pollution, via hypertension and other mechanisms, has detrimental effects on human health. Previous studies evaluating the associations between air pollution exposure and BP did not consider the effect that air pollutant mixtures may have on BP. We investigated the effect of exposure to single species or their synergistic effects as air pollution mixture on ambulatory BP. Using portable sensors, we measured personal concentrations of black carbon (BC), nitrogen dioxide (NO2), nitrogen monoxide (NO), carbon monoxide (CO), ozone (O3), and particles with aerodynamic diameters below 2.5 μm (PM2.5). We simultaneously collected ambulatory BP measurements (30-min intervals, N = 3319) of 221 participants over one day of their lives. Air pollution concentrations were averaged over 5 min to 1 h before each BP measurement, and inhaled doses were estimated across the same exposure windows using estimated ventilation rates. Fixed-effect linear models as well as quantile G-computation techniques were applied to associate air pollutants' individual and combined effects with BP, adjusting for potential confounders. In mixture models, a quartile increase in air pollutant concentrations (BC, NO2, NO, CO, and O3) in the previous 5 min was associated with a 1.92 mmHg (95% CI: 0.63, 3.20) higher systolic BP (SBP), while 30-min and 1-h exposures were not associated with SBP. However, the effects on diastolic BP (DBP) were inconsistent across exposure windows. Unlike concentration mixtures, inhalation mixtures in the previous 5 min to 1 h were associated with increased SBP. Out-of-home BC and O3 concentrations were more strongly associated with ambulatory BP outcomes than in-home concentrations. In contrast, only the in-home concentration of CO reduced DBP in stratified analyses. This study shows that exposure to a mixture of air pollutants (concentration and inhalation) was associated with elevated SBP.

Agreement of Step-Based Metrics From ActiGraph and ActivPAL Accelerometers Worn Concurrently Among Older Adults

Purpose

Our study evaluated the agreement of mean daily step counts, peak 1-min cadence, and peak 30-min cadence between the hip-worn ActiGraph GT3X+ accelerometer, using the normal filter (AG_N) and the low frequency extension (AG_LFE), and the thigh-worn activPAL3 micro (AP) accelerometer among older adults.

Methods

Nine-hundred and fifty-three older adults (≥65 years) were recruited to wear the ActiGraph device concurrently with the AP for 4-7 days beginning in 2016. Using the AP as the reference measure, device agreement for each step-based metric was assessed using mean differences (AG_N - AP and AG_LFE - AP), mean absolute percentage error (MAPE), and Pearson and concordance correlation coefficients.

Results

For AG_N - AP, the mean differences and MAPE were: daily steps -1,851 steps/day and 27.2%, peak 1-min cadence -16.2 steps/min and 16.3%, and peak 30-min cadence -17.7 steps/min and 24.0%. Pearson coefficients were .94, .85, and .91 and concordance coefficients were .81, .65, and .73, respectively. For AG_LFE - AP, the mean differences and MAPE were: daily steps 4,968 steps/day and 72.7%, peak 1-min cadence -1.4 steps/min and 4.7%, and peak 30-min cadence 1.4 steps/min and 7.0%. Pearson coefficients were .91, .91, and .95 and concordance coefficients were .49, .91, and .94, respectively.

Conclusions

Compared with estimates from the AP, the AG_N underestimated daily step counts by approximately 1,800 steps/day, while the AG_LFE overestimated by approximately 5,000 steps/day. However, peak step cadence estimates generated from the AG_LFE and AP had high agreement (MAPE ≤ 7.0%). Additional convergent validation studies of step-based metrics from concurrently worn accelerometers are needed for improved understanding of between-device agreement.

Application of machine learning to predict transport modes from GPS, accelerometer, and heart rate data

BACKGROUND

There has been an increased focus on active transport, but the measurement of active transport is still difficult and error-prone. Sensor data have been used to predict active transport. While heart rate data have very rarely been considered before, this study used random forests (RF) to predict transport modes using Global Positioning System (GPS), accelerometer, and heart rate data and paid attention to methodological issues related to the prediction strategy and post-processing.

METHODS

The RECORD MultiSensor study collected GPS, accelerometer, and heart rate data over seven days from 126 participants living in the Ile-de-France region. RF models were built to predict transport modes for every minute (ground truth information on modes is from a GPS-based mobility survey), splitting observations between a Training dataset and a Test dataset at the participant level instead at the minute level. Moreover, several window sizes were tested for the post-processing moving average of the predicted transport mode.

RESULTS

The minute-level prediction rate of being on trips vs. at a visited location was 90%. Final prediction rates of transport modes ranged from 65% for public transport to 95% for biking. Using minute-level observations from the same participants in the Training and Test sets (as RF spontaneously does) upwardly biases prediction rates. The inclusion of heart rate data improved prediction rates only for biking. A 3 to 5-min bandwidth moving average was optimum for a posteriori homogenization.

CONCLUSION

Heart rate only very slightly contributed to better predictions for specific transport modes. Moreover, our study shows that Training and Test sets must be carefully defined in RF models and that post-processing with carefully chosen moving average windows can improve predictions.

Acute ambulatory blood pressure response to short-term black carbon exposure: The MobiliSense sensor-based study

Documented relationships between black carbon (BC) exposure and blood pressure (BP) have been inconsistent. Very few studies measured both BC exposure and ambulatory BP across the multiple daily environments visited in the general population, and none adjusted for personal noise exposure, a major confounder. Our study addresses these gaps by considering 245 adults living in the Grand Paris region. Personal exposure to BC was monitored for 2 days using AE51 microaethalometers. Ambulatory BP was measured every 30 min after waking up using Arteriograph 24 monitors (n = 6772). Mixed effect models with a random intercept at the individual level and time-autocorrelation structure adjusted for personal noise exposure were used to evaluate the associations between BC exposure (averaged from 5 min to 1 h before each BP measurement) and BP. To increase the robustness of findings, we eliminated confounding by unmeasured time-invariant personal variables, by modelling the associations with fixed-effect models. All models were adjusted for potential confounders and short-term time trends. Results from mixed models show that a 1-μg/m³ increase in 5-minute averaged BC exposure was associated with an increase of 0.57 mmHg in ambulatory systolic blood pressure (SBP) (95 % CI: 0.30, 0.83) and with an increase of 0.36 mmHg in diastolic blood pressure (DBP) (95 % CI: 0.14, 0.58). The slope of the exposure-response relationship gradually decreased for both SBP and DBP with the increase in the averaging period of BC exposure from 5 min to 1 h preceding each BP measurement. Findings from the fixed-effect models were consistent with these results. There was no effect modification by noise in the associations, across all exposure windows. We found evidence of a relationship between BC exposure and acute increase in ambulatory SBP and DBP after adjustment for personal noise exposure, with potential implications for the development of adverse cardiovascular outcomes.

The impact of ActiGraph's low-frequency extension filter on measurement of children's physical activity

This study examines differences in accelerometer-based measurements of children's movement with and without applying ActiGraph's low-frequency extension (LFE) filter. Thirty children wore ActiGraph GT9X devices during structured physical activity (PA) periods. Raw accelerometry data for each activity period were processed with and without the LFE filter applied. For each activity period, paired t-tests were used to compare vector magnitude counts and minutes spent in moderate-to-vigorous physical activity (MVPA) with and without the LFE filter applied. Repeated measures MANOVA models were used for compositional data analysis of the percentage of time spent in sedentary behaviour and light, moderate, and vigorous PA with and without the LFE filter applied. Applying the LFE filter significantly increased vector magnitude counts and estimated minutes spent in MVPA for all activity periods when compared to the normal filter. For brisk walking, the LFE filter had a significant impact on the composition of time spent in sedentary behaviour and PA intensities. Children's activity data processed with the LFE filter may not be compatible with cut-points for activity levels developed with the normal filter, and caution should be taken when comparing children's activity levels or movement data between studies that do and do not use the LFE filter.

Personal exposure to concentrations and inhalation of black carbon according to transport mode use: The MobiliSense sensor-based study

INTRODUCTION

Epidemiological evidence suggests that motorized vehicle users have a higher air pollutant exposure (especially from vehicle exhaust) than active (walking or cycling) transport users. However, studies often relied on insufficiently diverse sample and ignored that minute ventilation has an effect on individuals' inhaled dose. This study examined commuters' breathing zone concentration and inhaled doses of black carbon (BC) when travelling by different transport modes in the Grand Paris region.

METHODS

Personal exposure to BC was continuously measured with MicroAethalometer (MicroAeth AE51) portable monitors strapped on participants' shoulder with tube inlet at the level of the neck (breathing zone), and inhaled doses were derived from several methods estimating ventilation [based on metabolic equivalents from accelerometry [METs], heart rate, and breathing rate]. Trip stages and transport modes were assessed from GPS and mobility survey data. Breathing zone concentrations and inhaled doses of BC were compared across transport modes at the trip stage level (n = 7495 for 283 participants) using linear mixed effect models with a random intercept at individual level.

RESULTS

Trip stages involving public transport and private motorized transport were associated with a 2.20 µg/m³ (95% CI: 1.99, 2.41) and 2.29 µg/m³ (95% CI: 2.10, 2.48) higher breathing zone concentration to BC than walking, respectively. Trip stages with other active modes had a 0.41 µg (95% CI: 0.25, 0.57) higher inhaled dose, while those involving public transport and private motorized transport had a 0.25 µg (95% CI: -0.35, -0.15) and 0.19 µg (95 %CI: -0.28, -0.10) lower inhaled dose of BC per 30 min than walking.

CONCLUSION

The ranking of transport modes in terms of personal exposure was markedly different when breathing zone concentrations and inhaled doses were considered. Future studies should take both into account to explore the relationship of air pollutants in transport microenvironments with physiological response.

Comparative assessment of ActiGraph data processing techniques for measuring sedentary behavior in adults with COPD

Objective.The ActiGraph is commonly used for measuring sedentary behavior (SB), but the best data processing technique is not established for sedentary adults with chronic illness. The purpose of this study was to process ActiGraph vertical axis and vector magnitude data with multiple combinations of filters, non-wear algorithm lengths, and cut-points and to compare ActiGraph estimates to activPAL-measured sedentary time in sedentary adults with chronic obstructive pulmonary disease (COPD).Approach.This study was a secondary analysis of adults ≥50 years (N = 59; mean age: 69.4 years;N = 31 males) with COPD. Participants woreActiGraph GT9XandactivPAL3for 7 d. ActiGraph vertical axis and vector magnitude data were processed using combinations of filters (normal, low frequency extension (LFE)), non-wear algorithm lengths (60, 90, 120 min), and cut-points for SB previously validated in older adults (two for vertical axis and three for vector magnitude data). The Bland-Altman method was used to assess concordance between sedentary time measured with 30 ActiGraph techniques and activPAL-measured sedentary time.Main results. Agreement between the two devices was moderate to strong for all techniques; concordance correlations ranged from 0.614 to 0.838. Limits of agreement were wide. The best overall technique was vector magnitude data with LFE filter, 120 min non-wear algorithm, and <40 counts/15 s SB cut-point (concordance correlation 0.838; mean difference -11.7 min d^-1).Significance. This analysis supports the use of ActiGraph vector magnitude data and LFE filter in adults with COPD, but also demonstrates that other techniques may be acceptable with appropriate cut-points. These results can guide ActiGraph data processing decisions.

Agreement Between StepWatch3 and ActiGraph wGT3X+ for Measuring Step-Based Metrics in People With Peripheral Artery Disease

The authors investigated the agreement between StepWatch3™ (SW3) and ActiGraph™ wGT3X+ monitors for measuring step-based metrics in patients with peripheral artery disease and older adults. In 23 patients with peripheral artery disease and 38 older participants, the authors compared the metrics obtained during an outdoor (400-m track) walking session (step count) and a 7-day free-living period (step count and 60/30/5/1-min maximal or peak step accumulation) using the SW3 (ankle) and the wGT3X+ (hip) with the low-frequency extension filter enabled (wGT3X+/LFE) or not (wGT3X+/N). During outdoor walking session, agreement was high, particularly for wGT3X+/LFE: correlations ≥.98, median absolute percentage errors <1%, and significant equivalence using a ± 15% equivalence zone or narrower. In free living, no wGT3X+ method was equivalent to SW3 for step count. The wGT3X+/LFE was equivalent to SW3 regarding all step accumulation metrics using a ± 20% equivalence zone or narrower, with median absolute percentage errors <11%. The wGT3X+/LFE method is the best option for comparisons with SW3 in peripheral artery disease and older adults.

Influence of Neighborhood Characteristics and Weather on Movement Behaviors at Age 3 and 5 Years in a Longitudinal Birth Cohort

BACKGROUND

Movement behaviors (physical activity, sedentary time, and sleep) established in early childhood track into adulthood and interact to influence health outcomes. This study examined the associations between neighborhood characteristics and weather with movement behaviors in preschoolers.

METHODS

A subset of Canadian Healthy Infant Longitudinal Development birth cohort (n = 385, 50.6% boys) with valid movement behaviors data were enrolled at age 3 years and followed through to age 5 years. Objective measures of neighborhood characteristics were derived by ArcGIS software, and weather variables were derived from the Government of Canada weather website. Random forest and linear mixed models were used to examine predictors of movement behaviors. Cross-sectional analyses were stratified by age and season (winter and nonwinter).

RESULTS

Neighborhood safety, temperature, green space, and roads were important neighborhood characteristics for movement behaviors in 3- and 5-year-olds. An increase in temperature was associated with greater light physical activity longitudinally from age 3 to 5 years and also in the winter at age 5 years in stratified analysis. A higher percentage of expressways was associated with less nonwinter moderate to vigorous physical activity at age 3 years.

CONCLUSIONS

Future initiatives to promote healthy movement behaviors in the early years should consider age differences, neighborhood characteristics, and season.

Development of a mind body program for obese knee osteoarthritis patients with comorbid depression

Knee osteoarthritis (OA) is the most common joint disorder in the U.S. and a leading cause of disability. Depression and obesity are highly comorbid among knee OA patients, and the combination of obesity and depression is associated with decreased physical activity, higher pain and disability, and more rapid cartilage degradation. Depression, obesity and OA exacerbate one another and share a common pathophysiology involving systemic inflammation and pro-inflammatory cytokines, reflecting a complex mind-body interaction. Current treatments for knee OA offer little to no benefit over placebo, and do not emphasize mind-body practices or physical activity to target the underlying pathophysiology. Mind-body interventions to lessen depressive symptoms and increase physical activity offer the ability to target biological, mechanical and psychological mechanisms of OA progression. Our long-term goals are to evaluate the mechanisms by which the Relaxation Response Resiliency Program (3RP) delivered via secure telehealth, and adapted for patients with depression, obesity and knee OA (GetActive-OA) promotes increases in physical activity and improved knee health. We hypothesize that the synergistic interaction between mindfulness, adaptive thinking, positive psychology and healthy living skills of the GetActive-OA will slow the progression of symptomatic knee OA by reducing pro-inflammatory cytokine expression and promoting optimal mechanical loading of the cartilage. Here we present the protocol for a mixed methods study that will adapt the 3RP for the needs of knee OA patients with depression and obesity with a focus on increasing physical activity (GetActive-OA), and iteratively maximize the feasibility, credibility and acceptability of the programs and research procedures.

Step and Distance Measurement From a Low-Cost Consumer-Based Hip and Wrist Activity Monitor: Protocol for a Validity and Reliability Assessment

Background

Self-tracking via wearable and mobile technologies is becoming an essential part of personal health management. At this point, however, little information is available to substantiate the validity and reliability of low-cost consumer-based hip and wrist activity monitors, with regard more specifically to the measurements of step counts and distance traveled while walking.

Objective

The aim of our study is to assess the validity and reliability of step and distance measurement from a low-cost consumer-based hip and wrist activity monitor specific in various walking conditions that are commonly encountered in daily life. Specifically, this study is designed to evaluate whether and to what extent validity and reliability could depend on the sensor placement on the human body and the walking task being performed.

Methods

Thirty healthy participants will be instructed to wear four PBN 2433 (Nakosite) activity monitors simultaneously, with one placed on each hip and each wrist. Participants will attend two experimental sessions separated by 1 week. During each experimental session, two separate studies will be performed. In study 1, participants will be instructed to complete a 2-minute walk test along a 30-meter indoor corridor under 3 walking speeds: very slow, slow, and usual speed. In study 2, participants will be required to complete the following 3 conditions performed at usual walking speed: walking on flat ground, upstairs, and downstairs. Activity monitor measured step count and distance values will be computed along with the actual step count (determined from video recordings) and distance (measured using a measuring tape) to determine validity and reliability for each activity monitor placement and each walking condition.

Results

Participant recruitment and data collection began in January 2020. As of June 2020, we enrolled 8 participants. Dissemination of study results in peer-reviewed journals is expected in spring 2021.

Conclusions

To the best of our knowledge, this is the first study that examines the validity and reliability of step and distance measurement during walking using the PBN 2433 (Nakosite) activity monitor. Results of this study will provide beneficial information on the effects of activity monitor placement, walking speed, and walking tasks on the validity and reliability of step and distance measurement. We believe such information is of utmost importance to general consumers, clinicians, and researchers.

International Registered Report Identifier (IRRID)

DERR1-10.2196/21262

Considerations in Processing Accelerometry Data to Explore Physical Activity and Sedentary Time in Older Adults

Processing decisions for accelerometry data can have important implications for outcome measures, yet little evidence exists exploring these in older adults. The aim of the current study was to investigate the impact of three potentially important criteria on older adults, physical activity, and sedentary time. Participants (n = 222: mean age 71.75 years [SD = 6.58], 57% male) wore ActiGraph GT3X+ for 7 days. Eight data processing combinations from three criteria were explored: low-frequency extension (on/off), nonwear time (90/120 min), and intensity cut points (moderate-to-vigorous physical activity ≥1,041 and >2,000 counts/min). Analyses included Wilcoxon signed-rank test, paired t tests, and correlation coefficients (significance, p < .05). Results for low-frequency extension on 90-min nonwear time and >1,041 counts/min showed significantly higher light and moderate-to-vigorous physical activity and lower sedentary time. Cut points had the greatest impact on physical activity and sedentary time. Processing criteria can significantly impact physical activity and/or sedentary time, potentially leading to data inaccuracies, preventing cross-study comparisons and influencing the accuracy of population surveillance.

Differences in Physical Activity and Sedentary Behavior Patterns of Postmenopausal Women With Normal vs. Low Total Hip Bone Mineral Density

Purpose: Recent evidence suggests that sedentary behavior (SB) may be associated with bone health. This study compares free-living physical activity (PA) and SB distribution patterns of postmenopausal women with normal vs. low total hip bone mineral density (BMD). Methods: Sixty nine post-menopausal women [mean (min-max) age: 61 (46-79) years] wore ActiGraph GT3X+ activity monitors on the bilateral ankles for 7 days in free-living. Participants were split into two groups: those with normal hip BMD (T-scores ≥-1.0; N = 34) and those with low hip BMD (T-scores <-1.0; N = 35) as defined by the World Health Organization. Daily active time, step counts, sedentary time, sedentary break number, and median sedentary bout length were estimated from ankle acceleration data. The distribution and accumulation patterns of time spent in sedentary bouts, sedentary breaks, and stepping bouts, and sedentary break and stepping bout lengths' variability were also investigated. Group differences were assessed using two-sampled t-tests and Mann-Whitney U-tests with significance levels of 0.5. Results: Significant between group differences (p < 0.05) were in total daily active time [median (IQR): 257 (209-326) vs. 249 (199-299) min], step count [14,188 (10,938-18,646) vs. 13,204 (10,337-16,630) steps], sedentary time [669 (584-731) vs. 687 (615-753) min], and sedentary break number [93 (68-129) breaks vs. 88 (64-113) breaks], as well as median sedentary bout length [15.1 (11.9-22.1) vs. 15.8 (12.1-24.9) min]. Participants' sedentary bouts were found to be power law distributed with 52% of sedentary time occurring in bouts ≥20 min for the normal BMD group, and 58% for the low BMD group. Significant differences were observed between groups in sedentary bouts' and sedentary breaks' power distribution exponents (p < 0.0001) and patterns of sedentary and stepping time accumulation using the Gini index (p ≤ 0.0014). Variability was significantly lower for sedentary break and stepping bout lengths for the low BMD group (p ≤ 0.0001). Participants with lower hip BMD have longer sedentary bouts with shorter and less complex activity bouts compared to participants with normal hip BMD. Conclusion: The results suggest healthier hip BMD may be associated with PA distributed more evenly throughout the day with shorter sedentary bouts. PA distribution should be considered in exercise-based bone health management programs.

Physical Activity Promotion for Apprentices in Nursing Care and Automotive Mechatronics-Competence Counts More than Volume

Apprentices in the area of nursing care and automotive mechatronics are exposed to increased health risks. In this context, the promotion of physical activity (PA) is considered an effective strategy for the assurance of work ability. The goal of the PArC-AVE study was therefore to better understand the role of PA for apprentices employed in these two sectors. In an exploratory study, 55 apprentices wore an ActiGraph accelerometer over seven consecutive days and were subject to activity analysis. The objective accelerometer data (18,979 ± 3780 steps/day; 471.00 ± 159.75 min of moderate-to-vigorous PA/week), complemented by questionnaire data, indicated that most met the volume-based PA recommendations. Subsequently, we conducted a multicenter study comprising 745 apprentices from six vocational education institutions. Path analyses showed that competencies for health-enhancing PA were significantly related to indicators of work ability (0.180 ≤ b ≤ 0.452) and psychophysical health (0.139 ≤ b ≤ 0.347), whereas mere volume of PA was not (-0.048 ≤ b ≤ 0.080). In summary, apprentices of nursing care and automotive mechatronics showed high levels of PA. However, the results highlight the importance of competencies for health-enhancing PA. The PAHCO model could provide a useful framework for the conceptualization of effective interventions.

Associations of Sedentary Time with Energy Expenditure and Anthropometric Measures

PURPOSE

To investigate associations between accelerometer-determined sedentary time (ST) in prolonged (≥30 min) and nonprolonged (<30 min) bouts with physical activity energy expenditure (PAEE) from doubly labeled water. Additionally, associations between ST and body mass index (BMI) and waist circumference were examined.

METHODS

Data from 736 women and 655 men age 43 to 82 yr were analyzed. Participants wore the Actigraph GT3X for 7 d on two occasions approximately 6 months apart, and the average of the measurements was used. Physical activity energy expenditure was estimated by subtracting resting metabolic rate and the thermic effect of food from doubly labeled water estimates of total daily energy expenditure. Cross-sectional associations were analyzed using isotemporal substitution modeling.

RESULTS

Reallocation of prolonged ST to nonprolonged was not associated with increased PAEE and only significantly associated with lower BMI (β = -0.57 kg·m; 95% confidence interval, -0.94 to -0.20) and waist circumference (β = -1.61 cm; 95% confidence interval, -2.61 to -0.60) in men. Replacing either type of ST with light or moderate-to-vigorous physical activity was significantly associated with higher PAEE, and lower BMI and waist circumference in both women and men.

CONCLUSIONS

Limiting time spent sedentary as well as decreasing ST accumulated in prolonged bouts may have beneficial effects on BMI and waist circumference. Replacing any type of ST with activities of light or higher intensity may also have a substantial impact on PAEE.

Step-counting accuracy of activity monitors in persons with Down syndrome

BACKGROUND

Accelerometers and pedometers have been used to monitor the number of steps. However, the evidence on the step-counting accuracy of these devices - especially accelerometers - is limited in persons with Down syndrome (DS). This study therefore examined the accuracy of accelerometers placed on the hip or wrist and of a pedometer with a uni-axial accelerometer mechanism in measuring steps in persons with DS and whether device error is associated with walking speed, height, weight, body mass index, waist circumference, leg length, age or sex.

METHOD

Seventeen persons with DS (eight women and nine men; age 33 ± 15 years) walked over-ground for 6 min at their preferred speed. The steps were measured with a hip-worn and a wrist-worn ActiGraph accelerometer using the manufacturer's default (DF) and low-frequency extension (LFE) filters, and with the NL-1000 New Lifestyles pedometer on the hip. Steps were also measured with hand tally which served as the criterion.

RESULTS

Absolute percent error was considerable and differed statistically between devices (P = 0.001); however, error improved for accelerometers when LFE was applied (Hip-DF: 31.6 ± 18.8%; Hip-LFE: 9.7 ± 12.8%; Wrist-DF: 32.7 ± 14.2%; Wrist-LFE: 13.6 ± 10.2%; Pedometer: 23.2 ± 22.8%). Bland-Altman plots indicated underestimation of steps for accelerometers and the pedometer. Application of LFE, however, improved the prediction of the accelerometers. The number of steps measured by the hip accelerometer with LFE and by the pedometer did not differ statistically from actual steps. Steps by the remaining methods were significantly lower than hand tally (P ≤ 0.001). Correlations between percent error for each device and walking speed, anthropometry, age or sex ranged between -0.28 and +0.48, and were non-significant, except for age.

CONCLUSIONS

The results demonstrated that the pedometer and ActiGraph accelerometers have considerable error in measuring steps of persons with DS. Application of LFE, however, significantly improved the step-counting performance of the Actigraph accelerometers.

Validity of objective methods for measuring sedentary behaviour in older adults: a systematic review

Background

The evidence showing the ill health effects of prolonged sedentary behaviour (SB) is growing. Most studies of SB in older adults have relied on self-report measures of SB. However, SB is difficult for older adults to recall and objective measures that combine accelerometry with inclinometry are now available for more accurately assessing SB. The aim of this systematic review was to assess the validity and reliability of these accelerometers for the assessment of SB in older adults.

Methods

EMBASE, PubMed and EBSCOhost databases were searched for articles published up to December 13, 2017. Articles were eligible if they: a) described reliability, calibration or validation studies of SB measurement in healthy, community-dwelling individuals, b) were published in English, Portuguese or Spanish, and c) were published or in press as journal articles in peer-reviewed journals.

Results

The review identified 15 studies in 17 papers. Of the included studies, 11 assessed the ActiGraph accelerometer. Of these, three examined reliability only, seven (in eight papers) examined validity only and one (in two papers) examined both. The strongest evidence from the studies reviewed is from studies that assessed the validity of the ActiGraph. These studies indicate that analysis of the data using 60-s epochs and a vertical magnitude cut-point < 200 cpm or using 30- or 60-s epochs with a machine learning algorithm provides the most valid estimates of SB. Non-wear algorithms of 90+ consecutive zeros is also suggested for the ActiGraph.

Conclusions

Few studies have examined the reliability and validity of accelerometers for measuring SB in older adults. Studies to date suggest that the criteria researchers use for classifying an epoch as sedentary instead of as non-wear time (e.g., the non-wear algorithm used) may need to be different for older adults than for younger adults. The required number of hours and days of wear for valid estimates of SB in older adults was not clear from studies to date. More older-adult-specific validation studies of accelerometers are needed, to inform future guidelines on the appropriate criteria to use for analysis of data from different accelerometer brands.

Trial registration

PROSPERO ID# CRD42017080754 registered December 12, 2017.

Electronic supplementary material

The online version of this article (10.1186/s12966-018-0749-2) contains supplementary material, which is available to authorized users.

Accelerometer Data Collection and Processing Criteria to Assess Physical Activity and Other Outcomes: A Systematic Review and Practical Considerations

BACKGROUND

Accelerometers are widely used to measure sedentary time, physical activity, physical activity energy expenditure (PAEE), and sleep-related behaviors, with the ActiGraph being the most frequently used brand by researchers. However, data collection and processing criteria have evolved in a myriad of ways out of the need to answer unique research questions; as a result there is no consensus.

OBJECTIVES

The purpose of this review was to: (1) compile and classify existing studies assessing sedentary time, physical activity, energy expenditure, or sleep using the ActiGraph GT3X/+ through data collection and processing criteria to improve data comparability and (2) review data collection and processing criteria when using GT3X/+ and provide age-specific practical considerations based on the validation/calibration studies identified.

METHODS

Two independent researchers conducted the search in PubMed and Web of Science. We included all original studies in which the GT3X/+ was used in laboratory, controlled, or free-living conditions published from 1 January 2010 to the 31 December 2015.

RESULTS

The present systematic review provides key information about the following data collection and processing criteria: placement, sampling frequency, filter, epoch length, non-wear-time, what constitutes a valid day and a valid week, cut-points for sedentary time and physical activity intensity classification, and algorithms to estimate PAEE and sleep-related behaviors. The information is organized by age group, since criteria are usually age-specific.

CONCLUSION

This review will help researchers and practitioners to make better decisions before (i.e., device placement and sampling frequency) and after (i.e., data processing criteria) data collection using the GT3X/+ accelerometer, in order to obtain more valid and comparable data.

PROSPERO REGISTRATION NUMBER

CRD42016039991.

Defining Accelerometer Nonwear Time to Maximize Detection of Sedentary Time in Youth

PURPOSE

The present study examined various accelerometer nonwear definitions and their impact on detection of sedentary time using different ActiGraph models, filters, and axes.

METHODS

In total, 61 youth (34 children and 27 adolescents; aged 5-17 y) wore a 7164 and GT3X+ ActiGraph on a hip-worn belt during a 90-minute structured sedentary activity. Data from GT3X+ were downloaded using the Normal filter (N) and low-frequency extension (LFE), and vertical axis (V) and vector magnitude (VM) counts were examined. Nine nonwear definitions were applied to the 7164 model (V), GT3X+LFE (V and VM), and GT3X+N (V and VM), and sedentary estimates were computed.

RESULTS

The GT3X+LFE-VM was most sensitive to movement and could accurately detect observed sedentary time with the shortest nonwear definition of 20 minutes of consecutive "0" counts for children and 40 minutes for adolescents. The GT3X+N-V was least sensitive to movement and required longer definitions to detect observed sedentary time (40 min for children and 90 min for adolescents). VM definitions were 10 minutes shorter than V definitions. LFE definitions were 40 minutes shorter than N definitions in adolescents.

CONCLUSION

Different nonwear definitions are needed for children and adolescents and for different model-filter-axis types. Authors need to consider nonwear definitions when comparing prevalence rates of sedentary behavior across studies.

A systematic literature review of reviews on techniques for physical activity measurement in adults: a DEDIPAC study

The links between increased participation in Physical Activity (PA) and improvements in health are well established. As this body of evidence has grown, so too has the search for measures of PA with high levels of methodological effectiveness (i.e. validity, reliability and responsiveness to change). The aim of this “review of reviews” was to provide a comprehensive overview of the methodological effectiveness of currently employed measures of PA, to aid researchers in their selection of an appropriate tool. A total of 63 review articles were included in this review, and the original articles cited by these reviews were included in order to extract detailed information on methodological effectiveness.

Self-report measures of PA have been most frequently examined for methodological effectiveness, with highly variable findings identified across a broad range of behaviours. The evidence-base for the methodological effectiveness of objective monitors, particularly accelerometers/activity monitors, is increasing, with lower levels of variability observed for validity and reliability when compared to subjective measures. Unfortunately, responsiveness to change across all measures and behaviours remains under-researched, with limited information available.

Other criteria beyond methodological effectiveness often influence tool selection, including cost and feasibility. However, researchers must be aware of the methodological effectiveness of any measure selected for use when examining PA. Although no “perfect” tool for the examination of PA in adults exists, it is suggested that researchers aim to incorporate appropriate objective measures, specific to the behaviours of interests, when examining PA in free-living environments.

Physical Activity Assessment with the ActiGraph GT3X and Doubly Labeled Water

PURPOSE

To compare the degree to which four accelerometer metrics-total activity counts per day (TAC per day), steps per day (steps per day), physical activity energy expenditure (PAEE) (kcal·kg·d), and moderate- to vigorous-intensity physical activity (MVPA) (min·d)-were correlated with PAEE measured by doubly labeled water (DLW). Additionally, accelerometer metrics based on vertical axis counts and triaxial counts were compared.

METHODS

This analysis included 684 women and 611 men age 43 to 83 yr. Participants wore the Actigraph GT3X on the hip for 7 d twice during the study and the average of the two measurements was used. Each participant also completed one DLW measurement, with a subset having a repeat. PAEE was estimated by subtracting resting metabolic rate and the thermic effect of food from total daily energy expenditure estimated by DLW. Partial Spearman correlations were used to estimate associations between PAEE and each accelerometer metric.

RESULTS

Correlations between the accelerometer metrics and DLW-determined PAEE were higher for triaxial counts than vertical axis counts. After adjusting for weight, age, accelerometer wear time, and fat free mass, the correlation between TAC per day based on triaxial counts and DLW-determined PAEE was 0.44 in women and 0.41 in men. Correlations for steps per day and accelerometer-estimated PAEE with DLW-determined PAEE were similar. After adjustment for within-person variation in DLW-determined PAEE, the correlations for TAC per day increased to 0.61 and 0.49, respectively. Correlations between MVPA and DLW-determined PAEE were lower, particularly for modified bouts of ≥10 min.

CONCLUSIONS

Accelerometer measures that represent total activity volume, including TAC per day, steps per day, and PAEE, were more highly correlated with DLW-determined PAEE than MVPA using traditional thresholds and should be considered by researchers seeking to reduce accelerometer data to a single metric.

Validation of the Global Physical Activity Questionnaire for self-administration in a European context

Background/aim

Little is known about the measurement properties of the self-administered Global Physical Activity Questionnaire (GPAQ) in Europe. The aim was to validate the self-administered GPAQ against accelerometry in Switzerland in German, French and Italian.

Methods

Participants of this cross-sectional study were recruited among members of the Swiss Food Panel (German-speaking and French-speaking samples) and as a convenience sample (Italian-speaking sample). They completed the GPAQ and wore an Actigraph GT3X+ accelerometer during 7 days in 2014/2015. GPAQ and accelerometer data on total physical activity and different intensities, as well as sitting time, were compared using Spearman correlations and Bland-Altman plots.

Results

Complete data were available for 354 participants (50.6% women, mean age: 47.0 years) on physical activity, and for 366 on sitting time. Correlations were highest for vigorous physical activity (r=0.46) and sitting time (r=0.47). A significant sex difference was apparent for vigorous physical activity (men: r=0.35 vs women: r=0.55; p=0.02). Some age differences were present especially for total physical activity, with the lowest correlations found for those aged 60+ years. The correlation for sitting time was significantly higher in the youngest age group (r=0.61) compared with the middle (r=0.38, p=0.01) and the oldest age groups (r=0.37, p=0.03). Total physical activity was 2.8 times higher according to the GPAQ than to accelerometer data.

Conclusions

The self-administered version of the GPAQ showed fair-to-moderate validity in the three languages tested, both for men and women and individuals aged ≤60 years. For older individuals, a careful interpretation of total physical activity is required.

Using GPS, GIS, and Accelerometer Data to Predict Transportation Modes

INTRODUCTION

Active transportation is a substantial source of physical activity, which has a positive influence on many health outcomes. A survey of transportation modes for each trip is challenging, time-consuming, and requires substantial financial investments. This study proposes a passive collection method and the prediction of modes at the trip level using random forests.

METHODS

The RECORD GPS study collected real-life trip data from 236 participants over 7 d, including the transportation mode, global positioning system, geographical information systems, and accelerometer data. A prediction model of transportation modes was constructed using the random forests method. Finally, we investigated the performance of models on the basis of a limited number of participants/trips to predict transportation modes for a large number of trips.

RESULTS

The full model had a correct prediction rate of 90%. A simpler model of global positioning system explanatory variables combined with geographical information systems variables performed nearly as well. Relatively good predictions could be made using a model based on the 991 trips of the first 30 participants.

CONCLUSIONS

This study uses real-life data from a large sample set to test a method for predicting transportation modes at the trip level, thereby providing a useful complement to time unit-level prediction methods. By enabling predictions on the basis of a limited number of observations, this method may decrease the workload for participants/researchers and provide relevant trip-level data to investigate relations between transportation and health.

Validation of the long international physical activity questionnaire: Influence of age and language region

Objective

Little is known about the influence of age, gender and language on the measurement properties of the long International Physical Activity Questionnaire (IPAQ). The aim was to validate the long IPAQ in adults aged 18–84 in the German-, French- and Italian-speaking parts of Switzerland, focusing on differences between gender, age groups and language regions.

Methods

This cross-sectional study was conducted in the frame of SAPALDIA (Swiss Cohort Study on Air Pollution and Lung and Heart Disease in Adults) in 2011. 346 participants (54.6% women, mean age 54.6 years) wore an Actigraph GT3X accelerometer during 8 days and completed the IPAQ. IPAQ and accelerometer data on total physical activity and on different intensities as well as sitting time were compared using Spearman correlations and Bland–Altman plots.

Results

Correlations were highest for vigorous physical activity (r = 0.41) and sitting time (r = 0.42). Significant gender differences were apparent for leisure-time physical activity (men: r = 0.35 versus women: r = 0.57, p = 0.012) and for sitting time (men: r = 0.28 versus women: r = 0.53, p = 0.007). Differences between age groups were present for sitting time (youngest: r = 0.72 versus middle: r = 0.36, p < 0.001; youngest versus oldest: r = 0.34, p = 0.001). Differences between language regions were present for vigorous physical activity (German: r = 0.28 versus Italian: r = 0.53, p = 0.033). IPAQ overestimated physical activity but underestimated sitting time.

Conclusion

The long IPAQ showed moderate validity similar to other studies when compared to accelerometer data in a diverse sample of individuals. Some sex, age and regional differences were observed but do not seem to limit its applicability in population sub groups.

•

We assessed the validity of the long IPAQ focusing on age and language differences.

•

The IPAQ showed moderate validity against accelerometers.

•

Correlations were highest for vigorous physical activity and for sitting time.

•

We observed only limited sex, age and regional differences regarding validity.

•

However, these do not limit the applicability of the IPAQ in specific sub groups.

Calibrating physical activity intensity for hip-worn accelerometry in women age 60 to 91 years: The Women's Health Initiative OPACH Calibration Study

OBJECTIVE

We conducted a laboratory-based calibration study to determine relevant cutpoints for a hip-worn accelerometer among women ≥60 years, considering both type and filtering of counts.

METHODS

Two hundred women wore an ActiGraph GT3X+ accelerometer on their hip while performing eight laboratory-based activities. Oxygen uptake was measured using an Oxycon portable calorimeter. Accelerometer data were analyzed in 15-second epochs for both normal and low frequency extension (LFE) filters. Receiver operating characteristic (ROC) curve analyses were used to calculate cutpoints for sedentary, light (low and high), and moderate to vigorous physical activity (MVPA) using the vertical axis and vector magnitude (VM) counts.

RESULTS

Mean age was 75.5 years (standard deviation 7.7). The Spearman correlation between oxygen uptake and accelerometry ranged from 0.77 to 0.85 for the normal and LFE filters and for both the vertical axis and VM. The area under the ROC curve was generally higher for VM compared to the vertical axis, and higher for cutpoints distinguishing MVPA compared to sedentary and light low activities. The VM better discriminated sedentary from light low activities compared to the vertical axis. The area under the ROC curves were better for the LFE filter compared to the normal filter for the vertical axis counts, but no meaningful differences were found by filter type for VM counts.

CONCLUSION

The cutpoints derived for this study among women ≥60 years can be applied to ongoing epidemiologic studies to define a range of physical activity intensities.

Practical considerations in using accelerometers to assess physical activity, sedentary behavior, and sleep

Increasingly, behavioral and epidemiological research uses activity-based measurements (accelerometry) to provide objective estimates of physical activity, sedentary behavior, and sleep in a variety of study designs. As interest in concurrently assessing these domains grows, there are key methodological considerations that influence the choice of monitoring instrument, analysis algorithm, and protocol for measuring these behaviors. The purpose of this review is to summarize evidence-guided information for 7 areas that are of importance in the design and interpretation of studies using actigraphy: (1) choice of cut-points; (2) impact of epoch length; (3) accelerometer placement; (4) duration of monitoring; (5) approaches for distinguishing sleep, nonwear times, and sedentary behavior; (6) role for a sleep and activity diary; and (7) epidemiological applications. Recommendations for future research are outlined and are intended to enhance the appropriate use of accelerometry for assessing physical activity, sedentary behavior, and sleep behaviors in research studies.

Self-Selected Walking Speed is Predictive of Daily Ambulatory Activity in Older Adults

Daily ambulatory activity is associated with health and functional status in older adults; however, assessment requires multiple days of activity monitoring. The objective of this study was to determine the relative capabilities of self-selected walking speed (SSWS), maximal walking speed (MWS), and walking speed reserve (WSR) to provide insight into daily ambulatory activity (steps per day) in community-dwelling older adults. Sixty-seven older adults completed testing and activity monitoring (age 80.39 [6.73] years). SSWS (R2 = .51), MWS (R2 = .35), and WSR calculated as a ratio (R2 = .06) were significant predictors of daily ambulatory activity in unadjusted linear regression. Cutpoints for participants achieving < 8,000 steps/day were identified for SSWS (≤ 0.97 m/s, 44.2% sensitivity, 95.7% specificity, 10.28 +LR, 0.58 -LR) and MWS (≤ 1.39 m/s, 60.5% sensitivity, 78.3% specificity, 2.79 +LR, 0.50 -LR). SSWS may be a feasible proxy for assessing and monitoring daily ambulatory activity in older adults.

Comparison of two accelerometer filter settings in individuals with Parkinson's disease

This study compared common free-living physical activity (PA) outcomes, assessed with the Actigraph GT3X+ accelerometer and processed with two different filter settings, in a sample of elderly individuals with Parkinson´s disease (PD). Sixty-six individuals (73.1  ±  5.8 years) with mild to moderate idiopathic PD carried an accelerometer for 7 d. Data were processed with the default filter setting and a low frequency extension filter (LFE). Significantly larger values were obtained with the LFE for mean counts and steps per day, and for minutes per day in low intensity- and lifestyle activities at moderate intensity, but not for moderate-to vigorous intensity ambulatory activities. The largest difference was observed for mean ± SD steps per day (default = 4730  ±  3210; LFE = 11 117  ±  4553). Intraclass correlation confidence intervals and limits of agreement were generally wide, indicating poor agreement. A sub-study, in which 15 individuals with PD performed a self-paced 3 min walk, demonstrated that neither filter setting differed from video-recorded steps (p ≥ 0.05). This suggests that the LFE might overestimate PA-outcomes in free-living conditions. Until new evidence supporting an extension of the lower filter-band is presented, it is recommended that the default filter setting be used when assessing PA in elderly individuals with PD.

Active transportation and public transportation use to achieve physical activity recommendations? A combined GPS, accelerometer, and mobility survey study

BACKGROUND

Accurate information is lacking on the extent of transportation as a source of physical activity, on the physical activity gains from public transportation use, and on the extent to which population shifts in the use of transportation modes could increase the percentage of people reaching official physical activity recommendations.

METHODS

In 2012-2013, 234 participants of the RECORD GPS Study (French Paris region, median age = 58) wore a portable GPS receiver and an accelerometer for 7 consecutive days and completed a 7-day GPS-based mobility survey (participation rate = 57.1%). Information on transportation modes and accelerometry data aggregated at the trip level [number of steps taken, energy expended, moderate to vigorous physical activity (MVPA), and sedentary time] were available for 7,644 trips. Associations between transportation modes and accelerometer-derived physical activity were estimated at the trip level with multilevel linear models.

RESULTS

Participants spent a median of 1 h 58 min per day in transportation (8.2% of total time). Thirty-eight per-cent of steps taken, 31% of energy expended, and 33% of MVPA over 7 days were attributable to transportation. Walking and biking trips but also public transportation trips with all four transit modes examined were associated with greater steps, MVPA, and energy expenditure when compared to trips by personal motorized vehicle. Two simulated scenarios, implying a shift of approximately 14% and 33% of all motorized trips to public transportation or walking, were associated with a predicted 6 point and 13 point increase in the percentage of participants achieving the current physical activity recommendation.

CONCLUSIONS

Collecting data with GPS receivers, accelerometers, and a GPS-based electronic mobility survey of activities and transportation modes allowed us to investigate relationships between transportation modes and physical activity at the trip level. Our findings suggest that an increase in active transportation participation and public transportation use may have substantial impacts on the percentage of people achieving physical activity recommendations.

Accelerometer data reduction in adolescents: effects on sample retention and bias

BACKGROUND

Accelerometry is increasingly being recognized as an accurate and reliable method to assess free-living physical activity (PA) in children and adolescents. However, accelerometer data reduction criteria remain inconsistent, and the consequences of excluding participants in for example intervention studies are not well described. In this study, we investigated how different data reduction criteria changed the composition of the adolescent population retained in accelerometer data analysis.

METHODS

Accelerometer data (Actigraph GT3X), anthropometric measures and survey data were obtained from 1348 adolescents aged 11-14 years enrolled in the Danish SPACE for physical activity study. Accelerometer data were analysed using different settings for each of the three key data reduction criteria: (1) number of valid days; (2) daily wear time; and (3) non-wear time. The effects of the selected setting on sample retention and PA counts were investigated and compared. Ordinal logistic regression and multilevel mixed-effect linear regression models were used to analyse the impact of differing non-wear time definitions in different subgroups defined by body mass index, age, sex, and self-reported PA and sedentary levels.

RESULTS

Increasing the minimum requirements for daily wear time and the number of valid days and applying shorter non-wear definitions, resulted in fewer adolescents retained in the dataset. Moreover, the different settings for non-wear time significantly influenced which participants would be retained in the accelerometer data analyses. Adolescents with a higher BMI (OR:0.93, CI:0.87-0.98, p=0.015) and older adolescents (OR:0.68, CI:0.49-0.95, p=0.025) were more likely to be excluded from analysis using 10 minutes of non-wear compared to longer non-wear time periods. Overweight and older adolescents accumulated more daily non-wear time if the non-wear time setting was short, and the relative difference between groups changed depending on the non-wear setting. Overweight and older adolescents did also accumulate more sedentary time, but this was not significant correlated to the non-wear setting used.

CONCLUSIONS

Even small differences in accelerometer data reduction criteria can have substantial impact on sample size and PA and sedentary outcomes. This study highlighted the risk of introducing bias with more overweight and older adolescents excluded from the analysis when using short non-wear time definitions.

Accelerometry analysis of physical activity and sedentary behavior in older adults: a systematic review and data analysis

Accelerometers objectively monitor physical activity and sedentary patterns and are increasingly used in the research setting. It is important to maintain consistency in data analysis and reporting, therefore, we: (1) systematically identified studies using accelerometry (ActiGraph, Pensacola, FL, USA) to measure moderate-to-vigorous physical activity (MVPA) and sedentary time in older adults, and (2) based on the review findings, we used different cut-points obtained to analyze accelerometry data from a sample of community-dwelling older women. We identified 59 articles with cut-points ranging between 574 and 3,250 counts/min for MVPA and 50 and 500 counts/min for sedentary time. Using these cut-points and data from women (mean age, 70 years), the median MVPA minutes per day ranged between 4 and 80 min while percentage of sedentary time per day ranged between 62 % and 86 %. These data highlight (1) the importance of reporting detailed information on the analysis assumptions and (2) that results can differ greatly depending on analysis parameters.

Comparison of older and newer generations of ActiGraph accelerometers with the normal filter and the low frequency extension

Background

Many studies used the older ActiGraph (7164) for physical activity measurement, but this model has been replaced with newer ones (e.g., GT3X+). The assumption that new generation models are more accurate has been questioned, especially for measuring lower intensity levels. The low-frequency extension (LFE) increases the low-intensity sensitivity of newer models, but its comparability with older models is unknown. This study compared step counts and physical activity collected with the 7164 and GT3X + using the Normal Filter and the LFE (GT3X+N and GT3X+LFE, respectively).

Findings

Twenty-five adults wore 2 accelerometer models simultaneously for 3Âdays and were instructed to engage in typical behaviors. Average daily step counts and minutes per day in nonwear, sedentary, light, moderate, and vigorous activity were calculated. Repeated measures ANOVAs with post-hoc pairwise comparisons were used to compare mean values. Means for the GT3X+N and 7164 were significantly different in 4 of the 6 categories (p < .05). The GT3X+N showed 2041 fewer steps per day and more sedentary, less light, and less moderate than the 7164 (+25.6, -31.2, -2.9 mins/day, respectively). The GT3X+LFE showed non-significant differences in 5 of 6 categories but recorded significantly more steps (+3597 steps/day; p < .001) than the 7164.

Conclusion

Studies using the newer ActiGraphs should employ the LFE for greater sensitivity to lower intensity activity and more comparable activity results with studies using the older models. Newer generation ActiGraphs do not produce comparable step counts to the older generation devices with the Normal filter or the LFE.