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      An Assessment of Health Behavior Peer Effects in Peking University Dormitories: A Randomized Cluster-Assignment Design for Interference

      research-article
      Author(s): 1 , 2 , 3 , 3 , * , 2 , 4
      Editor(s):
      Publication date (Electronic):
      Journal: PLoS ONE
      Publisher: Public Library of Science

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          Abstract

          Background

          Relatively little is known about the peer influence in health behaviors within university dormitory rooms. Moreover, in China, the problem of unhealthy behaviors among university students has not yet been sufficiently recognized. We thus investigated health behavior peer influence in Peking University dormitories utilizing a randomized cluster-assignment design.

          Methods

          Study design: Cross-sectional in-dormitory survey. Study population: Current students from Peking University Health Science Center from April to June, 2009. Measurement: Self-reported questionnaire on health behaviors: physical activity (including bicycling), dietary intake and tobacco use.

          Results

          Use of bicycle, moderate-intensity exercise, frequency of sweet food and soybean milk intake, frequency of roasted/baked/toasted food intake were behaviors significantly or marginally significantly affected by peer influence.

          Conclusion

          Health behavior peer effects exist within dormitory rooms among university students. This could provide guidance on room assignment, or inform intervention programs. Examining these may demand attention from university administrators and policy makers.

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          Most cited references15

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          Theories for social epidemiology in the 21st century: an ecosocial perspective.

          Nancy Krieger (2001)
          Article 0 comments Cited 543 times – based on 0 reviews     Review now
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            Power and sample size calculations for studies involving linear regression.

            W. D. Dupont, W. D. Plummer (1998)
            This article presents methods for sample size and power calculations for studies involving linear regression. These approaches are applicable to clinical trials designed to detect a regression slope of a given magnitude or to studies that test whether the slopes or intercepts of two independent regression lines differ by a given amount. The investigator may either specify the values of the independent (x) variable(s) of the regression line(s) or determine them observationally when the study is performed. In the latter case, the investigator must estimate the standard deviation(s) of the independent variable(s). This study gives examples using this method for both experimental and observational study designs. Cohen's method of power calculations for multiple linear regression models is also discussed and contrasted with the methods of this study. We have posted a computer program to perform these and other sample size calculations on the Internet (see http://www.mc.vanderbilt.edu/prevmed/psintro+ ++.htm). This program can determine the sample size needed to detect a specified alternative hypothesis with the required power, the power with which a specific alternative hypothesis can be detected with a given sample size, or the specific alternative hypotheses that can be detected with a given power and sample size. Context-specific help messages available on request make the use of this software largely self-explanatory.
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              Prevalence, Awareness, Treatment, and Control of Hypertension in China

              Dongfeng Gu, Kristi Reynolds, Xigui Wu … (2002)
              The objective of this study was to estimate the prevalence and distribution of hypertension and to determine the status of hypertension awareness, treatment, and control in the general adult population in China. The International Collaborative Study of Cardiovascular Disease in ASIA (InterASIA), conducted in 2000-2001, used a multistage cluster sampling method to select a nationally representative sample. A total of 15 540 adults, age 35 to 74 years, were examined. Three blood pressure measurements were obtained by trained observers by use of a standardized mercury sphygmomanometer after a 5-minute sitting rest. Information on history of hypertension and use of antihypertensive medications was obtained by use of a standard questionnaire. Hypertension was defined as a mean systolic blood pressure > or =140 mm Hg, diastolic blood pressure > or =90 mm Hg, and/or use of antihypertensive medications. Overall, 27.2% of the Chinese adult population age 35 to 74 years, representing 129 824 000 persons, had hypertension. The age-specific prevalence of hypertension was 17.4%, 28.2%, 40.7%, and 47.3% in men and 10.7%, 26.8%, 38.9%, and 50.2% in women age 35 to 44 years, 45 to 54 years, 55 to 64 years, and 65 to 74 years, respectively. Among hypertensive patients, only 44.7% were aware of their high blood pressure, 28.2% were taking antihypertensive medication, and 8.1% achieved blood pressure control (<140/90 mm Hg). Our results indicate that hypertension is highly prevalent in China. The percentages of those with hypertension who are aware, treated, and controlled are unacceptably low. These results underscore the urgent need to develop national strategies to improve prevention, detection, and treatment of hypertension in China.
              Article 0 comments Cited 136 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Lawrence Kazembe: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS ONE
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, USA )
                ISSN (Electronic): 1932-6203
                Publication date Collection: 2013
                Publication date (Electronic): 9 September 2013
                Volume: 8
                Issue: 9
                Electronic Location Identifier: e75009
                Affiliations
                [1 ]Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, United States of America
                [2 ]Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America
                [3 ]Department of Epidemiology and Biostatistics, Peking University School of Public Health, Beijing, China
                [4 ]Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America
                Chancellor College, University of Malawi, Malawi
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: JL. Performed the experiments: CY. Analyzed the data: CY TV. Contributed reagents/materials/analysis tools: CY TV. Wrote the paper: CY TV.

                Article
                Publisher ID: PONE-D-13-05575
                DOI: 10.1371/journal.pone.0075009
                PMC ID: 3767642
                PubMed ID: 24040377
                SO-VID: 076c3004-b111-4015-8661-a46f0db93680
                Copyright statement: Copyright @ 2013
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Date received : 5 February 2013
                Date accepted : 9 August 2013
                Page count
                Pages: 6
                Funding
                The authors have no support or funding to report.
                Categories
                Subject: Research Article

                ScienceOpen disciplines: Uncategorized
                Data availability:
                ScienceOpen disciplines: Uncategorized

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