ArticleJournal of medical Internet research2020
Digital Pain Mapping and Tracking in Patients With Chronic Pain: Longitudinal Study.
Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 43 citations in OpenAlex.
- Differences in phantom limb, residual limb, and bodily pain during pain recall and increasing activity intensity in persons with unilateral lower limb amputation.PM & R : the journal of injury, function, and rehabilitation · 2026Article
- The PKD Daily: Development and Content Validation of a Hybrid Diary for Tracking Urinary Events in Adults with Autosomal Dominant Polycystic Kidney Disease (ADPKD).Advances in therapy · 2026Article
- Pain distribution and functional outcomes after lateral unicompartmental versus total knee arthroplasty for isolated lateral compartment osteoarthritis with anterior knee pain.Frontiers in medicine · 2026Article
- A Summary of Pain Locations and Neuropathic Patterns Extracted Automatically from Patient Self-Reported Sensation Drawings.International journal of environmental research and public health · 2025Article
- Summary and Analysis of Digital Pain Manikin Data in Adults With Pain Experience: Scoping Review.Journal of medical Internet research · 2025Article
- Bodily maps of exercise-induced feelings.Scientific reports · 2025Article
- Types, method, and mode of implementation of pain/symptom maps in musculoskeletal pain rehabilitation: A scoping review protocol.PloS one · 2025Article
- Short-term variability of chronic musculoskeletal pain.Frontiers in pain research (Lausanne, Switzerland) · 2025Article
- Automated Pain Spots Recognition Algorithm Provided by a Web Service-Based Platform: Instrument Validation Study.JMIR mHealth and uHealth · 2024Article
- The Experience Sampling Method in Small Fiber Neuropathy: The Influence of Psychosocial Factors on Pain Intensity and Physical Activity.Journal of pain research · 2024Article
- Quantifying Pain Location and Intensity with Multimodal Pain Body Diagrams.Journal of visualized experiments : JoVE · 2023Article
- Acceptability, usability and feasibility of experienced sampling method in chronic secondary pain syndromes.Frontiers in neurology · 2023Article
- Visualizing and quantifying spatial and qualitative pain sensations.Scandinavian journal of pain · 2022Article
- Digital body mapping of pain quality and distribution in athletes with longstanding groin pain.Scientific reports · 2022Article
- User Engagement and Assessment of Treatment Effectiveness in Patients Using a Novel Digital mHealth App During Spinal Cord Stimulation Screening Trials.JMIR human factors · 2022Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDigital pain mapping allows for remote and ecological momentary assessment in patients over multiple time points spanning days to months. Frequent ecological assessments may reveal tendencies and fluctuations more clearly and provide insights into the trajectory of a patient's pain.
objectiveThe primary aim of this study is to remotely map and track the intensity and distribution of pain and discomfort (eg, burning, aching, and tingling) in patients with nonmalignant spinal referred pain over 12 weeks using a web-based app for digital pain mapping. The secondary aim is to explore the barriers of use by determining the differences in clinical and user characteristics between patients with good (regular users) and poor (nonregular users) reporting compliance.
methodsPatients (N=91; n=53 women) with spinal referred pain were recruited using web-based and traditional in-house strategies. Patients were asked to submit weekly digital pain reports for 12 weeks. Each pain report consisted of digital pain drawings on a pseudo-three-dimensional body chart and pain intensity ratings. The pain drawings captured the distribution of pain and discomfort (pain quality descriptors) expressed as the total extent and location. Differences in weekly pain reports were explored using the total extent (pixels), current and usual pain intensity ratings, frequency of quality descriptor selection, and Jaccard similarity index. Validated e-questionnaires were completed at baseline to determine the patients' characteristics (adapted Danish National Spine Register), disability (Oswestry Disability Index and Neck Disability Index), and pain catastrophizing (Pain Catastrophizing Scale) profiles. Barriers of use were assessed at 6 weeks using a health care-related usability and acceptance e-questionnaire and a self-developed technology-specific e-questionnaire to assess the accessibility and ease of access of the pain mapping app. Associations between total extent, pain intensity, disability, and catastrophizing were explored to further understand pain. Differences between regular and nonregular users were assessed to understand the pain mapping app reporting compliance.
resultsFluctuations were identified in pain reports for total extent and pain intensity ratings (P<.001). However, quality descriptor selection (P=.99) and pain drawing (P=.49), compared using the Jaccard index, were similar over time. Interestingly, current pain intensity was greater than usual pain intensity (P<.001), suggesting that the timing of pain reporting coincided with a more intense pain experience than usual. Usability and acceptance were similar between regular and nonregular users. Regular users were younger (P<.001) and reported a larger total extent of pain than nonregular users (P<.001).
conclusionsThis is the first study to examine digital reports of pain intensity and distribution in patients with nonmalignant spinal referred pain remotely for a sustained period and barriers of use and compliance using a digital pain mapping app. Differences in age, pain distribution, and current pain intensity may influence reporting behavior and compliance.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.