scholarly journals P095: Wellness, sleep and exercise in emergency medicine residents: an observational study

CJEM ◽  
2017 ◽  
Vol 19 (S1) ◽  
pp. S110
Author(s):  
Z. Poonja ◽  
P.S. O’Brien ◽  
E. Cross ◽  
C. Desrochers ◽  
P.K. Jaggi ◽  
...  
Keyword(s):  
Emergency Medicine ◽  
Statistical Power ◽  
Step Count ◽  
Daily Step ◽  
Data Set ◽  
Sleep Quantity ◽  
Metabolic Equivalents ◽  
Perceived Wellness ◽  
Daily Step Count ◽  
Wellness Initiatives

Introduction: Burnout is well documented in residents and emergency physicians. Wellness initiatives are becoming increasingly prevalent, but there is a lack of data supporting their efficacy. In some populations, a relationship between sleep, exercise and wellness has been documented, but this relationship has not been established in emergency medicine (EM) residents or physicians. We aim to determine whether exercise and sleep quality and quantity as measured by a Fitbit are associated with greater perceived wellness in EM residents. Methods: Fifteen EM residents from two training sites wore a Fitbit during a 4-week EM rotation. The Fitbit recorded data on sleep quantity (minutes sleeping)/quality (sleep disruptions) and exercise quantity (daily step count)/quality (daily active minutes performing activity of 3-6 and >6 metabolic equivalents). Participants completed an end-of-rotation Perceived Wellness Survey (PWS) which provided information on six domains of personal wellness (psychological, emotional, social, physical, spiritual and intellectual). Associations between PWS scores and the Fitbit markers were evaluated using a Mann-Whitney-U statistical analysis. Results: Preliminary results indicate that residents who scored ≥50th percentile for sleep quantity had significantly higher PWS scores than those who scored ≤50th percentile (median PWS 17.0 vs 13.0 respectively, p=0.04). There was no significant correlation between PWS scores, sleep interruptions, daily step count and average daily active minutes. Postgraduate Year PGY1 and PGY2-5 report median PWS scores of 13.9 and 17.2 respectively. Conclusion: To our knowledge, this is the first study to objectively measure the quality and quantity of sleep as well as exercise habits of EM residents using a Fitbit device. Our data indicates a significant relationship between better sleep quantity and higher wellness scores in this population. We aim to enroll 30 residents in order to obtain a more robust data set. A larger sample size will increase statistical power and allow us to more extensively evaluate the use of exercise and sleep monitoring devices in the efficacy assessment of wellness initiatives.

Abstract 108: Impact of a Wireless "Wearable" Device to Measure Daily Activity in Patients With Coronary Microvascular Disease Treated With Late Na Channel Inhibition (ranolazine): a Substudy of the RWISE Clinical Trial

2017 ◽  
Vol 10 (suppl_3) ◽  
Author(s):  
Kade Birkeland ◽  
Raj M Khandwalla ◽  
Ilan Kedan ◽  
Chrisandra L Shufelt ◽  
Puja K Mehta ◽  
...  
Keyword(s):  
Daily Life ◽  
Obstructive Coronary Artery Disease ◽  
Step Count ◽  
Wearable Device ◽  
Coronary Microvascular Dysfunction ◽  
Na Channel ◽  
Daily Step ◽  
Data Set ◽  
Channel Inhibition ◽  
Daily Step Count

Background: Since late Na channel inhibition (ranolazine) improves exercise duration in the stress laboratory among angina patients, we questioned if this benefit would translate to impact step-count during daily life assessed by a "wearable" device. Methods: We conducted a pilot substudy within a randomized, double-blinded, placebo-controlled, cross-over trial of subjects with angina, non-obstructive coronary artery disease and coronary microvascular dysfunction. Ranolazine was administered (500-1000mg BID for 2 weeks). The outcome of interest was difference in Fitbit Flex daily step-count during weeks 2 of ranolazine or placebo treatment. Other outcomes included angina, quality of life, ischemia, diastolic function. Results: 30 subjects were analyzed. Overall, late Na channel inhibition reduced daily step-count vs. placebo (5757 +/- 3076 vs. 6593 +/- 3393, p=0.01) and did not improve angina. However, among those with improved angina (SAQ-7 improvement), a direct correlation with increased step-count (0.42, p=0.02) was observed, most due to typical angina (0.57, p=0.05) (Fig). Conclusions: We report the "first" data set from a wearable monitor to measure step-count in a controlled late Na channel inhibition trial. Our results suggest short-term late Na channel inhibition (ranolazine) does not increase step-count during daily life.

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.

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.

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 Daily Step Count Profile Data for 61 Days

2016 ◽  
Author(s):  
Elijah Meyer ◽  
Mark Greenwood ◽  
Tan Tran
Keyword(s):  
Step Count ◽  
Daily Step ◽  
Profile Data ◽  
Daily Step Count

scholarly journals Feedback From Activity Trackers Improves Daily Step Count After Knee and Hip Arthroplasty: A Randomized Controlled Trial

2018 ◽  
Vol 33 (11) ◽  
pp. 3422-3428 ◽  
Author(s):  
Neill Van der Walt ◽  
Lucy J. Salmon ◽  
Benjamin Gooden ◽  
Matthew C. Lyons ◽  
Michael O'Sullivan ◽  
...  
Keyword(s):  
Randomized Controlled Trial ◽  
Hip Arthroplasty ◽  
Controlled Trial ◽  
Step Count ◽  
Daily Step ◽  
Randomized Controlled ◽  
Activity Trackers ◽  
Daily Step Count ◽  
Knee And Hip Arthroplasty

Associations between psychological factors and daily step count in patients with lumbar spinal stenosis

2020 ◽  
pp. 1-9
Author(s):  
Masakazu Minetama ◽  
Mamoru Kawakami ◽  
Masatoshi Teraguchi ◽  
Ryohei Kagotani ◽  
Yoshimasa Mera ◽  
...  
Keyword(s):  
Spinal Stenosis ◽  
Lumbar Spinal Stenosis ◽  
Psychological Factors ◽  
Step Count ◽  
Daily Step ◽  
Daily Step Count ◽  
Lumbar Spinal

scholarly journals Adding team-based financial incentives to the Carrot Rewards physical activity app increases daily step count on a population scale: a 24-week matched case control study

2020 ◽  
Vol 17 (1) ◽  
Author(s):  
Emma Pearson ◽  
Harry Prapavessis ◽  
Christopher Higgins ◽  
Robert Petrella ◽  
Lauren White ◽  
...  
Keyword(s):  
Physical Activity ◽  
Financial Incentives ◽  
Step Count ◽  
Daily Step ◽  
Individual Level ◽  
Significant Difference ◽  
Daily Step Count ◽  
Steps Per Day ◽  
The Impact ◽  
Experimental Group

Abstract Background Mobile health applications (mHealth apps) targeting physical inactivity have increased in popularity yet are usually limited by low engagement. This study examined the impact of adding team-based incentives (Step Together Challenges, STCs) to an existing mHealth app (Carrot Rewards) that rewarded individual physical activity achievements. Methods A 24-week quasi-experimental study (retrospective matched pairs design) was conducted in three Canadian provinces (pre-intervention: weeks 1–12; intervention: weeks 13–24). Participants who used Carrot Rewards and STCs (experimental group) were matched with those who used Carrot Rewards only (controls) on age, gender, province and baseline mean daily step count (±500 steps/d). Carrot Rewards users earned individual-level incentives (worth $0.04 CAD) each day they reached a personalized daily step goal. With a single partner, STC users could earn team incentives ($0.40 CAD) for collaboratively reaching individual daily step goals 10 times in seven days (e.g., Partner A completes four goals and Partner B completes six goals in a week). Results The main analysis included 61,170 users (mean age = 32 yrs.; % female = 64). Controlling for pre-intervention mean daily step count, a significant difference in intervention mean daily step count favoured the experimental group (p < 0.0001; ηp2 = 0.024). The estimated marginal mean group difference was 537 steps per day, or 3759 steps per week (about 40 walking min/wk). Linear regression suggested a dose-response relationship between the number of STCs completed (app engagement) and intervention mean daily step count (adjusted R2 = 0.699) with each new STC corresponding to approximately 200 more steps per day. Conclusion Despite an explosion of physical activity app interest, low engagement leading to small or no effects remains an industry hallmark. In this paper, we found that adding modest team-based incentives to the Carrot Rewards app increased mean daily step count, and importantly, app engagement moderated this effect. Others should consider novel small-teams based approaches to boost engagement and effects.

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