Variables related to continuance in a behavioral weight loss program

Pekarik, Gene; Blodgett, Christopher; Evans, Ronald G.; Wierzbicki, Michael

doi:10.1016/0306-4603(84)90044-3

Cited by 33 publications

(37 citation statements)

References 4 publications

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“…Results indicated that lower education and heavier smoking were strongly associated with, and depression was mildly associated with, lower overall app utilization. None of the other demographic or smoking-related characteristics previously observed to predict smoking cessation Web site utilization (being female, [24][25][26][27][28][29] age, 24,[28][29][30] and number of friends who smoke 29 ) or anxiety, 31,33,34 which predicts worse adherence to other behavioral interventions, were predictive of SmartQuit openings.…”

Section: Discussionmentioning

confidence: 99%

“…Prior research on smoking cessation Web sites, a technological predecessor of smoking cessation apps with a more mature empirical literature, suggests several possible demographic predictors of utilization. Specifically, eight studies of Web-delivered interventions found that the following user characteristics were predictive of higher utilization: (1) being female, [23][24][25][26][27][28] In addition to demographic characteristics, psychological factors such as depression [31][32][33] and anxiety 31,33,34 may negatively impact utilization because these are risk factors for low adherence to other types of behavioral interventions. The goal of this study was to examine the degree to which previously identified demographic factors (gender, age, education, level of smoking, friends who smoke) and psychological factors (depression and anxiety) are predictive of (a) overall SmartQuit use and (b) use of specific SmartQuit features predictive of cessation (i.e., Tracked Practice of ACT Skills, Tracked Practice of Letting Urges Pass, and Viewed Quit Plan).…”

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confidence: 99%

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Predictors of Utilization of a Novel Smoking Cessation Smartphone App

Zeng

Vilardaga

Heffner

et al. 2015

Telemedicine and e-Health

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Background: Understanding the characteristics of high and low utilizers of smartphone applications (apps) for smoking cessation would inform development of more engaging and effective apps, yet no studies to date have addressed this critical question. Informed by prior research on predictors of cessation Web site utilization, this study examines the degree to which baseline demographic factors (gender, age, and education), smoking-related factors (smoking level and friends' smoking), and psychological factors (depression and anxiety) are predictive of utilization of a smartphone app for smoking cessation called SmartQuit. Materials and Methods: Data came from 98 participants randomized to SmartQuit as part of a pilot trial from March to May 2013. We used negative binomial count regressions to examine the relationship between user characteristics and utilization of the app over an 8-week treatment period. Results: Lower education (risk ratio [RR] = 0.492; p = 0.021), heavier smoking (RR = 0.613; p = 0.033), and depression (RR = 0.958; p = 0.017) prospectively predicted lower app utilization. Women (RR = 0.320; p = 0.022), those with lower education (RR = 0.491; p = 0.013), and heavier smokers (RR = 0.418; p = 0.039) had lower utilization of app features known to predict smoking cessation. Conclusions: Many of the predictors of utilization of smoking cessation apps are the same as those of cessation Web sites. App-delivered smoking cessation treatment effectiveness could be enhanced by focusing on increasing engagement of women, those with lower education, heavy smokers, and those with current depressive symptoms.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Predictors of Utilization of a Novel Smoking Cessation Smartphone App

Zeng

Vilardaga

Heffner

et al. 2015

Telemedicine and e-Health

View full text Add to dashboard Cite

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“…Time of dropout is included in the analysis as recommended by Pekarik, Blodgett, Evans, and Wierzbicki (1984). Participants' weights were obtained using balance beam scales.…”

Section: Methodsmentioning

confidence: 99%

Binge eating disorder, retention, and dropout in an adult obesity program

Nichaman

Taylor

et al. 1995

Int. J. Eat. Disord.

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Objective: The purpose of this study was to identify factors associated with dropout in a weight reduction program among obese, nonpurging binge and nonbinge eaters. Method: We categorized 156 obese, nonpurging women previously randomized to a behavior modification‐based (BM) or a food addiction theory‐based (FD) weight reduction intervention by binge status using the DSM‐IV criteria for binge eating disorder (BED). Subjects were monitored prospectively for dropout during the 6‐month intervention period. Results: Results of a Cox proportional hazards survival analysis indicated binge status had a statistically significant (p = .04) protective effect against dropout. Binge eaters were half as likely to dropout versus nonbinge eaters. Discussion: We conclude that (1) the DSM‐IV criteria for BED is a useful method of classifying obese individuals in weight loss interventions and (2) the identification of subgroups among obese subjects is an important step in understanding retention in weight loss programs. Future research can confirm this finding. © 1995 by John Wiley & Sons, Inc.

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“…Defining attrition is further complicated by some studies distinguishing between early and late drop-outs [11,14,15]. It has been suggested that defining attrition as a homogenous group is a weakness of obesity attrition research [11,16] and may be partially responsible for inconsistent findings [17]. There may be important differences between early and late drop-outs, which could aid in the understanding of treatment attrition [11].…”

Section: Defining Attrition In Weight-loss Interventionsmentioning

confidence: 99%