Ethical considerations

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 question

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?

Information source and search strategy

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. 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. 2 (“endometriosis”[MeSH Terms] OR “endometriosis”[Title/Abstract] OR “endometrioma”[Title/Abstract] OR “endometriomas”[Title/Abstract])
3. 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. 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”).

Inclusion and exclusion criteria

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].

Study selection process

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.

Data extraction

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.

Study characteristics

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.

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Table 1. Basic characteristics of the included literature (n = 18).

https://doi.org/10.1371/journal.pone.0345756.t001

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Table 2. Digital health technologies for pain management mapped to core functions by technology type.

https://doi.org/10.1371/journal.pone.0345756.t002

Application forms

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.

Content elements

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].

Outcome indicators

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.

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Table 3. Summary of findings for digital health technologies in included RCTs.

https://doi.org/10.1371/journal.pone.0345756.t003

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Table 4. Summary of findings for digital health technologies in included quasi-experimental studies.

https://doi.org/10.1371/journal.pone.0345756.t004

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Table 5. Summary of findings for digital health technologies in included qualitative studies.

https://doi.org/10.1371/journal.pone.0345756.t005

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Table 6. Summary of findings for digital health technologies in included mixed studies.

https://doi.org/10.1371/journal.pone.0345756.t006

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Table 7. Summary of findings for digital health technologies in included observational studies.

https://doi.org/10.1371/journal.pone.0345756.t007

Preliminary success and potential of digital health technologies in endometriosis pain management

The findings of this study indicate that digital health technologies for pain management in patients with endometriosis are available in diverse forms, including remote network platforms, mobile applications, VR, and AI. These technologies offer multiple pathways for pain management, primarily involving assessment and monitoring, diagnosis, intervention, and education. Web-based patient-report outcome measurement tools generate continuous, real-time data, which quantifies and tracks patients’ pain status while minimizing biases associated with long-term recall [30]. The Endo app delivers a interactive and engaging pain management platform for patients, which helps alleviate pain and negative emotions, and to some extent, overcomes the temporal and spatial limitations of traditional interventions [18]. Professional pain education websites provide convenient, multifunctional support for patients, facilitating a better understanding and self-management of pain, as well as enhancing patients’ self-efficacy and confidence in seeking medical care [29]. Digital follow-up interventions can maintain patients’ long-term health outcomes without compromising the effectiveness of core therapeutic measures [33]. Collectively, these findings highlight digital health technologies as a valuable complement to traditional pain management strategies: they enhance the flexibility and accessibility of healthcare services, while delivering more continuous and personalized follow-up care and support for patients with chronic pain related to endometriosis.

Future directions of digital health technologies in endometriosis pain management

Optimize personalized digital health interventions

Among the 18 studies included in this review, several incorporated the opinions and needs of patients with endometriosis during the development of digital health technologies for pain assessment and education. Compared with patients with other chronic conditions, individuals with endometriosis are generally younger, conferring higher accessibility and adaptability to digital health services. However, current pain intervention programs remain largely standardized and fail to fully account for interindividual differences in patients’ pain types, psychological preferences, and behavioral characteristics, thereby affecting intervention experience and adherence. First, the lack of significant effects of the activity-stimulating VR interventions on improving pain catastrophizing and negative emotions may be associated with a mismatch between activity intensity and patients’ pain tolerance [15]. All study participants were patients with moderate to severe pain, some of whom may have had limited physical mobility. The design of activity-stimulating VR failed to consider individual differences in pain tolerance, which might instead cause discomfort and reduce adherence. Second, repeated immersion in the same VR environment twice daily may lead to patient boredom and even study discontinuation [23]. Therefore, future research should scientifically design progressive intervention protocols based on pain types and objective evidence to achieve dynamic, personalized adjustments to intervention content and frequency. For example, Piette et al. [35] adopted a reinforcement learning model to dynamically adjust therapists’ interaction modes and intervention content by analyzing patients’ pain scores, activity levels, and feedback data. To improve intervention adherence, prioritizing motivation sustainability and interactive experience optimization helps strengthen patients’ self-efficacy, with real-time feedback, progress visualization, and personalized reminders as practical examples.

Challenges and prospects of digital health technology in endometriosis pain management

While digital health technologies are advancing rapidly in pain management of patients with endometriosis, they face several challenges, which are detailed as follows: (i) Verify the long-term effectiveness: Existing studies have focused primarily on short-term outcomes, and the long-term effectiveness and sustainability of digital health technologies remain to be verified. Although Endo App has been shown to improve patients’ quality of life within a 12-week period [18], its long-term impacts on pain management and mechanisms underlying maintaining patient engagement remain unclear. An 8-week online mindfulness therapy intervention showed no significant effect on endometriosis-related pelvic pain, potentially due to the long disease duration of endometriosis, which makes it difficult for short-term interventions to alter pain-associated neural responses [20]. This also suggests that short-term evaluations may be insufficient to reflect the needs of long-term management for chronic conditions. Therefore, future studies should extend follow-up periods to verify the long-term effects of interventions, thereby supporting a more robust and sustainable role for digital health technologies in the management of EAP. (ii) Protect personal sensitive information: Digital health interventions typically involve collection and storage of patients’ personal information, such as symptom burden and psychological status. For individuals with endometriosis, topics such as pain and adverse sexual experiences often contribute to feelings of social isolation [36]. Without adequate safeguards, such sensitive health information is particularly vulnerable to privacy breaches, unauthorized access, and data leakage. An anonymous environment can foster a sense of safety and privacy, thereby enhancing treatment adherence. Therefore, future research should emphasize the protection of sensitive information and ensure a secure digital environment for patients. (iii) Addressing evidence gaps in adolescent populations: As frequent users of digital technologies, adolescents have unique physiological, psychological, developmental and cognitive characteristics, which offer substantial potential for digital health interventions to provide personalized care. Given the early onset of endometriosis-related symptoms and the critical importance of early pain intervention, digital health technologies are particularly promising for expanding pain education, improving symptom recognition, and supporting timely management in this population. However, current digital health tools rarely provide appropriate content matched to adolescents’ comprehension levels, and also face challenges such as risks of digital dependence, privacy concerns, and low acceptance among caregivers [37]. Future research should optimize intervention design through multidisciplinary collaboration, develop age‑appropriate pain education and psychological support content, establish strict content review and ethical safeguards, and promote coordinated implementation across schools, families, and clinical settings to prioritize the needs of adolescents. This will enable digital health technologies to provide timely, convenient, and personalized symptom management support for adolescents with endometriosis. (iv) Addressing regional bias in the evidence base and equity concerns: Current research evidence is predominantly derived from high-income countries, with insufficient data from low- and middle-income countries. Significant disparities exist between high-income countries and low- and middle-income countries in digital technology accessibility, healthcare resource allocation, and socioeconomic contexts, which restricts the global generalizability of the findings and raises important equity concerns. Future studies should expand their geographic scope, develop regionally adapted tools, and promote international collaboration to enhance the inclusivity and equity of digital health technologies for endometriosis pain management.

Limitations

Several limitations of this scoping review should be acknowledged. First, only studies published in English and Chinese were included, which may have resulted in the omission of relevant studies in other languages. Second, despite our comprehensive search strategy, some relevant literature may not have been captured, which could affect the interpretation of our findings. Third, this study has limited the main scope of digital health technologies to patient-facing interactive tools. While this approach may not encompass the full spectrum of digital health categories (such as electronic health records that are clinician-facing only with no patient engagement), it facilitates an in-depth analysis of the shared mechanisms through which these technologies enhance patient engagement and self-management. Future reviews could expand to include a broader range of digital health technology types. Finally, as a scoping review, this study primarily aimed to systematically map and summarize the breadth of available evidence, not to formally appraise its methodological quality. It is important to note that the included studies consisted predominantly of exploratory or pilot investigations. Specifically, interventional studies were often limited by small sample sizes, single-center designs, and a lack of blinding for participants and outcome assessors. These limitations collectively highlight the evolving nature of this field and underscore the need for more robust primary studies to build a conclusive evidence base.