Journal for the measurement of physical behaviour最新文献

{"title":"The Concurrent Validity of the ActiGraph LEAP Compared to the ActiGraph GT9X Link for Measuring Physical Activity.","authors":"Jethro Raphael M Suarez, Joon-Hyuk Park, Kworweinski Lafontant, Ladda Thiamwong","doi":"10.1123/jmpb.2025-0032","DOIUrl":"10.1123/jmpb.2025-0032","url":null,"abstract":"<p><strong>Background: </strong>The ActiGraph GT9X Link wearable accelerometer has been widely utilized for physical activity (PA) measurement but is no longer in production. The performance of the ActiGraph LEAP, regarded as its successor and released in 2023, remains unevaluated in comparison to other accelerometers. This study aimed to establish concurrent validity of the ActiGraph LEAP device through PA measurements and comparison with the GT9X Link.</p><p><strong>Methods: </strong>A total of 26 young adults (mean age = 21 ± 4 years) wore the ActiGraph LEAP and GT9X Link simultaneously for 7 consecutive days on their nondominant wrist. Monitor Independent Movement Summary units were utilized for calculation of daily sedentary time, light PA, and moderate to vigorous PA across valid days of wear (≥10 hr). Nonwear time was determined using a preexisting algorithm. Intraclass correlation coefficient, mean absolute percent error, equivalence tests (with GT9X Link as reference), and Bland-Altman plots assessed agreement between daily values of sedentary time, light PA, and moderate to vigorous PA recorded by each device across all valid days recorded among participants.</p><p><strong>Results: </strong>A total of 149 days of data were compared between the two devices. Across all PA levels, acceptable-excellent agreement was found (mean absolute percent error = 3.86%-13.86% and intraclass correlation coefficient = .94-.99), and Bland-Altman plots showed that at least 90% of recorded data fell within the 95% limits of agreement.</p><p><strong>Conclusions: </strong>The findings suggest that sedentary time and PA measurements using Monitor Independent Movement Summary units from the ActiGraph LEAP compare well with the GT9X Link, indicating that the LEAP performs similarly to the GT9X Link.</p>","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"9 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12916136/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146230076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validating the Longitudinal Assessment of Sedentary Behavior in a Randomized Controlled Trial via a Deep Learning Algorithm.","authors":"Jasmine Morales, Rong W Zablocki, Sheri J Hartman, Arun Kumar, Animesh Kumar, Jordan A Carlson, Mikael Anne Greenwood-Hickman, Paul R Hibbing, John Staudenmayer, Chongzhi Di, Jingjing Zou, Steve Nguyen, Andrea Z LaCroix, Lindsay Dillon, Howon Ryu, Lucy Shao, Weiwei Shi, Fatima Tuz-Zahra, Loki Natarajan","doi":"10.1123/jmpb.2025-0086","DOIUrl":"10.1123/jmpb.2025-0086","url":null,"abstract":"<p><strong>Background: </strong>Sedentary behavior (SB) is a risk factor for cardiometabolic disease. Existing cut-points of hip-worn accelerometers to quantify SB underestimate sedentary bout durations by ignoring posture. To address this, we previously developed CHAP, Convolutional Neural Network Hip Accelerometer Posture, a deep learning algorithm that predicts sitting and breaks from sitting. In this study, we applied CHAP to a randomized controlled trial to externally validate its accuracy for classifying sitting versus non-sitting and estimating intervention-related changes in SB.</p><p><strong>Methods: </strong>CHAP was applied to 30Hz-tri-axial hip-worn ActiGraph-GT3X+ (AG) accelerometer data from free-living postmenopausal women who are overweight (≥55-yrs) in the Rise for Health RCT(n=406). CHAP-predicted SB accuracy (e.g., sensitivity) and daily pattern metrics (e.g., total sedentary time) were compared to a ground-truth device activPAL (AP) and standard cut-point method (AG100), at two timepoints for intervention arms-Healthy Living (control), Reduce Sitting, and Increase Transitions.</p><p><strong>Results: </strong>CHAP's SB accuracy metrics were above 84.8%. Mean (SE, p) intervention effect estimates (mean change, intervention minus controls) from generalized estimating equation models for (I) total sedentary time were AP 65.1 min/day (16.2, p<0.001), CHAP 38.2 min/day (15.4, p=0.013), and AG100 0.28 min/day (13.9, p=0.98) (Reduce Sitting); (II) breaks from SB were AP 22/day (4.3, p<0.001), CHAP 4/day (1.5, p=0.003), and AG100 <1/day (2.1, p=0.860) (Increase Transitions).</p><p><strong>Conclusions: </strong>This study validated CHAP on an independent cohort. CHAP exhibited high prediction accuracy, and although less sensitive than AP, CHAP detected changes in SB more effectively than AG100. Thus, CHAP could make a strong contribution to SB intervention research.</p>","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"9 ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13641217/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149119693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-Time Versus Video-Recorded Step Count Validation Among Young Adults.","authors":"Sicong Ren, Mariya Boikova, Cayla R McAvoy, Agnes Bucko, Tahmina Begum, Mike Dulin, Spencer Ingoglia, Maha Raya, Leah Patterson, Daheia J Barr-Anderson, Trudy Moore-Harrison, Catrine Tudor-Locke, Laura H Gunn","doi":"10.1123/jmpb.2025-0035","DOIUrl":"10.1123/jmpb.2025-0035","url":null,"abstract":"<p><strong>Background: </strong>Consumer Technology Association guidelines recommend two independent researchers review video recordings of treadmill bouts to verify accuracy of the number of steps taken. Performing multiple post hoc validation checks requires significant resources for personnel time and costs, but it is unknown if the burden is necessary. The purpose of this study was to evaluate accuracy and efficiency of step counts assessed during direct observation compared with multiple reviews of video recordings.</p><p><strong>Methods: </strong>Participants ages 18-20 years (n = 72) completed up to twelve 5-min, zero-incline treadmill bouts, speeds ranging 0.2-2.7 m/s (0.5-6.0 mph). Directly observed steps were counted and hand-tallied in real-time. A sex-age stratified random sample of 30 participants' bouts were selected for analysis. Two independent researchers recounted steps from video recordings, producing three independent counts. Intraclass correlation coefficient determined interrater reliability. Mean average percent deviation computed deviation magnitude among the three counts. Correlation tests determined the relationship between absolute count differences and average counts for each pairwise comparison.</p><p><strong>Results: </strong>Interrater reliability was excellent with intraclass correlation coefficients ≥ .995 (p < .001) across all speeds. Average mean average percent deviation was 0.29% with bout-specific deviations ranging from 0.10% to 0.54%, indicating < 1% difference among the three counts for all bouts.</p><p><strong>Conclusions: </strong>A single real-time step count represents a sufficient and efficient approach to measure steps taken in a controlled treadmill setting, while video recounts serve as back-up to allay potential data loss concerns and/or for random selection verification. These data-driven findings are useful for optimizing personnel time and reducing costs associated with collecting quality data in large-scale treadmill-based step-counting studies.</p>","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"9 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13099236/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147791154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance evaluation of algorithms to estimate daily sedentary time using wrist-worn sensors in free-living adults.","authors":"Charles E Matthews, Pedro Saint-Maurice, Joshua R Freeman, Hayden A Hayes, Alaina H Shreves, Aiden Doherty, Eric T Hyde, Katie Ylarregui, Rena R Jones, Sarah K Keadle","doi":"10.1123/jmpb.2024-0051","DOIUrl":"https://doi.org/10.1123/jmpb.2024-0051","url":null,"abstract":"<p><strong>Purpose: </strong>Given the limited real-world testing of algorithms for wrist-worn sensors to estimate sedentary time, we examined the performance of 21 algorithms in free-living adults.</p><p><strong>Methods: </strong>Seventy-one adults (35-65 years) wore a GENEActiv (wrist) and an activPAL (thigh) sensor for up to 10 days. activPAL was our reference measure. We estimated sedentary time (hours/day) using 21 classification algorithms, including cut point and machine-learning methods. Valid days from each monitor were matched by date and mean values were calculated. Equivalence testing (±10%) and linear regression were used to compare each algorithm's estimate to the reference, over all participants and by sex and age.</p><p><strong>Results: </strong>activPAL recorded a mean of 9.4 hours/d sedentary. Five of 21 algorithms (24%) estimated sedentary time within 10% (±0.94 hours) of the reference. Two of these methods employed machine-learning algorithms (Trost Extended, OxWearables) and three employed cut points (GGIR ENMO 40mg; Bakrania ENMO 32.6mg; Fraysse ENMOa 62.5mg). Variance explained in linear regression was relatively high for the machine-learning (R²=0.44-0.63) and cut point algorithms developed for younger (R²=0.30-0.64) and older (R²=0.45-0.66) adults. More accurate performance was noted for algorithms developed in studies using posture-based ground truth measures and conducted in free-living settings.</p><p><strong>Conclusion: </strong>Fifteen of 21 (71%) algorithms produced estimates of sedentary time that were moderate-strongly correlated with the reference measure, but only five (24%) were within 10% of the reference. Free-living benchmarking studies like this can identify more accurate and precise algorithms to estimate sedentary time and identify characteristics of algorithm development studies that yield better results.</p>","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"8 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12363551/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144981283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Ability of Monitor-Independent Movement Summary Units, Euclidean Norm Minus One, and Mean Amplitude Deviation to Harmonize Accelerometry Data Across Research-Grade and Consumer Wearable Devices During Simulated Free-Living Physical Activity in Children.","authors":"Olivia L Finnegan, James W White, Bridget Armstrong, Elizabeth L Adams, Sarah Burkart, Michael W Beets, Srihari Nelakuditi, Zifei Zhong, Hongpeng Yang, Keagan P Kiely, Rahul Ghosal, Stuart J Fairclough, Gregory J Welk, R Glenn Weaver","doi":"10.1123/jmpb.2025-0002","DOIUrl":"10.1123/jmpb.2025-0002","url":null,"abstract":"<p><strong>Background: </strong>Epoch-level accelerometry summary metrics have the potential to be device-agnostic, meaning that similar estimates should be obtained regardless of the device used. The objective of this study was to identify which metric (ENMO, MAD, MIMS) best harmonizes data across devices in an applied setting measuring children's activity.</p><p><strong>Methods: </strong>Children (n=239; 9.3 ± 2.1 years, 47% female, 30% Black) wore ActiGraph GT9X (+/-8g, 50Hz) accelerometers and were randomized to wear two of three consumer wearables including Apple Watch Series 7 (+/-16g, 50Hz), Garmin Vivoactive 4S (+/-8g, 25Hz), and Fitbit Sense (+/-4g, 50Hz) on their non-dominant wrist, while participating in 60 minutes of simulated free-living activities (i.e., walking, running, soccer). The standard deviation (SD) across z-scores for ENMO, MAD and MIMS of each device was calculated at the second-level to quantify variability across devices. Lin's Concordance Correlation Coefficient (LCCC) was calculated for each combination of devices by metric to determine agreement. Multi-level intra-class correlation coefficients (ICC) were additionally used to quantify harmonization.</p><p><strong>Results: </strong>The SD of z-score across devices was the lowest, indicating better harmonization, for MAD (0.13±0.23), followed by ENMO (0.24±0.50), and then MIMS (0.26±0.39). LCCC was strongest for MAD, with coefficients ranging from 0.89 to 0.96. LCCC for MIMS ranged from 0.70 to 0.83, and was lowest for ENMO, ranging from 0.62 to 0.76. Overall ICC was highest for MAD (0.88), followed by MIMS (0.73), and ENMO (0.62).</p><p><strong>Conclusions: </strong>MAD appears to perform best at harmonizing across all the sampled research-grade and consumer devices in children.</p>","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"8 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13029015/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147576960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of Sleep and Physical Activity Metrics From Wrist-Worn ActiGraph wGT3X-BT and GT9X Accelerometers During Free-Living in Adults","authors":"Duncan S. Buchan","doi":"10.1123/jmpb.2023-0026","DOIUrl":"https://doi.org/10.1123/jmpb.2023-0026","url":null,"abstract":"Background: ActiGraph accelerometers can monitor sleep and physical activity (PA) during free-living, but there is a need to confirm agreement in outcomes between different models. Methods: Sleep and PA metrics from two ActiGraphs were compared after participants (N = 30) wore a GT9X and wGT3X-BT on their nondominant wrist for 7 days during free-living. PA metrics including total steps, counts, average acceleration—Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation, intensity gradient, the minimum acceleration value of the most active 10 and 30 min (M10, M30), time spent in activity intensities from vector magnitude (VM) counts, and ENMO cut points and sleep metrics (sleep period time window, sleep duration, sleep onset, and waking time) were compared. Results: Excellent agreement was evident for average acceleration-Mean Amplitude Deviation, counts, total steps, M10, and light PA (VM counts) with good agreement evident from the remaining PA metrics apart from moderate–vigorous PA (VM counts) which demonstrated moderate agreement. Mean bias for all PA metrics were low, as were the limits of agreement for the intensity gradient, average acceleration-Mean Amplitude Deviation, and inactive time (ENMO and VM counts). The limits of agreement for all other PA metrics were >10%. Excellent agreement, low mean bias, and narrow limits of agreement were evident for all sleep metrics. All sleep and PA metrics demonstrated equivalence (equivalence zone of ≤10%) apart from moderate–vigorous PA (ENMO) which needed an equivalence zone of 16%. Conclusions: Equivalent estimates of almost all PA and sleep metrics are provided from the GT9X and wGT3X-BT worn on the nondominant wrist.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"19 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140521255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing Multiple Approaches to Estimate Physical Activity, Sedentary Behavior, and Sleep in Pregnancy.","authors":"J B Gallagher, D E Boonstra, J D Borrowman, M Unke, M A Jones, C E Kline, B Barone Gibbs, K M Whitaker","doi":"10.1123/jmpb.2024-0007","DOIUrl":"10.1123/jmpb.2024-0007","url":null,"abstract":"<p><strong>Introduction: </strong>The purpose of this study was to compare estimates of 24-hour activity using the best practice of a thigh accelerometer (activPAL), wrist actigraphy (Actiwatch), and a sleep diary (<i>PAL + watch + diary</i>) to estimates from simpler procedures, such as the thigh accelerometer and diary (<i>PAL + diary</i>) or thigh monitor alone (<i>PAL only</i>) during pregnancy.</p><p><strong>Methods: </strong>Data collected during the 2^nd trimester from 40 randomly selected participants in the Pregnancy 24/7 cohort study were included. activPAL data were integrated with sleep time determined by wrist actigraphy (<i>PAL + watch + diary</i>) or diary-determined sleep (<i>PAL + diary</i>). In the <i>PAL only</i> analysis, average estimates were exported directly from the PAL software. Repeated measures ANOVA and intraclass correlations coefficients compared moderate-vigorous physical activity (MVPA), light physical activity (LPA), sedentary time, sleep, and wear time across measurement approaches. Pairwise comparisons using a Bonferroni correction explored significant differences identified from the omnibus ANOVA.</p><p><strong>Results: </strong>The three approaches arrived at consistent durations of physical activity (intraclass correlations coefficients > .95) but not for estimating sedentary behavior and sleep durations (intraclass correlations coefficients: .73-.82). PAL + diary overestimated MVPA by 2.3 min/day (p < .01) compared with PAL + diary + watch. PAL only overestimated sleep (25.3-29.0 min/day, p < .01) while underestimating MVPA (11.7-14.0 min/day, p < .01) compared with the other approaches.</p><p><strong>Conclusions: </strong>Since the inclusion of the wrist actigraphy provided only slight differences in MVPA estimates, <i>PAL + diary</i> may provide acceptable estimates of 24-hour activity during pregnancy in future research. <i>PAL only</i> may be acceptable when exclusively interested in physical activity.</p>","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11822988/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143416457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterizing ActiGraph's Idle Sleep Mode in Free-living Assessments of Physical Behavior.","authors":"Samuel R LaMunion, Robert J Brychta, Joshua R Freeman, Pedro F Saint-Maurice, Charles E Matthews, Asuka Ishihara, Kong Y Chen","doi":"10.1123/jmpb.2023-0038","DOIUrl":"10.1123/jmpb.2023-0038","url":null,"abstract":"","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11524531/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142549356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<i>agcounts</i>: An R Package to Calculate ActiGraph Activity Counts From Portable Accelerometers.","authors":"Brian C Helsel, Paul R Hibbing, Robert N Montgomery, Eric D Vidoni, Lauren T Ptomey, Jonathan Clutton, Richard A Washburn","doi":"10.1123/jmpb.2023-0037","DOIUrl":"10.1123/jmpb.2023-0037","url":null,"abstract":"<p><p>Portable accelerometers are used to capture physical activity in free-living individuals with the ActiGraph being one of the most widely used device brands in physical activity and health research. Recently, in February 2022, ActiGraph published their activity count algorithm and released a Python package for generating activity counts from raw acceleration data for five generations of ActiGraph devices. The nonproprietary derivation of the ActiGraph count improved the transparency and interpretation of accelerometer device-measured physical activity, but the Python release of the count algorithm does not integrate with packages developed by the physical activity research community using the R Statistical Programming Language. In this technical note, we describe our efforts to create an R-based translation of ActiGraph's Python package with additional extensions to make data processing easier and faster for end users. We call the resulting R package <i>agcounts</i> and provide an inside look at its key functionalities and extensions while discussing its prospective impacts on collaborative open-source software development in physical behavior research. We recommend that device manufacturers follow ActiGraph's lead by providing open-source access to their data processing algorithms and encourage physical activity researchers to contribute to the further development and refinement of <i>agcounts</i> and other open-source software.</p>","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11636644/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142820310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of Accelerometer Calibration on the Estimation of Objectively Measured Physical Activity: The Tromsø Study","authors":"Marc Weitz, B. Morseth, L. Hopstock, Alexander Horsch","doi":"10.1123/jmpb.2023-0019","DOIUrl":"https://doi.org/10.1123/jmpb.2023-0019","url":null,"abstract":"Accelerometers are increasingly used to observe human behavior such as physical activity under free-living conditions. An important prerequisite to obtain reliable results is the correct calibration of the sensors. However, accurate calibration is often neglected, leading to potentially biased results. Here, we demonstrate and quantify the effect of accelerometer miscalibration on the estimation of objectively measured physical activity under free-living conditions. The total volume of moderate to vigorous physical activity (MVPA) was significantly reduced after post hoc auto-calibration for uniaxial and triaxial count data, as well as for Euclidean Norm Minus One and mean amplitude deviation raw data. Weekly estimates of MVPA were reduced on average by 5.5, 9.2, 45.8, and 4.8 min, respectively, when compared to the original uncalibrated estimates. Our results indicate a general trend of overestimating physical activity when using factory-calibrated sensors. In particular, the accuracy of estimates derived from the Euclidean Norm Minus One feature suffered from uncalibrated sensors. For all modalities, the more uncalibrated the sensor was, the more MVPA was overestimated. This might especially affect studies with lower sample sizes.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"32 37","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139631382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}