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.

Comparison of Pedometer and Accelerometer Measures of Physical Activity in Preschool Children

2007 ◽  
Vol 19 (2) ◽  
pp. 205-214 ◽  
Author(s):  
Greet Cardon ◽  
Ilse De Bourdeaudhuij
Keyword(s):  
Physical Activity ◽  
Preschool Children ◽  
Vigorous Physical Activity ◽  
Step Count ◽  
Activity Levels ◽  
Accelerometer Data ◽  
Strongly Correlated ◽  
Daily Step ◽  
Step Counts ◽  
Daily Step Count

In this study, daily step counts were recorded for 4 consecutive days in 129 four- and five-year-old children. To compare daily Yamax Digiwalker step counts with minutes of engagement in moderate to vigorous physical activity (MVPA), concurrent accelerometer data were collected in a random subsample (n = 76). The average daily step count was 9,980 (± 2,605). Step counts and MVPA minutes were strongly correlated (r = .73, p < .001). The daily step count of 13,874, equating to 1-hr MVPA engagement, was reached by 8% of the children. Daily step counts in preschool children give valid information on physical activity levels—daily step counts in preschoolers are low.

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

scholarly journals Prediction of Optimal Daily Step Count Achievement from Segmented School Physical Activity

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Ryan D. Burns ◽  
Timothy A. Brusseau ◽  
James C. Hannon
Keyword(s):  
Physical Activity ◽  
Activity Patterns ◽  
Step Count ◽  
Activity Levels ◽  
Daily Step ◽  
Step Counts ◽  
School Aged Children ◽  
Daily Step Count ◽  
Generalized Linear Mixed Effects ◽  
Predictive Relationship

Optimizing physical activity in childhood is needed for prevention of disease and for healthy social and psychological development. There is limited research examining how segmented school physical activity patterns relate to a child achieving optimal physical activity levels. The purpose of this study was to examine the predictive relationship between step counts during specific school segments and achieving optimal school (6,000 steps/day) and daily (12,000 steps/day) step counts in children. Participants included 1,714 school-aged children (mean age =9.7±1.0years) recruited across six elementary schools. Physical activity was monitored for one week using pedometers. Generalized linear mixed effects models were used to determine the adjusted odds ratios (ORs) of achieving both school and daily step count standards for every 1,000 steps taken during each school segment. The school segment that related in strongest way to a student achieving 6,000 steps during school hours was afternoon recess (OR = 40.03;P<0.001) and for achieving 12,000 steps for the entire day was lunch recess (OR = 5.03;P<0.001). School segments including lunch and afternoon recess play an important role for optimizing daily physical activity in children.

Abstract P178: Pedometer Feedback Intervention Increases Time Spent in Moderate-to-Vigorous Physical Activity in Cardiac Rehabilitation Patients

Circulation ◽  
2013 ◽  
Vol 127 (suppl_12) ◽  
Author(s):  
Cemal Ozemek ◽  
Wonwoo Byun ◽  
Katrina Riggin ◽  
Scott Strath ◽  
Leonard Kaminsky
Keyword(s):  
Physical Activity ◽  
Cardiac Rehabilitation ◽  
Vigorous Physical Activity ◽  
Activity Level ◽  
Step Count ◽  
Intensity Level ◽  
Activity Levels ◽  
Daily Step ◽  
Light Physical Activity ◽  
Step Counts

Introduction: Pedometer feedback with step goals has previously been demonstrated to be effective in increasing daily steps in cardiac rehabilitation patients. These monitors allow the individual to track steps taken during a day, which may influence the frequency or duration of structured physical activity that is intended to achieve a step goal. However, it is not known whether an increase in step counts by pedometer feedback with step goals also increases time spent in recommended intensity levels for improved health, specifically moderate-to vigorous physical activity (MVPA), in cardiac rehabilitation patients. Hypothesis: Pedometer feedback with weekly step goals will increase time spent in MVPA, mediated by an increase in step counts in cardiac rehabilitation patients. Methods: A total of 31 (22 men and 9 women, age 62 ± 9 years) patients participated in a 12-week maintenance cardiac rehabilitation, pedometer based step goal intervention. Prior to the intervention, each subject’s one week baseline average daily step count was measured and 10% of this value was used to increase step goals during the intervention. Each week the step goal was met, the following week’s goal was appropriately increased. However, if the step goal for the week was not achieved, the step goal would not increase until the goal was fulfilled. Additionally, daily step counts and time spent in MVPA and light physical activity were assessed at baseline (without pedometer feedback) and for each intervention week (with pedometer feedback) using a Kenz Lifecorder PLUS monitor (Nagoya, Japan). Average time spent in light physical activity (activity level of 1-2) and MVPA (activity levels ≥3), were determined according to activity intensity level defined by the manufacturer’s analyses program. Results: The average step count for the baseline week was 5546 ± 2679 steps/day which significantly increased to 8348± 3613 steps/day by week 12 (p<0.01). The average time spent in MVPA also significantly increased (p<0.01) from 19 ± 16 min/day at baseline to 38 ± 23 min/day at week 12. In addition, there was a significant increase (p<0.05) in time spent in light physical activity from baseline (42 ± 18 min/day) to week 12 (51 ± 24 min/day). Conclusion: Findings of this study demonstrate that a 12-week pedometer feedback-based intervention was effective in increasing time spent in MVPA in maintenance cardiac rehabilitation patients. Cardiac rehabilitation facilities can utilize pedometer feedback and goal setting to promote increases in time spent in recommended activity levels previously associated with improved health outcomes.

scholarly journals Predictors of Daily Steps at 1-Year Poststroke

Stroke ◽  
2021 ◽  
Author(s):  
Reed Handlery ◽  
Elizabeth W. Regan ◽  
Jill C. Stewart ◽  
Christine Pellegrini ◽  
Courtney Monroe ◽  
...  
Keyword(s):  
Physical Activity ◽  
Characteristic Curve ◽  
Secondary Analysis ◽  
Berg Balance Scale ◽  
Step Count ◽  
Stroke Severity ◽  
Daily Step ◽  
Step Counts ◽  
Daily Step Count ◽  
Walking Speeds

Background and Purpose: Walking has the potential to improve endurance and community participation after stroke. Obtaining ≥6000 daily steps can decrease subsequent stroke risk. Early identification of those prone to low daily steps could facilitate interventions that lead to increased walking and improved health. The purpose of this study was to (1) determine which factors at 2 months poststroke can predict daily step counts at 1 year and (2) determine what step count at 2 months corresponds to obtaining ≥6000 daily steps at 1-year poststroke. Methods: This was a secondary analysis of data from the Locomotor Experience Applied Post Stroke trial, which enrolled participants with walking speeds <0.80 m/second at 2 months poststroke. Daily steps were assessed at 2 months and 1-year poststroke. Linear regression was used to predict daily step counts at 1 year based on factors including age, sex, race and/or ethnicity, stroke severity, walking speed, endurance, fitness, motor function, balance, and balance confidence. A receiver operating characteristic curve determined which step count corresponded to reaching ≥6000 steps at 1 year. Results: Data from 206 participants, mean age=63 (13) years, 43% female, mean baseline daily step count=2922 (2749) steps, were analyzed. The final model to predict daily steps at 1 year poststroke contained daily steps at 2 months and balance (Berg Balance Scale score); these factors explained 38% of the variability in daily steps at 1 year ( P ≤0.001). Participants obtaining ≥1632 daily steps at 2 months were 1.86 (95% CI, 1.52–2.27) times more likely to reach ≥6000 daily steps at 1-year poststroke. Conclusions: Daily steps and balance at 2 months poststroke were the strongest predictors of future daily steps. Improving daily physical activity and targeting balance early after stroke may be necessary to increase physical activity at 1-year poststroke.

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 Step-count guidelines referenced on 60-minutes of moderate/vigorous physical activity

2015 ◽  
Vol 21 (1) ◽  
pp. 92-99
Author(s):  
Fabio Eduardo Fontana ◽  
Michael Pereira da Silva ◽  
Ripley Marston ◽  
Kevin Finn ◽  
Jere Gallagher
Keyword(s):  
Physical Activity ◽  
Diagnostic Accuracy ◽  
Vigorous Physical Activity ◽  
Roc Curves ◽  
Sixth Grade ◽  
Step Count ◽  
Step Counts ◽  
Sixth Grade Students ◽  
Good Diagnostic Accuracy ◽  
Full Day

The purpose of this study was to establish step-count guidelines for sixth-grade students and assess the ability of step-counts to discriminate between students achieving and not achieving 60-minutes of moderate to vigorous physical activity daily. 201 sixth-grade students completed the study. They wore a pedometer and an accelerometer at the waist level for one full day. ROC curves were used to establish step-count guidelines and determine the diagnostic accuracy of step-counts. Sixth grade students need 12,118 steps/day to reach adequate daily levels of physical activity. The AUC indicated good diagnostic accuracy of step-counts. Suggested step-count guidelines can be a useful tool for identifying children who need to increase their daily levels of physical activity. The step-count cutoff proposed in this study is adequate for discriminating between sixth grade students reaching and not reaching recommended levels of physical activity.

scholarly journals Development of Step-Count Cut Points for School-Day Vigorous Physical Activity

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Ryan D. Burns ◽  
Timothy A. Brusseau ◽  
You Fu ◽  
Peng Zhang
Keyword(s):  
Physical Activity ◽  
Vigorous Physical Activity ◽  
Area Under The Curve ◽  
Roc Curves ◽  
Step Count ◽  
Convenience Sample ◽  
Cut Points ◽  
Step Counts ◽  
School Day ◽  
The Usa

Background. No study has established step-count cut points for varying amounts of accelerometer-assessed vigorous physical activity (VPA) accrued during the school day in children. The purpose of this study was to establish step-count cut points for discriminating children meeting VPA in 5 minutes, 10 minutes, 15 minutes, and 20 minutes per 7-hour school day. Methods. Participants were a convenience sample of 1,053 children (mean age = 8.4 (1.8) years) recruited from 5 schools from the Mountain West region of the USA. Data within students were observed across multiple semesters totaling 2,119 separate observations. Step counts and time in VPA were assessed using ActiGraph wGT3X-BT triaxial accelerometers that were worn during the entirety of a 7-hour school day for one school week. Average censored step counts and minutes in VPA were calculated across 3 to 5 days. Receiver operating characteristic (ROC) curves were employed to derive step counts via calculation of the maximum Youden J statistic. Results. Area-under-the-curve (AUC) scores ranged from AUC = 0.81 (95% CI: 0.78–0.83; p<0.001) for meeting at least 5 minutes of VPA to AUC = 0.94 (95% CI: 0.88–1.00, p<0.001) for meeting at least 20 minutes of VPA. Approximately 3,460 steps best discriminated children meeting at least 5 minutes of VPA (sensitivity = 74.0%, specificity = 74.0%, and accuracy = 74.1%) and approximately 5,628 steps best discriminated children meeting at least 20 minutes per day of VPA (sensitivity = 85.7%, specificity = 95.1%, and accuracy = 95.1%). Conclusion. Step counts can discriminate with reasonable accuracy children that meet at least 5 minutes of school-day VPA and with strong accuracy children that meet 20 minutes of school-day VPA.

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