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      Comparison of BMI changes in Japanese adults receiving face-to-face versus online counseling for specific health guidance: a noninferiority prospective observational study

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          Abstract

          Objectives

          This study aimed to evaluate the noninferiority of online counseling over face-to-face counseling for specific health guidance (SHG).

          Methods

          This prospective observational study was conducted using specific health checkup (SHC) and SHG data of individuals with health insurance in Japan. We analyzed data from 1431 participants who met the inclusion criteria, including those who underwent online or face-to-face counseling between April 1, 2020 and March 31, 2021, and received an SHC in the following year but no earlier than 90 days after their first counseling session. Assessed variables comprised demographics, counseling methods, and SHC results, including baseline questionnaire findings and body mass index (BMI) at follow-up. We performed inverse probability of treatment weighting (IPTW) using propensity scores, with changes in BMI as the objective variable and the counseling method as the explanatory variable. We set the noninferiority margin to 0.175, based on a previous study.

          Results

          The online and face-to-face counseling groups comprised 455 (31.8%) and 976 (68.2%) participants, respectively. The number of men and mean age were 214 (47.0%) and 49.9 years (SD: 6.9 years), respectively, in the online counseling group, and 491 (50.3%) and 51.1 years (SD: 7.6 years), respectively, in the face-to-face counseling group. IPTW using propensity scores revealed a regression coefficient of −0.014 (95% CI: −0.157 to 0.129) for the online group compared with the face-to-face group ( P = .847). The CI was within the noninferiority margin.

          Conclusions

          The effects of online counseling on BMI are likely noninferior to those of face-to-face counseling.

          Abstract

          What is already known on this topic.

          The coronavirus disease 2019 (COVID-19) pandemic has resulted in a swift surge in the use of online counseling as an alternative to traditional face-to-face counseling. Online counseling offers several benefits, such as heightened geographical inclusivity, facilitated participation for individuals facing travel constraints, and engagement with a broader spectrum of participants. Several previous studies have indicated that online counseling may be effective in improving overweight and obesity status.

          What this study adds.

          For specific health guidance in Japan, the potential effect of online counseling on BMI is expected to be comparable to that of face-to-face counseling.

          How this study might affect research, practice, or policy.

          As a practical implication, our results suggests that online counseling should be actively used when feasible. As a policy implication, our study indicates that the environment and system for online counseling should be improved, and that the skills of health professionals should be enhanced to develop effective online counseling.

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          Most cited references17

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          Investigation of the freely available easy-to-use software ‘EZR' for medical statistics

          Y Kanda (2012)
          Although there are many commercially available statistical software packages, only a few implement a competing risk analysis or a proportional hazards regression model with time-dependent covariates, which are necessary in studies on hematopoietic SCT. In addition, most packages are not clinician friendly, as they require that commands be written based on statistical languages. This report describes the statistical software ‘EZR' (Easy R), which is based on R and R commander. EZR enables the application of statistical functions that are frequently used in clinical studies, such as survival analyses, including competing risk analyses and the use of time-dependent covariates, receiver operating characteristics analyses, meta-analyses, sample size calculation and so on, by point-and-click access. EZR is freely available on our website (http://www.jichi.ac.jp/saitama-sct/SaitamaHP.files/statmed.html) and runs on both Windows (Microsoft Corporation, USA) and Mac OS X (Apple, USA). This report provides instructions for the installation and operation of EZR.
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            Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19·2 million participants

            Summary Background Underweight and severe and morbid obesity are associated with highly elevated risks of adverse health outcomes. We estimated trends in mean body-mass index (BMI), which characterises its population distribution, and in the prevalences of a complete set of BMI categories for adults in all countries. Methods We analysed, with use of a consistent protocol, population-based studies that had measured height and weight in adults aged 18 years and older. We applied a Bayesian hierarchical model to these data to estimate trends from 1975 to 2014 in mean BMI and in the prevalences of BMI categories (<18·5 kg/m2 [underweight], 18·5 kg/m2 to <20 kg/m2, 20 kg/m2 to <25 kg/m2, 25 kg/m2 to <30 kg/m2, 30 kg/m2 to <35 kg/m2, 35 kg/m2 to <40 kg/m2, ≥40 kg/m2 [morbid obesity]), by sex in 200 countries and territories, organised in 21 regions. We calculated the posterior probability of meeting the target of halting by 2025 the rise in obesity at its 2010 levels, if post-2000 trends continue. Findings We used 1698 population-based data sources, with more than 19·2 million adult participants (9·9 million men and 9·3 million women) in 186 of 200 countries for which estimates were made. Global age-standardised mean BMI increased from 21·7 kg/m2 (95% credible interval 21·3–22·1) in 1975 to 24·2 kg/m2 (24·0–24·4) in 2014 in men, and from 22·1 kg/m2 (21·7–22·5) in 1975 to 24·4 kg/m2 (24·2–24·6) in 2014 in women. Regional mean BMIs in 2014 for men ranged from 21·4 kg/m2 in central Africa and south Asia to 29·2 kg/m2 (28·6–29·8) in Polynesia and Micronesia; for women the range was from 21·8 kg/m2 (21·4–22·3) in south Asia to 32·2 kg/m2 (31·5–32·8) in Polynesia and Micronesia. Over these four decades, age-standardised global prevalence of underweight decreased from 13·8% (10·5–17·4) to 8·8% (7·4–10·3) in men and from 14·6% (11·6–17·9) to 9·7% (8·3–11·1) in women. South Asia had the highest prevalence of underweight in 2014, 23·4% (17·8–29·2) in men and 24·0% (18·9–29·3) in women. Age-standardised prevalence of obesity increased from 3·2% (2·4–4·1) in 1975 to 10·8% (9·7–12·0) in 2014 in men, and from 6·4% (5·1–7·8) to 14·9% (13·6–16·1) in women. 2·3% (2·0–2·7) of the world’s men and 5·0% (4·4–5·6) of women were severely obese (ie, have BMI ≥35 kg/m2). Globally, prevalence of morbid obesity was 0·64% (0·46–0·86) in men and 1·6% (1·3–1·9) in women. Interpretation If post-2000 trends continue, the probability of meeting the global obesity target is virtually zero. Rather, if these trends continue, by 2025, global obesity prevalence will reach 18% in men and surpass 21% in women; severe obesity will surpass 6% in men and 9% in women. Nonetheless, underweight remains prevalent in the world’s poorest regions, especially in south Asia. Funding Wellcome Trust, Grand Challenges Canada.
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              Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies

              The propensity score is defined as a subject's probability of treatment selection, conditional on observed baseline covariates. Weighting subjects by the inverse probability of treatment received creates a synthetic sample in which treatment assignment is independent of measured baseline covariates. Inverse probability of treatment weighting (IPTW) using the propensity score allows one to obtain unbiased estimates of average treatment effects. However, these estimates are only valid if there are no residual systematic differences in observed baseline characteristics between treated and control subjects in the sample weighted by the estimated inverse probability of treatment. We report on a systematic literature review, in which we found that the use of IPTW has increased rapidly in recent years, but that in the most recent year, a majority of studies did not formally examine whether weighting balanced measured covariates between treatment groups. We then proceed to describe a suite of quantitative and qualitative methods that allow one to assess whether measured baseline covariates are balanced between treatment groups in the weighted sample. The quantitative methods use the weighted standardized difference to compare means, prevalences, higher‐order moments, and interactions. The qualitative methods employ graphical methods to compare the distribution of continuous baseline covariates between treated and control subjects in the weighted sample. Finally, we illustrate the application of these methods in an empirical case study. We propose a formal set of balance diagnostics that contribute towards an evolving concept of ‘best practice’ when using IPTW to estimate causal treatment effects using observational data. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
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                Author and article information

                Contributors
                Journal
                J Occup Health
                J Occup Health
                joh
                Journal of Occupational Health
                Oxford University Press
                1341-9145
                1348-9585
                Jan-Dec 2024
                10 May 2024
                10 May 2024
                : 66
                : 1
                : uiae026
                Affiliations
                Graduate School of Public Health, Teikyo University , Tokyo, Japan
                Department of Preventive Medicine and Public Health, Tokyo Medical University , Tokyo, Japan
                Division of Health Support, Department Store Health Insurance Association , Tokyo, Japan
                Integrated Research Faculty for Advanced Medical Sciences, Dokkyo Medical University School of Medicine , Tochigi, Japan
                Author notes
                Corresponding author: Satoru Kanamori, ( satoru_kanamori@ 123456med.teikyo-u.ac.jp ).
                Article
                uiae026
                10.1093/joccuh/uiae026
                11170213
                38729214
                f4ccde48-9387-49c2-b078-eed59f0d9252
                © The Author(s) [2024]. Published by Oxford University Press on behalf of Journal of Occupational Health

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 15 November 2023
                : 25 April 2024
                : 02 May 2024
                : 13 June 2024
                Page count
                Pages: 08
                Categories
                Original Article
                AcademicSubjects/MED00010
                AcademicSubjects/MED00640

                internet,telemedicine,counseling,overweight,obesity,body mass index

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