Journal articles: 'Wrist-activity monitors'

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Park, Susan, Lindsay P. Toth, Scott E. Crouter, Cary M. Springer, Robert T. Marcotte, and David R. Bassett. "Effect of Monitor Placement on the Daily Step Counts of Wrist and Hip Activity Monitors." Journal for the Measurement of Physical Behaviour 3, no. 2 (June 1, 2020): 164–69. http://dx.doi.org/10.1123/jmpb.2019-0065.

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Purpose: To examine the effect of activity monitor placement on daily step counts when monitors are worn at different positions on the wrist/forearm and the hip. Methods: Participants (N = 18) wore eight different models (four wrist and four hip models) across four days. Each day, one hip and one wrist model were selected, and four identical monitors of each model were worn on the right hip and the non-dominant wrist/forearm, respectively, during all waking hours. Step counts of each monitor were compared to the same model worn in the referent position (wrist: proximal to ulnar styloid process; hip: midline of thigh). Percent of referent steps and mean difference between observed and referent positions were computed. Significant differences in steps between positions for each method were determined using one-way repeated measures ANOVAs. For significant main effects, pairwise comparisons with Bonferroni corrections were used to determine which positions were significantly different. Results: All wrist methods showed a significant main effect for placement (p < .05) and alternate positions were 1–16% lower than the referent position. For hip methods, only the Omron HJ-325 differed across positions (p < .05), but differences were among non-referent positions and all were within ±2% of steps recorded by the referent position. Conclusions: Researchers should be aware that positions that deviate from the manufacturer’s recommended position at the wrist could influence step counts. Of all hip methods examined, the Omron had a significant placement effect which did not constitute a practical difference.

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Hartung, Verena, Mustafa Sarshar, Viktoria Karle, Layal Shammas, Asarnusch Rashid, Paul Roullier, Caroline Eilers, et al. "Validity of Consumer Activity Monitors and an Algorithm Using Smartphone Data for Measuring Steps during Different Activity Types." International Journal of Environmental Research and Public Health 17, no. 24 (December 12, 2020): 9314. http://dx.doi.org/10.3390/ijerph17249314.

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Background: Consumer activity monitors and smartphones have gained relevance for the assessment and promotion of physical activity. The aim of this study was to determine the concurrent validity of various consumer activity monitor models and smartphone models for measuring steps. Methods: Participants completed three activity protocols: (1) overground walking with three different speeds (comfortable, slow, fast), (2) activities of daily living (ADLs) focusing on arm movements, and (3) intermittent walking. Participants wore 11 activity monitors (wrist: 8; hip: 2; ankle: 1) and four smartphones (hip: 3; calf: 1). Observed steps served as the criterion measure. The mean average percentage error (MAPE) was calculated for each device and protocol. Results: Eighteen healthy adults participated in the study (age: 28.8 ± 4.9 years). MAPEs ranged from 0.3–38.2% during overground walking, 48.2–861.2% during ADLs, and 11.2–47.3% during intermittent walking. Wrist-worn activity monitors tended to misclassify arm movements as steps. Smartphone data collected at the hip, analyzed with a separate algorithm, performed either equally or even superiorly to the research-grade ActiGraph. Conclusion: This study highlights the potential of smartphones for physical activity measurement. Measurement inaccuracies during intermittent walking and arm movements should be considered when interpreting study results and choosing activity monitors for evaluation purposes.

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Thiebaud, Robert S., Merrill D. Funk, Jacelyn C. Patton, Brook L. Massey, Terri E. Shay, Martin G. Schmidt, and Nicolas Giovannitti. "Validity of wrist-worn consumer products to measure heart rate and energy expenditure." DIGITAL HEALTH 4 (January 2018): 205520761877032. http://dx.doi.org/10.1177/2055207618770322.

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Introduction The ability to monitor physical activity throughout the day and during various activities continues to improve with the development of wrist-worn monitors. However, the accuracy of wrist-worn monitors to measure both heart rate and energy expenditure during physical activity is still unclear. The purpose of this study was to determine the accuracy of several popular wrist-worn monitors at measuring heart rate and energy expenditure. Methods Participants wore the TomTom Cardio, Microsoft Band and Fitbit Surge on randomly assigned locations on each wrist. The maximum number of monitors per wrist was two. The criteria used for heart rate and energy expenditure were a three-lead electrocardiogram and indirect calorimetry using a metabolic cart. Participants exercised on a treadmill at 3.2, 4.8, 6.4, 8 and 9.7 km/h for 3 minutes at each speed, with no rest between speeds. Heart rate and energy expenditure were manually recorded every minute throughout the protocol. Results Mean absolute percentage error for heart rate varied from 2.17 to 8.06% for the Fitbit Surge, from 1.01 to 7.49% for the TomTom Cardio and from 1.31 to 7.37% for the Microsoft Band. The mean absolute percentage error for energy expenditure varied from 25.4 to 61.8% for the Fitbit Surge, from 0.4 to 26.6% for the TomTom Cardio and from 1.8 to 9.4% for the Microsoft Band. Conclusion Data from these devices may be useful in obtaining an estimate of heart rate for everyday activities and general exercise, but energy expenditure from these devices may be significantly over- or underestimated.

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Smith, John. "Accuracy of Wrist-Worn Activity Monitors during Wheelchair Use." Medicine & Science in Sports & Exercise 50, no. 5S (May 2018): 291. http://dx.doi.org/10.1249/01.mss.0000536043.73093.eb.

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Hwang, Jungyun, Austin Fernandez, and Amy Lu. "Application and Validation of Activity Monitors’ Epoch Lengths and Placement Sites for Physical Activity Assessment in Exergaming." Journal of Clinical Medicine 7, no. 9 (September 11, 2018): 268. http://dx.doi.org/10.3390/jcm7090268.

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We assessed the agreement of two ActiGraph activity monitors (wGT3X vs. GT9X) placed at the hip and the wrist and determined an appropriate epoch length for physical activity levels in an exergaming setting. Forty-seven young adults played a 30-min exergame while wearing wGT3X and GT9X on both hip and wrist placement sites and a heart rate sensor below the chest. Intraclass correlation coefficient indicated that intermonitor agreement in steps and activity counts was excellent on the hip and good on the wrist. Bland-Altman plots indicated good intermonitor agreement in the steps and activity counts on both placement sites but a significant intermonitor difference was detected in steps on the wrist. Time spent in sedentary and physical activity intensity levels varied across six epoch lengths and depended on the placement sites, whereas time spent from a 1-s epoch of the hip-worn monitors most accurately matched the relative exercise intensity by heart rate. Hip placement site was associated with better step-counting accuracy for both activity monitors and more valid estimation of physical activity levels. A 1-s epoch was the most appropriate epoch length to detect short bursts of intense physical activity and may be the best choice for data processing and analysis in exergaming studies examining intermittent physical activities.

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Hurter, Liezel, Stuart Fairclough, Zoe Knowles, Lorna Porcellato, Anna Cooper-Ryan, and Lynne Boddy. "Establishing Raw Acceleration Thresholds to Classify Sedentary and Stationary Behaviour in Children." Children 5, no. 12 (December 19, 2018): 172. http://dx.doi.org/10.3390/children5120172.

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This study aimed to: (1) compare acceleration output between ActiGraph (AG) hip and wrist monitors and GENEActiv (GA) wrist monitors; (2) identify raw acceleration sedentary and stationary thresholds for the two brands and placements; and (3) validate the thresholds during a free-living period. Twenty-seven from 9- to 10-year-old children wore AG accelerometers on the right hip, dominant- and non-dominant wrists, GA accelerometers on both wrists, and an activPAL on the thigh, while completing seven sedentary and light-intensity physical activities, followed by 10 minutes of school recess. In a subsequent study, 21 children wore AG and GA wrist monitors and activPAL for two days of free-living. The main effects of activity and brand and a significant activity × brand × placement interaction were observed (all p < 0.0001). Output from the AG hip was lower than the AG wrist monitors (both p < 0.0001). Receiver operating characteristic (ROC) curves established AG sedentary thresholds of 32.6 mg for the hip, 55.6 mg and 48.1 mg for dominant and non-dominant wrists respectively. GA wrist thresholds were 56.5 mg (dominant) and 51.6 mg (non-dominant). Similar thresholds were observed for stationary behaviours. The AG non-dominant threshold came closest to achieving equivalency with activPAL during free-living.

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Pat Rapp, Mary, Francine Nelson, Melinda Oliver, Nancy Bergstrom, and Stanley G. Cron. "Comparison of Commonly Used Placement Sites for Activity Monitoring." Biological Research For Nursing 11, no. 3 (July 17, 2009): 302–9. http://dx.doi.org/10.1177/1099800409337526.

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Background: No accepted standard exists to evaluate nonsleep-related activity in nursing facility residents where monitors are variously placed at the ankle, waist, wrist, thigh, or embedded in sheeting and set to record activity frequency. Objectives: To determine optimal placement of activity monitors by site—at the ankle, waist, or wrist for nursing facility residents. Methods: Nursing facility residents (N = 16) wore accelerometers at three sites: the nondominant ankle, waist, and wrist, while recording activity in three modes: frequency, duration, and intensity. Results: The natural log activity mean for each mode by site and time revealed no significant differences between the three sites for activity intensity, F(2, 62.78) = .15, p = .86; activity duration, F(2, 69.84) = .50, p = .61; and activity frequency, F(2, 70.04) = 1.25, p = .29. There were no significant site—time interactions. The natural log activity by site and mode indicated no significant differences by site for the 24-hr mean, F(2, 107.64) = .20, p = .82; activity median, F(2, 100.42) = .47, p = .63; and activity standard deviation, F(2, 108.69) = 1.5, p = .23. A significant difference was seen by site for the acceleration index, F(2, 106.32) = 9.57, p < .001. No significant site—mode interactions were found. Conclusions: Similarity between ankle, waist, or wrist sites when measuring activity by various modes, frequency, duration, or intensity, suggests the monitors measure nonsleep-related activity equally well at any of the sites.

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Kwon, Eun Hye, and John D. Smith. "Accuracy Of Wrist-worn Activity Monitors During Walking And Swimming." Medicine & Science in Sports & Exercise 49, no. 5S (May 2017): 652. http://dx.doi.org/10.1249/01.mss.0000518717.79658.a4.

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Rowlands, Alex V., Tatiana Plekhanova, Tom Yates, Evgeny M. Mirkes, Melanie Davies, Kamlesh Khunti, and Charlotte L. Edwardson. "Providing a Basis for Harmonization of Accelerometer-Assessed Physical Activity Outcomes Across Epidemiological Datasets." Journal for the Measurement of Physical Behaviour 2, no. 3 (September 1, 2019): 131–42. http://dx.doi.org/10.1123/jmpb.2018-0073.

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Introduction: To capitalize on the increasing availability of accelerometry data for epidemiological research it is desirable to compare and/or pool data from surveys worldwide. This study aimed to establish whether free-living physical activity outcomes can be considered equivalent between three research-grade accelerometer brands worn on the dominant and non-dominant wrist. Of prime interest were the average acceleration (ACC) and the intensity gradient (IG). These two metrics describe the volume and intensity of the complete activity profile; further, they are comparable across populations making them ideal for comparing and/or pooling activity data. Methods: Forty-eight adults wore a GENEActiv, Axivity, and ActiGraph on both wrists for up to 7-days. Data were processed using open-source software (GGIR) to generate physical activity outcomes, including ACC and IG. Agreement was assessed using pairwise 95% equivalence tests (±10% equivalence zone) and intra-class correlation coefficients (ICC). Results: ACC was equivalent between brands when measured at the non-dominant wrist (ICC ≥ 0.93), but approximately 10% higher when measured at the dominant wrist (GENEActiv and Axivity only, ICC ≥ 0.83). The IG was equivalent irrespective of monitor brand or wrist (ICC ≥ 0.88). After adjusting ACC measured at the dominant wrist by −10% (GENEActiv and Axivity only), ACC was also within (or marginally outside) the 10% equivalence zone for all monitor pairings. Conclusion: If average acceleration is decreased by 10% for studies deploying monitors on the dominant wrist (GENEActiv and Axivity only), ACC and IG may be suitable for comparing and/or collating physical activity outcomes across accelerometer datasets, regardless of monitor brand and wrist.

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Steeves, Jeremy A., Scott A. Conger, Joe R. Mitrzyk, Trevor A. Perry, Elise Flanagan, Alecia K. Fox, Trystan Weisinger, and Alexander H. K. Montoye. "Using the Wrist-Worn Atlas Wristband2 Monitor to Objectively Measure Resistance Training Exercises." Journal for the Measurement of Physical Behaviour 2, no. 4 (December 1, 2019): 218–27. http://dx.doi.org/10.1123/jmpb.2019-0012.

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Background: Devices for monitoring physical activity have focused mainly on measuring aerobic activity; however, the 2018 Physical Activity Guidelines for Americans also recommend muscle-resistance training two or more days per week. Recently, a wrist-worn activity monitor, the Atlas Wristband2, was developed to recognize resistance training exercises. Purpose: To assess the ability of the Wristband2 to identify the type and number of repetitions of resistance training exercises, when worn on the left wrist as directed by the manufacturer, and when worn on the right wrist. Methods: While wearing monitors on both wrists, 159 participants completed a circuit-style workout consisting of two sets of 12 repetitions of 14 different resistance training exercises. Data from the monitors were used to determine classification accuracies for identifying exercise type verses direct observation. The average repetitions and mean absolute error (MAE) for repetitions were calculated for each exercise. Results: The Wristband2 classification accuracy for exercise type was 78.4 ± 2.5%, ranging from 54.7 ± 3.4% (dumbbell [DB] bench press) to 97.5 ± 1.0% (DB biceps curls), when worn on the left wrist. An average of 11.0 ± 0.2 repetitions, ranging from 9.0 ± 0.3 repetitions (DB lunges) to 11.9 ± 0.1 repetitions (push-ups), were identified. For all exercises, MAE ranged from 0.0–4.6 repetitions. When worn on the right wrist, exercise type classification accuracy dropped to 24.2 ± 5.1%, and repetitions decreased to 8.1 ± 0.8 out of 12. Conclusions: The Wristband2, worn on the left wrist, had acceptable exercise classification and repetition counting capabilities for many of the 14 exercises used in this study, and may be a useful tool to objectively track resistance training.

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Smith, John D. "Accuracy of Wrist-Worn Activity Monitors during Treadmill and Elliptical Ergometry." Medicine & Science in Sports & Exercise 49, no. 5S (May 2017): 365. http://dx.doi.org/10.1249/01.mss.0000517882.22655.92.

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Deka, Pallav, Bunny Pozehl, Joseph F. Norman, and Deepak Khazanchi. "Feasibility of using the Fitbit® Charge HR in validating self-reported exercise diaries in a community setting in patients with heart failure." European Journal of Cardiovascular Nursing 17, no. 7 (March 16, 2018): 605–11. http://dx.doi.org/10.1177/1474515118766037.

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Background: Use of wrist-worn activity monitors has increased over the past few years; however, the use of the Fitbit® Charge HR (FCHR) in a community setting in patients with heart failure has not been tested. Purpose: The purpose of the study was to assess the feasibility, practicality and acceptability of utilizing the FCHR to validate self-reported exercise diaries and monitor exercise in community dwelling patients with heart failure. Methods: Thirty heart failure patients (12 females and 18 males) aged 64.7 ± 11.5 years were provided with a FCHR. Participants were provided with an exercise routine and for eight weeks, recorded their exercise sessions in self-reported exercise diaries and used the FCHR to record those exercise sessions. Results: Exercise data from the self-reported exercise diaries were validated with data from the FCHR. Participants’ perception and acceptance of using the FCHR was positive. Validation of exercise and physical activity interventions using the FCHR appears feasible and acceptable in patients with heart failure. Conclusion: Wrist-worn activity monitors can be useful for objective measurement of exercise adherence and monitoring of physical activity in patients with heart failure in a community setting.

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Kim, Youngdeok, and Marc Lochbaum. "Comparison of Polar Active Watch and Waist- and Wrist-Worn ActiGraph Accelerometers for Measuring Children’s Physical Activity Levels during Unstructured Afterschool Programs." International Journal of Environmental Research and Public Health 15, no. 10 (October 16, 2018): 2268. http://dx.doi.org/10.3390/ijerph15102268.

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Background: The purpose of this study was to examine the convergent validity of the Polar Active Watch (PAW), a consumer-grade wrist-worn activity monitor, against waist- and wrist-worn research-grade monitors, the ActiGraph GT3X+/GT9X accelerometers, in children. Methods: Fifty-one children (18 boys; mean age = 10.30 ± 0.91 years) wore the three monitors (PAW, GT3X+, and GT9X) during an 80-min afterschool program across five school days. Time spent in sedentary, light-intensity (LPA), and moderate- and vigorous-intensity physical activity (MVPA) were estimated from each monitor. The correlation, mixed model, mean absolute percentage error, equivalence testing, and Bland-Altman analyses were used to examine the comparability of PA estimates of the PAW with GT3X+/GT9X accelerometers. Results: Moderate to strong correlations for sedentary and MVPA minutes, and weak correlation for LPA were observed between the PAW and GT3X+/GT9X accelerometers. Significant mean differences were found, where the PAW tended to overestimate time in sedentary and MVPA and underestimate LPA minutes, compared to the GT3X+/GT9X accelerometers. However, a non-significant mean difference in MVPA minutes was observed when using an adjusted MET threshold (≥4 METs) for the PAW, compared to the GT3X+ accelerometer. Conclusions: The PAW showed moderate convergent validity for sedentary and MVPA minutes against the GT3X+/GT9X accelerometers. However, caution is needed in the direct comparison between the monitors due to relatively large mean differences and within-group variability.

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Webster, Michael D., and Daniel P. Heil. "Reliably Measuring Habitual Free-Living Physical Activity with Hip- and Wrist-Worn Activity Monitors." Medicine & Science in Sports & Exercise 40, Supplement (May 2008): S199. http://dx.doi.org/10.1249/01.mss.0000322321.47803.be.

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Imboden, Mary T., Michael B. Nelson, Leonard A. Kaminsky, and Alexander HK Montoye. "Comparison of four Fitbit and Jawbone activity monitors with a research-grade ActiGraph accelerometer for estimating physical activity and energy expenditure." British Journal of Sports Medicine 52, no. 13 (May 8, 2017): 844–50. http://dx.doi.org/10.1136/bjsports-2016-096990.

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Background/aimConsumer-based physical activity (PA) monitors have become popular tools to track PA behaviours. Currently, little is known about the validity of the measurements provided by consumer monitors. We aimed to compare measures of steps, energy expenditure (EE) and active minutes of four consumer monitors with one research-grade accelerometer within a semistructured protocol.MethodsThirty men and women (18–80 years old) wore Fitbit One (worn at the waist), Fitbit Zip (waist), Fitbit Flex (wrist), Jawbone UP24 (wrist) and one waist-worn research-grade accelerometer (ActiGraph) while participating in an 80 min protocol. A validated EE prediction equation and active minute cut-points were applied to ActiGraph data. Criterion measures were assessed using direct observation (step count) and portable metabolic analyser (EE, active minutes). A repeated measures analysis of variance (ANOVA) was used to compare differences between consumer monitors, ActiGraph, and criterion measures. Similarly, a repeated measures ANOVA was applied to a subgroup of subjects who didn’t cycle.ResultsParticipants took 3321±571 steps, had 28±6 active min and expended 294±56 kcal based on criterion measures. Comparatively, all monitors underestimated steps and EE by 13%–32% (p<0.01); additionally the Fitbit Flex, UP24, and ActiGraph underestimated active minutes by 35%–65% (p<0.05). Underestimations of PA and EE variables were found to be similar in the subgroup analysis.ConclusionConsumer monitors had similar accuracy for PA assessment as the ActiGraph, which suggests that consumer monitors may serve to track personal PA behaviours and EE. However, due to discrepancies among monitors, individuals should be cautious when comparing relative and absolute differences in PA values obtained using different monitors.

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Ali, Eglal, Dipti Wani, Wen Ling, and Smita Rao. "Reliability and validity of wrist-worn activity monitors in healthy young adults." Physiotherapy Practice and Research 39, no. 2 (July 17, 2018): 117–24. http://dx.doi.org/10.3233/ppr-180114.

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Kressler, Jochen, Joshua Koeplin-Day, Benedikt Muendle, and Antoinette Domingo. "Accuracy of Wrist-Worn Activity Monitors during Wheelchair Use and Arm Ergometry." Medicine & Science in Sports & Exercise 48 (May 2016): 101. http://dx.doi.org/10.1249/01.mss.0000485307.55049.96.

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Lewis, Zakkoyya H., Maddison Cannon, Grace Rubio, Maria C. Swartz, and Elizabeth J. Lyons. "Analysis of the Behavioral Change and Utility Features of Electronic Activity Monitors." Technologies 8, no. 4 (December 5, 2020): 75. http://dx.doi.org/10.3390/technologies8040075.

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The aim of this study was to perform a content analysis of electronic activity monitors that also evaluates utility features, code behavior change techniques included in the monitoring systems, and align the results with intervention functions of the Behaviour Change Wheel program planning model to facilitate informed device selection. Devices were coded for the implemented behavior change techniques and device features. Three trained coders each wore a monitor for at least 1 week from December 2019–April 2020. Apple Watch Nike, Fitbit Versa 2, Fitbit Charge 3, Fitbit Ionic—Adidas Edition, Garmin Vivomove HR, Garmin Vivosmart 4, Amazfit Bip, Galaxy Watch Active, and Withings Steel HR were reviewed. The monitors all paired with a phone/tablet, tracked exercise sessions, and were wrist-worn. On average, the monitors implemented 27 behavior change techniques each. Fitbit devices implemented the most behavior change techniques, including techniques related to the intervention functions: education, enablement, environmental restructuring, coercion, incentivization, modeling, and persuasion. Garmin devices implemented the second highest number of behavior change techniques, including techniques related to enablement, environmental restructuring, and training. Researchers can use these results to guide selection of electronic activity monitors based on their research needs.

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Farina, Nicolas, and Ruth G. Lowry. "The Validity of Consumer-Level Activity Monitors in Healthy Older Adults in Free-Living Conditions." Journal of Aging and Physical Activity 26, no. 1 (January 1, 2018): 128–35. http://dx.doi.org/10.1123/japa.2016-0344.

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Consumer-level activity monitors, such as Fitbit and Misfit devices, are a popular and low-cost means of measuring physical activity. This study aims to compare the accuracy of step counts from two consumer-level activity monitors against two reference devices in healthy, community-dwelling older adults in free-living conditions. Twenty-five older adults (aged 65–84) simultaneously wore 5 devices (e.g., Misfit Shine and Fitbit Charge HR) over 7 consecutive days. All consumer-level activity monitors positively correlated with reference devices (p < .001). There was also substantial to near perfect agreement between all consumer-level activity monitors and reference devices. Compared to the ActiGraph GT3X+, the waist-worn Misfit Shine displayed the highest agreement amongst the devices worn (ICC = 0.96, 95% 0.91 to 0.99). The wrist-worn devices showed poorer agreement to reference devices. Future research needs to consider that not all consumer-level activity monitors are equal in terms of accuracy, design, and function.

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Smith, John D., and Gary Guerra. "Quantifying Step Count and Oxygen Consumption with Portable Technology during the 2-Min Walk Test in People with Lower Limb Amputation." Sensors 21, no. 6 (March 16, 2021): 2080. http://dx.doi.org/10.3390/s21062080.

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Step counts and oxygen consumption have yet to be reported during the 2-min walk test (2MWT) test in persons with lower-limb amputations (LLA). The purpose of this study was to determine step counts and oxygen consumption during the 2MWT in LLA. Thirty-five men and women walked for two minutes as quickly as possible while wearing activity monitors (ActiGraph Link on the wrist (LW) and ankle (LA), Garmin vivofit®3 on the wrist (VW) and ankle (VA), and a modus StepWatch on the ankle (SA), and a portable oxygen analyzer. The StepWatch on the ankle (SA) and the vivofit3 on the wrist (VW) had the least error and best accuracy of the activity monitors studied. While there were no significant differences in distance walked, oxygen consumption (VO2) or heart rate (HR) between sexes or level of amputation (p > 0.05), females took significantly more steps than males (p = 0.034), and those with unilateral transfemoral amputations took significantly fewer steps than those with unilateral transtibial amputations (p = 0.023). The VW and SA provided the most accurate step counts among the activity monitors and were not significantly different than hand counts. Oxygen consumption for all participants during the 2MWT was 8.9 ± 2.9 mL/kg/min, which is lower than moderate-intensity activity. While some may argue that steady-state activity has not yet been reached in the 2MWT, it may also be possible participants are not walking as fast as they can, thereby misclassifying their performance to a lower standard.

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Conger, Scott A., Stacy N. Scott, Eugene C. Fitzhugh, Dixie L. Thompson, and David R. Bassett. "Validity of Physical Activity Monitors for Estimating Energy Expenditure During Wheelchair Propulsion." Journal of Physical Activity and Health 12, no. 11 (November 2015): 1520–26. http://dx.doi.org/10.1123/jpah.2014-0376.

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Background:It is unknown if activity monitors can detect the increased energy expenditure (EE) of wheelchair propulsion at different speeds or on different surfaces.Methods:Individuals who used manual wheelchairs (n = 14) performed 5 wheeling activities: on a level surface at 3 speeds, on a rubberized track at 1 fixed speed and on a sidewalk course at a self-selected speed. EE was measured using a portable indirect calorimetry system and estimated by an Actical (AC) worn on the wrist and a SenseWear (SW) activity monitor worn on the upper arm. Repeated-measures ANOVA was used to compare measured EE to the estimates from the standard AC prediction equation and SW using 2 different equations.Results:Repeated-measures ANOVA demonstrated a significant main effect between measured EE and estimated EE. There were no differences between the criterion method and the AC across the 5 activities. The SW overestimated EE when wheeling at 3 speeds on a level surface, and during sidewalk wheeling. The wheelchair-specific SW equation improved the EE prediction during low intensity activities, but error progressively increased during higher intensity activities.Conclusions:During manual wheelchair propulsion, the wrist-mounted AC provided valid estimates of EE, whereas the SW tended to overestimate EE.

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Al Mushcab, Hayat, Kevin Curran, and Jonathan Doherty. "An Activity Monitoring Application for Windows Mobile Devices." International Journal of Ambient Computing and Intelligence 2, no. 3 (July 2010): 1–18. http://dx.doi.org/10.4018/jaci.2010070101.

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Obesity is rising at an alarming rate. A great challenge facing the health community is introducing population-wide approaches to weight management as existing health and medical provisions do not have the capacity to cope. Technology is fast becoming an important tool to combat this trend. The use of activity monitors is becoming more common in health care as a device to measure everyday activity levels of patients as activity is often linked to weight. This paper outlines a research project where Bluetooth technology can be used to connect a commercial wrist-worn activity monitor with a Windows Mobile device to allow the user to upload the activity data to a remote server.

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Brønd, Jan Christian, Natascha Holbæk Pedersen, Kristian Traberg Larsen, and Anders Grøntved. "Temporal Alignment of Dual Monitor Accelerometry Recordings." Sensors 21, no. 14 (July 13, 2021): 4777. http://dx.doi.org/10.3390/s21144777.

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Combining accelerometry from multiple independent activity monitors worn by the same subject have gained widespread interest with the assessment of physical activity behavior. However, a difference in the real time clock accuracy of the activity monitor introduces a substantial temporal misalignment with long duration recordings which is commonly not considered. In this study, a novel method not requiring human interaction is described for the temporal alignment of triaxial acceleration measured with two independent activity monitors and evaluating the performance with the misalignment manually identified. The method was evaluated with free-living recordings using both combined wrist/hip (n = 9) and thigh/hip device (n = 30) wear locations, and descriptive data on initial offset and accumulated day 7 drift in a large-scale population-based study (n = 2513) were calculated. The results from the Bland–Altman analysis show good agreement between the proposed algorithm and the reference suggesting that the described method is valid for reducing the temporal misalignment and thus reduce the measurement error with aggregated data. Applying the algorithm to the n = 2513 samples worn for 7-days suggest a wide and substantial issue with drift over time when each subject wears two independent activity monitors.

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Hofbauer, Lena M., and Francisca S. Rodriguez. "How is the usability of commercial activity monitors perceived by older adults and by researchers? A cross-sectional evaluation of community-living individuals." BMJ Open 12, no. 11 (November 2022): e063135. http://dx.doi.org/10.1136/bmjopen-2022-063135.

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ObjectivesUsing commercial activity monitors may advance research with older adults. However, usability for the older population is not sufficiently established. This study aims at evaluating the usability of three wrist-worn monitors for older adults. In addition, we report on usability (including data management) for research.DesignData were collected cross-sectionally. Between-person of three activity monitor type (Apple Watch 3, Fitbit Charge 4, Polar A370) were made.SettingThe activity monitors were worn in normal daily life in an urban community in Germany. The period of wear was 2 weeks.ParticipantsUsing convenience sampling, we recruited N=27 healthy older adults (≥60 years old) who were not already habitual users of activity monitors.OutcomesTo evaluate usability from the participant perspective, we used the System Usability Scale (SUS) as well as a study-specific qualitative checklist. Assessment further comprised age, highest academic degree, computer proficiency and affinity for technology interaction. Usability from the researchers’ perspective was assessed using quantitative data management markers and a study-specific qualitative check-list.ResultsThere was no significant difference between monitors in the SUS. Female gender was associated with higher SUS usability ratings. Qualitative participant-usability reports revealed distinctive shortcomings, for example, in terms of battery life and display readability. Usability for researchers came with problems in data management, such as completeness of the data download.ConclusionThe usability of the monitors compared in this work differed qualitatively. Yet, the overall usability ratings by participants were comparable. Conversely, from the researchers’ perspective, there were crucial differences in data management and usability that should be considered when making monitor choices for future studies.

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Carlin, Thomas, and Nicolas Vuillerme. "Step and Distance Measurement From a Low-Cost Consumer-Based Hip and Wrist Activity Monitor: Protocol for a Validity and Reliability Assessment." JMIR Research Protocols 10, no. 1 (January 13, 2021): e21262. http://dx.doi.org/10.2196/21262.

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Background Self-tracking via wearable and mobile technologies is becoming an essential part of personal health management. At this point, however, little information is available to substantiate the validity and reliability of low-cost consumer-based hip and wrist activity monitors, with regard more specifically to the measurements of step counts and distance traveled while walking. Objective The aim of our study is to assess the validity and reliability of step and distance measurement from a low-cost consumer-based hip and wrist activity monitor specific in various walking conditions that are commonly encountered in daily life. Specifically, this study is designed to evaluate whether and to what extent validity and reliability could depend on the sensor placement on the human body and the walking task being performed. Methods Thirty healthy participants will be instructed to wear four PBN 2433 (Nakosite) activity monitors simultaneously, with one placed on each hip and each wrist. Participants will attend two experimental sessions separated by 1 week. During each experimental session, two separate studies will be performed. In study 1, participants will be instructed to complete a 2-minute walk test along a 30-meter indoor corridor under 3 walking speeds: very slow, slow, and usual speed. In study 2, participants will be required to complete the following 3 conditions performed at usual walking speed: walking on flat ground, upstairs, and downstairs. Activity monitor measured step count and distance values will be computed along with the actual step count (determined from video recordings) and distance (measured using a measuring tape) to determine validity and reliability for each activity monitor placement and each walking condition. Results Participant recruitment and data collection began in January 2020. As of June 2020, we enrolled 8 participants. Dissemination of study results in peer-reviewed journals is expected in spring 2021. Conclusions To the best of our knowledge, this is the first study that examines the validity and reliability of step and distance measurement during walking using the PBN 2433 (Nakosite) activity monitor. Results of this study will provide beneficial information on the effects of activity monitor placement, walking speed, and walking tasks on the validity and reliability of step and distance measurement. We believe such information is of utmost importance to general consumers, clinicians, and researchers. International Registered Report Identifier (IRRID) DERR1-10.2196/21262

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Hargens, Trent A., Kayla N. Deyarmin, Kelsey M. Snyder, Allison G. Mihalik, and Lauren E. Sharpe. "Comparison of wrist-worn and hip-worn activity monitors under free living conditions." Journal of Medical Engineering & Technology 41, no. 3 (January 12, 2017): 200–207. http://dx.doi.org/10.1080/03091902.2016.1271046.

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Smith, John D. "Accuracy Of Wrist-worn Activity Monitors At Three Walking Speeds On The Treadmill." Medicine & Science in Sports & Exercise 48 (May 2016): 783. http://dx.doi.org/10.1249/01.mss.0000487352.64658.a5.

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Lynn, Rebekah, Rebekah Pfitzer, Rebecca R. Rogers, Christopher G. Ballmann, Tyler D. Williams, and Mallory R. Marshall. "Step-Counting Validity of Wrist-Worn Activity Monitors During Activities With Fixed Upper Extremities." Journal for the Measurement of Physical Behaviour 3, no. 3 (September 1, 2020): 197–203. http://dx.doi.org/10.1123/jmpb.2019-0055.

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Little is known about validity of wrist-worn physical activity monitors during activities when an arm-swing is not present. The purpose of this study was to compare the step-counting validity of wrist-worn activity monitors (Fitbit Charge HR Series 2, ActiGraph GT9X Link, Apple Watch Series 4) during functional physical activities with fixed upper extremities. Tasks included treadmill walking at 3 mph and five free-living tasks (walking with a baby doll on the left hip and the right hip, holding groceries, and pushing a stroller while walking and while jogging). Device step counts were compared to hand-counted steps from GoPro video footage. Fitbit Charge had less error when compared to the left ActiGraph in both stroller walking and jogging, treadmill walking, and grocery walking tasks (p < .001 to .020). For grocery walking, walking with a baby on the right, and walking with a baby on the left, device percentage errors ranged from 0 (0.5%) to −7.6 (15.8%). For stroller jogging, stroller walking, and treadmill walking, device percentage errors ranged from −8.3 (7.3%) to −94.3 (17.9%). Tasks with the hands fixed to an item that also had contact with the floor (stroller and treadmill) had more error than when participants held an item that was not in contact with the floor (doll and groceries). Though wrist-worn, consumer-grade step-counting devices typically undercount steps in general, consumers should be aware that their devices may particularly undercount steps during activities with the hands fixed. This may be especially true with items in contact with the floor.

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Farrow, Tom F. D., Michael D. Hunter, Rozana Haque, and Sean A. Spence. "Modafinil and unconstrained motor activity in schizophrenia." British Journal of Psychiatry 189, no. 5 (November 2006): 461–62. http://dx.doi.org/10.1192/bjp.bp.105.017335.

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SummaryAvolition affects quality of life in chronic schizophrenia. We investigated the effect of modafinil upon unconstrained motor activity in 18 male patients. In a randomised crossover design study wrist-worn actigraphic monitors were used to objectively record motor activity over a 20 h period. Patients' total activity was significantly greater when given the drug. These data suggest that modafinil increases quantifiable motor behaviour in schizophrenia and may have an impact on avolition.

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Conger, Scott A., Alexander H. K. Montoye, Olivia Anderson, Danielle E. Boss, and Jeremy A. Steeves. "Validity of a Wrist-Worn Activity Monitor During Resistance Training Exercises at Different Movement Speeds." Journal for the Measurement of Physical Behaviour 2, no. 4 (December 1, 2019): 247–55. http://dx.doi.org/10.1123/jmpb.2019-0025.

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Speed of movement has been shown to affect the validity of physical activity (PA) monitors during locomotion. Speed of movement may also affect the validity of accelerometer-based PA monitors during other types of exercise. Purpose: To assess the ability of the Atlas Wearables Wristband2 (a PA monitor developed specifically for resistance training [RT] exercise) to identify the individual RT exercise type and count repetitions during RT exercises at various movement speeds. Methods: 50 male and female participants completed seven sets of 10 repetitions for five different upper/lower body RT exercises while wearing a Wristband2 on the left wrist. The speed of each set was completed at different metronome-paced speeds ranging from a slow speed of 4 sec·rep−1 to a fast speed of 1 sec·rep−1. Repeated Measures ANOVAs were used to compare the actual exercise type/number of repetitions among the seven different speeds. Mean absolute percent error (MAPE) and bias were calculated for repetition counting. Results: For each exercise, there tended to be significant differences between the slower speeds and the fastest speed for activity type identification and repetition counting (p < .05). Across all exercises, the highest accuracy for activity type identification (91 ± 1.8% correct overall), repetition counting (8.77 ± 0.17 of 10 reps overall) and the lowest MAPE (14 ± 1.7% overall) and bias (−1.23 ± 0.17 reps overall) occurred during the 1.5 sec·rep−1 speed (the second fastest speed tested). Conclusions: The validity of the Atlas Wearables Wristband2 to identify exercise type and count repetitions varied based on the speed of movement during RT exercises.

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Park, Susan, Lindsay P. Toth, Paul R. Hibbing, Cary M. Springer, Andrew S. Kaplan, Mckenzie D. Feyerabend, Scott E. Crouter, and David R. Bassett. "Dominant vs. Non-Dominant Wrist Placement of Activity Monitors: Impact on Steps per Day." Journal for the Measurement of Physical Behaviour 2, no. 2 (June 2019): 118–23. http://dx.doi.org/10.1123/jmpb.2018-0060.

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Deyarmin, Kayla N., Kelsey M. Snyder, Allison G. Mihalik, and Trent A. Hargens. "Accuracy of Wrist and Hip-worn Commercial Physical Activity Monitors In Free Living Conditions." Medicine & Science in Sports & Exercise 48 (May 2016): 781. http://dx.doi.org/10.1249/01.mss.0000487344.93367.44.

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Dunican, Ian C., Kevin Murray, James A. Slater, Kathleen J. Maddison, Maddison J. Jones, Brian Dawson, Leon M. Straker, John A. Caldwell, Shona L. Halson, and Peter R. Eastwood. "Laboratory and home comparison of wrist-activity monitors and polysomnography in middle-aged adults." Sleep and Biological Rhythms 16, no. 1 (October 16, 2017): 85–97. http://dx.doi.org/10.1007/s41105-017-0130-x.

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Higgins, Simon, Emerson Bennett, and Richard Blackmon. "Classification Accuracy Of Wrist-worn Physical Activity Monitors Relative To Free-living Heart Rate." Medicine & Science in Sports & Exercise 52, no. 7S (July 2020): 404. http://dx.doi.org/10.1249/01.mss.0000678228.44684.68.

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Smith, John D., Gary Guerra, and Brian G. Burkholder. "The validity and accuracy of wrist-worn activity monitors in lower-limb prosthesis users." Disability and Rehabilitation 42, no. 22 (April 12, 2019): 3182–88. http://dx.doi.org/10.1080/09638288.2019.1587792.

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Kang, Minsoo, Nan Hee Lee, Hyun Chul Jung, Soeun Jeon, and Sukho Lee. "Impact Of Placement Of Wrist-worn Activity Monitors During The Lab And Free-living Settings." Medicine & Science in Sports & Exercise 50, no. 5S (May 2018): 294. http://dx.doi.org/10.1249/01.mss.0000536052.44384.c1.

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Bai, Yang, Connie Tompkins, Nancy Gell, Dakota Dione, Tao Zhang, and Wonwoo Byun. "Comprehensive comparison of Apple Watch and Fitbit monitors in a free-living setting." PLOS ONE 16, no. 5 (May 26, 2021): e0251975. http://dx.doi.org/10.1371/journal.pone.0251975.

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Objectives The aim of this study was to evaluate the accuracy of three consumer-based activity monitors, Fitbit Charge 2, Fitbit Alta, and the Apple Watch 2, all worn on the wrist, in estimating step counts, moderate-to-vigorous minutes (MVPA), and heart rate in a free-living setting. Methods Forty-eight participants (31 females, 17 males; ages 18–59) were asked to wear the three consumer-based monitors mentioned above on the wrist, concurrently with a Yamax pedometer as the criterion for step count, an ActiGraph GT3X+ (ActiGraph) for MVPA, and a Polar H7 chest strap for heart rate. Participants wore the monitors for a 24-hour free-living condition without changing their usual active routine. MVPA was calculated in bouts of ≥10 minutes. Pearson correlation, mean absolute percent error (MAPE), and equivalence testing were used to evaluate the measurement agreement. Results The average step counts recorded for each device were as follows: 11,734 (Charge2), 11,922 (Alta), 11,550 (Apple2), and 10,906 (Yamax). The correlations in steps for the above monitors ranged from 0.84 to 0.95 and MAPE ranged from 17.1% to 35.5%. For MVPA minutes, the average were 76.3 (Charge2), 63.3 (Alta), 49.5 (Apple2), and 47.8 (ActiGraph) minutes accumulated in bouts of 10 or greater minutes. The correlation from MVPA estimation for above monitors were 0.77, 0.91, and 0.66. MAPE from MVPA estimation ranged from 44.7% to 55.4% compared to ActiGraph. For heart rate, correlation for Charge2 and Apple2 was higher for sedentary behavior and lower for MVPA. The MAPE ranged from 4% to 16%. Conclusion All three consumer monitors estimated step counts fairly accurately, and both the Charge2 and Apple2 reported reasonable heart rate estimation. However, all monitors substantially underestimated MVPA in free-living settings.

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Menickelli, Justin, Dan P. Grube, and Sarah Lowell. "Convergent Validity of a Consumer-Grade Accelerometer with a Research-Grade Pedometer in a Physical Education Setting." International Journal of Physical Education, Fitness and Sports 7, no. 2 (June 30, 2018): 20–24. http://dx.doi.org/10.26524/ijpefs1823.

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The cost of activity monitors has substantially reduced in recent years, making them more feasible for use in physical education programs. This study examined the convergent validity of the consumer-grade Movband activity monitor with the research-grade NL-2000 pedometer. The NL-2000 was chosen as the criterion unit because it is unaffected by BMI, pedometer tilt, or waist circumference, and has been recommended for use in research [1]. One hundred and eleven elementary school aged children (53 boys, 58 girls; 9.2 ± 0.7 yr.) from three physical education classes wore an NL-2000 on their right hip and a Movband on each wrist during a 30 minute class in which participants walked or ran on a hiking trail. A repeated measures ANOVA of mean steps indicated a significant difference (p< .001) between the NL-2000 (2411.74 ± 514.87) and the Movband worn on either wrist (left= 1554.33 ± 340.81, right= 1532.26 ± 329.76). Pearson product-moment correlations indicated that NL-2000 steps and Moves were significantly and positively correlated (p< .001; left= .79, right= .85). The correlation coefficient between left and right wrists was .87. In general, the Movband can provide reasonable estimates of physical activity for physical education teachers.

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Farrow, Tom F. D., Michael D. Hunter, Iain D. Wilkinson, Russell D. J. Green, and Sean A. Spence. "Structural brain correlates of unconstrained motor activity in people with schizophrenia." British Journal of Psychiatry 187, no. 5 (November 2005): 481–82. http://dx.doi.org/10.1192/bjp.187.5.481.

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SummaryAvolition affects quality of life in chronic schizophrenia. We investigated the relationship between unconstrained motor activity and the volume of key executive brain regions in 16 male patients with schizophrenia. Wrist-worn actigraphy monitors were used to record motor activity over a 20 h period. Structural magnetic resonance imaging brain scans were parcellated and individual volumes for anterior cingulate cortex and dorsolateral prefrontal cortex extracted. Patients' total activity was positively correlated with volume of left anterior cingulate cortex. These data suggest that the volume of specific executive structures may affect (quantifiable) motor behaviours, having further implications for models of the ‘will’ and avolition.

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Hicks, Hilary, Alexandra Laffer, Kayla Meyer, and Amber Watts. "Estimates of Physical Activity in Older Adults Using the ActiGraph Low-Frequency Extension Filter." Journal for the Measurement of Physical Behaviour 4, no. 2 (June 1, 2021): 118–25. http://dx.doi.org/10.1123/jmpb.2020-0034.

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As a default setting, many body-worn research-grade activity monitors rely on software algorithms developed for young adults using waist-worn devices. ActiGraph offers the low-frequency extension (LFE) filter, which reduces the movement threshold to capture low acceleration activity, which is more common in older adults. It is unclear how this filter changes activity estimates and whether it is appropriate for all older adults. The authors compared activity estimates with and without the LFE filter on wrist-worn devices in a sample of 34 older adults who wore the ActiGraph GT9X on their nondominant wrist for 7 days in a free-living environment. The authors used participant characteristics to predict discrepancy in step count estimates generated with and without the LFE filter to determine which individuals are most accurately characterized. Estimates of steps per minute were higher (M = 21, SD = 1), and more activity was classified as moderate to vigorous intensity (M = 5.03%, SD = 3.92%) with the LFE filter (M = 11, SD = 1; M = 4.27%, SD = 3.52%) versus without the LFE filter (all ps < .001). The findings suggest that axes-based variables should be interpreted with caution when generated with wrist-worn data, and future studies should develop separate wrist and waist-worn standard estimates in older adults. Participation in a greater amount of moderate to vigorous intensity physical activity predicted a larger discrepancy in step counts generated with and without the filter (p < .009), suggesting that the LFE filter becomes increasingly inappropriate for use in highly active older individuals.

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Hicks, Hilary J., Alex Laffer, Genna Losinski, and Amber Watts. "ACTIGRAPH’S LOW-FREQUENCY EXTENSION FILTER FOR ESTIMATING WRIST-WORN PHYSICAL ACTIVITY IN OLDER ADULTS." Innovation in Aging 3, Supplement_1 (November 2019): S520—S521. http://dx.doi.org/10.1093/geroni/igz038.1918.

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Abstract Advancements in body-worn activity devices make them valuable for objective physical activity measurement. Research-grade monitors utilize software algorithms developed with younger populations using waist-worn devices. ActiGraph offers the low frequency extension (LFE) filter which reduces the movement threshold to capture low acceleration activity that is more common in older adults. It is unclear how this filter changes activity variable calculations in older adults. We investigated the effects of the LFE filter on wrist-worn activity estimates in this population. Participants were 21 older adults who wore the GT9X on their non-dominant wrist for 7 days in a free-living environment. Activity counts were estimated both with and without the LFE filter. Paired samples t-tests revealed that the LFE estimated significantly higher number of counts than non-LFE calculated counts per minute on all three axes (p < .001). Step count estimates were higher with (M = 20,780.09, SD = 5300.85) vs. without (M = 10,896.54, SD = 3489.45) the LFE filter, (t (20) = -22.21, p < .001). These differences have implications for calculations based on axis counts (e.g., Axis-1 calculated steps, intensity level classifications) that rely on waist-worn standards. For example, even without the filter, the GT9X calculated an average of 10,897 steps, which is likely an overestimate in this population. This suggests that axes-based variables should be interpreted with caution when generated with wrist-worn data, and future studies should aim to develop separate wrist and waist-worn standard estimates of these variables in older adult populations.

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Floegel, Theresa A., Alberto Florez-Pregonero, Eric B. Hekler, and Matthew P. Buman. "Validation of Consumer-Based Hip and Wrist Activity Monitors in Older Adults With Varied Ambulatory Abilities." Journals of Gerontology Series A: Biological Sciences and Medical Sciences 72, no. 2 (June 2, 2016): 229–36. http://dx.doi.org/10.1093/gerona/glw098.

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LaMunion, Samuel R., Andrew L. Blythe, Paul R. Hibbing, Andrew S. Kaplan, Brandon J. Clendenin, and Scott E. Crouter. "Use of consumer monitors for estimating energy expenditure in youth." Applied Physiology, Nutrition, and Metabolism 45, no. 2 (February 2020): 161–68. http://dx.doi.org/10.1139/apnm-2019-0129.

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The purpose of this study was to compare energy expenditure (EE) estimates from 5 consumer physical activity monitors (PAMs) to indirect calorimetry in a sample of youth. Eighty-nine youth (mean (SD); age, 12.3 (3.4) years; 50% female) performed 16 semi-structured activities. Activities were performed in duplicate across 2 visits. Participants wore a Cosmed K4b2(criterion for EE), an Apple Watch 2 (left wrist), Mymo Tracker (right hip), and Misfit Shine 2 devices (right hip; right shoe). Participants were randomized to wear a Samsung Gear Fit 2 or a Fitbit Charge 2 on the right wrist. Oxygen consumption was converted to EE by subtracting estimated basal EE (Schofield’s equation) from the measured gross EE. EE from each visit was summed across the 2 visit days for comparison with the total EE recorded from the PAMs. All consumer PAMs estimated gross EE, except for the Apple Watch 2 (net Active EE). Paired t tests were used to assess differences between estimated (PAM) and measured (K4b2) EE. Mean absolute percent error (MAPE) was used to assess individual-level error. The Mymo Tracker was not significantly different from measured EE and was within 15.9 kcal of measured kilocalories (p = 0.764). Mean percent errors ranged from 3.5% (Mymo Tracker) to 48.2% (Apple Watch 2). MAPE ranged from 16.8% (Misfit Shine 2 – right hip) to 49.9% (Mymo Tracker).Novelty Only the Mymo Tracker was not significantly different from measured EE but had the greatest individual error. The Misfit Shine 2 – right hip had the lowest individual error. Caution is warranted when using consumer PAMs in youth for tracking EE.

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Grant, Katharyn A., Traci L. Galinsky, and Peter W. Johnson. "Use of the Actigraph for Objective Quantification of Hand/Wrist Activity in Repetitive Work." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 37, no. 10 (October 1993): 720–24. http://dx.doi.org/10.1177/154193129303701016.

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Valid and reliable measures of hand/wrist activity are needed to address the relationship between work tasks and the development of upper extremity musculoskeletal disorders. The utility of the actigraph for measuring wrist activity in manual work was examined in this study. Ten grocery cashiers and four non-cashier retail workers wore actigraph monitors on both wrists and the left ankle during their normal work activities. Work activities were periodically observed and recorded on videotape. Data recorded by the actigraphs were matched against observational data. The results indicated that actigraphy was effective in detecting significant work-related variations in physical activity in the three limbs studied. Compared to traditional observational procedures, actigraphy represents a cost-effective approach for obtaining objective and quantitative information about the intensity and duration of work over long time periods. Traditional observational procedures, however, are necessary to provide additional information needed for a complete job analysis (e.g., postural data). Continuous activity recordings can be used in conjunction with sampling protocols to examine the relationship between work-related physical activities and musculoskeletal trauma.

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Duncan, Michael J., Alexandra Dobell, Mark Noon, Cain C. T. Clark, Clare M. P. Roscoe, Mark A. Faghy, David Stodden, Ryan Sacko, and Emma L. J. Eyre. "Calibration and Cross-Validation of Accelerometery for Estimating Movement Skills in Children Aged 8–12 Years." Sensors 20, no. 10 (May 13, 2020): 2776. http://dx.doi.org/10.3390/s20102776.

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(1) Background: This study sought to calibrate triaxial accelerometery, worn on both wrists, waist and both ankles, during children’s physical activity (PA), with particular attention to object control motor skills performed at a fast and slow cadence, and to cross-validate the accelerometer cut-points derived from the calibration using an independent dataset. (2) Methods: Twenty boys (10.1 ±1.5 years) undertook seven, five-minute bouts of activity lying supine, standing, running (4.5kmph−1) instep passing a football (fast and slow cadence), dribbling a football (fast and slow cadence), whilst wearing five GENEActiv accelerometers on their non-dominant and dominant wrists and ankles and waist. VO2 was assessed concurrently using indirect calorimetry. ROC curve analysis was used to generate cut-points representing sedentary, light and moderate PA. The cut-points were then cross-validated using independent data from 30 children (9.4 ± 1.4 years), who had undertaken similar activities whilst wearing accelerometers and being assessed for VO2. (3) Results: GENEActiv monitors were able to discriminate sedentary activity to an excellent level irrespective of wear location. For moderate PA, discrimination of activity was considered good for monitors placed on the dominant wrist, waist, non-dominant and dominant ankles but fair for the non-dominant wrist. Applying the cut-points to the cross-validation sample indicated that cut-points validated in the calibration were able to successfully discriminate sedentary behaviour and moderate PA to an excellent standard and light PA to a fair standard. (4) Conclusions: Cut-points derived from this calibration demonstrate an excellent ability to discriminate children’s sedentary behaviour and moderate intensity PA comprising motor skill activity.

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Evans, E. Whitney, Ana M. Abrantes, Eva Chen, and Elissa Jelalian. "Using Novel Technology within a School-Based Setting to Increase Physical Activity: A Pilot Study in School-Age Children from a Low-Income, Urban Community." BioMed Research International 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/4271483.

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Background. Less than half of American children meet national physical activity (PA) recommendations. This study tested the feasibility, acceptability, and preliminary effectiveness of using wearable PA monitors to increase PA in school-age children. Methods. In Phase 1 of this study, conducted in 2014, 32 fifth-grade students enrolled in a low-resource middle school were given a waist-worn Fitbit Zip monitor for 4 weeks to test its feasibility (adherence) and acceptability. Adherence, wear time of ≥8 hours per day, was examined. Feedback was solicited from parents through structured interviews. In Phase 2, conducted in 2015, 42 sixth-grade students were assigned, by classroom, to one of three conditions (Fitbit + goal and incentive-based intervention, Fitbit only, or control) to test the feasibility of the wrist-worn Fitbit Charge and its preliminary effectiveness in increasing PA over 6 weeks. Results. In Phase 1, average adherence was 64.1%. In Phase 2, it was 73.4% and 80.2% for participants in the Fitbit + intervention and Fitbit only groups, respectively (p=.07). After controlling for baseline values, weight status, and sex, there were no significant group differences in changes in MVPA or steps from baseline to follow-up. Conclusions. While moderately acceptable, wearable PA monitors did not increase PA levels in this sample. They may be more effective within a coordinated school-based physical activity program.

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Mesquita, Maria Eugênia, Maria Eliza Finazzi, Bruno Gonçalves, Lee Fu-I, Leandro L. Duarte, José Ricardo Lopes, José Alberto Del-Porto, and Luiz Menna-Barreto. "Activity/rest rhythm of depressed adolescents undergoing therapy: case studies." Trends in Psychiatry and Psychotherapy 38, no. 4 (December 2016): 216–20. http://dx.doi.org/10.1590/2237-6089-2015-0053.

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Abstract Introduction: Disorders of circadian rhythms have been reported in studies of both depressed children and of depressed adolescents. The aim of this study was to evaluate whether there is a relationship between the 24-hour spectral power (24h SP) of the activity/rest rhythm and the clinical course of depression in adolescents. Methods: Six 14 to 17-year-old adolescents were recruited for the study. They were all suffering from major depressive disorder, according to the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) criteria, as identified by the Schedule for Affective Disorders and Schizophrenia for School Aged Children: Present and Lifetime Version (K-SADS-PL). Depressive symptoms were assessed using the Children's Depression Rating Scale - Revised (CDRS-R) and clinical evaluations. Locomotor activity was monitored over a period of 13 consecutive weeks. Activity was measured for 10-minute periods using wrist-worn activity monitors. All patients were prescribed sertraline from after the first week up until the end of the study. Results: We found a relationship between high CDRS values and low 24-hour spectral power. Conclusions: The 24h SP of the activity/rest rhythm correlated significantly (negatively) with the clinical ratings of depression.

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Reddy, Ravi Kondama, Rubin Pooni, Dessi P. Zaharieva, Brian Senf, Joseph El Youssef, Eyal Dassau, Francis J. Doyle III, et al. "Accuracy of Wrist-Worn Activity Monitors During Common Daily Physical Activities and Types of Structured Exercise: Evaluation Study." JMIR mHealth and uHealth 6, no. 12 (December 10, 2018): e10338. http://dx.doi.org/10.2196/10338.

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Ekblom, Orjan, Gisela Nyberg, Elin Ekblom Bak, Ulf Ekelund, and Claude Marcus. "Validity and Comparability of a Wrist-Worn Accelerometer in Children." Journal of Physical Activity and Health 9, no. 3 (March 2012): 389–93. http://dx.doi.org/10.1123/jpah.9.3.389.

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Background:Wrist-worn accelerometers may provide an alternative to hip-worn monitors for assessing physical activity as they are easier to wear and may thus facilitate long-term recordings. The current study aimed at a) assessing the validity of the Actiwatch (wrist-worn) for estimating energy expenditure, b) determining cut-off values for light, moderate, and vigorous activities, c) studying the comparability between the Actiwatch and the Actigraph (hip-worn), and d) assessing reliability.Methods:For validity, indirect calorimetry was used as criterion measure. ROC-analyses were applied to identify cut-off values. Comparability was tested by simultaneously wearing of the 2 accelerometers during free-living condition. Reliability was tested in a mechanical shaker.Results:All-over correlation between accelerometer output and energy expenditure were found to be 0.80 (P < .001).Based on ROC-analysis, cut-off values for 1.5, 3, and 6 METs were found to be 80, 262, and 406 counts per 15 s, respectively. Energy expenditure estimates differed between the Actiwatch and the Actigraph (P < .05). The intra- and interinstrument coefficient of variation of the Actiwatch ranged between 0.72% and 8.4%.Conclusion:The wrist-worn Actiwatch appears to be valid and reliable for estimating energy expenditure and physical activity intensity in children aged 8 to 10 years.

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Dadhania, Seem, James Wei Wang, Boyu Yu, Waqar Saleem, Catherine Blake, Lillie Shahabi, and Matthew Williams. "Early effects of surgery and radiotherapy on activity levels in patients with brain tumours: preliminary data from the BrainWear trial." Neuro-Oncology 21, Supplement_4 (October 2019): iv12. http://dx.doi.org/10.1093/neuonc/noz167.048.

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Abstract Background BrainWear is a phase II observational clinical trial which collects data on patient activity levels, fatigue, Quality of Life (QoL) and imaging in patients with brain tumours Methods Newly diagnosed patients were offered wrist worn accelerometers (Axivity AX3) to be worn continuously throughout their treatment (surgery, chemoradiotherapy or radiotherapy) to monitor physical activity. We collected standardised measures of QoL, fatigue, MRI imaging data and disease progression. Here, we report early results on activity data 5 days before and after treatment. Results Of 23 patients recruited, we report complete pre and post treatment data in 4 patients (2 HGG, 2 metastatic) who underwent craniotomy (2), fractionated radiotherapy (1) and SRS (1). Both craniotomy patients experienced an immediate 60 – 70% reduction in activity, and were successfully discharged at day 2 post-op even though their activity was still significantly reduced. Both patients recovered another 10% in their activity levels following discharge. Radiotherapy patients experienced no early change within 5 days of starting treatment. Conclusion As expected craniotomy results in much larger changes in activity levels than SRS and radical radiotherapy. Activity levels recover post craniotomy, but this takes > 5 days. Using wearable activity monitors in brain tumour patients is feasible, although there are multiple practical problems. Interpreting such data will require consideration of inpatient vs. outpatient settings.

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Journal articles on the topic 'Wrist-activity monitors'

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