scholarly journals Association of Habitual Physical Activity With Home Blood Pressure in the Electronic Framingham Heart Study (eFHS): Cross-sectional Study (Preprint)

2020 ◽  
Author(s):  
Mayank Sardana ◽  
Honghuang Lin ◽  
Yuankai Zhang ◽  
Chunyu Liu ◽  
Ludovic Trinquart ◽  
...  
Keyword(s):  
Physical Activity ◽  
Blood Pressure ◽  
Habitual Physical Activity ◽  
Step Count ◽  
Daily Step ◽  
Community Based ◽  
Cross Sectional ◽  
Heart Study ◽  
Daily Step Count ◽  
The Mean

BACKGROUND When studied in community-based samples, the association of physical activity with blood pressure (BP) remains controversial and is perhaps dependent on the intensity of physical activity. Prior studies have not explored the association of smartwatch-measured physical activity with home BP. OBJECTIVE We aimed to study the association of habitual physical activity with home BP. METHODS Consenting electronic Framingham Heart Study (eFHS) participants were provided with a study smartwatch (Apple Watch Series 0) and Bluetooth-enabled home BP cuff. Participants were instructed to wear the watch daily and transmit BP values weekly. We measured habitual physical activity as the average daily step count determined by the smartwatch. We estimated the cross-sectional association between physical activity and average home BP using linear mixed effects models adjusting for age, sex, wear time, antihypertensive drug use, and familial structure. RESULTS We studied 660 eFHS participants (mean age 53 years, SD 9 years; 387 [58.6%] women; 602 [91.2%] White) who wore the smartwatch 5 or more hours per day for 30 or more days and transmitted three or more BP readings. The mean daily step count was 7595 (SD 2718). The mean home systolic and diastolic BP (mmHg) were 122 (SD 12) and 76 (SD 8). Every 1000 increase in the step count was associated with a 0.49 mmHg lower home systolic BP (<i>P</i>=.004) and 0.36 mmHg lower home diastolic BP (<i>P</i>=.003). The association, however, was attenuated and became statistically nonsignificant with further adjustment for BMI. CONCLUSIONS In this community-based sample of adults, higher daily habitual physical activity measured by a smartwatch was associated with a moderate, but statistically significant, reduction in home BP. Differences in BMI among study participants accounted for the majority of the observed association.

scholarly journals Association of Habitual Physical Activity With Home Blood Pressure in the Electronic Framingham Heart Study (eFHS): Cross-sectional Study

10.2196/25591 ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. e25591
Author(s):  
Mayank Sardana ◽  
Honghuang Lin ◽  
Yuankai Zhang ◽  
Chunyu Liu ◽  
Ludovic Trinquart ◽  
...  
Keyword(s):  
Physical Activity ◽  
Blood Pressure ◽  
Habitual Physical Activity ◽  
Step Count ◽  
Daily Step ◽  
Community Based ◽  
Cross Sectional ◽  
Heart Study ◽  
Daily Step Count ◽  
The Mean

Background When studied in community-based samples, the association of physical activity with blood pressure (BP) remains controversial and is perhaps dependent on the intensity of physical activity. Prior studies have not explored the association of smartwatch-measured physical activity with home BP. Objective We aimed to study the association of habitual physical activity with home BP. Methods Consenting electronic Framingham Heart Study (eFHS) participants were provided with a study smartwatch (Apple Watch Series 0) and Bluetooth-enabled home BP cuff. Participants were instructed to wear the watch daily and transmit BP values weekly. We measured habitual physical activity as the average daily step count determined by the smartwatch. We estimated the cross-sectional association between physical activity and average home BP using linear mixed effects models adjusting for age, sex, wear time, antihypertensive drug use, and familial structure. Results We studied 660 eFHS participants (mean age 53 years, SD 9 years; 387 [58.6%] women; 602 [91.2%] White) who wore the smartwatch 5 or more hours per day for 30 or more days and transmitted three or more BP readings. The mean daily step count was 7595 (SD 2718). The mean home systolic and diastolic BP (mmHg) were 122 (SD 12) and 76 (SD 8). Every 1000 increase in the step count was associated with a 0.49 mmHg lower home systolic BP (P=.004) and 0.36 mmHg lower home diastolic BP (P=.003). The association, however, was attenuated and became statistically nonsignificant with further adjustment for BMI. Conclusions In this community-based sample of adults, higher daily habitual physical activity measured by a smartwatch was associated with a moderate, but statistically significant, reduction in home BP. Differences in BMI among study participants accounted for the majority of the observed association.

scholarly journals Volume and Intensity of Stepping Activity and Cardiometabolic Risk Factors in a Multi-ethnic Asian Population

2020 ◽  
Vol 17 (3) ◽  
pp. 863 ◽  
Author(s):  
Jennifer Sumner ◽  
Léonie Uijtdewilligen ◽  
Anne Chu Hin Yee ◽  
Sheryl Ng Hui Xian ◽  
Tiago V Barreira ◽  
...  
Keyword(s):  
Physical Activity ◽  
Risk Factors ◽  
Cardiometabolic Risk ◽  
Asian Population ◽  
Cardiometabolic Risk Factors ◽  
Step Count ◽  
Daily Step ◽  
Future Health ◽  
Daily Step Count ◽  
The Mean

The health benefits of objectively measured physical activity volume versus intensity have rarely been studied, particularly in non-western populations. The aim of this study was to investigate the association between cardiometabolic risk factors and stepping activity including; volume (step count), intensity (cadence) or inactivity (zero-steps/minute/day), in a multi-ethnic Asian population. Participants clinical data was collected at baseline and their physical activity was monitored for seven days, using an accelerometer (Actigraph GT3X+) in 2016. Tertiles (low, moderate, high) of the mean daily step count, peak one-minute, 30-min, 60-min cadences and time/day spent at zero-steps/minute were calculated. Adjusted linear regressions explored the association between stepping activity tertiles and cardiometabolic risk factors. A total of 635 participants (41% male, 67% Chinese, mean age 48.4 years) were included in the analyses. The mean daily step count was 7605 (median daily step count 7310) and 7.8 h of awake time per day were spent inactive (zero-steps/minute). A greater number of associations were found for step intensity than volume. Higher step intensity was associated with reduced body mass index (BMI), waist circumference, blood pressures and higher high-density lipoprotein (HDL). Future health promotion initiatives should consider the greater role of step intensity to reduce cardiometabolic risk.

scholarly journals Efficacy of a Mobile Social Networking Intervention in Promoting Physical Activity: Quasi-Experimental Study (Preprint)

2018 ◽  
Author(s):  
Huong Ly Tong ◽  
Enrico Coiera ◽  
William Tong ◽  
Ying Wang ◽  
Juan C Quiroz ◽  
...  
Keyword(s):  
Physical Activity ◽  
Experimental Study ◽  
Social Networking ◽  
Step Count ◽  
Daily Step ◽  
Mobile Social Networking ◽  
System Usability ◽  
Daily Step Count ◽  
Quasi Experimental ◽  
The Mean

BACKGROUND Technological interventions such as mobile apps, Web-based social networks, and wearable trackers have the potential to influence physical activity; yet, only a few studies have examined the efficacy of an intervention bundle combining these different technologies. OBJECTIVE This study aimed to pilot test an intervention composed of a social networking mobile app, connected with a wearable tracker, and investigate its efficacy in improving physical activity, as well as explore participant engagement and the usability of the app. METHODS This was a pre-post quasi-experimental study with 1 arm, where participants were subjected to the intervention for a 6-month period. The primary outcome measure was the difference in daily step count between baseline and 6 months. Secondary outcome measures included engagement with the intervention and system usability. Descriptive and inferential statistical tests were conducted; posthoc subgroup analyses were carried out for participants with different levels of steps at baseline, app usage, and social features usage. RESULTS A total of 55 participants were enrolled in the study; the mean age was 23.6 years and 28 (51%) were female. There was a nonstatistically significant increase in the average daily step count between baseline and 6 months (mean change=14.5 steps/day, P=.98, 95% CI –1136.5 to 1107.5). Subgroup analysis comparing the higher and lower physical activity groups at baseline showed that the latter had a statistically significantly higher increase in their daily step count (group difference in mean change from baseline to 6 months=3025 steps per day, P=.008, 95% CI 837.9-5211.8). At 6 months, the retention rate was 82% (45/55); app usage decreased over time. The mean system usability score was 60.1 (SD 19.2). CONCLUSIONS This study showed the preliminary efficacy of a mobile social networking intervention, integrated with a wearable tracker to promote physical activity, particularly for less physically active subgroups of the population. Future research should explore how to address challenges faced by physically inactive people to provide tailored advices. In addition, users’ perspectives should be explored to shed light on factors that might influence their engagement with the intervention.

scholarly journals Association of Habitual Physical Activity With Cardiovascular Disease Risk

2020 ◽  
Vol 127 (10) ◽  
pp. 1253-1260
Author(s):  
Honghuang Lin ◽  
Mayank Sardana ◽  
Yuankai Zhang ◽  
Chunyu Liu ◽  
Ludovic Trinquart ◽  
...  
Keyword(s):  
Physical Activity ◽  
Cardiovascular Disease ◽  
Daily Activity ◽  
Interquartile Range ◽  
Habitual Physical Activity ◽  
Daily Step ◽  
Cvd Risk ◽  
Heart Study ◽  
Daily Step Count ◽  
Predicted Risk

Rationale: A sedentary lifestyle is associated with increased risk for cardiovascular disease (CVD). Smartwatches enable accurate daily activity monitoring for physical activity measurement and intervention. Few studies, however, have examined physical activity measures from smartwatches in relation to traditional risk factors associated with future risk for CVD. Objective: To investigate the association of habitual physical activity measured by smartwatch with predicted CVD risk in adults. Methods and Results: We enrolled consenting FHS (Framingham Heart Study) participants in an ongoing eFHS (electronic Framingham Heart Study) at the time of their FHS research center examination. We provided participants with a smartwatch (Apple Watch Series 0) and instructed them to wear it daily, which measured their habitual physical activity as the average daily step count. We estimated the 10-year predicted risk of CVD using the American College of Cardiology/American Heart Association 2013 pooled cohort risk equation. We estimated the association between physical activity and predicted risk of CVD using linear mixed effects models adjusting for age, sex, wear time, and familial structure. Our study included 903 eFHS participants (mean age 53±9 years, 61% women, 9% non-White) who wore the smartwatch ≥5 hours per day for ≥30 days. Median daily step count was similar among men (7202 with interquartile range 3619) and women (7260 with interquartile range 3068; P =0.52). Average 10-year predicted CVD risk was 4.5% (interquartile range, 6.1%) for men and 1.2% (interquartile range, 2.2%) for women ( P =1.3×10 −26 ). Every 1000 steps higher habitual physical activity was associated with 0.18% lower predicted CVD risk ( P =3.2×10 −4 ). The association was attenuated but remained significant after further adjustment for body mass index ( P =0.01). Conclusions: In this community-based sample of adults, higher daily physical activity measured by a study smartwatch was associated with lower predicted risk of CVD. Future research should examine the longitudinal association of prospectively measured daily activity and incident CVD.

scholarly journals AB1320-HPR THE ASSOCIATION BETWEEN PHYSICAL ACTIVITY AND CARDIORESPIRATORY FITNESS IN PATIENTS WITH RHEUMATOID ARTHRITIS AND HIGH CARDIOVASCULAR RISK

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1949.2-1950
Author(s):  
J. Van den Hoek ◽  
M. Van der Leeden ◽  
G. Metsios ◽  
G. Kitas ◽  
H. Jorstad ◽  
...  
Keyword(s):  
Physical Activity ◽  
Cardiovascular Disease ◽  
Cardiorespiratory Fitness ◽  
Vigorous Physical Activity ◽  
Step Count ◽  
Activity Levels ◽  
Daily Step ◽  
Research Support ◽  
Daily Step Count ◽  
The Mean

Background:Rheumatoid arthritis (RA) is associated with increased risk of cardiovascular disease (CVD) disease and CV mortality1. High values of cardiorespiratory fitness (CRF) are protective against CVD and CV mortality2. Physical activity levels in patients with RA are low. Knowledge on whether physical activity is associated with CRF in patients with RA and high CV risk is scarce. This knowledge is important because improving the level of physical activity could improve CRF and lower CV risk in this group of patients with RA and high CV risk. However, it is unclear whether physical activity is associated with CRF in this group of patients. This study presents the preliminary results at baseline of the association of physical activity with CRF from an ongoing pilot study aimed at improving CRF through exercise therapy in patients with RA and high CV risk.Objectives:To determine (i) the level of physical activity in patients with RA and high CV risk and (ii) whether physical activity is associated with CRF in patients with RA and high CV risk.Methods:Patients with RA and high CV risk participated in this pilot study. Increased 10-year risk of CV mortality was determined by using the Dutch SCORE-table. Anthropometrics and disease characteristics were collected. Physical activity was assessed with an Actigraph accelerometer to determine the number of steps and intensity of physical activity expressed in terms of sedentary, light, and moderate-to-vigorous time per day. Participants wore the accelerometer for seven days. A minimum of four measurement days with a wear time of at least 10 hours was required. The VO2max measured with a graded maximal exercise test was used to determine the CRF. Pearson correlation coefficients were calculated for the associations between the different measures of physical activity and VO2max. For the variables that were associated, linear regression analysis was carried out, with pain and disease activity as possible confounders.Results:Thirteen females and five males were included in the study. The mean age was 66.5 (± 15.0) years. Only 22% of the patients met public health physical activity guidelines for the minimal amount of 150 minutes a week. The mean step count was 6237 (± 2297) steps per day and mean moderate-to-vigorous physical activity time was 16.50 (± 23.56) minutes per day. The median VO2max was 16.23 [4.63] ml·kg-1·min-1, which is under the standard. Pearson correlations showed a significant positive association for step count with VO2max. No associations were found for sedentary, light, and moderate-to-vigorous physical activity with VO2max. The significant association between step count and VO2max(p = 0.01) was not confounded by disease severity and pain.Discussion:Since better CRF protects against CVD, increasing daily step count may be a simple way to reduce the risk of CVD in patients with RA and high CV risk. However, these results need to be confirmed in a larger study group. Future research should investigate if improving daily step count will lead to better CRF levels and ultimately will lead to a reduction in CV risk in patients with RA and high CV risk.Conclusion:Physical activity levels of patients with RA and high CV risk do not meet public health requirements for physical activity criteria and the VO2max was under the standard. Step count is positively associated with CRF.References:[1]Agca et al. Atherosclerotic cardiovascular disease in patients with chronic inflammatory joint disorders. Heart. 2016;102(10):790-795.[2]Lemes et al. Cardiorespiratory fitness and risk of all-cause, cardiovascular disease, and cancer mortality in men with musculoskeletal conditions. J Phys Act Health. 2019;16;134-140.Disclosure of Interests:Joëlle van den Hoek: None declared, Marike van der Leeden: None declared, George Metsios: None declared, Georeg Kitas: None declared, Harald Jorstad: None declared, WIllem Lems Grant/research support from: Pfizer, Consultant of: Lilly, Pfizer, Michael Nurmohamed Grant/research support from: Not related to this research, Consultant of: Not related to this research, Speakers bureau: Not related to this research, Martin van der Esch: None declared

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 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.

Sign in / Sign up

Export Citation Format

Share Document