Faculty Opinions recommendation of Just-in-time adaptive intervention to promote fluid consumption in patients with kidney stones.

2021 ◽  
Author(s):  
Alberto Trinchieri
Keyword(s):  
Kidney Stones ◽  
Just In Time ◽  
Fluid Consumption ◽  
Adaptive Intervention

scholarly journals Just-in-time adaptive intervention to promote fluid consumption in patients with kidney stones.

2020 ◽  
Vol 39 (12) ◽  
pp. 1062-1069
Author(s):  
David E. Conroy ◽  
Ashley B. West ◽  
Deborah Brunke-Reese ◽  
Edison Thomaz ◽  
Necole M. Streeper
Keyword(s):  
Kidney Stones ◽  
Just In Time ◽  
Fluid Consumption ◽  
Adaptive Intervention

scholarly journals Reinforcement Learning to Send Reminders at Right Moments in Smartphone Exercise Application: A Feasibility Study

2021 ◽  
Vol 18 (11) ◽  
pp. 6059
Author(s):  
Shihan Wang ◽  
Karlijn Sporrel ◽  
Herke van Hoof ◽  
Monique Simons ◽  
Rémi D. D. de Boer ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Physical Activities ◽  
Lessons Learned ◽  
Just In Time ◽  
Healthcare Intervention ◽  
Adaptive Intervention ◽  
Mobile Healthcare ◽  
Crucial Component ◽  
Application Data ◽  
The Right

Just-in-time adaptive intervention (JITAI) has gained attention recently and previous studies have indicated that it is an effective strategy in the field of mobile healthcare intervention. Identifying the right moment for the intervention is a crucial component. In this paper the reinforcement learning (RL) technique has been used in a smartphone exercise application to promote physical activity. This RL model determines the ‘right’ time to deliver a restricted number of notifications adaptively, with respect to users’ temporary context information (i.e., time and calendar). A four-week trial study was conducted to examine the feasibility of our model with real target users. JITAI reminders were sent by the RL model in the fourth week of the intervention, while the participants could only access the app’s other functionalities during the first 3 weeks. Eleven target users registered for this study, and the data from 7 participants using the application for 4 weeks and receiving the intervening reminders were analyzed. Not only were the reaction behaviors of users after receiving the reminders analyzed from the application data, but the user experience with the reminders was also explored in a questionnaire and exit interviews. The results show that 83.3% reminders sent at adaptive moments were able to elicit user reaction within 50 min, and 66.7% of physical activities in the intervention week were performed within 5 h of the delivery of a reminder. Our findings indicated the usability of the RL model, while the timing of the moments to deliver reminders can be further improved based on lessons learned.

scholarly journals Development of a Just-in-Time Adaptive Intervention for Smoking Cessation Among Korean American Emerging Adults

2017 ◽  
Vol 24 (5) ◽  
pp. 665-672 ◽  
Author(s):  
Christian Jules Cerrada ◽  
Eldin Dzubur ◽  
Kacie C. A. Blackman ◽  
Vickie Mays ◽  
Steven Shoptaw ◽  
...  
Keyword(s):  
Smoking Cessation ◽  
Emerging Adults ◽  
Korean American ◽  
Just In Time ◽  
Adaptive Intervention

Just-in-time Adaptive Mechanisms of Popular Mobile Applications for Individuals with Depression: Systematic Review (Preprint)

2021 ◽  
Author(s):  
Gisbert Wilhelm Teepe ◽  
Ashish Da Fonseca ◽  
Birgit Kleim ◽  
Nicholas C. Jacobson ◽  
Alicia Salamanca Sanabria ◽  
...  
Keyword(s):  
American Psychological Association ◽  
Just In Time ◽  
Full Potential ◽  
Adaptive Intervention ◽  
Scientific Databases ◽  
App Store ◽  
The United Kingdom ◽  
Self Reports ◽  
Passive Measurements ◽  
Google Play

BACKGROUND There is an increasing number of smartphone applications (apps) focusing on prevention, treatment, and diagnosis of depression. A promising approach to increase the effectiveness while reducing the individual’s burden is the use of just-in-time adaptive intervention (JITAI) mechanisms. OBJECTIVE With this work, we systematically assess the use of JITAI mechanisms in apps for individuals with depression. METHODS We systematically searched for apps addressing depression in the Apple App Store, the Google Play Store, and in curated lists from the Anxiety and Depression Association of America, the United Kingdom National Health Service, and the American Psychological Association in August 2020. Relevant apps were ranked according to the number of reviews (Apple App Store) or downloads (Google Play Store). For each app, two authors separately reviewed all publications concerning the app found within scientific databases (PubMed, Cochrane Register of Controlled Trials, PsycINFO, and Google Scholar), publications cited on the app’s website, information on the app’s website, and the app itself. RESULTS None of the 28 reviewed apps used JITAI mechanisms to tailor content to situations or individuals. Three apps did not use any measurements, 20 apps exclusively used self-reports that are insufficient to leverage the full potential of JITAIs, and the five apps employing self-reports and passive measurements used them as progress or task indicators only. While 23 of the 68 reviewed publications investigated the effectiveness and 14 publications investigated the efficacy of the apps, not one publication mentioned or evaluated JITAI mechanisms. CONCLUSIONS Promising JITAI mechanisms have not yet been translated into mainstream depression apps. The lack of publications investigating whether JITAI mechanisms lead to an increase of the apps’ effectiveness or efficacy highlights the need for further research, especially in real-world apps.

scholarly journals Return of the JITAI: Applying a Just-in-Time Adaptive Intervention Framework to the Development of m-Health Solutions for Addictive Behaviors

2017 ◽  
Vol 24 (5) ◽  
pp. 673-682 ◽  
Author(s):  
Stephanie P. Goldstein ◽  
Brittney C. Evans ◽  
Daniel Flack ◽  
Adrienne Juarascio ◽  
Stephanie Manasse ◽  
...  
Keyword(s):  
Just In Time ◽  
Addictive Behaviors ◽  
Adaptive Intervention

Randomized controlled trial of OnTrack, a just-in-time adaptive intervention designed to enhance weight loss

2019 ◽  
Vol 9 (6) ◽  
pp. 989-1001 ◽  
Author(s):  
Evan M Forman ◽  
Stephanie P Goldstein ◽  
Rebecca J Crochiere ◽  
Meghan L Butryn ◽  
Adrienne S Juarascio ◽  
...  
Keyword(s):  
Machine Learning ◽  
Weight Loss ◽  
Randomized Controlled Trial ◽  
Randomized Trial ◽  
Controlled Trial ◽  
Weight Loss Program ◽  
Just In Time ◽  
Adaptive Intervention ◽  
Randomized Controlled

This randomized trial demonstrated qualified support for the ability of a machine learning-powered, smartphone-based just-in-time, adaptive intervention to enhance weight loss over and above a commercial weight loss program.

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