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Digital Pain Assessment and Management (164) Registered Report (560) Web-based and Mobile Health Interventions (3297) Health Care Quality and Health Services Research (271) Tools, Programs and Algorithms (266)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/47284, first published .
Short-Term Findings From Testing EPIO, a Digital Self-Management Program for People Living With Chronic Pain: Randomized Controlled Trial

Short-Term Findings From Testing EPIO, a Digital Self-Management Program for People Living With Chronic Pain: Randomized Controlled Trial

Short-Term Findings From Testing EPIO, a Digital Self-Management Program for People Living With Chronic Pain: Randomized Controlled Trial

Authors of this article:

Katrine Bostrøm1, 2 Author Orcid Image ;   Elin Børøsund1, 3 Author Orcid Image ;   Hilde Eide1, 4 Author Orcid Image ;   Cecilie Varsi1, 5 Author Orcid Image ;   Ólöf Birna Kristjansdottir1, 6, 7 Author Orcid Image ;   Karlein M G Schreurs8 Author Orcid Image ;   Lori B Waxenberg9 Author Orcid Image ;   Karen E Weiss10 Author Orcid Image ;   Eleshia J Morrison10 Author Orcid Image ;   Hanne Stavenes Støle1, 11 Author Orcid Image ;   Milada Cvancarova Småstuen12 Author Orcid Image ;   Audun Stubhaug2, 13, 14 Author Orcid Image ;   Lise Solberg Nes1, 2, 10 Author Orcid Image
Article Authors Cited by (3) Tweetations (4) Metrics

    Original Paper

    1Department of Digital Health Research, Division of Medicine, Oslo University Hospital, Oslo, Norway

    2Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway

    3Department of Nursing and Health Sciences, Faculty of Health and Social Sciences, University of South-Eastern Norway, Drammen, Norway

    4Centre for Health and Technology, Faculty of Health and Social Sciences, University of South-Eastern Norway, Drammen, Norway

    5Faculty of Health and Social Sciences, University of South-Eastern Norway, Drammen, Norway

    6Norwegian National Advisory Unit on Learning and Mastery in Health, Oslo University Hospital, Oslo, Norway

    7Mental Health Team West, Primary Care of the Capital area, Reykjavik, Iceland

    8Department of Psychology, Health & Technology, University of Twente, Enschede, Netherlands

    9Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States

    10Department of Psychiatry and Psychology, College of Medicine and Science, Mayo Clinic, Rochester, MN, United States

    11Department of Child Health and Development, Norwegian Institute of Public Health, Oslo, Norway

    12Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway

    13Department of Pain Management and Research, Oslo University Hospital, Oslo, Norway

    14Regional Advisory Unit on Pain, Oslo University Hospital, Oslo, Norway

    Corresponding Author:

    Lise Solberg Nes, PhD

    Department of Digital Health Research

    Division of Medicine

    Oslo University Hospital

    Pb 4950 Nydalen

    Oslo, N-0424

    Norway

    Phone: 47 91332341

    Email: lise.solberg.nes@rr-research.no


    Background: Chronic pain conditions involve numerous physical and psychological challenges, and while psychosocial self-management interventions can be of benefit for people living with chronic pain, such in-person treatment is not always accessible. Digital self-management approaches could improve this disparity, potentially bolstering outreach and providing easy, relatively low-cost access to pain self-management interventions.

    Objective: This randomized controlled trial aimed to evaluate the short-term efficacy of EPIO (ie, inspired by the Greek goddess for the soothing of pain, Epione), a digital self-management intervention, for people living with chronic pain.

    Methods: Patients (N=266) were randomly assigned to either the EPIO intervention (n=132) or a care-as-usual control group (n=134). Outcome measures included pain interference (Brief Pain Inventory; primary outcome measure), anxiety and depression (Hospital Anxiety and Depression Scale), self-regulatory fatigue (Self-Regulatory Fatigue 18 scale), health-related quality of life (SF-36 Short Form Health Survey), pain catastrophizing (Pain Catastrophizing Scale), and pain acceptance (Chronic Pain Acceptance Questionnaire). Linear regression models used change scores as the dependent variables.

    Results: The participants were primarily female (210/259, 81.1%), with a median age of 49 (range 22-78) years and a variety of pain conditions. Analyses (n=229) after 3 months revealed no statistically significant changes for the primary outcome of pain interference (P=.84), but significant reductions in the secondary outcomes of depression (mean difference −0.90; P=.03) and self-regulatory fatigue (mean difference −2.76; P=.008) in favor of the intervention group. No other statistically significant changes were observed at 3 months (all P>.05). Participants described EPIO as useful (ie, totally agree or agree; 95/109, 87.2%) and easy to use (101/109, 92.7%), with easily understandable exercises (106/109, 97.2%).

    Conclusions: Evidence-informed, user-centered digital pain self-management interventions such as EPIO may have the potential to effectively support self-management and improve psychological functioning in the form of reduced symptoms of depression and improved capacity to regulate thoughts, feelings, and behavior for people living with chronic pain.

    Trial Registration: ClinicalTrials.gov NCT03705104; https://clinicaltrials.gov/ct2/show/NCT03705104

    J Med Internet Res 2023;25:e47284

    doi:10.2196/47284

    Keywords

    chronic pain; self-management; digital health; efficacy; cognitive behavioral therapy; acceptance and commitment therapy 


    Background

    Chronic pain conditions are common and can be disabling and distressing [1,2]. People living with chronic pain face daily challenges related to symptom management, activities, roles, and relationships, and coping with the emotional and sometimes cognitive impact of pain can be taxing. The many demands of living with chronic pain may also affect the ability to self-regulate (ie, regulate thoughts, emotions, and behavior) [3-5]. Given the complexity of chronic pain and living with chronic pain, a multidisciplinary treatment approach for pain management, aimed at supporting people in coping with physiological as well as psychological challenges, is recommended [2,6,7].

    Psychological treatment approaches such as cognitive behavioral therapy (CBT) [8,9] and acceptance and commitment therapy (ACT) [10,11] have shown to be beneficial for pain across classification and pain etiology, with links to reductions in symptoms of pain, distress, depression, and anxiety, as well as improved quality of life and pain acceptance [12,13]. Regrettably, such evidence-based in-person psychological treatments are not always offered or accessible [7,14], with barriers to treatment including availability, financial costs, distance to treatment site, transportation issues, and personal preferences [15,16]. The limited availability of in-person psychological interventions for chronic pain, combined with individual barriers to engagement with such interventions, highlights the need to expand the care and delivery of psychosocial support for people living with chronic pain.

    Digital solutions may have the potential to improve outreach and provide easy and relatively low-cost access to pain self-management interventions [17,18]. Existing digital pain self-management interventions have, for example, shown promising results in meeting unfulfilled needs, supporting psychological well-being, and improving health-related quality of life (HRQoL) [17-20]. However, there are several challenges with the existing digital pain management programs. Most such programs appear to focus on providing information about pain and collecting and monitoring data (eg, tracking medication use) and often lack a theoretical basis, such as evidence-based psychosocial interventional aspects [21,22]. Many existing digital interventions for pain self-management also lack user-centeredness, with the limited involvement of end users (ie, people with chronic pain) and health care providers in the development process [21,23,24]. In addition, attrition has surfaced as a serious challenge with digital interventions, highlighting the need for ways to bolster intervention adherence when designing and developing these interventions [25,26]. Finally, rigorous efficacy testing to establish efficacy appears to be scarce in the existing digital pain self-management interventions [17,27], with subsequent limited evidence of actual implementation into clinical care after study completion [22,28].

    In response to the identified limitations in existing digital pain self-management interventions, we designed, developed, and feasibility pilot-tested EPIO (ie, inspired by the Greek goddess for the soothing of pain, Epione), a digital pain self-management intervention aimed at supporting patients living with chronic pain [29-33]. Although the type, form, and intensity of pain may differ depending on the condition and diagnosis (eg, neck or back pain, fibromyalgia, or trigeminal neuralgia) and other individual factors, EPIO was developed aiming to target chronic pain in general (ie, across pain classification and etiology) based on user input and the CBT- and ACT-based treatment approaches for people living with chronic pain. The EPIO intervention was designed and developed in close collaboration with researchers, eHealth experts, end users, and health care providers [29-31].

    In line with recommendations from the Medical Research Council (MRC) framework for the evaluation of complex interventions [34,35], a feasibility pilot study was conducted, identifying EPIO as useful and easy to use, with excellent user satisfaction [32]. In line with the MRC guidelines, a qualitative study examining participants’ experiences when engaging with EPIO complemented the feasibility pilot findings, pointing to engagement factors such as the motivation to learn, fostering joy and enthusiasm, and personalization, as well as factors related to coping with pain in everyday life (eg, awareness and need for practice) and the value of engaging with EPIO (eg, EPIO—a friend and making peace with the presence of pain) [33]. Program optimization based on user feedback [32,33] was conducted before embarking on efficacy testing in a randomized controlled trial (RCT).

    Objectives

    This study aimed to examine short-term (ie, 3 months) efficacy findings from an RCT testing the digital pain self-management intervention program EPIO. It was hypothesized that participants receiving EPIO, compared with participants in a care-as-usual control group, would experience significant improvements in primary (ie, pain interference) and secondary (ie, depression, anxiety, self-regulatory fatigue, HRQoL, pain catastrophizing, and pain acceptance) outcomes after 3 months of access to the EPIO program. System use, usefulness, and ease of use were examined on an exploratory basis.


    Study Design

    A 2-armed RCT was used with participants randomly assigned to (1) the digital pain self-management intervention program EPIO or (2) a care-as-usual control group.

    Participants and Recruitment

    Participants were people living with chronic pain, recruited from November 2019 to February 2021 through a major medical institution (Oslo University Hospital, Norway), collaborating with local health care services and primary care practices, social media channels, and patient organizations’ web pages. The eligibility criteria were as follows: (1) living with chronic pain in general (ie, across pain classification and etiology); (2) pain duration ≥3 months (ie, self-reported); (3) age ≥18 years; (4) access to a smartphone or tablet; (5) ability to understand oral and written Norwegian; and (6) ability to attend an in-person introduction session either at a nearby health care facility or by using a secure video link (ie, due to pandemic restrictions as of spring 2020). The exclusion criteria included cancer-related pain, migraine, and untreated severe psychological illness (eg, psychosis), all of which were self-reported.

    Study Procedure

    Patients living with chronic pain were verbally informed about the EPIO study by collaborating partners or through flyers at various health care sites. If interested, the patient’s contact information was forwarded to the project team, who then provided additional information about study participation to those interested. Patients could also contact the study staff directly through a study phone number or website. Study information was also published on social media (eg, Facebook), and these posts were frequently republished by individuals or patient organizations.

    All participants provided written informed consent before completing the baseline outcome measures through a secure research server at the Services for Sensitive Data (Tjenester for Sensitive Data [TSD]; University of Oslo). Randomization, either to the EPIO intervention or the care-as-usual control group, was computerized (ie, using the R-tool software program, locally developed by the Department of Digital Health Research at Oslo University Hospital) and stratified by sex, with study arms 1:1 and a block size of 20. As participants were either assigned to receive the EPIO intervention or not, participation could not be blinded once group assignment was completed.

    The intervention group participated in a face-to-face introduction session where they received (1) an introduction to the EPIO intervention program, (2) help to download the EPIO app from the Apple App Store or Google Play Store, and (3) guidance on how to use the program. The introduction session was initially planned to be an in-person session but was also provided digitally (ie, videoconference) as of spring 2020 due to the ongoing COVID-19 pandemic.

    The study period was 3 months after the introduction session. The intervention group received follow-up phone calls from the members of the project team at approximately 3 and 7 weeks with standard questions about status (eg, to see how the use of the EPIO program was going and whether they had any use-related questions). The project team could also be contacted through the project study phone for questions or technical assistance. System use was logged, and outcome measures were completed through the secure research server. Participants completing at least 6 (67%) of the total 9 EPIO modules during the study period were defined as program completers for study purposes [32,36].

    The EPIO Intervention Program

    The app-based EPIO program consists of 9 CBT-based modules with aspects of ACT, each combining educational information (eg, thought challenges, coping strategies, values, and activity pacing) with practical and related exercises (eg, diaphragmatic breathing, progressive muscle relaxation, visualization, and mindfulness) for people living with chronic pain [30]. The 9 modules include information about (1) pain; (2) balance; (3) thoughts and feelings; (4) stress and coping; (5) what is important to me (ie, values); (6) behaviors and lifestyle; (7) communication, relations, and social support; (8) coping during difficult times; and (9) summary and the road ahead [30]. Through the EPIO program, progression is supported and guided by the EPIOS bird, an avatar accompanying the participants and their program progression. Participants are encouraged to practice content frequently to familiarize themselves with the psychoeducational content and exercises, and a 3-day practice mode delay has therefore been incorporated between modules (ie, from completing 1 module until the next module becomes available). The first 5 EPIO modules are sequential owing to the educational structure, while the participants can choose the order of modules 6 to 8 to allow for individualization. As participants proceed through the program, they can also create a list of favorites by highlighting exercises and topics, and they can opt to receive reminders based on their needs and preferences. EPIO also allows the user to choose between reading and listening or both, and there is an option to use the program offline. More detailed information about the design and development of the EPIO intervention program has been presented elsewhere [29-31]. See Figure 1 for screenshots of the EPIO program.

    ‎
    Figure 1. Example screenshots of the EPIO intervention program.

    Care-As-Usual Control Group

    Participants in the care-as-usual control group completed the study outcome measures at baseline and at 3 months, without any additional follow-up from the research team. The project team did not seek to monitor or control any type of additional care potentially sought by participants in the care-as-usual control group during the study period. If interested, participants in the control group received access to the EPIO program after the completion of the study (ie, 12 months).

    Data Collection and Outcome Measures

    All questionnaires and outcome measures were completed by the participants (ie, self-report) using the secure TSD server. Participants completed a study-specific sociodemographic and disease-related questionnaire at baseline, and outcome measures were collected at baseline (ie, before randomization) and at 3 months (ie, follow-up) after the introduction session.

    Psychosocial Outcome Measures
    Primary Outcome

    Pain interference was measured using the short version of the Brief Pain Inventory (BPI) [37]. The BPI consists of 7 items and measures the impact of pain on daily functioning. The questionnaire has been validated in a Norwegian chronic pain population sample [38] and has acceptable internal consistency and reliability [37]. The BPI score ranges from 0 to 10, with higher scores indicating greater pain interference.

    Secondary Outcomes

    Symptoms of anxiety and depression were measured using the Hospital Anxiety and Depression Scale (HADS) [39]. The HADS consists of 14 items measuring anxiety (7 items) and depressive (7 items) symptomatology. The questionnaire has acceptable internal consistency and reliability and has been validated in Norwegian primary care patients and the general population [40]. The HADS scores range from 0 to 21 for both scales, with higher scores indicating a higher presence of symptoms of anxiety or depression.

    Self-regulatory fatigue was measured using the Self-Regulatory Fatigue 18 scale [41]. The Self-Regulatory Fatigue 18 consists of 18 items measuring self-regulatory capacity with cognitive, emotional, and behavioral components. 8 items are positively phrased (eg, I rarely get frustrated, I have no trouble making decisions, and I handle stress well), and 10 items are negatively phrased (eg, I experience repeated unpleasant thoughts, I feel moody, and I get easily upset). The measure has been translated and tested in Norwegian cancer populations [42,43] and has acceptable internal consistency and reliability [41], and the score ranges from 18 to 90, with higher scores indicating higher self-regulatory fatigue.

    HRQoL was assessed using the noncommercial SF-36 Short Form Health Survey (RAND-36) [44,45]. The RAND-36 consists of 36 items measuring physical, role, emotional, cognitive, and social functions as well as physical, general, and global health. The questionnaire has acceptable internal consistency and reliability and has been validated in a Norwegian population sample with chronic pain [45]. The RAND-36 scores range from 0 to 100, with higher subscale scores indicating improved HRQoL.

    Pain catastrophizing was measured using the Pain Catastrophizing Scale (PCS) [46]. The PCS consists of 13 items measuring catastrophic thinking and maladaptive responses to pain, with 3 subscales measuring helplessness, magnification, and rumination. The questionnaire has acceptable internal consistency and reliability and has been validated in a Norwegian population sample with chronic pain [47]. The PCS score ranges from 0 to 52, with higher scores indicating a greater presence of catastrophic thoughts and feelings about pain.

    Pain acceptance was measured using the short form of the Chronic Pain Acceptance Questionnaire (CPAQ)–8 [48]. The CPAQ-8 consists of 8 items measuring pain acceptance, with 4 items gauging pain willingness and the other 4 activity engagement [48]. The questionnaire has acceptable internal consistency and reliability and has been validated in a Norwegian population sample with chronic pain [49]. The CPAQ-8 score ranges from 0 to 24, with higher scores indicating a higher acceptance of pain.

    System Use, Usefulness, and Ease of Use

    Details related to use and program progress (ie, system use) were collected automatically, stored on a secure research server (ie, TSD), and later extracted in accordance with informed consent and existing safety and privacy regulations. At the 3-month follow-up, participants completed a 6-item study-specific questionnaire related to usefulness and ease of use based on the research team’s previous experience [30,42], inspired by Davis [50]. Participants completed 3 questions rating acceptability and feasibility on a scale from 1 to 5 (ranging from totally agree to totally disagree) and 3 open-ended questions regarding preferences, perceived usefulness, ease of use, and further suggestions. The System Usability Scale (SUS), examining system and program usability [50,51] on a 10-item 5-point scale with options ranging from strongly disagree to agree, was also completed at 3 months. The scores were summarized and multiplied by 2.5, yielding a value range of 0 to 100. Scores above 80.3 are considered excellent.

    Power Analysis and Sample Estimates

    eHealth interventions for comparable samples have shown a Cohen d effect size of 0.30 to 0.40 on the primary outcome (ie, pain interference) [20,52]. On the basis of these findings, a sample size for the intervention effect on pain interference was calculated to allow the detection of Cohen d=0.4, with a Cronbach α value of .05 and 80% power (based on a 2 tailed t test). This study included 200 participants. To account for attrition and allow for adequate power in secondary outcome analyses, 266 participants were included.

    Statistical Analyses

    Baseline characteristics, usefulness, ease of use, and user patterns were summarized with means and SDs for normally distributed variables and medians and ranges for variables with skewed distributions. The type of distribution was assessed using the visual inspection of histograms and q-q plots and by comparing means and medians. Categorical data were presented as counts and percentages. Change scores, defined as a difference between scores at baseline and at 3 months, were calculated for pain interference, anxiety, depression, self-regulatory fatigue, HRQoL, pain catastrophizing, and pain acceptance and used as dependent variables in linear regression models. As statistically significant differences were observed between the intervention and care-as-usual control groups for age and years living with pain, these variables were included in the intention-to-treat analysis as possible confounders. We included only individuals with data on both assessment points; thus, there were no missing data and no imputation of missing values was necessary. Model fit was tested using visual inspection of the residual plots of histograms, and the model fit was satisfactory for all the presented variables (ie, change scores). P values <.05 were considered statistically significant. The results are presented as estimated mean difference (MD) in change with 95% CI and effect size (ie, standardized coefficients β) [53]. All the presented CIs for model estimates were derived using bootstrapping with bias-corrected and accelerated correction (95% CI). Statistical analyses were performed using SPSS software (release 25; IBM Corp).

    Ethics Approval, Informed Consent, and Participation

    The study was registered in ClinicalTrials.gov (NCT03705104) and approved by the Regional Committee for Medical and Health Research Ethics (REK 2018/8911) and the Hospital Privacy Protection Committee (ie, institutional review board equivalent; PVO 2017/6697). The study method and results were reported following the CONSORT-EHEALTH (Consolidated Standards of Reporting Trials of Electronic and Mobile Health Applications and Online Telehealth) checklist of information to include when reporting an RCT (Multimedia Appendix 1).

    All potential participants received information about the nature of the study and details about study participation before deciding whether to participate. All study participants provided written informed consent. The signed consent forms were stored separately from any study data under lock and key in a separate departmental cabinet. Data from questionnaires and outcome measures were collected through the secure TSD platform throughout the study, and all personally identifiable information was deidentified from the TSD database before exporting the study data to a local secure server for further analysis. The participants did not receive compensation for participation in the study.


    Sample Description

    An overview of recruitment and retention details from baseline to the 3-month follow-up is shown in the trial recruitment and participant flowchart in Figure 2. A total of 266 participants living with chronic pain were enrolled in the study. In all, 7 participants allocated to the intervention group withdrew from the study or did not respond before the introduction session and were therefore excluded from the study. The final study sample was 259, with 125 participants in the intervention group and 134 in the control group.

    ‎
    Figure 2. Recruitment and participant flow.

    Most participants (174/259, 67.2%) were recruited through social media, personally contacting the project via the study phone or website, and the remainder were recruited through collaboration with health care sites. The 259 participants were primarily White (n=251, 96.9%), with a median age of 49 (range 22-78) years at inclusion, and were mainly female (210/259, 81.1%). Most of the participants reported being on sick leave or disability benefits (186/259, 71.8%) at the time of enrollment and reported having lived with pain for ≥10 years (158/259, 61%). See Table 1 for an overview of baseline sociodemographic and disease-related characteristics.

    There were statistically significant differences between the groups in terms of age (ie, intervention group participants were significantly older than those in the control group; 49.2 years vs 46.0 years, respectively; P=.02) and years lived with pain (ie, more participants in the intervention group reported having lived with pain for ≥10 years than those in the control group; 91 participants vs 72 participants, respectively; P=.007). There were no other statistically significant differences between the groups in terms of sociodemographic and disease-related variables (all P>.05). Of the 259 participants, 229 (88.4%) completed the outcome measures at the 3-month follow-up (intervention group, n=109, 47.6%; control group, n=120, 52.4%). See Figure 2 for details regarding the final number of participants. Of the 109 participants in the intervention group, 39 (35.8%) had received the introduction session in person and 70 (64.2%) received it through a secure video link owing to the COVID-19 pandemic restrictions.

    Table 1. Baseline sociodemographic and disease-related characteristics (n=259).
    CharacteristicsIntervention (n=125)Control (n=134)P value
    Age (years), median (range)50 (26-74)48 (22-78).02
    Sex, n (%).64

    		Female103 (82.4)107 (79.8)

    		Male22 (17.6)27 (20.1)
    Marital status, n (%).30

    		Married or cohabitating79 (63.2)93 (69.4)

    		Single or divorced46 (36.8)41 (30.6)
    Education, n (%).49

    		Elementary or high school51 (40.8)58 (43.3)

    		University or college <4 years57 (45.6)48 (35.8)

    		University or college >4 years24 (19.2)26 (19.4)
    Employment, n (%).58

    		Full-time work or part-time work30 (24)27 (20.1)

    		Sick leave or disability benefits86 (68.8)100 (74.6)

    		Retired or others9 (7.2)7 (5.2)
    Income status (€a), n (%).92

    		<40,00028 (22.4)26 (19.4)

    		>40,000 to 60,00019 (15.2)22 (16.4)

    		>60,000 to 80,00032 (25.6)31 (23.1)

    		>80,000 to 100,00024 (19.2)31 (23.1)

    		>100,00022 (17.6)24 (17.9)
    Self-reported pain conditionsb, n (%)

    		Unspecific musculoskeletal pain27 (21.6)32 (23.9).77

    		Unspecified disk disorder15 (12)16 (11.9).93

    		Osteoarthritis22 (17.6)26 (19.4).87

    		Rheumatoid arthritis16 (12.8)13 (9.7).43

    		Fibromyalgia52 (41.6)54 (40.3).70

    		Neuropathic pain10 (8)9 (6.7).81

    		After injury or surgery12 (9.6)11 (8.2).58

    		Other23 (18.4)18 (13.4).16
    Years living with pain, n (%).03

    		>312 (9.6)22 (16.4).14

    		3-57 (5.6)17 (12.7).06

    		5-1020 (16)23 (17.2).62

    		>1091 (72.8)72 (53.7).007

    a€1 is approximately US $1.1 and approximately 10 Norwegian kroner (as of spring 2022).

    bParticipants could report having several types of self-reported pain conditions.

    Group Differences

    Primary and secondary outcomes, assessing between-group differences in changes from baseline to the 3-month follow-up, are reported in Multimedia Appendix 2. There was no statistically significant difference between the intervention and control groups in the primary outcome of pain interference on function at the 3-month follow-up (P=.84). However, there were significant reductions in symptoms of depression (ie, MD −0.90; P=.03) and self-regulatory fatigue (ie, MD −2.76; P=.008) in the EPIO intervention group compared with the care-as-usual control group. There were no statistically significant between-group differences in anxiety, pain acceptance, pain catastrophizing, or HRQoL at 3 months (all P>.05); a trend was observed for the HRQoL subscale of vitality (MD 4.25; P=.05) and the Chronic Pain Acceptance Item of Activity Engagement (MD 0.08; P=.08; Multimedia Appendix 2), but these results were not statistically significant.

    System Use, Usefulness, and Ease of Use

    In total, 63 of 125 (50.4%) participants in the intervention group completed at least 6 of the 9 modules within the 3-month study period and were considered completers. There were no significant differences in sociodemographic variables or outcome measures (ie, MD in change) between program completers and noncompleters (all P>.05). Participants (n=109) completing outcome measures at the 3-month follow-up described the program as useful (ie, totally agree or agree; 95/109, 87.2%) and easy to use (101/109, 92.7%) and as having easily understandable exercises (106/109, 97.2%). Participants particularly reported appreciating the exercises (eg, diaphragmatic breathing), the combination of theory and interconnected exercises, encouraging messages (eg, set reminders), and the functionality of being able to choose between listening and reading. The median system usability (ie, SUS) score was 92.5 (range 32.5-100), indicating excellent (ie, grade A; SUS score >80.3) system usability.


    Principal Findings

    In this RCT, people with chronic pain having access to the digital pain self-management intervention EPIO, compared with care-as-usual controls, did not report statistically significantly different changes in the primary outcome of pain interference on function after 3 months nor in the secondary outcomes of anxiety, HRQoL, pain catastrophizing, or pain acceptance. However, participants in the intervention group reported a significant decrease in symptoms of depression and self-regulatory fatigue compared with care-as-usual controls, indicating that people living with chronic pain may benefit from having access to evidence-informed, user-centered digital self-management programs, such as EPIO, specifically in terms of improving mood and self-regulation in the context of chronic pain.

    With pain not being a single entity, rather a subjective, individual experience with substantial heterogeneity (eg, depending on the pain condition and diagnosis, individual, available treatment, and culture), finding adequate pain treatment and conducting successful pain trials have long been a conundrum [54,55]. The fact that this short-term RCT did not show statistical significance for the primary outcome of pain interference on function does not necessarily mean that there was no impact on pain-related aspects in the intervention group. Although it is a validated and reliable multidimensional instrument, the global estimates for the BPI [37] may not be sensitive enough to assess the interference of pain (ie, general activity, walking, work, mood, enjoyment of life, relations with others, and sleep) in a heterogeneous study population, as in this study. Effect sizes in this study were also small (β<.2), even with statistical significance, but the data variability was large, which may indicate that although some participants did not seem to benefit from EPIO with respect to all outcome measures, others may have benefited greatly from the intervention. Interventions can also have clinical importance despite not reaching statistical significance, and aspects such as data variability and sample size may influence statistical results [56].

    The numerous physical and psychosocial challenges associated with living with chronic pain may naturally contribute to symptoms of depression [57,58]. In-person psychosocial interventions, particularly CBT-based interventions, can be effective in helping decrease the symptoms of depression [59], and this is also the case for CBT- and ACT-based interventions for people living with chronic pain [8,13]. Recently, digital CBT-based self-management programs have also shown promise in positively impacting depression [60,61]. The CBT-based content with aspects of ACT in EPIO [30] may as such have contributed to much of the significant decrease in symptoms of depression in the intervention group in this study. It is also possible that the way the EPIO program has been designed in line with existing recommendations [23,24], based on end-user and stakeholder input [30], plays a role in the perception, use, and ultimate effect of the program. The blended care delivery model used in this study, although simple, promoting knowledge that the research team is only one phone call or text message away, could also have provided a sense of connectedness with the research team for the participants [62,63]. This was also underlined when examining aspects of engagement with EPIO, with participants describing the EPIO program as fostering communication and social support [33].

    Participants receiving EPIO, compared with participants in the care-as-usual control group, also reported a statistically significant decrease in self-regulatory fatigue after 3 months of use. Self-regulation, that is, a person’s ability to control or regulate cognitions, emotions, behavior, and to some extent physiology, is a vital part of life and the self [64,65]. However, depending on demands (eg, situational and environmental), self-regulatory capacity appears to be a limited resource that may be depleted or fatigued [65,66]. People living with chronic pain conditions face several physical (eg, pain and fatigue), behavioral (eg, overdoing or underdoing activities), cognitive (eg, rumination and worry), and emotional (eg, anxiety and depression) challenges, all of which entail a need for self-control or self-regulation [3], and people living with chronic pain conditions may, because of their condition, be susceptible to self-regulatory fatigue, perhaps even persistent self-regulatory fatigue [4].

    Considering the possibility that self-regulatory fatigue may be a missing link in comprehending the complex aspects of chronic pain conditions [5], finding ways to improve self-regulatory capacity is vital [3-5,67]. This could potentially be done by aiming to target areas such as stress management, cognitive behavioral aspects, physical activity, nutrition, and sleep [5,68]. The CBT- and ACT-based EPIO intervention contains a balance between educational (eg, coping, problem solving, thought challenges, and activity pacing) and relaxation-type (eg, diaphragmatic breathing, progressive muscle relaxation, and visualization) exercises [30]. Therefore, it is possible that the EPIO intervention through content does target self-regulation. Other CBT-based digital interventions combining educational and relaxation-based content, tested in other patient groups, support this notion, showing positive self-regulatory impact [42,43].

    At the 3-month follow-up, half of the participants had completed at least 6 out of 9 modules, slightly below the completion rate in the EPIO feasibility pilot study (62%) [32]. It is possible that the in-person introduction sessions (ie, 100% in the feasibility pilot study vs 34% in this RCT) could have contributed to the differences in completion or adherence between the 2 studies. However, a comparison between participants receiving the introduction session in person (42/125, 33.6%) versus via video (83/125, 66.4%) in this study could not be justified as the change in delivery method was solely due to the onset of the COVID-19 pandemic. It is also possible that 3 months may not be enough time for participants living with chronic pain to complete the program, particularly as they were all instructed to spend as much time as possible practicing the module content in this study, rather than trying to complete the program quickly. In addition, even though the completion rate in this study may appear relatively low, 50% seems to be an average adherence rate for eHealth interventions [69], and program completion rates at 20% to 40% are not uncommon [70]. Further research is needed to examine whether access for longer periods (ie, >3 months) may help improve eHealth intervention adherence or whether other aspects, for example, additional follow-up or both, are called for.

    There are several professional challenges in the transfer of traditional in-person service models to digital self-management interventions. In-person treatment entails several evident values, such as the therapist-patient relationship, but little is known about the type and frequency of human interaction that may be preferred or that may provide optimal effect. Reviews have indicated that professionally guided digital self-management interventions may be more effective than self-guided interventions [23,71] and that guided digital CBT-based programs may be more beneficial than unguided programs in terms of depression [72]. As such, the prioritization of human contact and exploration of increasingly personalized digital programs have been suggested [71]. It is possible that the simple blended care delivery in this study, combined with the avatar-like EPIOS bird guiding participants through the program, may have provided a certain combination of guidance and human contact. Blended care delivery, retaining advantages from in-person and digital solutions, may therefore be a recommended way to deliver such interventions in the future [23,33].

    Finally, participants in this study described the EPIO program as useful (95/109, 87.2%) and easy to use (101/109, 92.7%), with easily understandable exercises (106/109, 97.2%) and excellent system usability (92.5 of 100). These findings are in line with those from the EPIO feasibility pilot study [32], with even higher system usability scores in this study (92.5 vs 85.7, respectively).

    Study Limitations, Strengths, and Future Directions

    This study had several limitations. First, participants were recruited through social media and by collaborating with project partners, which may suggest that the study population consisted of highly motivated individuals. Therefore, it is not clear whether people with chronic pain would, in general, be interested in or benefit from a digital self-management intervention such as EPIO. However, therapeutic interventions are likely mainly effective if the participants are indeed interested in participating, which may support the notion that interested participants may be more likely to benefit from such self-management programs.

    Second, most participants in this study were female, were White, had higher education, and had lived with chronic pain for many years. Randomization in this study was computerized and stratified by sex to ensure even sex group distribution, but future studies should seek to identify ways to include more balanced proportions of participants (eg, sex, ethnicity, education, and years with pain) in self-management interventions [73,74].

    Third, the EPIO intervention was developed aiming to target chronic pain in general (ie, across pain classification and etiology) by providing educational information and related exercises based on CBT and aspects of ACT for pain self-management and targeting a broader range of aspects related to self-management skills. Given the heterogeneity of chronic pain (eg, neck or back pain, spinal cord injury, fibromyalgia, or trigeminal neuralgia), future research should seek to explore whether tailored self-management interventions could benefit from a more pain condition–specific approach, with a more rigorous definition of inclusion.

    Fourth, the intervention group was compared with a care-as-usual control group, in which the participants did not receive the EPIO intervention program or any other care from the project team. However, as there was no way of controlling or assessing whether participants in the control group sought any other type of care (eg, self-management courses or mindfulness interventions) during the study period, there is a potential risk that this unknown factor could have impacted the study results. Given this challenge with rather pragmatic RCTs, inquiring about the potential additional external care received by members of the care-as-usual control group might have provided further details about this unknown factor.

    Fifth, the study did not include a numerical primary outcome measure cut-off score for study inclusion, and it is possible that such a cut-off score (eg, only including participants providing a score above mild pain interference, such as >3 or >5) could have affected the primary outcome results of this study. However, with pain and the interference of pain being an individual and subjective experience, the rationale for excluding people living with chronic pain based on subjective scoring must be thoroughly considered before implementation.

    Sixth, although the statistical power for the between-group effects was adequate, future studies may consider testing potential moderating effects, for example, related to baseline medical comorbidities, in larger study samples. Finally, this study explored short-term (ie, 3 months) findings from an RCT testing the EPIO intervention program. Future research should examine these long-term findings and follow-up. Qualitative explorations may also, in line with recommendations from the MRC [34,35], contribute to a better understanding of the impact and nuances of using pain self-management interventions [33,35].

    The fact that the EPIO intervention program was designed and developed based on existing recommendations for digital interventions, including a solid theoretical foundation and development in close collaboration with end users and other stakeholders (eg, health care professionals), is a clear study strength. The simple blended care delivery model used in the study may also guide how such digital interventions could be delivered and implemented in the future, and future research should also aim to test and compare various delivery approaches (eg, blended care delivery vs digital self-management only). This type of research could add value to the understanding of what may constitute the optimal digital intervention and its delivery for those living with chronic pain and also identify the population groups, stages, or types of condition that may benefit from digital pain self-management.

    Conclusions

    Digital pain self-management interventions, such as EPIO, delivered in a simple blended care model, may have the potential to support self-management and improve coping and psychological functioning for people living with chronic pain. Despite not showing a statistically significant impact on the primary outcome of pain interference on function after 3 months in this RCT, participants with chronic pain conditions having access to the EPIO intervention showed a statistically significant decrease in depressive symptoms and self-regulatory fatigue (ie, increased capacity to regulate thoughts, feelings, and behavior), compared with those in the care-as-usual control group. Therefore, long-term efficacy testing is warranted and in progress.

    Acknowledgments

    The authors would like to thank the patients who participated in this study. Without them, there would be no such research. The authors would also like to thank all collaborating partners and participating health care providers for enabling recruitment and supporting this project. Finally, the authors would like to thank the research project team members, including the content development group and the design and software team at the Department of Digital Health Research at Oslo University Hospital, for their exceptional efforts throughout the project.

    Data Availability

    Data sets from this study are, due to the nature of patient-sensitive information, not available for public sharing through public archives or repositories. However, deidentified data from this study will be made available in accordance with institutional standards upon contacting the corresponding author.

    Conflicts of Interest

    LSN is an unpaid board member of dHealth AS, a company eventually aiming to commercialize the EPIO program. All other authors declare no other conflicts of interest.

    Multimedia Appendix 1

    CONSORT-eHEALTH (Consolidated Standards of Reporting Trials of Electronic and Mobile Health Applications and Online Telehealth) checklist (V 1.6.1).

    PDF File (Adobe PDF File), 3260 KB
    Multimedia Appendix 2

    Estimates at both time points and between-group differences in the change from baseline to the 3-month follow-up.

    DOCX File , 29 KB
    1. Mills SE, Nicolson KP, Smith BH. Chronic pain: a review of its epidemiology and associated factors in population-based studies. Br J Anaesth. Aug 2019;123(2):e273-e283. [FREE Full text] [CrossRef] [Medline]
    2. Breivik H, Eisenberg E, O'Brien T, OPENMinds. The individual and societal burden of chronic pain in Europe: the case for strategic prioritisation and action to improve knowledge and availability of appropriate care. BMC Public Health. Dec 24, 2013;13:1229. [FREE Full text] [CrossRef] [Medline]
    3. Solberg Nes L, Roach AR, Segerstrom SC. Executive functions, self-regulation, and chronic pain: a review. Ann Behav Med. Apr 2009;37(2):173-183. [CrossRef] [Medline]
    4. Nes LS, Carlson CR, Crofford LJ, de Leeuw R, Segerstrom SC. Self-regulatory deficits in fibromyalgia and temporomandibular disorders. Pain. Oct 2010;151(1):37-44. [CrossRef] [Medline]
    5. Nes LS, Ehlers SL, Whipple MO, Vincent A. Self-regulatory fatigue: a missing link in understanding fibromyalgia and other chronic multisymptom illnesses. Pain Pract. Apr 2017;17(4):460-469. [CrossRef] [Medline]
    6. Joypaul S, Kelly F, McMillan SS, King MA. Multi-disciplinary interventions for chronic pain involving education: a systematic review. PLoS One. Oct 02, 2019;14(10):e0223306. [FREE Full text] [CrossRef] [Medline]
    7. Kress HG, Aldington D, Alon E, Coaccioli S, Collett B, Coluzzi F, et al. A holistic approach to chronic pain management that involves all stakeholders: change is needed. Curr Med Res Opin. 2015;31(9):1743-1754. [CrossRef] [Medline]
    8. Williams AC, Fisher E, Hearn L, Eccleston C. Psychological therapies for the management of chronic pain (excluding headache) in adults. Cochrane Database Syst Rev. Aug 12, 2020;8(8):CD007407. [FREE Full text] [CrossRef] [Medline]
    9. Beck JS, Beck AT. Cognitive Behavior Therapy: Basics and Beyond. 3rd edition. London, UK. The Guilford Press; 2020.
    10. Hayes SC. Acceptance and commitment therapy, relational frame theory, and the third wave of behavioral and cognitive therapies - republished article. Behav Ther. Nov 2016;47(6):869-885. [CrossRef] [Medline]
    11. Feliu-Soler A, Montesinos F, Gutiérrez-Martínez O, Scott W, McCracken LM, Luciano JV. Current status of acceptance and commitment therapy for chronic pain: a narrative review. J Pain Res. Oct 02, 2018;11:2145-2159. [FREE Full text] [CrossRef] [Medline]
    12. Eccleston C, Morley SJ, Williams AC. Psychological approaches to chronic pain management: evidence and challenges. Br J Anaesth. Jul 2013;111(1):59-63. [FREE Full text] [CrossRef] [Medline]
    13. Urits I, Hubble A, Peterson E, Orhurhu V, Ernst CA, Kaye AD, et al. An update on cognitive therapy for the management of chronic pain: a comprehensive review. Curr Pain Headache Rep. Jul 10, 2019;23(8):57. [CrossRef] [Medline]
    14. Driscoll MA, Edwards RR, Becker WC, Kaptchuk TJ, Kerns RD. Psychological interventions for the treatment of chronic pain in adults. Psychol Sci Public Interest. Sep 2021;22(2):52-95. [CrossRef] [Medline]
    15. Becker WC, Dorflinger L, Edmond SN, Islam L, Heapy AA, Fraenkel L. Barriers and facilitators to use of non-pharmacological treatments in chronic pain. BMC Fam Pract. Mar 20, 2017;18(1):41. [FREE Full text] [CrossRef] [Medline]
    16. Ankawi B, Kerns RD, Edmond SN. Enhancing motivation for change in the management of chronic painful conditions: a review of recent literature. Curr Pain Headache Rep. Aug 06, 2019;23(10):75. [CrossRef] [Medline]
    17. Thurnheer SE, Gravestock I, Pichierri G, Steurer J, Burgstaller JM. Benefits of mobile apps in pain management: systematic review. JMIR Mhealth Uhealth. Oct 22, 2018;6(10):e11231. [FREE Full text] [CrossRef] [Medline]
    18. Eccleston C, Fisher E, Craig L, Duggan GB, Rosser BA, Keogh E. Psychological therapies (internet-delivered) for the management of chronic pain in adults. Cochrane Database Syst Rev. Feb 26, 2014;2014(2):CD010152. [FREE Full text] [CrossRef] [Medline]
    19. Moman RN, Dvorkin J, Pollard EM, Wanderman R, Murad MH, Warner DO, et al. A systematic review and meta-analysis of unguided electronic and mobile health technologies for chronic pain-is it time to start prescribing electronic health applications? Pain Med. Nov 01, 2019;20(11):2238-2255. [CrossRef] [Medline]
    20. Trompetter HR, Bohlmeijer ET, Veehof MM, Schreurs KM. Internet-based guided self-help intervention for chronic pain based on acceptance and commitment therapy: a randomized controlled trial. J Behav Med. Feb 2015;38(1):66-80. [CrossRef] [Medline]
    21. Zhao P, Yoo I, Lancey R, Varghese E. Mobile applications for pain management: an app analysis for clinical usage. BMC Med Inform Decis Mak. May 30, 2019;19(1):106. [FREE Full text] [CrossRef] [Medline]
    22. Devan H, Farmery D, Peebles L, Grainger R. Evaluation of self-management support functions in apps for people with persistent pain: systematic review. JMIR Mhealth Uhealth. Feb 12, 2019;7(2):e13080. [FREE Full text] [CrossRef] [Medline]
    23. Talboom-Kamp EP, Verdijk NA, Kasteleyn MJ, Numans ME, Chavannes NH. From chronic disease management to person-centered eHealth; a review on the necessity for blended care. Clin eHealth. Mar 2018;1(1):3-7. [FREE Full text] [CrossRef]
    24. Huygens MW, Vermeulen J, Swinkels IC, Friele RD, van Schayck OC, de Witte LP. Expectations and needs of patients with a chronic disease toward self-management and eHealth for self-management purposes. BMC Health Serv Res. Jul 08, 2016;16:232. [FREE Full text] [CrossRef] [Medline]
    25. Ludden GD, van Rompay TJ, Kelders SM, van Gemert-Pijnen JE. How to increase reach and adherence of web-based interventions: a design research viewpoint. J Med Internet Res. Jul 10, 2015;17(7):e172. [FREE Full text] [CrossRef] [Medline]
    26. van Gemert-Pijnen JE, Nijland N, van Limburg M, Ossebaard HC, Kelders SM, Eysenbach G, et al. A holistic framework to improve the uptake and impact of eHealth technologies. J Med Internet Res. Dec 05, 2011;13(4):e111. [FREE Full text] [CrossRef] [Medline]
    27. Lee JA, Choi M, Lee SA, Jiang N. Effective behavioral intervention strategies using mobile health applications for chronic disease management: a systematic review. BMC Med Inform Decis Mak. Mar 20, 2018;18(1):12. [FREE Full text] [CrossRef] [Medline]
    28. Varsi C, Solberg Nes L, Kristjansdottir OB, Kelders SM, Stenberg U, Zangi HA, et al. Implementation strategies to enhance the implementation of eHealth programs for patients with chronic illnesses: realist systematic review. J Med Internet Res. Sep 27, 2019;21(9):e14255. [FREE Full text] [CrossRef] [Medline]
    29. Ledel Solem IK, Varsi C, Eide H, Kristjansdottir OB, Mirkovic J, Børøsund E, et al. Patients' needs and requirements for eHealth pain management interventions: qualitative study. J Med Internet Res. Apr 01, 2019;21(4):e13205. [FREE Full text] [CrossRef] [Medline]
    30. Ledel Solem IK, Varsi C, Eide H, Kristjansdottir OB, Børøsund E, Schreurs KM, et al. A user-centered approach to an evidence-based electronic health pain management intervention for people with chronic pain: design and development of EPIO. J Med Internet Res. Jan 21, 2020;22(1):e15889. [FREE Full text] [CrossRef] [Medline]
    31. Varsi C, Ledel Solem IK, Eide H, Børøsund E, Kristjansdottir OB, Heldal K, 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. Mar 25, 2021;21(1):275. [FREE Full text] [CrossRef] [Medline]
    32. Bostrøm K, Børøsund E, Varsi C, Eide H, Flakk Nordang E, Schreurs KM, et al. Digital self-management in support of patients living with chronic pain: feasibility pilot study. JMIR Form Res. Oct 23, 2020;4(10):e23893. [FREE Full text] [CrossRef] [Medline]
    33. Bostrøm K, Varsi C, Eide H, Børøsund E, Kristjansdottir Ó, Schreurs KM, et al. Engaging with EPIO, a digital pain self-management program: a qualitative study. BMC Health Serv Res. Apr 29, 2022;22(1):577. [FREE Full text] [CrossRef] [Medline]
    34. Craig P, Dieppe P, Macintyre S, Michie S, Nazareth I, Petticrew M, et al. Medical Research Council Guidance. Developing and evaluating complex interventions: the new medical research council guidance. BMJ. Sep 29, 2008;337:a1655. [FREE Full text] [CrossRef] [Medline]
    35. Skivington K, Matthews L, Simpson SA, Craig P, Baird J, Blazeby JM, et al. A new framework for developing and evaluating complex interventions: update of medical research council guidance. BMJ. Sep 30, 2021;374:n2061. [FREE Full text] [CrossRef] [Medline]
    36. Børøsund E, Ehlers SL, Varsi C, Clark MM, Andrykowski MA, Cvancarova M, et al. Results from a randomized controlled trial testing StressProffen; an application-based stress-management intervention for cancer survivors. Cancer Med. Jun 2020;9(11):3775-3785. [FREE Full text] [CrossRef] [Medline]
    37. Keller S, Bann CM, Dodd SL, Schein J, Mendoza TR, Cleeland CS. Validity of the brief pain inventory for use in documenting the outcomes of patients with noncancer pain. Clin J Pain. Sep 2004;20(5):309-318. [CrossRef] [Medline]
    38. Klepstad P, Loge JH, Borchgrevink PC, Mendoza TR, Cleeland CS, Kaasa S. The Norwegian brief pain inventory questionnaire: translation and validation in cancer pain patients. J Pain Symptom Manage. Nov 2002;24(5):517-525. [FREE Full text] [CrossRef] [Medline]
    39. Zigmond AS, Snaith RP. The hospital anxiety and depression scale. Acta Psychiatr Scand. Jun 1983;67(6):361-370. [CrossRef] [Medline]
    40. Leiknes KA, Dalsbø TK, Siqveland J. Psychometric assessment of the Norwegian version of the Hospital Anxiety and Depression Scale (HADS). Norwegian Institute of Public Health. 2016. URL: https://www.fhi.no/publ/2016/maleegenskaper-ved-den-norske-versj/ [accessed 2023-04-11]
    41. Nes LS, Ehlers SL, Whipple MO, Vincent A. Self-regulatory fatigue in chronic multisymptom illnesses: scale development, fatigue, and self-control. J Pain Res. 2013;6:181-188. [FREE Full text] [CrossRef] [Medline]
    42. Børøsund E, Varsi C, Clark MM, Ehlers SL, Andrykowski MA, Sleveland HR, et al. Pilot testing an app-based stress management intervention for cancer survivors. Transl Behav Med. Aug 07, 2020;10(3):770-780. [FREE Full text] [CrossRef] [Medline]
    43. Børøsund E, Ehlers SL, Clark MM, Andrykowski MA, Cvancarova Småstuen M, Solberg Nes L. Digital stress management in cancer: testing StressProffen in a 12-month randomized controlled trial. Cancer. Apr 01, 2022;128(7):1503-1512. [FREE Full text] [CrossRef] [Medline]
    44. Ware Jr JE, Sherbourne CD. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med Care. Jun 1992;30(6):473-483. [Medline]
    45. Loge JH, Kaasa S, Hjermstad MJ, Kvien TK. Translation and performance of the Norwegian SF-36 health survey in patients with rheumatoid arthritis. I. Data quality, scaling assumptions, reliability, and construct validity. J Clin Epidemiol. Nov 1998;51(11):1069-1076. [CrossRef] [Medline]
    46. Sullivan MJ, Bishop SR, Pivik J. The pain catastrophizing scale: development and validation. Psychol Assess. 1995;7(4):524-532. [FREE Full text] [CrossRef]
    47. Fernandes L, Storheim K, Lochting I, Grotle M. Cross-cultural adaptation and validation of the Norwegian pain catastrophizing scale in patients with low back pain. BMC Musculoskelet Disord. Jun 22, 2012;13:111. [FREE Full text] [CrossRef] [Medline]
    48. McCracken LM, Vowles KE, Eccleston C. Acceptance of chronic pain: component analysis and a revised assessment method. Pain. Jan 2004;107(1-2):159-166. [CrossRef] [Medline]
    49. Eide H, Leren L, Sørebø Ø. The Norwegian versions of the Chronic Pain Acceptance Questionnaire CPAQ-20 and CPAQ-8 - validation and reliability studies. Disabil Rehabil. Jul 2017;39(14):1441-1448. [CrossRef] [Medline]
    50. Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. Sep 1989;13(3):319-340. [FREE Full text] [CrossRef]
    51. Brook J. SUS: a 'quick and dirty' usability scale. In: Jordan PW, Thomas B, McClelland IL, Weerdmeester B, editors. Usability Evaluation in Industry. London, UK. Taylor & Francis; 1996;189-194.
    52. Buhrman M, Gordh T, Andersson G. Internet interventions for chronic pain including headache: a systematic review. Internet Interv. Jan 03, 2016;4:17-34. [FREE Full text] [CrossRef] [Medline]
    53. Schäfer T, Schwarz MA. The meaningfulness of effect sizes in psychological research: differences between sub-disciplines and the impact of potential biases. Front Psychol. Apr 11, 2019;10:813. [FREE Full text] [CrossRef] [Medline]
    54. Sachau J, Sendel M, Péchard M, Schnabel K, Schmieg I, Medkour T, et al. Patient reported outcome measures in chronic neuropathic pain clinical trials - a systematic literature review. J Pain. Jan 2023;24(1):38-54. [FREE Full text] [CrossRef] [Medline]
    55. Mouraux A, Bannister K, Becker S, Finn DP, Pickering G, Pogatzki-Zahn E, et al. Challenges and opportunities in translational pain research - an opinion paper of the working group on translational pain research of the European pain federation (EFIC). Eur J Pain. Apr 2021;25(4):731-756. [FREE Full text] [CrossRef] [Medline]
    56. Guyatt G, Osoba D, Wu AW, Wyrwich KW, Norman GR. Methods for explaining the clinical significance of health status measures. Clinical Therapeutics. Jan 2002;24(2):5. [FREE Full text] [CrossRef]
    57. IsHak WW, Wen RY, Naghdechi L, Vanle B, Dang J, Knosp M, et al. Pain and depression: a systematic review. Harv Rev Psychiatry. Nov 2018;26(6):352-363. [CrossRef] [Medline]
    58. Goesling J, Clauw DJ, Hassett AL. Pain and depression: an integrative review of neurobiological and psychological factors. Curr Psychiatry Rep. Dec 2013;15(12):421. [CrossRef] [Medline]
    59. Lepping P, Whittington R, Sambhi RS, Lane S, Poole R, Leucht S, et al. Clinical relevance of findings in trials of CBT for depression. Eur Psychiatry. Sep 2017;45:207-211. [CrossRef] [Medline]
    60. Moshe I, Terhorst Y, Philippi P, Domhardt M, Cuijpers P, Cristea I, et al. Digital interventions for the treatment of depression: a meta-analytic review. Psychol Bull. Aug 2021;147(8):749-786. [CrossRef] [Medline]
    61. Pang Y, Zhang X, Gao R, Xu L, Shen M, Shi H, et al. Efficacy of web-based self-management interventions for depressive symptoms: a meta-analysis of randomized controlled trials. BMC Psychiatry. Aug 11, 2021;21(1):398. [FREE Full text] [CrossRef] [Medline]
    62. Aerts JE, van Dam A. Blended e-Health in cognitive behavioural therapy: usage intensity, attitude and therapeutic alliance in clinical practice. Psychol. Sep 2018;9(10):2422-2435. [FREE Full text] [CrossRef]
    63. Topooco N, Berg M, Johansson S, Liljethörn L, Radvogin E, Vlaescu G, et al. Chat- and internet-based cognitive-behavioural therapy in treatment of adolescent depression: randomised controlled trial. BJPsych Open. Jun 26, 2018;4(4):199-207. [FREE Full text] [CrossRef] [Medline]
    64. Segerstrom SC, Nes LS. Heart rate variability reflects self-regulatory strength, effort, and fatigue. Psychol Sci. Mar 2007;18(3):275-281. [CrossRef] [Medline]
    65. Baumeister RF, Bratslavsky E, Muraven M, Tice DM. Ego depletion: is the active self a limited resource? J Pers Soc Psychol. May 1998;74(5):1252-1265. [CrossRef] [Medline]
    66. Baumeister RF, Tice DM, Vohs KD. The strength model of self-regulation: conclusions from the second decade of willpower research. Perspect Psychol Sci. Mar 2018;13(2):141-145. [CrossRef] [Medline]
    67. Baumeister RF, Vohs KD, Tice DM. The strength model of self-control. Curr Dir Psychol Sci. Dec 2007;16(6):351-355. [FREE Full text] [CrossRef]
    68. McGonigal K. The Willpower Instinct: How Self-Control Works, Why It Matters, and What You Can Do to Get More of It. New York, NY, USA. Avery Publishing Group Inc; 2013.
    69. Kelders SM, Kok RN, Ossebaard HC, Van Gemert-Pijnen JE. Persuasive system design does matter: a systematic review of adherence to web-based interventions. J Med Internet Res. Nov 14, 2012;14(6):e152. [FREE Full text] [CrossRef] [Medline]
    70. Sieverink F, Kelders SM, van Gemert-Pijnen JE. Clarifying the concept of adherence to eHealth technology: systematic review on when usage becomes adherence. J Med Internet Res. Dec 06, 2017;19(12):e402. [FREE Full text] [CrossRef] [Medline]
    71. Obro LF, Heiselberg K, Krogh PG, Handberg C, Ammentorp J, Pihl GT, et al. Combining mHealth and health-coaching for improving self-management in chronic care. A scoping review. Patient Educ Couns. Apr 2021;104(4):680-688. [FREE Full text] [CrossRef] [Medline]
    72. Arean PA, Hallgren KA, Jordan JT, Gazzaley A, Atkins DC, Heagerty PJ, et al. The use and effectiveness of mobile apps for depression: results from a fully remote clinical trial. J Med Internet Res. Dec 20, 2016;18(12):e330. [FREE Full text] [CrossRef] [Medline]
    73. Seidler ZE, Rice SM, Ogrodniczuk JS, Oliffe JL, Dhillon HM. Engaging men in psychological treatment: a scoping review. Am J Mens Health. Nov 2018;12(6):1882-1900. [FREE Full text] [CrossRef] [Medline]
    74. Liddon L, Kingerlee R, Barry JA. Gender differences in preferences for psychological treatment, coping strategies, and triggers to help-seeking. Br J Clin Psychol. Mar 2018;57(1):42-58. [CrossRef] [Medline]

    ‎
    ACT: acceptance and commitment therapy
    BPI: Brief Pain Inventory
    CBT: cognitive behavioral therapy
    CONSORT-EHEALTH: Consolidated Standards of Reporting Trials of Electronic and Mobile Health Applications and Online Telehealth
    CPAQ: Chronic Pain Acceptance Questionnaire
    HADS: Hospital Anxiety and Depression Scale
    HRQoL: health-related quality of life
    MD: mean difference
    MRC: Medical Research Council
    PCS: Pain Catastrophizing Scale
    RAND-36: SF-36 Short Form Health Survey
    RCT: randomized controlled trial
    SUS: System Usability Scale
    TSD: Tjenester for Sensitive Data (Services for Sensitive Data)

    Edited by T de Azevedo Cardoso; submitted 14.03.23; peer-reviewed by P Zhao, R Grainger; comments to author 17.04.23; revised version received 23.04.23; accepted 20.06.23; published 25.08.23.

    Copyright

    ©Katrine Bostrøm, Elin Børøsund, Hilde Eide, Cecilie Varsi, Ólöf Birna Kristjansdottir, Karlein M G Schreurs, Lori B Waxenberg, Karen E Weiss, Eleshia J Morrison, Hanne Stavenes Støle, Milada Cvancarova Småstuen, Audun Stubhaug, Lise Solberg Nes. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 25.08.2023.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.