By Jelu Roy Elixes Bito-onon Calimpong, RN;

Maria An Versa Ledesma Losa, RN

Dante Nepacena, RN

Rowell Alden, RN

Francis Coteng, RN

Roison Andro Narvaez, MSN RN

St. Paul University Philippines – Graduate School

Citation: Calimpong, J. R., Ledesma Losa, M. A., Nepacena, D., Alden, R., Coteng, F., & Narvaez, R. A.. (2024). Effectiveness of digital body mapping in patients with chronic pain disorders. Canadian Journal of Nursing Informatics, 19(4). https://cjni.net/journal/?p=14007

Abstract

Background

Across the world, chronic pain has been considered to be a debilitating condition that directly and indirectly impacts the lives of many people. Digital body mapping using current technologies has evolved to become a better tool for physicians to diagnose and manage treatment of chronic pain.

Aim

To understand the impact of using digital body mapping on the diagnosis, management, and treatment of patients with chronic pain disorders.

Design

This study used an integrative review research design.

Results

A review of 20 studies revealed several themes regarding Digital Pain Mapping, including its utility as a tool for pain mapping, processing, communication, and comparisons, as well as its reliability and user-friendliness. The advantages highlighted included its ability to offer more accurate pain measurements, accessibility, facilitation of communication, and practicality. However, disadvantages included inconsistencies between patients and physicians in identifying pain locations and lack of measurement for psychological aspects. Overall, all 20 studies indicated enhancements in chronic pain assessment and treatment management with the use of Digital Pain Mapping.

Conclusion

Digital body mapping revolutionizes pain management by leveraging technology to create detailed representations of pain, enabling personalized care and improved outcomes. Its ability to capture the complexity of chronic pain, facilitate communication, support data-driven decision-making, and drive research advancements makes it a crucial tool in the realm of pain science and clinical practice, ultimately enhancing the quality of life for those with chronic pain conditions.

Background

Chronic pain is operationally characterized by its persistence or recurrence for a duration surpassing three months, often emerging as the primary or predominant complaint necessitating specialized treatment and care (Treede et al., 2019). Unlike acute pain, chronic pain does not merely constitute a temporal extension of nociception but is sustained by complex factors remote from its initial cause, including central sensitization, altered pain modulation, glial activation, and neuroimmune signaling (Raffaeli et al., 2019). McQuay et al. (2018) underscored the profound societal implications of chronic pain, affecting nearly half of all adults and predominantly afflicting older individuals, with significant consequences for health, well-being, and workforce productivity.

Standardized current approaches, including self-reporting instruments such as Numerical Rating Scales (NRS), Visual Analogue Scales (VAS), and observational measures like the PAINAD scale, are commonly utilized alongside digital tools within Electronic Health Records (EHRs) to assess pain intensity, location, and interference. These assessment methods have been identified as crucial components in contemporary pain management practices, as highlighted by Fillingim et al. (2019). Additionally, research conducted by Hjermstad et al. (2011) has compared various rating scales, indicating that ordered categorical scales with descriptive adjectives are frequently utilized to categorize pain intensity levels. Moreover, widely used questionnaires like the Brief Pain Inventory (BPI), as proposed by Miettinen et al. (2019), offer a structured approach to evaluating pain interference.

This integrative review delves into the existing literature and research surrounding the use of digital body mapping in patients with chronic pain disorders. Currently, there are limited resources that cover integrative reviews of the current and ongoing studies about digital body mapping for chronic pain. Thus, this integrative review explores the potential benefits of this technology, including improved pain assessment accuracy, enhanced communication between patients and healthcare providers, and more tailored treatment plans. Additionally, it will analyze the current state of the research, identify gaps in knowledge, and discuss implications for future research and clinical practice. Ultimately, this examination of digital body mapping seeks to shed light on its effectiveness and contribute to the ongoing efforts to enhance care for those living with chronic pain.

Method

Design

This study is an integrative review of all available evidence-based literature on the topic, whether quantitative, qualitative, or mixed methods with diverse methodologies (Whittemore & Knafl, 2005). The steps are as follows: identification of clinical subjects being studied by discussing the use of digital pain mapping techniques and procedures, followed by the completion of literature synthesis, analysis, and evaluation of information gathered.

Search Strategy

This review was conducted from March to April 2024 using online sources such as Pubmed, NCBI, Researchgate, SageJournal, Elsevier, Proquest, and CINAHL. Keywords used were: Chronic Pain, Digital Mapping of Chronic Pain, Demographics of Chronic Pain, Digital Body Mapping, Body Mapping, and Body Drawing. As illustrated in Figure 1, utilizing the PRISMA diagram, using the keywords, the initial search resulted in 340 resources based on title, from which 100 were reviewed. Upon validating if the inclusion criteria were met, 50 articles were fully reviewed, and upon application of the exclusion criteria, 20 studies were identified as eligible.

Figure 1

 PRISMA Flow Diagram of the effectiveness of digital body mapping in patients with chronic pain disorders

The Inclusion and Exclusion Criteria

Table 1 shows the eligibility criteria. The included studies are peer-reviewed empirical studies that utilize an array of techniques and devices to assess and map pain. Qualitative, quantitative, and mixed methods studies were included, whereas opinion papers, blogs, and personal reviews were excluded. Articles included were also written in comprehendible basic English. Studies included were 2010 and beyond and excluded the ones prior to that to ensure that the data available are up to date in terms of technology.

Table 1

Inclusion and Exclusion Criteria

Below is a comprehensive table detailing the inclusion and exclusion criteria utilized in the study. These criteria serve as essential guidelines for participant selection, ensuring the relevance and reliability of the research outcomes.

Data Evaluation and Quality Appraisal

The researchers have manually gone through the studies included with consideration for purpose, methods, and findings. Online sources that fit the criteria were added in this integrative review. The team conducted meetings for consensus and to resolve disagreements.

As shown in Table 2, each selected study was classified among the five categories of study design, nine qualitative, nine quantitative, and two used mixed methods. Furthermore, the level of evidence was established using Melynk and Fineout-Overholt’s (2022) critical appraisal of evidence to guarantee the quality of this integrative review.

Table 2

Characteristics of the study

The table below provides a detailed overview of key factors and attributes that define the framework and scope of the research. These characteristics are its authors, designs, methodology, aim, result, and level of evidence.

Results

As shown in Table 3, twenty research studies that were conducted between 2010 to 2023 have been selected and synthesized and are included in this integrative review. The design of the studies was nine qualitative, nine quantitative and two mixed methods. Most of the Level of Evidence is Level VI (n=9), Level V (n=5), one, two, three for Level III (n=1), Level IV (n=2) and Level VII (n=3).

Table 3

Role of Digital Body Mapping in the Study

Table 3 unveils the significance of employing digital body mapping techniques as a pivotal tool within the research framework. This table serves as a comprehensive guide which details the diverse applications and implications of digital body mapping in the study.

One study was conducted in each of the countries of Brazil, Germany, The Netherlands, Qatar, Spain, Switzerland, Timor Leste, and the USA. Three studies were from Australia while four of were done in Denmark and the United Kingdom. Methods and instrumentation employed in their investigations were Digital Body Mapping, interviews and surveys in which data were collected through Digital Body Mapping applications, body drawings, manikins, and questionnaires. The respondents for these studies ranged from 10 to 3100 individuals and were all included as a sample size.

The overarching aim of these investigations was to assess the effectiveness of Digital Body Mapping as a pain mapping tool, explore the use of body mapping as pain processing tools, as a communication tool, and as an accessible and reliable tool to be used to assess, document, and to enhance management, programs, and solution approaches for pain. The synthesis of twenty studies has led to the identification of several themes regarding the effects of utilizing digital body mapping in the identification and management of patients with chronic pain disorders.

Digital Body Mapping as a Pain Mapping Tool

Four studies explored the application of Digital Body Mapping (DBM) as a tool for pain mapping. Villa et al. (2020) observed that current pain intensity exceeded usual levels proposing that the instances of reporting pain aligned with more severe pain experiences than usual. Serner et al. (2022) found that using DBM for athletes with multiple issues worked while those with single concerns exhibited clearer patterns, often reflecting a combination of adductor and inguinal groin pain. Thomas et al. (2018) demonstrated that DBM revealed musculoskeletal disorders extending beyond back pain, including significant discomfort in the shoulders and neck. Finally, Muracki et al. (2019) identified distinct areas of pain related to physical contact, muscle strain, and joint overloading or injury.

Digital Body mapping as a Pain Processing Tool

Four studies were included in these themes, where an algorithm was created to generate pain frequency maps using freehand pain drawings. Participants created a visualization of pain occurrences across different regions using a web browser. Users had the flexibility to specify the layout according to the dimensions of the body regions (Dixit et al.,2022). In another study, the Rheuma Buddy app yielded a comprehensive system providing feedback on clinical and psychosocial aspects of coping with and managing the disease, while also addressing everyday practicalities associated with living with Rheumatoid arthritis (Studenic et al.2022).The findings from a pen-on-paper pain drawing analysis offered robust support for both the relative and absolute reliability on the  level of measurement error that clinicians can confidently differentiate from genuine changes in painful symptoms (Barbero et al.,2024). Finally, the development of an innovative artificial intelligence framework aimed at enhancing therapeutic strategies for chronic pain was done. This framework integrates psychological and traditional biomedical approaches, potentially improving treatment outcomes (Goldstein et al., 2020).

Digital Body mapping as a Pain Communication Tool

Five studies examined the use of Digital Body Mapping as a pain communication tool. Henderson et al. (2023) found that healthcare providers lacked knowledge and experience regarding male sexual and reproductive health when utilizing body mapping. Egsgaard et al. (2016) reported that patients appreciated the 3D body chart for its detailed representation of pain. Van Schelven et al. (2023) documented significant treatment-related challenges faced by adolescents with chronic conditions, including emotional side effects, and identified coping strategies such as seeking support and maintaining a positive outlook. Vaughan et al. (2023) discovered a wide range of somatic sensations linked to anxiety through body mapping, while Ryan et al. (2021) observed disruptions in eating and exercise behaviors in women due to premenstrual symptoms.

Digital Body mapping as a Pain Comparative Tool

Two studies were included in this theme: Plisinga et al., (2022) in their research noted that there is no significant difference when pain and size is considered however, there is a minimal difference when pain location is measured. On the other hand, Jud et al., (2010) used DBM to compare the difference of pain among breast cancer survivors. The findings indicated a notable distinction in the pain experienced by patients undergoing Breast Conserving Therapy (BCT) compared to those undergoing Modified Radical Mastectomy (MCM), as well as for patients with Lymphedema versus those without. Nevertheless, there was no significant difference observed based on variables such as size of the tumour, complementary radiotherapy, and even nodal status, did not influence the extent of the pain area marked.

User Experience: Acceptability and Reliability

Five studies examined the use of Digital Body Mapping for acceptability and reliability. Ali et al. (2023) found that using a smart-based manikin for self-reporting pain was both practical and acceptable. Luque-Suarez et al. (2023) observed a significant relationship between the level of pain extent and pain intensity but found there is no association between psychological measures and pain-related infirmity. Caseiro et al. (2019) perceived excellent levels of intra- and inter-rater reliability when replicating paper pain drawings into digitized versions. Brigden et al. (2023) emphasized the importance of exploring smartphones and other technology to capture pain data in real-world settings, alongside other pertinent variables. Additionally, they highlighted the need for developmentally appropriate methods to capture pain quality data and effective ways to provide clinically meaningful feedback to key stakeholders. Finally, Adiyanto et al. (2022) emphasized that information collected through body mapping, combined with data from health surveillance, risk assessments, and online tools, can provide valuable insights into workplace and health issues within an organization.

Role of Digital Body Mapping

Table 3 illustrates the functions of Digital Body Mapping (DBM) within the researched studies. DBM served various roles, including acting as an application software, mapping pain on manikins, and capturing pain through drawings. It served as a communication tool to assess pain distribution, frequency, and location for musculoskeletal pain, women experiencing pain and cancer, as well as for sexual and reproductive health issues. Additionally, it served as an assessment tool for pediatric patients’ pain.

Advantages and Disadvantages of Digital Body Mapping

Table 4 outlines the pros and cons of employing Digital Body Mapping in the examined studies. The acknowledged benefits of DBM include its reliability and effectiveness, accessibility, user-friendliness, feasibility, and its role as a facilitator of communication. On the flip side, drawbacks include discrepancies in the interpretation of pain between patients and physicians and its inability to fully support psychological measures due to the individualized nature of pain experienced by each patient.

Table 4

Advantages and Disadvantages of Digital Body Mapping in the studies

The table below identifies a comprehensive examination of the benefits and drawbacks inherent in employing Digital Body Mapping in patients with chronic pain.

Notes: A: Advantages    D: Disadvantages

Discussion

The synthesis of twenty research studies spanning from 2010 to 2023 provides a comprehensive understanding of the multifaceted use of Digital Body Mapping (DBM) in pain assessment and management. This integrative review shows the various roles DBM plays, its effectiveness, acceptability, and limitations across diverse populations and contexts. A discussion the key findings and implications of this research review are presented below.

Effectiveness of DBM as a Pain Mapping Tool

DBM emerges as a valuable tool for mapping pain experiences, offering insights into the distribution, intensity, and nature of pain across different populations and conditions. Studies highlighted in this review revealed DBM’s ability to uncover patterns in pain experiences beyond traditional assessments. For instance, findings by Villa et al. (2020) underscored the potential of DBM in capturing instances of heightened pain intensity, crucial for tailoring interventions to individual needs. Similarly, Serner et al. (2022) and Thomas et al. (2018) shed light on DBM’s efficacy in identifying pain patterns in athletes and patients with musculoskeletal disorders, respectively. Such insights can inform targeted interventions and enhance pain management strategies.

Effectiveness of DBM as a Pain Processing and Communication Tool

The integration of DBM with advanced algorithms and visualization techniques amplifies its utility as a pain processing and communication tool. Studenic et al.  (2022) and Dixit et al. (2022) demonstrated the potential of DBM-generated pain frequency maps and interactive visualizations in facilitating data interpretation and communication among healthcare professionals and patients. Moreover, DBM’s role in capturing pain experiences, as evidenced by studies by Egsgaard et al. (2016) and Vaughan et al. (2023), enhances patient-provider communication and fosters a deeper understanding of pain-related challenges.

Effectiveness of DBM as a Comparative and Assessment Tool

DBM’s versatility extends to comparative analyses and comprehensive pain assessments. Studies by Plisinga et al. (2022) and Jud et al. (2010) illustrated DBM’s capacity to discern differences in pain experiences among distinct patient groups and conditions. Such insights can inform personalized treatment approaches and advance understanding of pain mechanisms. Additionally, DBM’s role as an assessment tool, as highlighted by Brigden et al. (2023), underscored its potential for real-world pain data collection and clinical feedback, particularly in pediatric populations.

Acceptability and Reliability of DBM

The reviewed studies underscored the acceptability and reliability of DBM across diverse settings and populations. Ali et al. (2023) and Caseiro et al. (2019) highlighted the practicality and reliability of DBM-based pain assessments, emphasizing its potential for self-reporting and inter-rater consistency. Furthermore, Loque-Suarez et al. (2023) and Adiyanto et al. (2022) underscored DBM’s utility in capturing meaningful insights into pain extent and associated health issues, enhancing workplace surveillance and risk assessment efforts.

Advantages and Disadvantages of DBM

While DBM offers numerous advantages in pain assessment and management, including its reliability, accessibility, and user-friendliness, challenges remain. Discrepancies in pain interpretation between patients and healthcare providers, as well as limitations in capturing psychological measures, pose notable challenges. However, ongoing advancements in technology and methodologies hold promise for addressing these limitations and maximizing the utility of DBM in clinical practice.

The synthesis of these 20 research studies underscores the diverse roles and utility of Digital Body Mapping in pain assessment, communication, and management. From its effectiveness as a pain mapping and processing tool to its acceptability and reliability across varied populations, DBM holds immense potential for enhancing clinical practice and advancing pain research. As technology continues to evolve, future studies should focus on refining DBM methodologies, addressing existing limitations, and exploring its integration with complementary approaches to optimize pain care delivery.

Implication for Practice

Digital Body Mapping has significant impacts and implications for health practice. DBM can improve diagnosis where doctors can use the digital map to understand the location, intensity, and patterns of pain experienced by patients more accurately. This can lead to more precise diagnoses and targeted treatment plans. Next, personalized treatment plans can be improved. With detailed information about the pain’s characteristics, healthcare practitioners can tailor treatment plans to individual patients, considering factors like pain triggers, response to previous treatments, and lifestyle factors. DBM also supports better pain monitoring, wherein the digital map allows for ongoing monitoring of a patient’s pain levels and response to treatment. This helps doctors and nurses track progress over time and make necessary adjustments to the treatment plan.

Other implications for healthcare practice are enhanced communication where having a visual representation of pain can facilitate communication between patients and healthcare providers. Patients can use the map to describe their pain more effectively, leading to better understanding and collaboration in managing their condition. Research and development are also supported, where aggregated data from digital pain maps can contribute to research efforts aimed at understanding pain mechanisms, developing new treatments, and improving overall pain management strategies. Finally, patient empowerment is supported by involving patients in mapping their pain and understanding the data.

Overall, the availability of a digital pain map can significantly enhance the quality of care provided to patients with chronic pain conditions and contribute to advancements in pain management practices.

Limitations and Recommendations

The accuracy and consistency of digital body mapping may be affected by variations in how patients use technology, the reliability of the software and hardware used, and the quality of the data collected. A minority of patients may have access to the necessary technology or be comfortable using digital tools for mapping their pain, which may have restrictions on the applicability of this approach in certain populations. The interpretation of digital body mapping data can be complex and may require specialized training or expertise to analyze and understand the results effectively.

Thus, it is recommended that researchers and clinicians who use body mapping should develop standardized protocols and guidelines for conducting digital body mapping in patients with chronic pain disorders to ensure consistency and reliability of the data collected. Alos, training and support for both healthcare providers and patients on how to use digital body mapping technology effectively and interpret the results accurately is recommended. Also, further research should be conducted to validate the reliability and validity of digital body mapping as a tool for assessing pain in patients with chronic pain disorders. Clinicians should integrate digital body mapping with other assessment tools and techniques to provide a comprehensive understanding of their patient’s pain experience and treatment needs. Lastly, health professionals should incorporate patient feedback and preferences into the design and implementation of digital body mapping to ensure that it is user-friendly and relevant to the individual patient’s needs and experiences.

Conclusion

Digital body mapping is a crucial tool for pain management, especially for chronic pain patients, creating detailed body representations via technology. This method enables healthcare providers to engage patients comprehensively, leading to improved outcomes. Its strength lies in capturing the complexity of chronic pain, aiding in nuanced diagnosis and treatment planning. Moreover, it fosters clear communication and data-driven decision-making, enhancing overall patient care. Overall, digital body mapping represents a significant step forward in the effective assessment and management of chronic pain.

References

Adiyanto, O., Mohamad, E., Jaafar, R., Ma’ruf, F., Faishal, M., & Anggraeni, A. (2022). Application of Nordic body map and rapid upper limb assessment for assessing work-related musculoskeletal disorders: A case study in small and medium enterprises. International Journal of Integrated Engineering, 14(4), 10-19.

Ali, S. M., Selby, D. A., Bourke, D., Santisteban, R. D. B., Chiarotto, A., Firth, J., James, B., Parker, B., Dixon, W. G., & Van Der Veer, S. N. (2023). Feasibility and acceptability to use a smartphone-based manikin for daily longitudinal self-reporting of chronic pain. Digital Health, 9. https://doi.org/10.1177/20552076231194544

Barbero, M., Cescon, C., Schneebeli, A., Falla, D., Landolfi, G., Derboni, M., Giuffrida, V., Rizzoli, A. E., Maino, P., & Koetsier, E. (2023). Reliability of the pen-on-paper pain drawing analysis using different scanning procedures. Journal of Pain and Symptom Management 67(2), e129–e136. https://doi.org/10.1016/j.jpainsymman.2023.10.019

Boudreau, S. (2020). Digital pain in mapping and tracking in patients with chronic pain: Longitudinal and mechanisms. The Journal of Pain, 17(9), T10-T20. https://doi.org/10.1016/j.jpain.2015.08.010

Brigden, A., Garg, M., Deighan, M. T., Rai, M., Leveret, J., & Crawley, E. (2023). Digital tools for assessing chronic pain in children (5–11 years): Systematic review. Paediatric & Neonatal Pain, 6(3), 89–97. https://doi.org/10.1002/pne2.12106

Caraceni, A., & Shkodra, M. (2019). Cancer pain assessment and classification. Cancers, 11(4), 510. https://doi.org/10.3390/cancers11040510

Carnago L, O’Regan A, & Hughes JM. Diagnosing and treating chronic pain: Are we doing this right? Journal of Primary Care & Community Health, 12, 21501327211008055. https://doi.org/10.1177/21501327211008055

Caseiro, M., Woznowski?Vu, A., De Oliveira, A. S., Reis, F. J. J. D., & Wideman, T. H. (2019). From paper to digitalized body map: A reliability study of the pain area. Pain Practice (Print), 19(6), 602–608. https://doi.org/10.1111/papr.12780

Chaudhry, B. (2016). No gain without pain: using pain tracking mobile apps. mHealth, 2, 27. https://doi.org/10.21037/mhealth.2016.06.03

Dixit, A., & Lee, M. C. (2022b). Quantification of digital body maps for pain: Development and application of an algorithm for generating pain frequency maps. JMIR Formative Research, 6(6), e36687. https://doi.org/10.2196/36687

Egsgaard, L. L., Christensen, T. S., Petersen, I. M., Brønnum, D., & Boudreau, S. (2016). Do gender-specific and high-resolution three-dimensional body charts facilitate the communication of pain for women? A quantitative and qualitative study. JMIR Human Factors, 3(2), e19. https://doi.org/10.2196/humanfactors.5693

Fillingim, R. B., Loeser, J. D., Baron, R., & Edwards, R. R. (2016). Assessment of chronic pain: Domains, methods, and mechanisms. The Journal of Pain, 17(9), T10-T20. https://doi.org/10.1016/j.jpain.2015.08.010

Goldstein, P., Ashar, Y. K., Tesarz, J., Kazgan, M., Cetin, B., & Wager, T. D. (2020). Emerging clinical technology: Application of machine learning to chronic pain assessments based on emotional body maps. Neurotherapeutics (Online), 17(3), 774–783. https://doi.org/10.1007/s13311-020-00886-7

Henderson, H., Da Silva, A. M., Da Silva, M., Xavier, H. a. S., Mendonca, S. A., De Araújo, R. M., Vaughan, C., & Bohren, M. A. (2023). Body mapping as a participatory method to explore health provider beliefs and understanding about sexual and reproductive health and rights in Timor-Leste. SSM – Qualitative Research in Health, 3, 100205. https://doi.org/10.1016/j.ssmqr.2022.100205

Hjermstad, M. J., Fayers, P. M., Haugen, D. F., Caraceni, A., Hanks, G. W., Loge, J. H., Fainsinger, R., Aass, N., Kaasa, S., & European Palliative Care Research Collaborative (EPCRC). (2011). Studies comparing Numerical Rating Scales, Verbal Rating Scales, and Visual Analogue Scales for assessment of pain intensity in adults: A systematic literature review. Journal of Pain and Symptom Management, 41(6), 1073–1093. https://doi.org/10.1016/j.jpainsymman.2010.08.016

Jud, S. M., Fasching, P. A., Maihöfner, C., Heusinger, K., Loehberg, C. R., Hatko, R., Rauh, C., Bani, H., Lux, M. P., Beckmann, M. W., & Bani, M. R. (2010). Pain perception and detailed visual pain mapping in breast cancer survivors. Breast Cancer Research and Treatment, 119(1), 105-10. https://doi.org/10.1007/s10549-009-0485-z

Lu, H., & Javier, F. (2011). Prevalence and treatment of chronic pain in the Philippines. Philippine Journal of Internal Medicine, 49(2), 61-69.

Luque-Suárez, A., Falla, D., Barbero, M., Galán, C. P., Derboni, M., Giuffrida, V., & Martínez-Calderón, J. (2022). Digital pain extent is associated with pain intensity but not with pain-related cognitions and disability in people with chronic musculoskeletal pain: A cross-sectional study. BMC Musculoskeletal Disorders (Online), 23(1). https://doi.org/10.1186/s12891-022-05700-3

Melnyk, B. M., & Fineout-Overholt, E. (2022). Evidence-based practice in nursing & healthcare: A guide to best practice. Lippincott Williams & Wilkins.

Miettinen, T., Kautiainen, H., Mäntyselkä, P., Linton, S. J., & Kalso, E. (2019). Pain interference type and level guide the assessment process in chronic pain: Categorizing pain patients entering tertiary pain treatment with the Brief Pain Inventory. PloS One, 14(8), e0221437.

McQuay, H. J., Smith, L. A., & Moore, R. A. (2018). Chronic pain. In Health care needs assessment (pp. 519-600). CRC Press.

Muracki, J., Kumorek, M., Kisilewicz, A., Po?arowszczyk, B., Larsen, D. B., Kawczy?ski, A., & Boudreau, S. (2019). Practical use of the Navigate Pain application for the assessment of the area, location, and frequency of the pain location in young soccer goalkeepers. Journal of Human Kinetics, 69(1), 125–135. https://doi.org/10.2478/hukin-2019-0091

Nahin, R. L., Feinberg, T., Kapos, F. P., & Terman, G. W. (2023). Estimated rates of incident and persistent chronic pain among US adults, 2019-2020. JAMA Network Open, 6(5), e2313563-e2313563. http://doi.org/10.1001/jamanetworkopen.2023.13563

Plinsinga, M. L., Boudreau, S., Coombes, B. K., Mellor, R., Hayes, S. C., & Vicenzino, B. (2022). Comparing what the clinician draws on a digital pain map to that of persons who have greater trochanteric pain syndrome. Scandinavian Journal of Pain (Print), 22(3), 506–514. https://doi.org/10.1515/sjpain-2021-0135

Raffaeli, W., Tenti, M., Corraro, A., Malafoglia, V., Ilari, S., Balzani, E., & Bonci, A. (2021). Chronic pain: what does it mean? A review on the use of the term chronic pain in clinical practice. Journal of Pain Research, 14, 827–835. https://doi.org/10.2147/JPR.S303186

Rikard, S. M., Strahan, A. E., Schmit, K. M., & Guy, G. P. (2023). Chronic pain among adults — United States, 2019–2021. Morbidity and Mortality Weekly Report, 72(15), 379–385. https://doi.org/10.15585/mmwr.mm7215a1

Ryan, S., Ussher, J. M., & Hawkey, A. (2021). Managing the premenstrual body: A body mapping study of women’s negotiation of premenstrual food cravings and exercise. Journal of Eating Disorders, 9(1), 125. https://doi.org/10.1186/s40337-021-00478-6

Serner, A., Reboul, G., Lichau, O., Weir, A., Heijboer, W., Vuckovic, Z., & Boudreau, S. (2022). Digital body mapping of pain quality and distribution in athletes with longstanding groin pain. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-13847-1

Studenic, P., Stamm, T., Seidler, Y., Dam, A., Weibrecht, N., Zauner, G., Jakobsen, T. H., Hansen, R. L., Popper, N., Wilhelmer, T. C., Radner, H., Ramos, R., Rickmann, J., Urach, C., Kristensen, L. E., & Jørgensen, T. S. (2022). AB1393 RHEUMA BUDDY 4.0 leading the path to a patient-driven electronic support and monitoring tool. Annals of the Rheumatic Diseases, 81(Suppl 1), 1801.2-1802. https://doi.org/10.1136/annrheumdis-2022-eular.1748

Thomas, D., Hare, B., & Cameron, I. (2018). Using body mapping as part of the risk assessment process – a case study. Policy and Practice in Health and Safety, 16(2), 224-240. https://doi.org/10.1080/14773996.2018.1491146

Treede, R. D., Rief, W., Barke, A., Aziz, Q., Bennett, M. I., Benoliel, R., Cohen, M., Evers, S., Finnerup, N. B., First, M. B., Giamberardino, M. A., Kaasa, S., Korwisi, B., Kosek, E., Lavand’homme, P., Nicholas, M., Perrot, S., Scholz, J., Schug, S., Smith, B. H., … Wang, S. J. (2019). Chronic pain as a symptom or a disease: the IASP Classification of Chronic Pain for the International Classification of Diseases (ICD-11). Pain, 160(1), 19-27.  https://doi.org/10.1097/j.pain.0000000000001384

Van Schelven, F., Van Der Meulen, E., Wessels, E., & Boeije, H. (2023). Let us talk treatment: Using a digital body map tool to examine treatment burden and coping strategies among young people with a chronic condition. Patient Preference and Adherence, 17, 517–529. https://doi.org/10.2147/ppa.s400702

Vaughan, P., Tewson, A., Morgan, P., & Boydell, K. M. (2023). “Chains Weigh Heavy”: Body mapping embodied experiences of anxiety. The Qualitative Report, 28(2), 583-606. https://doi.org/10.46743/2160-3715/2023.5712

Villa, M. G., Pálsson, Þ. S., Royo, A. C., Bjarkam, C. R., & Boudreau, S. (2020b). Digital pain mapping and tracking in patients with chronic pain: Longitudinal study. JMIR. Journal of Medical Internet Research, 22(10), e21475. https://doi.org/10.2196/21475

Whittemore, R., & Knafl, K. A. (2005). The integrative review: Updated methodology. Journal of Advanced Nursing, 52(5), 546–553. https://doi.org/10.1111/j.1365-2648.2005.03621.x