Adapting Natural Language Processing and Sentiment Analysis Methods for an Intervention in Older Adults: Positive Perceptions of Health and Technology

Background: 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.

Research Aim: 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/m).

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.

Conclusions: This analysis demonstrates that sentiment analysis and natural language processing in program review identified an association between perception and topics with clinical outcomes.