Journal articles on the topic 'Accelerometry'
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Roth, Marilyn A., and Jennifer S. Mindell. "Who Provides Accelerometry Data? Correlates of Adherence to Wearing an Accelerometry Motion Sensor: The 2008 Health Survey for England." Journal of Physical Activity and Health 10, no. 1 (January 2013): 70–78. http://dx.doi.org/10.1123/jpah.10.1.70.
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Background:Use of objective physical activity measures is rising. We investigated the representativeness of survey participants who wore an accelerometer.Methods:4273 adults aged 16+ from a cross-sectional survey of a random, nationally representative general population sample in England in 2008 were categorized as 1) provided sufficient accelerometry data [4−7 valid days (10+ hrs/d), n = 1724], 2) less than that (n = 237), or 3) declined (n = 302). Multinomial logistic regression identified demographic, socioeconomic, health, lifestyle, and biological correlates of participants in these latter 2 groups, compared with those who provided sufficient accelerometry data (4+ valid days).Results:Those in the random subsample offered the accelerometer were older and more likely to be retired and to report having a longstanding limiting illness than the rest of the adult Health Survey for England participants. Compared with those providing sufficient accelerometery data, those wearing the accelerometer less were younger, less likely to be in paid employment, and more likely to be a current smoker. Those who declined to wear an accelerometer did not differ significantly from those who wore it for sufficient time.Conclusions:We found response bias in wearing the accelerometers for sufficient time, but refusers did not differ from those providing sufficient data. Differences should be acknowledged by data users.
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Evenson, Kelly R., Elissa Scherer, Kennedy M. Peter, Carmen C. Cuthbertson, and Stephanie Eckman. "Historical development of accelerometry measures and methods for physical activity and sedentary behavior research worldwide: A scoping review of observational studies of adults." PLOS ONE 17, no. 11 (November 21, 2022): e0276890. http://dx.doi.org/10.1371/journal.pone.0276890.
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This scoping review identified observational studies of adults that utilized accelerometry to assess physical activity and sedentary behavior. Key elements on accelerometry data collection were abstracted to describe current practices and completeness of reporting. We searched three databases (PubMed, Web of Science, and SPORTDiscus) on June 1, 2021 for articles published up to that date. We included studies of non-institutionalized adults with an analytic sample size of at least 500. The search returned 5686 unique records. After reviewing 1027 full-text publications, we identified and abstracted accelerometry characteristics on 155 unique observational studies (154 cross-sectional/cohort studies and 1 case control study). The countries with the highest number of studies included the United States, the United Kingdom, and Japan. Fewer studies were identified from the continent of Africa. Five of these studies were distributed donor studies, where participants connected their devices to an application and voluntarily shared data with researchers. Data collection occurred between 1999 to 2019. Most studies used one accelerometer (94.2%), but 8 studies (5.2%) used 2 accelerometers and 1 study (0.6%) used 4 accelerometers. Accelerometers were more commonly worn on the hip (48.4%) as compared to the wrist (22.3%), thigh (5.4%), other locations (14.9%), or not reported (9.0%). Overall, 12.7% of the accelerometers collected raw accelerations and 44.6% were worn for 24 hours/day throughout the collection period. The review identified 155 observational studies of adults that collected accelerometry, utilizing a wide range of accelerometer data processing methods. Researchers inconsistently reported key aspects of the process from collection to analysis, which needs addressing to support accurate comparisons across studies.
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Bolton, Samantha, Nick Cave, Naomi Cogger, and G. R. Colborne. "Use of a Collar-Mounted Triaxial Accelerometer to Predict Speed and Gait in Dogs." Animals 11, no. 5 (April 27, 2021): 1262. http://dx.doi.org/10.3390/ani11051262.
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Accelerometry has been used to measure treatment efficacy in dogs with osteoarthritis, although interpretation is difficult. Simplification of the output into speed or gait categories could simplify interpretation. We aimed to determine whether collar-mounted accelerometry could estimate the speed and categorise dogs’ gait on a treadmill. Eight Huntaway dogs were fitted with a triaxial accelerometer and then recorded using high-speed video on a treadmill at a slow and fast walk, trot, and canter. The accelerometer data (delta-G) was aligned with the video data and records of the treadmill speed and gait. Mixed linear and logistic regression models that included delta-G and a term accounting for the dogs’ skeletal sizes were used to predict speed and gait, respectively, from the accelerometer signal. Gait could be categorised (pseudo-R2 = 0.87) into binary categories of walking and faster (trot or canter), but not into the separate faster gaits. The estimation of speed above 3 m/s was inaccurate, though it is not clear whether that inaccuracy was due to the sampling frequency of the particular device, or whether that is an inherent limitation of collar-mounted accelerometers in dogs. Thus, collar-mounted accelerometry can reliably categorise dogs’ gaits into two categories, but finer gait descriptions or speed estimates require individual dog modelling and validation. Nonetheless, this accelerometry method could improve the use of accelerometry to detect treatment effects in osteoarthritis by allowing the selection of periods of activity that are most affected by treatment.
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Gewolb, Ira H., and Frank L. Vice. "Use of a non-invasive accelerometric method for diagnosing gastroesophageal reflux in premature infants." Journal of Perinatology 41, no. 8 (March 23, 2021): 1879–85. http://dx.doi.org/10.1038/s41372-021-01034-5.
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Abstract Objective To evaluate the clinical usefulness of a non-invasive accelerometric device to diagnose GER in preterm babies. Study design An accelerometer was taped over the sub-xiphoid process in 110 preterm (GA 29.6 ± 3.3 wk) infants (133 studies). Low frequency, sub-audible signals were captured via digital recording (sampling rate 200 Hz), then re-sampled (rate = 60 Hz) to create a spectrogram (focused range 0–30 Hz). Mean amplitude in the focused range was calculated. Results Of 85 studies with simultaneous pH-metry and accelerometry, 18 had concurrent positive and 23 had concurrent negative scores, 42 had negative pH scores when accelerometry was positive (≥1 µV), consistent with non-acid reflux. Eleven infants at high risk of aspiration received surgical interventions. All but 1 had negative pH scores while 10/11 had positive accelerometry. Conclusions The non-invasiveness of this accelerometric technique allows for GER screening and for repeated testing to assess efficacy of interventions.
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Sjöros, Tanja, Henri Vähä-Ypyä, Saara Laine, Taru Garthwaite, Eliisa Löyttyniemi, Harri Sievänen, Kari K. Kalliokoski, Juhani Knuuti, Tommi Vasankari, and Ilkka H. A. Heinonen. "Influence of the Duration and Timing of Data Collection on Accelerometer-Measured Physical Activity, Sedentary Time and Associated Insulin Resistance." International Journal of Environmental Research and Public Health 18, no. 9 (May 6, 2021): 4950. http://dx.doi.org/10.3390/ijerph18094950.
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Accelerometry is a commonly used method to determine physical activity in clinical studies, but the duration and timing of measurement have seldom been addressed. We aimed to evaluate possible changes in the measured outcomes and associations with insulin resistance during four weeks of accelerometry data collection. This study included 143 participants (median age of 59 (IQR9) years; mean BMI of 30.7 (SD4) kg/m2; 41 men). Sedentary and standing time, breaks in sedentary time, and different intensities of physical activity were measured with hip-worn accelerometers. Differences in the accelerometer-based results between weeks 1, 2, 3 and 4 were analyzed by mixed models, differences during winter and summer by two-way ANOVA, and the associations between insulin resistance and cumulative means of accelerometer results during weeks 1 to 4 by linear models. Mean accelerometry duration was 24 (SD3) days. Sedentary time decreased after three weeks of measurement. More physical activity was measured during summer compared to winter. The associations between insulin resistance and sedentary behavior and light physical activity were non-significant after the first week of measurement, but the associations turned significant in two to three weeks. If the purpose of data collection is to reveal associations between accelerometer-measured outcomes and tenuous health outcomes, such as insulin sensitivity, data collection for at least three weeks may be needed.
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Wainberg, Michael, Samuel E. Jones, Lindsay Melhuish Beaupre, Sean L. Hill, Daniel Felsky, Manuel A. Rivas, Andrew S. P. Lim, Hanna M. Ollila, and Shreejoy J. Tripathy. "Association of accelerometer-derived sleep measures with lifetime psychiatric diagnoses: A cross-sectional study of 89,205 participants from the UK Biobank." PLOS Medicine 18, no. 10 (October 12, 2021): e1003782. http://dx.doi.org/10.1371/journal.pmed.1003782.
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Background Sleep problems are both symptoms of and modifiable risk factors for many psychiatric disorders. Wrist-worn accelerometers enable objective measurement of sleep at scale. Here, we aimed to examine the association of accelerometer-derived sleep measures with psychiatric diagnoses and polygenic risk scores in a large community-based cohort. Methods and findings In this post hoc cross-sectional analysis of the UK Biobank cohort, 10 interpretable sleep measures—bedtime, wake-up time, sleep duration, wake after sleep onset, sleep efficiency, number of awakenings, duration of longest sleep bout, number of naps, and variability in bedtime and sleep duration—were derived from 7-day accelerometry recordings across 89,205 participants (aged 43 to 79, 56% female, 97% self-reported white) taken between 2013 and 2015. These measures were examined for association with lifetime inpatient diagnoses of major depressive disorder, anxiety disorders, bipolar disorder/mania, and schizophrenia spectrum disorders from any time before the date of accelerometry, as well as polygenic risk scores for major depression, bipolar disorder, and schizophrenia. Covariates consisted of age and season at the time of the accelerometry recording, sex, Townsend deprivation index (an indicator of socioeconomic status), and the top 10 genotype principal components. We found that sleep pattern differences were ubiquitous across diagnoses: each diagnosis was associated with a median of 8.5 of the 10 accelerometer-derived sleep measures, with measures of sleep quality (for instance, sleep efficiency) generally more affected than mere sleep duration. Effect sizes were generally small: for instance, the largest magnitude effect size across the 4 diagnoses was β = −0.11 (95% confidence interval −0.13 to −0.10, p = 3 × 10−56, FDR = 6 × 10−55) for the association between lifetime inpatient major depressive disorder diagnosis and sleep efficiency. Associations largely replicated across ancestries and sexes, and accelerometry-derived measures were concordant with self-reported sleep properties. Limitations include the use of accelerometer-based sleep measurement and the time lag between psychiatric diagnoses and accelerometry. Conclusions In this study, we observed that sleep pattern differences are a transdiagnostic feature of individuals with lifetime mental illness, suggesting that they should be considered regardless of diagnosis. Accelerometry provides a scalable way to objectively measure sleep properties in psychiatric clinical research and practice, even across tens of thousands of individuals.
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Kwon, Soyang, Patricia Zavos, Katherine Nickele, Albert Sugianto, and Mark V. Albert. "Hip and Wrist-Worn Accelerometer Data Analysis for Toddler Activities." International Journal of Environmental Research and Public Health 16, no. 14 (July 21, 2019): 2598. http://dx.doi.org/10.3390/ijerph16142598.
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Although accelerometry data are widely utilized to estimate physical activity and sedentary behavior among children age 3 years or older, for toddlers age 1 and 2 year(s), accelerometry data recorded during such behaviors have been far less examined. In particular, toddler’s unique behaviors, such as riding in a stroller or being carried by an adult, have not yet been examined. The objective of this study was to describe accelerometry signal outputs recorded during participation in nine types of behaviors (i.e., running, walking, climbing up/down, crawling, riding a ride-on toy, standing, sitting, riding in a stroller/wagon, and being carried by an adult) among toddlers. Twenty-four toddlers aged 13 to 35 months (50% girls) performed various prescribed behaviors during free play in a commercial indoor playroom while wearing ActiGraph wGT3X-BT accelerometers on a hip and a wrist. Participants’ performances were video-recorded. Based on the video data, accelerometer data were annotated with behavior labels to examine accelerometry signal outputs while performing the nine types of behaviors. Accelerometer data collected during 664 behavior assessments from the 21 participants were used for analysis. Hip vertical axis counts for walking were low (median = 49 counts/5 s). They were significantly lower than those recorded while a toddler was “carried” by an adult (median = 144 counts/5 s; p < 0.01). While standing, sitting, and riding in a stroller, very low hip vertical axis counts were registered (median ≤ 5 counts/5 s). Although wrist vertical axis and vector magnitude counts for “carried” were not higher than those for walking, they were higher than the cut-points for sedentary behaviors. Using various accelerometry signal features, machine learning techniques showed 89% accuracy to differentiate the “carried” behavior from ambulatory movements such as running, walking, crawling, and climbing. In conclusion, hip vertical axis counts alone may be unable to capture walking as physical activity and “carried” as sedentary behavior among toddlers. Machine learning techniques that utilize additional accelerometry signal features could help to recognize behavior types, especially to differentiate being “carried” from ambulatory movements.
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Oliver, Melody, Hannah Badland, Suzanne Mavoa, Mitch J. Duncan, and Scott Duncan. "Combining GPS, GIS, and Accelerometry: Methodological Issues in the Assessment of Location and Intensity of Travel Behaviors." Journal of Physical Activity and Health 7, no. 1 (January 2010): 102–8. http://dx.doi.org/10.1123/jpah.7.1.102.
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Background:Global positioning systems (GPS), geographic information systems (GIS), and accelerometers are powerful tools to explain activity within a built environment, yet little integration of these tools has taken place. This study aimed to assess the feasibility of combining GPS, GIS, and accelerometry to understand transport-related physical activity (TPA) in adults.Methods:Forty adults wore an accelerometer and portable GPS unit over 7 consecutive days and completed a demographics questionnaire and 7-day travel log. Accelerometer and GPS data were extracted for commutes to/from workplace and integrated into a GIS database. GIS maps were generated to visually explore physical activity intensity, GPS speeds and routes traveled.Results:GPS, accelerometer, and survey data were collected for 37 participants. Loss of GPS data was substantial due to a range of methodological issues, such as low battery life, signal drop out, and participant noncompliance. Nonetheless, greater travel distances and significantly higher speeds were observed for motorized trips when compared with TPA.Conclusions:Pragmatic issues of using GPS monitoring to understand TPA behaviors and methodological recommendations for future research were identified. Although methodologically challenging, the combination of GPS monitoring, accelerometry and GIS technologies holds promise for understanding TPA within the built environment.
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Nedergaard, Niels J., Mark A. Robinson, Elena Eusterwiemann, Barry Drust, Paulo J. Lisboa, and Jos Vanrenterghem. "The Relationship Between Whole-Body External Loading and Body-Worn Accelerometry During Team-Sport Movements." International Journal of Sports Physiology and Performance 12, no. 1 (January 2017): 18–26. http://dx.doi.org/10.1123/ijspp.2015-0712.
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Purpose:To investigate the relationship between whole-body accelerations and body-worn accelerometry during team-sport movements.Methods:Twenty male team-sport players performed forward running and anticipated 45° and 90° side-cuts at approach speeds of 2, 3, 4, and 5 m/s. Whole-body center-of-mass (CoM) accelerations were determined from ground-reaction forces collected from 1 foot–ground contact, and segmental accelerations were measured from a commercial GPS accelerometer unit on the upper trunk. Three higher-specification accelerometers were also positioned on the GPS unit, the dorsal aspect of the pelvis, and the shaft of the tibia. Associations between mechanical load variables (peak acceleration, loading rate, and impulse) calculated from both CoM accelerations and segmental accelerations were explored using regression analysis. In addition, 1-dimensional statistical parametric mapping (SPM) was used to explore the relationships between peak segmental accelerations and CoM-acceleration profiles during the whole foot–ground contact.Results:A weak relationship was observed for the investigated mechanical load variables regardless of accelerometer location and task (R2 values across accelerometer locations and tasks: peak acceleration .08–.55, loading rate .27–.59, and impulse .02–.59). Segmental accelerations generally overestimated whole-body mechanical load. SPM analysis showed that peak segmental accelerations were mostly related to CoM accelerations during the first 40–50% of contact phase.Conclusions:While body-worn accelerometry correlates to whole-body loading in team-sport movements and can reveal useful estimates concerning loading, these correlations are not strong. Body-worn accelerometry should therefore be used with caution to monitor whole-body mechanical loading in the field.
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Schrack, Jennifer, and Amal Wanigatunga. "MOVING, THINKING, AND SLEEPING: NOVEL INSIGHTS INTO PHYSICAL AND COGNITIVE HEALTH FROM ACCELEROMETRY DATA." Innovation in Aging 6, Supplement_1 (November 1, 2022): 330. http://dx.doi.org/10.1093/geroni/igac059.1303.
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Abstract Physical activity and sleep are well-established predictors of health and longevity with aging. Wrist accelerometers, that produce high-frequency time series data, capture multiple aspects of daily physical activity and sleep 24-hours/day. Historically, the majority of accelerometry-based activity research has employed summary metrics to understand the associations of total daily physical activity and sleep with physical and cognitive health. Although these measures are important for understanding conformity with physical activity and sleep recommendations, they underutilize the potential of these data. Further, the summary metrics may differ by accelerometer type/brand, making it difficult to translate results across device types and studies. This symposium will examine the associations between accelerometry-derived physical activity and various aging-related health outcomes, and compare the measurement properties of two commonly used accelerometers for measuring sleep. Ms. Marino will discuss the association of physical activity volume and fragmentation with the presence of the Apolipoprotein-ε4 genotype in the Baltimore Longitudinal Study of Aging (BLSA), overall and by time of day. Dr. Wanigatunga will present evidence on the association of physical activity patterns with beta amyloid plaques in the BLSA. Dr. Schrack will present the association of physical activity fragmentation and diurnal patterns with peripheral artery disease in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Finally, Ms. Liu will compare measurement of sleep variables derived from two commonly used accelerometers. Collectively, these presentations highlight ways to utilize the richness of accelerometry data to illuminate more sensitive associations between movement and health outcomes to advance prevention science and promote health aging.
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Kalisch, Tobias, Christoph Theil, Georg Gosheger, Thomas Ackmann, Isabell Schoenhals, and Burkhard Moellenbeck. "Measuring sedentary behavior using waist- and thigh-worn accelerometers and inclinometers – are the results comparable?" Therapeutic Advances in Musculoskeletal Disease 14 (January 2022): 1759720X2210792. http://dx.doi.org/10.1177/1759720x221079256.
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Background: Objective sensor-based quantification of sedentary behavior is an important tool for planning and evaluating interventions for excessive sedentary behavior in patients with musculoskeletal diseases. Although waist-worn accelerometers are the standard for physical activity (PA) assessment, only thigh-worn inclinometers can clearly distinguish sedentary behavior from any light PA or standing activity. Methods: In this study, 53 adults (ages 20–85 years) wore two ActiGraph wGT3X-BT monitors, each containing an inclinometer and accelerometer (set for acquisition of slow movements in all three planes), attached to the right waist and thigh for a period of about 4 days. Both monitors recorded total sedentary time and continuous sedentary 10-min bouts by synchronous accelerometry and inclinometry. Differences and correlations between methods and wearing positions were evaluated against participant age, body mass index (BMI), and number of steps taken. Thigh-worn inclinometry was used as reference. Results: Data from thigh-worn inclinometry and waist-worn accelerometry were highly correlated for total sedentary time [rho = 0.888; intraclass correlation coefficient (ICC) = 0.937] and time in sedentary bouts (rho = 0.818; ICC = 0.848). Nevertheless, accelerometry at the waist underestimated sedentary time by ≈17% ( p < 0.001) and time in sedentary bouts by ≈54% ( p < 0.001). A satisfactory concordance thus could be demonstrated only for total sedentary time, based on the Bland–Altmann method (≈96% of data within the limits of agreement). The differences between waist-worn accelerometry and thigh-worn inclinometry did not correlate with age but did correlate with BMI and PA for both sedentary behavior parameters ( r ⩾ 0.240, p ⩽ 0.043). Conclusion: A waist-worn accelerometer can be used to determine total sedentary time under free-living conditions with sufficient accuracy if the correct settings are chosen. Further investigations are necessary to investigate why short sedentary bouts cannot be reliably assessed. Trial registration: DRKS00024060 (German Clinical Trials Register)
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Kelly, Louise A., John J. Reilly, Sheila C. Fairweather, Sarah Barrie, Stanley Grant, and James Y. Paton. "Comparison of Two Accelerometers for Assessment of Physical Activity in Preschool Children." Pediatric Exercise Science 16, no. 4 (November 2004): 324–33. http://dx.doi.org/10.1123/pes.16.4.324.
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The primary aim of this study was to test the validity of two accelerometers, CSA/MTI WAM-7164 and Actiwatch®, against direct observation of physical activity using the Children’s Physical Activity Form (CPAF). CSA/MTI WAM-7164 and Actiwatch accelerometers simultaneously measured activity during structured-play classes in 3- to 4-year olds. Accelerometry output was synchronized to CPAF assessments of physical activity in 78 children. Rank order correlations between accelerometry and direct observation evaluated the ability of the accelerometers to assess total physical activity. Within-child minute-to-minute correlations were calculated between accelerometry output and direct observation. For total physical activity, CSA/MTI output was significantly correlated with CPAF (r = .72, p < .001), but output from the Actiwatch was not (r = .16, p > .05).
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Crane, Tracy E., Meghan B. Skiba, Austin Miller, David O. Garcia, and Cynthia A. Thomson. "Development and Evaluation of an Accelerometer-Based Protocol for Measuring Physical Activity Levels in Cancer Survivors: Development and Usability Study." JMIR mHealth and uHealth 8, no. 9 (September 24, 2020): e18491. http://dx.doi.org/10.2196/18491.
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Background The collection of self-reported physical activity using validated questionnaires has known bias and measurement error. Objective Accelerometry, an objective measure of daily activity, increases the rigor and accuracy of physical activity measurements. Here, we describe the methodology and related protocols for accelerometry data collection and quality assurance using the Actigraph GT9X accelerometer data collection in a convenience sample of ovarian cancer survivors enrolled in GOG/NRG 0225, a 24-month randomized controlled trial of diet and physical activity intervention versus attention control. Methods From July 2015 to December 2019, accelerometers were mailed on 1337 separate occasions to 580 study participants to wear at 4 time points (baseline, 6, 12, and 24 months) for 7 consecutive days. Study staff contacted participants via telephone to confirm their availability to wear the accelerometers and reviewed instructions and procedures regarding the return of the accelerometers and assisted with any technology concerns. Results We evaluated factors associated with wear compliance, including activity tracking, use of a mobile app, and demographic characteristics with chi-square tests and logistic regression. Compliant data, defined as ≥4 consecutive days with ≥10 hours daily wear time, exceeded 90% at all study time points. Activity tracking, but no other characteristics, was significantly associated with compliant data at all time points (P<.001). This implementation of data collection through accelerometry provided highly compliant and usable activity data in women who recently completed treatment for ovarian cancer. Conclusions The high compliance and data quality associated with this protocol suggest that it could be disseminated to support researchers who seek to collect robust objective activity data in cancer survivors residing in a wide geographic area.
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Clark, Cain C. T., Claire M. Barnes, Mark Holton, Huw D. Summers, and Gareth Stratton. "SlamTracker Accuracy under Static and Controlled Movement Conditions." Sport Science Review 25, no. 5-6 (December 1, 2016): 374–83. http://dx.doi.org/10.1515/ssr-2016-0020.
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Abstract Accelerometry is the de facto standard in objective physical activity monitoring. However traditional accelerometer units undergo proprietary pre-processing, resulting in the ‘black-box’ phenomenon, where researchers are unaware of the processes and filters used on their data. Raw accelerometers where all frequencies related to human movement are included in the signal, would facilitate novel analyses, such as frequency domain analysis and pattern recognition. The aim of this study was to quantify the mean, standard deviation and variance of the SlamTracker raw accelerometer at a range of speeds. Four tri-axial accelerometers underwent a one minute static condition test nine movement condition tests. Accelerometers were assessed for mean, standard deviation, sample variance and coefficient of variation throughout in all axes for all experimental conditions. The sample variance was <0.001g across all speeds and axes during the movement condition tests. In conclusion, the SlamTracker is shown to be an accurate and reliable device for measuring the raw accelerations of movement.
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Kelly, Stephen J., Aron J. Murphy, Mark L. Watsford, Damien Austin, and Michael Rennie. "Reliability and Validity of Sports Accelerometers During Static and Dynamic Testing." International Journal of Sports Physiology and Performance 10, no. 1 (January 2015): 106–11. http://dx.doi.org/10.1123/ijspp.2013-0408.
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Purpose:To investigate the validity and reliability of accelerometry of the SPI-ProX II dual data logger (GPSports, Canberra, Australia).Methods:Controlled laboratory assessments determined the accuracy and reproducibility of raw accelerometer data. Intra- and interdevice reliability assessed the ability of the SPI-ProX II accelerometers to repeatedly measure peak gravitational accelerations (g) during impact-based testing. Static and dynamic validity testing assessed the accuracy of SPI-ProX II accelerometers against a criterion-referenced accelerometer. Dynamic validity was assessed over a range of frequencies from 5 to 15 Hz.Results:Intradevice reliability found no differences (P < .05) between 4 SPI-ProX II accelerometers, with a low coefficient of variation (1.87–2.21%). SPI-ProX II accelerometers demonstrated small to medium effect-size (ES) differences (0.10–0.44) between groups and excellent interdevice reliability, with no difference found between units (F = 0.826, P = .484). Validity testing revealed significant differences between devices (P = .001), with high percentage differences (27.5–30.5%) and a large ES (>3.44).Conclusions:SPI-ProX II accelerometers demonstrated excellent intra- and interaccelerometer reliability. However, static and dynamic validity were poor, and caution is recommended when measuring the absolute magnitude of acceleration, particularly for high-frequency movements. Regular assessment of individual devices is advised, particularly for mechanical damage and signal-drift errors. It is recommended that guidelines be provided by the manufacturer on measuring shifts in the base accelerometer signal, including time frames for assessing accelerometer axis, magnitude of errors, and calibration of accelerometers from a stable reference point.
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Ortolá, Rosario, Esther García-Esquinas, Verónica Cabanas-Sánchez, Jairo H. Migueles, David Martínez-Gómez, and Fernando Rodríguez-Artalejo. "Association of Physical Activity, Sedentary Behavior, and Sleep With Unhealthy Aging: Consistent Results for Device-Measured and Self-reported Behaviors Using Isotemporal Substitution Models." Journals of Gerontology: Series A 76, no. 1 (July 23, 2020): 85–94. http://dx.doi.org/10.1093/gerona/glaa177.
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Abstract Background We examined the association of time allocation among physical activity (PA), sedentary behavior (SB), and sleep with unhealthy aging (UA), using both accelerometry and self-reports. Method We used cross-sectional data from 2312 individuals aged 65 years and older. Physical activity, SB, and sleep were ascertained by both wrist accelerometers and validated questionnaires, and UA was measured with a 52-item health-deficit accumulation index. Analyses used isotemporal substitution linear regression models. Results Less deficit accumulation was observed when the distribution of activities was 30 min/d less of SB and 30 min/d more of PA for both accelerometer (fully adjusted β [95% CI]: –0.75 [–0.90, –0.61]) and self-reports (–0.55 [–0.65, –0.45]), as well as less long sleep and more PA (accelerometer: –1.44 [–1.86, –1.01]; self-reports: –2.35 [–3.35, –1.36]) or more SB (accelerometer: –0.45 [–0.86, –0.05]; self-reports: –1.28 [–2.29, –0.28]), less normal sleep and more moderate-to-vigorous PA (accelerometer: –1.70 [–2.28, –1.13]; self-reports: –0.65 [–0.99, –0.31]), and less accelerometer light PA and more moderate-to-vigorous PA (–1.62 [–2.17, –1.07]). However, more deficit accumulation was observed when less sleep was accompanied by either more SB or more light PA in short sleepers. Self-reports captured differential associations by activity: walking appeared to be as beneficial as more vigorous activities, such as cycling or sports, and reading was associated with less UA than more mentally passive SBs, such as watching TV. Conclusions More PA was associated with less UA when accompanied by less SB time or sleep in long/normal sleepers, but not in short sleepers, where the opposite was found. Accelerometry and self-reports provided consistent associations.
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Clark, Emma L. M., Lauren D. Gulley, Allison M. Hilkin, Bonny Rockette-Wagner, Heather J. Leach, Rachel G. Lucas-Thompson, Marian Tanofsky-Kraff, et al. "Feasibility and Acceptability of Accelerometer Measurement of Physical Activity in Pregnant Adolescents." International Journal of Environmental Research and Public Health 18, no. 5 (February 24, 2021): 2216. http://dx.doi.org/10.3390/ijerph18052216.
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During pregnancy, physical activity relates to better maternal and child mental and physical health. Accelerometry is thought to be effective for assessing free-living physical activity, but the feasibility/acceptability of accelerometer use in pregnant adolescents has not been reported. In this short communication, we conducted secondary analysis of a small pilot study to describe the feasibility/acceptability of accelerometry in pregnant adolescents and the preliminary results of physical activity characteristics. Participants were recruited from a multidisciplinary adolescent perinatal clinic. Physical activity was assessed with wrist-worn accelerometers. Feasibility was described as median days of valid wear (≥10 h of wear/day) for the total sample and the number/percentage of participants with ≥4 days of valid wear. Sensitivity analyses of wear time were performed. Acceptability ratings were collected by structured interview. Thirty-six pregnant (14.6 ± 2.1 gestational weeks) adolescents (17.9 ± 1.0 years) participated. Median days of valid wear were 4 days. Seventeen participants (51.5%) had ≥4 days of valid wear. There were no differences in characteristics of adolescents with vs. without ≥4 days of valid wear. Twenty participants (60.6%) had ≥3 days of valid wear, 24 (72.7%) ≥2 valid days, and 27 (81.8%) ≥1 valid wear day. Acceptability ratings were neutral. Assessing physical activity with accelerometry in pregnant adolescents was neither feasible nor acceptable with the current conditions. Future research should investigate additional incentives and the potential utility of a lower wear-time criterion in pregnant adolescents.
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Moore, Ryan, Kristin R. Archer, and Leena Choi. "Statistical and Machine Learning Models for Classification of Human Wear and Delivery Days in Accelerometry Data." Sensors 21, no. 8 (April 13, 2021): 2726. http://dx.doi.org/10.3390/s21082726.
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Accelerometers are increasingly being used in biomedical research, but the analysis of accelerometry data is often complicated by both the massive size of the datasets and the collection of unwanted data from the process of delivery to study participants. Current methods for removing delivery data involve arduous manual review of dense datasets. We aimed to develop models for the classification of days in accelerometry data as activity from human wear or the delivery process. These models can be used to automate the cleaning of accelerometry datasets that are adulterated with activity from delivery. We developed statistical and machine learning models for the classification of accelerometry data in a supervised learning context using a large human activity and delivery labeled accelerometry dataset. Model performances were assessed and compared using Monte Carlo cross-validation. We found that a hybrid convolutional recurrent neural network performed best in the classification task with an F1 score of 0.960 but simpler models such as logistic regression and random forest also had excellent performance with F1 scores of 0.951 and 0.957, respectively. The best performing models and related data processing techniques are made publicly available in the R package, Physical Activity.
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Brown, Barbara B., and Carol M. Werner. "Using Accelerometer Feedback to Identify Walking Destinations, Activity Overestimates, and Stealth Exercise in Obese and Nonobese Individuals." Journal of Physical Activity and Health 5, no. 6 (November 2008): 882–93. http://dx.doi.org/10.1123/jpah.5.6.882.
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Background:Accelerometer output feedback might enable assessment of recall biases for moderate bouts by obese and nonobese individuals; accelerometry might also help residents recall destinations for moderate-intensity walking bouts.Methods:Adult residents’ 1-week accelerometer-measured physical activity and obesity status were measured before and after a new rail stop opened (n = 51 Time 1; n = 47 Time 2). Participants recalled the week’s walking bouts, described them as brisk (moderate) or not, and reported a rail stop destination or not.Results:At the end of the week, we provided accelerometry output to residents as a prompt. Recall of activity intensity was accurate for about 60% of bouts. Nonobese participants had more moderate bouts and more “stealth exercise” —moderate bouts recalled as not brisk—than did obese individuals. Obese participants had more overestimates—recalling light bouts as brisk walks—than did nonobese individuals. Compared with unprompted recall, accelerometry-prompted recalls allowed residents to describe where significantly more moderate bouts of activity occurred.Conclusion:Coupling accelerometry feedback with self-report improves research by measuring the duration, intensity, and destination of walking bouts. Recall errors and different patterns of errors by obese and nonobese individuals underscore the importance of validation by accelerometry.
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O’Halloran, Paul, Courtney Sullivan, Kiera Staley, Matthew Nicholson, Erica Randle, Adrian Bauman, Alex Donaldson, et al. "Measuring change in adolescent physical activity: Responsiveness of a single item." PLOS ONE 17, no. 6 (June 3, 2022): e0268459. http://dx.doi.org/10.1371/journal.pone.0268459.
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Self-report measures are frequently used to assess change in physical activity (PA) levels. Given the limited data from adolescent populations, the primary objective of this study was to examine the responsiveness of a single item measure (SIM) of PA for adolescents to detect change in moderate-to-vigorous physical activity (MVPA) using accelerometer data as the reference measure. A secondary objective was to provide further data on the validity of the measure at one point in time. The validity of the SIM to determine the number of days ≥60 minutes of MVPA was based on data from 200 participants (62% female; age: 14.0 ± 1.6 years) and analysis of change was based on data from 177 participants (65% female; age: 14.0 ± 1.6 years). Validity of change in days ≥60 minutes of MVPA was examined through agreement in classification of change between the SIM and accelerometry as the reference measurement and Spearman’s correlation. Cohen’s d and standardised response means were used to assess the responsiveness to change of the measure. The responsiveness of the SIM and accelerometer data were comparable and modest (0.27–0.38). The correlation for change in number of days ≥60 minutes MVPA between the SIM and accelerometery was low (r = 0.11) and the accuracy of the SIM for detecting change, using accelerometry as the reference, was only marginally above chance (53%). Therefore, the adolescent version of the SIM is adequate for assessing PA at a single time point but not recommended for assessing change.
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Ullrich, Antje, Sophie Baumann, Lisa Voigt, Ulrich John, and Sabina Ulbricht. "Measurement Reactivity of Accelerometer-Based Sedentary Behavior and Physical Activity in 2 Assessment Periods." Journal of Physical Activity and Health 18, no. 2 (February 1, 2021): 185–91. http://dx.doi.org/10.1123/jpah.2020-0331.
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Background: The purposes of this study were to examine accelerometer measurement reactivity (AMR) in sedentary behavior (SB), physical activity (PA), and accelerometer wear time in 2 measurement periods and to quantify AMR as a human-related source of bias for the reproducibility of SB and PA estimates. Methods: In total, 136 participants (65% women, mean age = 54.6 y) received 7-day accelerometry at the baseline and after 12 months. Latent growth models were used to identify AMR. Intraclass correlations were calculated to examine the reproducibility using 2-level mixed-effects linear regression analyses. Results: Within each 7-day accelerometry assessment, the participants increased their time spent in SB (b = 2.4 min/d; b = 3.8 min/d) and reduced their time spent in light PA (b = −2.0 min/d; b = −3.2 min/d), but did not change moderate to vigorous PA. The participants reduced their wear time (b = −5.2 min/d) only at the baseline. The intraclass correlations ranged from .42 for accelerometer wear time to .74 for SB. The AMR was not identified as a source of bias in any regression model. Conclusions: AMR may influence SB and PA estimates differentially. Although 7-day accelerometry seems to be a reproducible measure, our findings highlight accelerometer wear time as a crucial confounder in analyzing SB and PA data.
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Stevens, Matthew L., Nidhi Gupta, Elif Inan Eroglu, Patrick Joseph Crowley, Barbaros Eroglu, Adrian Bauman, Malcolm Granat, et al. "Thigh-worn accelerometry for measuring movement and posture across the 24-hour cycle: a scoping review and expert statement." BMJ Open Sport & Exercise Medicine 6, no. 1 (December 2020): e000874. http://dx.doi.org/10.1136/bmjsem-2020-000874.
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IntroductionThe Prospective Physical Activity Sitting and Sleep consortium (ProPASS) is an international collaboration platform committed to harmonise thigh-worn accelerometry data. The aim of this paper is to (1) outline observational thigh-worn accelerometry studies and (2) summarise key strategic directions arising from the inaugural ProPASS meeting.Methods(1) We performed a systematic scoping review for observational studies of thigh-worn triaxial accelerometers in free-living adults (n≥100, 24 hours monitoring protocols). (2)Attendees of the inaugural ProPASS meeting were sent a survey focused on areas related to developing ProPASS: important terminology (Q1); accelerometry constructs (Q2); advantages and distinct contribution of the consortium (Q3); data pooling and harmonisation (Q4); data access and sharing (Q5 and Q6).Results(1) Eighty eligible articles were identified (22 primary studies; n~17 685). The accelerometers used most often were the ActivPAL3 and ActiGraph GT3X. The most commonly collected health outcomes were cardiometabolic and musculoskeletal. (2) None of the survey questions elicited the predefined 60% agreement. Survey responses recommended that ProPASS: use the term physical behaviour or movement behaviour rather than ‘physical activity’ for the data we are collecting (Q1); make only minor changes to ProPASS’s accelerometry construct (Q2); prioritise developing standardised protocols/tools (Q4); facilitate flexible methods of data sharing and access (Q5 and Q6).ConclusionsThigh-worn accelerometry is an emerging method of capturing movement and posture across the 24 hours cycle. In 2020, the literature is limited to 22 primary studies from high-income western countries. This work identified ProPASS’s strategic directions—indicating areas where ProPASS can most benefit the field of research: use of clear terminology, refinement of the measured construct, standardised protocols/tools and flexible data sharing.
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Zbogar, Dominik, Janice J. Eng, William C. Miller, Andrei V. Krassioukov, and Mary C. Verrier. "Reliability and validity of daily physical activity measures during inpatient spinal cord injury rehabilitation." SAGE Open Medicine 4 (January 1, 2016): 205031211666694. http://dx.doi.org/10.1177/2050312116666941.
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Objectives: To assess the test–retest reliability and convergent validity of daily physical activity measures during inpatient spinal cord injury rehabilitation. Design: Observational study. Setting: Two inpatient spinal cord injury rehabilitation centres. Subjects: Participants ( n = 106) were recruited from consecutive admissions to rehabilitation. Methods: Physical activity during inpatient spinal cord injury rehabilitation stay was recorded on two days via (1) wrist accelerometer, (2) hip accelerometer if ambulatory, and (3) self-report (Physical Activity Recall Assessment for People with Spinal Cord Injury questionnaire). Spearman’s correlations and Bland–Altman plots were utilized for test–retest reliability. Correlations between physical activity measures and clinical measures (functional independence, hand function, and ambulation) were performed. Results: Correlations for physical activity measures between Day 1 and Day 2 were moderate to high (ρ = 0.53–0.89). Bland–Altman plots showed minimal bias and more within-subject differences in more active individuals and wide limits of agreement. None of these three physical activity measures correlated with one another. A moderate correlation was found between wrist accelerometry counts and grip strength (ρ = 0.58) and between step counts and measures of ambulation (ρ = 0.62). Functional independence was related to wrist accelerometry (ρ = 0.70) and step counts (ρ = 0.56), but not with self-report. Conclusion: The test–retest reliability and convergent validity of the instrumented measures suggest that wrist and hip accelerometers are appropriate tools for use in research studies of daily physical activity in the spinal cord injury rehabilitation setting but are too variable for individual use.
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Bento, Teresa, António Cortinhas, José Carlos Leitão, and Maria Paula Mota. "Use of accelerometry to measure physical activity in adults and the elderly." Revista de Saúde Pública 46, no. 3 (June 2012): 561–70. http://dx.doi.org/10.1590/s0034-89102012005000022.
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OBJECTIVE: To review the use of accelerometry as an objective measure of physical activity in adults and elderly people. METHODS: A systematic review of studies on the use of accelerometty as an objective measure to assess physical activity in adults were examined in PubMed Central, Web of Knowledge, EBSCO and Medline databases from March 29 to April 15, 2010. The following keywords were used: "accelerometry," "accelerometer," "physical activity," "PA," "patterns," "levels," "adults," "older adults," and "elderly," either alone or in combination using "AND" or "OR." The reference lists of the articles retrieved were examined to capture any other potentially relevant article. Of 899 studies initially identified, only 18 were fully reviewed, and their outcome measures abstracted and analyzed. RESULTS: Eleven studies were conducted in North America (United States), five in Europe, one in Africa (Cameroon) and one in Australia. Very few enrolled older people, and only one study reported the season or time of year when data was collected. The articles selected had different methods, analyses, and results, which prevented comparison between studies. CONCLUSIONS: There is a need to standardize study methods for data reporting to allow comparisons of results across studies and monitor changes in populations. These data can help design more adequate strategies for monitoring and promotion of physical activity.
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Larrivée, Samuel, Frédéric Balg, Guillaume Léonard, Sonia Bédard, Michel Tousignant, and Patrick Boissy. "Wrist-Based Accelerometers and Visual Analog Scales as Outcome Measures for Shoulder Activity During Daily Living in Patients With Rotator Cuff Tendinopathy: Instrument Validation Study." JMIR Rehabilitation and Assistive Technologies 6, no. 2 (December 3, 2019): e14468. http://dx.doi.org/10.2196/14468.
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Background Shoulder pain secondary to rotator cuff tendinopathy affects a large proportion of patients in orthopedic surgery practices. Corticosteroid injections are a common intervention proposed for these patients. The clinical evaluation of a response to corticosteroid injections is usually based only on the patient’s self-evaluation of his function, activity, and pain by multiple questionnaires with varying metrological qualities. Objective measures of upper extremity functions are lacking, but wearable sensors are emerging as potential tools to assess upper extremity function and activity. Objective This study aimed (1) to evaluate and compare test-retest reliability and sensitivity to change of known clinical assessments of shoulder function to wrist-based accelerometer measures and visual analog scales (VAS) of shoulder activity during daily living in patients with rotator cuff tendinopathy convergent validity and (2) to determine the acceptability and compliance of using wrist-based wearable sensors. Methods A total of 38 patients affected by rotator cuff tendinopathy wore wrist accelerometers on the affected side for a total of 5 weeks. Western Ontario Rotator Cuff (WORC) index; Short version of the Disability of the Arm, Shoulder, and Hand questionnaire (QuickDASH); and clinical examination (range of motion and strength) were performed the week before the corticosteroid injections, the day of the corticosteroid injections, and 2 and 4 weeks after the corticosteroid injections. Daily Single Assessment Numeric Evaluation (SANE) and VAS were filled by participants to record shoulder pain and activity. Accelerometer data were processed to extract daily upper extremity activity in the form of active time; activity counts; and ratio of low-intensity activities, medium-intensity activities, and high-intensity activities. Results Daily pain measured using VAS and SANE correlated well with the WORC and QuickDASH questionnaires (r=0.564-0.815) but not with accelerometry measures, amplitude, and strength. Daily activity measured with VAS had good correlation with active time (r=0.484, P=.02). All questionnaires had excellent test-retest reliability at 1 week before corticosteroid injections (intraclass correlation coefficient [ICC]=0.883-0.950). Acceptable reliability was observed with accelerometry (ICC=0.621-0.724), apart from low-intensity activities (ICC=0.104). Sensitivity to change was excellent at 2 and 4 weeks for all questionnaires (standardized response mean=1.039-2.094) except for activity VAS (standardized response mean=0.50). Accelerometry measures had low sensitivity to change at 2 weeks, but excellent sensitivity at 4 weeks (standardized response mean=0.803-1.032). Conclusions Daily pain VAS and SANE had good correlation with the validated questionnaires, excellent reliability at 1 week, and excellent sensitivity to change at 2 and 4 weeks. Daily activity VAS and accelerometry-derived active time correlated well together. Activity VAS had excellent reliability, but moderate sensitivity to change. Accelerometry measures had moderate reliability and acceptable sensitivity to change at 4 weeks.
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Ishikawa-Takata, Kazuko, Kayoko Kaneko, Kayo Koizumi, and Chinatsu Ito. "Comparison of physical activity energy expenditure in Japanese adolescents assessed by EW4800P triaxial accelerometry and the doubly labelled water method." British Journal of Nutrition 110, no. 7 (April 2, 2013): 1347–55. http://dx.doi.org/10.1017/s0007114513000603.
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The present study compared the accuracy of triaxial accelerometry and the doubly labelled water (DLW) method for measuring physical activity (PA) in Japanese adolescents. A total of sixty adolescents aged 12–15 years were analysed. The total energy expenditure (TEE) was measured over 7 d by the DLW method and with an EW4800P triaxial accelerometer (Panasonic Corporation). The measured (RMRm) and predicted RMR (RMRp) were 5·7 (sd 0·9) and 6·0 (sd 1·0) MJ/d, respectively. TEE measured by the DLW method and accelerometry using RMRm or RMRp were 11·0 (sd 2·6), 10·3 (sd 1·9), and 10·7 (sd 2·1) MJ/d, respectively. The PA levels (PAL) measured by the DLW method using RMRm or RMRp were 1·97 (sd 0·31) and 1·94 (sd 0·31) in subjects who exercised, and 1·85 (sd 0·27) and 1·74 (sd 0·29) in subjects who did not exercise. The percentage of body fat correlated significantly with the percentage difference between RMRmv. RMRp, TEE, PA energy expenditure (PAEE) and PAL using RMRp, and PAL using RMRm assessed by the DLW method and accelerometry. The present data showed that while accelerometry estimated TEE accurately, it did not provide the precise measurement of PAEE and PAL. The error in accelerometry was attributed to the prediction error of RMR and assessment in exercise.
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Lewis, Liane S., James Hernon, Allan Clark, and John M. Saxton. "Validation of the IPAQ Against Different Accelerometer Cut-Points in Older Cancer Survivors and Adults at Risk of Cancer." Journal of Aging and Physical Activity 26, no. 1 (January 1, 2018): 34–40. http://dx.doi.org/10.1123/japa.2016-0207.
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The present study investigated the convergent validity of an interview-administered IPAQ long version (IPAQ-L) in an older population by comparison with objective accelerometry movement data. Data from 52 participants (mean age 67.9 years, 62% male) were included in the analysis. Treadmill derived (TM-ACC: 1,952–5,724 cpm) and free-living physical activity (PA) derived (FL-ACC: 760–5,724 cpm) accelerometer cut-points were used as criterion. IPAQ-L measures (total PA, leisure-time, walking-time, sedentary time) were significantly correlated with accelerometry (P ≤ .05). Differences in sex were observed. Bland-Altman Limits of Agreement analysis showed that the IPAQ-L overestimated PA in relation to accelerometry. Our results show that an interview-administered IPAQ-L shows low to moderate convergent validity with objective PA measures in this population but there may be differences between males and females which should be further investigated.
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Vanderloo, Leigh M., Natascja A. Di Cristofaro, Nicole A. Proudfoot, Patricia Tucker, and Brian W. Timmons. "Comparing the Actical and ActiGraph Approach to Measuring Young Children’s Physical Activity Levels and Sedentary Time." Pediatric Exercise Science 28, no. 1 (February 2016): 133–42. http://dx.doi.org/10.1123/pes.2014-0218.
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Young children’s activity and sedentary time were simultaneously measured via the Actical method (i.e., Actical accelerometer and specific cut-points) and the ActiGraph method (i.e., ActiGraph accelerometer and specific cut-points) at both 15-s and 60-s epochs to explore possible differences between these 2 measurement approaches. For 7 consecutive days, participants (n = 23) wore both the Actical and ActiGraph side-by-side on an elastic neoprene belt. Device-specific cut-points were applied. Paired sample t tests were conducted to determine the differences in participants’ daily average activity levels and sedentary time (min/h) measured by the 2 devices at 15-s and 60-s time sampling intervals. Bland-Altman plots were used to examine agreement between Actical and ActiGraph accelerometers. Regardless of epoch length, Actical accelerometers reported significantly higher rates of sedentary time (15 s: 42.7 min/h vs 33.5 min/h; 60 s: 39.4 min/h vs 27.1 min/h). ActiGraph accelerometers captured significantly higher rates of moderate-to-vigorous physical activity (15 s: 9.2 min/h vs 2.6 min/h; 60 s: 8.0 min/h vs 1.27 min/h) and total physical activity (15 s: 31.7 min/h vs 22.3 min/h; 60 s: = 39.4 min/h vs 25.2 min/h) in comparison with Actical accelerometers. These results highlight the present accelerometry-related issues with interpretation of datasets derived from different monitors.
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Zainol Abidin, Nurdiana, Wendy J. Brown, Bronwyn Clark, Ahmad Munir Che Muhamed, and Rabindarjeet Singh. "Physical Activity Measurement by Accelerometry Among Older Malay Adults Living in Semi-Rural Areas—A Feasibility Study." Journal of Aging and Physical Activity 24, no. 4 (October 2016): 533–39. http://dx.doi.org/10.1123/japa.2015-0157.
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We evaluated feasibility of physical activity measurement by accelerometry among older Malay adults living in semi-rural areas in Malaysia. Results showed that 95% of 146 participants (aged [SD] 67.6 [6.4] years) were compliant in wearing the accelerometer for at least five days. Fifteen participants were asked for re-wear the accelerometer because they did not have enough valid days during the first assessment. Participants wore the accelerometer an average of 15.3 hr in a 24-hr day, with 6.5 (1.2) valid wear days. No significant difference in valid wear day and time was found between men and women. Participants who are single provide more valid wear days compared with married participants (p < .05), and participants with higher levels of education provide longer periods of accelerometer wearing hours (p < .01). Eighty-seven percent of participants reported ‘no issues’ with wearing the meter. This study suggests that accelerometry is a feasible method to assess the physical activity level among older Malay adults living in semi-rural areas.
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de Graauw, Suzanne M., Janke F. de Groot, Marco van Brussel, Marjolein F. Streur, and Tim Takken. "Review of Prediction Models to Estimate Activity-Related Energy Expenditure in Children and Adolescents." International Journal of Pediatrics 2010 (2010): 1–14. http://dx.doi.org/10.1155/2010/489304.
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Purpose. To critically review the validity of accelerometry-based prediction models to estimate activity energy expenditure (AEE) in children and adolescents.Methods. The CINAHL, EMBASE, PsycINFO, and PubMed/MEDLINE databases were searched. Inclusion criteria were development or validation of an accelerometer-based prediction model for the estimation of AEE in healthy children or adolescents (6–18 years), criterion measure: indirect calorimetry, or doubly labelled water, and language: Dutch, English or German.Results. Nine studies were included. Median methodological quality was5.5±2.0 IR (out of a maximum 10 points). Prediction models combining heart rate and counts explained 86–91% of the variance in measured AEE. A prediction model based on a triaxial accelerometer explained 90%. Models derived during free-living explained up to 45%.Conclusions. Accelerometry-based prediction models may provide an accurate estimate of AEE in children on a group level. Best results are retrieved when the model combines accelerometer counts with heart rate or when a triaxial accelerometer is used. Future development of AEE prediction models applicable to free-living scenarios is needed.
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ZEALEAR, DAVID L., GARRETT D. HERZON, and MARJORIE KORFF. "EVOKED ACCELEROMETRY." Laryngoscope 98, no. 5 (May 1988): 568???572. http://dx.doi.org/10.1288/00005537-198805000-00019.
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Tuckwell, Georgia A., James A. Keal, Charlotte C. Gupta, Sally A. Ferguson, Jarrad D. Kowlessar, and Grace E. Vincent. "A Deep Learning Approach to Classify Sitting and Sleep History from Raw Accelerometry Data during Simulated Driving." Sensors 22, no. 17 (September 1, 2022): 6598. http://dx.doi.org/10.3390/s22176598.
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Prolonged sitting and inadequate sleep can impact driving performance. Therefore, objective knowledge of a driver’s recent sitting and sleep history could help reduce safety risks. This study aimed to apply deep learning to raw accelerometry data collected during a simulated driving task to classify recent sitting and sleep history. Participants (n = 84, Mean ± SD age = 23.5 ± 4.8, 49% Female) completed a seven-day laboratory study. Raw accelerometry data were collected from a thigh-worn accelerometer during a 20-min simulated drive (8:10 h and 17:30 h each day). Two convolutional neural networks (CNNs; ResNet-18 and DixonNet) were trained to classify accelerometry data into four classes (sitting or breaking up sitting and 9-h or 5-h sleep). Accuracy was determined using five-fold cross-validation. ResNet-18 produced higher accuracy scores: 88.6 ± 1.3% for activity (compared to 77.2 ± 2.6% from DixonNet) and 88.6 ± 1.1% for sleep history (compared to 75.2 ± 2.6% from DixonNet). Class activation mapping revealed distinct patterns of movement and postural changes between classes. Findings demonstrate the suitability of CNNs in classifying sitting and sleep history using thigh-worn accelerometer data collected during a simulated drive. This approach has implications for the identification of drivers at risk of fatigue-related impairment.
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Eston, Roger G., Ann V. Rowlands, and David K. Ingledew. "Validity of heart rate, pedometry, and accelerometry for predicting the energy cost of children’s activities." Journal of Applied Physiology 84, no. 1 (January 1, 1998): 362–71. http://dx.doi.org/10.1152/jappl.1998.84.1.362.
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Eston, Roger G., Ann V. Rowlands, and David K. Ingledew.Validity of heart rate, pedometry, and accelerometry for predicting the energy cost of children’s activities. J. Appl. Physiol. 84(1): 362–371, 1998.—Heart rate telemetry is frequently used to estimate daily activity in children and to validate other methods. This study compared the accuracy of heart rate monitoring, pedometry, triaxial accelerometry, and uniaxial accelerometry for estimating oxygen consumption during typical children’s activities. Thirty Welsh children (mean age 9.2 ± 0.8 yr) walked (4 and 6 km/h) and ran (8 and 10 km/h) on a treadmill, played catch, played hopscotch, and sat and crayoned. Heart rate, body accelerations in three axes, pedometry counts, and oxygen uptake were measured continuously during each 4-min activity. Oxygen uptake was expressed as a ratio of body mass raised to the power of 0.75 [scaled oxygen uptake (sV˙o 2)]. All measures correlated significantly ( P < 0.001) with sV˙o 2. A multiple-regression equation that included triaxial accelerometry counts and heart rate predicted sV˙o 2 better than any measure alone ( R 2 = 0.85, standard error of the estimate = 9.7 ml ⋅ kg−0.75 ⋅ min−1). The best of the single measures was triaxial accelerometry ( R 2 = 0.83, standard error of the estimate = 10.3 ml ⋅ kg−0.75 ⋅ min−1). It is concluded that a triaxial accelerometer provides the best assessment of activity. Pedometry offers potential for large population studies.
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Garnotel, M., T. Bastian, H. M. Romero-Ugalde, A. Maire, J. Dugas, A. Zahariev, M. Doron, et al. "Prior automatic posture and activity identification improves physical activity energy expenditure prediction from hip-worn triaxial accelerometry." Journal of Applied Physiology 124, no. 3 (March 1, 2018): 780–90. http://dx.doi.org/10.1152/japplphysiol.00556.2017.
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Accelerometry is increasingly used to quantify physical activity (PA) and related energy expenditure (EE). Linear regression models designed to derive PAEE from accelerometry-counts have shown their limits, mostly due to the lack of consideration of the nature of activities performed. Here we tested whether a model coupling an automatic activity/posture recognition (AAR) algorithm with an activity-specific count-based model, developed in 61 subjects in laboratory conditions, improved PAEE and total EE (TEE) predictions from a hip-worn triaxial-accelerometer (ActigraphGT3X+) in free-living conditions. Data from two independent subject groups of varying body mass index and age were considered: 20 subjects engaged in a 3-h urban-circuit, with activity-by-activity reference PAEE from combined heart-rate and accelerometry monitoring (Actiheart); and 56 subjects involved in a 14-day trial, with PAEE and TEE measured using the doubly-labeled water method. PAEE was estimated from accelerometry using the activity-specific model coupled to the AAR algorithm (AAR model), a simple linear model (SLM), and equations provided by the companion-software of used activity-devices (Freedson and Actiheart models). AAR-model predictions were in closer agreement with selected references than those from other count-based models, both for PAEE during the urban-circuit (RMSE = 6.19 vs 7.90 for SLM and 9.62 kJ/min for Freedson) and for EE over the 14-day trial, reaching Actiheart performances in the latter (PAEE: RMSE = 0.93 vs. 1.53 for SLM, 1.43 for Freedson, 0.91 MJ/day for Actiheart; TEE: RMSE = 1.05 vs. 1.57 for SLM, 1.70 for Freedson, 0.95 MJ/day for Actiheart). Overall, the AAR model resulted in a 43% increase of daily PAEE variance explained by accelerometry predictions.NEW & NOTEWORTHY Although triaxial accelerometry is widely used in free-living conditions to assess the impact of physical activity energy expenditure (PAEE) on health, its precision and accuracy are often debated. Here we developed and validated an activity-specific model which, coupled with an automatic activity-recognition algorithm, improved the variance explained by the predictions from accelerometry counts by 43% of daily PAEE compared with models relying on a simple relationship between accelerometry counts and EE.
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Shi, Chengjian, Jacek Urbanek, Niser Babiker, Alan Gonzolez, Jovany Soto, Andrey Rzhestsky, and Megan Huisingh-Scheetz. "Hip Accelerometry Activity Patterns Improve Machine Learning Prediction of 1-Year MoCA Score Change." Innovation in Aging 5, Supplement_1 (December 1, 2021): 444. http://dx.doi.org/10.1093/geroni/igab046.1723.
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Abstract We tested whether free-living hip accelerometry measures improved prediction of 1-year change in Montreal Cognitive Assessment (MoCA) scores beyond clinically available information. We analyzed data (n=126) from predominantly African American (78.2%) older adults without moderate-severe dementia residing near our geriatrics clinic. Age (73.6 ±6.1 years), gender, education, comorbidities, income, and MoCA performance were collected at baseline; participants then wore a right hip, triaxial Actigraph accelerometer (30Hz) continuously for 7 days. A MoCA was repeated at 1 year. Six measures were calculated from the daytime (7am-5pm) data: mean/variance of hourly counts per minute, mean/variance of daily percent of time spent in the lowest activity quartile, and mean/variance of daily percent of time spent in the highest activity quartile. In a random forest model containing baseline MoCA, demographics and comorbidities, the accelerometry measures improved prediction of 1-year MoCA performance by ~17.8%. Accelerometry data may be clinically useful for predicting early cognitive decline.
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Visser, Marjolein, Robert J. Brychta, Kong Y. Chen, and Annemarie Koster. "Self-Reported Adherence to the Physical Activity Recommendation and Determinants of Misperception in Older Adults." Journal of Aging and Physical Activity 22, no. 2 (April 2014): 226–34. http://dx.doi.org/10.1123/japa.2012-0219.
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We aimed to compare self-reported adherence to the physical activity recommendation with accelerometry in older adults and to identify determinants of misperception. The sample included 138 adults age 65–75 yr old participating in the Longitudinal Aging Study Amsterdam. Participants completed a lifestyle questionnaire and wore an accelerometer for one week. More than half (56.8%) of the participants reported to adhere to the physical activity recommendation (in 5-min bouts), however, based on accelerometry, this percentage was only 24.6%. Of those who reported to adhere, 65.3% did not do so based on accelerometry. The misperceivers were older (p< .009), more often female (p= .007), had a poorer walking performance (p= .02), reported a lower social support (p= .04), and tended to have a lower self-efficacy (p= .09) compared with those who correctly perceived their adherence to the recommendation. These results suggest that misperception of adherence to the physical activity recommendation is highly prevalent among specific subgroups of older adults.
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García-Soidán, Jose L., Raquel Leirós-Rodríguez, Vicente Romo-Pérez, and Jesús García-Liñeira. "Accelerometric Assessment of Postural Balance in Children: A Systematic Review." Diagnostics 11, no. 1 (December 22, 2020): 8. http://dx.doi.org/10.3390/diagnostics11010008.
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The correct development of postural control in children is fundamental to ensure that they fully reach their psychomotor capacities. However, this capacity is one of the least studied in the clinical and academic scope regarding children. The objective of this study was to analyze the degree of implementation of accelerometry as an evaluation technique for postural control in children and how it is being used. Methods: A systematic search was conducted in PubMed, SpringerLink, SportsDiscus, Medline, Scopus, and Web of Science with the following terms: balance, postural control, children, kids, accelerometry, and accelerometer. Results: The search generated a total of 18 articles. Two groups of studies were differentiated: those which exclusively included healthy individuals (n = 5) and those which included children with pathologies (n = 13). Accelerometry is being used in children mainly to assess the gait and static balance, as well as to identify the differences between healthy children and children with developmental disorders. Conclusions: Accelerometry has a discrete degree of implementation as an evaluation tool to assess postural control. It is necessary to define a systematic method for the evaluation of postural control in pediatrics, in order to delve into the development of this capacity and its alterations in different neurodevelopmental disorders.
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Roomkham, S., D. Lovell, I. Szollosi, and D. Perrin. "P122 Comparison of sleep estimation using Apple Watch accelerometry against polysomnography." SLEEP Advances 2, Supplement_1 (October 1, 2021): A61. http://dx.doi.org/10.1093/sleepadvances/zpab014.163.
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Abstract Introduction Consumer wearables offer new ways to improve our health and well-being, including sleep. Researchers are interested in consumer wearables because their widespread adoption creates the potential for larger studies than could be run with clinically validated measurement methods, as those are more expensive or less convenient. This study investigates sleep tracking using sensor data from Apple Watch in comparison to the gold standard polysomnography (PSG). Method We used Apple Watch accelerometer data to establish both activity and heart rate (using ballistocardiography). Thirty participants (13 female, 17 male) wore the Apple Watch on their non-dominant wrist during clinical PSG. We compared predicted sleep status at the epoch level and overall sleep parameters, taking PSG as the ground truth. Results Our method achieved sleep-wake classification accuracy of 84%, sensitivity of 95%, and specificity of 47%. Apple Watch overestimated total sleep time (mean+SD) by 39.4 + 57.7 mins, underestimated WASO by 45.5 + 54.6 mins and the number of awakenings by 5.0 + 6.9. We observed worse performance for participants who had PSGs exhibiting frequent respiratory events. Discussion Accelerometry cannot replace PSG for diagnostic purposes. However, the Apple Watch results compare favourably to previously published Actiwatch-PSG comparisons. The performance we measured suggests that Apple Watch based accelerometry could be used in longitudinal studies to gather information similar to clinically validated accelerometers, potentially on a larger scale for lower cost. Further study is needed to understand how sleep disorders affect this kind of measurement.
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Elliott, Kyle H., Maryline Le Vaillant, Akiko Kato, John R. Speakman, and Yan Ropert-Coudert. "Accelerometry predicts daily energy expenditure in a bird with high activity levels." Biology Letters 9, no. 1 (February 23, 2013): 20120919. http://dx.doi.org/10.1098/rsbl.2012.0919.
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Animal ecology is shaped by energy costs, yet it is difficult to measure fine-scale energy expenditure in the wild. Because metabolism is often closely correlated with mechanical work, accelerometers have the potential to provide detailed information on energy expenditure of wild animals over fine temporal scales. Nonetheless, accelerometry needs to be validated on wild animals, especially across different locomotory modes. We merged data collected on 20 thick-billed murres ( Uria lomvia ) from miniature accelerometers with measurements of daily energy expenditure over 24 h using doubly labelled water. Across three different locomotory modes (swimming, flying and movement on land), dynamic body acceleration was a good predictor of daily energy expenditure as measured independently by doubly labelled water ( R 2 = 0.73). The most parsimonious model suggested that different equations were needed to predict energy expenditure from accelerometry for flying than for surface swimming or activity on land ( R 2 = 0.81). Our results demonstrate that accelerometers can provide an accurate integrated measure of energy expenditure in wild animals using many different locomotory modes.
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García-Prieto, Jorge Cañete, Vicente Martinez-Vizcaino, Antonio García-Hermoso, Mairena Sánchez-López, Natalia Arias-Palencia, Juan Fernando Ortega Fonseca, and Ricardo Mora-Rodriguez. "Energy Expenditure in Playground Games in Primary School Children Measured by Accelerometer and Heart Rate Monitors." International Journal of Sport Nutrition and Exercise Metabolism 27, no. 5 (October 2017): 467–74. http://dx.doi.org/10.1123/ijsnem.2016-0122.
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The aim of this study was to examine the energy expenditure (EE) measured using indirect calorimetry (IC) during playground games and to assess the validity of heart rate (HR) and accelerometry counts as indirect indicators of EE in children´s physical activity games. 32 primary school children (9.9 ± 0.6 years old, 19.8 ± 4.9 kg · m-2 BMI and 37.6 ± 7.2 ml · kg-1 · min-1 VO2max). Indirect calorimetry (IC), accelerometry and HR data were simultaneously collected for each child during a 90 min session of 30 playground games. Thirty-eight sessions were recorded in 32 different children. Each game was recorded at least in three occasions in other three children. The intersubject coefficient of variation within a game was 27% for IC, 37% for accelerometry and 13% for HR. The overall mean EE in the games was 4.2 ± 1.4 kcals · min-1 per game, totaling to 375 ± 122 kcals/per 90 min/session. The correlation coefficient between indirect calorimetry and accelerometer counts was 0.48 (p = .026) for endurance games and 0.21 (p = .574) for strength games. The correlation coefficient between indirect calorimetry and HR was 0.71 (p = .032) for endurance games and 0.48 (p = .026) for strength games. Our data indicate that both accelerometer and HR monitors are useful devices for estimating EE during endurance games, but only HR monitors estimates are accurate for endurance games.
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Evenson, Kelly R., John Bellettiere, Carmen C. Cuthbertson, Chongzhi Di, Rimma Dushkes, Annie Green Howard, Humberto Parada Jr., et al. "Cohort profile: the Women’s Health Accelerometry Collaboration." BMJ Open 11, no. 11 (November 2021): e052038. http://dx.doi.org/10.1136/bmjopen-2021-052038.
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PurposeThis paper describes the Women’s Health Accelerometry Collaboration, a consortium of two prospective cohort studies of women age 62 years or older, harmonised to explore the association of accelerometer-assessed physical activity and sedentary behaviour with cancer incidence and mortality.ParticipantsA total of 23 443 women (age mean 73.4, SD 6.8) living in the USA and participating in an observational study were included; 17 061 from the Women’s Health Study (WHS) and 6382 from the Women’s Health Initiative Objective Physical Activity and Cardiovascular Health (WHI/OPACH) Study.Findings to dateAccelerometry, cancer outcomes and covariate harmonisation was conducted to align the two cohort studies. Physical activity and sedentary behaviour were measured using similar procedures with an ActiGraph GT3X+ accelerometer, worn at the hip for 1 week, during 2011–2014 for WHS and 2012–2014 for WHI/OPACH. Cancer outcomes were ascertained via ongoing surveillance using physician adjudicated cancer diagnosis. Relevant covariates were measured using questionnaire or physical assessments. Among 23 443 women who wore the accelerometer for at least 10 hours on a single day, 22 868 women wore the accelerometer at least 10 hours/day on ≥4 of 7 days. The analytical sample (n=22 852) averaged 4976 (SD 2669) steps/day and engaged in an average of 80.8 (SD 46.5) min/day of moderate-to-vigorous, 105.5 (SD 33.3) min/day of light high and 182.1 (SD 46.1) min/day of light low physical activity. A mean of 8.7 (SD 1.7) hours/day were spent in sedentary behaviour. Overall, 11.8% of the cohort had a cancer diagnosis (other than non-melanoma skin cancer) at the time of accelerometry measurement. During an average of 5.9 (SD 1.6) years of follow-up, 1378 cancer events among which 414 were fatal have occurred.Future plansUsing the harmonised cohort, we will access ongoing cancer surveillance to quantify the associations of physical activity and sedentary behaviour with cancer incidence and mortality.
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Hayward, Kathryn S., Janice J. Eng, Lara A. Boyd, Bimal Lakhani, Julie Bernhardt, and Catherine E. Lang. "Exploring the Role of Accelerometers in the Measurement of Real World Upper-Limb Use After Stroke." Brain Impairment 17, no. 1 (November 10, 2015): 16–33. http://dx.doi.org/10.1017/brimp.2015.21.
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The ultimate goal of upper-limb rehabilitation after stroke is to promote real-world use, that is, use of the paretic upper-limb in everyday activities outside the clinic or laboratory. Although real-world use can be collected through self-report questionnaires, an objective indicator is preferred. Accelerometers are a promising tool. The current paper aims to explore the feasibility of accelerometers to measure upper-limb use after stroke and discuss the translation of this measurement tool into clinical practice. Accelerometers are non-invasive, wearable sensors that measure movement in arbitrary units called activity counts. Research to date indicates that activity counts are a reliable and valid index of upper-limb use. While most accelerometers are unable to distinguish between the type and quality of movements performed, recent advancements have used accelerometry data to produce clinically meaningful information for clinicians, patients, family and care givers. Despite this, widespread uptake in research and clinical environments remains limited. If uptake was enhanced, we could build a deeper understanding of how people with stroke use their arm in real-world environments. In order to facilitate greater uptake, however, there is a need for greater consistency in protocol development, accelerometer application and data interpretation.
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Schrack, Jennifer, Jacek Urbanek, and Todd Manini. "Novel Applications of Accelerometry Data for Health Outcomes in Older Adults: Thinking Beyond MVPA." Innovation in Aging 5, Supplement_1 (December 1, 2021): 334–35. http://dx.doi.org/10.1093/geroni/igab046.1298.
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Abstract Physical activity is a well-established predictor of health and longevity. Wearable accelerometers produce high-frequency, time series data that capture multiple aspects of daily physical activity across the spectrum of intensity. Historically, the majority of accelerometry-based physical activity research has employed summary threshold metrics such as moderate-to-vigorous physical activity, or “MVPA.” Although these measures are important for understanding compliance with physical activity guidelines, they underutilize the potential of this data. To advance the science of physical activity in older adults, more sensitive, clinically translatable measures are needed. This symposium will examine the associations between novel measures of accelerometry-derived physical activity and various aging-related health outcomes. Dr. Wanigatunga will discuss the association of physical activity volume and fragmentation with the frailty phenotype in the Study to Understand Vitamin D and Fall Reduction in You (STURDY). Dr. Cai will present evidence on the association of physical activity quantities and patterns with measures of visual impairment in the Baltimore Longitudinal Study of Aging. Ms. Qiao will present a novel accelerometry-derived measure of performance fatigability in the Developmental Epidemiologic Cohort Study. Finally, Dr. Urbanek will discuss the role of accelerometry-derived free-living gait cadence in defining fall risk in STURDY. Collectively, these presentations highlight critical associations between objective measures of physical activity and health outcomes in older adults and illuminate the need for thinking beyond MVPA to improve prevention and intervention efforts.
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Schütte, Kurt H., Saint Sackey, Rachel Venter, and Benedicte Vanwanseele. "Energy cost of running instability evaluated with wearable trunk accelerometry." Journal of Applied Physiology 124, no. 2 (February 1, 2018): 462–72. http://dx.doi.org/10.1152/japplphysiol.00429.2017.
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Maintaining stability under dynamic conditions is an inherent challenge to bipedal running. This challenge may impose an energetic cost (Ec) thus hampering endurance running performance, yet the underlying mechanisms are not clear. Wireless triaxial trunk accelerometry is a simple tool that could be used to unobtrusively evaluate these mechanisms. Here, we test a cost of instability hypothesis by examining the contribution of trunk accelerometry-based measures (triaxial root mean square, step and stride regularity, and sample entropy) to interindividual variance in Ec (J/m) during treadmill running. Accelerometry and indirect calorimetry data were collected concurrently from 30 recreational runners (16 men; 14 women) running at their highest steady-state running speed (80.65 ± 5.99% V̇o2max). After reducing dimensionality with factor analysis, the effect of dynamic stability features on Ec was evaluated using hierarchical multiple regression analysis. Three accelerometry-based measures could explain an additional 10.4% of interindividual variance in Ec after controlling for body mass, attributed to anteroposterior stride regularity (5.2%), anteroposterior root mean square ratio (3.2%), and mediolateral sample entropy (2.0%). Our results lend support to a cost of instability hypothesis, with trunk acceleration waveform signals that are 1) more consistent between strides anteroposterioly, 2) larger in amplitude variability anteroposterioly, and 3) more complex mediolaterally and are energetically advantageous to endurance running performance. This study shows that wearable trunk accelerometry is a useful tool for understanding the Ec of running and that running stability is important for economy in recreational runners. NEW & NOTEWORTHY This study evaluates and more directly lends support to a cost of instability hypothesis between runners. Moreover, this hypothesis was tested using a minimalist setup including a single triaxial trunk mounted accelerometer, with potential transferability to biomechanical and performance analyses in typical outdoor settings.
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Connes, Pierre. "Absolute astronomical accelerometry." Astrophysics and Space Science 110, no. 2 (1985): 211–55. http://dx.doi.org/10.1007/bf00653671.
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Garner, Amelia Jane, Reza Saatchi, Oliver Ward, Harriet Nwaizu, and Daniel Philip Hawley. "Proof-of-Concept Study of the Use of Accelerometry to Quantify Knee Joint Movement and Assist with the Diagnosis of Juvenile Idiopathic Arthritis." Technologies 10, no. 4 (June 23, 2022): 76. http://dx.doi.org/10.3390/technologies10040076.
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Juvenile idiopathic arthritis (JIA) is the most common rheumatic disease in childhood. Seven children and young people (CYP) with a diagnosis of JIA and suspected active arthritis of a single knee joint were recruited for this proof-of-concept study. The presence of active arthritis was confirmed by clinical examination. Four tri-axial accelerometers were integrated individually in elastic bands and placed above and below each knee. Participants performed ten periodic flexion-extensions of each knee joint while lying down, followed by walking ten meters in a straight path. The contralateral (non-inflamed) knee joint acted as a control. Accelerometry data were concordant with the results of clinical examination in six out of the seven patients recruited. There was a significant difference between the accelerometry measured range of movement (ROM, p-value = 0.032) of the knees with active arthritis and the healthy contralateral knees during flexion-extension. No statistically significant difference was identified between the ROM of the knee joints with active arthritis and healthy knee joints during the walking test. The study demonstrated that accelerometry may help in differentiating between healthy knee joints and those with active arthritis; however, further research is required to confirm these findings.
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Nedergaard, Niels J., Jasper Verheul, Barry Drust, Terence Etchells, Paulo Lisboa, Mark A. Robinson, and Jos Vanrenterghem. "The feasibility of predicting ground reaction forces during running from a trunk accelerometry driven mass-spring-damper model." PeerJ 6 (December 20, 2018): e6105. http://dx.doi.org/10.7717/peerj.6105.
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Background Monitoring the external ground reaction forces (GRF) acting on the human body during running could help to understand how external loads influence tissue adaptation over time. Although mass-spring-damper (MSD) models have the potential to simulate the complex multi-segmental mechanics of the human body and predict GRF, these models currently require input from measured GRF limiting their application in field settings. Based on the hypothesis that the acceleration of the MSD-model’s upper mass primarily represents the acceleration of the trunk segment, this paper explored the feasibility of using measured trunk accelerometry to estimate the MSD-model parameters required to predict resultant GRF during running. Methods Twenty male athletes ran at approach speeds between 2–5 m s−1. Resultant trunk accelerometry was used as a surrogate of the MSD-model upper mass acceleration to estimate the MSD-model parameters (ACCparam) required to predict resultant GRF. A purpose-built gradient descent optimisation routine was used where the MSD-model’s upper mass acceleration was fitted to the measured trunk accelerometer signal. Root mean squared errors (RMSE) were calculated to evaluate the accuracy of the trunk accelerometry fitting and GRF predictions. In addition, MSD-model parameters were estimated from fitting measured resultant GRF (GRFparam), to explore the difference between ACCparam and GRFparam. Results Despite a good match between the measured trunk accelerometry and the MSD-model’s upper mass acceleration (median RMSE between 0.16 and 0.22 g), poor GRF predictions (median RMSE between 6.68 and 12.77 N kg−1) were observed. In contrast, the MSD-model was able to replicate the measured GRF with high accuracy (median RMSE between 0.45 and 0.59 N kg−1) across running speeds from GRFparam. The ACCparam from measured trunk accelerometry under- or overestimated the GRFparam obtained from measured GRF, and generally demonstrated larger within parameter variations. Discussion Despite the potential of obtaining a close fit between the MSD-model’s upper mass acceleration and the measured trunk accelerometry, the ACCparam estimated from this process were inadequate to predict resultant GRF waveforms during slow to moderate speed running. We therefore conclude that trunk-mounted accelerometry alone is inappropriate as input for the MSD-model to predict meaningful GRF waveforms. Further investigations are needed to continue to explore the feasibility of using body-worn micro sensor technology to drive simple human body models that would allow practitioners and researchers to estimate and monitor GRF waveforms in field settings.
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Quiles, Norberto N., Aston K. McCullough, and Lin Piao. "Validity and Reliability of the Exercise Vital Sign Questionnaire in an Ethnically Diverse Group: A Pilot Study." Journal of Primary Care & Community Health 10 (January 2019): 215013271984406. http://dx.doi.org/10.1177/2150132719844062.
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The purpose of this study was to determine the validity and reliability of the Exercise Vital Sign (EVS) questionnaire in an ethnically diverse sample. Participants (N = 39) were asked to wear an accelerometer at the hip for at least 7 days and to complete the EVS at the beginning (T1) and end (T2) of the wear period. The EVS questionnaire validity was determined against accelerometry, and bias was calculated as the mean difference between measures. The sensitivity and specificity of the EVS questionnaire were also evaluated. The reliability of the questionnaire was calculated using intraclass correlation coefficient (ICC) between EVS responses at T1 and T2. The mean difference in EVS- and accelerometer-determined time in MVPA was 24 min/wk. The reliability for the questionnaire was excellent (ICC = 0.98). The EVS specificity and sensitivity at T2 were 56% and 78%, respectively. The EVS questionnaire may be an acceptable measure of weekly MVPA time compared to accelerometry in an ethnically diverse sample; however, further research is needed to confirm these findings.
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Staunton, Craig A., Mikael Swarén, Thomas Stöggl, Dennis-Peter Born, and Glenn Björklund. "The Relationship Between Cardiorespiratory and Accelerometer-Derived Measures in Trail Running and the Influence of Sensor Location." International Journal of Sports Physiology and Performance 17, no. 3 (March 1, 2022): 474–83. http://dx.doi.org/10.1123/ijspp.2021-0220.
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Purpose: To examine the relationship between cardiorespiratory and accelerometer-derived measures of exercise during trail running and determine the influence of accelerometer location. Methods: Eight trail runners (7 males and 1 female; age 26 [5] y; maximal oxygen consumption [] 70 [6] mL·kg−1·min−1) completed a 7-km trail run (elevation gain: 486 m), with concurrent measurements of , heart rate, and accelerations recorded from 3 triaxial accelerometers attached at the upper spine, lower spine, and pelvis. External exercise intensity was quantified from the accelerometers using PlayerLoad™ per minute and accelerometry-derived average net force. External exercise volume was calculated using accumulated PlayerLoad and the product of average net force and duration (impulse). Internal intensity was calculated using heart rate and -metrics; internal volume was calculated from total energy expenditure (work). All metrics were analyzed during both uphill (UH) and downhill (DH) sections of the trail run. Results: PlayerLoad and average net force were greater during DH compared with UH for all sensor locations (P ≤ .004). For all accelerometer metrics, there was a sensor position × gradient interaction (F2,1429.003; P <.001). The upper spine was lower compared with both pelvis (P ≤ .003) and lower spine (P ≤ .002) for all accelerometer metrics during both UH and DH running. Relationships between accelerometer and cardiorespiratory measures during UH running ranged from moderate negative to moderate positive (r = −.31 to .41). Relationships were stronger during DH running where there was a nearly perfect correlation between work and impulse (r = .91; P < .001). Conclusions: Simultaneous monitoring of cardiorespiratory and accelerometer-derived measures during trail running is suggested because of the disparity between internal and external intensities during changes in gradient. Sensor positioning close to the center of mass is recommended.
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Fourcade, M. "Final report: First European comparison in accelerometry, using two standard accelerometers." Metrologia 34, no. 2 (April 1997): 197–98. http://dx.doi.org/10.1088/0026-1394/34/2/13.
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