Perception of People Diagnosed With Fibromyalgia About Information and Communication Technologies for Chronic Pain Management: Cross-Sectional Survey Study
Bibliographic record
Abstract
BACKGROUND: Chronic pain is prevalent in our society, with conditions such as fibromyalgia being notably widespread. The gold standard for aiding individuals dealing with chronic pain involves interdisciplinary approaches rooted in a biopsychosocial perspective. Regrettably, access to such care is difficult for most of the people in need. Information and communication technologies (ICTs) have been used as a way of overcoming access barriers (among other advantages). OBJECTIVE: This study addressed the little explored area of how individuals with fibromyalgia use and perceive different types of ICTs for pain management. METHODS: A cross-sectional study was conducted using an online survey. This survey was created to assess the use of different ICT tools for pain management, satisfaction with the tools used, and perceived advantages and disadvantages. In addition, data collection encompassed sociodemographic variables and pain-related variables, pain intensity, the impact of pain on daily life activities, and fear of movement/injury beliefs. In total, 265 individuals diagnosed with fibromyalgia completed the survey. RESULTS: Only 2 (0.75%) participants reported not having used any ICT tool for pain management. Among those who used ICT tools, an average of 10.94 (SD 4.48) of 14 different tools were used, with the most used options being instant messaging apps, websites dedicated to managing fibromyalgia, phone calls with health professionals, and online multimedia resources. Satisfaction rates were relatively modest (mean 2.09, SD 0.38) on a scale from 0 to 5, with instant messaging apps, phone calls with health professionals, fibromyalgia management websites, and online multimedia resources being the ones with higher satisfaction. Participants appreciated the ability to receive treatment from home, access to specialized treatment, and using ICTs as a supplement to in-person interventions. However, they also highlighted drawbacks, such as a lack of close contact with health professionals, difficulty expressing emotions, and a lack of knowledge or resources to use ICTs. The use of ICTs was influenced by age and educational background. Additionally, there was a negative correlation between satisfaction with ICT tools and fear of movement/injury. CONCLUSIONS: People with fibromyalgia are prone to using ICTs for pain management, especially those tools that allow them to be in contact with health professionals and have access to online resources. However, there is still a need to improve ICT tools since satisfaction ratings are modest. Moreover, strategies aimed at older people, those with lower levels of education, and those with higher levels of fear of movement/injury can be useful to potentiate the use of ICTs among them.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".