The reliability of end of day and ecological momentary assessments of pain and pain interference in individuals with spinal cord injury
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This study investigated the most efficient means of measuring pain intensity and pain interference comparing ecological momentary assessment (EMA) to end of day (EOD) data, with the highest level of measurement reliability as examined in individuals with spinal cord injury.
EMA (five times throughout the day) and EOD ratings of pain and pain interference were collected over a 7-day period. Multilevel models were used to examine the reliability for both EOD and EMA assessments in order to determine the amount of variability in these assessments over the course of a week or the day, and a multilevel version of the Spearman–Brown Prophecy formula was used to estimate values for reliability.
Findings indicate the minimum of number of EOD and EMA assessments needed to achieve different levels of reliability (“adequate” > 0.70, “good” > 0.80 and excellent > 0.90). In addition, the time of day (either morning, midday or evening) did not impact the estimated reliability for the EMA assessments.
These findings can help researchers and clinician balance the cost/benefit tradeoffs of these different types of assessments by providing specific cutoffs for the numbers of each type of assessment that are needed to achieve excellent reliability.
KeywordsFeasibility Spinal cord injury Ecological momentary assessment Daily diaries Pain Pain interference
We thank Siera Goodnight, Kristen Pickup, Daniela Ristova-Trendov, Christopher Garbaccio, Jessica Mackelprang-Carter and Angela Garza for collecting and managing these data. A sincere thanks to all of our study participants for their effort in being in this study.
Research reported in this publication was supported by the Craig H. Neilsen Foundation under Award Number 287372 (PI: Kratz). The content is solely the responsibility of the authors and does not necessarily represent the views of the Craig H. Neilsen Foundation. Dr. Kratz was supported during manuscript preparation by a Grant from the National Institute of Arthritis and Musculoskeletal and Skin Diseases (Award Number K01AR064275).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflicts of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
Informed consent was obtained from all individual participants included in the study.
- 20.Mehta, S., McIntyre, A., Janzen, S., Loh, E., & Teasell, R. (2016). Spinal cord injury rehabilitation evidence T. Systematic review of pharmacologic treatments of pain after spinal cord injury: An update. Archives of Physical Medicine and Rehabilitation, 97(8), 1381–1391 e1381.CrossRefGoogle Scholar
- 22.Kratz, A. L., Ehde, D. M., Bombardier, C. H., Kalpakjian, C. Z., & Hanks, R. A. (2017). Pain acceptance decouples the momentary associations between pain, pain interference, and physical activity in the daily lives of people with chronic pain and spinal cord injury. The Journal of Pain, 18(3), 319–331.CrossRefGoogle Scholar
- 27.Czajkowski, S. M., Cella, D., Stone, A. A., Amtmann, D., & Keefe, F. (2010). Patient-Reported Outcomes Measurement Information System (Promis): Using new theory and technology to improve measurement of patient-reported outcomes in clinical research. Annals of Behavioral Medicine, 39, 46–46.Google Scholar
- 28.Cella, D., Rothrock, N., Choi, S., Lai, J. S., Yount, S., & Gershon, R. (2010). Promis overview: Development of new tools for measuring health-related quality of life and related outcomes in patients with chronic diseases. Annals of Behavioral Medicine, 39, 47–47.Google Scholar
- 33.Cohen, M. L., Kisala, P. A., Dyson-Hudson, T. A., & Tulsky, D. S. (2017). Measuring pain phenomena after spinal cord injury: Development and psychometric properties of the SCI-QOL Pain Interference and Pain Behavior assessment tools. The Journal of Spinal Cord Medicine, 41(3), 267–280.CrossRefGoogle Scholar
- 34.Cleeland, C. S. (1989). Measurement of pain by subjective report. In C. R. Chapman & J. D. Loeser (Eds.), Advances in pain research and therapy (Vol. 12, pp. 391–403). New York: Raven Press.Google Scholar
- 35.Cleeland, C. S., & Ryan, K. M. (1994). Pain assessment: Global use of the Brief Pain Inventory. Annals of the Academy of Medicine (Singapore), 23(2), 129–138.Google Scholar
- 39.Raudenbush, S. W., & Bryk, A. S. (1992). Hierarchical linear models in social and behavioral research: Applications and data analysis methods (2 edn.). ed. Thousand Oaks, CA: Sage Publications.Google Scholar
- 40.Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd edn.). New York: Academic Press.Google Scholar
- 41.DeVellis, R. (2017). Scale development: Theory and applications (4th edn.). Los Angeles: Sage.Google Scholar
- 42.Rasbash, J., Steele, F., Browne, W. J., Goldstein, H. (2012). User’s Guide to MLwiN, v2.26. Bristol: University of Bristol: Centre for Multilevel Modelling.Google Scholar
- 43.Nunnally, J., & Bernstein, I. (1994). Psychometric theory. New York: McGraw-Hill.Google Scholar
- 45.Stone, A., Shiffman, S., Atienza, A., & Nebling, L. (Eds.). (2007). The science of real-time data capture: Self-reports in health research. New York: Oxford University.Google Scholar
- 50.Kratz, A. L., Ansari, S., Duda, M., et al. (2017). Feasibility of the E4 Wristband to Assess Sleep in Adults with and without Fibromyalgia. 36th Annual Scientific Meeting of the American Pain Society; Pittsburgh, PAGoogle Scholar