scholarly journals Evaluation of the FitBark Activity Monitor for Measuring Physical Activity in Dogs

Animals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 781
Author(s):  
Jessica Colpoys ◽  
Dean DeCock
Keyword(s):  
Physical Activity ◽  
Video Analysis ◽  
Activity Monitor ◽  
Criterion Validity ◽  
Step Count ◽  
Phase 3 ◽  
Activity Data ◽  
Phase Approach ◽  
Low Correlation ◽  
Three Phase

Accelerometers track changes in physical activity which can indicate health and welfare concerns in dogs. The FitBark 2 (FitBark) is an accelerometer for use with dogs; however, no studies have externally validated this tool. The objective of this study was to evaluate FitBark criterion validity by correlating FitBark activity data to dog step count. Dogs (n = 26) were fitted with a collar-mounted FitBark and individually recorded for 30 min using a three-phase approach: (1) off-leash room explore; (2) human–dog interaction; and (3) on-leash walk. Video analysis was used to count the number of times the front right paw touched the ground (step count). Dog step count and FitBark activity were moderately correlated across all phases (r = 0.65, p < 0.001). High correlations between step count and FitBark activity were observed during phases 1 (r = 0.795, p < 0.001) and 2 (r = 0.758, p < 0.001), and a low correlation was observed during phase 3 (r = 0.498, p < 0.001). In conclusion, the FitBark is a valid tool for tracking physical activity in off-leash dogs; however, more work should be done to identify the best method of tracking on-leash activity.

scholarly journals Formative Evaluation of Consumer-Grade Activity Monitors Worn by Older Adults: Test-Retest Reliability and Criterion Validity of Step Counts (Preprint)

2019 ◽  
Author(s):  
Stephanie A Maganja ◽  
David C Clarke ◽  
Scott A Lear ◽  
Dawn C Mackey
Keyword(s):  
Older Adults ◽  
Activity Monitor ◽  
Criterion Validity ◽  
Step Count ◽  
Retest Reliability ◽  
Activity Monitors ◽  
Limited Mobility ◽  
Step Counts ◽  
Test Retest Reliability ◽  
Mobility Status

BACKGROUND To assess whether commercial-grade activity monitors are appropriate for measuring step counts in older adults, it is essential to evaluate their measurement properties in this population. OBJECTIVE This study aimed to evaluate test-retest reliability and criterion validity of step counting in older adults with self-reported intact and limited mobility from 6 commercial-grade activity monitors: Fitbit Charge, Fitbit One, Garmin vívofit 2, Jawbone UP2, Misfit Shine, and New-Lifestyles NL-1000. METHODS For test-retest reliability, participants completed two 100-step overground walks at a usual pace while wearing all monitors. We tested the effects of the activity monitor and mobility status on the absolute difference in step count error (%) and computed the standard error of measurement (SEM) between repeat trials. To assess criterion validity, participants completed two 400-meter overground walks at a usual pace while wearing all monitors. The first walk was continuous; the second walk incorporated interruptions to mimic the conditions of daily walking. Criterion step counts were from the researcher tally count. We estimated the effects of the activity monitor, mobility status, and walk interruptions on step count error (%). We also generated Bland-Altman plots and conducted equivalence tests. RESULTS A total of 36 individuals participated (n=20 intact mobility and n=16 limited mobility; 19/36, 53% female) with a mean age of 71.4 (SD 4.7) years and BMI of 29.4 (SD 5.9) kg/m<sup>2</sup>. Considering test-retest reliability, there was an effect of the activity monitor (<i>P</i>&lt;.001). The Fitbit One (1.0%, 95% CI 0.6% to 1.3%), the New-Lifestyles NL-1000 (2.6%, 95% CI 1.3% to 3.9%), and the Garmin vívofit 2 (6.0%, 95 CI 3.2% to 8.8%) had the smallest mean absolute differences in step count errors. The SEM values ranged from 1.0% (Fitbit One) to 23.5% (Jawbone UP2). Regarding criterion validity, all monitors undercounted the steps. Step count error was affected by the activity monitor (<i>P</i>&lt;.001) and walk interruptions (<i>P</i>=.02). Three monitors had small mean step count errors: Misfit Shine (−1.3%, 95% CI −19.5% to 16.8%), Fitbit One (−2.1%, 95% CI −6.1% to 2.0%), and New-Lifestyles NL-1000 (−4.3%, 95 CI −18.9% to 10.3%). Mean step count error was larger during interrupted walking than continuous walking (−5.5% vs −3.6%; <i>P</i>=.02). Bland-Altman plots illustrated nonsystematic bias and small limits of agreement for Fitbit One and Jawbone UP2. Mean step count error lay within an equivalence bound of ±5% for Fitbit One (<i>P</i>&lt;.001) and Misfit Shine (<i>P</i>=.001). CONCLUSIONS Test-retest reliability and criterion validity of step counting varied across 6 consumer-grade activity monitors worn by older adults with self-reported intact and limited mobility. Walk interruptions increased the step count error for all monitors, whereas mobility status did not affect the step count error. The hip-worn Fitbit One was the only monitor with high test-retest reliability and criterion validity.

The Preliminary Criterion Validity of the Activ8 Activity Monitor to Measure Physical Activity in Youth Using a Wheelchair

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Kristel Lankhorst ◽  
Marleen Sol ◽  
Rita van den Berg-Emons ◽  
Herwin Horemans ◽  
Janke de Groot
Keyword(s):  
Physical Activity ◽  
Activity Monitor ◽  
Criterion Validity

Validation of the SOPLAY Direct Observation Tool With an Accelerometry-Based Physical Activity Monitor

2011 ◽  
Vol 8 (8) ◽  
pp. 1108-1116 ◽  
Author(s):  
Pedro F. Saint-Maurice ◽  
Greg Welk ◽  
Michelle A. Ihmels ◽  
Julia Richards Krapfl
Keyword(s):  
Physical Activity ◽  
Direct Observation ◽  
Convergent Validity ◽  
Leisure Activities ◽  
Physical Activity Behavior ◽  
Activity Monitor ◽  
After School ◽  
Activity Data ◽  
Observation Instrument ◽  
Matched Data

Background:The System for Observing Play and Leisure Activities (SOPLAY) is a direct observation instrument designed to assess group physical activity and environmental contexts. The purpose of this study was to test the convergent validity of the SOPLAY using temporally matched data from an accelerometry-based activity monitor.Methods:Accelerometry-based physical activity data were obtained from 160 elementary school children from 9 after-school activity programs. SOPLAY coding was used to directly observe physical activity during these sessions. Analyses evaluated agreement between the monitored and observed physical activity behavior by comparing the percent of youth engaging in physical activity with the 2 assessments.Results:Agreement varied widely depending on the way the SOPLAY codes were interpreted. Estimates from SOPLAY were significantly higher than accelerometer PA levels when codes of walking and vigorous were used (in combination) to reflect participation in moderate to vigorous PA (MVPA). Estimates were similar when only SOPLAY codes of vigorous were used to define MVPA (Difference = 1.33 ± 22.06%).Conclusions:SOPLAY codes of walking corresponded well with estimates of Light intensity PA. Observations provide valid indicators of MVPA if coding is based on the percentage of youth classified as “vigorous.”

Validation of a Noninvasive, Disposable Activity Monitor for Clinical Applications

2017 ◽  
Vol 14 (7) ◽  
pp. 546-551 ◽  
Author(s):  
Greg Welk ◽  
Youngwon Kim ◽  
Robin P. Shook ◽  
Laura Ellingson ◽  
Roberto L. Lobelo
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Physical Activity Behavior ◽  
Activity Monitor ◽  
Criterion Validity ◽  
Sensewear Armband ◽  
Statistical Equivalence ◽  
Activity Behavior ◽  
Highly Correlated ◽  
And Behavior

Background:The study evaluated the concurrent and criterion validity of a new, disposable activity monitor designed to provide objective data on physical activity and energy expenditure in clinical populations.Methods:A sample of healthy adults (n = 52) wore the disposable Metria IH1 along with the established Sensewear armband (SWA) monitor for a 1-week period. Concurrent validity was examined by evaluating the statistical equivalence of estimates from the Metria and the SWA. Criterion validity was examined by comparing the relative accuracy of the Metria IH1 and the SWA for assessing walking/running. The absolute validity of the 2 monitors was compared by computing correlations and mean absolute percent error (MAPE) relative to criterion data from a portable metabolic analyzer.Results:The output from 2 monitors was highly correlated (correlations > 0.90) and the summary measures yielded nearly identical allocations of time spent in physical activity and energy expenditure. The monitors yielded statistically equivalent estimates and had similar absolute validity relative to the criterion measure (12% to 15% error).Conclusions:The disposable nature of the adhesive Metria IH1 monitor offers promise for clinical evaluation of physical activity behavior in patients. Additional research is needed to test utility for counseling and behavior applications.

scholarly journals Formative Evaluation of Consumer-Grade Activity Monitors Worn by Older Adults: Test-Retest Reliability and Criterion Validity of Step Counts

10.2196/16537 ◽  
2020 ◽  
Vol 4 (8) ◽  
pp. e16537 ◽  
Author(s):  
Stephanie A Maganja ◽  
David C Clarke ◽  
Scott A Lear ◽  
Dawn C Mackey
Keyword(s):  
Older Adults ◽  
Activity Monitor ◽  
Criterion Validity ◽  
Step Count ◽  
Retest Reliability ◽  
Activity Monitors ◽  
Limited Mobility ◽  
Step Counts ◽  
Test Retest Reliability ◽  
Mobility Status

Background To assess whether commercial-grade activity monitors are appropriate for measuring step counts in older adults, it is essential to evaluate their measurement properties in this population. Objective This study aimed to evaluate test-retest reliability and criterion validity of step counting in older adults with self-reported intact and limited mobility from 6 commercial-grade activity monitors: Fitbit Charge, Fitbit One, Garmin vívofit 2, Jawbone UP2, Misfit Shine, and New-Lifestyles NL-1000. Methods For test-retest reliability, participants completed two 100-step overground walks at a usual pace while wearing all monitors. We tested the effects of the activity monitor and mobility status on the absolute difference in step count error (%) and computed the standard error of measurement (SEM) between repeat trials. To assess criterion validity, participants completed two 400-meter overground walks at a usual pace while wearing all monitors. The first walk was continuous; the second walk incorporated interruptions to mimic the conditions of daily walking. Criterion step counts were from the researcher tally count. We estimated the effects of the activity monitor, mobility status, and walk interruptions on step count error (%). We also generated Bland-Altman plots and conducted equivalence tests. Results A total of 36 individuals participated (n=20 intact mobility and n=16 limited mobility; 19/36, 53% female) with a mean age of 71.4 (SD 4.7) years and BMI of 29.4 (SD 5.9) kg/m2. Considering test-retest reliability, there was an effect of the activity monitor (P<.001). The Fitbit One (1.0%, 95% CI 0.6% to 1.3%), the New-Lifestyles NL-1000 (2.6%, 95% CI 1.3% to 3.9%), and the Garmin vívofit 2 (6.0%, 95 CI 3.2% to 8.8%) had the smallest mean absolute differences in step count errors. The SEM values ranged from 1.0% (Fitbit One) to 23.5% (Jawbone UP2). Regarding criterion validity, all monitors undercounted the steps. Step count error was affected by the activity monitor (P<.001) and walk interruptions (P=.02). Three monitors had small mean step count errors: Misfit Shine (−1.3%, 95% CI −19.5% to 16.8%), Fitbit One (−2.1%, 95% CI −6.1% to 2.0%), and New-Lifestyles NL-1000 (−4.3%, 95 CI −18.9% to 10.3%). Mean step count error was larger during interrupted walking than continuous walking (−5.5% vs −3.6%; P=.02). Bland-Altman plots illustrated nonsystematic bias and small limits of agreement for Fitbit One and Jawbone UP2. Mean step count error lay within an equivalence bound of ±5% for Fitbit One (P<.001) and Misfit Shine (P=.001). Conclusions Test-retest reliability and criterion validity of step counting varied across 6 consumer-grade activity monitors worn by older adults with self-reported intact and limited mobility. Walk interruptions increased the step count error for all monitors, whereas mobility status did not affect the step count error. The hip-worn Fitbit One was the only monitor with high test-retest reliability and criterion validity.

Associations of Activity Monitor Output and an Estimate of Aerobic Fitness With Pulse Wave Velocities: The Nakanojo Study

2015 ◽  
Vol 12 (1) ◽  
pp. 139-144 ◽  
Author(s):  
Makoto Ayabe ◽  
Sungjin Park ◽  
Roy J. Shephard ◽  
Yukitoshi Aoyagi
Keyword(s):  
Physical Activity ◽  
Arterial Stiffness ◽  
Aerobic Fitness ◽  
Walking Speed ◽  
Pulse Wave ◽  
Activity Monitor ◽  
Step Count ◽  
Daily Step ◽  
Metabolic Equivalents ◽  
Daily Step Count

Background:We examined the relative contributions of habitual physical activity and aerobic fitness to the prevention of arteriosclerosis.Methods:Elderly individuals (97 men and 109 women, aged > 65 y) each wore a uniaxial activity monitor continuously for 1 year, with activity data summarized as an average daily step count and duration of activity > 3 metabolic equivalents (METs). Aerobic fitness was assessed by a standardized 5-m walking test measure of maximal walking speed. Central arterial stiffness was determined using an automatic waveform analyzer measure of cardio-femoral pulse wave velocity (cfPWV).Results:The cfPWV was negatively associated with daily step count, duration of activity > 3 METs, and maximal walking speed (P < .05). Multiple stepwise regression analysis revealed that the step count, duration of activity > 3 METs, and maximal walking speed were all significant predictors of cfPWV, accounting for 11%, 7%, and 4% of total variance, respectively.Conclusions:In contrast to findings from studies using potentially fallible questionnaires, our data suggest that a measure of health (arterial stiffness) is more closely related to objective measures of physical activity than to an estimate of aerobic fitness.

scholarly journals Integration of Consumer-Based Activity Monitors into Clinical Practice for Children with Type 1 Diabetes: A Feasibility Study

2021 ◽  
Vol 18 (20) ◽  
pp. 10611
Author(s):  
Jason R. Jaggers ◽  
Timothy McKay ◽  
Kristi M. King ◽  
Bradly J. Thrasher ◽  
Kupper A. Wintergerst
Keyword(s):  
Physical Activity ◽  
Type 1 Diabetes ◽  
Insulin Pump ◽  
Final Analysis ◽  
Total Sample ◽  
Activity Monitor ◽  
Activity Data ◽  
Glucose Response ◽  
Activity Monitors

Current technology commonly utilized in diabetes care includes continuous glucose monitors (CGMs) and insulin pumps. One often overlooked critical component to the human glucose response is daily physical activity habits. Consumer-based activity monitors may be a valid way for clinics to collect physical activity data, but whether or not children with type 1 diabetes (T1D) would wear them or use the associated mobile application is unknown. Therefore, the purpose of this study was to test the feasibility of implementing a consumer-based accelerometer directly into ongoing care for adolescents managing T1D. Methods: Adolescents with T1D were invited to participate in this study and instructed to wear a mobile physical activity monitor while also completing a diet log for a minimum of 3 days. Clinical compliance was defined as the number of participants who were compliant with all measures while also having adequate glucose recordings using either a CGM, insulin pump, or on the diet log. Feasibility was defined as >50% of the total sample reaching clinical compliance. Results: A total of 57 children and teenagers between the ages of 7 and 19 agreed to participate in this study and were included in the final analysis. Chi-square results indicated significant compliance for activity tracking (p < 0.001), diet logs (p = 0.04), and overall clinical compliance (p = 0.04). Conclusion: More than half the children in this study were compliant for both activity monitoring and diet logs. This indicates that it is feasible for children with T1D to wear a consumer-based activity monitor while also recording their diet for a minimum of three days.

Are people with rheumatoid arthritis who undertake activity pacing at risk of being too physically inactive?

2012 ◽  
Vol 26 (11) ◽  
pp. 1048-1052 ◽  
Author(s):  
Nienke Cuperus ◽  
Thomas J Hoogeboom ◽  
Yvette Neijland ◽  
Cornelia HM van den Ende ◽  
Noël LW Keijsers
Keyword(s):  
Rheumatoid Arthritis ◽  
Physical Activity ◽  
At Risk ◽  
Activity Monitor ◽  
Cross Sectional Study ◽  
Activity Levels ◽  
Activity Data ◽  
Cross Sectional ◽  
Specialized Nurse ◽  
The Relationship

Objective: To gain insight into the relationship between activity pacing and physical inactivity. Design: A cross-sectional study. Setting: Outpatient clinic of a rheumatology department. Subjects: Men and women diagnosed with rheumatoid arthritis Main measures: Physical activity was assessed using self-reported measures and an accelerometer-based activity monitor. An occupational therapist and specialized nurse analysed the self-reported physical activity data and classified on the basis of consensus the pacing of activities of all patients as ‘adequate’ or ‘not adequate’. Results: Thirty rheumatoid arthritis patients participated in this study of whom nine were categorized as adequate activity pacers. None of these nine undertook sufficient exercise whereas 6 of the 20 people who did not pace activity appropriately did. Physical activity levels assessed by self-reported measures were significantly higher than when assessed by an accelerometer-based activity monitor. Conclusions: Activity pacing was associated with lower levels of physical activity. Since patients with rheumatoid arthritis are already at risk for inactivity, further inactivation by activity pacing might potentially be harmful.

scholarly journals Measuring Criterion Validity of Microinteraction Ecological Momentary Assessment (Micro-EMA): A Pilot Study with Physical Activity Measurement (Preprint)

2020 ◽  
Author(s):  
Aditya Ponnada ◽  
Binod Thapa Chhetry ◽  
Justin Manjourides ◽  
Stephen Intille
Keyword(s):  
Physical Activity ◽  
Pilot Study ◽  
Ecological Momentary Assessment ◽  
Activity Monitor ◽  
Criterion Validity ◽  
Self Report ◽  
Activity Measurement ◽  
Research Grade ◽  
Ecological Momentary ◽  
Momentary Assessment

BACKGROUND Ecological momentary assessment (EMA) is an in-situ method of gathering self-report on behaviors using mobile devices. Microinteraction-EMA (Micro-EMA or μEMA) is a type of EMA where all the self-report prompts are single-question surveys that can be answered using a one-tap glanceable microinteraction, conveniently on a smartwatch. Prior work suggests that μEMA may permit a substantially higher prompting rate than EMA with higher response rates. However, the validity of μEMA self-report has not yet been assessed. OBJECTIVE In this pilot study, we evaluated the criterion validity of μEMA on a smartwatch, using physical activity (PA) assessment as an example behavior of interest. METHODS Seventeen participants answered 72 μEMA prompts each day for one-week, self-reporting whether they were doing sedentary, light/standing, moderate/walking, or vigorous activities at each prompt. Responses were then compared with a research-grade activity monitor worn on the dominant ankle continuously measuring PA. RESULTS We observed significantly higher (P <.001) momentary PA levels on the activity monitor when participants self-reported (using μEMA) engaging in moderate/walking or vigorous activities as compared to sedentary or light/standing activities. CONCLUSIONS For PA measurement, high-frequency μEMA self-report could be used to capture the information comparable to that of a research-grade continuous sensor – suggesting criterion validity.

Sign in / Sign up

Export Citation Format

Share Document