The Health Belief Model and preventive health behavior: an analysis of alternative models of causal relationships

Three areas of preventive health behavior using data from the National Survey of Preventive Health Practices and Consequences were investigated. First the latent constructs of preventive health behaviors were examined for fourteen preventive health behaviors. A confirmatory factor analysis using LISREL was conducted on three different hypothesized models, each representing a different way to conceptualize health behaviors. The model based on a common approach to grouping health behavior resulted in a model that was empirically unsound suggesting a need to identify the mechanisms that facilitate the clustering of preventive health actions;The second research question investigated the Health Belief Model along with demographic variables was modifying factors in an attempt to account for varying degrees of involvement in preventive health behavior. Health behaviors of physical activity, preventive medical care, and risk avoidance behavior across three different adult age groups (20-35, 36-54, 55-64) were analyzed using structural equation modeling. Results indicated that vulnerability, health concern cues, perception of health status, and barriers to preventive health varied in their power to explain preventive health behavior in each of the three age groups. Preventive health behavior was influenced by the modifying factors, particularly education in young adults, income in older adults, and gender in all age groups;The third research question employed six different two-wave, two-variable models to investigate the crosslag, stability, and contemporaneous effect of health status and health habits. Results indicated that health habits and health status at time 1 were strong predictors of those behaviors one year later. The crosslag effect of health status at time 1 on health habits at time 2 was the strongest crosslag effect; the relationship was small in magnitude due to the strong stability effects. Implications for future research and panel data analyses are discussed