scholarly journals Daily step count of British military males with bilateral lower limb amputations: A comparison of in-patient rehabilitation with the consecutive leave period between admissions

2018 ◽  
Vol 43 (2) ◽  
pp. 188-195 ◽  
Author(s):  
Kate Sherman ◽  
Andrew Roberts ◽  
Kevin Murray ◽  
Sarah Deans ◽  
Hannah Jarvis

Background: Reduced function and health in individuals with lower limb amputation is well documented. Step count measurement could facilitate rehabilitation and help monitor functional health outcomes. Objectives: To determine whether mean daily step count changed between in-patient rehabilitation and consecutive leave periods. Study Design: Observational study. Methods: Nine individuals with bilateral traumatic amputations attending rehabilitation at the Defence Medical Rehabilitation Centre during a 4-month period were invited to participate in the study (two bilateral transfemoral, two bilateral transfemoral/knee disarticulation, two transfemoral/transtibial, one bilateral transfemoral plus transradial, one bilateral transfemoral plus transhumeral and one transfemoral/transtibial/transradial). Prostheses worn by each participant were fitted with an activity monitor (LAM2TM; PAL Technologies Ltd, Glasgow). Mean daily step count was analysed for each participant following 2 weeks in-patient rehabilitation and consecutive 2 weeks away from rehabilitation. Results: Nine participants completed the study (time since injury: 19 ± 7 months, age: 26 ± 6 years). Mean daily step count significantly decreased from 2258 ± 192 during in-patient rehabilitation to 1387 ± 363 at home ( p < 0.01). Conclusion: The step count decreased when away from rehabilitation, confirming the hypothesis that the mean daily step count would change between in-patient rehabilitation and consecutive leave period. Clinical relevance These data provide an indication of the step count achievable by young, military male personnel with bilateral lower limb amputations and highlights differences between intensive in-patient rehabilitation and consecutive leave periods. It is suggested that further investigation and support of clinical monitoring could facilitate rehabilitation tailored to the individual.

2014 ◽  
Vol 33 (10) ◽  
pp. 1051-1057 ◽  
Author(s):  
Marieke De Craemer ◽  
Ellen De Decker ◽  
Ilse De Bourdeaudhuij ◽  
Maïté Verloigne ◽  
Yannis Manios ◽  
...  

Circulation ◽  
2015 ◽  
Vol 131 (suppl_1) ◽  
Author(s):  
Seth S Martin ◽  
David I Feldman ◽  
Roger S Blumenthal ◽  
Steven R Jones ◽  
Wendy S Post ◽  
...  

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.


10.2196/18142 ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. e18142
Author(s):  
Ramin Mohammadi ◽  
Mursal Atif ◽  
Amanda Jayne Centi ◽  
Stephen Agboola ◽  
Kamal Jethwani ◽  
...  

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.


2016 ◽  
Author(s):  
Elijah Meyer ◽  
Mark Greenwood ◽  
Tan Tran

2006 ◽  
Vol 30 (2) ◽  
pp. 155-164 ◽  
Author(s):  
Ji Cheol Shin ◽  
Eun Joo Kim ◽  
Chang Il Park ◽  
Eun Sook Park ◽  
Kyoo-Ho Shin

The objectives of this study were to evaluate the clinical features and outcomes of 43 bilateral lower limb amputees. The clinical features obtained included the causes of amputation, level of amputation, concurrent medical problems, and stump condition. Outcome measures were obtained using the activities of daily living (ADL) index, the Frenchay Activities Index (FAI), and mobility grading with prostheses or wheelchair. Of 33 amputees who were prosthetic ambulators, 22 (67%), mainly bilateral trans-tibial (TT) amputees, were community ambulators, and participated in activities which included stair-walking, and six of 11 household ambulators were combination trans-femoral (TF) and TT amputees. Of 10 amputees who were wheelchair ambulators, only one was able to perform wheelchair transfers independently and five were independent wheelchair ambulators. Using the ADL index and FAI, there was no significant difference in scores according to the level of amputation ( p > 0.05), but the scores of community prosthetic ambulators were significantly higher than those of wheelchair ambulators ( p < 0.05). Age was found to be negatively correlated with ADL index and FAI scores ( r = −0.518 vs. r = −0.550) ( p < 0.01). This study concludes that overall independence in ADL after bilateral lower limb amputation improved with young age and prosthetic mobility.


2018 ◽  
Vol 33 (11) ◽  
pp. 3422-3428 ◽  
Author(s):  
Neill Van der Walt ◽  
Lucy J. Salmon ◽  
Benjamin Gooden ◽  
Matthew C. Lyons ◽  
Michael O'Sullivan ◽  
...  

Author(s):  
Masakazu Minetama ◽  
Mamoru Kawakami ◽  
Masatoshi Teraguchi ◽  
Ryohei Kagotani ◽  
Yoshimasa Mera ◽  
...  

Author(s):  
Emma Pearson ◽  
Harry Prapavessis ◽  
Christopher Higgins ◽  
Robert Petrella ◽  
Lauren White ◽  
...  

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.


2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Marcel Ballin ◽  
Peter Nordström ◽  
Johan Niklasson ◽  
Antti Alamäki ◽  
Joan Condell ◽  
...  

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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