Relationship Between the Change in Daily Step Count and Brachial-Ankle Wave Velocity During a Pedometer-Based Physical Activity Program for Older Adults

Abstract Background One in four older adults in Denmark and almost half of the very old above 75 do not meet the World Health Organization’s recommendations for a minimum of physical activity (PA). A cost-efficient and effective way to increase focus on and motivation for daily walking might be to use Physical Activity Monitors (PAMs) in combination with behavioural change intervention. Thus, the objective of this randomized controlled study was to investigate the effect of Motivational Interviewing (MI) as an add-on intervention to a PAM-based intervention measured in community-dwelling older adults. Methods This two-arm parallel group randomized controlled effectiveness trial compared a 12-weeks PAM-based intervention with additional MI (PAM+MI group) with a PAM-based intervention alone (PAM group). The primary outcome, average daily step count, was analysed with a linear regression model, adjusted for sex and baseline daily step count. Following the intention-to-treat principle, multiple imputation based on baseline step count, sex and age was performed. Results In total, 38 participants were randomized to the PAM intervention and 32 to the PAM+MI intervention arm. During the intervention period, PAM+MI participants walked on average 909 more steps per day than PAM participants, however insignificant (95%CI: − 71; 1889) and reported 2.3 points less on the UCLA Loneliness Scale (95%CI: − 4.5; − 1.24). Conclusion The use of MI, in addition to a PAM-based intervention among older adults in PA promoting interventions hold a potential clinically relevant effect on physical activity and should thus be investigated further with adequately powered RCTs. Trial registration This study was pre-registered in the clinicaltrials.gov database with identifier: NCT03906162.

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Logging steps using the website, app, Fitbit or a combination influences engagement with the 10,000 Steps physical activity program: An observational study (Preprint)

10.2196/preprints.22151 ◽

2020 ◽

Author(s):

Anna Therese Rayward ◽

Corneel Vandelanotte ◽

Anetta Van Itallie ◽

Mitchell Jon Duncan

Keyword(s):

Physical Activity ◽

Real World ◽

Physical Activity Program ◽

Combination Group ◽

Daily Step ◽

Activity Program ◽

Daily Step Count ◽

User Groups ◽

The Mean ◽

Tracking Devices

BACKGROUND Engagement with online health behaviour-change programs is positively associated with their effectiveness. However, it is unclear whether tracking devices that sync data automatically (e.g., Fitbit) result in different engagement levels compared with manually entering data. OBJECTIVE This study examined how different methods of logging steps in the freely-available 10,000 Steps physical activity program influence engagement with the program. METHODS A subsample of users (n=22,142, 71% female) of the free 10,000 Steps physical activity program (www.10000steps.org.au) were classified into one of five user-groups based on the method used to log steps: Website-only (n=14,617; 66.0%); App-only (n=2,100; 9.5%); Fitbit-only (n=1,705; 7.7%); Web-and-App (n=2,057; 9.3%); and Fitbit-Combination (combination of web-app-and-Fitbit; n=1,663; 7.5%). Users’ engagement was assessed using generalised linear regression models to examine differences between user groups in website sessions, minutes per session, page views, pages per session, daily step count and total step log entries. Binary logistic regression was used to measure associations between user groups in participation in Challenges and Tournaments and receiving and sending friend requests. Time to non-usage attrition was assessed using Cox proportion hazards regression. RESULTS All outcomes are relative to the Website-only group. The App-only group had significantly fewer website sessions (-6.9, 95% CI -7.6--6.2), and the Fitbit-only (10.6, 95% CI 8.8-12.3), Web-and-App (1.5, 95% CI 0.4-2.6) and Fitbit-Combination (8.0, 95% CI 6.2-9.7) groups had significantly more. The App-only (-0.7, 95% CI -0.9--0.4) and Fitbit-only (-0.5, 95% CI -0.7--0.2) groups spent significantly fewer minutes, whilst the Fitbit-Combination group (0.2, 95% CI 0.0-0.5) spent significantly more minutes on the website each session. All groups, except the Fitbit-Combination group, viewed significantly fewer website pages per session. The mean daily step count was significantly lower for the App-only (-201.9, 95% CI -387.7--116.0) and Fitbit-only (-492.9, 95% CI -679.9--305.8) groups, but it was significantly higher for the Web-and-App group (258.0, 95% CI 76.9-439.2). The Fitbit-only (5.0, 95% CI 3.4-6.6), Web-and-App (7.2, 95% CI 5.9-8.6), and Fitbit-Combination (15.6, 95% CI 13.7-17.5) groups all significantly logged a greater number of step entries. The App-only group was less likely (OR 0.65, 95% CI 0.46-0.94) and all other groups were significantly more likely to participate in Challenges. All groups, except the App-only group, had a significant difference in time to non-usage attrition relative to the Website-only group (HR range=0.55 to 0.75; p <.001). The mean time to non-usage attrition was 35±26 days (Website-only=32±22; App-only=33±23; Fitbit-only=40±29, Web-and-App=39±27; Fitbit-Combination=50±40). CONCLUSIONS The use of a Fitbit in combination with the 10,000 Steps app and/or website enhanced engagement with a real-world physical activity program. Integrating tracking devices that sync data automatically into real-world physical activity interventions is one strategy to improve engagement.

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Effects of a DVD-delivered randomized controlled physical activity intervention on functional health in cancer survivors

BMC Cancer ◽

10.1186/s12885-021-08608-8 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Elizabeth A. Salerno ◽

Neha P. Gothe ◽

Jason Fanning ◽

Lindsay L. Peterson ◽

Graham A. Colditz ◽

...

Keyword(s):

Physical Activity ◽

Older Adults ◽

Cancer Survivors ◽

Physical Activity Intervention ◽

Physical Activity Program ◽

Functional Health ◽

Link Type ◽

Time Interaction ◽

Activity Program ◽

Group Time

Abstract Background Supervised physical activity interventions improve functional health during cancer survivorship, but remain costly and inaccessible for many. We previously reported on the benefits of a DVD-delivered physical activity program (FlexToBa™) in older adults. This is a secondary analysis of the intervention effects among cancer survivors in the original sample. Methods Low active, older adults who self-reported a history of cancer (N = 46; M time since diagnosis = 10.7 ± 9.4 years) participated in a 6-month, home-based physical activity intervention. Participants were randomized to either the DVD-delivered physical activity program focused on flexibility, toning, and balance (FlexToBa™; n = 22) or an attentional control condition (n = 24). Physical function was assessed by the Short Physical Performance Battery (SPPB) at baseline, end of intervention, and at 12 and 24 months after baseline. Results Repeated measures linear mixed models indicated a significant group*time interaction for the SPPB total score (β = − 1.14, p = 0.048), driven by improved function from baseline to six months in the FlexToBa™ group. The intervention group also had improved balance (β = − 0.56, p = 0.041) compared with controls. Similar trends emerged for the SPPB total score during follow-up; the group*time interaction from 0 to 12 months approached significance (β = − 0.97, p = 0.089) and was significant from 0 to 24 months (β = − 1.84, p = 0.012). No significant interactions emerged for other outcomes (ps > 0.11). Conclusions A DVD-delivered physical activity intervention designed for cancer-free older adults was capable of eliciting and maintaining clinically meaningful functional improvements in a subgroup of cancer survivors, with similar effects to the original full sample. These findings inform the dissemination of evidence-based physical activity programs during survivorship. Trial registration ClinicalTrials.govNCT01030419. Registered 11 December 2009

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The translation of preschoolers’ physical activity guidelines into a daily step count target

Journal of Sports Sciences ◽

10.1080/02640414.2014.981850 ◽

2014 ◽

Vol 33 (10) ◽

pp. 1051-1057 ◽

Cited By ~ 24

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

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Determining the reach of a home-based physical activity program for older adults within the context of a randomized controlled trial

Health Education Research ◽

10.1093/her/cyu049 ◽

2014 ◽

Vol 29 (5) ◽

pp. 861-869 ◽

Cited By ~ 5

Author(s):

S. M. Harden ◽

J. T. Fanning ◽

R. W. Motl ◽

E. McAuley ◽

P. A. Estabrooks

Keyword(s):

Physical Activity ◽

Older Adults ◽

Randomized Controlled Trial ◽

Controlled Trial ◽

Physical Activity Program ◽

Randomized Controlled ◽

Activity Program ◽

Home Based

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Effects Of A Long-term Physical Activity Program On Accelerometry-based Sedentary Time In Older Adults

Medicine & Science in Sports & Exercise ◽

10.1249/01.mss.0000477849.39535.d3 ◽

2015 ◽

Vol 47 ◽

pp. 515-516

Author(s):

Amal A. Wanigatunga ◽

Walter T. Ambrosius ◽

Mary M. McDermott ◽

Abby C. King ◽

Roger A. Fielding ◽

...

Keyword(s):

Physical Activity ◽

Older Adults ◽

Sedentary Time ◽

Physical Activity Program ◽

Activity Program

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Abstract 11: mActive: A Blinded, Randomized mHealth Trial Supporting Digital Tracking and Smart Texting for Promotion of Physical Activity

Circulation ◽

10.1161/circ.131.suppl_1.11 ◽

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.

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Neural Network–Based Algorithm for Adjusting Activity Targets to Sustain Exercise Engagement Among People Using Activity Trackers: Retrospective Observation and Algorithm Development Study

JMIR mhealth and uhealth ◽

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.

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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

International Journal of Behavioral Nutrition and Physical Activity ◽

10.1186/s12966-020-01043-1 ◽

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

BMC Public Health ◽

10.1186/s12889-020-09929-2 ◽

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.

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