scholarly journals The impact of late liver allograft dysfunction on physical activity of liver transplant recipients

2021 ◽  
Vol 13 (4) ◽  
pp. 356-366
Author(s):  
Yu. O. Malinovskaya ◽  
K. Yu. Kokina ◽  
Ya. G. Moysyuk ◽  
O. V. Sumtsova
Keyword(s):  
Physical Activity ◽  
Liver Transplantation ◽  
Social Life ◽  
Normal Liver ◽  
Step Count ◽  
Anthropometric Parameters ◽  
Liver Allograft ◽  
Allograft Dysfunction ◽  
Activity Trackers ◽  
The Impact

Introduction. Liver transplantation restores patients' physical and social life, and its quality. The prevalence of low physical activity in liver recipients is unknown as well as the impact of late liver allograft dysfunction on it. Liver transplantation enhances patient's return to the usual physical and social activity and improves the quality of life. However, the prevalence of low physical activity among liver recipients and the impact of the late allograft dysfunction on it, which is a risk factor for obesity and cardiovascular diseases, require studying.The aim of the study was to identify whether the late liver allograft dysfunction influences the physical activity of recipients.Material and methods. The study included 87 liver recipients. We measured anthropometric parameters, physical performance (SPPB, LFI, 6-min walk test), mean step count per day. Late liver allograft dysfunction was determined if elevated transaminases and/or cholestatic enzymes or hepatic failure have been diagnosed later than 3 months posttransplant. Activity trackers were provided to assess physical activity.Results. Median age was 54 years [45;61], 33% were men. The median follow-up period was 36 months [16;64]. The median of the average steps count was 5.9 [4.1;8.7] thousand per day. 60.5% of recipients were sedentary and low active, 24.4% were somewhat active, 15.1% were active. In cases of liver allograft dysfunction, the mean step count was significantly lower than in patients with normal liver function: 4.1 thousand [2.6;5.3] versus 6.8 thousand [4.2;9.4], p=0.003, despite no differences in the physical activity test results.Conclusion. In case of a late liver allograft dysfunction, the physical activity can decrease; 60.5% of liver recipients, in the absence of pathological restriction of movement, have a sedentary and low active lifestyle. Activity trackers may allow identifying patients who need additional check-up or physical training.

scholarly journals Body Mass, Physical Activity and Eating Habits Changes during the First COVID-19 Pandemic Lockdown in Poland

2021 ◽  
Vol 18 (11) ◽  
pp. 5682
Author(s):  
Hubert Dobrowolski ◽  
Dariusz Włodarek
Keyword(s):  
Physical Activity ◽  
Body Weight ◽  
Body Mass ◽  
Social Life ◽  
Eating Habits ◽  
High Energy ◽  
High Energy Density ◽  
Average Weight ◽  
Study Participants ◽  
The Impact

The outbreak of the COVID-19 pandemic caused a number of changes in social life around the world. In response to the growing number of infections, some countries have introduced restrictions that may have resulted in the change of the lifestyle. The aim of our study was to investigate the impact of the lockdown on body weight, physical activity and some eating habits of the society. The survey involving 183 people was conducted using a proprietary questionnaire. The mean age of the study participants was 33 ± 11 and mean height 169 ± 8 cm. An average increase in body weight was observed in 49.18% by 0.63 ± 3.7 kg which was the result of a decrease in physical activity and an increase in food consumption. We also observed a decrease in PAL from 1.64 ± 0.15 to 1.58 ± 0.13 and changes in the amount of food and individual groups of products consumption, including alcohol. Among the study participants who did not lose body mass, there was an average weight gain of 2.25 ± 2.5 kg. In conclusion, an increase of weight was shown in about half of the respondents in the study group which was associated with a decrease in physical activity and an increase in the consumption of total food and high energy density products.

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.

Intervention Mediators in a Randomized Controlled Trial to Increase Physical Activity and Fatigue Self-management Behaviors Among Adults With Multiple Sclerosis

2019 ◽  
Author(s):  
Matthew Plow ◽  
Robert W Motl ◽  
Marcia Finlayson ◽  
Francois Bethoux
Keyword(s):  
Physical Activity ◽  
Multiple Sclerosis ◽  
Goal Setting ◽  
Self Efficacy ◽  
Outcome Expectations ◽  
Step Count ◽  
Self Management ◽  
Post Test ◽  
The Impact

Abstract Background People with multiple sclerosis (MS) often experience fatigue, which is aggravated by inactivity. Identifying mediators of changes in physical activity (PA) and fatigue self-management (FSM) behaviors could optimize future interventions that reduce the impact of MS fatigue. Purpose To examine the effects of telephone-delivered interventions on Social Cognitive Theory constructs and test whether these constructs mediated secondary outcomes of PA and FSM behaviors. Methods Participants with MS (n = 208; Mean age = 52.1; Female = 84.6%) were randomized into contact–control intervention (CC), PA-only intervention, and PA+FSM intervention. Step count (Actigraphy) and FSM behaviors as well as self-efficacy, outcome expectations, and goal setting for PA and FSM were measured at baseline, post-test (12 weeks), and follow-up (24 weeks). Path analyses using bias-corrected bootstrapped 95% confidence intervals (CI) determined whether constructs at post-test mediated behaviors at follow-up when adjusting for baseline measures. Results Path analysis indicated that PA-only (β = 0.50, p < .001) and PA+FSM interventions (β = 0.42, p < .010) had an effect on goal setting for PA, and that PA + FSM intervention had an effect on self-efficacy for FSM (β = 0.48, p = .011) and outcome expectations for FSM (β = 0.42, p = .029). Goal setting for PA at post-test mediated the effects of PA-only (β = 159.45, CI = 5.399, 371.996) and PA + FSM interventions (β = 133.17, CI = 3.104, 355.349) on step count at follow-up. Outcome expectations for FSM at post-test mediated the effects of PA + FSM intervention on FSM behaviors at follow-up (β = 0.02, CI = 0.001, 0.058). Conclusions Goal setting for PA and outcome expectations for FSM may be important constructs to target in telephone-delivered interventions designed to reduce the impact of MS fatigue. Trial registration Clinicaltrials.gov (NCT01572714)

Effect of interventions using physical activity trackers on physical activity in people aged 60 years and over: a systematic review and meta-analysis

2019 ◽  
Vol 54 (20) ◽  
pp. 1188-1194 ◽  
Author(s):  
Juliana S Oliveira ◽  
Cathie Sherrington ◽  
Elizabeth R Y Zheng ◽  
Marcia Rodrigues Franco ◽  
Anne Tiedemann
Keyword(s):  
Quality Of Life ◽  
Mental Health ◽  
Physical Activity ◽  
Systematic Review ◽  
Older People ◽  
Activity Levels ◽  
Activity Tracker ◽  
Activity Trackers ◽  
The Impact

BackgroundOlder people are at high risk of physical inactivity. Activity trackers can facilitate physical activity. We aimed to investigate the effect of interventions using activity trackers on physical activity, mobility, quality of life and mental health among people aged 60+ years.MethodsFor this systematic review, we searched eight databases, including MEDLINE, Embase and CENTRAL from inception to April 2018. Randomised controlled trials of interventions that used activity trackers to promote physical activity among people aged 60+ years were included in the analyses. The study protocol was registered with PROSPERO, number CRD42017065250.ResultsWe identified 23 eligible trials. Interventions using activity trackers had a moderate effect on physical activity (23 studies; standardised mean difference (SMD)=0.55; 95% CI 0.40 to 0.70; I2=86%) and increased steps/day by 1558 (95% CI 1099 to 2018 steps/day; I2=92%) compared with usual care, no intervention and wait-list control. Longer duration activity tracker-based interventions were more effective than short duration interventions (18 studies, SMD=0.70; 95% CI 0.47 to 0.93 vs 5 studies, SMD=0.14; 95% CI −0.26 to 0.54, p for comparison=0.02). Interventions that used activity trackers improved mobility (three studies; SMD=0.61; 95% CI 0.31 to 0.90; I2=10%), but not quality of life (nine studies; SMD=0.09; 95% CI −0.07 to 0.25; I2=45%). Only one trial included mental health outcomes and it reported similar effects of the activity tracker intervention compared with control.ConclusionsInterventions using activity trackers improve physical activity levels and mobility among older people compared with control. However, the impact of activity tracker interventions on quality of life, and mental health is unknown.

scholarly journals 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

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.

scholarly journals The impact of postreperfusion syndrome on short-term patient and liver allograft outcome in patients undergoing orthotopic liver transplantation

2008 ◽  
Vol 14 (4) ◽  
pp. 504-508 ◽  
Author(s):  
Ibtesam Hilmi ◽  
Charles N. Horton ◽  
Raymond M. Planinsic ◽  
Tetsuro Sakai ◽  
Ramona Nicolau-Raducu ◽  
...  
Keyword(s):  
Liver Transplantation ◽  
Orthotopic Liver Transplantation ◽  
Short Term ◽  
Liver Allograft ◽  
Allograft Outcome ◽  
The Impact

scholarly journals Convergent Validity of the Fitbit Charge 2 to Measure Sedentary Behavior and Physical Activity in Overweight and Obese Adults

2020 ◽  
pp. 1-8
Author(s):  
Joanne A. McVeigh ◽  
Jennifer Ellis ◽  
Caitlin Ross ◽  
Kim Tang ◽  
Phoebe Wan ◽  
...  
Keyword(s):  
Physical Activity ◽  
Confidence Interval ◽  
Sedentary Behavior ◽  
Convergent Validity ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Living Environment ◽  
Step Count ◽  
Activity Trackers ◽  
Relative Differences

Activity trackers provide real-time sedentary behavior (SB) and physical activity (PA) data enabling feedback to support behavior change. The validity of activity trackers in an obese population in a free-living environment is largely unknown. This study determined the convergent validity of the Fitbit Charge 2 in measuring SB and PA in overweight adults. The participants (n = 59; M ± SD: age = 48 ± 11 years; body mass index = 34 ± 4 kg/m2) concurrently wore a Charge 2 and ActiGraph GT3X+ accelerometer for 8 days. The same waking wear periods were analyzed, and standard cut points for GT3X+ and proprietary algorithms for the Charge 2, together with a daily step count, were used. Associations between outputs, mean difference (MD) and limits of agreement (LOA), and relative differences were assessed. There was substantial association between devices (intraclass correlation coefficients from .504, 95% confidence interval [.287, .672] for SB, to .925, 95% confidence interval [.877, .955] for step count). In comparison to the GT3X+, the Charge 2 overestimated SB (MD = 37, LOA = −129 to 204 min/day), moderate to vigorous PA (MD = 15, LOA = −49 to 79 min/day), and steps (MD = 1,813, LOA = −1,066 to 4,691 steps/day), and underestimated light PA (MD = −32, LOA = −123 to 58 min/day). The Charge 2 may be a useful tool for self-monitoring of SB and PA in an overweight population, as mostly good agreement was demonstrated with the GT3X+. However, there were mean and relative differences, and the implications of these need to be considered for overweight adult populations who are already at risk of being highly sedentary and insufficiently active.

scholarly journals Use of Featforward Mobile Phone App Associated with Decreased Cardiometabolic Risk Factors in Patients with Chronic Conditions (Preprint)

2018 ◽  
Author(s):  
Sara B Golas ◽  
Ramya Palacholla ◽  
Amanda Centi ◽  
Odeta Dyrmishi ◽  
Stephen Agboola ◽  
...  
Keyword(s):  
Physical Activity ◽  
Mobile Phone ◽  
Chronic Conditions ◽  
Primary Outcome ◽  
Fasting Blood Glucose ◽  
Intervention Group ◽  
Step Count ◽  
Control Group ◽  
Activity Trackers ◽  
Secondary Outcomes

BACKGROUND Physical inactivity is one of the leading risk factors contributing to rising rates of chronic diseases and has been associated with deleterious health outcomes in patients with chronic disease conditions. FeatForward is a mobile phone app designed to encourage patients with cardiometabolic risk (CMR) factors to increase their levels of physical activity. OBJECTIVE To evaluate the effect of the FeatForward mobile phone app on physical activity levels (primary outcome) and global CMR factors (secondary outcomes) in patients with chronic conditions. METHODS In this 6-month, 2-arm randomized controlled trial, adult participants endorsing at least 1 study-eligible condition (obesity, [pre-]diabetes, [pre-]hypertension) were enrolled and assigned to either the intervention group (FeatForward app and standard care) or control group (standard care only). The primary and secondary outcomes were, respectively, change from baseline in physical activity (step count) and CMR factors (weight, body mass index [BMI], waist circumference, glycated hemoglobin [HbA1c], fasting blood glucose, systolic/diastolic blood pressures, serum lipids, C-reactive protein [CRP]). CMR data were collected at 3 time-points: baseline, 3 months, and 6 months. Step count data were recorded continuously by patients’ study-issued activity trackers and collected in batches at 3 and 6 months. At study end, patients’ weekly average step counts (WAS) were calculated as total steps taken divided by days of step data (0-7) for each of 26 study weeks. Mixed-effects linear regression models evaluated change over time between groups for the primary outcome and secondary outcomes. All models controlled for baseline values. The step count model additionally controlled for proportion of days without data, defined as (7 – days of data) / 7. Analyses were conducted for both groups overall, and by disease cohort (obesity, diabetes, hypertension). RESULTS Step count and CMR data were analyzed for 128 intervention and 133 control patients. There were no demographic differences between groups. While there was an overall downward trend in WAS for both groups, the intervention group decreased significantly less than the control group, with a slope of -29.3 steps per week compared to controls’ -57.9 (P=.02). Intervention patients with obesity slightly increased their step count overtime, differing significantly from controls (slope of 0.9 vs -90.2; P<.001). Intervention patients significantly lowered their BMI per study month compared to controls (slopes -0.23 vs -0.02; P=.04). Additionally, intervention patients with hypertension significantly decreased weight (P=.003), BMI (P=.002), and CRP (P=.03) per month compared to the control group. Waist circumference, HbA1c, fasting blood glucose, blood pressure, and lipids did not differ significantly by group or disease cohort over time. CONCLUSIONS While it is common for patient engagement with physical activity trackers to decrease over the course of a study, patients using the FeatFoward app had a slower decline in physical activity compared to controls. Intervention patients experienced a reduction in their BMI from a mean of 34.3 to 33.4, compared to controls’ 34.8 to 35.0. Patients with hypertension experienced significant decreases in BMI, weight, and CRP compared to controls. Future analyses will evaluate the impact of app engagement levels on step counts and CMR factors for the intervention group.

scholarly journals B.01 Impact of work-hours and sleep on well-being and burnout for physicians-in-training: the prospective RATE Study

2017 ◽  
Vol 44 (S2) ◽  
pp. S10-S10
Author(s):  
D Mendelsohn ◽  
I Despot ◽  
PA Gooderham ◽  
A Singhal ◽  
GJ Redekop ◽  
...  
Keyword(s):  
Physical Activity ◽  
Job Satisfaction ◽  
Well Being ◽  
Work Hours ◽  
Surgical Residents ◽  
Activity Trackers ◽  
Steps Per Day ◽  
Wearable Activity Trackers ◽  
Rate Study ◽  
The Impact

Background: Wearable activity trackers are an innovative tool for measuring sleep and physical activity. The Resident Activity Tracker Evaluation (RATE) is a prospective observational study evaluating the impact of work-hours, sleep, and physical activity on resident well-being, burnout, and job satisfaction. Methods: Residents were recruited from: 1. general surgery and orthopedics (SURG), 2. internal medicine and neurology (MED) and 3. anesthesia and radiology (RCD). Groups 1 and 2 do not enforce on-call duration restrictions and group 3 had 12-hour restricted-call durations (RCD). Participants wore FitBit activity trackers for 14 days and completed four validated surveys assessing self-reported health, sleepiness, burnout, and job satisfaction. Results: Fifty-nine residents completed the study. 778 days of activity and 244 on-call periods were tracked. Surgical residents worked 24 more hours per week than non-surgical residents (84.3 vs 60.7). Surgical residents had 7 less hours of sleep per week and reported significantly higher Epworth Sleepiness scores. Nearly two-thirds of participants (61%) scored high burnout on the Maslach depersonalization subscore. Total steps per day and self-reported well-being, burnout, and job satisfaction were comparable between the groups. Conclusions: Despite a positive correlation between work-hours and sleepiness, burnout and well-being were similar among residents. Physical activity did not prevent burnout. These findings are relevant to work-hours policies.

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