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 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 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

scholarly journals P0648LEVELS OF PHYSICAL ACTIVITY FOLLOWING AN EPISODE OF STAGE 3 AKI ARE ASSOCIATED WITH RENAL RECOVERY

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Anam Asad ◽  
Maurice Dungey ◽  
Katherine Hull ◽  
James Burton ◽  
Daniel March
Keyword(s):  
Physical Activity ◽  
Renal Function ◽  
Functional Capacity ◽  
Step Count ◽  
Renal Recovery ◽  
Activity Levels ◽  
Daily Step ◽  
Exercise Interventions ◽  
Physical Activity Levels ◽  
Daily Step Count

Abstract Background and Aims Acute kidney injury (AKI) is a known risk factor for the development of chronic kidney disease (CKD). Animal studies have demonstrated the potentially reno-protective effects of physical activity, both against the development of AKI and in promoting renal recovery. However, this has not been investigated in humans. The aim of the study was to investigate the association between physical activity levels and recovery in kidney function, measured by eGFR and creatinine, following an episode of stage 3 AKI. Method Twelve hospitalised participants with non-obstructive stage 3 AKI (as per KDIGO criteria) were asked to complete two questionnaires; the General Practitioner Physical Activity Questionnaire (GPPAQ), a measure of physical activity and; the Duke Activity Status Index, a measure of functional capacity. Baseline questionnaires were completed whilst in hospital (where participants were asked to recall their physical activity and functional capacity levels before hospitalisation) and again at 6-months post discharge. In addition, participants wore a pedometer for 7 consecutive days following discharge to ascertain their daily step count. Baseline renal function was collected using eGFR and creatinine measurements within the 12-months prior to admission; further readings were collected 25 ± 46 days after discharge as a measure of renal recovery (referred to as recovered creatinine). Results Data from the 12 participants who provided step count information were analysed. At diagnosis of stage 3 AKI, participants had a mean creatinine of 547 ± 280 with their mean baseline and recovered creatinine as follows; 95 ± 35 and 172 ± 83. A higher daily step count after discharge was associated with both a higher baseline eGFR (r=0.73, p<0.01) and significant improvements to their renal recovery (r=0.69, p=0.01). There were positive associations between renal recovery and physical activity levels measured using the GPPAQ (r=0.55, p=0.06) and functional capacity (0.17, p=0.6), although not to statistical significance. The participants were divided into two groups based on their recovered creatinine levels. Those who recovered renal function back to within 25% of baseline (n=5) had a higher mean step count compared to those whose renal recovery was less pronounced (n=7); (3712 ±3960 vs 3334 ± 2254, respectively). Conclusion These results show a positive association between physical activity levels and renal recovery following an episode of AKI. This suggests that higher levels of physical activity may be protective and promote recovery of renal function following an episode of AKI. Physical activity and exercise interventions should be tested in the setting of AKI to see whether they are efficacious in promoting renal recovery.

scholarly journals Examining physical activity and quality of life in adults with autism spectrum disorder and intellectual disability

2021 ◽  
pp. 174462952110334
Author(s):  
Brianne Tomaszewski ◽  
Melissa N Savage ◽  
Kara Hume
Keyword(s):  
Quality Of Life ◽  
Physical Activity ◽  
Intellectual Disability ◽  
Step Count ◽  
Daily Step ◽  
Adults With Autism ◽  
Activity Tracker ◽  
Daily Step Count ◽  
The Relationship

Adults with autism and co-occurring intellectual disability engage in low levels of physical activity and are at increased risk of developing secondary health conditions attributed to physical inactivity compared to adults in the general population. Few studies have examined the use of objective measures to characterize physical activity levels for adults with autism and intellectual disability. The current study aimed to examine the relationship between physical activity, using an activity tracker, and quality of life in adults with autism and intellectual disability. In the current study, 38 adults with autism and intellectual disability, ages 18–55, wore a Fitbit Flex 2® activity tracker for 1 week, and completed the Quality of Life Questionnaire. The relationship between average daily step count quality of life was examined. Most adults in the sample were overweight and taking fewer daily steps than recommended guidelines. Increased average daily step count was significantly associated with quality of life.

Simple daily step count predicts long-term changes in arterial stiffness in symptomatic COPD patients

2016 ◽  
Author(s):  
Motohiko Miura ◽  
Toshiyasu Yatsu ◽  
Katsuya Takita ◽  
Musashi Abe ◽  
Ayumi Ito ◽  
...  
Keyword(s):  
Arterial Stiffness ◽  
Step Count ◽  
Daily Step ◽  
Copd Patients ◽  
Long Term Changes ◽  
Daily Step Count

Does Patient Preference for Mode of Intervention Delivery Impact Intervention Efficacy and Attrition?

2019 ◽  
Vol 34 (1) ◽  
pp. 63-66
Author(s):  
Ronald C. Plotnikoff ◽  
Fiona G. Stacey ◽  
Anna K. Jansson ◽  
Benjamin Ewald ◽  
Natalie A. Johnson ◽  
...  
Keyword(s):  
Controlled Trial ◽  
Intervention Group ◽  
Step Count ◽  
Future Research ◽  
Study Group ◽  
Daily Step ◽  
Study Participation ◽  
Face To Face ◽  
Exercise Specialist ◽  
Daily Step Count

Purpose: To explore whether there was a difference in objectively measured physical activity and study participation between people who received their preferred study group allocation (matched) and those who did not receive their preferred study group (mismatched). Design: Secondary data from the NewCOACH randomized controlled trial. Setting: Insufficiently active patients in the primary care settings in Sydney and Newcastle, Australia. Participants: One hundred seventy-two adults aged 20 to 81 years. Intervention: Participants indicated their intervention preference at baseline for (1) five face-to-face visits with an exercise specialist, (2) one face-to-face visit and 4 telephone follow-ups with an exercise specialist, (3) written material, or (4) slight-to-no preference. Participants were then allocated to an intervention group and categorized as either “matched” or “mismatched” based on their indications. Participants who reported a slight-to-no preference was categorized as “matched.” Measures: Daily step count as measured by pedometers and study participation. Analysis: Mean differences between groups in daily step count at 3 and 12 months (multiple linear regression models) and study participation at baseline, 3 months, and 12 months (χ2 tests). Results: Preference for an intervention group prior to randomization did not significantly (all P’s > .05 using 95% confidence interval) impact step counts (differences of <600 steps/day between groups) or study participation. Conclusion: Future research should continue to address whether the strength of preferences influence study outcome and participation and whether the study preferences change over time.

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 (Preprint)

2020 ◽  
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.

The translation of preschoolers’ physical activity guidelines into a daily step count target

2014 ◽  
Vol 33 (10) ◽  
pp. 1051-1057 ◽  
Author(s):  
Marieke De Craemer ◽  
Ellen De Decker ◽  
Ilse De Bourdeaudhuij ◽  
Maïté Verloigne ◽  
Yannis Manios ◽  
...  
Keyword(s):  
Physical Activity ◽  
Step Count ◽  
Daily Step ◽  
Physical Activity Guidelines ◽  
Daily Step Count

Abstract 11: mActive: A Blinded, Randomized mHealth Trial Supporting Digital Tracking and Smart Texting for Promotion of Physical Activity

Circulation ◽  
2015 ◽  
Vol 131 (suppl_1) ◽  
Author(s):  
Seth S Martin ◽  
David I Feldman ◽  
Roger S Blumenthal ◽  
Steven R Jones ◽  
Wendy S Post ◽  
...  
Keyword(s):  
Physical Activity ◽  
Phase I ◽  
Real Time ◽  
Phase Ii ◽  
The United States ◽  
Step Count ◽  
Daily Step ◽  
Baseline Activity ◽  
Daily Step Count ◽  
Digital Tracking

Introduction: The recent advent of smartphone-linked wearable pedometers offers a novel opportunity to promote physical activity using mobile health (mHealth) technology. Hypothesis: We hypothesized that digital activity tracking and smart (automated, real-time, personalized) texting would increase physical activity. Methods: mActive (NCT01917812) was a 5-week, blinded, sequentially-randomized, parallel group trial that enrolled patients at an academic preventive cardiovascular center in Baltimore, MD, USA from January 17 th to May 20 th , 2014. Eligible patients were 18-69 year old smartphone users who reported low leisure-time physical activity by a standardized survey. After establishing baseline activity during a 1-week blinded run-in, we randomized 2:1 to unblinded or blinded tracking in phase I (2 weeks), then randomized unblinded participants 1:1 to receive or not receive smart texts in phase II (2 weeks). Smart texts provided automated, personalized, real-time coaching 3 times/day towards a daily goal of 10,000 steps. The primary outcome was change in daily step count. Results: Forty-eight patients (22 women, 26 men) enrolled with a mean (SD) age of 58 (8) years, body mass index of 31 (6), and baseline daily step count of 9670 (4350). The phase I change in activity was non-significantly higher in unblinded participants versus blinded controls by 1024 steps/day (95% CI -580-2628, p=0.21). In phase II, smart text receiving participants increased their daily steps over those not receiving texts by 2534 (1318-3750, p<0.001) and over blinded controls by 3376 (1951-4801, p<0.001). The unblinded-texts group had the highest proportion attaining the 10,000 steps/day goal (p=0.02) (Figure). Conclusions: In present-day adult smartphone users receiving preventive cardiovascular care in the United States, a technologically-integrated mHealth strategy combining digital tracking with automated, personalized, real-time text message coaching resulted in a large short-term increase in physical activity.

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