Sentiment And Confidence Interpretation Of Older Adult Fall Prevention Materials: A Pilot

PURPOSE: Currently, falling represents a major risk factor and a global concern for older adults with elevated frailty. One potentially overlooked factor is the negative consequences of ageism as it relates to decreased confidence and associated competence in one’s ability to prevent falling and stay active. The purpose of this study is to use linguistic content and sentiment analyses to explore how emotional tones found within fall prevention language could affect a person’s confidence. METHODS: One researcher searched the CDC website pages and promotional materials pertaining to fall prevention in older adults. Three coders rated all statements based on two constructs: Sentiment (positive, negative, or neutral) and Mobility Confidence (promote mobility confidence, erode mobility confidence, or neutral effect on mobility confidence). Inter-rater reliability for each construct was assessed using Intraclass Correlation Coefficients (ICC) using two-way random effects models. Descriptive statistics were then generated for the codes. RESULTS: A total of 842 sentences from twenty documents were analyzed. Average measure ICC between the three raters (ages 34, 41, and 63) showed moderate reliability for both constructs: Sentiment ICC = 0.54 (p<0.001) and Mobility Confidence ICC = 0.543 (p<0.001). Single measure ICC for Sentiment was 0.281 (p<0.001) and for Mobility Confidence was 0.283 (p<0.001). Overall, 48% of the statements were coded as having negative sentiment and 72% were coded as potentially mobility confidence eroding; while only 6% of statements were coded as having positive sentiment and 2% were coded as mobility confidence promoting. CONCLUSIONS: Though average ICC showed moderate reliability, single measure ICC showed a greater level of bias between coders. This indicates that interpretation of statements around falling is highly variable, likely due to individual variation and preconceived notions of aging and falls. Overall findings from the analysis show the majority of statements are negative in sentiment and potentially erode confidence among older adult readers. Future research should analyze whether these sentiments have a confidence erosion effect on a representative sample of older adults and use the data to inform machine learning algorithms for more large-scale analyses.