scholarly journals Criterion validity of a wrist-worn activity tracker in laboratory and free-living setting in patients with chronic pain (Preprint)

2020 ◽  
Author(s):  
Veronica Sjöberg ◽  
Jens Westergren ◽  
Andreas Monnier ◽  
Ricardo LoMartire ◽  
Maria Hagströmer ◽  
...  
Keyword(s):  
Chronic Pain ◽  
Criterion Validity ◽  
Step Count ◽  
Fair Agreement ◽  
Laboratory Setting ◽  
Poor Agreement ◽  
Activity Tracking ◽  
Free Living ◽  
Activity Tracker ◽  
Tracking Devices

BACKGROUND Physical Activity (PA) is evidently a crucial part of the rehabilitation process for patients suffering from chronic pain. Modern wrist-worn activity tracking devices seemingly have a great potential to provide objective feedback and assist in the adoption of healthy PA behavior by supplying data of energy expenditure expressed as Metabolic Equivalents (METS). However, no studies have been found of any wrist-worn activity tracking devices’ criterion validity in estimating METS, heart rate (HR), or step count in patients with chronic pain. OBJECTIVE The aim was to determine the criterion validity of wrist-worn activity tracking devices for estimations of METS, HR, and step count in a controlled laboratory setting and free-living settings for patients with chronic pain. METHODS In this combined laboratory and field validation study, METS, HR, and step count were simultaneously estimated by a wrist-worn activity tracker (Fitbit Versa), indirect calorimetry (Jaeger Oxycon Pro), and a research-grade hip-worn accelerometer (ActiGraph GT3X) during a treadmill walk at three speeds (3.0, 4.5, and 6.0 km/h) in a laboratory setting. METS and step count were also estimated by the wrist-worn activity tracker in free-living settings for 72 hours. The criterion validity was determined by conventional statistics (ICC and Spearman rho) and graphical plots (Bland-Altman Plots) as well as by Mean Absolute Percentage Error (MAPE). Analysis of Variance (ANOVA) was used to determine any significant systematic differences between estimations. RESULTS A total of 42 patients (76% females), 25-66 years of age, with chronic pain, were included. Results showed that the wrist-worn activity tracking devices (Fitbit Versa) systematically overestimated METS when compared to the criterion measurement (Jaeger Oxycon Pro) and the relative criterion measurement (ActiGraph GT3X). Poor agreement and correlation was shown in estimated METS between Fitbit Versa and both Jaeger Oxycon Pro and ActiGraph GT3X at all treadmill speeds. Estimations of HR emerged with poor to fair agreement during laboratory-based treadmill walks. For step count, the wrist-worn devices showed a fair agreement and fair correlation at most treadmill speeds. In free-living settings, however, the agreement of step count between wrist-worn devices and waist-worn accelerometer was good, and the correlation was excellent. CONCLUSIONS The wrist-worn device systematically overestimated METS and showed poor agreement and correlation compared to the criterion measurement (Jaeger Oxycon Pro) and the relative criterion measurement (ActiGraph GT3X), which needs to be considered when used clinically. Step count measured from the wrist, however, seemed to be a valid estimation, suggesting that future guidelines could include such variables in this group with chronic pain. CLINICALTRIAL Not applicable in this study

Abstract P276: Activity Tracker Increases Daily Step Count Post-Cardiac Rehabilitation Compared to Placebo Device

Circulation ◽  
2018 ◽  
Vol 137 (suppl_1) ◽  
Author(s):  
Erica Schorr ◽  
Hilton Dahl ◽  
Alicia Sarkinen ◽  
Rebecca Brown
Keyword(s):  
Cardiac Rehabilitation ◽  
Functional Capacity ◽  
Intervention Group ◽  
Step Count ◽  
Daily Step ◽  
Activity Tracking ◽  
Activity Tracker ◽  
Daily Step Count ◽  
Tracking Devices

Introduction: Despite optimal levels of physical activity (PA) among patients immediately post-cardiac rehabilitation, little is known about PA levels long-term. Further, interventions to maintain recommended PA levels and functional capacity achieved during cardiac rehabilitation are lacking. Objective: To test the effect of wearing a Garmin vÍvofit for 3 months post-cardiac rehabilitation on PA levels and functional capacity compared to a placebo device. Methods: Change in daily step count and 6-minute walk test (6MWT) were assessed over 3 months using the vÍvofit activity tracker in 35 patients (mean age 62±8 years; 83% male; 94% Caucasian) post-cardiac rehabilitation. Goal was 10,000 steps for all participants. Patients were randomized into the control or intervention group with control devices displaying a digital clock. VÍvofit step data were recorded continuously; the 6MWT was conducted at 0, 9, 12, and 15 weeks. Comparisons between the 2 groups were made using test of proportions, t-test, and logistic and linear regression. Results: Control and intervention groups were balanced with respect to age, gender, education, baseline step count, and body composition. Although all participants exhibited above average daily step counts (>8,000 steps at 3, 9, and 15 weeks); step counts for intervention group participants were higher at 3, 9, and 15 weeks (by 2,537 steps, 2,022 steps, and 1,545 steps, respectively). Intervention group participants (N=17) increased the distance covered during the 6MWT by 138 feet (sd=28), compared to a 48 foot (sd=18) improvement among control group participants (p=0.500); although not statistically significant, but perhaps clinically relevant. Conclusion: These data provide preliminary support for using wrist-worn activity tracking devices to continuously monitor and maintain PA levels post-cardiac rehabilitation. There is a need for larger trials testing the effectiveness of these devices with a more diverse sample over a longer period of time. Wrist worn activity tracking devices should be coupled with other components known to support long-term behavior change (e.g., social support and text messaging) to develop effective interventions for secondary cardiovascular disease prevention.

scholarly journals Criterion validity of a wrist-worn activity tracker in laboratory and free-living setting in patients with chronic pain (Preprint)

10.2196/24806 ◽  
2020 ◽  
Author(s):  
Veronica Sjöberg ◽  
Jens Westergren ◽  
Andreas Monnier ◽  
Ricardo LoMartire ◽  
Maria Hagströmer ◽  
...  
Keyword(s):  
Chronic Pain ◽  
Criterion Validity ◽  
Free Living ◽  
Activity Tracker

Abstract 17026: Effects of Supervised Exercise Therapy on Free-Living Physical Activity Among Patients With Symptomatic Peripheral Artery Disease

Circulation ◽  
2018 ◽  
Vol 138 (Suppl_1) ◽  
Author(s):  
Erica Schorr ◽  
Mary Whipple ◽  
Diane Treat-Jacobson
Keyword(s):  
Physical Activity ◽  
Peripheral Artery Disease ◽  
Exercise Therapy ◽  
Sedentary Time ◽  
Peripheral Artery ◽  
Activity Tracking ◽  
Free Living ◽  
Supervised Exercise ◽  
Artery Disease ◽  
Tracking Devices

Introduction: Evidence supporting the effects of supervised exercise therapy (SET) on alleviating symptoms and improving walking ability for patients with symptomatic peripheral artery disease (PAD) is robust and well recognized. However, little is known about the impact of SET on free-living physical activity (PA). The aim of this study was to examine the relationship between participation in SET and changes in free-living PA among individuals in the the EX ercise Training to Reduce Claudication: Arm ER gometry versus T readmill Walking ( EXERT ) trial. Methods: In this randomized, controlled trial, 104 participants (mean age 68±9; 29% female) were allocated to receive treadmill (TM) exercise (n=41), upper body ergometry (UBE) exercise (n=42), or usual-care (UC) (n=21) for 12 weeks. Exercise participants attended SET three times per week; UC participants met with study staff weekly. PA was measured over 7 days via waist-worn ActiGraph accelerometers at baseline, 6, and 12 weeks. Steps per day was the primary outcome. Secondary outcomes were proportion of time in light and moderate to vigorous physical activity (MVPA), and sedentary time. PA was controlled for in TM participants by using SET logs. Results were analyzed using descriptive statistics, two-sample t-tests, and analysis of variance. Results: Regardless of randomization, average daily steps were low at baseline and 6 weeks (4,013 steps, p =.72; and 3,911 steps, p =.84, respectively), and slightly higher at 12 weeks (4,307 steps; p =.93). Although not statistically significant but perhaps clinically relevant, UBE participants exhibited greater increases in MVPA over 12 weeks (0.9% to 1.3%; F =.48, p =.62) compared to TM (1.2% to 1.3%; F =.35, p =.71) and UC (1.3% to 1.5%, F =.03, p =.97); similarly all participants exhibited reductions in sedentary time and increases in free-living PA between baseline and 12 weeks. Conclusions: These data suggest individuals with PAD attending SET replace sedentary time with light or moderate intensity PA regardless of exercise modality. Despite study participants meeting the recommended daily steps for adults with chronic conditions (3,500-5,500 steps), it is suspected that they did not reach the daily goal of 30 minutes of enhanced PA to reduce health risks. Future research should incorporate activity tracking devices that can provide feedback on PA as an approach to meet daily PA goals. Activity tracking devices used in conjunction with SET may further improve walking distance, symptom management, and quality of life among patients with symptomatic PAD.

Step count accuracy and reliability of two activity tracking devices in people after stroke

2017 ◽  
Vol 33 (10) ◽  
pp. 788-796 ◽  
Author(s):  
Samuel D. Schaffer ◽  
Simon D. Holzapfel ◽  
George Fulk ◽  
Pamela Rogers Bosch
Keyword(s):  
Step Count ◽  
Activity Tracking ◽  
Tracking Devices

scholarly journals The validity of activity trackers is affected by walking speed: the criterion validity of Garmin Vivosmart® HR and StepWatch™ 3 for measuring steps at various walking speeds under controlled conditions

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9381
Author(s):  
Frederik Rose Svarre ◽  
Mads Møller Jensen ◽  
Josephine Nielsen ◽  
Morten Villumsen
Keyword(s):  
Criterion Validity ◽  
Step Count ◽  
Healthcare Personnel ◽  
Percentage Error ◽  
Activity Tracker ◽  
Activity Trackers ◽  
Controlled Conditions ◽  
Healthcare It ◽  
Graded Activity ◽  
Walking Speeds

Introduction The use of activity trackers has increased both among private consumers and in healthcare. It is therefore relevant to consider whether a consumer-graded activity tracker is comparable to or may substitute a research-graded activity tracker, which could further increase the use of activity trackers in healthcare and rehabilitation. Such use will require knowledge of their accuracy as the clinical implications may be significant. Studies have indicated that activity trackers are not sufficiently accurate, especially at lower walking speeds. The present study seeks to inform decision makers and healthcare personnel considering implementing physical activity trackers in clinical practice. This study investigates the criterion validity of the consumer-graded Garmin Vivosmart® HR and the research-graded StepWatch™ 3 compared with manual step count (gold standard) at different walking speeds under controlled conditions. Methods Thirty participants, wearing Garmin Vivosmart® HR at the wrist and StepWatch™ 3 at the ankle, completed six trials on a treadmill at different walking speeds: 1.6 km/h, 2.4 km/h, 3.2 km/h, 4.0 km/h, 4.8 km/h, and 5.6 km/h. The participants were video recorded, and steps were registered by manual step count. Medians and inter-quartile ranges (IQR) were calculated for steps and differences in steps between manually counted steps and the two devices. In order to assess the clinical relevance of the tested devices, the mean absolute percentage error (MAPE) was determined at each speed. A MAPE ≤3% was considered to be clinically irrelevant. Furthermore, differences between manually counted steps and steps recorded by the two devices were presented in Bland–Altman style plots. Results The median of differences in steps between Garmin Vivosmart® HR and manual step count ranged from −49.5 (IQR = 101) at 1.6 km/h to −1 (IQR = 4) at 4.0 km/h. The median of differences in steps between StepWatch™ 3 and manual step count were 4 (IQR = 14) at 1.6 km/h and 0 (IQR = 1) at all other walking speeds. The results of the MAPE showed that differences in steps counted by Garmin Vivosmart® HR were clinically irrelevant at walking speeds 3.2–4.8 km/h (MAPE: 0.61–1.27%) as the values were below 3%. Differences in steps counted by StepWatch™ 3 were clinically irrelevant at walking speeds 2.4–5.6 km/h (MAPE: 0.08–0.35%). Conclusion Garmin Vivosmart® HR tended to undercount steps compared with the manual step count, and StepWatch™ 3 slightly overcounted steps compared with the manual step count. Both the consumer-graded activity tracker (Garmin Vivosmart® HR) and the research-graded (StepWatch™ 3) are valid in detecting steps at selected walking speeds in healthy adults under controlled conditions. However, both activity trackers miscount steps at slow walking speeds, and the consumer graded activity tracker also miscounts steps at fast walking speeds.

scholarly journals Neural Network–Based Algorithm for Adjusting Activity Targets to Sustain Exercise Engagement Among People Using Activity Trackers: Retrospective Observation and Algorithm Development Study

10.2196/18142 ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. e18142
Author(s):  
Ramin Mohammadi ◽  
Mursal Atif ◽  
Amanda Jayne Centi ◽  
Stephen Agboola ◽  
Kamal Jethwani ◽  
...  
Keyword(s):  
Neural Network ◽  
Physical Activity ◽  
Machine Learning ◽  
Learning Model ◽  
Step Count ◽  
Daily Step ◽  
Activity Tracker ◽  
Activity Trackers ◽  
Machine Learning Model ◽  
Daily Step Count

Background It is well established that lack of physical activity is detrimental to the overall health of an individual. Modern-day activity trackers enable individuals to monitor their daily activities to meet and maintain targets. This is expected to promote activity encouraging behavior, but the benefits of activity trackers attenuate over time due to waning adherence. One of the key approaches to improving adherence to goals is to motivate individuals to improve on their historic performance metrics. Objective The aim of this work was to build a machine learning model to predict an achievable weekly activity target by considering (1) patterns in the user’s activity tracker data in the previous week and (2) behavior and environment characteristics. By setting realistic goals, ones that are neither too easy nor too difficult to achieve, activity tracker users can be encouraged to continue to meet these goals, and at the same time, to find utility in their activity tracker. Methods We built a neural network model that prescribes a weekly activity target for an individual that can be realistically achieved. The inputs to the model were user-specific personal, social, and environmental factors, daily step count from the previous 7 days, and an entropy measure that characterized the pattern of daily step count. Data for training and evaluating the machine learning model were collected over a duration of 9 weeks. Results Of 30 individuals who were enrolled, data from 20 participants were used. The model predicted target daily count with a mean absolute error of 1545 (95% CI 1383-1706) steps for an 8-week period. Conclusions Artificial intelligence applied to physical activity data combined with behavioral data can be used to set personalized goals in accordance with the individual’s level of activity and thereby improve adherence to a fitness tracker; this could be used to increase engagement with activity trackers. A follow-up prospective study is ongoing to determine the performance of the engagement algorithm.

scholarly journals Use of Free-Living Step Count Monitoring for Heart Failure Functional Classification: Validation Study (Preprint)

2018 ◽  
Author(s):  
Jonathan-F. Baril ◽  
Simon Bromberg ◽  
Yasbanoo Moayedi ◽  
Babak Taati ◽  
Cedric Manlhiot ◽  
...  
Keyword(s):  
Heart Failure ◽  
Pilot Study ◽  
Ejection Fraction ◽  
Functional Class ◽  
Step Count ◽  
Functional Classification ◽  
Free Living ◽  
Reduced Ejection Fraction ◽  
Nyha Functional Class ◽  
Daily Total

BACKGROUND The New York Heart Association (NYHA) functional classification system has poor inter-rater reproducibility. A previously published pilot study showed a statistically significant difference between the daily step counts of heart failure (with reduced ejection fraction) patients classified as NYHA functional class II and III as measured by wrist-worn activity monitors. However, the study’s small sample size severely limits scientific confidence in the generalizability of this finding to a larger heart failure (HF) population. OBJECTIVE This study aimed to validate the pilot study on a larger sample of patients with HF with reduced ejection fraction (HFrEF) and attempt to characterize the step count distribution to gain insight into a more objective method of assessing NYHA functional class. METHODS We repeated the analysis performed during the pilot study on an independently recorded dataset comprising a total of 50 patients with HFrEF (35 NYHA II and 15 NYHA III) patients. Participants were monitored for step count with a Fitbit Flex for a period of 2 weeks in a free-living environment. RESULTS Comparing group medians, patients exhibiting NYHA class III symptoms had significantly lower recorded 2-week mean daily total step count (3541 vs 5729 [steps], P=.04), lower 2-week maximum daily total step count (10,792 vs 5904 [steps], P=.03), lower 2-week recorded mean daily mean step count (4.0 vs 2.5 [steps/minute], P=.04,), and lower 2-week mean and 2-week maximum daily per minute step count maximums (88.1 vs 96.1 and 111.0 vs 123.0 [steps/minute]; P=.02 and .004, respectively). CONCLUSIONS Patients with NYHA II and III symptoms differed significantly by various aggregate measures of free-living step count including the (1) mean and (2) maximum daily total step count as well as by the (3) mean of daily mean step count and by the (4) mean and (5) maximum of the daily per minute step count maximum. These findings affirm that the degree of exercise intolerance of NYHA II and III patients as a group is quantifiable in a replicable manner. This is a novel and promising finding that suggests the existence of a possible, completely objective measure of assessing HF functional class, something which would be a great boon in the continuing quest to improve patient outcomes for this burdensome and costly disease.

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.

Activity-tracking devices for older adults: comparison and preferences

2017 ◽  
Vol 17 (2) ◽  
pp. 411-419 ◽  
Author(s):  
Anika Steinert ◽  
Marten Haesner ◽  
Elisabeth Steinhagen-Thiessen
Keyword(s):  
Older Adults ◽  
Activity Tracking ◽  
Tracking Devices
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