This paper is in the following e-collection/theme issue:

Mobile Health (mhealth) (3162) Smoking Cessation (594) mHealth for Wellness, Behavior Change and Prevention (3104) Engagement with and Adherence to Digital Health Interventions, Law of Attrition (539) Web-based and Mobile Health Interventions (3646)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/43629, first published .
Can a Single Variable Predict Early Dropout From Digital Health Interventions? Comparison of Predictive Models From Two Large Randomized Trials

Can a Single Variable Predict Early Dropout From Digital Health Interventions? Comparison of Predictive Models From Two Large Randomized Trials

Can a Single Variable Predict Early Dropout From Digital Health Interventions? Comparison of Predictive Models From Two Large Randomized Trials

Authors of this article:

Jonathan Bricker1, 2 Author Orcid Image ;   Zhen Miao3 Author Orcid Image ;   Kristin Mull1 Author Orcid Image ;   Margarita Santiago-Torres1 Author Orcid Image ;   David M Vock4 Author Orcid Image
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Chidambara .ML. Chidambara .ML. @chidambara09

RT @jmirpub: New in JMIR: Can a Single Variable Predict Early Dropout From #DigitalHealth Interventions? Comparison of Predictive Models Fr…

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Asmâhidki Asmâhidki @Drasmaahidki

Can a Single Variable Predict Early Dropout From Digital Health Interventions? Comparison of Predictive Models From… https://t.co/CP8yjKVpgY

11:26 PM · Jan 22, 2023

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山本陵平 山本陵平 @yamamotoryohei_

禁煙アプリの二重盲検ランダム化比較試験(4試験4529人)のデータを利用し、試験開始後1週間のログイン回数による早期アプリ離脱(one week user)の予測能をロジスティック回帰、決定木、support vector machine, neural networkの4モデルで比較したらほぼ同等でした 解析データxlsxが公開されています https://t.co/2FTZ916Hva

10:31 PM · May 12, 2023

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