Dropout, Nonusage Attrition, and Pretreatment Predictors of Nonusage Attrition in a Commercial Web-Based Weight Loss Program

Background An understanding of the factors that predict retention and website use are critical to the development of effective Web-based weight loss interventions. However, poor retention (dropout attrition) and website utilization (nonusage attrition) are major inhibitors to the effectiveness of Web-based programs. Objective The study aimed to (1) describe the prevalence of dropout and nonusage attrition and (2) examine pretreatment predictors of nonusage attrition in a cohort of commercial Web-based weight loss program participants. Methods Participants enrolled in the online program, The Biggest Loser Club, Australia, from August 15, 2007, to May 31, 2008. Only those who subscribed for 12 or 52 weeks were included in this study. All data were collected by the program proprietors, SP Health Co Pty Ltd (Sydney, Australia), and provided in “deidentified” form. Data collected included responses to a pretreatment survey (sociodemographic and behavioral characteristics), subscription history (date of enrollment and subscription end), and website use (log-ins, food and exercise diary entries, weigh-ins, and forum posts). Participants were classified as a member of the program at 12 or 52 weeks if they held an active subscription plan at that point in time. Participants were classified as nonusers at 12 or 52 weeks if they had stopped using all of the website features and had not returned. Predictors of nonusage attrition were explored using Cox proportional hazards regression analysis. Results Of the 9599 eligible participants, 6943 (72%) subscribed to the program for 12 weeks, and 2656 (28%) subscribed for 52 weeks. Of all participants, 31% (2975/9599) were classified as overweight, 61% (5866/9599) were classified as obese, 86% (8279/9599) were female, and participants’ mean (SD) age was 35.7 (9.5) years. The 12 week and 52 week subscribers’ retention rates were 97% and 77% respectively. Of 12 week subscribers, 35% were classified as program “users” after 12 weeks, and 30% of 52 week subscribers were classified as “users” after 52 weeks. Significant predictors of nonusage attrition among 12 week subscribers included age (hazard ratio for 45 to 55 years of age = 0.83, 95% confidence interval [CI] 0.73 - 0.93, P = .001; hazard ratio for 55 to 65 years of age = 0.80, 95% CI 0.66 - 0.99, P = .04), exercise level (hazard ratio = 0.76, 95% CI 0.72 - 0.81, P < .001), emotional eating (hazard ratio = 1.11, 95% CI 1.04 - 1.18, P = .001), eating breakfast (hazard ratio = 0.88, 95% CI 0.82 - 0.95, P = .001), and skipping meals (hazard ratio = 1.12, 95% CI 1.04 -1.19, P = .001). For 52 week subscribers, eating breakfast (hazard ratio = 0.88, 95% CI 0.79 - 0.99, P = .04) and not drinking tea or coffee with sugar (hazard ratio = 1.23, 95% CI 1.11 - 1.37, P < .001) were the pretreatment characteristics that significantly decreased risk of nonusage attrition. Conclusions The findings demonstrate a high prevalence of nonusage attrition among a cohort of commercial Web-based weight loss program participants. Several sociodemographic and behavioral factors were shown to independently predict nonusage attrition.

[1]  Boris P. Kovatchev,et al.  A Behavior Change Model for Internet Interventions , 2009, Annals of behavioral medicine : a publication of the Society of Behavioral Medicine.

[2]  R. Wing,et al.  A Statewide Intervention Reduces BMI in Adults: Shape Up Rhode Island Results , 2009, Obesity.

[3]  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.

[4]  C. Vandelanotte,et al.  Website-delivered physical activity interventions a review of the literature. , 2007, American journal of preventive medicine.

[5]  D. Tate,et al.  A randomized comparison of two motivationally enhanced Internet behavioral weight loss programs. , 2008, Behaviour research and therapy.

[6]  Noel D. Duncan,et al.  Evaluating an internet weight loss program for diabetes prevention. , 2005, Health promotion international.

[7]  R. Callister,et al.  The SHED‐IT Randomized Controlled Trial: Evaluation of an Internet‐based Weight‐loss Program for Men , 2009, Obesity.

[8]  D. Wantland,et al.  The Effectiveness of Web-Based vs. Non-Web-Based Interventions: A Meta-Analysis of Behavioral Change Outcomes , 2004, Journal of medical Internet research.

[9]  K. Elfhag,et al.  Who succeeds in maintaining weight loss? A conceptual review of factors associated with weight loss maintenance and weight regain , 2005, Obesity reviews : an official journal of the International Association for the Study of Obesity.

[10]  J. Honas,et al.  Predictors of attrition in a large clinic-based weight-loss program. , 2003, Obesity research.

[11]  Robert Hurling,et al.  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 , 2008, Journal of medical Internet research.

[12]  R. Winett,et al.  Using Internet technology to deliver a behavioral weight loss program. , 2001, JAMA.

[13]  T. Wadden,et al.  A randomized controlled trial of a commercial internet weight loss program. , 2004, Obesity research.

[14]  L. Houtkooper,et al.  Pretreatment predictors of attrition and successful weight management in women , 2004, International Journal of Obesity.

[15]  Russell E Glasgow,et al.  eHealth evaluation and dissemination research. , 2007, American journal of preventive medicine.

[16]  J. Harvey-Berino,et al.  The Feasibility of Using Internet Support for the Maintenance of Weight Loss , 2002, Behavior modification.

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

[18]  Ruth Petersen,et al.  Effectiveness of Employee Internet-Based Weight Management Program , 2008, Journal of occupational and environmental medicine.

[19]  M. Neve,et al.  Effectiveness of web‐based interventions in achieving weight loss and weight loss maintenance in overweight and obese adults: a systematic review with meta‐analysis , 2010, Obesity reviews : an official journal of the International Association for the Study of Obesity.

[20]  C. Dacey,et al.  Examination of pretreatment predictors of attrition in a VLCD and behavior therapy weight-loss program. , 1993, Addictive behaviors.

[21]  Truls Østbye,et al.  An Internet-Based Weight Loss Intervention Initiated by a Newspaper , 2009, Preventing chronic disease.

[22]  S. Rössner,et al.  An Internet-based weight loss programme — a feasibility study with preliminary results from 4209 completers , 2009, Scandinavian journal of public health.

[23]  D. Tate,et al.  Effects of Internet behavioral counseling on weight loss in adults at risk for type 2 diabetes: a randomized trial. , 2003, JAMA.

[24]  Reneé H Moore,et al.  Predictors of attrition and weight loss success: Results from a randomized controlled trial. , 2009, Behaviour research and therapy.

[25]  A. Bandura.  Social Foundations of Thought and Action: A Social Cognitive Theory , 1985 .

[26]  Christine M Hunter,et al.  Weight management using the internet a randomized controlled trial. , 2008, American journal of preventive medicine.

[27]  R. Dalle Grave,et al.  Weight loss expectations in obese patients and treatment attrition: an observational multicenter study. , 2005, Obesity research.

[28]  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.

[29]  Jennifer L. Pomeranz,et al.  The Delivery of Public Health Interventions via the Internet: Actualizing Their Potential , 2011 .

[30]  Lucas J Carr,et al.  Internet-delivered behavior change program increases physical activity and improves cardiometabolic disease risk factors in sedentary adults: results of a randomized controlled trial. , 2008, Preventive medicine.

[31]  Scott B Going,et al.  Maintenance of Weight Loss in Overweight Middle‐aged Women Through the Internet , 2008, Obesity.

[32]  J. Harvey-Berino,et al.  Weight Loss on the Web: A Pilot Study Comparing a Structured Behavioral Intervention to a Commercial Program , 2007, Obesity.

[33]  D. Tate,et al.  Perinatal mortality and congenital anomalies in babies of women with type 1 or type 2 diabetes in England, Wales, and Northern Ireland: population based study , 2006, BMJ : British Medical Journal.

[34]  Martin Binks,et al.  Utilization Patterns and User Characteristics of an Ad Libitum Internet Weight Loss Program , 2010, Journal of medical Internet research.

[35]  T. Wadden,et al.  Behavioral treatment of obesity. , 2000, The Medical clinics of North America.

[36]  E. Molinari,et al.  Complexity of attrition in the treatment of obesity: clues from a structured telephone interview , 2006, International Journal of Obesity.

[37]  G. Sergi,et al.  Predictors of drop-out in overweight and obese outpatients , 2005, International Journal of Obesity.

[38]  Howard Cabral,et al.  Weight, Blood Pressure, and Dietary Benefits After 12 Months of a Web-based Nutrition Education Program (DASH for Health): Longitudinal Observational Study , 2008, Journal of medical Internet research.

[39]  Miriam Wanner,et al.  Comparison of Trial Participants and Open Access Users of a Web-Based Physical Activity Intervention Regarding Adherence, Attrition, and Repeated Participation , 2010, Journal of medical Internet research.

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