Ringwald, Whitney
(2024)
Refining Behavioral Phenotypes for Binge Drinking from Broad Liabilities to Proximal Predictors with Multiple Raters of Personality and Smartphone Sensor Data.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
This is the latest version of this item.
Abstract
Binge drinking in young adults is a significant and growing public health problem. Despite the known hazards of binge drinking, and despite decades of intervention development, currently available alcohol use treatments have only modest efficacy. A major barrier to effective interventions is an imprecise understanding of differences between people in alcohol use behavior and of the proximal behaviors that precipitate binge drinking episodes. This imprecision is due in part to reliance on self-report methods that provide an incomplete picture of binge drinking risk. The current study overcomes these issues by combining self-reports, informant reports of personality, and passive sensing data to refine behavioral phenotypes for binge drinking from broad liabilities to proximal predictors. Data for this project came from a sample of young adults (N = 156) who completed a 120-day ambulatory assessment protocol of daily self-reported alcohol use and passive recording of behavior detected by smartphone sensors. There were three specific aims of this study. For aim 1, I established associations between self- and informant ratings of Big Five personality traits with alcohol use. For aim 2, I used Random Forest models to identify passively sensed predictors of binge drinking in the 3, 12, 24, and 48 hours prior to a drinking episode. For aim 3, I examined whether passively sensed behaviors accounted for associations between personality traits and binge drinking patterns. Results at the individual difference level showed that young adults who are less conscientious and more open to experiences, and those who are seen by friends and family as being highly agreeable, tend to binge drink more often. I also identified multiple passively sensed patterns of behavior that may account for personality-related risk. At the proximal level, I found that the most predictive smartphone sensor variables were related to physical mobility and how people interacted with their phone, which suggests that young adults tend to binge drink after being out and about and coordinating plans with friends in the hours leading up to a drinking episode. Overall, this study puts the field one step closer to refining multi-method behavioral phenotypes that can start reversing the dangerous trend in binge drinking among young adults.
Share
| Citation/Export: |
|
| Social Networking: |
|
Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
|
| Date: |
27 August 2024 |
| Date Type: |
Publication |
| Defense Date: |
29 June 2023 |
| Approval Date: |
27 August 2024 |
| Submission Date: |
1 August 2023 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
73 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Dietrich School of Arts and Sciences > Psychology |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
machine learning, personality traits, binge drinking, passive sensing |
| Date Deposited: |
27 Aug 2024 14:37 |
| Last Modified: |
27 Aug 2024 14:37 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/45398 |
Available Versions of this Item
-
Refining Behavioral Phenotypes for Binge Drinking from Broad Liabilities to Proximal Predictors with Multiple Raters of Personality and Smartphone Sensor Data. (deposited 27 Aug 2024 14:37)
[Currently Displayed]
Metrics
Monthly Views for the past 3 years
Plum Analytics
Actions (login required)
 |
View Item |
![[feed]](/images/feed-icon-32x32.png){kind=icon}