Adapting natural language processing and sentiment analysis methods for intervention in older adults: Positive perceptions of health and technology
Curtis L. Petersen MPH PhD, Xingyi Li BS, Courtney J. Stevens PhD, Tyler L. Goodinge BA, Elizabeth A. Carpenter-Song PhD, John A. Batsis MD*
Full text PDF 
( Download count: 612)
AbstractBackground: Older adults frequently participate in behavior change studies, yet it is not clear how to quantify a potential relationship between their perception of the intervention and its efficacy.
Objective: We assessed the relationship between participant sentiment toward the intervention from follow-up interviews with physical activity and questionnaires for the perception of health.
Methods: Sentiment was calculated using the transcripts of exit interviews through a bag of words approach defined as the sum of positive and negative words in 28 older adults with obesity (body mass index ≥30kg/m2).
Results: Mean age was 73 years (82% female), and 54% lost ≥5% weight loss. Through linear regression we describe a significant association between positive sentiment about the intervention and weight loss; positive sentiment on technology and change in PROMIS-10 physical health and reduced physical activity time, while controlling for sex and age.
Conclusion: This analysis demonstrates that sentiment analysis and natural language processing in program review identified an association between perception and topics with clinical outcomes.Keywords: older adults, mHealth, sentiment analysis, obesity, weight loss, natural language processing
Curtis L. Petersen MPH PhD, Xingyi Li BS, Courtney J. Stevens PhD, Tyler L. Goodinge BA, Elizabeth A. Carpenter-Song PhD, John A. Batsis MD* (2023). Adapting natural language processing and sentiment analysis methods for intervention in older adults: Positive perceptions of health and technology. Gerontechnology, 22(1), 1-6
https://doi.org/10.4017/gt.2023.22.1.824.06