scholarly journals Not Now, Ask Later: Users Weaken Their Behavior Change Regimen Over Time, But Expect To Re-Strengthen It Imminently

Author(s):  
Geza Kovacs ◽  
Zhengxuan Wu ◽  
Michael S. Bernstein
Keyword(s):  
2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 827-827
Author(s):  
Jaime Hughes ◽  
Susan Hughes ◽  
Mina Raj ◽  
Janet Bettger

Abstract Behavior change is an inherent aspect of routine geriatric care. However, most research and clinical programs emphasis how to initiate behavior change with less emphasis placed on skills and strategies to maintain behaviors over time, including after an intervention has concluded. This presentation will provide an introduction to the symposium, including a review of prior work and our rationale for studying the critical yet overlooked construct of maintenance in older adults. Several key considerations in our work include the impact of multiple chronic conditions, declines in cognitive and functional capacity over time, changes in environmental context and/or social support, and sustainability of community and population-level programs and services.


10.2196/23369 ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. e23369
Author(s):  
Lauren Bell ◽  
Claire Garnett ◽  
Tianchen Qian ◽  
Olga Perski ◽  
Elizabeth Williamson ◽  
...  

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.


10.2196/15563 ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. e15563 ◽  
Author(s):  
Sean D Young

A growing number of interventions incorporate digital and social technologies (eg, social media, mobile phone apps, and wearable devices) into their design for behavior change. However, because of a number of factors, including changing trends in the use of technology over time, results on the efficacy of these interventions have been mixed. An updated framework is needed to help researchers better plan behavioral technology interventions by anticipating the needed resources and potential changes in trends that may affect interventions over time. Focusing on the domain of health interventions as a use case, we present the Adaptive Behavioral Components (ABC) model for technology-based behavioral interventions. ABC is composed of five components: basic behavior change; intervention, or problem-focused characteristics; population, social, and behavioral characteristics; individual-level and personality characteristics; and technology characteristics. ABC was designed with the goals of (1) guiding high-level development for digital technology–based interventions; (2) helping interventionists consider, plan for, and adapt to potential barriers that may arise during longitudinal interventions; and (3) providing a framework to potentially help increase the consistency of findings among digital technology intervention studies. We describe the planning of an HIV prevention intervention as a case study for how to implement ABC into intervention design. Using the ABC model to plan future interventions might help to improve the design of and adherence to longitudinal behavior change intervention protocols; allow these interventions to adapt, anticipate, and prepare for changes that may arise over time; and help to potentially improve intervention behavior change outcomes. Additional research is needed on the influence of each of ABC’s components to help improve intervention design and implementation.


2017 ◽  
Vol 31 (3) ◽  
pp. 219-232 ◽  
Author(s):  
Monique K. Vallabhan ◽  
Alberta S. Kong ◽  
Elizabeth Yakes Jimenez ◽  
Linda C. Summers ◽  
Conni J. DeBlieck ◽  
...  

Background and Purpose: Adolescent obesity is a global epidemic. Motivational interviewing (MI) is a promising strategy to address adolescent obesity risk behaviors. However, primary care providers (PCPs) tend to express discomfort with learning and adopting MI practices and with addressing patient weight issues. PCP proficiency in using MI to discuss body mass index, health screening results, and nutrition and physical activity behaviors after receiving training and coaching from an MI expert and practicing the technique was evaluated. We hypothesized that comfort with MI would increase consistently over time. Methods: Self-assessment surveys in MI proficiency were administered to PCPs after every youth participant MI session. MI comfort as determined by proficiency was categorized into low, medium, and high comfort according to survey Likert scale responses. Data were analyzed using analysis of variance (ANOVA) and Fisher’s exact tests. Results: Two hundred twenty-seven youth were seen for MI-based discussions by 4 PCPs. Two hundred twenty-six surveys had complete data for analysis. As anticipated, overall PCPs reported significantly more comfort with MI from the first to the final MI session over a 2- to 3-month period (p < .001). Comfort scores did not increase linearly over time for all PCPs. Despite standard training practices, overall MI proficiency as measured by comfort scores varied by PCP (p < .01). Implications for Practice: This type of MI training program should be considered for clinical nurses and nurse practitioners during their nursing education training to facilitate their ability to consistently and effectively support youth behavior change for conditions such as obesity (ClinicalTrials.gov Number NCT02502383).


2015 ◽  
Vol 27 (3) ◽  
pp. 283-290 ◽  
Author(s):  
Mathew C. Uretsky ◽  
Bethany R. Lee ◽  
Elizabeth J. Greeno ◽  
Richard P. Barth

Objective: The purpose of this study is to examine the correlates of child behavior change over time in a replication of the KEEP intervention. Method: The study sample was drawn from the treatment group of the Maryland replication of KEEP (n=65). Change over time was analyzed using multilevel linear mixed modeling. Results: Parents’ use of positive reinforcement relative to discipline was associated with the rate of child behavior change among program participants; parents with the lowest initial levels of reinforcement reported the greatest decrease in child problem behaviors. Other participant characteristics were not associated with child behavior change during the study period. Conclusions: The results indicate the efficacy of an evidence-based foster parent training program for reducing child problem behaviors and underscore the utility of teaching parents to use more positive responses relative to discipline as a robust path to improved child outcomes.


Author(s):  
Alberto Morando ◽  
Pnina Gershon ◽  
Bruce Mehler ◽  
Bryan Reimer

Previous research indicates that drivers may forgo their supervisory role with partial-automation. We investigated if this behavior change is the result of the time automation was active. Naturalistic data was collected from 16 Tesla owners driving under free-flow highway conditions. We coded glance location and steering-wheel control level around Tesla Autopilot (AP) engagements, driver-initiated AP disengagements, and AP steady-state use in-between engagement and disengagement. Results indicated that immediately after AP engagement, glances downwards and to the center-stack increased above 18% and there was a 32% increase in the proportion of hands-free driving. The decrease in driver engagement in driving was not gradual over-time but occurred immediately after engaging AP. These behaviors were maintained throughout the drive with AP until drivers approached AP disengagement. In conclusion, drivers may not be using AP as recommended (intentionally or not), reinforcing the call for improved ways to ensure drivers’ supervisory role when using partial-automation.


2016 ◽  
Vol 24 (2) ◽  
pp. 172-188 ◽  
Author(s):  
Nicole Asmussen ◽  
Jinhee Jo

Existing methods for estimating ideal points of legislators that are comparable across time and chambers make restrictive assumptions regarding how legislators' ideal points can move over time, either by fixing some legislators' ideal points or by constraining their movement over time. These assumptions are clearly contradictory to some theories of congressional responsiveness to election dynamics and changes in constituency. Instead of using legislators as anchors, our approach relies on matching roll calls in one chamber and session with roll calls or cosponsorship decisions on identical bills introduced in a different chamber or session. By using these “bridge decisions” to achieve comparability, we can remove any assumptions about the movement of legislators' ideal points. We produce these estimates for both chambers from the 102nd (1991–92) to 111th (2009–11) Congresses, and we show that our estimates provide interesting insights into the nature of legislative behavior change.


2017 ◽  
Vol 39 (3) ◽  
pp. 514-533
Author(s):  
Andrew L. Moskowitz ◽  
Jennifer L. Krull ◽  
K. Alex Trickey ◽  
Bruce F. Chorpita

2019 ◽  
Author(s):  
Sean D Young

UNSTRUCTURED A growing number of interventions incorporate digital and social technologies (eg, social media, mobile phone apps, and wearable devices) into their design for behavior change. However, because of a number of factors, including changing trends in the use of technology over time, results on the efficacy of these interventions have been mixed. An updated framework is needed to help researchers better plan behavioral technology interventions by anticipating the needed resources and potential changes in trends that may affect interventions over time. Focusing on the domain of health interventions as a use case, we present the Adaptive Behavioral Components (ABC) model for technology-based behavioral interventions. ABC is composed of five components: basic behavior change; intervention, or problem-focused characteristics; population, social, and behavioral characteristics; individual-level and personality characteristics; and technology characteristics. ABC was designed with the goals of (1) guiding high-level development for digital technology–based interventions; (2) helping interventionists consider, plan for, and adapt to potential barriers that may arise during longitudinal interventions; and (3) providing a framework to potentially help increase the consistency of findings among digital technology intervention studies. We describe the planning of an HIV prevention intervention as a case study for how to implement ABC into intervention design. Using the ABC model to plan future interventions might help to improve the design of and adherence to longitudinal behavior change intervention protocols; allow these interventions to adapt, anticipate, and prepare for changes that may arise over time; and help to potentially improve intervention behavior change outcomes. Additional research is needed on the influence of each of ABC’s components to help improve intervention design and implementation.


Sign in / Sign up

Export Citation Format

Share Document