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THU0645 Optimizing the efficiency of patient data capture using smartphone technology: evaluation of the correlation between promis instruments for pro data capture
  1. WB Nowell1,
  2. H Yun2,
  3. J Beaumont3,
  4. S Yang4,
  5. J Willig4,
  6. S Ginsberg5,
  7. KV Clayton5,
  8. S Hazel5,
  9. C Wiedmeyer5,
  10. JR Curtis4
  1. 1Global Healthy Living Foundation, Upper Nyack
  2. 2University of Alabama at Birmingham School of Public Health, Birmingham
  3. 3Northwestern University Feinberg School of Medicine, Evanston
  4. 4University of Alabama at Birmingham, Birmingham
  5. 5CreakyJoints, Global Healthy Living Foundation, Upper Nyack, United States

Abstract

Background Patient-reported outcomes (PROs) are key to enabling the comprehensive assessment of patient-centered benefits in comparative effectiveness research (CER). However, the relationships between different PROMIS instruments and condition-specific disease activity measures in diseases such as rheumatoid arthritis (RA) have not been well studied.

Objectives The objectives of this analysis were to evaluate the longitudinal relationship between different PROMIS instruments and the RAPID3, a measure of self-reported patient disease activity.

Methods Four NIH PROMIS instruments (Pain Interference, Physical Function, Sleep Disturbance and Fatigue) and the RAPID3 were administered to participants in the PCORI-funded ArthritisPower patient registry. After descriptive analytics, we estimated multiple correlations between PROMIS instruments and the RAPID3. For each PRO instrument and with each assessment used as the unit of measure, we calculated the R-squared using mixed models to evaluate how the PROs were related to each other. Using Pain Interference as an example, we evaluated R-squared for each model with additional PROs and demographic factors including enrollment age, sex, race, Twitter account, region, and visit times.

Results A total of 1,590 unique participants who answered the survey one or more times were included in the analysis, with mean (SD) age of 49 (12) years. The mean score for Pain Interference was 63.7 (SD: 7.0), Physical Function 37.5 (8.7), Sleep Disturbance 58.4 (8.7), Fatigue 63.8 (8.8), and RAPID3 15.5 (5.7). Most PROMIS instruments were low to moderately correlated (around 0.2) with each other and the RAPID3. Using Pain Interference as an example, R-squared measures revealed a high total variance explained (R2=49%) between Pain Interference and Physical Function (Table); those involving Pain Interference, Physical Function, Fatigue, Sleep Disturbance and RAPID3 also revealed a higher variance contribution with these additional PROs (66%). Additional adjustment for demographic factors added little variance explanation (1.4%).

Conclusions PROMIS Pain Interference, Physical Function, Sleep Disturbance, Fatigue instruments and RAPID3 are reasonably correlated to each other. Age, gender, race and other demographic factors play little role in explaining variance in PROs. These results suggest potential efficiencies in using some measures to predict or impute the values for other measures and to optimize the frequency of patient data collection using at-home technologies including Smartphone Apps like ArthritisPower.

Disclosure of Interest None declared

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