Sage Journals: Discover world-class research

Abstract

Background:

Chronic musculoskeletal pain is a major health issue worldwide, characterized by a significant disease burden that leads to disability and reduced quality of life. Its prevalence is increasing, particularly among the aging population. Research is needed to provide effective, individualized, and theory-based eHealth self-management interventions to improve clinical outcomes, and access to care. There is a lack of consensus in the literature on the concept of eHealth self-management support for chronic musculoskeletal pain.

Objective:

The aim of this study is to conduct a concept analysis of eHealth self-management support for chronic pain within the framework of the Social Cognitive Theory.

Methods:

Our study was guided by Mckenna’s 9-step process for concept analysis as a framework: (1) Select the concept; (2) Determine the purpose of the analysis; (3) Identify the uses; (4) Determine attributes; (5) Identify a ‘model case’; (6) Identify alternative cases; (7) Identify antecedents and consequences; (8) Consider context and values; and (9) Identify empirical indicators. We searched the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Ovid MEDLINE, Web of Science, and other sources between 2000 and 2025. Studies were included if they met the predetermined inclusion and exclusion criteria.

Results:

A total of 12 studies were included to define the main attributes, antecedents, and consequences. The attributes were: (1) Person-centered, accessible, personal, and facilitated behavioral change; (2) Web-based multidisciplinary education, modeling, and vicarious learning; (3) Web-based goal setting, activity planning, self-monitoring, and automated personalized feedback for motivational enhancement; and (4) Web-based social support and communication with healthcare providers.

Conclusion:

This study has delineated the concept of eHealth self-management support for chronic pain and provided a foundation for future research.

Keywords

eHealth internet self-management chronic pain concept analysis

Introduction

Approximately 1.71 billion people have musculoskeletal (MSK) conditions worldwide, and they are the leading contributor to disability worldwide.¹ They are defined as conditions affecting muscles, bones, joints, and connective tissue and are often characterized by persistent pain and limitations in mobility and dexterity. Chronic pain has been defined as persisting or recurring for more than three months.^2,3 Self-management refers to the tasks an individual undertakes to control or reduce the impact of disease on their health.⁴ Self-management support is defined as “the systematic provision of education and supportive interventions by health care staff to increase patients’ skills and confidence in managing their health problems.”⁵

Electronic health (eHealth) can improve the accessibility of self-management supports, regardless of geographical location.⁶ eHealth is an overarching term to describe the use of information and communications technologies (ICTs) in healthcare for a wide range of purposes, including healthcare delivery⁷ eHealth can support the delivery of self-management interventions leading to goal achievement and well-being.⁸

The Social Cognitive Theory (SCT) by Bandura and Walters⁹ will be used as a context to elucidate our concept. Although the concept of self-management has been described in the literature and is part of several theories,^10,11 frameworks and models,¹² the concept of eHealth self-management has not been adequately described. Theories are key in personalizing eHealth interventions,¹³ and self-management of health habits is effective in reducing risk factors for diseases.^11,14 We will focus on the SCT because it is supported by empirical evidence that SCT-based strategies can improve outcomes in chronic conditions, such as arthritis and asthma. The theory posits that learning occurs through observation and modeling, and that self-efficacy—an individual’s confidence in their ability to manage behavior plays a critical role in initiating and sustaining coping behaviors to overcome barriers.¹⁵ Other key concepts include self-regulation, which is promoted through ongoing self-influence and underpins behavioral change,¹⁶ and reciprocal determinism: the continuous interaction between behavioral, cognitive, and environmental influences in shaping human behavior.¹⁷ Additionally, the SCT is the foundation for Cognitive Behavioral Therapy (CBT),^18,19 which is a group of therapeutic strategies aimed at changing maladaptive cognitions leading to emotional distress and problematic behaviors.^19-21

Kelly et al²² reported that internet-based interventions for chronic pain are effective in reducing pain interference. However, they concluded that there is significant variation in the components of the different interventions, and there is a need to create a set of standardized core outcomes for eHealth self-management interventions for chronic pain. This will enable future researchers to compare the effectiveness of various interventions. It is empirical that an underlying theory informs research to inform the understanding of the phenomenon.²³ Although there are published concept analyses on each concept separately, “eHealth”²⁴ and “self-management support for chronic conditions,”^25,26 there is a lack of definition for both terms combined. Thus, the aim of this analysis is to address these gaps by delineating the concept of eHealth self-management for individuals with chronic pain by defining its key attributes, antecedents, and consequences based on the SCT. Subsequently, this study can be used for future research on theory development or evaluation in the field of digital behavioral health.

Methods

McKenna et al.’s²⁷ process for concept analysis was used as a framework to guide our study, following a 9-step process based on previous work:^28-30

Step 1: select the concept

Step 2: determine the aims or purpose of the analysis

Step 3: identify all uses of the concept and select the literature

Step 4: define the attributes

Step 5: identify a model case

Step 6: identify alternative cases

Step 7: identify antecedents and consequences

Step 8: consider context and values

Step 9: define empirical indicators (referents)

Search Strategy

To define the attributes, antecedents, and consequences, we searched the following sources between 2000 and 2025: Databases (Cumulative Index for Nursing and Allied Health Literature CINAHL, Ovid MEDLINE, and Web of Science), in addition to other sources, including Google Scholar and searching reference lists. The search terms included the following: pain management, self-management, self-care, telemedicine, eHealth, and internet (Figure 1).

Inclusion criteria: Studies were included if: (1) participants were adults (>18 years old) with chronic musculoskeletal non-cancer pain; (2) the intervention was eHealth self-management support with a biopsychosocial model³¹ which addresses physiological, psychological, and sociocultural variables. Thus, the program was multifaceted, including education, exercise, and psychosocial support; and (3) a theory, model, or framework was explicitly specified as part of the study design or theory development; and included the SCT and/or CBT.

Exclusion criteria: Studies were excluded if (1) the intervention offered one component only physical or psychosocial; (2) the theoretical basis was not specified or not SCT and/or CBT; and (3) the Language was not English.

Figure 1.

Search strategy flow chart.

Results

Steps 3: Uses of the Concept

Walker and Avant²⁹ suggested identifying as many uses of a concept as available across disciplines. The concept of eHealth, digital, virtual, online, internet, or mobile health, self-management/self-care support is used in the fields of research, medicine, nursing, health sciences (physical and occupational therapy), pain science, rehabilitation, psychology, social work, public health, population health, digital behavioral health, engineering, and computer science.^32,40 However, the scope of this study was eHealth self-management in health care, specifically interventions designed for individuals with chronic musculoskeletal conditions. Gee et al⁴¹ stated the following definition of eHealth for chronic disease self-management: “To promote positive health outcomes by using a new frame of mind that incorporates ICTs in the presence of a complete feedback loop and enables the use of data and information, to generate health management knowledge and wisdom.” Digital self-management was defined by Shirish⁴² as the use of digital tools and services as a substitute for in-person health services aiming to enhance health and well-being. Yardley et al⁴³ defined digital behavior change interventions as “interventions that employ digital technologies to encourage and support behavior change that will promote and maintain health, through primary or secondary prevention and management of health problems.” We define eHealth self-management for chronic MSK conditions by an amalgamation of the definition of “eHealth”⁷ and “self-management”⁵ for MSK conditions: “the use of ICTs for healthcare delivery to provide education and supportive interventions to increase patients’ skills and confidence in managing their own chronic MSK health condition.”^5,7 (Table 1)

Table 1.

Summary of Results of Concept Analysis Steps 2, 3, 4, and 7.

Step	Description
2. Determine the aims or purpose
The aim of this analysis is to delineate the concept of eHealth self-management for chronic pain by defining its key attributes, antecedents, and consequences.
3. Identify all uses of the concept and select the literature
Author	Definition
Gee et al⁴¹	eHealth for chronic disease self-management: “Promote positive health outcomes by using ICTs with a complete feedback loop enabling data use to generate health management knowledge and wisdom.”
Shirish⁴²	Digital self-management: Use of digital tools/services as a substitute for in-person health services to enhance health and well-being.
Yardley et al⁴³	Digital behavior change interventions: “Interventions that employ digital technologies to encourage and support behavior change to promote and maintain health.”
Current Study Definition	eHealth self-management for chronic MSK conditions: “Use of ICTs for healthcare delivery to provide education and supportive interventions to increase patients’ skills and confidence in managing their own condition.”
4. Define the attributes
Attribute	Definition
Person-centered, accessible, personal, and facilitates behavioral change	Developed using a person-centered approach or via clinical experts; simple and user-friendly; lay language; web-based; accessible via mobile; personal and facilitates behavioral change; offered in English/French; 6 to 16 weeks duration; targets chronic MSK conditions.
Web-based multidisciplinary education; modelling and vicarious learning	Modeling lifestyle self‑management; through videos of a fictional character managing her condition using a multidisciplinary approach: video tutorials providing information on pain types, medications, sleep, fatigue, depression, stress, diet, cognitive pain management, pacing, and a tailored graded exercise program.
Web-based goal setting, activity planning, and self-monitoring with feedback	Web-based goal setting; activity planning; self‑monitoring; problem‑solving; managing flare‑ups; cognitive symptom management (relaxation, distraction, and self‑talk); automated personalized feedback for motivational enhancement.
Web-based social support and communication with health providers	Peer chat rooms; communication with health providers; moderator assistance; caregiver and family involvement; community/legal service information.
7. Identify antecedents and consequences
Antecedents	Internet accessDigital health literacySelf-efficacyPositive outcome expectations (physical, social, and self-evaluative)Overcoming impediments
Consequences	Disability reduction or increase in physical abilityPain reductionImproved self-efficacyHealth distress reductionReduction of kinesiophobiaGlobal health improvement

Step 4: The Attributes

Attributes are the characteristics most associated with the concept.^32,34 A total of 12 studies were included (Table 2) to define the main attributes. These were extracted from the studies coded, then organized into themes (Table 1).²⁶

Table 2.

Included Studies to Define Attributes, Antecedents, and Consequences.

No	Authors	Year	Title	Type/study design
1	Bandura et al¹⁴ (USA)	1998	Health promotion from the perspective of social cognitive theory	Theory
2	Geraghty et al⁴⁴ (UK)	2015	Using an internet intervention to support self-management of low back pain in primary care: protocol for a randomised controlled feasibility trial (SupportBack)	Protocol for RCT
3	Geraghty et al³⁵ (UK)	2018	Using an internet intervention to support self-management of low back pain in primary care: findings from a randomised controlled feasibility trial (SupportBack)	Feasibility RCT
4	Gogovor et al⁴⁵ (Canada)	2017	Informing the development of an Internet-based chronic pain self-management program	Qualitative
5	Lorig et al³⁷ (USA)	2008	The internet-based arthritis self-management program: a one-year randomized trial for patients with arthritis or fibromyalgia	RCT
6	Rosser et al⁴⁶ (UK)	2011	Technology-mediated therapy for chronic pain management: the challenges of adapting behavior change interventions for delivery with pervasive communication technology	Qualitative
7	Schultz et al³⁶ (Australia)	2018	Pilot Trial of the Reboot Online Program: An Internet-Delivered, Multidisciplinary Pain Management Program for Chronic Pain	Pilot RCT
8	Smith et al⁴⁷ (Australia)	2019	Reboot Online: A Randomized Controlled Trial Comparing an Online Multidisciplinary Pain Management Program with Usual Care for Chronic Pain.	RCT
9	Varsi et al⁴⁸ (Norway)	2021	Health care providers’ experiences of pain management and attitudes towards digitally supported self-management interventions for chronic pain: a qualitative study.	Qualitative
10	Fernandes et al⁴⁹ (Brazil)	2022	At my own pace, space, and place: a systematic review of qualitative studies of enablers and barriers to telehealth interventions for people with chronic pain.	Qualitative
11	Gardner et al⁵⁰ (Australia)	2022	The Effect of Adjunct Telephone Support on Adherence and Outcomes of the Reboot Online Pain Management Program: Randomized Controlled Trial	RCT
12	Geraghty et al⁵¹ (UK)	2024	Supporting self-management of low back pain with an internet intervention with and without telephone support in primary care (SupportBack 2): a randomised controlled trial of clinical and cost-effectiveness	RCT

Person-Centered, Accessible, Personal, and Facilitates Behavioral Change

There were some general attributes reported in the literature, including: developed using a person-centered approach,⁴⁴ or via collaboration of clinical experts,^36,48 personal, simple, facilitating, and sustaining behavioral change.⁴⁸ It is user-friendly,⁴⁹ uses lay language, and was offered in English and other languages (French).⁴⁵ It was web-based^37,44,47,51 and could be easily accessed,^{44,45,48,49,51} for example, via mobile devices.^44,51 The program was of 6-week duration^37,44 or 8 sessions to be completed over 16 weeks.^36,47 It aimed at managing chronic musculoskeletal conditions^36,45,47 or targeted specific conditions such as low back pain,^44,51 rheumatoid arthritis, osteoarthritis, and fibromyalgia.³⁷

Web-Based Multidisciplinary Education, Modeling, and Vicarious Learning

Lifestyle habits have a major impact on human health. Thus, self-management of these habits can enhance health.¹⁴ Participants were taught the principles of self-management, specifically to take greater initiative in their healthcare and deal with their condition through modeling of self-management skills.^{14,37,44,47,51,52} The participants engaged with a video featuring a fictional character who acquires skills to manage her chronic pain through a multidisciplinary strategy.⁴⁷ Additionally programs included information with videos, tutorials, and questions and answers about the following topics:⁴⁵ chronic pain education and management,^36,37,45,47 types of pain conditions,⁴⁵ holistic lifestyle including diet and stress and cognitive pain management^{14,36,37,45,48} such as relaxation, distraction, and self-talk;³⁷ depression;^37,45 medications;³⁷ sleep, fatigue, and energy conservation;^36,37 problem-solving;³⁷ tailored exercise program;^37,44 pacing;⁴⁷ potential barriers to physical activity;⁴⁴ cognitive symptoms management;^36,37,47 dealing with pain flare ups;⁴⁷ and community and legal services for disability status.⁴⁵ Moreover, the programs provided information about the health professionals involved in the care and their roles,^37,45 as well as information for caregivers and family members.⁴⁵ Participants were provided with a reference or help book which contained all the program content, information about medications, and drawings of exercises.³⁷ A graded web-based exercise program was included, featuring video demonstrations that highlighted key components, specifically strength, stability, flexibility, and cardiovascular exercise.^36,47 The aim of these educational sessions was to support patients to become experts in self-managing their conditions and to emphasize the evidence-based recommendations for chronic musculoskeletal pain of remaining active.³⁵

Web-Based Goal Setting, Activity Planning, Self-Monitoring, and Automated Personalized Feedback for Motivational Enhancement

Participants monitored the behavior they sought to change and set individualized attainable goals and action plans, then received personalized feedback on their progress, which further motivated them for self-directed change.^{14,36,37,44-47,51} Self-motivation requires both goal challenges and performance feedback.¹⁴ This could be done through videos on goal setting and coping skills,⁴⁵ and self-monitoring through exercise and medication logs.³⁷ In addition, automatic tailored information and feedback based on individual progress and monitoring were sent, for example, automated follow-up messages¹⁴ or pop-ups, a reward system for participation, and daily positive thoughts.^44,45 One program offered phone support by a physical therapist.^44,50,51 Real-time monitoring and feedback of behavior could be done through a mobile phone accelerometer and a global positioning system (GPS).⁴⁶ Participants had access to the web-based intervention after the program had ended, and adherence was encouraged through automated weekly email reminders.^44,51

Web-Based Social Support and Communication With Healthcare Providers

It is important to have communication with healthcare providers when their expertise is needed.¹⁴ Peer chat room and integrated email application were used for interaction with healthcare professionals.⁴⁵ Moderators who were previously trained online assisted participants by reminding them to log on, modeling action planning and problem-solving, in addition to offering encouragement and posting to the bulletin board.³⁷

Steps 5 and 6

Model cases are real-life cases representing the concept.^27,53 Contrary cases can be borderline, related, contrary, or invented cases.^27,53 The following cases were written by the author LE.

Model Case

Mrs. A is a 65-year-old retired female with chronic right knee osteoarthritis for over 10 years. During the Covid-19 Pandemic lock down, her pain progressively increased to 8/10 on the pain numeric rating scale (PNRS), until she had difficulty walking and was no longer able to take walks in the park beside her home. She had a telephone consultation with her nurse practitioner, who referred her to a 6-week online self-management support program. She felt empowered after attending the first few sessions. She learned about chronic pain and how to manage it effectively, received a personalized exercise program, and set short- and long-term goals for herself. She wanted to be able to walk for 30 minutes in the park. She logged her exercises daily and received weekly emails to encourage her progress toward her goal. She also chatted about her progress with her peers online, which motivated her to walk and exercise daily.

Contrary Case

Mr. C is a 70-year-old retired male with chronic low back pain for over 15 years. In the previous year, his pain increased to 6/10 on the PNRS and became constant without any apparent reason. He had difficulty sitting for more than 20 minutes and felts better with walking for a short distance. He consulted his family physician, who prescribed him Nonsteroidal Anti-inflamatory Drugs and advised him to stay active. Mr. C refused to take his medication, and he did not want to walk because he was afraid he would hurt his back. He wanted to have physiotherapy sessions because, in the past, he had received electrotherapy, which he believed helped him.

Step 7: Antecedents and Consequences

These are the events or situations that prompt or stimulate a concept.^27,53 The antecedents and consequences (Table 1) were identified based on the 12 included studies (Table 2).

Antecedents

Participants in eHealth self-management programs require internet access and a computer.³⁷ They also need to be able to log in to the program and fill out online questionnaires; thus,³⁷ some degree of digital health literacy is needed.⁴⁹ Limited digital health literacy reduced engagement in telehealth interventions. Participants struggled with logging in, navigating content, and often required external support.⁴⁹ The SCT of self-regulation has explanatory, predictive, and operational value in effecting change.¹⁴ The variables from the prediction model are the same ones that inform the intervention model. Therefore, the antecedents based on this theory would be self-efficacy, positive outcome expectations, and overcoming impediments.

Self-efficacy is the belief in one’s ability to achieve the desired behavior,¹⁴ in our case is effective self-management. Empowering patients with information promotes self-efficacy, favoring patient engagement in eHealth interventions for chronic pain.⁴⁹ Belief about efficacy can be influenced by experience mastery i.e., success with the new goal or behvior and vicarious learning through social models.

Positive Outcome Expectations are positive (not negative) expectations about the lifestyle habit, including physical (pleasant sensory experience and physical pleasure), social (reaction that the behavior evokes), and self-valuative (self-satisfaction).¹⁴ Outcome persuasion or belief that intervention will be effective can be achieved through patients sharing their success stories within the online program.³⁵

Overcoming Impediments is getting past barriers to behavioral change, which can be personal or related to the health system, such as the availability of resources for healthy behavior.¹⁴ This can be done online by connecting the participants with online peer support, integrated email communication⁴⁵ or telephone support by health professionals.³⁵

Consequences

Improvement in self-efficacy and some health status outcomes are a consequence of eHealth self-management support.^37,47,51 A randomized controlled trial (RCT) by Lorig et al³⁷ showed that after one year, the intervention group receiving an internet-based self-management program for arthritis and fibromyalgia had 44% improvement in (≥0.30 effect size) three or more outcomes (pain, activity limitation, global health, and health distress) compared to 30% in the usual care group (P < .001). Smith et al⁴⁷ concluded from an RCT that Reboot online multidisciplinary chronic pain self-management was significantly more effective than usual care in improving pain self-efficacy (effect size = 0.69) at post-treatment, with these improvements sustained at follow-up. It also showed greater effectiveness than usual care on secondary outcomes such as movement-based fear avoidance and pain-related disability at both post-treatment and follow-up. However, Reboot Online did not produce significant reductions in pain interference or depression compared with usual care. The program required minimal clinician involvement, and adherence was moderate, with 61% of the participants completing all eight lessons.⁴⁷ Geraghy et al⁵¹ conducted an RCT comparing usual care, SupportBack digital intervention alone, and SupportBack combined with telephone support for managing back pain. The primary outcome, measured by the Roland Morris Disability Questionnaire (RMDQ) over 12 months, showed no statistically significant difference between either SupportBack intervention and usual care at the predefined significance level (SupportBack alone: adjusted mean difference: −0.5, P = .085; SupportBack with telephone support: −0.6, P = .048). Economically, SupportBack alone was more effective and less costly than usual care, with both interventions likely cost-effective at a threshold of £20,000 per quality-adjusted life year.⁵¹

Step 8: Considering Context and Value

Phenomena and concepts alter depending on the context within which they occur, and values have different meanings for different people in different settings.³⁰ eHealth self-management support was considered in the health context. The main barriers to engaging with eHealth interventions for pain self-management included information overload, the impersonal aspect of telehealth, and a lack of flexibility in content.⁴⁹

Step 9: Empirical Referents

These are the means by which it is possible to recognize or measure the defining characteristics or attributes.^27,53 Table 3 provides a summary of commonly used indicators of eHealth self-management based on the included studies in this concept analysis.

Table 3.

Results of Step 9 Empirical Indicators.

Domain	Empirical Indicator	Reference
Disability reduction/increase in physical ability	Roland Morris Disability Questionnaire (RMDQ)	Geraghty et al^35,44,51
Disability reduction/increase in physical ability	Pain Disability Index (PDI)	Schultz et al,³⁶ Smith et al,¹⁰ and Gardner et al⁵⁰
	Health Assessment Questionnaire	Lorig et al³⁷
Activity limitations	Activities Limitation Scale	Lorig et al³⁷
Health behaviors (physical activity/stress management)	Aerobic/stretch/strength exercise/stress management (minutes per week)	Lorig et al³⁷
	International Physical Activity Questionnaire Short Form (IPAQ-SF)	Geraghty et al^35,44,51 and Gardner et al⁵⁰
	Godin Physical Activity ScaleBack-related physical activity (in days)	Geraghty et al⁵¹
Pain intensity	Numeric Rating Scale (NRS)	Geraghty et al^35,44,51
Pain intensity	Visual Analogue Scale (VAS)	Lorig et al³⁷
Pain duration	Days in pain	Geraghty et al^35,44
Pain severity and interference on daily function	The Brief Pain Inventory (BPI)	Schultz et al,³⁶ Smith et al,¹⁰ and Gardner et al⁵⁰
Acceptance of chronic pain	Chronic Pain Acceptance Questionnaire (CPAQ)	Schultz et al³⁶ and Smith et al¹⁰
Risk of persistent disability	StarT Back Screening Tool (SBST)	Geraghty et al^35,44,51
Fatigue	Visual Analog Scale (VAS)	Lorig et al³⁷
Enablement coping/satisfaction	Modified Pain Enablement Instrument (PEI)	Geraghty et al^35,44
Global improvement	Self-Rated Global Health Scale	Lorig et al³⁷
Self-efficacy	Modified Self-Efficacy for Exercise Scale (SEES)	Geraghty et al^35,44
	Self-efficacy for low back pain	Geraghty et al⁵¹
	Pain Self-Efficacy Questionnaire (PSEQ)	Schultz et al,³⁶ Smith et al,¹⁰ Gardner et al,⁵⁰ and Geraghty et al^35,44,51
	Arthritis Self-Efficacy Scale	Lorig et al³⁷
Health distress	Health Distress Scale	Lorig et al³⁷
Fear of movement	Tampa Scale of Kinesiophobia (TSK)	Geraghty et al,^35,44,51 Schultz et al,³⁶ Smith et al,¹⁰ and Gardner et al⁵⁰
Catastrophizing or negative orientation toward pain	Pain Catastrophizing Scale (PCS)	Geraghty et al,^35,44,51 Schultz et al,³⁶ and Smith et al¹⁰
Depression, anxiety, and stress	Depression Anxiety and Stress Scale-21 (DASS-21)Kessler 10-item Psychological Stress Scale (K10)Patient Health Questionnaire-9 (PHQ-9)	Schultz et al³⁶
	Kessler 10-item Psychological Stress Scale (K10)Patient Health Questionnaire-9 (PHQ-9)	Smith et al¹⁰ and Gardner et al⁵⁰
	Patient Health Questionnaire (PHQ)PHQ-4 AnxietyPHQ-4 Depression	Geraghty et al⁵¹
Beliefs about effectiveness and credibility of the interventions	Modified Credibility and Expectancy Questionnaire (CEQ)	Geraghty et al^35,44
Reasons for non-adherence to exercise	Problematic Experiences of Therapy Scale (PETS)	Geraghty et al^35,44
Quality of life	European Quality of Life Five Dimensions EuroQol (EQ-5D)	Geraghty et al^35,44,51
Patient satisfaction	Satisfaction questionnaire	Schultz et al,³⁶ Gardner et al,⁵⁰ and Geraghty et al⁵¹
Health care resource use	Medication use, number of GP visits, chiropractic, and physical therapy visits	Geraghty et al^35,44,51 and Lorig et al³⁷
Time off work	Days of work absence due to pain	Geraghty et al^35,44,51
Healthcare utilization (past 6 months)	GP/physiotherapist/secondary care consultations for back pain and back pain-related prescriptions	Geraghty et al⁵¹
	Physician/emergency/chiropractic/physical therapy visits, days in hospital	Lorig et al³⁷

Discussion

Our study provides valuable insights into eHealth self-management for chronic musculoskeletal pain in the context of the SCT. The key attributes identified were: (1) Person-centered, accessible, personal, and facilitates behavioral change; (2) Web-based multidisciplinary education, modeling, and vicarious learning; (3) Web-based goal setting, activity planning, self-monitoring, and automated personalized feedback for motivational enhancement; and (4) Web-based social support and communication with health providers. These elements form the core components of effective eHealth interventions for chronic pain management support. Our study also highlights important antecedents such as internet access, digital health literacy, self-efficacy, positive outcome expectations, and the ability to overcome impediments. These factors are crucial for successful implementation and adoption of eHealth self-management strategies. The consequences of such interventions include improved self-efficacy and other health outcomes such as pain, activity limitation, kinesiophobia, global health, and health distress, demonstrating the potential benefits of eHealth approaches. By defining eHealth self-management in the context of chronic musculoskeletal conditions, this study has established a clear framework for future research and intervention development. The theoretical foundation of the SCT provides a solid basis for understanding the mechanisms underlying eHealth self-management. The major limitation of the existing models or frameworks to inform the design of digital behavioral intervention technologies is that the parameters for use are not included,⁵⁴ which are the conditions needed for the practical application to reflect the theoretical model. These parameters, or what we defined here as the attributes, can be lost in translation, leading to an ineffective intervention.^54,55 In a scoping review, a total of 46 frameworks to validate digital behavioral change interventions were identified; only 4 (9%) incorporated theory.⁵⁶ This is the major strength of this study, since concepts are building blocks of theory; we have set a theoretical foundation for eHealth self-management support. While this study has limitations, such as not being an exhaustive systematic review, it sets the stage for developing more comprehensive eHealth and digital behavior change theories specific to chronic musculoskeletal pain self-management. Future empirical research can focus on using eHealth data to advance theories and models of behavior change⁵⁷ by testing the described concept of eHealth self-management within the SCT. This will evaluate the theoretical basis and lead to an effective personalized intervention. Future research for chronic pain applications should use a person-centered or co-design approach with end-users, people with chronic pain, including older adults, their caregivers, pain self-management experts, health technologists, and clinicians.^36,40,44 This is to ensure that the newly developed applications are rigorous, feasible, effective, and engaging for users. Smartphone applications can offer a personalized, efficient, cost-effective, and accessible pain management experience.⁴⁰ Digital platform providers should tailor their features to meet the diverse needs of different patients.⁵² Educators and policymakers should support the funding and implementation of theory-based eHealth behavioral change interventions for adults with chronic musculoskeletal pain.

Conclusion

This concept analysis clarifies and defines eHealth self-management for chronic musculoskeletal pain within the framework of SCT, offering a theoretically grounded foundation for future digital health innovation. By identifying the core attributes, antecedents, and consequences of eHealth self-management support, this study provides a structured understanding of what effective, theory-informed digital interventions should entail. The findings highlight the essential components-person-centered design, multidisciplinary web-based education, goal-setting, action planning strategies, motivational feedback, and integrated social and professional support-that underpin meaningful behavior change in digital contexts.

Footnotes

Acknowledgements

This project was undertaken in partial fulfillment of the PhD in Aging and Health at Queen’s University, Kingston, Canada. This project was partially supported by Queen’s University, Kingston, Ontario, Canada and Johns Hopkins Aramco Healthcare, Dhahran, Eastern Province, Saudi Arabia.

ORCID iD

Lina Elsabbagh

Ethical Considerations

Ethical approval was not required by the Queen’s University Research Ethics Board (REB). Research that is exempt from REB review includes research that relies exclusively on publicly available information or involves using certain documents or records for which the individual's consent has already been provided.

Author Contributions

LE and KW contributed to the conceptualization of this article. LE curated the data and drafted the manuscript. KW supervised, reviewed, and edited all versions.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors received financial support for the open access publication fees for this article. This study was supported by the Toronto Grace Health Centre and Johns Hopkins Aramco Healthcare.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Guarantor

LE.

References

WHO. Musculoskeletal Health. 2022. WHO.

Merskey

. Classification of chronic pain: descriptions of chronic pain syndromes and definitions of pain terms. Pain. 1986;3:226-226.

Treede

Rief

Barke

, et al. A classification of chronic pain for ICD-11. Pain. 2015;156:1003-1007. doi:10.1097/j.pain.0000000000000160

Barlow

Wright

Sheasby

, et al. Self-management approaches for people with chronic conditions: a review. Patient Educ Couns. 2002;48:177-187. doi:10.1016/s0738-3991(02)00032-0

Institute of Medicine Committee on the Crossing the Quality Chasm. Chapter 5, Patient self-management support. In: Adams

Greiner

Corrigan

, eds. The 1st Annual Crossing the Quality Chasm Summit: A Focus on Communities. National Academies Press (US); 2004: 57.

Campbell

Hudspith

Choinière

, et al. An Action Plan for Pain in Canada. Health Canada; 2021.

Government of Canada. eHealth. 2010. Accessed November 20, 2022. https://www.canada.ca/en/health-canada/services/health-care-system/ehealth.html

van Olmen

. The promise of digital self-management: a reflection about the effects of patient-targeted e-health tools on self-management and wellbeing. Int J Environ Res Public Health 2022;19:20220126. doi:10.3390/ijerph19031360

Bandura

Walters

. Social Learning Theory. Englewood cliffs Prentice Hall; 1977.

10.

Smith

Garcia-Torres

Coughlin

, et al. Effectiveness of social cognitive theory-based interventions for glycemic control in adults with type 2 diabetes mellitus: protocol for a systematic review and meta-analysis. JMIR Res Protoc. 2020;9:e17148. doi:10.2196/17148

11.

Bandura

. Social cognitive theory: an agentic perspective. Annu Rev Psychol. 2001;52:1-26. doi:10.1146/annurev.psych.52.1.1

12.

El-Osta

Sasco

Barbanti

, et al. Tools for measuring individual self-care capability: a scoping review. BMC Public Health. 2023;23:1312. doi:10.1186/s12889-023-16194-6

13.

Riley

Rivera

Atienza

, et al. Health behavior models in the age of mobile interventions: are our theories up to the task? Transl Behav Med. 2011;1:53-71. doi:10.1007/s13142-011-0021-7

14.

Bandura

. Health promotion from the perspective of social cognitive theory. Psychol Health. 1998;13:623-649. doi:10.1080/08870449808407422

15.

Bandura

. Self-efficacy: toward a unifying theory of behavioral change. Psychol Rev. 1977;84:191-215. doi:10.1037/0033-295X.84.2.191

16.

Bandura

. Social cognitive theory of self-regulation. Organ Behav Hum Decis Process. 1991;50:248-287. doi:10.1016/0749-5978(91)90022-L

17.

Bandura

. The self system in reciprocal determinism. Am Psychologist. 1978;33:344-358. doi:10.1037/0003-066X.33.4.344

18.

Whitham

Sterling

Lin

, et al. Social cognitive learning theory. In: Volkmar

, ed. Encyclopedia of Autism Spectrum Disorders. Springer New York; 2013 2884-2893.

19.

Beck

. Cognitive therapy: nature and relation to behavior therapy. Behav Ther. 1970;1:184-200. doi:10.1016/S0005-7894(70)80030-2

20.

Beck

. A 60-year evolution of cognitive theory and therapy. Perspect. Psychol. Sci. 2019;14:16-20. doi:10.1177/1745691618804187

21.

Beck

. Cognitive Behavior Therapy. Guilford Press; 2021.

22.

Kelly

Fullen

Martin

, et al. eHealth interventions to support self-management in people with musculoskeletal disorders, “eHealth: it’s TIME”-a scoping review. Phys Ther. 2022;102:pzab307. doi:10.1093/ptj/pzab307

23.

Varpio

Paradis

Uijtdehaage

, et al. The distinctions between theory, theoretical framework, and conceptual framework. Acad Med. 2020;95:989-994. doi:10.1097/acm.0000000000003075

24.

Reed

. eHealth: a concept analysis from a nursing perspective. Can J Nurs Informatics. 2014;9.

25.

Miller

Lasiter

Bartlett Ellis

, et al. Chronic disease self-management: a hybrid concept analysis. Nurs Outlook. 2015;63:154-161. doi:10.1016/j.outlook.2014.07.005

26.

Van de Velde

De Zutter

Satink

, et al. Delineating the concept of self-management in chronic conditions: a concept analysis. BMJ Open. 2019;9:e027775. doi:10.1136/bmjopen-2018-027775

27.

McKenna

Pajinkihar

Murphy

. Fundamentals of Nursing Models, Theories and Practice. PublisherJohn Wiley & Sons, Inc.; 2014.

28.

McKenna

. Nursing Theories and Models. 1st ed. Routledge; 1997.

29.

Walker

Avant

. Strategies for Theory Construction in Nursing. 6th ed. Pearson; 2019.

30.

Cutcliffe

McKenna

. The Essential Concepts of Nursing. Elsevier; 2005.

31.

Miles

. Biopsychosocial model. In: Gellman

Turner

, eds. Encyclopedia of Behavioral Medicine. Springer New York;2013:227-228.

32.

Morton

Dennison

May

, et al. Using digital interventions for self-management of chronic physical health conditions: a meta-ethnography review of published studies. Patient Educ Couns. 2017;100:616-635. doi:10.1016/j.pec.2016.10.019

33.

Liu

Cai

, et al. The efficacy of e-health in the self-management of chronic low back pain: a meta analysis. Int J Nurs Stud. 2020;106:103507. doi:10.1016/j.ijnurstu.2019.103507

34.

Rejula

Anitha

Belfin

, et al. Chronic pain treatment and digital health era-an opinion. Front Public Health. 2021;9:779328. doi:10.3389/fpubh.2021.779328

35.

Geraghty

AWA

Stanford

Stuart

, et al. Using an internet intervention to support self-management of low back pain in primary care: findings from a randomised controlled feasibility trial (SupportBack). BMJ Open. 2018;8:e016768. doi:10.1136/bmjopen-2017-016768

36.

Schultz

Smith

Newby

, et al. Pilot trial of the reboot online program: an internet-delivered, multidisciplinary pain management program for chronic pain. Pain Res Manag. 2018;2018:9634727. doi:10.1155/2018/9634727

37.

Lorig

Ritter

Laurent

, et al. The internet-based arthritis self-management program: a one-year randomized trial for patients with arthritis or fibromyalgia. Arthritis Rheum. 2008;59:1009-1017. doi:10.1002/art.23817

38.

Fioratti

Miyamoto

Fandim

, et al. Feasibility, usability, and implementation context of an internet-based pain education and exercise program for chronic musculoskeletal pain: pilot trial of the ReabilitaDOR program. JMIR Form Res. 2022;6:e35743. doi:10.2196/35743

39.

Alhussein

Hadjileontiadis

. Digital health technologies for long-term self-management of osteoporosis: systematic review and meta-analysis. JMIR Mhealth Uhealth. 2022;10:e32557. doi:10.2196/32557

40.

Dunham

Bonacaro

Schofield

, et al. Smartphone applications designed to improve older people’s chronic pain management: an integrated systematic review. Geriatrics. 2021;6:40. doi:10.3390/geriatrics6020040

41.

Gee

Greenwood

Paterniti

, et al. The eHealth enhanced chronic care model: a theory derivation approach. J Med Internet Res. 2015;17:e86. doi:10.2196/jmir.4067

42.

Shirish

. Positive Technology: A Growing Market With a Potential to Rebuild a Resilient Society During and After the COVID-19 Crisis. IGI Global; 2021:293.

43.

Yardley

Spring

Riper

, et al. Understanding and promoting effective engagement with digital behavior change interventions. Am J Prev Med. 2016;51:833-842. doi:10.1016/j.amepre.2016.06.015

44.

Geraghty

Stanford

Little

, et al. Using an internet intervention to support self-management of low back pain in primary care: protocol for a randomised controlled feasibility trial (SupportBack). BMJ Open. 2015;5:e009524. doi:10.1136/bmjopen-2015-009524

45.

Gogovor

Visca

Auger

, et al. Informing the development of an Internet-based chronic pain self-management program. Int J Med Inform. 2017;97:109-119. doi:10.1016/j.ijmedinf.2016.10.005

46.

Rosser

McCullagh

Davies

, et al. Technology-mediated therapy for chronic pain management: the challenges of adapting behavior change interventions for delivery with pervasive communication technology. Telemed J E Health. 2011;17:211-216. doi:10.1089/tmj.2010.0136

47.

Smith

Faux

Gardner

, et al. Reboot online: a randomized controlled trial comparing an online multidisciplinary pain management program with usual care for chronic pain. Pain Med. 2019;20:2385-2396. doi:10.1093/pm/pnz208

48.

Varsi

Ledel Solem

Eide

, et al. Health care providers’ experiences of pain management and attitudes towards digitally supported self-management interventions for chronic pain: a qualitative study. BMC Health Serv Res. 2021;21:275. doi:10.1186/s12913-021-06278-7

49.

Fernandes

Devan

Fioratti

, et al. At my own pace, space, and place: a systematic review of qualitative studies of enablers and barriers to telehealth interventions for people with chronic pain. Pain. 2022;163:e165-e181. doi:10.1097/j.pain.0000000000002364

50.

Gardner

Schultz

Haskelberg

, et al. The effect of adjunct telephone support on adherence and outcomes of the reboot online pain management program: randomized controlled trial. J Med Internet Res. 2022;24:e30880. doi:10.2196/30880

51.

Geraghty

AWA

Becque

Roberts

, et al. Supporting self-management of low back pain with an internet intervention with and without telephone support in primary care (SupportBack 2): a randomised controlled trial of clinical and cost-effectiveness. Lancet Rheumatol. 2024;6:e424-e437. doi:10.1016/S2665-9913(24)00086-9

52.

Cenamor

. Use of health self-management platform features: the case of a specialist ehealth app. Technol Forecast Soc Change. 2022;185:122066. doi:10.1016/j.techfore.2022.122066

53.

Walker

Avant

. Strategies for Theory Construction in Nursing. 5th ed. Prentice Hall; 2011.

54.

Mohr

Schueller

Montague

, et al. The behavioral intervention technology model: an integrated conceptual and technological framework for eHealth and mHealth interventions. J Med Internet Res. 2014;16:e146. doi:10.2196/jmir.3077

55.

Crutzen

. The behavioral intervention technology model and intervention mapping: the best of both worlds. J Med Internet Res. 2014;16:e188. doi:10.2196/jmir.3620

56.

Carbonnel

Ninot

. Identifying frameworks for validation and monitoring of consensual behavioral intervention technologies: narrative review. J Med Internet Res. 2019;21:e13606. doi:10.2196/13606

57.

Michie

Yardley

West

, et al. Developing and evaluating digital interventions to promote behavior change in health and health care: recommendations resulting from an international workshop. J Med Internet Res. 2017;19:e232. doi:10.2196/jmir.7126

Delineating the Concept of eHealth Self-Management for Chronic Musculoskeletal Pain: A Concept Analysis in the Context of the Social Cognitive Theory

Abstract

Background:

Objective:

Methods:

Results:

Conclusion:

Keywords

Introduction

Methods

Search Strategy

Results

Steps 3: Uses of the Concept

Step 4: The Attributes

Person-Centered, Accessible, Personal, and Facilitates Behavioral Change

Web-Based Multidisciplinary Education, Modeling, and Vicarious Learning

Web-Based Goal Setting, Activity Planning, Self-Monitoring, and Automated Personalized Feedback for Motivational Enhancement

Web-Based Social Support and Communication With Healthcare Providers

Steps 5 and 6

Model Case

Contrary Case

Step 7: Antecedents and Consequences

Antecedents

Consequences

Step 8: Considering Context and Value

Step 9: Empirical Referents

Discussion

Conclusion

Footnotes

Acknowledgements

ORCID iD

Ethical Considerations

Author Contributions

Funding

Declaration of Conflicting Interests

Guarantor

References