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

      research-article
      , BND (Hons) 1 , , , PhD BSc Dip Nutr&Diet Dip Clin Epi 1 , , PhD BEd (HPE) (Hons1) 2
      (Reviewer), (Reviewer), (Reviewer)
      Journal of Medical Internet Research
      Gunther Eysenbach
      Weight loss, Internet, commercial sector, retention

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          Abstract

          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.

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          Most cited references43

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          Who succeeds in maintaining weight loss? A conceptual review of factors associated with weight loss maintenance and weight regain.

          Weight loss is difficult to achieve and maintaining the weight loss is an even greater challenge. The identification of factors associated with weight loss maintenance can enhance our understanding for the behaviours and prerequisites that are crucial in sustaining a lowered body weight. In this paper we have reviewed the literature on factors associated with weight loss maintenance and weight regain. We have used a definition of weight maintenance implying intentional weight loss that has subsequently been maintained for at least 6 months. According to our review, successful weight maintenance is associated with more initial weight loss, reaching a self-determined goal weight, having a physically active lifestyle, a regular meal rhythm including breakfast and healthier eating, control of over-eating and self-monitoring of behaviours. Weight maintenance is further associated with an internal motivation to lose weight, social support, better coping strategies and ability to handle life stress, self-efficacy, autonomy, assuming responsibility in life, and overall more psychological strength and stability. Factors that may pose a risk for weight regain include a history of weight cycling, disinhibited eating, binge eating, more hunger, eating in response to negative emotions and stress, and more passive reactions to problems.
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            Social Foundations of Thought and Action: A Social-Cognitive View

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              The Effectiveness of Web-Based vs. Non-Web-Based Interventions: A Meta-Analysis of Behavioral Change Outcomes

              Background A primary focus of self-care interventions for chronic illness is the encouragement of an individual's behavior change necessitating knowledge sharing, education, and understanding of the condition. The use of the Internet to deliver Web-based interventions to patients is increasing rapidly. In a 7-year period (1996 to 2003), there was a 12-fold increase in MEDLINE citations for “Web-based therapies.” The use and effectiveness of Web-based interventions to encourage an individual's change in behavior compared to non-Web-based interventions have not been substantially reviewed. Objective This meta-analysis was undertaken to provide further information on patient/client knowledge and behavioral change outcomes after Web-based interventions as compared to outcomes seen after implementation of non-Web-based interventions. Methods The MEDLINE, CINAHL, Cochrane Library, EMBASE, ERIC, and PSYCHInfo databases were searched for relevant citations between the years 1996 and 2003. Identified articles were retrieved, reviewed, and assessed according to established criteria for quality and inclusion/exclusion in the study. Twenty-two articles were deemed appropriate for the study and selected for analysis. Effect sizes were calculated to ascertain a standardized difference between the intervention (Web-based) and control (non-Web-based) groups by applying the appropriate meta-analytic technique. Homogeneity analysis, forest plot review, and sensitivity analyses were performed to ascertain the comparability of the studies. Results Aggregation of participant data revealed a total of 11,754 participants (5,841 women and 5,729 men). The average age of participants was 41.5 years. In those studies reporting attrition rates, the average drop out rate was 21% for both the intervention and control groups. For the five Web-based studies that reported usage statistics, time spent/session/person ranged from 4.5 to 45 minutes. Session logons/person/week ranged from 2.6 logons/person over 32 weeks to 1008 logons/person over 36 weeks. The intervention designs included one-time Web-participant health outcome studies compared to non-Web participant health outcomes, self-paced interventions, and longitudinal, repeated measure intervention studies. Longitudinal studies ranged from 3 weeks to 78 weeks in duration. The effect sizes for the studied outcomes ranged from -.01 to .75. Broad variability in the focus of the studied outcomes precluded the calculation of an overall effect size for the compared outcome variables in the Web-based compared to the non-Web-based interventions. Homogeneity statistic estimation also revealed widely differing study parameters (Qw16 = 49.993, P ≤ .001). There was no significant difference between study length and effect size. Sixteen of the 17 studied effect outcomes revealed improved knowledge and/or improved behavioral outcomes for participants using the Web-based interventions. Five studies provided group information to compare the validity of Web-based vs. non-Web-based instruments using one-time cross-sectional studies. These studies revealed effect sizes ranging from -.25 to +.29. Homogeneity statistic estimation again revealed widely differing study parameters (Qw4 = 18.238, P ≤ .001). Conclusions The effect size comparisons in the use of Web-based interventions compared to non-Web-based interventions showed an improvement in outcomes for individuals using Web-based interventions to achieve the specified knowledge and/or behavior change for the studied outcome variables. These outcomes included increased exercise time, increased knowledge of nutritional status, increased knowledge of asthma treatment, increased participation in healthcare, slower health decline, improved body shape perception, and 18-month weight loss maintenance.
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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 2010
                14 December 2010
                : 12
                : 4
                : e69
                Affiliations
                [2] 2simpleSchool of Education simpleFaculty of Education and Arts simpleUniversity of Newcastle CallaghanAustralia
                [1] 1simpleSchool of Health Sciences simpleFaculty of Health simpleThe University of Newcastle CallaghanAustralia
                Article
                v12i4e69
                10.2196/jmir.1640
                3056525
                21156470
                d0995bb1-c361-4159-b582-f016b852a0e1
                ©Melinda J Neve, Clare E Collins, Philip J Morgan. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 14.12.2010  

                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
                : 01 September 2010
                : 20 September 2010
                : 07 October 2010
                : 16 November 2010
                Categories
                Original Paper

                Medicine
                weight loss,internet,commercial sector,retention
                Medicine
                weight loss, internet, commercial sector, retention

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