scholarly journals Physical Activity Levels of Adolescents and Adults With Cerebral Palsy in Urban South Africa

2021 ◽  
Vol 12 ◽  
Author(s):  
Roshaan Salie ◽  
Maaike M. Eken ◽  
Kirsten A. Donald ◽  
A. Graham Fieggen ◽  
Nelleke G. Langerak
Keyword(s):  
Physical Activity ◽  
South Africa ◽  
Step Count ◽  
Daily Step ◽  
Physically Active ◽  
Urban South ◽  
Daily Step Count ◽  
Adolescents And Adults ◽  
Level I ◽  
Gmfcs Level

Background: Research in high income countries shows that people with cerebral palsy (CP) are less physically active than typically developing (TD) peers, but less is known regarding physical activity (PA) in those with CP in low-to-middle income countries. The aim of this study was to determine daily step count and levels of PA in adolescents and adults with CP living in urban South Africa, compared to TD peers, and to determine associations with sex, Gross Motor Function Classification System (GMFCS) level, body mass index and socio-economic status.Materials and Methods: This case–control study included 26 adolescents and 22 adults with CP (GMFCS Level I-V) and matched TD peers (25 and 30, respectively). Participants wore an ActiGraph GT3X for 7 consecutive days to determine step count and time (minutes per hour) spent in PA levels, including sedentary (SED), low physical activity (LPA) and moderate to vigorous physical activity (MVPA).Results: The daily step count and PA levels for ambulant adolescents with CP (GMFCS level I-III) were similar to TD peers, while this was less for adolescents classified in GMFCS level IV-V. Daily step count, SED and MVPA were similar for adults classified in GMFCS level I-II compared to TD adults, while all parameters were lower for adults using assistive devices (GMFCS level III) and non-ambulant adults (GMFCS level IV-V) compared to TD peers. Daily step count and PA levels were inversely associated with GMFCS, while no other associations were found.Conclusion: People with CP who were more mobile dependent (higher GMFCS level) were less active. However, adolescents and adults with CP classified as GMFCS levels I-II living in urban South Africa recorded similar step count and PA levels as their TD peers. This was also the case for adolescents using assistive devices, though not for those in the adult group (GMFCS level III). Furthermore, it was apparent that even the ambulant individuals with CP and TD cohorts were relatively inactive. Intervention programs for CP and TD adolescents should be aimed at finding strategies to keep adolescents physically active well into adulthood, in order to promote physical health, social and emotional well-being and independence.

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.

A Pilot Study Comparing Pedometer Counts with Reported Physical Activity in Elementary Schoolchildren

2004 ◽  
Vol 16 (4) ◽  
pp. 355-367 ◽  
Author(s):  
Greet Cardon ◽  
Ilse De Bourdeaudhuij
Keyword(s):  
Physical Activity ◽  
Pilot Study ◽  
Vigorous Physical Activity ◽  
Step Count ◽  
Regression Equations ◽  
Moderate Correlation ◽  
Daily Step ◽  
Step Counts ◽  
Daily Step Count

In this study pedometer counts were recorded for 6 consecutive days for 92 children (mean age = 9.6 years; range 6.5–12.7) and were compared with the number of minutes per day in which the participants engaged in moderate-to-vigorous physical activity (MVPA). Diaries filled out with the assistance of one of the children’s parents were used to determine minutes of MVPA. The average daily step count was significantly higher in boys than in girls, although the average daily MVPA engagement in minutes did not vary significantly between genders. Based on the regression equations, 60 min of MVPA was equivalent to 15,340 step counts in boys, 11,317 step counts in girls, and 13,130 step counts when results for both genders were combined. A moderate correlation (r = .39, p < .001) was found between pedometer step counts and reported minutes of MVPA. According to the present study findings, however, predictions and promotion of daily MVPA engagement in children based on pedometer counts per day should be made with caution.

scholarly journals Self-reported task-oriented physical activity: A comparison with objective daily step count in COPD

2018 ◽  
Vol 140 ◽  
pp. 63-70 ◽  
Author(s):  
Emily S. Wan ◽  
Ana Kantorowski ◽  
Diana Homsy ◽  
Reema Kadri ◽  
Caroline R. Richardson ◽  
...  
Keyword(s):  
Physical Activity ◽  
Step Count ◽  
Daily Step ◽  
Daily Step Count ◽  
Task Oriented

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 7500 steps per day is associated with lower cardiovascular risk in rural residents with a high prevalence of sedentary lifestyle

2021 ◽  
Vol 20 (Supplement_1) ◽  
Author(s):  
I Madujibeya ◽  
L Misook ◽  
T Lennie ◽  
G Mudd-Martin ◽  
M Biddle ◽  
...  
Keyword(s):  
Risk Score ◽  
Rural Areas ◽  
Framingham Risk Score ◽  
Step Count ◽  
Daily Step ◽  
Cvd Risk ◽  
Rural Residents ◽  
Physically Active ◽  
Framingham Risk ◽  
Daily Step Count

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Sedentary lifestyle is one of the modifiable risk factors that account for the high prevalence of cardiovascular diseases (CVD) in the rural areas.1,2 Daily step counting, using wearable devices, is increasingly used to monitor physical activity levels in interventions targeting CVD risk reduction in the rural areas.3,4 However, there is a lack of evidence to support a daily step count threshold that may reduce CVD risk among rural residents. The purpose of this study was to examine the relationship between daily step count cut-points and CVD risk. Methods This secondary analysis included 312 adults living in the rural areas in the southern United States. Daily step counts from pedometers were collected for 14 consecutive days. Established cut-points were used to categorize participants into groups based on mean daily step count as sedentary (≤ 5000 steps/day), less active (5000 -7499 steps/per), and physically active (≥7500 steps/day).5-7 CVD risk was measured with the Framingham risk score. Generalized additive models were used to examine differences among the 3 activity groups in Framingham risk score, controlling for educational level, perceived physical health status, depressive, marital status, and years of residence in a rural county. Results Among the participants (75% female, mean age 50.1 (±13.6) years), 40.7% were sedentary, 35.2% were less active, and 24.0% were physically active. The average Framingham risk score was 11.2% (±9.4%). Framingham risk scores were 1.7% lower in the less active compared to the sedentary group, but the effect was not significant (p &lt;  .11), and 2.6% lower in the physically active compared to the sedentary group (p &lt; .04). The model accounted for 22% of the variation in Framingham risk scores. Conclusion These findings indicate that rural residents who averaged 7500 steps or more per day had lower CVD risk, and the difference is clinically significant.

scholarly journals An m-Health Intervention Using a Smartphone App to Improve Physical Activity in College Students: A Randomized Controlled Trial (P16-025-19)

2019 ◽  
Vol 3 (Supplement_1) ◽  
Author(s):  
Hala Nawaiseh ◽  
William Mcintoch
Keyword(s):  
Physical Activity ◽  
College Students ◽  
Body Fat ◽  
Fat Mass ◽  
Step Count ◽  
Smartphone App ◽  
Daily Step ◽  
Post Intervention ◽  
Intervention Effects ◽  
Daily Step Count

Abstract Objectives Determine the efficacy of a 12-week mobile health (m-Health) intervention with the goal of increasing daily step counts on physical activity, improve body mass index (BMI), and body fat mass among college students. Methods A 12- week randomized control trial was conducted. College students (n = 130) between 18–30 years of age were randomized to one of two conditions: Intervention (n = 65) and control (n = 65). All participants then had the Smartphone app downloaded onto their mobile phone to record their daily step count in order to provide a measurement of their baseline physical activity levels. Intervention group received physical activity goals of (10,000 step/day), information on the benefits of exercise, and automatic feedback. Control group received information on the benefits of exercise without any kind of intervention. The primary change was daily step count between baseline and follow-up. Results In this study, there were no significant intervention effects for BMI, fat mass and % body fat. Significant intervention effects were found for body weight (mean ± SE: 0.419 ± 0.164; P = 0.013). Physical activity as expressed by step counts significantly increased from baseline to post intervention (10,022 weekly/step; P = 0.008). Despite this, post intervention changes in outcomes were not significantly different from controls. Conclusions The m-Health appeared to be feasible and acceptable. In this study, the results demonstrate that (m-Health) app can significantly increase physical activity in a college student sample by setting specific goal, using self monitoring and feedback. Funding Sources None.

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.

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