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Engagement With a Behavior Change App for Alcohol Reduction: Data Visualization for Longitudinal Observational Study

BACKGROUND: Behavior change apps can develop iteratively, where the app evolves into a complex, dynamic, or personalized intervention through cycles of research, development, and implementation. Understanding how existing users engage with an app (eg, frequency, amount, depth, and duration of use) c...

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Autores principales: Bell, Lauren, Garnett, Claire, Qian, Tianchen, Perski, Olga, Williamson, Elizabeth, Potts, Henry WW
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7762688/
https://www.ncbi.nlm.nih.gov/pubmed/33306026
http://dx.doi.org/10.2196/23369
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author Bell, Lauren
Garnett, Claire
Qian, Tianchen
Perski, Olga
Williamson, Elizabeth
Potts, Henry WW
author_facet Bell, Lauren
Garnett, Claire
Qian, Tianchen
Perski, Olga
Williamson, Elizabeth
Potts, Henry WW
author_sort Bell, Lauren
collection PubMed
description BACKGROUND: Behavior change apps can develop iteratively, where the app evolves into a complex, dynamic, or personalized intervention through cycles of research, development, and implementation. Understanding how existing users engage with an app (eg, frequency, amount, depth, and duration of use) can help guide further incremental improvements. We aim to explore how simple visualizations can provide a good understanding of temporal patterns of engagement, as usage data are often longitudinal and rich. OBJECTIVE: This study aims to visualize behavioral engagement with Drink Less, a behavior change app to help reduce hazardous and harmful alcohol consumption in the general adult population of the United Kingdom. METHODS: We explored behavioral engagement among 19,233 existing users of Drink Less. Users were included in the sample if they were from the United Kingdom; were 18 years or older; were interested in reducing their alcohol consumption; had a baseline Alcohol Use Disorders Identification Test score of 8 or above, indicative of excessive drinking; and had downloaded the app between May 17, 2017, and January 22, 2019 (615 days). Measures of when sessions begin, length of sessions, time to disengagement, and patterns of use were visualized with heat maps, timeline plots, k-modes clustering analyses, and Kaplan-Meier plots. RESULTS: The daily 11 AM notification is strongly associated with a change in engagement in the following hour; reduction in behavioral engagement over time, with 50.00% (9617/19,233) of users disengaging (defined as no use for 7 or more consecutive days) 22 days after download; identification of 3 distinct trajectories of use, namely engagers (4651/19,233, 24.18% of users), slow disengagers (3679/19,233, 19.13% of users), and fast disengagers (10,903/19,233, 56.68% of users); and limited depth of engagement with 85.076% (7,095,348/8,340,005) of screen views occurring within the Self-monitoring and Feedback module. In addition, a peak of both frequency and amount of time spent per session was observed in the evenings. CONCLUSIONS: Visualizations play an important role in understanding engagement with behavior change apps. Here, we discuss how simple visualizations helped identify important patterns of engagement with Drink Less. Our visualizations of behavioral engagement suggest that the daily notification substantially impacts engagement. Furthermore, the visualizations suggest that a fixed notification policy can be effective for maintaining engagement for some users but ineffective for others. We conclude that optimizing the notification policy to target both effectiveness and engagement is a worthwhile investment. Our future goal is to both understand the causal effect of the notification on engagement and further optimize the notification policy within Drink Less by tailoring to contextual circumstances of individuals over time. Such tailoring will be informed from the findings of our micro-randomized trial (MRT), and these visualizations were useful in both gaining a better understanding of engagement and designing the MRT.
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spelling pubmed-77626882020-12-29 Engagement With a Behavior Change App for Alcohol Reduction: Data Visualization for Longitudinal Observational Study Bell, Lauren Garnett, Claire Qian, Tianchen Perski, Olga Williamson, Elizabeth Potts, Henry WW J Med Internet Res Original Paper BACKGROUND: Behavior change apps can develop iteratively, where the app evolves into a complex, dynamic, or personalized intervention through cycles of research, development, and implementation. Understanding how existing users engage with an app (eg, frequency, amount, depth, and duration of use) can help guide further incremental improvements. We aim to explore how simple visualizations can provide a good understanding of temporal patterns of engagement, as usage data are often longitudinal and rich. OBJECTIVE: This study aims to visualize behavioral engagement with Drink Less, a behavior change app to help reduce hazardous and harmful alcohol consumption in the general adult population of the United Kingdom. METHODS: We explored behavioral engagement among 19,233 existing users of Drink Less. Users were included in the sample if they were from the United Kingdom; were 18 years or older; were interested in reducing their alcohol consumption; had a baseline Alcohol Use Disorders Identification Test score of 8 or above, indicative of excessive drinking; and had downloaded the app between May 17, 2017, and January 22, 2019 (615 days). Measures of when sessions begin, length of sessions, time to disengagement, and patterns of use were visualized with heat maps, timeline plots, k-modes clustering analyses, and Kaplan-Meier plots. RESULTS: The daily 11 AM notification is strongly associated with a change in engagement in the following hour; reduction in behavioral engagement over time, with 50.00% (9617/19,233) of users disengaging (defined as no use for 7 or more consecutive days) 22 days after download; identification of 3 distinct trajectories of use, namely engagers (4651/19,233, 24.18% of users), slow disengagers (3679/19,233, 19.13% of users), and fast disengagers (10,903/19,233, 56.68% of users); and limited depth of engagement with 85.076% (7,095,348/8,340,005) of screen views occurring within the Self-monitoring and Feedback module. In addition, a peak of both frequency and amount of time spent per session was observed in the evenings. CONCLUSIONS: Visualizations play an important role in understanding engagement with behavior change apps. Here, we discuss how simple visualizations helped identify important patterns of engagement with Drink Less. Our visualizations of behavioral engagement suggest that the daily notification substantially impacts engagement. Furthermore, the visualizations suggest that a fixed notification policy can be effective for maintaining engagement for some users but ineffective for others. We conclude that optimizing the notification policy to target both effectiveness and engagement is a worthwhile investment. Our future goal is to both understand the causal effect of the notification on engagement and further optimize the notification policy within Drink Less by tailoring to contextual circumstances of individuals over time. Such tailoring will be informed from the findings of our micro-randomized trial (MRT), and these visualizations were useful in both gaining a better understanding of engagement and designing the MRT. JMIR Publications 2020-12-11 /pmc/articles/PMC7762688/ /pubmed/33306026 http://dx.doi.org/10.2196/23369 Text en ©Lauren Bell, Claire Garnett, Tianchen Qian, Olga Perski, Elizabeth Williamson, Henry WW Potts. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 11.12.2020. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Bell, Lauren
Garnett, Claire
Qian, Tianchen
Perski, Olga
Williamson, Elizabeth
Potts, Henry WW
Engagement With a Behavior Change App for Alcohol Reduction: Data Visualization for Longitudinal Observational Study
title Engagement With a Behavior Change App for Alcohol Reduction: Data Visualization for Longitudinal Observational Study
title_full Engagement With a Behavior Change App for Alcohol Reduction: Data Visualization for Longitudinal Observational Study
title_fullStr Engagement With a Behavior Change App for Alcohol Reduction: Data Visualization for Longitudinal Observational Study
title_full_unstemmed Engagement With a Behavior Change App for Alcohol Reduction: Data Visualization for Longitudinal Observational Study
title_short Engagement With a Behavior Change App for Alcohol Reduction: Data Visualization for Longitudinal Observational Study
title_sort engagement with a behavior change app for alcohol reduction: data visualization for longitudinal observational study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7762688/
https://www.ncbi.nlm.nih.gov/pubmed/33306026
http://dx.doi.org/10.2196/23369
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