Comparison of stepping-based metrics from ActiGraph accelerometers worn concurrently on the non-dominant wrist and waist among young adults.

Step counts can be estimated from wrist-worn accelerometers through the Verisense Step Count Algorithm. No study has assessed agreement between stepping metrics from ActiGraph accelerometers during free-living. Thirty-four participants (age: 22.9 ± 3.4 years) provided 24 h accelerometer data (non-dominant wrist) and waist. Agreement of two Verisense Algorithms (Verisense 1 & 2) for estimating daily steps, moderate-to-vigorous physical activity (MVPA), peak 1-min and 30-min accumulated steps, against the waist and ActiLife step-count Algorithm was assessed. Mean bias ± 95% limits of agreement (LoA) for daily steps was +1255 ± 3780 steps/day (mean absolute percent error (MAPE): 21%) (Verisense 1) and +1357 ± 3434 steps/day (MAPE: 20%) (Verisense 2). For peak 1-min accumulated steps, mean bias and 95% LoA was -17 ± 23 steps/min (MAPE: 17%) (Verisense 1) and -6 ± 5 steps/min (MAPE: 9%) with Verisense 2. For peak 30-min accumulated steps, mean bias and 95% LoA was -12 ± 45 steps/min (MAPE: 25%) (Verisense 1) and -2 ± 38 steps/min (MAPE: 13%) (Verisense 2). For MVPA steps/day, mean bias and 95% LoA was -1450 ± 3194 steps/day (MAPE: 420%) (Verisense 1) and -844 ± 2571 steps/day (MAPE: 211%) (Verisense 2). For MVPA min/day, mean bias and 95% LoA was -13 ± 27 min/day (MAPE: 368%) (Verisense 1) and -8 ± 24 min/day (MAPE: 209%) (Verisense 2). The Verisense 2 algorithm enhanced agreement for stepping intensity metrics but further refinement is needed to enhance agreement for MVPA against the waist.

Intensity or volume: the role of physical activity in longevity.

AIMS: To investigate how physical activity (PA) volume, intensity, duration, and fragmentation are associated with the risk of all-cause and cardiovascular disease mortality. To produce centile curves for PA volume and intensity representative of US adults. METHODS: This study is based on the observational 2011-2014 National Health and Nutrition Examination Survey (NHANES). Adults (age ≥20) with valid accelerometer, covariate, and mortality data were included. Average acceleration (AvAcc), intensity gradient (IG), and total PA served as proxies for volume, intensity, and duration of PA, respectively. Weighted Cox proportional hazard models estimated associations between outcome and PA metrics. RESULTS: In 7518 participants (52.0% women, weighted median age 49), there were curvilinear inverse dose-response relationships of all-cause mortality risk (81-month follow-up) with both AvAcc (-14.4% [95% CI -8.3 to -20.1%] risk reduction from 25th to 50th percentile) and IG (-37.1% [95% CI -30.0 to -43.4%] risk reduction from 25th to 50th percentile), but for cardiovascular disease mortality risk (N=7016, 82-month follow-up) only with IG (-41.0% [95% CI -26.7 to -52.4%] risk reduction from the 25th to 50th percentile). These relationships plateau at AvAcc: ∼35-45 mg and IG: -2.7 to -2.5. Associations of PA with all-cause and cardiovascular disease mortality are primarily driven by intensity and secondary by volume. Centile curves for volume and intensity were generated. CONCLUSION: Intensity is a main driver of reduced mortality risk suggesting that the intensity of PA rather than the quantity matters for longevity. The centile curves offer guidance for achieving desirable PA levels for longevity.

This study shows that the distribution of the intensity of physical activity accumulated across the day may be more important for mortality reduction than the quantity (volume), underscoring the relevance of integrating physical activity of higher intensity into daily routines for health optimisation.Higher physical activity intensity is more closely associated with reduced mortality risk than physical activity volume, particularly for cardiovascular disease mortality.We provide initial evidence suggesting health benefits when accumulating intense physical activity in continuous bouts rather than sporadically across the day.

Wrist-worn accelerometers: Influence of decisions during data collection and processing: A cross-sectional study.

Background and Aims: Accelerometers collect data in an objective way, however, a number of decisions must be done during data collection, processing and output-interpretation. The influence of those decisions is seldom investigated, reported, or discussed. Herein, we examined the influence of different decisions on the outcomes: daily minutes of moderate-to-vigorous physical activity (MVPA), inactivity and light physical activity (LPA). Methods: In total, 156 participants wore an accelerometer (ActiGraph wGT3X-BT) on their nondominant wrist for 7 days. Data collection was conducted from February 2017 to June 2018. Data was processed using the R-package GGIR and default settings were compared to by-the-literature-suggested options. The output was examined using paired t-tests. Results: When comparing two commonly used MVPA-cut-points, default and Hildebrand et al. we found a marginal difference (0.4 min, 1.0%, p < 0.001) in MVPA/day. When no bout criteria for MVPA/day was applied, MVPA/day was twice as high as bouted MVPA/day. Further, when we changed the epoch-length from 5 to 1 s, statistically significant changes were seen for MVPA/day (-6.6 min, 19%, p < 0.001), inactivity/day (-22 min, 3.0%, p < 0.001) and LPA/day (28 min, 81%, p < 0.001). Conclusion: Decisions made during data processing of wrist-worn accelerometers has an influence on the output and thus, may influence the conclusions drawn. However, there may be situations when these settings are changed. If so, we recommend examining if the variables of interest are affected. We encourage researchers to report decisions made during data collection, processing and output-interpretation, to facilitate comparisons between different studies.

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

PURPOSE: The aim was to use accelerometer data to describe day-to-day variability in physical activity in a single week, according to sociodemographic variables, in mid-aged Australian adults. METHODS: Data were from participants in the How Areas in Brisbane Influence HealTh and AcTivity (HABITAT) study who took part in a 2014 sub-study (N = 612; Mean age 60.6 [SD 6.9; range 48-73]). Participants wore a triaxial accelerometer (ActiGraph wGT3X-BT) on their non-dominant wrist for seven days, and data were expressed as acceleration in gravitational equivalent units (1 mg = 0.001 g). These were, used to estimate daily acceleration (during waking hours) and daily time spent in moderate-vigorous physical activity (MVPA, defined as ≥ 100mg). Coefficient of variation (calculated as [standard deviation/mean of acceleration and MVPA across the seven measurement days] * 100%) was used to describe day-to-day variability. RESULTS: Average values for both acceleration (24.1-24.8 mg/day) and MVPA (75.9-79.7 mins/day) were consistent across days of the week, suggesting little day-to-day variability (at the group level). However, over seven days, average individual day-to-day variability in acceleration was 18.8% (SD 9.3%; range 3.4-87.7%) and in MVPA was 35.4% (SD 15.6%; range 7.3-124.6%), indicating considerable day-to-day variability in some participants. While blue collar workers had the highest average acceleration (28.6 mg/day) and MVPA (102.5 mins/day), their day-to-day variability was low (18.3% for acceleration and 31.9% for MVPA). In contrast, variability in acceleration was highest in men, those in professional occupations and those with high income; and variability in MVPA was higher in men than in women. CONCLUSION: Results show group-level estimates of average acceleration and MVPA in a single week conceal considerable day-to-day variation in how mid-age Australians accumulate their acceleration and MVPA on a daily basis. Overall, there was no clear relationship between overall volume of activity and variability. Future studies with larger sample sizes and longitudinal data are needed to build on the findings from this study and increase the generalisability of these findings to other population groups.

Physical Activity Assessed by Wrist and Thigh Worn Accelerometry and Associations with Cardiometabolic Health.

Physical activity is increasingly being captured by accelerometers worn on different body locations. The aim of this study was to examine the associations between physical activity volume (average acceleration), intensity (intensity gradient) and cardiometabolic health when assessed by a thigh-worn and wrist-worn accelerometer. A sample of 659 office workers wore an Axivity AX3 on the non-dominant wrist and an activPAL3 micro on the right thigh concurrently for 24 h a day for 8 days. An average acceleration (proxy for physical activity volume) and intensity gradient (intensity distribution) were calculated from both devices using the open-source raw accelerometer processing software GGIR. Clustered cardiometabolic risk (CMR) was calculated using markers of cardiometabolic health, including waist circumference, triglycerides, HDL-cholesterol, mean arterial pressure and fasting glucose. Linear regression analysis assessed the associations between physical activity volume and intensity gradient with cardiometabolic health. Physical activity volume derived from the thigh-worn activPAL and the wrist-worn Axivity were beneficially associated with CMR and the majority of individual health markers, but associations only remained significant after adjusting for physical activity intensity in the thigh-worn activPAL. Physical activity intensity was associated with CMR score and individual health markers when derived from the wrist-worn Axivity, and these associations were independent of volume. Associations between cardiometabolic health and physical activity volume were similarly captured by the thigh-worn activPAL and the wrist-worn Axivity. However, only the wrist-worn Axivity captured aspects of the intensity distribution associated with cardiometabolic health. This may relate to the reduced range of accelerations detected by the thigh-worn activPAL.

Accelerometer-Assessed Physical Activity in People with Type 2 Diabetes: Accounting for Sleep when Determining Associations with Markers of Health.

High physical activity levels during wake are beneficial for health, while high movement levels during sleep are detrimental to health. Our aim was to compare the associations of accelerometer-assessed physical activity and sleep disruption with adiposity and fitness using standardized and individualized wake and sleep windows. People (N = 609) with type 2 diabetes wore an accelerometer for up to 8 days. Waist circumference, body fat percentage, Short Physical Performance Battery (SPPB) test score, sit-to-stands, and resting heart rate were assessed. Physical activity was assessed via the average acceleration and intensity distribution (intensity gradient) over standardized (most active 16 continuous hours (M16h)) and individualized wake windows. Sleep disruption was assessed via the average acceleration over standardized (least active 8 continuous hours (L8h)) and individualized sleep windows. Average acceleration and intensity distribution during the wake window were beneficially associated with adiposity and fitness, while average acceleration during the sleep window was detrimentally associated with adiposity and fitness. Point estimates for the associations were slightly stronger for the standardized than for individualized wake/sleep windows. In conclusion, standardized wake and sleep windows may have stronger associations with health due to capturing variations in sleep durations across individuals, while individualized windows represent a purer measure of wake/sleep behaviors.

Comparison of physical activity metrics from two research-grade accelerometers worn on the non-dominant wrist and thigh in children.

This study compared physical activity metrics from the activPAL (AP) worn on the thigh with the ActiGraph worn on the non-dominant wrist using open-source methods. Measures included average acceleration, intensity gradient (IG) and the minimum acceleration value of the most active X mins (MX). Fifty-two children (26 boys; age: 10.4 ± 0.6 years) provided≥1 day (24 h) of concurrent wear time from the activPAL and ActiGraph. Measures tended to be lower from the activPAL versus the ActiGraph. Poor agreement was evident for average acceleration but good for the IG. For the IG, the absolute and relative zones needed to reach equivalence was 4% and 0.4 SDs, respectively and for average acceleration were 10% and 1.2 SDs, respectively. Good agreement was evident for M60, M30, M20, M15 and M10 between devices. Regardless of the reference device used, equivalent estimates for the intensity gradient, M60, M30, M20, M15 and M10 were observed with relative and absolute equivalence zones being≤4% and≤0.5 SDs, respectively. The IG, M60, M30, M20, M15 and M10 appear good candidates for comparing activity data collected from the activPAL and ActiGraph. Future research can use the AP to report on sedentary behaviours as well as PA outcomes.

Accelerometer-measured physical activity in mid-age Australian adults.

BACKGROUND: Raw data from accelerometers can provide valuable insights into specific attributes of physical activity, such as time spent in intensity-specific activity. The aim of this study was to describe physical activity assessed with raw data from triaxial wrist-worn accelerometers in mid-age Australian adults. METHODS: Data were from 700 mid-age adults living in Brisbane, Australia (mean age: 60.4; SD:7.1 years). Data from a non-dominant wrist worn triaxial accelerometer (Actigraph wGT3X-BT), expressed as acceleration in gravitational equivalent units (1 mg = 0.001 g), were used to estimate time spent in moderate-vigorous intensity physical activity (MVPA; >100 mg) using different bout criteria (non-bouted, 1-, 5-, and 10-min bouts), and the proportion of participants who spent an average of at least one minute per day in vigorous physical activity. RESULTS: Mean acceleration was 23.2 mg (SD: 7.5) and did not vary by gender (men: 22.4; women: 23.7; p-value: 0.073) or education (p-value: 0.375). On average, mean acceleration was 10% (2.5 mg) lower per decade of age from age 55y. The median durations in non-bouted, 1-min, 5-min and 10-min MVPA bouts were, respectively, 68 (25th -75th : 45-99), 26 (25th -75th : 12-46), 10 (25th -75th : 3-24) and 8 (25th -75th : 0-19) min/day. Around one third of the sample did at least one minute per day in vigorous intensity activities. CONCLUSION: This population-based cohort provided a detailed description of physical activity based on raw data from accelerometers in mid-age adults in Australia. Such data can be used to investigate how different patterns and intensities of physical activity vary across the day/week and influence health outcomes.

Evaluation of Wrist Accelerometer Cut-Points for Classifying Physical Activity Intensity in Youth.

Background: Wrist worn accelerometers are convenient to wear and provide greater compliance. However, methods to transform the resultant output into predictions of physical activity (PA) intensity have been slow to evolve, with most investigators continuing the practice of applying intensity-based thresholds or cut-points. The current study evaluated the classification accuracy of seven sets of previously published youth-specific cut-points for wrist worn ActiGraph accelerometer data. Methods: Eighteen children and adolescents [mean age (± SD) 14.6 ± 2.4 years, 10 boys, 8 girls] completed 12 standardized activity trials. During each trial, participants wore an ActiGraph GT3X+ tri-axial accelerometer on the wrist and energy expenditure (Youth METs) was measured directly using the Oxycon Mobile portable calorimetry system. Seven previously published sets of ActiGraph cut-points were evaluated: Crouter regression vertical axis, Crouter regression vector magnitude, Crouter ROC curve vertical axis, Crouter ROC curve vector magnitude, Chandler ROC curve vertical axis, Chandler ROC curve vector magnitude, and Hildebrand ENMO. Classification accuracy was evaluated via weighted Kappa. Confusion matrices were generated to summarize classification accuracy and identify patterns of misclassification. Results: The cut-points exhibited only moderate agreement with directly measured PA intensity, with Kappa ranging from 0.45 to 0.58. Although the cut-points classified sedentary behavior accurately (> 95%), classification accuracy for the light (3-51%), moderate (12-45%), and vigorous-intensity trials (30-88%) was generally poor. All cut-points underestimated the true intensity of the walking trials, with error rates ranging from 35 to 100%, while the intensity of activity trials requiring significant upper body and/or arm movements was consistently overestimated. The Hildebrand cut-points which serve as the default option in the popular GGIR software package misclassified 30% of the light intensity trials as sedentary and underestimated the intensity of moderate and vigorous intensity trials 75% of the time. Conclusion: Published ActiGraph cut-points for the wrist, developed specifically for school-aged youth, do not provide acceptable classification accuracy for estimating daily time spent in light, moderate, and vigorous intensity physical activity. The development and deployment of more robust accelerometer data reduction methods such as functional data analysis and machine learning approaches continues to be a research priority.

Equivalence of activity outcomes derived from three research grade accelerometers worn simultaneously on each wrist.

This study evaluated the equivalence of activity outcomes from three accelerometer brands worn on both wrists during free living. Forty-four adults wore a GENEActiv, ActiGraph and Axivity accelerometer for 7 days. Outcomes were assessed between and within accelerometer brand and wrist location with average acceleration and the intensity gradient (IG) being of particular interest. Pairwise 95% equivalence tests and intra-class correlation coefficients (ICC) evaluated agreement. Average acceleration and the IG were largely equivalent between combinations of accelerometer device and wrists when applying a 10% equivalence zone. There was largely a lack of equivalence between pairings for time spent in acceleration values ≥100 mg. However, equivalence was largely achieved when applying an equivalence zone that encompassed values ranging from 0.3 to 0.45 SDs for IG and time spent above 100 mg and 150 mg. Agreement between pairings tended to be stronger between different brands on the non-dominant (ICCs ≥ 0.73-0.97) versus the dominant wrist (ICCs ≥ 0.57-0.97) and between wrists for the same accelerometer (ICCs ≥ 0.59-0.97) for average acceleration and the IG. These are important findings since device placement is not consistent in studies. Further work that applies an equivalence zone reflecting the variability of the outcome measure is encouraged.

Differences in Accelerometer-Measured Patterns of Physical Activity and Sleep/Rest Between Ethnic Groups and Age: An Analysis of UK Biobank.

BACKGROUND: Physical activity and sleep are important for health; whether device-measured physical activity and sleep differ by ethnicity is unclear. This study aimed to compare physical activity and sleep/rest in white, South Asian (SA), and black adults by age. METHODS: Physical activity and sleep/rest quality were assessed using accelerometer data from UK Biobank. Linear regressions, stratified by sex, were used to analyze differences in activity and sleep/rest. An ethnicity × age group interaction term was used to assess whether ethnic differences were consistent across age groups. RESULTS: Data from 95,914 participants, aged 45-79 years, were included. Overall activity was 7% higher in black, and 5% lower in SA individuals compared with white individuals. Minority ethnic groups had poorer sleep/rest quality. Lower physical activity and poorer sleep quality occurred at a later age in black and SA adults (>65 y), than white adults (>55 y). CONCLUSIONS: While black adults are more active, and SA adults less active, than white adults, the age-related reduction appears to be delayed in black and SA adults. Sleep/rest quality is poorer in black and SA adults than in white adults. Understanding ethnic differences in physical activity and rest differ may provide insight into chronic conditions with differing prevalence across ethnicities.

Comparação entre processamentos de dados do acelerômetro gt3x+ para atividade física moderada e vigorosa / Comparison between data processing from gt3x+ accelerometer for vigorous and moderate physical activity

RESUMO O uso do acelerômetro para mensurar a atividade física em pesquisas epidemiológicas, apresenta desafios para aumentar a comparabilidade entre os estudos que utilizam esse equipamento. Nesse sentido o objetivo deste trabalho é comparar estimativas de tempo em AFMV para adultos provenientes de diferentes métodos de processamentos de dados, através do acelerômetro Actigraph GT3X+. Trata-se de um estudo transversal, da linha de base do estudo piloto do Estudo Longitudinal dos Determinantes da Atividade Física. Amostra contou com 31 funcionários terceirizados de ambos os sexos, com idade média de 47.05anos (DP=9.35). Os participantes utilizaram acelerômetros do modelo GT3X+ durante sete dias consecutivos. A estimativa de tempo de AFMV foi gerada através de software Actilife e R-package GGIR. Análises estatísticas descritivas, ANOVA e pos-hoc de Bonferroni para comparabilidade foram realizadas no software R. Análise de Bland-Altman foi realizado no SigmaPlot para avaliação de viés e concordância. Houve diferença significativa no tempo médio de AFMV entre os dados baseados em counts e dados brutos (p<0,001). O tempo médio em AFMV foi menor a partir do processamento por dados brutos do que o em counts (-264,81min/dia; p<0,001). Concluindo que os achados sugerem não haver, estatisticamente, equivalência entre os métodos comparados para estimar tempo de AFMV.

ABSTRACT The use of accelerometers to measure physical activity in epidemiological research presents challenges to increase comparability between studies that use this equipment. In this sense, the objective of this work is to compare time estimates in MVPA for adults from different data processing methods, using the Actigraph GT3X+ accelerometer. This is a cross-sectional study, from the baseline of the pilot study of the Longitudinal Study of the Determinants of Physical Activity. Sample had 31 outsourced employees of both genders, with an average age of 47.05 years (SD=9.35). Participants used GT3X+ model accelerometers for seven consecutive days. The MVPA time estimate was generated using Actilife and R-package GGIR software. Descriptive statistical analyses, ANOVA and Bonferroni post-hoc for comparability were performed in the R software. Bland-Altman analysis was performed in SigmaPlot to assess bias and agreement. There was a significant difference in the mean time of MVPA between count-based data and raw data (p<0.001). The average time in MVPA was shorter from processing by raw data than in counts (-264.81 min/day; p<0.001). Concluding that the findings suggest that there is no statistically equivalence between the methods compared to estimate MVPA time.

The Dilemma of Analyzing Physical Activity and Sedentary Behavior with Wrist Accelerometer Data: Challenges and Opportunities.

Physical behaviors (e.g., physical activity and sedentary behavior) have been the focus among many researchers in the biomedical and behavioral science fields. The recent shift from hip- to wrist-worn accelerometers in these fields has signaled the need to develop novel approaches to process raw acceleration data of physical activity and sedentary behavior. However, there is currently no consensus regarding the best practices for analyzing wrist-worn accelerometer data to accurately predict individuals' energy expenditure and the times spent in different intensities of free-living physical activity and sedentary behavior. To this end, accurately analyzing and interpreting wrist-worn accelerometer data has become a major challenge facing many clinicians and researchers. In response, this paper attempts to review different methodologies for analyzing wrist-worn accelerometer data and offer cutting edge, yet appropriate analysis plans for wrist-worn accelerometer data in the assessment of physical behavior. In this paper, we first discuss the fundamentals of wrist-worn accelerometer data, followed by various methods of processing these data (e.g., cut points, steps per minute, machine learning), and then we discuss the opportunities, challenges, and directions for future studies in this area of inquiry. This is the most comprehensive review paper to date regarding the analysis and interpretation of free-living physical activity data derived from wrist-worn accelerometers, aiming to help establish a blueprint for processing wrist-derived accelerometer data.

Average acceleration and intensity gradient of primary school children and associations with indicators of health and well-being.

Average acceleration (AvAcc) and intensity gradient (IG) have been proposed as standardised metrics describing physical activity (PA) volume and intensity, respectively. We examined hypothesised between-group PA differences in AvAcc and IG, and their associations with health and well-being indicators in children. ActiGraph GT9X wrist accelerometers were worn for 24-h·d-1 over 7days by 145 children aged 9-10. Raw accelerations were averaged per 5-s epoch to represent AvAcc over 24-h. IG represented the relationship between log values for intensity and time. Moderate-to-vigorous PA (MVPA) was estimated using youth cutpoints. BMI z-scores, waist-to-height ratio (WHtR), peak oxygen uptake (VO2peak), Metabolic Syndrome risk (MetS score), and well-being were assessed cross-sectionally, and 8-weeks later. Hypothesised between-group differences were consistently observed for IG only (p < .001). AvAcc was strongly correlated with MVPA (r = 0.96), while moderate correlations were observed between IG and MVPA (r = 0.50) and AvAcc (r = 0.54). IG was significantly associated with health indicators, independent of AvAcc (p < .001). AvAcc was associated with well-being, independent of IG (p < .05). IG was significantly associated with WHtR (p < .01) and MetS score (p < .05) at 8-weeks follow-up. IG is sensitive as a gauge of PA intensity that is independent of total PA volume, and which relates to important health indicators in children.

A comparison of physical activity from Actigraph GT3X+ accelerometers worn on the dominant and non-dominant wrist.

The purpose of this study was to evaluate the agreement between several activity measures using raw acceleration data from accelerometers worn concurrently on the dominant and non-dominant wrist. Fifty-five adults (31·9 ± 9·7 years, 26 males) wore two ActiGraph GT3X+ monitors continuously for 1 day, one on their non-dominant wrist and the other on their dominant wrist. Paired t-tests were undertaken with sequential Holm-Bonferroni corrections to compare wear time, moderate-vigorous physical activity (MVPA), time spent in 10-min bouts of MVPA (MVPA10 min ) and the average magnitude of dynamic wrist acceleration (ENMO). Level of agreement between outcome variables from the wrists was examined using intraclass correlation coefficients (ICC, single measures, absolute agreement) with 95% confidence intervals and limits of agreement (LoA). Time spent across acceleration levels in 40 mg resolution were also examined. There were no significant differences between the non-dominant and dominant wrist for ENMO, wear time, MVPA or MVPA10 min . Agreement between wrists was strong for most outcomes (ICC ≥0·92) including wear time, ENMO, MVPA, MVPA10 min and the distribution of time across acceleration levels. Agreement was strong in the low acceleration bands (ICC = 0·970 and 0·922) with a mean bias of 3·08 min (LoA -55·18 to 61·34) and -5·43 (LoA -43·47 to 32·62). In summary, ENMO, MVPA, MVPA10 min , wear time and the distribution of time across acceleration levels compared well at the group level. The LOA from the two lowest acceleration levels suggest further work over a longer monitoring period is needed to determine whether outputs from each wrist are comparable.

Comparing physical activity estimates in children from hip-worn Actigraph GT3X+ accelerometers using raw and counts based processing methods.

This study examined differences in physical activity (PA) estimates provided from raw and counts processing methods. One hundred and sixty-five children (87 girls) wore a hip-mounted ActiGraph GT3X+ accelerometer for 7 days. Data were available for 129 participants. Time in moderate PA (MPA), vigorous PA (VPA) and moderate-vigorous PA (MVPA) were calculated using R-package GGIR and ActiLife. Participants meeting the wear time criteria for both processing methods were included in the analysis. Time spent in MPA (-21.4 min.d-1, 95%CI -21 to -20) and VPA (-36 min.d-1, 95%CI -40 to -33) from count data were higher (P < 0.001) than raw data. Time spent in MVPA between the two processing methods revealed significant differences (All P < 0.001). Bland-Altman plots suggest that the mean bias for time spent in MPA, VPA and MVPA were large when comparing raw and count methods. Equivalence tests showed that estimates from raw and count processing methods across all activity intensities lacked equivalence. Lack of equivalence and poor agreement between raw and count processing methods suggest the two approaches to estimate PA are not comparable. Further work to facilitate the comparison of findings between studies that process and report raw and count physical activity data may be necessary.