scholarly journals Daily step count and all-cause mortality in a sample of Japanese elderly people: a cohort study

2018 ◽  
Vol 18 (1) ◽  
Author(s):  
Naofumi Yamamoto ◽  
Hideo Miyazaki ◽  
Mieko Shimada ◽  
Naoki Nakagawa ◽  
Susumu S. Sawada ◽  
...  
Keyword(s):  
Cohort Study ◽  
Elderly People ◽  
Step Count ◽  
Daily Step ◽  
All Cause Mortality ◽  
Daily Step Count ◽  
Japanese Elderly

scholarly journals Daily step count and incident diabetes in community-dwelling 70-year-olds: a prospective cohort study

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Marcel Ballin ◽  
Peter Nordström ◽  
Johan Niklasson ◽  
Antti Alamäki ◽  
Joan Condell ◽  
...  
Keyword(s):  
Older Adults ◽  
Cohort Study ◽  
Dose Response ◽  
Prospective Cohort ◽  
Lower Risk ◽  
Community Dwelling ◽  
Step Count ◽  
Incident Diabetes ◽  
Daily Step ◽  
Daily Step Count

Abstract Background Older adults with diabetes take fewer steps per day than those without diabetes. The purpose of the present study was to investigate the association of daily step count with incident diabetes in community-dwelling 70-year-olds. Methods This prospective cohort study included N = 3055 community-dwelling 70-year-olds (52% women) who participated in a health examination in Umeå, Sweden during 2012–2017, and who were free from diabetes at baseline. Daily step count was measured for 1 week using Actigraph GT3X+ accelerometers. Cases of diabetes were collected from the Swedish National Patient Register. The dose-response association was evaluated graphically using a flexible parametric model, and hazard ratios (HR) with 95% confidence intervals (CI) were calculated using Cox regressions. Results During a mean follow-up of 2.6 years, diabetes was diagnosed in 81 participants. There was an inverse nonlinear dose-response association between daily step count and incident diabetes, with a steep decline in risk of diabetes from a higher daily step count until around 6000 steps/day. From there, the risk decreased at a slower rate until it leveled off at around 8000 steps/day. A threshold of 4500 steps/day was found to best distinguish participants with the lowest risk of diabetes, where those taking ≥ 4500 steps/day, had 59% lower risk of diabetes, compared to those taking fewer steps (HR, 0.41, 95% CI, 0.25–0.66). Adjusting for visceral adipose tissue (VAT) attenuated the association (HR, 0.64, 95% CI, 0.38–1.06), which was marginally altered after further adjusting for sedentary time, education and other cardiometabolic risk factors and diseases (HR, 0.58, 95% CI, 0.32–1.05). Conclusions A higher daily step count is associated with lower risk of incident diabetes in community-dwelling 70-year-olds. The greatest benefits occur at the lower end of the activity range, and much earlier than 10,000 steps/day. With the limitation of being an observational study, these findings suggest that promoting even a modest increase in daily step count may help to reduce the risk of diabetes in older adults. Because VAT appears to partly mediate the association, lifestyle interventions targeting diabetes should apart from promoting physical activity also aim to prevent and reduce central obesity.

scholarly journals Association of change in daily step count over five years with insulin sensitivity and adiposity: population based cohort study

BMJ ◽  
2011 ◽  
Vol 342 (jan13 1) ◽  
pp. c7249-c7249 ◽  
Author(s):  
T. Dwyer ◽  
A.-L. Ponsonby ◽  
O. C. Ukoumunne ◽  
A. Pezic ◽  
A. Venn ◽  
...  
Keyword(s):  
Cohort Study ◽  
Insulin Sensitivity ◽  
Population Based ◽  
Step Count ◽  
Daily Step ◽  
Daily Step Count

Cutoff Points for Step Count to Predict 1-year All-Cause Mortality in Patients with Idiopathic Pulmonary Fibrosis

Respiration ◽  
2021 ◽  
pp. 1-7
Author(s):  
Kazuya Shingai ◽  
Toshiaki Matsuda ◽  
Yasuhiro Kondoh ◽  
Tomoki Kimura ◽  
Kensuke Kataoka ◽  
...  
Keyword(s):  
Physical Activity ◽  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
Step Count ◽  
Cutoff Point ◽  
Daily Step ◽  
All Cause Mortality ◽  
Cutoff Points ◽  
Daily Step Count ◽  
Steps Per Day

<b><i>Background:</i></b> Although physical activity is associated with mortality in patients with idiopathic pulmonary fibrosis (IPF), reference values to interpret levels of physical activity are lacking. <b><i>Objectives:</i></b> This study aimed to investigate the prognostic significance of physical activity assessed by step count and its cutoff points for all-cause mortality. <b><i>Methods:</i></b> We measured physical activity (steps per day) using an accelerometer in patients with IPF at the time of diagnosis. Relationships among physical activity and mortality, as well as cutoff points of daily step count to predict all-cause mortality were examined. <b><i>Results:</i></b> Eighty-seven patients (73 males) were enrolled. Forty-four patients (50.1%) died during the follow-up (median 54 months). In analysis adjusting for Gender-Age-Physiology stage and 6-min walk distance, daily step count was an independent predictor of all-cause mortality (hazard ratio (HR) = 0.820, 95% confidence interval (CI) = 0.694–0.968, <i>p</i> = 0.019). The optimal cutoff point (receiving operating characteristic analysis) for 1-year mortality was 3,473 steps per day (sensitivity = 0.818 and specificity = 0.724). Mortality was significantly lower in patients with a daily step count exceeding 3,473 steps than in those whose count was 3,473 or less (HR = 0.395, 95% CI = 0.218–0.715, <i>p</i> = 0.002). <b><i>Conclusions:</i></b> Step count, an easily interpretable measurement, was a significant predictor of all-cause mortality in patients with IPF. At the time of diagnosis, a count that exceeded the cutoff point of 3,473 steps/day more than halved mortality. These findings highlight the importance of assessing physical activity in this patient population.

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.

Sign in / Sign up

Export Citation Format

Share Document