scholarly journals Detection of Flares by Decrease in Physical Activity, Collected Using Wearable Activity Trackers in Rheumatoid Arthritis or Axial Spondyloarthritis: An Application of Machine Learning Analyses in Rheumatology

2019 ◽  
Vol 71 (10) ◽  
pp. 1336-1343 ◽  
Author(s):  
Laure Gossec ◽  
Frédéric Guyard ◽  
Didier Leroy ◽  
Thomas Lafargue ◽  
Michel Seiler ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Physical Activity ◽  
Machine Learning ◽  
Axial Spondyloarthritis ◽  
Activity Trackers ◽  
Wearable Activity Trackers

scholarly journals Physical Activity Assessment Using an Activity Tracker in Patients with Rheumatoid Arthritis and Axial Spondyloarthritis: Prospective Observational Study (Preprint)

2017 ◽  
Author(s):  
Charlotte Jacquemin ◽  
Hervé Servy ◽  
Anna Molto ◽  
Jérémie Sellam ◽  
Violaine Foltz ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Physical Activity ◽  
Observational Study ◽  
Mobile Devices ◽  
Prospective Observational Study ◽  
Axial Spondyloarthritis ◽  
World Health ◽  
Activity Tracker ◽  
Activity Trackers ◽  
Number Of Patients

BACKGROUND Physical activity can be tracked using mobile devices and is recommended in rheumatoid arthritis (RA) and axial spondyloarthritis (axSpA) management. The World Health Organization (WHO) recommends at least 150 min per week of moderate to vigorous physical activity (MVPA). OBJECTIVE The objectives of this study were to assess and compare physical activity and its patterns in patients with RA and axSpA using an activity tracker and to assess the feasibility of mobile devices in this population. METHODS This multicentric prospective observational study (ActConnect) included patients who had definite RA or axSpA, and a smartphone. Physical activity was assessed over 3 months using a mobile activity tracker, recording the number of steps per minute. The number of patients reaching the WHO recommendations was calculated. RA and axSpA were compared, using linear mixed models, for number of steps, proportion of morning steps, duration of total activity, and MVPA. Physical activity trajectories were identified using the K-means method, and factors related to the low activity trajectory were explored by logistic regression. Acceptability was assessed by the mean number of days the tracker was worn over the 3 months (ie, adherence), the percentage of wearing time, and by an acceptability questionnaire. RESULTS A total of 157 patients (83 RA and 74 axSpA) were analyzed; 36.3% (57/157) patients were males, and their mean age was 46 (standard deviation [SD] 12) years and mean disease duration was 11 (SD 9) years. RA and axSpA patients had similar physical activity levels of 16 (SD 11) and 15 (SD 12) min per day of MVPA (P=.80), respectively. Only 27.4% (43/157) patients reached the recommendations with a mean MVPA of 106 (SD 77) min per week. The following three trajectories were identified with constant activity: low (54.1% [85/157] of patients), moderate (42.7% [67/157] of patients), and high (3.2% [5/157] of patients) levels of MVPA. A higher body mass index was significantly related to less physical activity (odds ratio 1.12, 95% CI 1.11-1.14). The activity trackers were worn during a mean of 79 (SD 17) days over the 90 days follow-up. Overall, patients considered the use of the tracker very acceptable, with a mean score of 8 out 10. CONCLUSIONS Patients with RA and axSpA performed insufficient physical activity with similar levels in both groups, despite the differences between the 2 diseases. Activity trackers allow longitudinal assessment of physical activity in these patients. The good adherence to this study and the good acceptability of wearing activity trackers confirmed the feasibility of the use of a mobile activity tracker in patients with rheumatic diseases.

scholarly journals The Acceptability, Feasibility and Effectiveness of Wearable Activity Trackers for Increasing Physical Activity in Children and Adolescents: A Systematic Review

2021 ◽  
Vol 18 (12) ◽  
pp. 6211
Author(s):  
Amy V. Creaser ◽  
Stacy A. Clemes ◽  
Silvia Costa ◽  
Jennifer Hall ◽  
Nicola D. Ridgers ◽  
...  
Keyword(s):  
Physical Activity ◽  
Systematic Review ◽  
Children And Adolescents ◽  
Behaviour Change Techniques ◽  
Physically Active ◽  
Self Monitoring ◽  
Start Date ◽  
Activity Trackers ◽  
Wearable Activity Trackers

Wearable activity trackers (wearables) embed numerous behaviour change techniques (BCTs) that have previously been shown to increase adult physical activity (PA). With few children and adolescents achieving PA guidelines, it is crucial to explore ways to increase their PA. This systematic review examined the acceptability, feasibility, and effectiveness of wearables and their potential mechanisms of action for increasing PA in 5 to 19-year-olds. A systematic search of six databases was conducted, including data from the start date of each database to December 2019 (PROSPERO registration: CRD42020164506). Thirty-three studies were included. Most studies (70%) included only adolescents (10 to 19 years). There was some—but largely mixed—evidence that wearables increase steps and moderate-to-vigorous-intensity PA and reduce sedentary behaviour. There were no apparent differences in effectiveness based on the number of BCTs used and between studies using a wearable alone or as part of a multi-component intervention. Qualitative findings suggested wearables increased motivation to be physically active via self-monitoring, goal setting, feedback, and competition. However, children and adolescents reported technical difficulties and a novelty effect when using wearables, which may impact wearables’ long-term use. More rigorous and long-term studies investigating the acceptability, feasibility, and effectiveness of wearables in 5 to 19-year-olds are warranted.

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.

Who Uses Wearable Activity Trackers and Why? A Comparison of Former and Current Users in the United States

2020 ◽  
Vol 34 (7) ◽  
pp. 762-769
Author(s):  
Ciarán P. Friel ◽  
Carol Ewing Garber
Keyword(s):  
Physical Activity ◽  
Average Length ◽  
Physical Activity Behavior ◽  
The United States ◽  
Web Based ◽  
Shared Data ◽  
Activity Trackers ◽  
Wearable Activity Trackers ◽  
Average Current ◽  
Activity Behavior

Background: There has been an explosion in the use of wearable activity trackers (WATs), but we do not fully understand who wears them and why. This study’s purpose was to describe the characteristics of WAT users and to compare current and former users. Materials and Methods: A variety of internet-based resources (eg, Craigslist, Facebook) were used to recruit current and former WAT users. Respondents completed a web-based survey, where they provided information on sociodemographic characteristics, health, physical activity behavior, and about their WAT use. Results: Of the 2826 respondents who gave informed consent, 70.8% (n = 2002) met inclusion criteria for this analysis. Respondents ranged from 18 to 81 years old (mean 32.9 ± 12.2 standard deviation) with 73.8% women. Most were current WAT users (68.7%), and the average length of WAT use overall was 9.3 ± 9.7 months. On average, current users wore the device for 3.7 months longer than former users. Compared to current users, former users had a lower body mass index (1.2 kg/m2 less), reported fewer medical conditions, shared data from their device less often, and received the device as a gift more frequently. Conclusions: Current and former users varied in their reasons for using a WAT and how they used their device. Differences identified between these groups support further exploration of associations between WAT users’ profiles and their physical activity behavior.

Wearable sensors and Mobile Health (mHealth) technologies to assess and promote physical activity in stroke: a narrative review

2016 ◽  
Vol 17 (1) ◽  
pp. 34-42 ◽  
Author(s):  
Shamala Thilarajah ◽  
Ross A Clark ◽  
Gavin Williams
Keyword(s):  
Physical Activity ◽  
Behaviour Change ◽  
Mobile Health ◽  
Wearable Sensors ◽  
Chronic Illnesses ◽  
Behaviour Change Techniques ◽  
Health Issues ◽  
Activity Trackers ◽  
Wearable Activity Trackers ◽  
Smartphone Technology

Stroke is a leading cause of disability worldwide, with approximately one third of people left with permanent deficits impacting on their function. This may contribute to a physically inactive lifestyle and further associated health issues. Current research suggests that people after stroke are not meeting the recommended levels of physical activity, and are less active than people with other chronic illnesses. Thus, it is important to understand how to support people after stroke to uptake and maintain physical activity. Wearable sensors and mobile health (mHealth) technologies are a potential platform to measure and promote physical activity. Some of these technologies may incorporate behaviour change techniques such as real-time feedback. Although wearable activity trackers and smartphone technology are widely available, the feasibility and applicability of these technologies for people after stroke is unclear. This article reviews the devices available for assessment of physical activity in stroke and discusses the potential for advances in technology to promote physical activity in this population.

scholarly journals Cardiovascular Autonomic Function Changes and Predictors During a 2-Year Physical Activity Program in Rheumatoid Arthritis: A PARA 2010 Substudy

2021 ◽  
Vol 8 ◽  
Author(s):  
David Hupin ◽  
Philip Sarajlic ◽  
Ashwin Venkateshvaran ◽  
Cecilia Fridén ◽  
Birgitta Nordgren ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Physical Activity ◽  
Machine Learning ◽  
Heart Rate ◽  
Autonomic Function ◽  
Maximal Exercise ◽  
Machine Learning Algorithms ◽  
Physical Activity Program ◽  
Activity Program ◽  
Increased Risk

Background: Chronic inflammation leads to autonomic dysfunction, which may contribute to the increased risk of cardiovascular diseases (CVD) in patients with rheumatoid arthritis (RA). Exercise is known to restore autonomic nervous system (ANS) activity and particularly its parasympathetic component. A practical clinical tool to assess autonomic function, and in particular parasympathetic tone, is heart rate recovery (HRR). The aim of this substudy from the prospective PARA 2010 study was to determine changes in HRR post-maximal exercise electrocardiogram (ECG) after a 2-year physical activity program and to determine the main predictive factors associated with effects on HRR in RA.Methods: Twenty-five participants performed physiotherapist-guided aerobic and muscle-strengthening exercises for 1 year and were instructed to continue the unsupervised physical activity program autonomously in the next year. All participants were examined at baseline and at years 1 and 2 with a maximal exercise ECG on a cycle ergometer. HRR was measured at 1, 2, 3, 4, and 5 min following peak heart rate during exercise. Machine-learning algorithms with the elastic net linear regression models were performed to predict changes in HRR1 and HRR2 at 1 year and 2 years of the PARA program.Results: Mean age was 60 years, range of 41–73 years (88% women). Both HRR1 and HRR2 increased significantly from baseline to year 1 with guided physical activity and decreased significantly from year 1 to year 2 with unsupervised physical activity. Blood pressure response to exercise, low BMI, and muscular strength were the best predictors of HRR1/HRR2 increase during the first year and HRR1/HRR2 decrease during the second year of the PARA program.Conclusion: ANS activity in RA assessed by HRR was improved by guided physical activity, and machine learning allowed to identify predictors of the HRR response at the different time points. HRR could be a relevant marker of the effectiveness of physical activity recommended in patients with RA at high risk of CVD. Very inactive and/or high CVD risk RA patients may get substantial benefits from a physical activity program.

scholarly journals P59 Monitoring the recovery time of children after elective tonsillectomies using commercial activity trackers, an prospective feasibility study

2019 ◽  
Vol 104 (6) ◽  
pp. e41.2-e42
Author(s):  
PIP Lambrechtse ◽  
VC Ziesenitz ◽  
A Atkinson ◽  
EJ Bos ◽  
T Welzel ◽  
...  
Keyword(s):  
Physical Activity ◽  
Feasibility Study ◽  
Recovery Time ◽  
List Type ◽  
Age Group ◽  
Validity And Reliability ◽  
Open Label ◽  
Activity Data ◽  
Activity Trackers ◽  
Wearable Activity Trackers

IntroductionWearable activity trackers are increasingly incorporated into daily life and are advancing in their technology in means of accuracy, validity and acceptability,1-6 however there is deficient knowledge on using these devices in a paediatric setting. The objective of this pilot study was to assess the feasibility of physical activity tracking in children7 before and after a standardized surgical intervention and to assess the recovery time after surgery.MethodsThis was a single centre, open-label, prospective feasibility study. We aimed at recruiting 24 children and adolescents 4–16 years of age undergoing elective tonsillectomy. The preoperative period was 10 days before surgery and the postoperative period was 28 days. Activity data were gathered with activity trackers.8 Reference activity was defined as the individual mean of daily steps preoperatively. Recovery time was defined as the number of days that the patient needed to reach reference activity postoperatively. The population was stratified according to age (4–7, 8–16 years).ResultsTwelve male and twelve female patients participated (mean age 6yr, mean BMI percentile 44.7). The age group 4–7 years had a mean recovery time of 11.2 days (SD 5.0) compared to 8.3 days (SD 1.7) in the age group 8–16. The difference was 2.9 days. The tracker datasets were 58% complete. The rate of technical failures of the trackers was 29.2% for the total study period.ConclusionsActivity trackers are a potential tool viable to assess recovery time after surgery in children. Recovery time after tonsillectomy seems to be age-dependent with older children recovering faster. For future studies, we recommend using trackers as a part of assessing physical activity as a parameter of general wellbeing of child during or after an intervention. Using wearable activity trackers is a more modern and appropriate method to assess physical activity,9-14 especially in a paediatric population.ReferencesBrooke SM, An HS, Kang SK, Noble JM, Berg KE, Lee JM. Concurrent validity of wearable activity trackers under free-living conditions. J Strength Cond Res 2017;31(4).Fokkema T, Kooiman TJM, Krijnen WP, Van Der Schans CP, De Groot M. Reliability and validity of ten consumer activity trackers depend on walking speed. Med Sci Sports Exerc. 2017;49(4).Evenson KR, Goto MM, Furberg RD. Systematic review of the validity and reliability of consumer-wearable activity trackers. Vol. 12, International Journal of Behavioral Nutrition and Physical Activity 2015.Huang Y, Xu J, Yu B, Shull PB. Validity of FitBit, Jawbone UP, Nike+ and other wearable devices for level and stair walking. Gait Posture 2016;Hein IM, Troost PW, De Vries MC, Knibbe CAJ, Van Goudoever JB, Lindauer RJL. Why do children decide not to participate in clinical research: A quantitative and qualitative study. Pediatr Res 2015;Van Berge Henegouwen MTH, Van Driel HF, Kasteleijn-Nolst Trenité DGA. A patient diary as a tool to improve medicine compliance. Pharm World Sci 1999;21(1):21–4.Stone AA. Patient non-compliance with paper diaries. BMJ 2002;Disclosure(s)Nothing to disclose

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