Psychological factors associated with daily step count in knee osteoarthritis

ABSTRACTGovernment restrictions applied during the COVID-19 pandemic in the UK led to the disruption of many people’s physical activity routines, with sports and leisure facilities closed and outdoor exercise only permitted once per day. In this study we investigated which population groups were impacted most in terms of reduced physical activity levels during these periods, and which groups benefitted in terms of increasing their usual level of physical activity. We surveyed UK residents, sampled through users of a rewards-for-exercise app (Sweatcoin; n=749) and an online panel (Prolific; n=907). Of the app users, n=487 further provided daily step-count data collected by the app, prior to, and during the periods of restrictions between March and June 2020. Regression models were applied to investigate factors associated with subjective change (perceived change in physical activity) and objective change (log-percentage change in daily step-count) in physical activity during the periods of restrictions. ANOVAs were used to further investigate the significant factors identified. Key factors associated with a substantial subjective reduction in physical activity included those classed as obese, gym users and people living in urban areas. All participants had a reduced step count during restrictions, with Black, Asian and minority ethnic (BAME) groups, students and urban dwellers showing the largest reductions. Therefore, targeted interventions are required to ensure that the physical and mental health impacts of sedentary behaviour are not exacerbated over the long-term by significant reductions in physical activity identified in these groups, particularly those who are also more vulnerable to the COVID-19 virus.

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Continuous Wrist-Worn Accelerometry Captures Change in Average Daily Step Count in People with Multiple Sclerosis Over One Year

10.26226/morressier.59a3eda0d462b8028d89455d ◽

2017 ◽

Author(s):

Valerie J. Block

Keyword(s):

Multiple Sclerosis ◽

Step Count ◽

Daily Step ◽

Daily Step Count ◽

One Year

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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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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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Are Psychological Factors Associated with Pain Worsening in Individuals with Knee Osteoarthritis? A Systematic Review

Journal of Novel Physiotherapies ◽

10.4172/2165-7025.1000268 ◽

2015 ◽

Vol 05 (04) ◽

Cited By ~ 1

Author(s):

Ryo Tanaka

Keyword(s):

Systematic Review ◽

Knee Osteoarthritis ◽

Psychological Factors ◽

Factors Associated

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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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Daily Step Count Profile Data for 61 Days

10.15788/m2bc7c ◽

2016 ◽

Cited By ~ 1

Author(s):

Elijah Meyer ◽

Mark Greenwood ◽

Tan Tran

Keyword(s):

Step Count ◽

Daily Step ◽

Profile Data ◽

Daily Step Count

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Feedback From Activity Trackers Improves Daily Step Count After Knee and Hip Arthroplasty: A Randomized Controlled Trial

The Journal of Arthroplasty ◽

10.1016/j.arth.2018.06.024 ◽

2018 ◽

Vol 33 (11) ◽

pp. 3422-3428 ◽

Cited By ~ 13

Author(s):

Neill Van der Walt ◽

Lucy J. Salmon ◽

Benjamin Gooden ◽

Matthew C. Lyons ◽

Michael O'Sullivan ◽

...

Keyword(s):

Randomized Controlled Trial ◽

Hip Arthroplasty ◽

Controlled Trial ◽

Step Count ◽

Daily Step ◽

Randomized Controlled ◽

Activity Trackers ◽

Daily Step Count ◽

Knee And Hip Arthroplasty

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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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