Patient Self-report Measures of Chronic Pain Consultation Measures: A Systematic Review

2010 ◽  
Vol 26 (3) ◽  
pp. 235-243 ◽  
Author(s):  
Norman Jay Stomski ◽  
Shylie Mackintosh ◽  
Mandy Stanley
2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 1006.1-1006
Author(s):  
S. Mustafa Ali ◽  
R. Lee ◽  
A. Chiarotto ◽  
J. Mcbeth ◽  
S. Van der Veer ◽  
...  

Background:Chronic pain is common in rheumatic and musculoskeletal conditions, and a major driver of disability worldwide. Knowledge gaps exist with respect to correct estimates of chronic pain [1], what causes it and how best to manage it [2]. To address this, researchers need validated methods to measure pain in large, representative populations. Though many authors have recognised the potential benefits of paper-based and digital pain manikins [3]–[5], it is unknown to what extent studies have adopted digital manikins as a data collection tool.Objectives:The objective of our review was to identify and characterise published studies that have used digital pain manikins as a data collection tool.Methods:We systematically searched six electronic databases, including Medline, CINAHL, Embase, Scopus, IEEE Xplore digital library, ACM Digital Library, on 3-4 of November 2020 by using a pre-defined search strategy. We included a study in our review if it used a digital manikin for self-reporting any pain aspect (e.g., intensity, type) by people suffering from pain, and if its full text was published in English. We conducted this review by following the PRISMA reporting guidelines and conducted a descriptive synthesis of findings, including manikin-derived outcome measures.Results:Our search yielded 4,685 unique studies. After full text screening of 705 articles, we included 14 studies in our review. Most articles were excluded because they used either paper-based manikins or didn’t include enough details to determine that the manikin was digital (n=386). The majority of included studies were published in Europe (n=11). Most studies collected data on a manikin once (n=11); from people with pain conditions (n=9); and in clinical settings (n=9). There was only one study that collected digital pain manikin data in a large sized (i.e., ~20,000) population-based survey.In most studies participants shaded any painful area on manikin (n=9) and did not enable participants to record location-specific pain aspects (n=11). None of the manikins enabled participants to record location-specific pain intensity. Pain distribution (i.e. number or percentage of pre-defined body areas or locations experiencing pain) and pain extent (i.e. number or percentage of shaded pixels) were commonly used manikin-derived outcome measures. In six studies, a heat map was used to summarise the extent of pain across the population.Conclusion:Digital pain manikins have been available since the 1990s but their adoption in research has been slow. Few manikins enabled location-specific pain recording suggesting that the digital nature of the manikin is not yet fully utilised. Future development of a validated digital pain manikin supporting self-reporting of the location and intensity of pain, usable across any device and screen size, may increase uptake and value.References:[1]S. E. E. Mills, K. P. Nicolson, and B. H. Smith, “Chronic pain: a review of its epidemiology and associated factors in population-based studies,” Br. J. Anaesth., vol. 123, no. 2, pp. e273–e283, Aug. 2019.[2]D. B. Reuben et al., “National Institutes of Health Pathways to Prevention Workshop: The Role of Opioids in the Treatment of Chronic Pain,” Ann. Intern. Med., vol. 162, no. 4, p. 295, Feb. 2015.[3]R. Waller, P. Manuel, and L. Williamson, “The Swindon Foot and Ankle Questionnaire: Is a Picture Worth a Thousand Words?,” ISRN Rheumatol., vol. 2012, pp. 1–8, 2012.[4]M. Barbero et al., “Clinical Significance and Diagnostic Value of Pain Extent Extracted from Pain Drawings: A Scoping Review,” Diagnostics, vol. 10, no. 8, p. 604, Aug. 2020.[5]S. M. Ali, W. J. Lau, J. McBeth, W. G. Dixon, and S. N. van der Veer, “Digital manikins to self-report pain on a smartphone: A systematic review of mobile apps,” Eur. J. Pain, vol. 25, no. 2, pp. 327–338, Feb. 2021.Disclosure of Interests:None declared


2018 ◽  
Vol 19 (8) ◽  
pp. 960-972 ◽  
Author(s):  
Daniele Nascimento Gouveia ◽  
Lícia Tairiny Santos Pina ◽  
Thallita Kelly Rabelo ◽  
Wagner Barbosa da Rocha Santos ◽  
Jullyana Souza Siqueira Quintans ◽  
...  

2020 ◽  
Author(s):  
Lili Zhang ◽  
Himanshu Vashisht ◽  
Alekhya Nethra ◽  
Brian Slattery ◽  
Tomas Ward

BACKGROUND Chronic pain is a significant world-wide health problem. It has been reported that people with chronic pain experience decision-making impairments, but these findings have been based on conventional lab experiments to date. In such experiments researchers have extensive control of conditions and can more precisely eliminate potential confounds. In contrast, there is much less known regarding how chronic pain impacts decision-making captured via lab-in-the-field experiments. Although such settings can introduce more experimental uncertainty, it is believed that collecting data in more ecologically valid contexts can better characterize the real-world impact of chronic pain. OBJECTIVE We aim to quantify decision-making differences between chronic pain individuals and healthy controls in a lab-in-the-field environment through taking advantage of internet technologies and social media. METHODS A cross-sectional design with independent groups was employed. A convenience sample of 45 participants were recruited through social media - 20 participants who self-reported living with chronic pain, and 25 people with no pain or who were living with pain for less than 6 months acting as controls. All participants completed a self-report questionnaire assessing their pain experiences and a neuropsychological task measuring their decision-making, i.e. the Iowa Gambling Task (IGT) in their web browser at a time and location of their choice without supervision. RESULTS Standard behavioral analysis revealed no differences in learning strategies between the two groups although qualitative differences could be observed in learning curves. However, computational modelling revealed that individuals with chronic pain were quicker to update their behavior relative to healthy controls, which reflected their increased learning rate (95% HDI from 0.66 to 0.99) when fitted with the VPP model. This result was further validated and extended on the ORL model because higher differences (95% HDI from 0.16 to 0.47) between the reward and punishment learning rates were observed when fitted on this model, indicating that chronic pain individuals were more sensitive to rewards. It was also found that they were less persistent in their choices during the IGT compared to controls, a fact reflected by their decreased outcome perseverance (95% HDI from -4.38 to -0.21) when fitted using the ORL model. Moreover, correlation analysis revealed that the estimated parameters had predictive value for the self-reported pain experiences, suggesting that the altered cognitive parameters could be potential candidates for inclusion in chronic pain assessments. CONCLUSIONS We found that individuals with chronic pain were more driven by rewards and less consistent when making decisions in our lab-in-the-field experiment. In this case study, it was demonstrated that compared to standard statistical summaries of behavioral performance, computational approaches offered superior ability to resolve, understand and explain the differences in decision- making behavior in the context of chronic pain outside the lab.


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