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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.
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.
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.
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.
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.
Public health interventions delivered via the Internet are becoming increasingly popular, and evidence to support their ability to achieve health-related behavior change and positive health outcomes is growing [
Retention rates published to date for Web-based weight loss programs range from 20% to 100%, with the majority less than 80% [
The majority of Web-based weight loss interventions report low website usage and experience a steady drop in usage over time [
As participants can potentially fail to drop out of Web-based interventions but stop using the website, Eysenbach [
To date, studies investigating Web-based weight loss programs have predominantly been randomized controlled trials (RCTs). However, RCTs could potentially overestimate or underestimate participant attrition and website use due to the inherent characteristics of volunteers and study rigor (eg, motivated participants, additional assessment sessions, subject retention strategies, greater accountability, and contact with study staff). Therefore, RCTs may not represent attrition or website usage in the “real world.” Studies that follow real-world participants of Web-based weight loss programs are, therefore, needed to ascertain true dropout and nonusage attrition rates in order to enhance program effectiveness.
Therefore, the first aim of this study was to describe in a large cohort of real-world users of a commercial Web-based weight loss program, the prevalence of dropout and nonusage attrition. The second aim was to determine which pretreatment sociodemographic and behavioral characteristics predict nonusage attrition.
Participants were adults 18 to 75 years of age who enrolled in a commercial Web-based weight loss program from August 15, 2007, through May 31, 2008, and paid a subscription to access the program. A self-reported body mass index (BMI) of greater than or equal to 22 kg/m2 was required to enroll in the program. Only participants who subscribed for 12 or 52 weeks were included in this study, as they are the most predominant subscription lengths. Participants who did not pay for their initial subscription (eg, free promotional program trials) were excluded. Data related to free or nonconsecutive memberships (≥ 7 days apart) were also not included in the analysis. Membership status and website use were tracked for the duration of the subscription.
SP Health Co Pty Ltd (Sydney, Australia) developed the Web-based weight loss platform that is commercially available as The Biggest Loser Club. In summary, the online program incorporates key evidence-based weight management strategies and aligns with key elements of social cognitive theory [
The proprietors of program, SP Health Co, store all data entered by participants accessing the program website. Data stored include responses to an enrollment survey, subscription plans held, and use of a number of the website features (log-ins, online food and activity diary entries, weigh-ins, and posts to the discussion forum). SP Health Co extracted stored data in “deidentifiable” form for up to 52 weeks from enrollment for all participants who met the inclusion criteria. Ethics approval for the study was obtained from the University of Newcastle Human Research Ethics Committee.
Participants’ pretreatment characteristics were captured from the enrollment survey. Participants’ self-reported height and weight were used to calculate BMI (weight in kilograms divided by height in meters squared), which was categorized as healthy, overweight, or obese using the World Health Organization’s BMI classification [
Website use was assessed by summing available usage data. Participants were classified as having used the website on any given day if they logged in, made an entry in the diary, posted to the forum, and/or weighed in. The total number of days per 4 week period each participant “used” the website was calculated and categorized as 0 days, 1 to 3 days, 4 to 7 days, 8 to 15 days and 16 or more days. All website use variables were calculated from enrollment to 12 and 52 weeks for the 12- and 52 week subscribers respectively.
The date a participant enrolled in program and the date membership ceased were used to calculate the number of days each participant was a member of the program (ie, duration of membership). The date membership ceased was the end date of the participant’s subscription plan unless there were special circumstances that prevented the participant from completing the subscription (eg, pregnancy or financial constraints). Participants were classified as members of the program at 12 or 52 weeks if they held an active subscription plan at that point in time (≥ 78 days for 12 week subscriptions and ≥ 359 days for 52 week subscriptions). Otherwise they were classified as a dropout.
Nonusage attrition was only considered for participants who completed their subscription (ie, they did not drop out). Participants were classified as a nonuser at 12 or 52 weeks if they stopped using the website features (ie, no log-ins, food/activity diary entries, weigh-ins, or posts to the discussion forum). The week a participant was classified as a nonuser was the week he or she ceased using the website and did not return.
Data analysis was undertaken using Stata 11 IC (StataCorp LP, College Station, USA). Participant pretreatment characteristics were described as means (SD) for continuous variables and percentage for categorical variables. Subscription length (12 and 52 weeks) group differences were tested using independent
Of the 11,341 participants who enrolled in the commercial Web-based weight loss program between August 15, 2007, and May 31, 2008, 9599 were eligible for inclusion in the study, and 1742 were excluded (
Participant flow
The characteristics of the eligible participants are outlined in
Pretreatment characteristics
Descriptor | Total | 12 Weeks | 52 Weeks |
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n = 9599 | n = 6943 | n = 2656 | |||
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Mean (SD) | 35.7 (9.5) | 35.3 (9.4) | 36.7 (9.6) | < .001 | |
18 to 25 years, % | 12.8 | 13.5 | 10.8 | < .001 | |
25 to 35 years, % | 37.4 | 38.6 | 34.5 | ||
35 to 45 years, % | 33.2 | 32.3 | 35.6 | ||
45 to 55 years, % | 13.2 | 12.6 | 14.7 | ||
55 to 65 years, % | 3.1 | 2.8 | 4.0 | ||
65 to 75 years, % | 0.4 | 0.4 | 0.4 | ||
Female (%) | 86.3 | 86.5 | 85.7 | .30 | |
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Mean (SD) | 32.9 (6.7) | 31.8 (6.1) | 35.8 (7.1) | < .001 | |
Healthy weight, % | 7.9 | 9.7 | 3.1 | < .001 | |
Overweight, % | 31.0 | 35.7 | 18.7 | ||
Obese, % | 61.1 | 54.6 | 78.2 | ||
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1-2, % | 5.8 | 4.9 | 8.0 | < .001 | |
3-4, % | 9.4 | 9.1 | 10.3 | ||
5-6, % | 18.2 | 17.4 | 20.2 | ||
7-8, % | 29.3 | 29.5 | 28.7 | ||
9-10, % | 37.4 | 39.1 | 32.8 | ||
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Major city, % | 75.4 | 76.4 | 72.7 | .001 | |
Regional, % | 23.2 | 22.3 | 25.8 | ||
Remote, % | 1.4 | 1.3 | 1.6 | ||
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0-1 days, % | 51.0 | 50.6 | 51.8 | < .001 | |
2 or more days, % | 49.0 | 49.4 | 48.2 | ||
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Fry foods, % | 37.9 | 36.4 | 42.4 | < .001 | |
Use butter in cooking, % | 36.1 | 35.4 | 38.2 | .01 | |
Drink full sugar soft drinks, % | 29.4 | 28.2 | 32.6 | < .001 | |
Skip meals, % | 53.1 | 51.3 | 58.0 | < .001 | |
Drink tea or coffee with sugar, % | 43.7 | 44.4 | 41.9 | .03 | |
Eat breakfast, % | 73.5 | 74.7 | 70.3 | < .001 | |
Use low fat products, % | 65.3 | 66.3 | 62.7 | .001 | |
Keep snack foods in the house, % | 59.8 | 58.9 | 62.1 | .004 | |
Drink 6+ glasses of water a day, % | 40.7 | 41.2 | 39.4 | .10 | |
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To ease emotional upset, % | 56.0 | 55.0 | 58.7 | .001 | |
For the joy of it, % | 55.9 | 53.4 | 56.9 | .002 | |
To reduce stress, % | 44.6 | 44.0 | 46.1 | .07 | |
Out of boredom, % | 78.6 | 78.9 | 77.9 | .26 | |
One or more health-related reasons for weight loss, % | 54.7 | 53.2 | 58.9 | < .001 |
a Total n = 9455; at 12 weeks n = 6841; and at 52 weeks, n = 2614
b Total n = 9456; at 12 weeks, n = 6842; and at 52 weeks, n = 2614
c Total n = 9569; at 12 weeks, n = 6923; and at 52 weeks, n = 2646
Statistically significant differences in pretreatment characteristics of 12- and 52 week subscribers were evident, with the mean (SD) age of participants who subscribed for 52 weeks being significantly greater (35.8 [7.1] years of age vs 31.8 [6.1] years of age), having a higher mean (SD) BMI (36.7 [9.6] vs 35.3 [9.4]), being of lower socioeconomic status (82% vs 86% with an ISRAD of 5 to 10), and a lower proportion residing in major cities of Australia (73% vs 76%) when compared with 12 week subscribers. A significantly higher proportion of 52 week subscribers reported poor eating habits (eg, frying foods or drinking full sugar soft drinks), exercising less than 2 days per week, eating for emotional reasons or for the joy of it, and having health-related reasons for wanting to lose weight.
Website use from enrollment to 12 weeks among 12 week subscribers
Website use from enrollment to 52 weeks among 52 week subscribers
Dropout attrition and nonusage attrition from enrollment to 12 weeks among 12 week subscribers
Dropout and nonusage attrition from enrollment to 52 weeks among 52 week subscribers
Of the 2051 participants who completed their 52 week subscription, 622 participants (30%) were “users” of the program at 52 weeks. The proportion of participants who stopped using the program remained steady from week 1 to week 44 (1% to 2% stopped using per week) but increased rapidly thereafter. By week 46, greater than 50% of the 52 week subscribers were nonusers of the program (
Risk of nonusage attrition for 12 week subscribers
Risk Factor | Unadjusted (n = 6705) | Adjusted (n = 6686)d | ||||
Hazard Ratio |
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Hazard Ratio |
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|||
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Male | 1.00 (reference) | |||||
Female | 0.85 (0.78 - 0.92) | < .001 | ||||
|
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18 to 25 | 1.00 (reference) | 1.00 (reference) | ||||
25 to 35 | 0.92 (0.84 -1.01) | .09 | 0.93 (0.85 - 1.02) | .12 | ||
35 to 45 | 0.92 (0.84 -1.01) | .09 | 0.93 (0.85 - 1.03) | .15 | ||
45 to 55 | 0.81 (0.72 - 0.91) | < .001 | 0.83 (0.73 - 0.93) | .001 | ||
55 to 65 | 0.77 (0.63 - 0.95) | .01 | 0.80 (0.66 - 0.99) | .04 | ||
65 to 75 | 0.54 (0.29 - 1.01) | .05 | 0.63 (0.34 - 1.17) | .14 | ||
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1-2 | 1.00 (reference) | |||||
3-4 | 1.04 (0.88 - 1.23) | .67 | ||||
5-6 | 0.97 (0.83 - 1.13) | .69 | ||||
7-8 | 1.01 (0.87 - 1.17) | .93 | ||||
9-10 | 1.03 (0.89 - 1.19) | .72 | ||||
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Major cities of Australia | 1.00 (reference) | |||||
Regional Australia | 0.97 (0.90 - 1.04) | .35 | ||||
Rural/remote Australia | 1.17 (0.91 - 1.49) | .21 | ||||
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Healthy weight | 1.00 (reference) | |||||
Overweight | 1.02 (0.92 - 1.14) | .66 | ||||
Obese | 1.11 (1.00 - 1.24) | .04 | ||||
0 to 1 days | 1.00 (reference) | 1.00 (reference) | ||||
2 or more days | 0.74 (0.69 - 0.78) | < .001 | 0.76 (0.72 - 0.81) | < .001 | ||
|
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To ease emotional upset | 1.07 (1.01 - 1.14) | .01 | 1.11 (1.04 - 1.18) | .001 | ||
For the joy of it | 0.99 (0.93 - 1.05) | .63 | ||||
To reduce stress | 1.10 (1.03 - 1.16) | .002 | ||||
Out of boredom | 0.98 (0.91 - 1.05) | .59 | ||||
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Fry foods | 1.07 (0.99 - 1.13) | .07 | ||||
Use butter in cooking | 1.06 (0.99 - 1.13) | .07 | ||||
Drink full sugar soft drinks | 1.16 (1.09 - 1.24) | < .001 | ||||
Skip meals | 1.23 (1.16 - 1.31) | < .001 | 1.12 (1.04 - 1.19) | .001 | ||
Drink tea or coffee with sugar | 0.99 (0.94 - 1.05) | .84 | ||||
Eat breakfast | 0.77 (0.72 - 0.82) | < .001 | 0.88 (0.82 - 0.95) | .001 | ||
Use low fat products | 0.85 (0.79 - 0.90) | < .001 | ||||
Keep snack foods in the house | 1.03 (0.97 - 1.09) | .33 | ||||
Drink 6 or more glasses of water a day | 0.92 (0.86 - 0.97) | .004 | ||||
1 or more health-related reasons for weight loss | 0.97 (0.92 - 1.03) | .37 |
a n = 6610
b n = 6611
c n = 6686 (all unadjusted)
d Stratified by gender
Risk of nonusage attrition for 52 week subscribers
Risk Factors | Unadjusted (n = 2051) | Adjusted (n = 2043)d | ||||||||
Hazard Ratio |
|
Hazard Ratio |
|
|||||||
|
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Male | 1.00 (reference) | |||||||||
Female | 0.90 (0.78 - 1.04) | |||||||||
|
||||||||||
18 to 25 | 1.00 (reference) | |||||||||
25 to 35 | 0.96 (0.79 - 1.16) | .66 | ||||||||
35 to 45 | 0.93 (0.77 - 1.16) | .45 | ||||||||
45 to 55 | 0.79 (0.63 - 0.97) | .03 | ||||||||
55 to 65 | 0.68 (0.49 - 0.91) | .01 | ||||||||
65 to 75 | 0.20 (0.02 - 1.44) | .11 | ||||||||
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1-2 | 1.00 (reference) | |||||||||
3-4 | 0.92 (0.71 - 1.18) | .49 | ||||||||
5-6 | 0.82 (0.66 - 1.03) | .08 | ||||||||
7-8 | 0.89 (0.72 - 1.10) | .29 | ||||||||
9-10 | 0.82 (0.66 - 1.01) | .06 | ||||||||
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Major cities of Australia | 1.00 (reference) | |||||||||
Regional Australia | 1.03 (0.91 - 1.16) | .66 | ||||||||
Rural/remote Australia | 1.05 (0.64 - 1.71) | .89 | ||||||||
|
||||||||||
Healthy weight | 1.00 (reference) | |||||||||
Overweight | 1.02 (0.74 - 1.42) | .89 | ||||||||
Obese | 1.04 (0.76 - 1.41) | .83 | ||||||||
|
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0 to 1 day | 1.00 (reference) | |||||||||
2 or more days | 0.70 (0.63 - 0.78) | < .001 | ||||||||
|
||||||||||
To ease emotional upset | 0.98 (0.88 - 1.08) | .64 | ||||||||
For the joy of it | 0.96 (0.87 - 1.07) | .46 | ||||||||
To reduce stress | 0.93 (0.84 - 1.03) | .17 | ||||||||
Out of boredom | 0.97 (0.86 - 1.10) | .62 | ||||||||
|
||||||||||
Fry foods | 1.12 (1.00 - 1.24) | .04 | ||||||||
Use butter in cooking | 1.16 (1.04 - 1.29) | .007 | ||||||||
Drink full sugar soft drinks | 1.12 (1.01 - 1.26) | .04 | ||||||||
Skip meals | 1.22 (1.10 - 1.36) | < .001 | ||||||||
Drink tea or coffee with sugar | 1.25 (1.13 - 1.39) | < .001 | 1.23 (1.11 - 1.37) | < .001 | ||||||
Eat breakfast | 0.82 (0.73 - 0.92) | .001 | 0.88 (0.79 - 0.99) | .04 | ||||||
Use low fat products | 0.84 (0.75 - 0.93) | .001 | ||||||||
Keep snack foods in the house | 1.09 (0.98 - 1.21) | .13 | ||||||||
Drink 6 or more glasses of water a day | 0.93 (0.83 - 1.03) | .15 | ||||||||
1 or more health-related reasons for weight loss | 0.90 (0.81 - 1.01) | .06 |
a n = 2019
b n = 2019
c n = 2043 (all unadjusted)
d Stratified by exercise level
This study is one of only a small number of studies [
The findings from this study are consistent with other studies that have demonstrated that individuals in the mid-to-older age group (45 to 65 years) are at decreased risk of nonusage [
The study findings suggest that people with poor eating or physical activity habits prior to enrolling in a commercial Web-based weight loss program are most likely to stop using the program. This includes participants who exercised less than 2 days per week, skipped meals, did not eat breakfast, drank tea or coffee with added sugar, or identified eating to ease emotional upset. This suggests that these at-risk individuals may require alternate or additional support to remain an active participant of Web-based programs, particularly in the short-term. Alternatively, it may be that the Web-based program in its current form did not engage this group of participants. A research priority is, therefore, to determine whether different or extra website features can improve website usage in this group of at-risk individuals.
This study highlights the importance of investigating nonusage attrition to accurately describe attrition rates. The retention rates for the commercial Web-based weight loss program of 97% after 12 weeks and 77% after 52 weeks were high in comparison with observational [
To our knowledge, only 2 other studies have investigated nonusage attrition rates in a Web-based interventions aiming to achieve weight loss [
Potential limitations of this study include that only pretreatment characteristics were considered as potential predictors of nonusage attrition. It is possible that other factors such as satisfaction with the program, initial and ongoing weight loss, and external factors also influenced program use. However, the aim of this study was to determine whether it is possible to predict who will use the program at enrollment. Furthermore, although a large number of pretreatment characteristics were explored as potential predictors of nonusage attrition, the study could have been improved by including a larger range of pretreatment characteristics (eg, motivation and stage of change), as well as through the use of validated measures to more comprehensively assess eating and physical activity behaviors. In addition, the study did not track the use of all features of the commercial Web-based weight loss program (eg, weekly tutorials and menu plans), as these data were not available at the time of the study. This may have overestimated nonusage attrition rates. Furthermore, the methodology assumes that nonusage is a negative behavior. It has been suggested, however, that participants may consider Web-based interventions differently from other treatment options [
Adherence has been acknowledged as one of the main determinants of effectiveness [
The findings from this study also highlight key pretreatment sociodemographic and behavioral predictors of nonusage attrition. The findings are similar to other weight loss [
Previous research has identified optimization of participant retention and website use as key challenges for all Web-based interventions [
We acknowledge the work of Ben Noblet in retrieving the data, Anna Crook and Penelope Jones for assistance with interpreting the data set, and Patrick McElduff for assistance with design and implementation of the statistical analyses. MJ Neve is funded by an Australian Postgraduate Award scholarship and a scholarship top-up from SP Health Co Pty Ltd. CE Collins is supported by an Australian National Health and Medical Research Council Career Development Award research fellowship
MJ Neve receives a postgraduate scholarship top-up from SP Health Co. CE Collins is a consultant dietitian to SP Health Co. PJ Morgan and CE Collins hold an Australian Research Council (ARC) Linkage project grant that is evaluating a weight loss program with SP Health Co.
body mass index
Index of Relative of Socioeconomic Advantage and Disadvantage
randomized controlled trial
short message service