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      Rates and Determinants of Uptake and Use of an Internet Physical Activity and Weight Management Program in Office and Manufacturing Work Sites in England: Cohort Study

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          Abstract

          Background

          Internet-based physical activity (PA) and weight management programs have the potential to improve employees’ health in large occupational health settings. To be successful, the program must engage a wide range of employees, especially those at risk of weight gain or ill health.

          Objective

          The aim of the study was to assess the use and nonuse (user attrition) of a Web-based and monitoring device–based PA and weight management program in a range of employees and to determine if engagement with the program was related to the employees’ baseline characteristics or measured outcomes.

          Methods

          Longitudinal observational study of a cohort of employees having access to the MiLife Web-based automated behavior change system. Employees were recruited from manufacturing and office sites in the North West and the South of England. Baseline health data were collected, and participants were given devices to monitor their weight and PA via data upload to the website. Website use, PA, and weight data were collected throughout the 12-week program.

          Results

          Overall, 12% of employees at the four sites (265/2302) agreed to participate in the program, with 130 men (49%) and 135 women (51%), and of these, 233 went on to start the program. During the program, the dropout rate was 5% (11/233). Of the remaining 222 Web program users, 173 (78%) were using the program at the end of the 12 weeks, with 69% (153/222) continuing after this period. Engagement with the program varied by site but was not significantly different between the office and factory sites. During the first 2 weeks, participants used the website, on average, 6 times per week, suggesting an initial learning period after which the frequency of website log-in was typically 2 visits per week and 7 minutes per visit. Employees who uploaded weight data had a significant reduction in weight (−2.6 kg, SD 3.2, P< .001). The reduction in weight was largest for employees using the program’s weight loss mode (−3.4 kg, SD 3.5). Mean PA level recorded throughout the program was 173 minutes (SE 12.8) of moderate/high intensity PA per week. Website interaction time was higher and attrition rates were lower (OR 1.38, P= .03) in those individuals with the greatest weight loss.

          Conclusions

          This Web-based PA and weight management program showed high levels of engagement across a wide range of employees, including overweight or obese workers, shift workers, and those who do not work with computers. Weight loss was observed at both office and manufacturing sites. The use of monitoring devices to capture and send data to the automated Web-based coaching program may have influenced the high levels of engagement observed in this study. When combined with objective monitoring devices for PA and weight, both use of the website and outcomes can be tracked, allowing the online coaching program to become more personalized to the individual.

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          Most cited references40

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          Compendium of physical activities: classification of energy costs of human physical activities.

          A coding scheme is presented for classifying physical activity by rate of energy expenditure, i.e., by intensity. Energy cost was established by a review of published and unpublished data. This coding scheme employs five digits that classify activity by purpose (i.e., sports, occupation, self-care), the specific type of activity, and its intensity as the ratio of work metabolic rate to resting metabolic rate (METs). Energy expenditure in kilocalories or kilocalories per kilogram body weight can be estimated for all activities, specific activities, or activity types. General use of this coding system would enhance the comparability of results across studies using self reports of physical activity.
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            A review of eHealth interventions for physical activity and dietary behavior change.

            To review eHealth intervention studies for adults and children that targeted behavior change for physical activity, healthy eating, or both behaviors. Systematic literature searches were performed using five databases: MEDLINE, PsychInfo, CINAHL, ERIC, and the Cochrane Library to retrieve articles. Articles published in scientific journals were included if they evaluated an intervention for physical activity and/or dietary behaviors, or focused on weight loss, used randomized or quasi-experimental designs, measured outcomes at baseline and a follow-up period, and included an intervention where participants interacted with some type of electronic technology either as the main intervention or an adjunct component. All studies were published between 2000 and 2005. Eighty-six publications were initially identified, of which 49 met the inclusion criteria (13 physical activity publications, 16 dietary behaviors publications, and 20 weight loss or both physical activity and diet publications), and represented 47 different studies. Studies were described on multiple dimensions, including sample characteristics, design, intervention, measures, and results. eHealth interventions were superior to comparison groups for 21 of 41 (51%) studies (3 physical activity, 7 diet, 11 weight loss/physical activity and diet). Twenty-four studies had indeterminate results, and in four studies the comparison conditions outperformed eHealth interventions. Published studies of eHealth interventions for physical activity and dietary behavior change are in their infancy. Results indicated mixed findings related to the effectiveness of eHealth interventions. Interventions that feature interactive technologies need to be refined and more rigorously evaluated to fully determine their potential as tools to facilitate health behavior change.
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              The technology of accelerometry-based activity monitors: current and future.

              This paper reviews accelerometry-based activity monitors, including single-site first-generation devices, emerging technologies, and analytical approaches to predict energy expenditure, with suggestions for further research and development. The physics and measurement principles of the accelerometer are described, including the sensor properties, data collections, filtering, and integration analyses. The paper also compares these properties in several commonly used single-site accelerometers. The emerging accelerometry technologies introduced include the multisensor arrays and the combination of accelerometers with physiological sensors. The outputs of accelerometers are compared with criterion measures of energy expenditure (indirect calorimeters and double-labeled water) to develop mathematical models (linear, nonlinear, and variability approaches). The technologies of the sensor and data processing directly influence the results of the outcome measurement (activity counts and energy expenditure predictions). Multisite assessment and combining accelerometers with physiological measures may offer additional advantages. Nonlinear approaches to predict energy expenditure using accelerometer outputs from multiple sites and orientation can enhance accuracy. The development of portable accelerometers has made objective assessments of physical activity possible. Future technological improvements will include examining raw acceleration signals and developing advanced models for accurate energy expenditure predictions.
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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                JMIR
                Journal of Medical Internet Research
                Gunther Eysenbach (Centre for Global eHealth Innovation, Toronto, Canada )
                1438-8871
                Oct-Dec 2008
                31 December 2008
                : 10
                : 4
                : e56
                Affiliations
                [6] 6simpleDepartment of Psychiatry and Behavioral Sciences simpleBaylor College of Medicine HoustonTXUSA
                [5] 5QuornLeicesterUK
                [4] 4Unilever Occupational HealthLondonUK
                [3] 3Tessella Support Services PLCAbingdonOxfordshireUK
                [2] 2Unilever ResearchColworthBedfordUK
                [1] 1MiLife Coaching LimitedColworthBedfordUK
                Article
                v10i4e56
                10.2196/jmir.1108
                2629365
                19117828
                af24d0d3-ff31-4450-8715-9fac09e834ac
                © Lisa J Ware, Robert Hurling, Ogi Bataveljic, Bruce W Fairley, Tina L Hurst, Peter Murray, Kirsten L Rennie, Chris E Tomkins, Anne Finn, Mark R Cobain, Dympna A Pearson, John P Foreyt. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 31.12.2008.  

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

                History
                : 27 June 2008
                : 20 July 2008
                : 29 October 2008
                : 21 November 2008
                Categories
                Original Paper

                Medicine
                employee health,internet,device,behavior change,body weight,psychology,physical activity,occupational health,diet,technology

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