From Epidemiology to Decision Making: A Systems Science Approach to Evaluate Effectiveness of Complex Behavioral Interventions Open Access
Shao, Yuefan (Summer 2022)
Abstract
Modifiable health behaviors are key to cardiometabolic disease prevention. A significant number of behavioral interventions have been proposed for healthy behavior promotion. However, identifying the most effective type of behavioral intervention for a given population remains challenging due to two main reasons. First, there is a lack of data for behavioral intervention effectiveness evaluation. Second, effectiveness of complex behavior intervention is dependent on multi-level factors, which poses challenges in intervention outcome evaluation. To address these challenges, this dissertation first empirically identified contributing factors associated with patterns of cigarette smoking and physical activity throughout the life-course. In addition, a complex systems modeling approach was used to evaluate effectiveness of different types of behavioral intervention, given target population network characteristics and individual behavior incentive.
Aim 1 characterized trajectories of physical activity and cigarette smoking from early adolescence to adulthood. Using latent class growth mixture model, results showed that there are three sub-groups of individuals sharing similar patterns of physical activity and past 30-day cigarette smoking behavior from early adolescence to adulthood. Age, socio-demographic and early-life psychological factors are important predictors of trajectories for both behaviors.
Aim 2 used social network analysis and regression methods to evaluate the association between social network characteristics and physical activity/cigarette smoking behaviors during adolescence and the adolescence to young adulthood transition. Results suggest that individuals’ health behaviors at younger age are the strongest predictors of health behaviors during young adulthood. In addition, an individual’s social position during adolescence is a predictor for physical activity level during young adulthood but not for cigarette smoking.
Using computational models, Aim 3 showed that when taking into consideration diffusion of interventions within a network, a highly clustered network does not imply the necessity of network-based intervention. Paradoxically, for networks with longer average path length or unknown network structure, incentivizing individuals might be more effective than interventions on popular opinion leader.
Collectively, findings highlighted that both social network and individual-level heterogeneity are key to shaping population level distributions of health behaviors. In addition, researchers need to embrace a systems science lens when evaluating complex behavioral intervention outcomes.
Table of Contents
TABLE OF CONTENTS
Chapter 1: Introduction. 1
Background and Literature Review. 1
Recent Advancement in Cardiovascular Disease Prevention and Modifiable Behavioral
Risk Factors. 1
Overview of Interventions for Physical Activity Promotion. 2
Overview of Interventions Targeting Tobacco Product Use. 4
Tobacco Control Efforts Targeting Conventional Tobacco Products. 4
Tobacco Control Effort Targeting Electronic Cigarettes. 5
Intervention Effectiveness for Physical Activity Promotion and Tobacco Cessation. 7
Using a Systems Science Approach to Evaluate Behavioral Intervention
Effectiveness 8
Dissertation Aims. 11
Public Health Significance. 11
Chapter 2: Characterization of Trajectories of Physical Activity and Cigarette Smoking from Early Adolescence to Adulthood. 13
Introduction. 13
Methods. 14
Results. 19
Discussion. 23
Conclusion. 28
Chapter 3: The impact of Social Network Characteristics on Cigarette Smoking and Physical Activity during Adolescence and Transition from Adolescence to Young Adulthood.40
Introduction. 40
Methods. 42
Results. 48
Discussion. 50
Conclusion. 52
Chapter 4: From Individual Will to Population Outcomes: a Complex Systems Framework to Evaluate Behavioral Intervention Effectiveness on Social Networks. 63
Introduction. 63
Methods. 65
Results. 70
Discussion. 73
Conclusion. 76
Chapter 5: Public Health Implications and Future Research Direction. 87
Public Health Implications. 87
Future research Direction. 90
Funding. 91
References. 92
LIST OF TABLES
Table 2-1. Characteristics of the Add Health Study Participants across Five Waves of Study Follow-up, 1994 – 2018. 29
Table 2-2. Baseline Class Member Profile of Physical Activity Trajectory. 30
Table 2-3. Baseline Class Member Profile of Past 30-day Cigarette Smoking Intensity Trajectory. 31
Table 2-4. Class membership of physical activity trajectories conditioned on cigarette smoking trajectory class membership. 32
Table 2-5. Predictors of Physical Activity Trajectory Class Membership. 33
Table 2-6. Predictors of P30-day Cigarette Smoking Intensity Trajectory Class Membership. 35
Supplemental Table 2-1. Model Selection Criteria for Trajectories of Physical Activity Score and Cigarette Smoking Intensity from Early Adolescence to Adulthood. 37
Table 3-1. Baseline Characteristics of Eligible Study Participants. 54
Table 3-2. Association of Individual and Social Network Characteristics and Cigarette Smoking Behavior at Wave I, the Add Health Study. 55
Table 3-3. Association of Individual and Social Network Characteristics and Physical Activity Level at Wave I, the Add Health Study. 57
Table 3-4. Association of Early Life Social Network Characteristics and Cigarette Smoking Behavior in Young Adult, the Add Health Study. 59
Table 3-5. Association of Early Life Social Network Characteristics and Physical Activity Level in Young Adult, the Add Health Study. 60
Supplemental Table 3-1. Terminology and Definition for Social Network Analysis. 61
Table 4-1. Model Parameter Definition for Computational Experiments. 77
Table 4-2. Prevalence of Smoking and Mean Opinion for Baseline Simulation. 78
Table 4-3. Prevalence of Smoking with Network-based Intervention and No Alteration on
Individual Incentive. 81
Table 4-4. Prevalence of Smoking with Network-based Intervention and Random Seeding Strategy, Varying Network Average Path Length and Sampling Distribution of Individual Inertia for Opinion Updating. 85
LIST OF FIGURES
Figure 2-1. Subject-specific Trajectories of Standardized Physical Activity Score from Early Adolescence to Adulthood. 38
Figure 2-2. Subject-specific Trajectories of Log (Past 30-day Cigarette Smoking Intensity) from Early Adolescence to Adulthood. 39
Supplemental Figure 3-1. Inclusion Criteria of Study Population. 62
Figure 4-1. Baseline Scenario. 79
Figure 4-2. Computational Experiment with Individual Inertia for Opinion Updating Following Sampling distribution of β (3,3), prev0 = 0.50. 80
Figure 4-3. Network-based Intervention with Individual Inertia for Opinion Updating Following Sampling Distribution of β (3,3), prev0 = 0.50, Varying Initial Seeding Strategy. 82
Figure 4-4. Network-based Intervention with Individual Inertia for Opinion Updating Following Sampling distribution of β (3,3), prev0 = 0.50,
Varying Network Average Path Length. 83
Figure 4-5. Network-based Intervention with Individual Inertia for Opinion Updating Following Sampling Distribution of β (3,3), prev0 = 0.50,
Varying Network Global Clustering Coefficient. 84
Figure 4-6. Computational Experiment with Individual Incentive Altering: Altering Structure, Random Seeding, 50% Initial Prevalence. 86
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