scholarly journals The influence of powered prostheses on user perspectives, metabolics, and activity: a randomized crossover trial

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Jay Kim ◽  
Jeffrey Wensman ◽  
Natalie Colabianchi ◽  
Deanna H. Gates
Keyword(s):  
Walking Speed ◽  
Daily Life ◽  
Step Count ◽  
Free Form ◽  
Objective Measures ◽  
Daily Step ◽  
Link Type ◽  
Metabolic Costs ◽  
Daily Step Count ◽  
Powered Prosthesis

Abstract Background Powered prosthetic ankles provide battery-powered mechanical push-off, with the aim of reducing the metabolic demands of walking for people with transtibial amputations. The efficacy of powered ankles has been shown in active, high functioning individuals with transtibial amputation, but is less clear in other populations. Additionally, it is unclear how use of a powered prosthesis influences everyday physical activity and mobility. Methods Individuals with unilateral transtibial amputations participated in a randomized clinical trial comparing their prescribed, unpowered prosthesis and the BiOM powered prosthesis. Participants’ metabolic costs and self-selected walking speeds were measured in the laboratory and daily step count, daily steps away from home, and walking speed were measured over two weeks of at-home prosthesis use. Participants also rated their perception of mobility and quality of life and provided free-form feedback. Dependent measures were compared between prostheses and the relationships between metabolic cost, perception of mobility, and characteristics of walking in daily life were explored using Pearson’s correlations. Results Twelve people were randomly allocated to the powered prosthesis first (n = 7) or unpowered prosthesis first (n = 5) and ten completed the full study. There were no differences in metabolic costs (p = 0.585), daily step count (p = 0.995), walking speed in-lab (p = 0.145) and in daily life (p = 0.226), or perception of mobility between prostheses (p ≥ 0.058). Changes varied across participants, however. There were several medium-sized effects for device comparisons. With the powered prosthesis, participants had increased self-reported ambulation (g = 0.682) and decreased frustration (g = 0.506). Conclusions There were no universal benefits of the powered prosthesis on function in the lab or home environment. However, the effects were subject-specific, with some reporting preference for power and improved mobility, and some increasing their activity and decreasing their metabolic effort. Additionally, self-reported preferences did not often correlate with objective measures of function. This highlights the need for future clinical research to include both perception and objective measures to better inform prosthetic prescription. Trial registration: https://clinicaltrials.gov, #NCT02828982. Registered 12 July 2016, https://clinicaltrials.gov/ct2/show/NCT02828982

scholarly journals The Influence of Powered Prostheses on User Perspectives, Metabolics and Activity: A Randomized Crossover Trial

2020 ◽  
Author(s):  
Jay Kim ◽  
Jeffrey Wensman ◽  
Natalie Colabianchi ◽  
Deanna Gates
Keyword(s):  
Walking Speed ◽  
Daily Life ◽  
Step Count ◽  
Free Form ◽  
Objective Measures ◽  
Daily Step ◽  
Metabolic Costs ◽  
Daily Step Count ◽  
Powered Prosthesis ◽  
Walking Speeds

Abstract Background: Powered prosthetic ankles provide battery-powered mechanical push-off, with the aim of reducing the metabolic demands of walking for people with transtibial amputations. The efficacy of powered ankles has been shown in active, high functioning individuals with transtibial amputation, but is less clear in other populations. Additionally, it is unclear how powered prosthetic use influences everyday physical activity and mobility.Methods: Individuals with unilateral transtibial amputations participated in a randomized clinical trial comparing their prescribed, unpowered prosthesis and the BiOM powered prosthesis. Participants’ metabolic costs and self-selected walking speeds were measured in the laboratory and daily step count, daily steps away from home, and walking speed were measured over two weeks of at-home prosthetic use. Participants also rated their perception of mobility and quality of life and provided free-form feedback. Dependent measures were compared between prostheses and the relationships between metabolic cost, perception of mobility, and characteristics of walking in daily life were explored using Pearson’s correlations.Results: Twelve people were randomly allocated to the powered prosthesis first (n = 7) or unpowered prosthesis first (n = 5) and ten completed the full study. There were no differences in metabolic costs (p = 0.585), daily step count (p = 0.995), walking speed (p ≥ 0.145), or perception of mobility between prostheses (p ≥ 0.058). Changes varied across participants, however. There were several medium- and large-sized effects for device comparisons. With the powered prosthesis, participants had decreased walking speeds in daily life (g = 0.310), increased self-reported ambulation (g = 0.682), and decreased frustration (g = 0.506). Greater preference for the powered prosthesis was moderately correlated with increased everyday walking speed (r = 0.636, p = 0.090).Conclusions: There were no universal benefits of the powered prosthesis on function in the lab or home environment. However, the effects were subject-specific, with some reporting preference for power and improved mobility, and some increasing their activity and decreasing their metabolic effort. Additionally, self-reported preferences did not often correlate with objective measures of function. This highlights the need for future clinical research to include both perception and objective measures to better inform prosthetic prescription.Trial registration: https://clinicaltrials.gov, #NCT02828982. Registered 12 July 2016, https://clinicaltrials.gov/ct2/show/NCT02828982

scholarly journals Daily step count and walking speed as general measures of patient wellbeing

2020 ◽  
Vol 6 (3) ◽  
pp. 635-636
Author(s):  
Ralph J. Mobbs ◽  
Callum Betteridge
Keyword(s):  
Walking Speed ◽  
Step Count ◽  
Daily Step ◽  
Daily Step Count

Abstract 108: Impact of a Wireless "Wearable" Device to Measure Daily Activity in Patients With Coronary Microvascular Disease Treated With Late Na Channel Inhibition (ranolazine): a Substudy of the RWISE Clinical Trial

2017 ◽  
Vol 10 (suppl_3) ◽  
Author(s):  
Kade Birkeland ◽  
Raj M Khandwalla ◽  
Ilan Kedan ◽  
Chrisandra L Shufelt ◽  
Puja K Mehta ◽  
...  
Keyword(s):  
Daily Life ◽  
Obstructive Coronary Artery Disease ◽  
Step Count ◽  
Wearable Device ◽  
Coronary Microvascular Dysfunction ◽  
Na Channel ◽  
Daily Step ◽  
Data Set ◽  
Channel Inhibition ◽  
Daily Step Count

Background: Since late Na channel inhibition (ranolazine) improves exercise duration in the stress laboratory among angina patients, we questioned if this benefit would translate to impact step-count during daily life assessed by a "wearable" device. Methods: We conducted a pilot substudy within a randomized, double-blinded, placebo-controlled, cross-over trial of subjects with angina, non-obstructive coronary artery disease and coronary microvascular dysfunction. Ranolazine was administered (500-1000mg BID for 2 weeks). The outcome of interest was difference in Fitbit Flex daily step-count during weeks 2 of ranolazine or placebo treatment. Other outcomes included angina, quality of life, ischemia, diastolic function. Results: 30 subjects were analyzed. Overall, late Na channel inhibition reduced daily step-count vs. placebo (5757 +/- 3076 vs. 6593 +/- 3393, p=0.01) and did not improve angina. However, among those with improved angina (SAQ-7 improvement), a direct correlation with increased step-count (0.42, p=0.02) was observed, most due to typical angina (0.57, p=0.05) (Fig). Conclusions: We report the "first" data set from a wearable monitor to measure step-count in a controlled late Na channel inhibition trial. Our results suggest short-term late Na channel inhibition (ranolazine) does not increase step-count during daily life.

The Pain-Note smartphone app as a tool to measure the relationship between step count and pain levels (Preprint)

2020 ◽  
Author(s):  
Takahisa Ogawa ◽  
Luis Castelo-Branco ◽  
Chie Usui
Keyword(s):  
Chronic Pain ◽  
Pain Perception ◽  
Daily Life ◽  
Pain Scale ◽  
Step Count ◽  
Daily Step ◽  
Cross Sectional ◽  
Daily Step Count ◽  
Different Response ◽  
The Relationship

BACKGROUND Chronic pain is the leading cause of disability, affecting nearly half of the global population. One of the recommended treatments for chronic pain is physical activity, which can be measured in daily life by a pedometer. However, low adherence to the pedometer use could result in incorrect measurements. Due to the ubiquitous use of smartphones, we developed the “Pain-Note” app to collect step count and moving distance. With the use of the Pain-Note app, we obtained the real-world information with a smartphone built-in pedometer and assessed the relationship between daily life step count and pain. OBJECTIVE The aim of our research is (1) to evaluate the association between the daily step count and pain level using a pedometer developed on the iPhone smartphone among patients with chronic pain, and (2) determine if the association between the daily step count and pain levels is curvilinear. METHODS We conducted cross-sectional research with data collected from the “Pain-Note” app on step count and questionnaires, including the duration and intensity of pain, the widespread pain index (WPI) and symptom severity score (SS score), the insomnia severity scale (ISS), and 7 questions for depressive symptoms. We analyzed the association between step count and pain levels considering a non-linear relationship using a restricted cubic spline model. RESULTS Between June 1, 2018 to June 11, 2020, a total of 6,138 records were identified and a total of 1,323 were analyzed. Participants in the 4th quartile (more than 5793 steps a day) had an increased number of step count significantly associated with less pain in numeric pain scale (mean difference, -0.38; 95%CI, -0.74- -0.02; P=.037), compared to the 1st quartile and the restricted cubic splines for the association between step count and pain scale displayed a steep decline followed by a moderate decrease as the step count increased. However, this association was not observed among those who met the fibromyalgia criteria. CONCLUSIONS Step count measured by the “Pain-Note”-based pedometer showed an association with pain levels with an inflection point among individuals with chronic pain, whereas among participants who met the fibromyalgia criteria there was no association. These findings suggest that participants who meet the criteria for fibromyalgia present a different response between walking and pain perception than those in the general chronic pain population.

Associations of Activity Monitor Output and an Estimate of Aerobic Fitness With Pulse Wave Velocities: The Nakanojo Study

2015 ◽  
Vol 12 (1) ◽  
pp. 139-144 ◽  
Author(s):  
Makoto Ayabe ◽  
Sungjin Park ◽  
Roy J. Shephard ◽  
Yukitoshi Aoyagi
Keyword(s):  
Physical Activity ◽  
Arterial Stiffness ◽  
Aerobic Fitness ◽  
Walking Speed ◽  
Pulse Wave ◽  
Activity Monitor ◽  
Step Count ◽  
Daily Step ◽  
Metabolic Equivalents ◽  
Daily Step Count

Background:We examined the relative contributions of habitual physical activity and aerobic fitness to the prevention of arteriosclerosis.Methods:Elderly individuals (97 men and 109 women, aged > 65 y) each wore a uniaxial activity monitor continuously for 1 year, with activity data summarized as an average daily step count and duration of activity > 3 metabolic equivalents (METs). Aerobic fitness was assessed by a standardized 5-m walking test measure of maximal walking speed. Central arterial stiffness was determined using an automatic waveform analyzer measure of cardio-femoral pulse wave velocity (cfPWV).Results:The cfPWV was negatively associated with daily step count, duration of activity > 3 METs, and maximal walking speed (P < .05). Multiple stepwise regression analysis revealed that the step count, duration of activity > 3 METs, and maximal walking speed were all significant predictors of cfPWV, accounting for 11%, 7%, and 4% of total variance, respectively.Conclusions:In contrast to findings from studies using potentially fallible questionnaires, our data suggest that a measure of health (arterial stiffness) is more closely related to objective measures of physical activity than to an estimate of aerobic fitness.

scholarly journals Proposed objective scoring algorithm for walking performance, based on relevant gait metrics: the Simplified Mobility Score (SMoS™)—observational study

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Callum Betteridge ◽  
Ralph Jasper Mobbs ◽  
Daniel Ho
Keyword(s):  
Walking Speed ◽  
Objective Measure ◽  
Simple Algorithm ◽  
Tertiary Hospital ◽  
Step Count ◽  
Gait Velocity ◽  
Activity Levels ◽  
Daily Step ◽  
Daily Step Count ◽  
Mobility Score

Abstract Background Walking is a fundamental part of living, and its importance is not limited by age or medical status. Reduced walking speed (WS), or gait velocity, is a sign of advancing age, various disease states, cognitive impairment, mental illness and early mortality. Activity levels, as defined in the literature as “daily step count” (DSC), is also a relevant measure of health status. A deterioration in our walking metrics, such as reduced WS and DSC, is associated with poor health outcomes. These objective measures are of such importance, that walking speed has been dubbed “the 6th vital sign”. We report a new objective measure that scores walking using the relevant metrics of walking speed and daily step count, into an easy-to-understand score from 0 (nil mobility) to 100 (excellent mobility), termed the Simplified Mobility Score (SMoS™). We have provided equal weighting to walking speed and daily step count, using a simple algorithm to score each metric out of 50. Methods Gait data was collected from 182 patients presenting to a tertiary hospital spinal unit with complaints of pain and reduced mobility. Walking speed was measured from a timed walk along an unobstructed pathway. Daily step count information was obtained from patients who had enabled step count tracking on their devices. The SMoS of the sample group were compared to expected population values calculated from the literature using 2-tailed Z tests. Results There were significantly reduced SMoS in patients who presented to the spinal unit than those expected at each age group for both genders, except for the 50–59 age bracket where no statistically significant reduction was observed. Even lower scores were present in those that went on to have surgical management. There was a significant correlation of SMoS scores with subjective disability scores such as the Oswestry Disability Index (ODI) and Visual Analogue Scale (VAS) in this cohort. Conclusions The SMoS is a simple and effective scoring tool which is demonstrably altered in spinal patients across age and gender brackets and correlates well with subjective disability scores. The SMoS has the potential to be used as a screening tool in primary and specialised care settings.

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.

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