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

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.

[1]  V. H. Hildebrandt,et al.  Rates and Determinants of Repeated Participation in a Web-Based Behavior Change Program for Healthy Body Weight and Healthy Lifestyle , 2007, Journal of medical Internet research.

[2]  P. Sheeran,et al.  Combining motivational and volitional interventions to promote exercise participation: protection motivation theory and implementation intentions. , 2002, British journal of health psychology.

[3]  R. Muniyappa,et al.  The diabetes prevention program , 2003, Current diabetes reports.

[4]  L. Festinger A Theory of Social Comparison Processes , 1954 .

[5]  J. D. Janssen,et al.  A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity , 1997, IEEE Transactions on Biomedical Engineering.

[6]  M. Perugini,et al.  Goal desires moderate intention-behaviour relations. , 2008, The British journal of social psychology.

[7]  G. Eysenbach The Law of Attrition , 2005, Journal of medical Internet research.

[8]  N. N. Available World medical association declaration of Helsinki , 2000, Chinese Journal of Integrative Medicine.

[9]  Mick P Couper,et al.  Reach, Engagement, and Retention in an Internet-Based Weight Loss Program in a Multi-Site Randomized Controlled Trial , 2007, Journal of medical Internet research.

[10]  R. Bagby,et al.  Usage and Longitudinal Effectiveness of a Web-Based Self-Help Cognitive Behavioral Therapy Program for Panic Disorder , 2005, Journal of medical Internet research.

[11]  A. Mcguire Tackling obesity in England: Report by the comptroller and auditor general, HC 220 Session 2000-2001: 15 February 2001HC 220 Session 2000-2001: 15 February 2001 , 2001 .

[12]  Rosalind W. Picard,et al.  Establishing the computer-patient working alliance in automated health behavior change interventions. , 2005, Patient education and counseling.

[13]  Yves Schutz,et al.  The use of uniaxial accelerometry for the assessment of physical-activity-related energy expenditure: a validation study against whole-body indirect calorimetry. , 2004, The British journal of nutrition.

[14]  P. Williams,et al.  Physical activity and public health. , 1995, JAMA.

[15]  S le Cessie,et al.  Using internet technology to deliver a home-based physical activity intervention for patients with rheumatoid arthritis: A randomized controlled trial. , 2006, Arthritis and rheumatism.

[16]  B. G. Celler,et al.  Classification of basic daily movements using a triaxial accelerometer , 2004, Medical and Biological Engineering and Computing.

[17]  Robert Hurling,et al.  Internet-based exercise intervention systems: Are more interactive designs better? , 2006 .

[18]  Sandra L Saperstein,et al.  The impact of Internet use for weight loss , 2007, Obesity reviews : an official journal of the International Association for the Study of Obesity.

[19]  J. Prochaska,et al.  A randomized controlled trial of single versus multiple health behavior change: promoting physical activity and nutrition among adolescents. , 2004, Health psychology : official journal of the Division of Health Psychology, American Psychological Association.

[20]  P. Macfarlane,et al.  Using the WHO (Rose) angina questionnaire in cardiovascular epidemiology. , 1989, International journal of epidemiology.

[21]  Sid J. Schneider,et al.  Computerized communication as a medium for behavioral smoking cessation treatment: controlled evaluation , 1990 .

[22]  I. Janis,et al.  Decision Making: A Psychological Analysis of Conflict, Choice, and Commitment , 1977 .

[23]  Issa Zakeri,et al.  Prediction of activity energy expenditure using accelerometers in children. , 2004, Medicine and science in sports and exercise.

[24]  Pim Cuijpers,et al.  Effectiveness of a Web-Based Self-Help Intervention for Symptoms of Depression, Anxiety, and Stress: Randomized Controlled Trial , 2008, Journal of medical Internet research.

[25]  C. Champagne,et al.  Design and Implementation of an Interactive Website to Support Long-Term Maintenance of Weight Loss , 2008, Journal of medical Internet research.

[26]  Daniel J Buysse,et al.  The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research , 1989, Psychiatry Research.

[27]  P Zimmet,et al.  Ethnic comparisons of the cross‐sectional relationships between measures of body size with diabetes and hypertension , 2008, Obesity reviews : an official journal of the International Association for the Study of Obesity.

[28]  K. Patrick,et al.  Physical Activity and Public Health: A Recommendation From the Centers for Disease Control and Prevention and the American College of Sports Medicine , 1995 .

[29]  J. S. Sodhi,et al.  Using Internet and Mobile Phone Technology to Deliver an Automated Physical Activity Program: Randomized Controlled Trial , 2007, Journal of medical Internet research.

[30]  C. Yarborough,et al.  Characteristics of Participants and Nonparticipants in Worksite Health Promotion , 1996, American journal of health promotion : AJHP.

[31]  Judith Wylie-Rosett,et al.  Achieving weight and activity goals among diabetes prevention program lifestyle participants. , 2004, Obesity research.

[32]  K. Croteau,et al.  A Preliminary Study on the Impact of a Pedometer-Based Intervention on Daily Steps , 2004, American journal of health promotion : AJHP.

[33]  A. Hwang,et al.  Development and validation of a computerized exercise intervention program for patients with type 2 diabetes mellitus in Korea. , 2003, Yonsei medical journal.

[34]  J F Sallis,et al.  Compendium of physical activities: classification of energy costs of human physical activities. , 1993, Medicine and science in sports and exercise.

[35]  B. Buunk,et al.  The relevance of social comparison processes for prevention and health care. , 2002, Patient education and counseling.

[36]  Stuart Linke,et al.  Internet-Based Interactive Health Intervention for the Promotion of Sensible Drinking: Patterns of Use and Potential Impact on Members of the General Public , 2007, Journal of medical Internet research.

[37]  Audie A Atienza,et al.  A review of eHealth interventions for physical activity and dietary behavior change. , 2007, American journal of preventive medicine.

[38]  Tom Baranowski,et al.  The Fun, Food, and Fitness Project (FFFP): the Baylor GEMS pilot study. , 2003, Ethnicity & disease.

[39]  M. H. van den Berg,et al.  Internet-Based Physical Activity Interventions: A Systematic Review of the Literature , 2007, Journal of medical Internet research.

[40]  R. Cook,et al.  A Field Test of a Web-Based Workplace Health Promotion Program to Improve Dietary Practices, Reduce Stress, and Increase Physical Activity: Randomized Controlled Trial , 2007, Journal of medical Internet research.

[41]  D. Bassett,et al.  The technology of accelerometry-based activity monitors: current and future. , 2005, Medicine and science in sports and exercise.

[42]  Caroline R Richardson,et al.  A randomized trial comparing structured and lifestyle goals in an internet-mediated walking program for people with type 2 diabetes , 2007, The international journal of behavioral nutrition and physical activity.

[43]  M. Perugini,et al.  Can the effects of implementation intentions on exercise be enhanced using text messages? , 2009, Psychology & health.

[44]  B. Alexy Factors associated with participation or nonparticipation in a workplace wellness center. , 1991, Research in Nursing and Health.

[45]  A. Bauman,et al.  Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. , 2007, Circulation.

[46]  Christiane,et al.  World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. , 2004, Journal international de bioethique = International journal of bioethics.

[47]  Peter M. Gollwitzer,et al.  Self-regulatory strategy and executive control: implementation intentions modulate task switching and Simon task performance , 2007, Psychological research.