Skip to main content
Hauptrubriken
CME Facharzt-Training Webinare Zeitschriften Bücher e.Medpedia Podcast
Service
Jobbörse Info & Hilfe
Fachgebiete
Anästhesiologie Allgemeinmedizin Arbeitsmedizin Augenheilkunde Chirurgie Dermatologie Gynäkologie und Geburtshilfe HNO Innere Medizin Kardiologie Neurologie Onkologie und Hämatologie Orthopädie und Unfallchirurgie Pädiatrie Pathologie Psychiatrie Radiologie Rechtsmedizin Urologie Zahnmedizin
Themen
Klimawandel und Gesundheit Neues aus dem Markt COVID-19 Praxis und Beruf Seltene Erkrankungen GOÄ & EBM Panorama
Sammlungen
Kasuistiken Algorithmen & Infografiken Blickdiagnosen Arthropedia FlapFinder (für Dermatochirurgen) Videos Kongressberichterstattung Medizin für Apothekerinnen und Apotheker Für Ärztinnen und Ärzte in Weiterbildung Für Medizinstudierende
Erweiterte Suche
Anmelden
nach oben
Quality of Life Research
Erschienen in: Quality of Life Research 11/2018

02.08.2018

The reliability of end of day and ecological momentary assessments of pain and pain interference in individuals with spinal cord injury

verfasst von: Noelle E. Carlozzi, Stephen Schilling, Jenna Freedman, Claire Z. Kalpakjian, Anna L. Kratz

Erschienen in: Quality of Life Research | Ausgabe 11/2018

Einloggen, um Zugang zu erhalten

Abstract

Purpose

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.

Methods

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.

Results

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.

Conclusions

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.
Literatur
1.
Broderick, J. E., Stone, A. A., Calvanese, P., Schwartz, J. E., & Turk, D. C. (2006). Recalled pain ratings: A complex and poorly defined task. The Journal of Pain, 7(2), 142–149.CrossRef
2.
Schneider, S., Stone, A. A., Schwartz, J. E., & Broderick, J. E. (2011). Peak and end effects in patients’ daily recall of pain and fatigue: A within-subjects analysis. The Journal of Pain, 12(2), 228–235.CrossRef
3.
Shiffman, S., Stone, A. A., & Hufford, M. R. (2008). Ecological momentary assessment. Annual Review of Clinical Psychology, 4, 1–32.CrossRef
4.
Schneider, S., & Stone, A. A. (2016). Ambulatory and diary methods can facilitate the measurement of patient-reported outcomes. Quality of Life Research, 25(3), 497–506.CrossRef
5.
Heapy, A., Dziura, J., Buta, E., Goulet, J., Kulas, J. F., & Kerns, R. D. (2014). Using multiple daily pain ratings to improve reliability and assay sensitivity: How many is enough? The Journal of Pain, 15(12), 1360–1365.CrossRef
6.
Broderick, J. E., Schwartz, J. E., Schneider, S., & Stone, A. A. (2009). Can End-of-day reports replace momentary assessment of pain and fatigue? The Journal of Pain, 10(3), 274–281.CrossRef
7.
Salaffi, F., Sarzi-Puttini, P., & Atzeni, F. (2015). How to measure chronic pain: New concepts. Best Practice & Research: Clinical Rheumatology, 29(1), 164–186.CrossRef
8.
Perkins, D. O., Wyatt, R. J., & Bartko, J. J. (2000). Penny-wise and pound-foolish: The impact of measurement error on sample size requirements in clinical trials. Biological Psychiatry, 47(8), 762–766.CrossRef
9.
Stone, A. A., & Broderick, J. E. (2007). Real-time data collection for pain: Appraisal and current status. Pain Medicine, 8(Suppl 3), S85–S93.CrossRef
10.
Stone, A. A., Broderick, J. E., & Schwartz, J. E. (2010). Validity of average, minimum, and maximum end-of-day recall assessments of pain and fatigue. Contemporary Clinical Trials, 31(5), 483–490.CrossRef
11.
Kratz, A. L., Kalpakjian, C. Z., & Hanks, R. A. (2017). Are intensive data collection methods in pain research feasible in those with physical disability? A study in persons with chronic pain and spinal cord injury. Quality of Life Research, 26(3), 587–600.CrossRef
12.
Siddall, P. J., McClelland, J. M., Rutkowski, S. B., & Cousins, M. J. (2003). A longitudinal study of the prevalence and characteristics of pain in the first 5 years following spinal cord injury. Pain, 103(3), 249–257.CrossRef
13.
Dijkers, M., Bryce, T., & Zanca, J. (2009). Prevalence of chronic pain after traumatic spinal cord injury: A systematic review. Journal of Rehabilitation Research & Development, 46(1), 13–29.CrossRef
14.
Siddall, P. J., & Loeser, J. D. (2001). Pain following spinal cord injury. Spinal Cord, 39(2), 63–73.CrossRef
15.
Cardenas, D. D., & Jensen, M. P. (2006). Treatments for chronic pain in persons with spinal cord injury: A survey study. The Journal of Spinal Cord Medicine, 29(2), 109–117.CrossRef
16.
Budh, C. N., & Osteraker, A. L. (2007). Life satisfaction in individuals with a spinal cord injury and pain. Clinical Rehabilitation, 21(1), 89–96.CrossRef
17.
Cruz-Almeida, Y., Alameda, G., & Widerstrom-Noga, E. G. (2009). Differentiation between pain-related interference and interference caused by the functional impairments of spinal cord injury. Spinal Cord, 47(5), 390–395.CrossRef
18.
Ataoglu, E., Tiftik, T., Kara, M., Tunc, H., Ersoz, M., & Akkus, S. (2013). Effects of chronic pain on quality of life and depression in patients with spinal cord injury. Spinal Cord, 51(1), 23–26.CrossRef
19.
Finnerup, N. B. (2013). Pain in patients with spinal cord injury. Pain, 154(Suppl 1), S71–S76.CrossRef
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.CrossRef
21.
Kalpakjian, C. Z., Farrell, D. J., Albright, K. J., Chiodo, A., & Young, E. A. (2009). Association of daily stressors and salivary cortisol in spinal cord injury. Rehabilitation Psychology, 54(3), 288–298.CrossRef
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.CrossRef
23.
Kirshblum, S. C., Burns, S. P., Biering-Sorensen, F., et al. (2011). International standards for neurological classification of spinal cord injury (revised 2011). The Journal of Spinal Cord Medicine, 34(6), 535–546.CrossRef
24.
Kirshblum, S. C., Waring, W., Biering-Sorensen, F., et al. (2011). Reference for the 2011 revision of the International Standards for Neurological Classification of Spinal Cord Injury. The Journal of Spinal Cord Medicine, 34(6), 547–554.CrossRef
25.
Riley, W. T., Rothrock, N., Bruce, B., et al. (2010). Patient-reported outcomes measurement information system (PROMIS) domain names and definitions revisions: Further evaluation of content validity in IRT-derived item banks. Quality of Life Research, 19(9), 1311–1321.CrossRef
26.
Cella, D., Riley, W., Stone, A., et al. (2010). The Patient-Reported Outcomes Measurement Information System (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005–2008. Journal of Clinical Epidemiology, 63(11), 1179–1194.CrossRef
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.
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.
29.
Donders, J., Tulsky, D. S., & Zhu, J. (2001). Criterion validity of new WAIS-II subtest scores after traumatic brain injury. Journal of the International Neuropsychological Society, 7(7), 892–898.PubMed
30.
Tulsky, D. S., Kisala, P. A., Victorson, D., et al. (2015). Methodology for the development and calibration of the SCI-QOL item banks. The Journal of Spinal Cord Medicine, 38(3), 270–287.CrossRef
31.
Tulsky, D. S., Kisala, P. A., Victorson, D., et al. (2015). Overview of the Spinal Cord Injury–Quality of Life (SCI-QOL) measurement system. The Journal of Spinal Cord Medicine, 38(3), 257–269.CrossRef
32.
Tulsky, D. S., & Kisala, P. A. (2015). The Spinal Cord Injury–Quality of Life (SCI-QOL) measurement system: Development, psychometrics, and item bank calibration. The Journal of Spinal Cord Medicine, 38(3), 251–256.CrossRef
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.CrossRef
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.
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.
36.
Osborne, T. L., Jensen, M. P., Ehde, D. M., Hanley, M. A., & Kraft, G. (2007). Psychosocial factors associated with pain intensity, pain-related interference, and psychological functioning in persons with multiple sclerosis and pain. Pain, 127(1–2), 52–62.CrossRef
37.
Osborne, T. L., Raichle, K. A., Jensen, M. P., Ehde, D. M., & Kraft, G. (2006). The reliability and validity of pain interference measures in persons with multiple sclerosis. Journal of Pain and Symptom Management, 32(3), 217–229.CrossRef
38.
Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7(2), 147–177.CrossRef
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.
40.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd edn.). New York: Academic Press.
41.
DeVellis, R. (2017). Scale development: Theory and applications (4th edn.). Los Angeles: Sage.
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.
43.
Nunnally, J., & Bernstein, I. (1994). Psychometric theory. New York: McGraw-Hill.
44.
Felix, E. R., Cruz-Almeida, Y., & Widerstrom-Noga, E. G. (2007). Chronic pain after spinal cord injury: What characteristics make some pains more disturbing than others? Journal of Rehabilitation Research & Development, 44(5), 703–715.CrossRef
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.
46.
Stone, A. A., Shiffman, S., Schwartz, J. E., Broderick, J. E., & Hufford, M. R. (2003). Patient compliance with paper and electronic diaries. Controlled Clinical Trials, 24(2), 182–199.CrossRef
47.
Stone, A. A., Shiffman, S., Schwartz, J. E., Broderick, J. E., & Hufford, M. R. (2002). Patient non-compliance with paper diaries. Brit Med J., 324(7347), 1193–1194.CrossRef
48.
Stone, A. A., Broderick, J. E., Schwartz, J. E., Shiffman, S., Litcher-Kelly, L., & Calvanese, P. (2003). Intensive momentary reporting of pain with an electronic diary: Reactivity, compliance, and patient satisfaction. Pain, 104(1–2), 343–351.CrossRef
49.
Kratz, A. L., Davis, M. C., & Zautra, A. J. (2012). Attachment predicts daily catastrophizing and social coping in women with pain. Health Psychology, 31(3), 278–285.CrossRef
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, PA
Metadaten
Titel
The reliability of end of day and ecological momentary assessments of pain and pain interference in individuals with spinal cord injury
verfasst von
Noelle E. Carlozzi
Stephen Schilling
Jenna Freedman
Claire Z. Kalpakjian
Anna L. Kratz
Publikationsdatum
02.08.2018
Verlag
Springer International Publishing
Erschienen in
Quality of Life Research / Ausgabe 11/2018
Print ISSN: 0962-9343
Elektronische ISSN: 1573-2649
DOI
https://doi.org/10.1007/s11136-018-1952-y

Weitere Artikel der Ausgabe 11/2018

Quality of Life Research 11/2018 Zur Ausgabe

OriginalPaper

Physical, sensory, and cognitive functioning among centenarians: a comparison between the Tokyo and Georgia centenarian studies

OriginalPaper

Psychiatric comorbidity and quality of life in South African alcohol use disorder patients

OriginalPaper

Modelling a preference-based index for EQ-5D using a non-parametric Bayesian method

Brief Communication

Psychometric properties of the World Health Organization WHOQOL-BREF Quality of Life assessment in Singapore

OriginalPaper

The Portuguese Rheumatoid Arthritis Impact of Disease (RAID) score and its measurement equivalence in three countries: validation study using Rasch Models

OriginalPaper

Acceptance, social support, benefit-finding, and depression in women with gynecological cancer