scholarly journals Measuring Free-Living Physical Activity With Three Commercially Available Activity Monitors for Telemonitoring Purposes: Validation Study

10.2196/11489 ◽  
2019 ◽  
Vol 3 (2) ◽  
pp. e11489 ◽  
Author(s):  
Martine JM Breteler ◽  
Joris H Janssen ◽  
Wilko Spiering ◽  
Cor J Kalkman ◽  
Wouter W van Solinge ◽  
...  
Keyword(s):  
Physical Activity ◽  
Validation Study ◽  
Free Living ◽  
Activity Monitors

Analyzing Free-Living Physical Activity of Older Adults in Different Environments Using Body-Worn Activity Monitors

2010 ◽  
Vol 18 (2) ◽  
pp. 171-184 ◽  
Author(s):  
P. Margaret Grant ◽  
Malcolm H. Granat ◽  
Morag K. Thow ◽  
William M. Maclaren
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Sedentary Time ◽  
Likelihood Estimation ◽  
Day Hospital ◽  
Free Living ◽  
Activity Monitors ◽  
The Third ◽  
Patient Groups ◽  
Restricted Maximum Likelihood Estimation

This study measured objectively the postural physical activity of 4 groups of older adults (≥65 yr). The participants (N= 70) comprised 3 patient groups—2 from rehabilitation wards (cityn= 20, 81.8 ± 6.7 yr; ruraln= 10, 79.4 ± 4.7 yr) and the third from a city day hospital (n= 20, 74.7 ± 7.9 yr)—and a healthy group to provide context (n= 20, 73.7 ± 5.5 yr). The participants wore an activity monitor (activPAL) for a week. A restricted maximum-likelihood-estimation analysis of hourly upright time (standing and walking) revealed significant differences between day, hour, and location and the interaction between location and hour (p< .001). Differences in the manner in which groups accumulated upright and sedentary time (sitting and lying) were found, with the ward-based groups sedentary for prolonged periods and upright for short episodes. This information may be used by clinicians to design appropriate rehabilitation interventions and monitor patient progress.

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).

Physical Activity Monitor Accuracy for Overground Walking and Free-Living Conditions Among Pregnant Women

2020 ◽  
Vol 3 (2) ◽  
pp. 100-109
Author(s):  
Christopher P. Connolly ◽  
Jordana Dahmen ◽  
Robert D. Catena ◽  
Nigel Campbell ◽  
Alexander H.K. Montoye
Keyword(s):  
Physical Activity ◽  
Pregnant Women ◽  
Living Conditions ◽  
Overground Walking ◽  
Third Trimester ◽  
Free Living ◽  
Activity Monitors ◽  
Step Counting ◽  
Physical Activity Monitors ◽  
Free Living Conditions

Purpose: We aimed to determine the step-count validity of commonly used physical activity monitors for pregnancy overground walking and during free-living conditions. Methods: Participants (n = 39, 12–38 weeks gestational age) completed six 100-step overground walking trials (three self-selected “normal pace”, three “brisk pace”) while wearing five physical activity monitors: Omron HJ-720 (OM), New Lifestyles 2000 (NL), Fitbit Flex (FF), ActiGraph Link (AG), and Modus StepWatch (SW). For each walking trial, monitor-recorded steps and criterion-measured steps were assessed. Participants also wore all activity monitors for an extended free-living period (72 hours), with the SW used as the criterion device. Mean absolute percent error (MAPE) was calculated for overground walking and free-living protocols and compared across monitors. Results: For overground walking, the OM, NL, and SW performed well (<5% MAPE) for normal and brisk pace walking trials, and also when trials were analyzed by actual speeds. The AG and FF had significantly greater MAPE for overground walking trials (11.9–14.7%). Trimester did affect device accuracy to some degree for the AG, FF, and SW, with error being lower in the third trimester compared to the second. For the free-living period, the OM, NL, AG, and FF significantly underestimated (>32% MAPE) actual steps taken per day as measured by the criterion SW (M [SD] = 9,350 [3,910]). MAPE for the OM was particularly high (45.3%). Conclusion: The OM, NL, and SW monitors are valid measures for overground step-counting during pregnancy walking. However, the OM and NL significantly underestimate steps by second and third trimester pregnant women in free-living conditions.

Comparison of Six Activity Monitors during a 440-m Walk and over 24-hours of Free-Living Physical Activity

2006 ◽  
Vol 38 (Supplement) ◽  
pp. S554
Author(s):  
Charles D. Kemble ◽  
Terrance S. Robinson ◽  
Ronald L. Starnes ◽  
Dalma H. Barber ◽  
Sheila K. Murphy ◽  
...  
Keyword(s):  
Physical Activity ◽  
Free Living ◽  
Activity Monitors

Comparison of Free-living Physical Activity Measured by Us and Japanese Activity Monitors

2015 ◽  
Vol 47 ◽  
pp. 269-270
Author(s):  
Hiroyuki Sasai ◽  
Robert J. Brychta ◽  
Nancy W. Glynn ◽  
Brittney Lange-Maia ◽  
Annemarie Koster ◽  
...  
Keyword(s):  
Physical Activity ◽  
Free Living ◽  
Activity Monitors

scholarly journals Validity of hip-mounted uniaxial accelerometry with heart-rate monitoring vs. triaxial accelerometry in the assessment of free-living energy expenditure in young children: the IDEFICS Validation Study

2012 ◽  
Vol 113 (10) ◽  
pp. 1530-1536 ◽  
Author(s):  
Robert Ojiambo ◽  
Kenn Konstabel ◽  
Toomas Veidebaum ◽  
John Reilly ◽  
Vera Verbestel ◽  
...  
Keyword(s):  
Physical Activity ◽  
Heart Rate ◽  
Energy Expenditure ◽  
Young Children ◽  
Validation Study ◽  
Longitudinal Axis ◽  
Activity Level ◽  
Free Living ◽  
Model Combining ◽  
Sedentary Activities

One of the aims of Identification and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants (IDEFICS) validation study is to validate field measures of physical activity (PA) and energy expenditure (EE) in young children. This study compared the validity of uniaxial accelerometry with heart-rate (HR) monitoring vs. triaxial accelerometry against doubly labeled water (DLW) criterion method for assessment of free-living EE in young children. Forty-nine European children (25 female, 24 male) aged 4–10 yr (mean age: 6.9 ± 1.5 yr) were assessed by uniaxial ActiTrainer with HR, uniaxial 3DNX, and triaxial 3DNX accelerometry. Total energy expenditure (TEE) was estimated using DLW over a 1-wk period. The longitudinal axis of both devices and triaxial 3DNX counts per minute (CPM) were significantly ( P < 0.05) associated with physical activity level (PAL; r = 0.51 ActiTrainer, r = 0.49 uniaxial-3DNX, and r = 0.42 triaxial Σ3DNX). Eight-six percent of the variance in TEE could be predicted by a model combining body mass (partial r2 = 71%; P < 0.05), CPM-ActiTrainer (partial r2 = 11%; P < 0.05), and difference between HR at moderate and sedentary activities (ModHR − SedHR) (partial r2 = 4%; P < 0.05). The SE of TEE estimate for ActiTrainer and 3DNX models ranged from 0.44 to 0.74 MJ/days or ∼7–11% of the average TEE. The SE of activity-induced energy expenditure (AEE) model estimates ranged from 0.38 to 0.57 MJ/day or 24–26% of the average AEE. It is concluded that the comparative validity of hip-mounted uniaxial and triaxial accelerometers for assessing PA and EE is similar.

scholarly journals Measuring Free-Living Physical Activity With Three Commercially Available Activity Monitors for Telemonitoring Purposes: Validation Study (Preprint)

2018 ◽  
Author(s):  
Martine JM Breteler ◽  
Joris H Janssen ◽  
Wilko Spiering ◽  
Cor J Kalkman ◽  
Wouter W van Solinge ◽  
...  
Keyword(s):  
Physical Activity ◽  
Validation Study ◽  
Cross Validation ◽  
Usability Evaluation ◽  
Step Count ◽  
Added Value ◽  
Reference Standard ◽  
Free Living ◽  
Activity Trackers ◽  
Leave One Out

BACKGROUND Remote monitoring of physical activity in patients with chronic conditions could be useful to offer care professionals real-time assessment of their patient’s daily activity pattern to adjust appropriate treatment. However, the validity of commercially available activity trackers that can be used for telemonitoring purposes is limited. OBJECTIVE The purpose of this study was to test usability and determine the validity of 3 consumer-level activity trackers as a measure of free-living activity. METHODS A usability evaluation (study 1) and validation study (study 2) were conducted. In study 1, 10 individuals wore one activity tracker for a period of 30 days and filled in a questionnaire on ease of use and wearability. In study 2, we validated three selected activity trackers (Apple Watch, Misfit Shine, and iHealth Edge) and a fourth pedometer (Yamax Digiwalker) against the reference standard (Actigraph GT3X) in 30 healthy participants for 72 hours. Outcome measures were 95% limits of agreement (LoA) and bias (Bland-Altman analysis). Furthermore, median absolute differences (MAD) were calculated. Correction for bias was estimated and validated using leave-one-out cross validation. RESULTS Usability evaluation of study 1 showed that iHealth Edge and Apple Watch were more comfortable to wear as compared with the Misfit Flash. Therefore, the Misfit Flash was replaced by Misfit Shine in study 2. During study 2, the total number of steps of the reference standard was 21,527 (interquartile range, IQR 17,475-24,809). Bias and LoA for number of steps from the Apple Watch and iHealth Edge were 968 (IQR −5478 to 7414) and 2021 (IQR −4994 to 9036) steps. For Misfit Shine and Yamax Digiwalker, bias was −1874 and 2004, both with wide LoA of (13,869 to 10,121) and (−10,932 to 14,940) steps, respectively. The Apple Watch noted the smallest MAD of 7.7% with the Actigraph, whereas the Yamax Digiwalker noted the highest MAD (20.3%). After leave-one-out cross validation, accuracy estimates of MAD of the iHealth Edge and Misfit Shine were within acceptable limits with 10.7% and 11.3%, respectively. CONCLUSIONS Overall, the Apple Watch and iHealth Edge were positively evaluated after wearing. Validity varied widely between devices, with the Apple Watch being the most accurate and Yamax Digiwalker the least accurate for step count in free-living conditions. The iHealth Edge underestimates number of steps but can be considered reliable for activity monitoring after correction for bias. Misfit Shine overestimated number of steps and cannot be considered suitable for step count because of the low agreement. Future studies should focus on the added value of remotely monitoring activity patterns over time in chronic patients.

scholarly journals Using Activity Monitors to Measure Physical Activity in Free-Living Conditions

2006 ◽  
Vol 86 (8) ◽  
pp. 1137-1145 ◽  
Author(s):  
Jaime E Berlin ◽  
Kristi L Storti ◽  
Jennifer S Brach
Keyword(s):  
Physical Activity ◽  
Living Conditions ◽  
Free Living ◽  
Activity Monitors ◽  
Free Living Conditions
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