scholarly journals Comparison of four Fitbit and Jawbone activity monitors with a research-grade ActiGraph accelerometer for estimating physical activity and energy expenditure

2017 ◽  
Vol 52 (13) ◽  
pp. 844-850 ◽  
Author(s):  
Mary T Imboden ◽  
Michael B Nelson ◽  
Leonard A Kaminsky ◽  
Alexander HK Montoye
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Repeated Measures ◽  
Prediction Equation ◽  
Activity Monitors ◽  
Actigraph Accelerometer ◽  
Repeated Measures Analysis ◽  
Research Grade ◽  
Similar Accuracy ◽  
Data Criterion

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.

Objectively Assessing Treadmill Walking During the Second and Third Pregnancy Trimesters

2012 ◽  
Vol 9 (1) ◽  
pp. 21-28 ◽  
Author(s):  
Jennifer M. DiNallo ◽  
Danielle Symons Downs ◽  
Guy Le Masurier
Keyword(s):  
Physical Activity ◽  
Pregnant Women ◽  
Outcome Measures ◽  
Repeated Measures ◽  
Activity Monitor ◽  
Treadmill Walking ◽  
Walking Test ◽  
Activity Monitors ◽  
Actigraph Accelerometer ◽  
Physical Changes

Background:To effectively promote physical activity (PA) and quantify the effects of PA interventions for pregnant women, PA measurement during pregnancy needs improvement. The purpose of this study was to assess PA monitor output during a controlled, treadmill walking protocol among pregnant women at 20- and 32-weeks gestation.Methods:Women (N = 43) wore an Actigraph accelerometer, NL1000, and Yamax pedometer during a 20-minute treadmill walking test [5-minute periods at 4 different speeds (54, 67, 80, and 94 m·min−1)] at 20- and 32-weeks gestation.Results:Repeated-measures ANOVAs indicated that Actigraph total counts/minute and minutes of moderate-vigorous PA (MVPA), NL1000 steps and minutes MVPA, and Yamax steps decreased from 20- to 32-weeks gestation (P ≤ .05), while body girth circumference and activity monitor tilt increased (P ≤ .05). Repeated measures ANCOVAs, controlling for changes in body girth and monitor tilt, yielded no significant differences in any outcome measures from 20- to 32-weeks gestation.Conclusions:Preliminary results suggest physical changes during pregnancy impact activity monitor output in controlled settings. Accurately measuring and statistically controlling for changes in body girth at monitor placement site and monitor tilt may improve the accuracy of activity monitors for use with pregnant populations.

Validity of Physical Activity Monitors for Estimating Energy Expenditure During Wheelchair Propulsion

2015 ◽  
Vol 12 (11) ◽  
pp. 1520-1526 ◽  
Author(s):  
Scott A. Conger ◽  
Stacy N. Scott ◽  
Eugene C. Fitzhugh ◽  
Dixie L. Thompson ◽  
David R. Bassett
Keyword(s):  
Energy Expenditure ◽  
Repeated Measures ◽  
Prediction Equation ◽  
Level Surface ◽  
Wheelchair Propulsion ◽  
Activity Monitors ◽  
Repeated Measures Anova ◽  
Main Effect ◽  
Physical Activity Monitors ◽  
Criterion Method

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.

Location, Location, Location: Accelerometer Placement Affects Steps-Based Physical Activity Outcomes During Pregnancy and Postpartum

2021 ◽  
pp. 155982762110304
Author(s):  
Mallory R. Marshall ◽  
Alexander H. K. Montoye ◽  
Michelle R. Conway ◽  
Rebecca A. Schlaff ◽  
Karin A. Pfeiffer ◽  
...  
Keyword(s):  
Physical Activity ◽  
Repeated Measures ◽  
Time Points ◽  
Repeated Measures Analysis ◽  
Pregnancy And Postpartum ◽  
Vigorous Intensity ◽  
The Right ◽  
Physical Changes ◽  
Intensity Classification ◽  
Measurement Validity

As pregnancy progresses, physical changes may affect physical activity (PA) measurement validity. n = 11 pregnant women (30.1 ± 3.8 years) wore ActiGraph GT3X+ accelerometers on the right hip, right ankle, and non-dominant wrist for 3–7 days during the second and third trimesters (21 and 32 weeks, respectively) and 12 weeks postpartum. Data were downloaded into 60-second epochs from which stepping cadence was calculated; repeated-measures analysis of variance was used to determine significant differences among placements. At all time points, the wrist accelerometer measured significantly more daily steps (9930–10 452 steps/d) and faster average stepping cadence (14.5–14.6 steps/min) than either the hip (4972–5944 steps/d, 7.1–8.6 steps/min) or ankle (7161–8205 steps/d, 10.3–11.9 steps/min) placement, while moderate- to vigorous-intensity activity at the wrist (1.2–1.7 min/d) was significantly less than either hip (3.0–5.9 min/d) or ankle (6.1–7.3 min/d). Steps, cadence, and counts were significantly lower for the hip than the ankle at all time points. Kappa calculated for agreement in intensity classification between the various pairwise comparisons ranged from .06 to .41, with Kappa for hip–ankle agreement (.34–.41) significantly higher than for wrist–ankle (.09–.11) or wrist–hip (.06–.16). These data indicate that wrist accelerometer placement during pregnancy likely results in over counting of PA parameters and should be used with caution.

Physical Activity Patterns of Free Living Dogs Diagnosed with Osteoarthritis

2021 ◽  
Author(s):  
Anne H Lee ◽  
Katelyn B Detweiler ◽  
Tisha A Harper ◽  
Kim E Knap ◽  
Maria R C de Godoy ◽  
...  
Keyword(s):  
Physical Activity ◽  
Pain Intensity ◽  
Repeated Measures ◽  
Pearson Correlation ◽  
Clinical Signs ◽  
Total Activity ◽  
Activity Count ◽  
Free Living ◽  
Activity Monitors ◽  
Average Activity

Abstract Osteoarthritis (OA) affects about 90% of dogs &gt; 5 yr of age in the US, resulting in reduced range of motion, difficulty climbing and jumping, reduced physical activity, and lower quality of life. Our objective was to use activity monitors to measure physical activity and identify how activity counts correlate with age, body weight (BW), body condition score (BCS), serum inflammatory markers, veterinarian pain assessment, and owner perception of pain in free-living dogs with OA. The University of Illinois Institutional Animal Care and Use Committee approved the study and owner consent was received prior to experimentation. Fifty-six client-owned dogs (mean age = 7.8 yr; mean BCS = 6.1) with clinical signs and veterinary diagnosis of OA wore HeyRex activity collars continuously over a 49-d period. Blood samples were collected on d 0 and 49, and dog owners completed canine brief pain inventory (CBPI) and Liverpool osteoarthritis in dogs (LOAD) surveys on d 0, 21, 35, and 49. All data were analyzed using SAS 9.3 using repeated measures and R Studio 1.0.136 was used to generate Pearson correlation coefficients between data outcomes. Average activity throughout the study demonstrated greater activity levels on weekends. It also showed that 24-h activity spiked twice daily, once in the morning and another in the afternoon. Serum C-reactive protein concentration was lower (P &lt; 0.01) at d 49 compared to d 0. Survey data indicated lower (P &lt; 0.05) overall pain intensity and severity score on d 21, 35 and 49 compared to d 0. BW was correlated with average activity counts (p=0.02; r=-0.12) and run activity (p=0.10; r=-0.24). Weekend average activity counts were correlated with owner pain intensity scores (p=0.0813; r=-0.2311), but weekday average activity count was not. Age was not correlated with total activity count, sleep activity, or run activity, but it was correlated with scratch (p=0.03; r=-0.10), alert (p=0.03; r=-0.13) and walk (p=0.09; r=-0.23) activities. Total activity counts and activity type (sleep, scratch, alert, walk, run) were not correlated with pain scored by veterinarians, pain intensity or severity scored by owners, or baseline BCS. Even though the lack of controls and/or information on the individual living conditions of dogs resulted in a high level of variability in this study, our data suggest that the use of activity monitors have the potential to aid in the management of OA and other conditions affecting activity (e.g., allergy; anxiety).

scholarly journals How consumer physical activity monitors could transform human physiology research

2017 ◽  
Vol 312 (3) ◽  
pp. R358-R367 ◽  
Author(s):  
Stephen P. Wright ◽  
Tyish S. Hall Brown ◽  
Scott R. Collier ◽  
Kathryn Sandberg
Keyword(s):  
Physical Activity ◽  
Human Physiology ◽  
Health Outcomes ◽  
Real Life ◽  
Smartphone Apps ◽  
Activity Monitors ◽  
Adverse Health Outcomes ◽  
Change Behavior ◽  
Physical Activity Monitors ◽  
Research Grade

A sedentary lifestyle and lack of physical activity are well-established risk factors for chronic disease and adverse health outcomes. Thus, there is enormous interest in measuring physical activity in biomedical research. Many consumer physical activity monitors, including Basis Health Tracker, BodyMedia Fit, DirectLife, Fitbit Flex, Fitbit One, Fitbit Zip, Garmin Vivofit, Jawbone UP, MisFit Shine, Nike FuelBand, Polar Loop, Withings Pulse O2, and others have accuracies similar to that of research-grade physical activity monitors for measuring steps. This review focuses on the unprecedented opportunities that consumer physical activity monitors offer for human physiology and pathophysiology research because of their ability to measure activity continuously under real-life conditions and because they are already widely used by consumers. We examine current and potential uses of consumer physical activity monitors as a measuring or monitoring device, or as an intervention in strategies to change behavior and predict health outcomes. The accuracy, reliability, reproducibility, and validity of consumer physical activity monitors are reviewed, as are limitations and challenges associated with using these devices in research. Other topics covered include how smartphone apps and platforms, such as the Apple ResearchKit, can be used in conjunction with consumer physical activity monitors for research. Lastly, the future of consumer physical activity monitors and related technology is considered: pattern recognition, integration of sleep monitors, and other biosensors in combination with new forms of information processing.

Comparison of Lifecorder and MTI ActiGraph Accelerometer Estimates of Measuring Physical Activity Energy Expenditure

2006 ◽  
Vol 38 (Supplement) ◽  
pp. S103
Author(s):  
Tracy L. Washington ◽  
Cora L. Craig ◽  
James J. McClain ◽  
Susan S. Sisson ◽  
Catrine Tudor-Locke
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Physical Activity Energy Expenditure ◽  
Actigraph Accelerometer ◽  
Activity Energy

scholarly journals In-school and Out-of-school Physical Activity in Preschool Children

2016 ◽  
Vol 13 (6) ◽  
pp. 606-610 ◽  
Author(s):  
Jennifer R. O’Neill ◽  
Karin A. Pfeiffer ◽  
Marsha Dowda ◽  
Russell R. Pate
Keyword(s):  
Physical Activity ◽  
Preschool Children ◽  
Repeated Measures ◽  
Vigorous Physical Activity ◽  
Pearson Correlation ◽  
Full Sample ◽  
Repeated Measures Analysis ◽  
The Relationship ◽  
Children’S Physical Activity ◽  
Out Of School

Background:Little is known about the relationship between children’s physical activity (PA) in preschool (in-school) and outside of preschool (out-of-school). This study described this relationship.Methods:Participants were 341 children (4.6 ± 0.3 years) in 16 preschools. Accelerometers measured moderate-to-vigorous physical activity (MVPA) and total physical activity (TPA) in-school and out-of-school. In the full sample, Pearson correlation was used to describe associations between in-school and out-of-school PA. In addition, children were categorized as meeting or not meeting a PA guideline during school. MVPA and TPA were compared between the 2 groups and in-school and out-of-school using 2-way repeated-measures analysis of variance.Results:In the full sample, in-school and out-of-school PA were positively correlated for MVPA (r = .13, P = .02) and TPA (r = .15, P = .01). Children who met the guideline in-school remained comparably active out-of-school. However, those who did not meet the guideline were more active out-of-school than in-school. The groups were active at comparable levels while out-of-school. Identical patterns were seen for MVPA and TPA.Conclusions:Children’s in-school PA was positively associated with out-of-school PA. Children who did not meet the guideline in-school were more active out-of-school than in-school, suggesting preschool and classroom factors may reduce some children’s PA in-school.

scholarly journals Pengaruh latihan mandiri dalam rangka pembatasan kegiatan masyarakat: Study kasus atlet pencak silat Kabupaten Karawang

2021 ◽  
Vol 9 (2) ◽  
pp. 290-298
Author(s):  
Rian Tri Prayogo ◽  
Sendy Mohamad Anugrah ◽  
Ardhika Falaahudin ◽  
Dody Tri Iwandana ◽  
Rifqi Festiawan
Keyword(s):  
Physical Activity ◽  
Uric Acid ◽  
Repeated Measures ◽  
Repeated Measure ◽  
Sports Activity ◽  
Repeated Measures Analysis ◽  
Significant Difference ◽  
Quasi Experimental ◽  
Measure Analysis ◽  
Repeated Measure Analysis

Pembatasan kegiatan masyarakat akibat penyebaran virus covid-19 telah mengubah olahraga di Indonesia sehingga memaksa para atlet untuk melakukan latihan mandiri di tempat tinggalnya masing-masing dengan program latihan yang dibuat oleh para pelatihnya. Tujuan penelitian ini adalah membandingkan kapasitas aerobik, aspek kelincahan, dan daya tahan otot lokal atlet pencak silat sebelum dan sesudah masa latihan mandiri. Metode penelitian yang digunakan adalah deskritptif dengan melibatkan 12 atlet pencak silat putra kategori tanding Kabupaten Karawang. Hasil penelitian menunjukan bahwa terjadi penurunan signifikan pada kapasitas aerobik (p= 0.025) dan performa kelincahan (0.042) namun pada daya tahan otot lokal tidak terdapat perbedaan. Kesimpulan penelitian ini adalah terjadi penurunan pada kapasitas aerobik dan aspek kelincahan pada atlet pencak silat Kabupaten Karawang setelah masa latihan di tempat tinggalnya masing-masing akibat dari pemberlakukan pembatasan kegiatan masyarakat (PPKM), namun pada komponen daya tahan otot-otot lokal tidak terdapat perubahan.The sports activity of badminton and responses to changes in blood uric acid at productive age AbstractThis study aims to determine whether there was a response to changes in uric acid levels due to the physical activity of badminton. The design of this study is a quasi-experimental. The sample used in this study was eight respondents with certain criteria. Treat physical activity twice on different days with 4 measurements of uric acid levels. The method of this study is repeated measure analysis. When subjects are measured repeatedly, requiring fewer subjects per experiment, then repeated measures analysis can be used. The results showed that the treatment of badminton had a significant effect on changes in uric acid levels with a probability value of 0,038. These results were obtained by using the Greenhouse-Geisser test where the assumptions of normality and homogeneity were satisfied. From the marginal test results using pairwise comparisons, there was a significant difference in the average uric acid levels at 15 minutes after exercise and 9 hours the following day, where there was a decrease of 1.169 mg/dl. Badminton can reduce uric acid levels, which is indicated by a decrease of 0.15 mg/dl at 09.00 the next day compared to before exercise. Marginally, this decrease is not statistically significant, but regular badminton can be an option for physical activity for those who want to reduce uric acid levels.

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