Predicting Attrition in a Text-Based Nutrition Education Program: Survival Analysis of Text2BHealthy

Grutzmacher, Stephanie; Munger, Ashley; Speirs, Katherine E.; Vafai, Yassaman; Hilberg, Evan; Duru, Erin Braunscheidel; Worthington, L.; Lachenmayr, L.

doi:10.2196/mhealth.9967

Cited by 13 publications

(10 citation statements)

References 19 publications

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“…In contrast, psychedelic-specific factors were found to be poor predictors of attrition. Methods for reducing attrition, which have been validated through other fields, such as text messaging [ 86 ], gamification [ 87 , 88 ], monetary incentives [ 89 , 90 ], or the creation of web-based participant communities [ 91 ], are thus likely to be applicable also to observational studies on psychedelics, which should be investigated in future studies. While not without prevailing limitations, for which there is significant scope for improvement, these findings somewhat support the reliability and validity of large-scale prospective web-based data collection as a methodology for studying the predictors and processes of changes related to psychedelic use.…”

Section: Discussionmentioning

confidence: 99%

Turn on, Tune in, and Drop out: Predictors of Attrition in a Prospective Observational Cohort Study on Psychedelic Use

Hübner¹,

Haijen²,

Kaelen³

et al. 2021

J Med Internet Res

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Background The resurgence of research and public interest in the positive psychological effects of psychedelics, together with advancements in digital data collection techniques, have brought forth a new type of research design, which involves prospectively gathering large-scale naturalistic data from psychedelic users; that is, before and after the use of a psychedelic compound. A methodological limitation of such studies is their high attrition rate, particularly owing to participants who stop responding after initial study enrollment. Importantly, study dropout can introduce systematic biases that may affect the interpretability of results. Objective Based on a previously collected sample (baseline n=654), here we investigated potential determinants of study attrition in web-based prospective studies on psychedelic use. Methods Logistic regression models were used to examine demographic, psychological trait and state, and psychedelic-specific predictors of dropout. Predictors were assessed 1 week before, 1 day after, and 2 weeks after psychedelic use, with attrition being defined as noncompletion of the key endpoint 4 weeks post experience. Results Predictors of attrition were found among demographic variables including age (β=0.024; P=.007) and educational levels, as well as personality traits, specifically conscientiousness (β=–0.079; P=.02) and extraversion (β=0.082; P=.01). Contrary to prior hypotheses, neither baseline attitudes toward psychedelics nor the intensity of acute challenging experiences were predictive of dropout. Conclusions The baseline predictors of attrition identified here are consistent with those reported in longitudinal studies in other scientific disciplines, suggesting their transdisciplinary relevance. Moreover, the lack of an association between attrition and psychedelic advocacy or negative drug experiences in our sample contextualizes concerns about problematic biases in these and related data.

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Section: Discussionmentioning

confidence: 99%

Turn on, Tune in, and Drop out: Predictors of Attrition in a Prospective Observational Cohort Study on Psychedelic Use

Hübner¹,

Haijen²,

Kaelen³

et al. 2021

J Med Internet Res

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show abstract

“…Dropout is primarily related to the program provider, outline of the intervention, activity in the platform (engagement), and, to a lower degree, the demographic variables available in this study. Multiple other studies have found attrition to be related to demographic variables [22,15], but these were not available in this dataset.…”

Section: Discussionmentioning

confidence: 99%

Predicting Dropouts From an Electronic Health Platform for Lifestyle Interventions: Analysis of Methods and Predictors

Pedersen¹,

Mansourvar

Sortsø³

et al. 2019

J Med Internet Res

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BackgroundThe increasing prevalence and economic impact of chronic diseases challenge health care systems globally. Digital solutions can potentially improve efficiency and quality of care, but these initiatives struggle with nonusage attrition. Machine learning methods have been proven to predict dropouts in other settings but lack implementation in health care.ObjectiveThis study aimed to gain insight into the causes of attrition for patients in an electronic health (eHealth) intervention for chronic lifestyle diseases and evaluate if attrition can be predicted and consequently prevented. We aimed to build predictive models that can identify patients in a digital lifestyle intervention at high risk of dropout by analyzing several predictor variables applied in different models and to further assess the possibilities and impact of implementing such models into an eHealth platform.MethodsData from 2684 patients using an eHealth platform were iteratively analyzed using logistic regression, decision trees, and random forest models. The dataset was split into a 79.99% (2147/2684) training and cross-validation set and a 20.0% (537/2684) holdout test set. Trends in activity patterns were analyzed to assess engagement over time. Development and implementation were performed iteratively with health coaches.ResultsPatients in the test dataset were classified as dropouts with an 89% precision using a random forest model and 11 predictor variables. The most significant predictors were the provider of the intervention, 2 weeks inactivity, and the number of advices received from the health coach. Engagement in the platform dropped significantly leading up to the time of dropout.ConclusionsDropouts from eHealth lifestyle interventions can be predicted using various data mining methods. This can support health coaches in preventing attrition by receiving proactive warnings. The best performing predictive model was found to be the random forest.

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“…However, survival analysis can also be applied to other contexts. For instance, recent studies used survival analysis to evaluate students’ engagement within online learning environments [8] , [9] , [10] , [11] , [12] , [13] , [14] . In this study, we applied this approach to evaluate students’ engagement – and, conversely, dropout rates – over time for different time periods as observed for the online learning environment Bettermarks .…”

Section: Introductionmentioning

confidence: 99%

Evaluating students’ engagement with an online learning environment during and after COVID-19 related school closures: A survival analysis approach

Spitzer

Gutsfeld

Wirzberger

et al. 2021

Trends in Neuroscience and Education

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Predicting Attrition in a Text-Based Nutrition Education Program: Survival Analysis of Text2BHealthy

Cited by 13 publications

References 19 publications

Turn on, Tune in, and Drop out: Predictors of Attrition in a Prospective Observational Cohort Study on Psychedelic Use

Turn on, Tune in, and Drop out: Predictors of Attrition in a Prospective Observational Cohort Study on Psychedelic Use

Predicting Dropouts From an Electronic Health Platform for Lifestyle Interventions: Analysis of Methods and Predictors

Evaluating students’ engagement with an online learning environment during and after COVID-19 related school closures: A survival analysis approach

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