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      Effect of Mobility Restrictions During the Coronavirus Disease Epidemic on Body Composition and Exercise Tolerance in Patients With Obesity: Single Institutional Retrospective Cohort Study

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          Abstract

          Background: This study investigated the effects of mobility restrictions (MRs) during the COVID-19 epidemic on physical activity, body composition, and exercise tolerance in patients with obesity. Methods: We analyzed data of obesity patients participating in a 6-month weight loss program in February 2020, and after, when the epidemic was considered to have had some effect on outdoor activity in Osaka, Japan (MR group). MR group patients were compared to patients with obesity attending the program in 2018 and 2019 (non-MR group) who had a similar number of months as MR group. Changes in physical activity, body composition, and exercise tolerance (O 2 consumption; VO 2) owing to the weight loss program were analyzed between both groups using analysis of covariance and logistic regression analysis. Results: Decreases in body fat were significantly higher in MR group than in non-MR group. However, increases in physical activity, VO 2 at anaerobic threshold, and peak VO 2 were significantly lower in MR group; however, increases in peak VO 2 owing to the weight loss program were less likely to be achieved in MR group (odds ratio, 0.21; 95% confidence interval, 0.06–0.81). Conclusion: MR during the COVID-19 epidemic may have affected the exercise tolerance of patients with obesity.

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          Physical inactivity is a global concern, but diverse physical activity measures in use prevent international comparisons. The International Physical Activity Questionnaire (IPAQ) was developed as an instrument for cross-national monitoring of physical activity and inactivity. Between 1997 and 1998, an International Consensus Group developed four long and four short forms of the IPAQ instruments (administered by telephone interview or self-administration, with two alternate reference periods, either the "last 7 d" or a "usual week" of recalled physical activity). During 2000, 14 centers from 12 countries collected reliability and/or validity data on at least two of the eight IPAQ instruments. Test-retest repeatability was assessed within the same week. Concurrent (inter-method) validity was assessed at the same administration, and criterion IPAQ validity was assessed against the CSA (now MTI) accelerometer. Spearman's correlation coefficients are reported, based on the total reported physical activity. Overall, the IPAQ questionnaires produced repeatable data (Spearman's rho clustered around 0.8), with comparable data from short and long forms. Criterion validity had a median rho of about 0.30, which was comparable to most other self-report validation studies. The "usual week" and "last 7 d" reference periods performed similarly, and the reliability of telephone administration was similar to the self-administered mode. The IPAQ instruments have acceptable measurement properties, at least as good as other established self-reports. Considering the diverse samples in this study, IPAQ has reasonable measurement properties for monitoring population levels of physical activity among 18- to 65-yr-old adults in diverse settings. The short IPAQ form "last 7 d recall" is recommended for national monitoring and the long form for research requiring more detailed assessment.
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            The Transtheoretical Model of Health Behavior Change

            The transtheoretical model posits that health behavior change involves progress through six stages of change: precontemplation, contemplation, preparation, action, maintenance, and termination. Ten processes of change have been identified for producing progress along with decisional balance, self-efficacy, and temptations. Basic research has generated a rule of thumb for at-risk populations: 40% in precontemplation, 40% in contemplation, and 20% in preparation. Across 12 health behaviors, consistent patterns have been found between the pros and cons of changing and the stages of change. Applied research has demonstrated dramatic improvements in recruitment, retention, and progress using stage-matched interventions and proactive recruitment procedures. The most promising outcomes to data have been found with computer-based individualized and interactive interventions. The most promising enhancement to the computer-based programs are personalized counselors. One of the most striking results to date for stage-matched programs is the similarity between participants reactively recruited who reached us for help and those proactively recruited who we reached out to help. If results with stage-matched interventions continue to be replicated, health promotion programs will be able to produce unprecedented impacts on entire at-risk populations.
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              The rule of thumb that logistic and Cox models should be used with a minimum of 10 outcome events per predictor variable (EPV), based on two simulation studies, may be too conservative. The authors conducted a large simulation study of other influences on confidence interval coverage, type I error, relative bias, and other model performance measures. They found a range of circumstances in which coverage and bias were within acceptable levels despite less than 10 EPV, as well as other factors that were as influential as or more influential than EPV. They conclude that this rule can be relaxed, in particular for sensitivity analyses undertaken to demonstrate adequate control of confounding.
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                Author and article information

                Journal
                Journal of Physical Activity and Health
                Human Kinetics
                1543-3080
                1543-5474
                May 1 2022
                May 1 2022
                : 19
                : 5
                : 351-357
                Affiliations
                [1 ]Department of Medical Nutrition, Graduate School of Human Life Science, Osaka City University, Sumiyoshi-ku, Osaka, Japan
                [2 ]Search Institute for Bone and Arthritis Disease (SINBAD), Shirahama Foundation for Health and Welfare, Nishimuro-gun Shirahama-cho, Wakayama, Japan
                [3 ]Department of Health Science, Kansai Medical University, Hirakata, Osaka, Japan
                [4 ]Health Science Center, Kansai Medical University Hirakata Hospital, Hirakata, Osaka, Japan
                [5 ]Department of Nutrition, Kansai Medical University Hirakata Hospital, Hirakata, Osaka, Japan
                Article
                10.1123/jpah.2021-0649
                c31bd76b-f388-4112-94d6-b4e5402d73f8
                © 2022
                History

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