Adults' Reaction to Public Health Messaging: Recall, Media Type, and Behavior Change Motivation.
Kimberly J M KellerDonna Mehrle ElliottJo Britt-RankinPublished in: Journal of prevention (2022) (2022)
This paper focuses on effective messaging practices identified in data collected after 10 years of implementing a gain-framed messaging campaign encouraging healthier behaviors in middle-aged and older adults. In Study 1, we measured message recall and intended health behaviors in an intercept survey of 733 adults. Binary logistic regression indicated that women were more likely than men to report intent to change behavior. Recalling messages from billboards or fliers was associated with a lower likelihood of intended behavior change, and media type was associated with intended behavior for those who saw the message online (reducing screen time) or on television (increasing physical activity and ceasing smoking). Study 2 focused on adult generational differences in response to the campaign and types of media used to access information. Data from an intercept survey of 604 clients at agencies serving low-income adults were segmented into three age groups: under 35, ages 35-54, and ages 55+. Recall and reaction to campaign materials differed by age group, and the influence of life stage factors and health costs varied across age groups. Television and newspapers were most frequently reported by the oldest group, and social media and online news/blogs were most frequently chosen by the youngest group. Campaign response of adults older than age 35 aligned with goals of improving health behaviors. Together, these studies indicate that diffuse messaging strategies may raise overall awareness, and targeted strategies may be more influential in motivating behavior change. Influential factors and media should be differentially leveraged to target different age cohorts of adults.
Keyphrases
- social media
- public health
- health information
- physical activity
- healthcare
- smoking cessation
- mental health
- primary care
- cross sectional
- polycystic ovary syndrome
- machine learning
- body mass index
- big data
- middle aged
- young adults
- type diabetes
- electronic health record
- global health
- hiv infected
- skeletal muscle
- health promotion
- high grade
- cancer therapy
- drug delivery
- adipose tissue
- deep learning