Digital health technologies in pain management for patients with endometriosis: A scoping review

Endometriosis is a benign estrogen-dependent inflammatory disease characterized by the presence, growth, and infiltration of endometrial tissue outside the uterus [ 1 ]. It is estimated that approximately 2%−10% of women of reproductive age worldwide are affected by endometriosis [ 2 ]. These symptoms, which primarily include dysmenorrhea, chronic pelvic pain, and dyspareunia, are collectively designated as endometriosis-related pain and have a marked adverse effect on patients’ quality of life [ 3 , 4 ]. Currently, no effective treatment exists for endometriosis-associated pain (EAP). Guidelines recommend long-term management with medication, surgery, and psychotherapy [ 5 ]. However, the complexity of pain poses major challenges: assessments lack precision and real-time monitoring, treatment options remain limited, outcomes are often unsatisfactory with high relapse after discontinuation, and patients frequently show low self-management engagement and poor access to care [ 6 – 8 ]. Digital health technologies refer to digital technologies that enable the provision of remote health-related services to patients, encompassing electronic health records, telemedicine, artificial intelligence, and various device-supported mobile health technologies [ 9 ]. For the purpose of this review, we have adopted a more focused operational definition, specifically including interactive tools that directly engage patients in their care, while excluding tools used exclusively by healthcare providers. Digital health technologies encompass the remote communication function between patients and providers inherent in telemedicine, and extend beyond it by aiming to empower patients through features that support continuous engagement and self-management. Within the context of chronic pain management, these technologies deliver comprehensive support, encompassing remote patient monitoring, clinical decision support, personalized therapeutic interventions, and peer-based social engagement [ 10 , 11 ]. These characteristics provide a novel strategy for EAP management: patients with endometriosis are predominantly young women, who show high acceptance of digital health technologies in diverse forms; the real-time monitoring function of these technologies can accurately match the chronically fluctuating symptomatic nature of EAP; at the same time, they can break through barriers to healthcare such as geographical limitations, thereby offering more continuous, flexible, and personalized support for the long-term management of EAP. Despite their growing use in chronic pain management, the application of these technologies to EAP remains limited and largely exploratory. Significant variability exists in aspects such as its application forms, specific content, assessment tools, and effectiveness outcomes, with a lack of systematic research and synthesis. This study synthesizes and analyzes existing research on the use of digital health technologies in pain management for patients with endometriosis, aiming to provide insights for further research in this field and the optimization of clinical practice.

Following an initial database search yielding 1359 records, 174 studies were selected after duplicate removal and title/abstract screening. Full-text assessment resulted in the final inclusion of 18 publications (please see Fig 1 ). Eighteen included studies were published between 2020 and 2025, with the distribution of regional sources as follows: Germany (n = 4) [ 15 – 18 ], Canada (n = 3) [ 19 – 21 ], France (n = 3) [ 22 – 24 ], the United States (n = 2) [ 25 , 26 ], China (n = 2) [ 27 , 28 ], the United Kingdom (n = 2) [ 29 , 30 ], Australia (n = 1) [ 31 ] and the Netherlands (n = 1) [ 32 ]. The included studies comprised randomized controlled trials (n = 4) [ 18 , 23 , 24 , 31 ], quasi-experimental study (n = 3) [ 15 , 27 , 28 ], qualitative studies (n = 3) [ 16 , 21 , 30 ], mixed studies (n = 4) [ 19 , 20 , 26 , 29 ] and observational studies (n = 4) [ 17 , 22 , 25 , 32 ]. The basic characteristics of the included literature are shown in Table 1 . Table 2 presents a mapping of digital health technologies for pain management to core functions, categorized by technology type. Note: Study population: A. Endometriosis B. EAP C. Endometriosis-related dysmenorrhea D. Endometriosis-related pelvic pain E. Endometriosis-related dyspareunia S ample size: “1/2” corresponds to the sample sizes of the intervention group/ control group; “1/2/3” corresponds to the sample sizes of the three intervention groups, which are the intervention group 1, intervention group 2, and control group in sequence. Content elements: ①Pain diagnosis ②Pain assessment and monitoring ③Pain intervention ④Pain education Outcome indicators: a. Pain-related indicators b. Psychosocial indicators c. Feasibility evaluation Currently, the applications of digital health technologies for pain management in patients with endometriosis fall into four main categories:①Remote network platforms [ 19 – 21 , 27 – 29 , 31 ]: Relevant services can be provided by leveraging existing website platforms or developing dedicated health education websites. ②Mobile application [ 16 – 18 , 22 , 26 , 30 , 32 ]: With mobile devices such as smartphones and tablets as the primary carriers, these apps integrate functions including real-time symptom recording, health education, exercise guidance and psychological support, covering the full-process needs of patients from symptom monitoring to daily health management. ③Virtual Reality (VR) [ 15 , 23 , 24 , 31 ]: Highly immersive virtual scenarios are created via VR headsets, delivering multi-sensory stimulation to patients through visual, auditory and other sensory channels. ④Artificial Intelligence (AI) [ 25 ]: Machine learning algorithms are applied to analyze pain-related data, so as to assist clinicians in clinical disease diagnosis. Digital health technologies contribute to pain management in patients with endometriosis across four core dimensions: ①Pain assessment and monitoring. This dimension primarily relies on patient-reported outcome measures. Two studies developed the Endometriosis Symptom Diary (ESD), the Endometriosis Impact Scale (EIS), and the Endometriosis Daily Diary (EDD) to assess the type, location and duration of pain in patients with endometriosis, as well as the impact of pain on daily activities and mood [ 26 , 30 ]. One study applied the MEASuRE tool for dynamic monitoring of pain symptoms in patients with EAP. By administering daily symptom questionnaires to patients over a 28-day period, it systematically collected data on pain, sleep, diet, and medication use, thereby enabling accurate capture of pain symptoms and their temporal changes [ 32 ]. ②Pain diagnosis. One study conducted cluster analysis on 155 pain locations, identifying 15 distinct pain site clusters. It then leveraged machine learning algorithms to calculate the relative risk of endometriosis based on combinations of pain locations and types, revealing specific diagnostic predictive patterns [ 25 ]. ③Pain intervention. Two studies involved psychological interventions incorporating digital storytelling, group meditation, and emotion regulation training [ 20 , 21 ]. Three studies adopted VR-based interventions, using headsets to deliver therapeutic virtual environments featuring natural sounds and simple visual stimuli, which provided diverse relaxation or activity scenarios [ 15 , 23 , 24 ]. One study designed three study arms: a 1-hour supervised telehealth exercise group (combining cardiopulmonary interval training and lumbopelvic stabilization), a VR group (10 minutes of pain distraction plus 50 minutes of aerobic exercise via VR applications), and a control group without structured exercise. This study compared the immediate pelvic pain relief effects of the two digital health-supported exercise modalities against the control group [ 31 ]. Four studies utilized the Endo or School of Endo programs to deliver multimodal self-management for EAP, including exercise guidance, symptom tracking, and social support [ 16 – 18 , 22 ]. Another study adopted a blended online-offline traditional Chinese medicine program to guide patients in acupoint massage. [ 27 ]. ④Pain education. One study disseminated educational materials on the pathophysiological mechanisms and clinical management of endometriosis-related dysmenorrhea [ 28 ]. Additionally, dedicated educational websites focusing on dyspareunia and pelvic pain were shown to further enhance patients’ access to targeted health information [ 19 , 29 ]. The outcome indicators for digital health technology-based interventions pertain to three domains: pain-related indicators, psychosocial indicators and feasibility assessment. ①Pain-related indicators included pain intensity, frequency of pain medication use and impact of pain on function. Eight studies evaluated pain management through effective tools such as the Visual Analogue Scale (VAS), the Numeric Rating Scale (NRS) and the Pain Self-Efficacy Questionnaire (PSEQ). Of these, seven studies reported observed improvements associated with digital health technology-based interventions, including reduced pain levels, lower rates of pain medication utilization, diminished pain-related disruption to daily life, and enhanced self-efficacy in patients [ 15 , 18 , 22 – 24 , 27 , 31 ]. Notably, one study showed no significant effect on alleviating pain intensity in the short term [ 20 ]. ②Psychosocial indicators: quality of life, anxiety, depression, sleep quality, stress and fatigue. Seven studies reported improvements in patient quality of life associated with the use of digital health technologies, as assessed by tools including the Endometriosis Health Profile-5 (EHP-5), Endometriosis Health Profile-30 (EHP-30), World Health Organization Quality of Life-100 (WHOQOL-100), Spitzer Quality of Life Index (SQLI), and Short-Form Health Survey (SF-36) [ 17 , 18 , 20 , 22 , 23 , 27 , 28 ]. Evidence from four studies supported the role of digital health interventions in improving negative emotions. Measurements were conducted using standardized tools, namely the Patient Health Questionnaire-9 (PHQ-9) and the Generalized Anxiety Disorder-7 (GAD-7) scale [ 15 , 18 , 22 , 27 ]. ③Feasibility assessment: the feasibility of implementing digital health technologies was uniformly confirmed in eight studies through through the analysis of patient satisfaction, adherence and user experience [ 16 , 19 , 21 , 25 , 26 , 29 , 30 , 32 ]. To more clearly illustrate the key findings of the included studies, the detailed information on interventions, types of pain and outcome directions was further tabulated separately and presented in Tables 3–7 . Note: Endo-App: A medical-grade application developed by Endo Health GmbH in Germany, incorporating psychosocial support, exercise therapy, nutritional therapy, and more. Endocare: A virtual reality digital therapeutic tool integrating auditory (e.g., alpha/theta binaural beats, nature-based sounds) and visual (e.g., bilateral alternative stimulations) components in a 3D VR environment. Note: School of Endo-APP : an application developed based on cognitive behavioral therapy and the endometriosis health profile, integrating multidisciplinary self-management tools such as disease education and dietary guidance.

This study systematically reviewed the research on digital health technologies for pain management in patients with endometriosis, with a focus on the content elements, application forms, and outcome indicators. The findings constitute a preliminary evidence base for the potential of these technologies in reducing pain and improving quality of life. However, challenges remain regarding the refinement of objective physiological indicators for pain assessment and the maintenance of long-term intervention effects, underscoring the early-stage nature of this research field, particularly in China. Future studies should leverage digital technologies to develop evidence-based, individualized pain management strategies and conduct long-term follow-ups to dynamically optimize intervention effects, thereby achieving sustained management of EAP.

We reported this review according to the preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews (PRISMA-ScR) [ 12 ]. The protocol for this review was registered in OSF (Open Science Framework- https://osf.io/a3wdb ). This study is a secondary analysis of previously published literature and does not involve the direct collection of any new raw data from human participants or animals. Therefore, this study does not require separate approval from an institutional ethics review committee. Research questions: (i) what are the application forms of digital health technologies of endometriosis patients? (ii) what are the content elements of digital health technologies of endometriosis patients? (iii) what are the outcome indicators for digital health technologies in pain management of endometriosis patients? (iv) what are the intervention effects of digital health technologies of endometriosis patients? A search was conducted in the electronic databases PubMed, CINAHL, Web of Science, Embase, Cochrane Library, China Knowledge Network, Wanfang Database, and China Biomedical Literature Database, covering literature in both English and Chinese published up to June 6, 2025. References were also tracked throughout the review process. To ensure search precision, the strategy was constructed by combining medical subject terms and keywords, and the English database was searched with PubMed as an example of search formula: 1 (“Digital Health”[MeSH Terms] OR “digital health technolog*”[Title/Abstract] OR ‘‘digital therapeutics’’[Title/Abstract] OR “telemedicine”[MeSH Terms] OR “Mobile Health”[Title/Abstract] OR “mhealth”[Title/Abstract] OR “telehealth”[Title/Abstract] OR “ehealth”[Title/Abstract] OR “telecare”[Title/Abstract] OR “Internet-Based Intervention”[MeSH Terms] OR “Web-based Intervention”[Title/Abstract] OR “Online Intervention”[Title/Abstract] OR “website”[Title/Abstract] OR “internet”[Title/Abstract] OR “Mobile Applications”[MeSH Terms] OR “Portable Software App”[Title/Abstract] OR “mobile phone’’[Title/Abstract] OR “Wearable Electronic Devices”[MeSH Terms] OR “electronic handheld device”[Title/Abstract] OR “Virtual Reality”[MeSH Terms] OR “virtual”[Title/Abstract] OR “internet of things”[MeSH Terms] OR “Artificial Intelligence”[MeSH Terms] OR “AI”[Title/Abstract]) 2 (“endometriosis”[MeSH Terms] OR “endometriosis”[Title/Abstract] OR “endometrioma”[Title/Abstract] OR “endometriomas”[Title/Abstract]) 3 (“pain”[MeSH Terms] OR “pain*”[Title/Abstract] OR “dysuria”[MeSH Terms] OR “dysuria”[Title/Abstract] OR “dyspareunia”[MeSH Terms] OR “dyspareunia”[Title/Abstract] OR “dyschezia”[Title/Abstract] OR “abdominal pain”[Title/Abstract] OR “chronic pelvic pain”[Title/Abstract]) 4 #1 AND #2 AND #3 Chinese databases were searched on China National Knowledge Infrastructure, for example, with the search formula: (Subject: “endometriosis”) and (Subject: “pain” or “dysmenorrhea” or “chronic pelvic pain” or “dyspareunia” or “acute abdominal pain” or “dyschezia” or “dysuria”) and (Subject: “mobile health” or “telehealth” or “digital health” or “e-health” or “web-based intervention” or “internet” or “computer” or “website” or “mobile platform” or “software” or “online” or “mobile application” or “WeChat” or “wearable device” or “virtual reality” or “artificial intelligence” or “big data” or “internet of things”). We established the inclusion criteria according to the PCC principle [ 13 ]. (i) Participants (P): patients with endometriosis (18 years old and older); (ii) Concept (C): involving the original literature on the application and potential impact of digital health technologies in pain management of endometriosis patients; (iii) Context (C): the various settings where pain management is implemented, such as communities, nursing institutions or hospitals. The type of study was limited to original quantitative, qualitative, and mixed-methods studies. Exclusion criteria: (i) studies unrelated to digital health technologies; (ii) studies unrelated to the pain management of endometriosis; (iii) studies focusing solely on strictly provider-facing tools, website use tracking, general health or fitness trackers; (iv) research protocols, policy opinions, guidelines, etc.; (v) full text not available [ 14 ]. After removing duplicates using EndNote X9 software, the study screening was conducted independently by two researchers in two phases, strictly adhering to the predefined inclusion and exclusion criteria. First, titles and abstracts of all retrieved records were reviewed. Subsequently, the full texts of studies deemed potentially eligible were further assessed for final inclusion. To ensure the consistency and reliability of the screening process, an inter-rater reliability check was performed on a random sample comprising 15% of records from each phase (n = 179 in the title/abstract screening phase; n = 26 in the full-text screening phase). Any disagreements identified during this calibration check or throughout the overall screening process were first resolved through discussion between the two primary reviewers. In cases where consensus could not be reached, a third senior researcher was consulted to make a final decision. Following the JBI methodology, we first conducted a pilot test of the data extraction tool on three randomly selected full-text articles to ensure its reliability and consistency. Subsequently, two reviewers independently reviewed full-text and extracted data into Microsoft Excel. All core extraction variables extracted in this review were pre-specified in the OSF including general information such as author, publication date, country, study design, study population, sample size, application forms, content elements, intervention duration, and outcome indicators.

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