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The Pain Course: exploring predictors of clinical response to an Internet-delivered pain management program
Abstract
There is significant interest in the potential of Internet-delivered pain management programs for adults with chronic pain. Understanding the characteristics of people who do and do not benefit from Internet-delivered programs will help to guide their safe and effective use. Using a large sample from a previous randomised controlled trial of an established Internet-delivered pain management program, the Pain Course, this study (n = 463) examined whether several demographic, clinical, psychological, and treatment-related variables could be used to predict clinical response in levels of disability, depression, anxiety, or average pain. Multiple univariate and multivariate stepwise logistic regressions were used to identify unique predictors of clinical improvement, which, consistent with recommendations, was defined as a ≥30% reduction in symptoms or difficulties from baseline. Several unique predictors of clinical improvement were found. However, no particularly decisive or dominant predictors emerged that were common across time points or across the outcome domains. Reflecting this, the identified predictors explained only 18.1%, 13.7%, 7.6%, and 9.5% of the variance in the likelihood of making a clinical improvement in disability, depression, anxiety, and average pain levels, respectively. The current findings suggest that a broad range of patients may benefit from emerging Internet-delivered pain management programs and that it may not be possible to predict who will or will not benefit on the basis of patients' demographic, clinical, and psychological characteristics.
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The PHQ-9: Validity of a brief depression severity measure
Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2001) · Journal of General Internal Medicine · doi:10.1046/j.1525-1497.2001.016009606.x · Cited by 44,910
Model selection and multimodel inference: a practical information-theoretic approach.
Guthery, F. S., Burnham, K. P., & Anderson, D. R. (2003) · The Journal of Wildlife Management · doi:10.2307/3802723 · Cited by 41,495
A Brief Measure for Assessing Generalized Anxiety Disorder: The GAD-7
Spitzer, R. L., Kroenke, K., Williams, J. B. W., & Löwe, B. (2006) · Archives of Internal Medicine · doi:10.1001/archinte.166.10.1092 · Cited by 31,765
Regression Modeling Strategies
Harrell, F. E. (2001) · Springer series in statistics · doi:10.1007/978-1-4757-3462-1 · Cited by 9,270
Löwe, B., Decker, O., Müller, S., Brähler, E., Schellberg, D., Herzog, W., & Herzberg, P. Y. (2008) · Medical Care · doi:10.1097/mlr.0b013e318160d093 · Cited by 4,527
A Study of the Natural History of Back Pain: Part I
ROLAND, M., & MORRIS, R. (1983) · Spine · doi:10.1097/00007632-198303000-00004 · Cited by 3,395
Model selection and multimodel inference : a practical information-theoretic approach
Burnham, K. P., Anderson, D. R., & Burnham, K. P. (2002) · Thammasat University Digital Collections · Cited by 2,907
Mediators and Moderators of Treatment Effects in Randomized Clinical Trials
Kraemer, H. C., Wilson, G. T., Fairburn, C. G., & Agras, W. S. (2002) · Archives of General Psychiatry · doi:10.1001/archpsyc.59.10.877 · Cited by 2,562
Fear of movement/(re)injury in chronic low back pain and its relation to behavioral performance
Vlaeyen, J. W. S., Kole-Snijders, A. M. J., Boeren, R. G. B., & van Eek, H. (1995) · Pain · doi:10.1016/0304-3959(94)00279-n · Cited by 2,446
Feature selection: evaluation, application, and small sample performance
Jain, A. K., & Zongker, D. E. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.574797 · Cited by 2,186
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Friesen, L. N., Hadjistavropoulos, H. D., Schneider, L. H., Alberts, N. M., Titov, N., & Dear, B. F. (2017) · Pain · doi:10.1097/j.pain.0000000000000802 · Cited by 89
Self-citationSlater, H., Dear, B. F., Merolli, M. A., Li, L. C., & Briggs, A. M. (2016) · Best Practice & Research Clinical Rheumatology · doi:10.1016/j.berh.2016.08.006 · Cited by 88
Self-citationVugts, M. A. P., Joosen, M. C. W., van der Geer, J. E., Zedlitz, A. M. E. E., & Vrijhoef, H. J. M. (2018) · PLOS ONE · doi:10.1371/journal.pone.0196467 · Cited by 85
Dear, B. F., Gandy, M., Karin, E., Ricciardi, T., Fogliati, V. J., McDonald, S., Staples, L. G., Perry, K. N., Sharpe, L., Nicholas, M. K., et al. (2017) · Pain · doi:10.1097/j.pain.0000000000000916 · Cited by 57
Self-citationDear, B. F., Gandy, M., Karin, E., Fogliati, R., Fogliati, V. J., Staples, L. G., Wootton, B. M., Sharpe, L., & Titov, N. (2018) · The Journal of Pain · doi:10.1016/j.jpain.2018.07.005 · Cited by 51
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Metadata
- Authors
- Dear, B. F., Gandy, M., Karin, E., Ricciardi, T., Langman, N., Staples, L. G., Fogliati, V. J., Sharpe, L., McLellan, L. F., & Titov, N.
- Affiliations
- Macquarie University (AU)
- The University of Sydney (AU)
- Corresponding author
- Dear, B.F.
- Venue
- Pain
- Published
- 2016-05-31
- Volume
- 157
- Issue
- 10
- Pages
- 2257-2268
- ISSN
- 0304-3959 (ISSN-L)
- 1872-6623
- OpenAlex
- W2415210715
- Cited by
- 40
- References
- 56
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 3 · 2023: 3 · 2022: 3 · 2021: 4 · 2020: 2 · 2019: 4 · 2018: 9 · 2017: 4 · 2016: 3
- FWCI
- 5.40
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Pain (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Musculoskeletal pain and rehabilitation
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Pediatric Pain Management Techniques
- Keywords
- chronic pain, predictors of treatment response, disability, depression, anxiety, pain intensity, online psychological intervention
- MeSH terms
- Adolescent, Adult, Aged, Aged, 80 and over, Anxiety, Disability Evaluation, Female, Humans, Male, Middle Aged, Pain, Predictive Value of Tests, Psychotherapy, Surveys and Questionnaires, Logistic Models, Treatment Outcome, Internet, Young Adult, Pain Management
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