Published on in Vol 19, No 5 (2017): May

Digital Support Interventions for the Self-Management of Low Back Pain: A Systematic Review

Digital Support Interventions for the Self-Management of Low Back Pain: A Systematic Review

Digital Support Interventions for the Self-Management of Low Back Pain: A Systematic Review

Original Paper

1General Practice & Primary Care, Institute of Health & Wellbeing, University of Glasgow, Glasgow, United Kingdom

2Department of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark

3Nordic Institute of Chiropractic and Clinical Biomechanics, Odense, Denmark

4Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway

*these authors contributed equally

Corresponding Author:

Barbara I Nicholl, BSc, PGDip, PhD

General Practice & Primary Care

Institute of Health & Wellbeing

University of Glasgow

1 Horselethill Road

Glasgow, G12 9LX

United Kingdom

Phone: 44 141 3308327

Fax:44 141 3308327


Background: Low back pain (LBP) is a common cause of disability and is ranked as the most burdensome health condition globally. Self-management, including components on increased knowledge, monitoring of symptoms, and physical activity, are consistently recommended in clinical guidelines as cost-effective strategies for LBP management and there is increasing interest in the potential role of digital health.

Objective: The study aimed to synthesize and critically appraise published evidence concerning the use of interactive digital interventions to support self-management of LBP. The following specific questions were examined: (1) What are the key components of digital self-management interventions for LBP, including theoretical underpinnings? (2) What outcome measures have been used in randomized trials of digital self-management interventions in LBP and what effect, if any, did the intervention have on these? and (3) What specific characteristics or components, if any, of interventions appear to be associated with beneficial outcomes?

Methods: Bibliographic databases searched from 2000 to March 2016 included Medline, Embase, CINAHL, PsycINFO, Cochrane Library, DoPHER and TRoPHI, Social Science Citation Index, and Science Citation Index. Reference and citation searching was also undertaken. Search strategy combined the following concepts: (1) back pain, (2) digital intervention, and (3) self-management. Only randomized controlled trial (RCT) protocols or completed RCTs involving adults with LBP published in peer-reviewed journals were included. Two reviewers independently screened titles and abstracts, full-text articles, extracted data, and assessed risk of bias using Cochrane risk of bias tool. An independent third reviewer adjudicated on disagreements. Data were synthesized narratively.

Results: Of the total 7014 references identified, 11 were included, describing 9 studies: 6 completed RCTs and 3 protocols for future RCTs. The completed RCTs included a total of 2706 participants (range of 114-1343 participants per study) and varied considerably in the nature and delivery of the interventions, the duration/definition of LBP, the outcomes measured, and the effectiveness of the interventions. Participants were generally white, middle aged, and in 5 of 6 RCT reports, the majority were female and most reported educational level as time at college or higher. Only one study reported between-group differences in favor of the digital intervention. There was considerable variation in the extent of reporting the characteristics, components, and theories underpinning each intervention. None of the studies showed evidence of harm.

Conclusions: The literature is extremely heterogeneous, making it difficult to understand what might work best, for whom, and in what circumstances. Participants were predominantly female, white, well educated, and middle aged, and thus the wider applicability of digital self-management interventions remains uncertain. No information on cost-effectiveness was reported. The evidence base for interactive digital interventions to support patient self-management of LBP remains weak.

J Med Internet Res 2017;19(5):e179



The point prevalence of low back pain (LBP) is estimated to be 12% and one-month prevalence 23% across the globe [1]. The Global Burden of Disease study reported that LBP is the greatest contributor to disability in 12 of 21 world regions studied [2]. When considering years lived with disability, LBP is one of the leading causes of burden worldwide out of 291 conditions considered [2,3]. It is among the most common causes of long-term work absence and has a major impact on productivity at work [4,5]. Annual costs of LBP have been estimated to be approximately £10.7 billion for indirect factors in the United Kingdom [6,7] and up to US $200 billion in the United States [8], including workplace productivity costs; personal costs include a reduction in everyday functioning and quality of life [9].

Optimizing treatment strategies that are cost-effective, safe, and easy to administer for individuals with LBP is essential. Self-management is consistently recommended in international guidelines on the management of LBP [10,11]. Self-management focuses on the patient’s ability to manage their own condition rather than treatment being based within the health care system or centered on a health care professional. The aim is to restore autonomy to the patient and include educational, or learning, components to position the patient at the center of their own management process and to help them acquire and maintain competencies to enable them to efficiently manage their condition [12].

A systematic review of the effectiveness of the self-management of LBP published in 2012 reports moderate quality evidence that self-management interventions have small, but clinically relevant, effects on reducing pain and disability for people with LBP when compared with minimal interventions [13]. The content and mode of delivery varied across the studies included, from receiving written information, attending face-to-face educational programs, functional movement training programs to information from websites [13].

Digital interventions (ie, interventions accessed via computer, mobile phone, or other handheld devices, including Web-based, desktop computer programs, or apps), providing self-management information have been proposed as a promising mode of delivery for self-management interventions. In a Cochrane Review from 2005, the use of such digital interventions was evaluated in people with chronic diseases and found to have a significant positive effect on knowledge, social support, and clinical outcomes in conditions such as diabetes and obesity [14]. Digital interventions have also been shown to effectively improve chronic pain, including chronic LBP, when compared with control groups (no care, waiting list, placebo, or care as usual) [15]. Providing supported self-management through digital platforms may enable individuals with LBP to better manage their symptoms. Garg et al [16] identified 9 randomized controlled trials (RCTs) for a systematic review of Web-based interventions to support individuals with LBP; included studies were grouped into cognitive behavioral therapy (CBT), a dialogue-based therapy that has been shown to have some efficacy for individuals with LBP [17], or knowledge improvement approaches with an interactive component [16]. Web-based methods were found to be useful, particularly CBT and those that offered an interactive support component; however, there was caution placed on the external validity of all studies included. Consequently, it appears that digital interventions hold potential in supporting the self-management of LBP but not enough is known about their content, delivery, and benefits, if any, or whether these interventions can be expected to be an improvement on traditional self-management approaches. Although it appears that the majority of digital interventions in this area have targeted individuals with chronic LBP (LBP for 3 months or longer), there is little known about the sociodemographic characteristics of individuals with LBP who are either targeted or who subsequently engage with such interventions.

The purpose of this systematic review was to synthesize published evidence concerning the characteristics, components, and effects of interactive digital interventions to support patient self-management of LBP. More specifically, the review aimed to address the following questions:

What are the key characteristics and components of digital self-management interventions for LBP, including theoretical underpinnings?

What outcome measures have been used in randomized trials of digital self-management interventions in LBP and what effect, if any, did the intervention have on these?

What specific characteristics or components, if any, of interventions appear to be associated with beneficial outcomes?

Inclusion and exclusion criteria.

Inclusion criteria

Participants: adults (18 years or above) with nonspecific LBP

Digital intervention:

  • Any intervention accessed through a computer (work or home), mobile phone, or hand-held device, and included Web-based or desktop computer programs or apps that provided self-management information or material, which is in keeping with previous reviews in this sphere [19].
  • Element of interaction between the user and digital interface: interaction was defined as patients entering data into the program or app, either by entering personal data or making choices that alter the pathways in the program and produce feedback in response to the patients’ inputted data or choices.
  • Interactive component as an add-on to face to-face health professional contact (eg, regularly seeing doctor but reporting pain levels electronically and receiving automated messages advising on physical activity level between visits).

Control group: usual care or digital noninteractive or nondigital self-management interventions for LBP

Study design: published randomized controlled trials (RCTs) or protocols for RCTs from peer-reviewed journals

Language: studies published in English, Danish, or Norwegian

Exclusion criteria

Digital intervention:

  • Studies that only involved sending information to a remotely located health professional and receiving advice directly from the health professional.

Study design: all non-RCT reports and protocols

Textbox 1. Inclusion and exclusion criteria.

Study Design

The systematic literature review followed an a priori defined protocol as registered in PROSPERO (reference number 42016037954) and reporting is consistent with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [18]. Inclusion and exclusion criteria are outlined in Textbox 1.

Information Sources and Search Strategy

A systematic search of the following databases was undertaken: Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Database of Systematic Reviews (CDSR), Cochrane Central Register of Controlled Trials (CENTRAL), Cochrane Library (including Database of Abstracts of Reviews of Effects [DARE] and Health Technology Assessment [HTA] databases), Database of Promoting Health Effectiveness Reviews (DoPHER), Embase, MEDLINE, PsycINFO, Trials Register of Promoting Health Interventions (TRoPHI) and Web of Science (Social Science Citation and Science Citation Index). All databases were searched from 2000 until March 2016. Reference and citation searching were also undertaken. The searches were performed by an experienced Librarian at the Norwegian University of Science and Technology (NTNU). The search strategy included subject indexing terms and free-text terms for title, abstract, and keyword searching. The search terms were grouped into 3 concepts: (1) back pain, (2) digital interventions, and (3) self-management. The search terms were selected with reference to previous systematic reviews of interactive digital interventions for hypertension [19,20] and asthma [21,22] and after discussion with the review team. The full version of the search terms used, including specifications on use of title, keywords, or abstract screening, is documented for the example of MEDLINE in Multimedia Appendix 1.

Study Selection

All identified citations from the searched databases were uploaded to Distiller software (Evidence Partners). An integrated duplication detection tool was used to identify duplicates. All suggested duplicate pairs were screened for correctness by one reviewer (LS). Title and abstract screening was performed for each article by two independent reviewers from four (LS, BN, MM, NS). Disagreement between the two reviewers resulted in inclusion of the citation to full-text screening. Full-text screening was similarly performed by two independent reviewers from four (LS, BN, MM, NS), assessing the eligibility of the citation. Any disagreement was resolved through discussion mediated by a third reviewer (PJM).

Data Collection

Similar to the study selection process, data extraction was performed independently by two of four reviewers (LS, BN, MM, NS) using the Distiller software. Discrepancies in data extracted were considered by LS by revisiting the original paper to adjudicate on appropriateness and discussed and finalized with BN where required. Data were systematically extracted on study settings (country, inclusion and exclusion criteria, recruitment and participation numbers); study population (baseline characteristics such as age, gender, ethnicity, duration of symptoms, comorbidities); description of the intervention (details on the key components, characteristics, and underlying theoretical concepts); and outcome measures (time-points for outcome assessment, choice of primary outcomes, included secondary outcomes and effects, if any, noted as well as attrition rates, where available).

Outcome Measures

Our primary and secondary outcomes of particular interest are outlined in Textbox 2. These outcomes were a priori defined as of interest, however all outcomes reported were included in the data synthesis. For this review, pain-related disability was of special interest, as it measures a construct of the physical functioning domain, which has been recommended as a core domain in LBP research by several authors and guidelines [23-25].

Quality Appraisal

The methodological quality of all included studies was assessed using the Cochrane Collaboration tool for assessing risk of bias in randomized trials [26]. Two reviewers independently assessed selection bias (allocation concealment and randomization procedure); blinding of participants, personnel, and outcomes assessors; completeness of data; selective outcome reporting; and other potential biases. Any disagreements were resolved through discussion by the two independent reviewers (BN, PK). Papers were not excluded from the study on the basis of quality.

Data Synthesis

The study population, intervention components, outcomes, and characteristics of the included studies were narratively described. In our protocol we stated that we would conduct a meta-analysis if included studies were sufficiently homogeneous; however, due to the heterogeneity of identified studies, meta-analysis was not possible. Quantitative results from all outcomes reported in the completed RCT studies were described as either favoring the intervention group, no difference between groups, or favoring the control group. The outcomes reported in Textbox 2 were used as a basis to structure the results for research question 2. Included protocols for future RCTs were used to consider intervention components, characteristics, and outcome measures, but were not included in synthesis of intervention effects.

Study Selection

We identified a total of 7014 citations, including 8 from searching reference lists of included studies. From these, 2316 were excluded as duplicates, and thus a total of 4698 titles were screened, resulting in the screening of 729 abstracts and 89 full-text papers. A total of 11 references concerning 9 different studies that described 5 RCT study protocols and 6 RCT reports met the inclusion criteria [27-37]. The PRISMA flow diagram demonstrating the screening process is illustrated in Figure 1 (adapted from Moher et al [18]).

Outcome measures of interest.

Primary outcome

Details of outcome measures used to determine the effects of interventions for self-management of LBP pain-related disability

Secondary outcomes

  • Pain intensity
  • Quality of life
  • Depression
  • Fear avoidance
  • Pain catastrophizing
  • Physical activity
  • Medication use
  • Health care utilization (eg, primary and secondary care visits, emergency department visits)
  • Health care costs
  • Knowledge of LBP
  • Markers of self-care
  • Self-efficacy
Textbox 2. Outcome measures of interest.
Figure 1. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram illustrating the screening process of papers.
View this figure

Description of Included Studies

Four of the 9 separate studies were undertaken in the United States [28,29,32,33], 2 in Germany [35,37], one in the United Kingdom [31], one in Australia [27], and one in Spain [36]. The studies were published between 2010 and 2016. The 6 completed RCT reports included a total of 2706 participants, with a range of 114-1343 participants per study (Table 1).

Study Population

The characteristics of the study population in each of the studies are described in Table 1. There was considerable variation between studies in the duration of LBP symptoms, content and delivery of the interventions, and the measured outcomes. In 6 studies, LBP was defined by participant self-report [27-29,32,35,37] and in 3 studies by general practitioner evaluations [31,36] or diagnosis codes from medical records [33]. Seven studies included participants with pain for more than 3 months [27-29,32,33,36,37]. Only one study included participants with current LBP at the time of screening (or within the past 2 weeks) [31], while Simon et al [35] included only acute LBP participants, defined as participants who had experienced pain for less than 3 months. The included populations had a mean age ranging from 42.5 to 52.7 years; one study did not report the age of the population, except to say they were 18-65 years [32], a further study also had an upper age limit of 65 years [36]. All the remaining 7 studies did not report any upper limit in their inclusion criteria, yet only one study reported the age range of participants, 18-79 years [29]. In 5 [28,29,32,35,37] of the 6 RCT reports, the majority of the participants were female (58%-83%). The 6th study, which was conducted within the American Department of Veterans Affairs, included only 11% females in the intervention group and 14% in the control group [33]. Included participants were generally Caucasian (74%-87%) and the majority (42%-75%) reported educational levels as time spent at college or higher.

Table 1. Included studies and population characteristics.
StudyDefinition of LBPaNumberAge, mean (SD)bSex (%)Ethnicity (%)Comorbid (%)SES (%)c
Chiauzzi et al [29]

United States
LBP ≥10 days/month for at least 3 consecutive months



I=47.3 (12.2)
C=45.0 (11.7)
C=F (68)
report partial college or associates degree or higher

report ≥US $25,000/year
Simon et al [35]

Acute LBP (<3 months)N=1343


I=45.8 (12.7)h
C=45.3 (13.0)h
F (82)hN/RN/REducation
60% “high” education levelh
Carpenter et al [28]

United States
Noncancer LBP ≥6 monthsN=141


42.5 (10.3)

F (83)White (77)N/REducation
54% ≤2 years college
Krein et al [33]

Krein et al [34]i

United States
Patients with ≥2 outpatient encounters within the past 12 months with a diagnosis of back pain with no neurologic findings (ICD­-9-­CM codes 724.2, 724.5, 846.0­-846.9)



I=51.2 (12.5)
C=51.9 (12.8)
I=F (11%)
C=F (14)
reported some college or higher

≥US $10,000/year

Irvine et al [32]

United States
Nonspecific LBP within the past 3 monthsN=398


N/RI=F (58%)
C=F (63)
report some college or higher

≥US $ 20,000/year
Weymann et al [37]

Dirmaier et al [30]i

Chronic LBP: pain almost every day for >12 weeksN=368h


I=52.2 (13.1)h
C=52.7 (13.0)h
I=F, 162 (59)h
C=F, 162 (59)h
reported >10 years of education
Geraghty et al [31]i

United Kingdom
LBP in the past 3 months recorded in General Practitioner records and current LBP (or within the past 2 weeks) at the time of screening20-30/group-----
Valenzuela-Pascual et al [36]i

Chronic LBP >6 months, confirmed by clinician29/group-----
Amorim et al [27]i

Chronic LBP persisting for
>12 weeks but without radicular symptoms

aLBP: low back pain.

bSD: standard deviation.

cSES: socioeconomic status.

dI: intervention group.

eC: control group.

fF: female.

gN/R: not reported.

hPopulation comprising more conditions than LBP, numbers refer to the general population and were not available for LBP group only.

iProtocol paper, no data available unless reported alongside the RCT results paper.

jPlanned number to recruit based on protocol paper.

Quality Assessment

Results for the 7 items of the Cochrane Risk of Bias tool for the 6 completed RCTs are reported in Table 2. Four studies had one or more items rated as unclear risk of bias [28,29,32,35], 4 studies had one item with high risk of bias [28,32,35,37], and only one study had a low risk of bias for all 7 items [33]. One study was assessed to have a high risk of incomplete data [35] because the attrition rate was extremely high. Two studies were rated as high risk of selective outcome reporting as one had no published protocol and reported the outcome with highest effect size as a primary outcome [28], and the other did not report on the primary outcome stated in the published protocol [32]. One study was assessed to have high risk of bias for other potential biases due to differences in educational level between the groups [37].

Key Intervention Components and Theoretical Underpinning of Digital Self-Management Interventions for LBP


The extent of descriptions of the intervention content varied across studies (Table 3), but the level of details provided was generally sparse. The content of the digital interventions can be grouped into the following categories: (1) Pain education material: all studies report educational material as part of the intervention, which included information on pain origin, mechanisms and management, epidemiology of LBP, psychological aspects (eg, role of depression and mood), diagnostics and treatment-options; (2) General well-being activities: information concerning well-being, such as meditation, relaxation, general physical activity, and sleep hygiene, was reported in 4 studies [27-29,31]; (3) Exercise advice and goals: 5 studies described exercise advice, such as recommendations and goal-setting [27,29,31-33]. Two studies included short videos of exercises [32,33]; (4) E-community: one study reported a discussion forum with peers and health professionals in addition to the educational material [33]; (5) Narratives: one study included patient stories as part of the content [28].

Table 2. Quality assessment: risk of bias assessment with Cochrane Risk of Bias tool (low risk of bias, unclear risk of bias, high risk of bias).
StudyRandom sequence generationAllocation concealmentBlindingIncomplete dataSelective reporting
Other biases


Chiauzzi et al [29]LowUnclearUnclearLowLowUnclearUnclear
Simon et al [35]LowLowUnclearLowHighLowLow
Carpenter et al [28]LowUnclearUnclearLowLowHighUnclear
Krein et al [33]LowLowLowLowLowLowLow
Irvine et al [32]UnclearUnclearUnclearLowLowHighUnclear
Weymann et al [37]LowLowLowLowLowLowHigh
Theoretical Underpinnings

Four of the 9 studies reported a theoretical underpinning to their intervention development (Table 3) [28,29,32,33]; cognitive behavior theory [28,29], collaborative decision making [29], social cognitive theory [32,33], theory of planned behavior [32], and acceptance and commitment therapy [28] were reported. The following approaches were mentioned as underpinning or rationales for the intervention: mindfulness [28], person-based approach [31], and self-management principles (not specified further) [29]; and tools such as goal setting [33] and information on pain and pain etiology [32,36]. Finally, 2 studies also reported that the advice given to participants was based on treatment guidelines, either evidence-based or recommendations from Governmental Institutes [27,37]. With regard to the tailoring element of interventions, 2 of the 7 studies that reported a tailoring element to their intervention described a systematic theoretical underpinning for the tailoring: Simon et al used the Ottawa Decision Support framework [35], whereas Weymann et al used the Avoidance Endurance Model and Health-Literacy as tailoring frameworks, as described in the study protocol [30,37].

Table 3. Intervention components and theoretical underpinnings.
StudyContentTheoretical underpinning of content
Chiauzzi et al [29]Educational material: content not more specifically described
Wellness activities: enhance good sleep, nutrition, stress management, exercise practices
Cognitive behavior theory

Collaborative decision making

Simon et al [35]Condition-specific information: epidemiology, etiology, diagnostics, treatment optionsN/Ra

Carpenter et al [28]Educational chapters: all about pain, thoughts and pain, stress and relaxation, getting active
Didactic material and interactive exercises
Patient stories
Guided relaxation and meditation exercises
Cognitive therapy, behavioral activation

Acceptance and commitment therapy

Krein et al [33]

Krein et alc[34]
Educational material: Handouts about topics (body mechanics, use of cold packs, lumbar rolls, and good posture); videos demonstrating specific strengthening and stretching exercises

Pedometer data: weekly goals for steps

E-community: participants to post suggestions, ask questions, and share stories
Social cognitive theoryb

Irvine et al [32]Education and behavioral strategies to manage and prevent pain: thirty 1-4 min videos on pain management, cognitive, and behavioral strategies; videos gain-framed messages with animated whiteboard-style coach; videos of ergonomics and exercisesSocial cognitive theory

Theory of planned behavior

Weymann et al [37]

Dirmaier et alc[30]
Educational information: physiology of pain, acute versus chronic pain; “chronification”; epidemiology; psychological aspects; coping and pain management

Diagnostic procedures
Treatment options
Geraghty et alc[31]Educational information: goal review; feedback on achievements; sessions on sleep, pain relief, flare-up, work, mood daily living.
Supporting advice: managing pain; modeling expectation through patient stories; reinforcing positive behavior through automated feedback; simple instructions on back exercises/behavior
Valenzuela-Pascual et alc [36]Content not yet developed, but will be based on qualitative study including interviews with patientsN/R
Amorim et alc [27]Educational material: “make your move—sit less, be active for life!”
Information on how to increase physical activity and decrease sedentary behavior
Health-coaching by health care professional
FitBit activity monitor/feedback device

aN/R: not reported.

bInformation given in the protocol but not stated in the randomized controlled trial report.

cProtocol paper.

Outcome Measures Used in Digital Self-Management Interventions for LBP

Primary Outcomes

A wide range of outcomes were included in the RCTs (Table 4), with a total of 16 different outcomes being reported as a “primary outcome” measure. The number of primary outcomes per study ranged from 1 to 4. The primary outcome measures covered the domains of pain-related disability, pain intensity, attitude, depression, physical activity, knowledge of LBP, markers of self-care, and participant’s assessment of change over time. Of the 6 completed trials, 4 studies [32,33,35,37] did not find a statistically significant effect on the primary outcome measures in favor of the intervention group; one study [28] reported a statistically significant effect in favor of the intervention compared with the control group on 6 of 7 subscales of their primary outcome—Survey of Pain Attitudes (SOPA)—following 3 weeks of intervention use (F statistic ranged from 5.1 to 44.7); while Chiauzzi et al [29] reported a favorable effect in the intervention arm but only in one of 4 primary outcomes that they measured (the Patient Global Impression Change Scale).

Pain-Related Disability

Pain-related disability was considered as the primary outcome in 4 of the 9 studies. The Roland-Morris Disability Questionnaire (RMDQ) was used in 2 of the 6 completed RCTs [28,33]. Carpenter et al [28] reported a significant difference in favor of the intervention group in RMDQ after 3 weeks of Web-based intervention compared with a waiting list control group (a reduction in RMDQ score of 2.8 for the intervention group compared with 0.8 for the control group; P=.01). Krein et al [33] similarly used the RMDQ, but observed reduced disability in chronic LBP with a 12-month, pedometer-based, Internet-supported, intervention of the same magnitude as the control group. The 3 protocols for RCT trials [27,31,36] all expected to use RMDQ as a measure of pain-related disability. The Oswestry Disability Index (ODI) was stated as the primary outcome measure in two RCT reports [29,32]. Chiauzzi et al [29] did not find a difference in ODI score between the intervention and control group after 4 weeks of access to a pain information website compared with static participant information. Irvine et al [32] did not report the trial results for ODI even though it was stated as a primary outcome in their Web-based trial registration.

Secondary Outcomes

A large variety of secondary outcome measures were described (Table 4 and Multimedia Appendix 2 provide a more detailed view). The outcome measures covered the following domains: pain-related disability; pain; health-related disability; depression/mood; fear of movement; pain catastrophizing; physical activity; knowledge of LBP, markers of self-care, and a range of other outcomes not held within our a priori defined domains. For the 3 protocols of future RCTs [27,31,36], a more consistent choice of outcomes was seen, as 2 outcomes—RMDQ and pain intensity—were planned to be measured in all 3 RCT protocols and 3 outcomes—the Tampa Scale of Kinesiophobia (TSK) [31,36], Pain Catastrophizing Scale (PCS) [31,36], and the International Physical Activity Questionnaire (IPAQ) [27,31]—were planned in 2 of the 3 RCT protocols. Below we provide an overview from the 6 included RCT reports of the treatment effects observed for the secondary outcomes that we had identified as being of interest in our systematic review protocol.

Pain Intensity

Pain intensity measured with either an 11-point Numerical Rating Scale (NRS) or a 100-mm Visual Analogue Scale (VAS) was reported in 3 of 6 RCT reports [28,32,33]. Only one study [32] reported that the digital intervention had a beneficial effect on pain intensity, 16 weeks post-intervention (eta-square = 0.43, P=.002); however, this was reported as a composite pain measure combining pain intensity, duration, and frequency.

Quality of Life

Health-related quality of life was reported in 2 studies using the Dartmouth Primary Care Cooperative Information Project (CO-OP) [32] and the Short-Form 12-Item questionnaire (SF-12) [34]. Only one of these studies actually reported the effect in the RCT report. Again Irvine et al used a composite outcome measure, incorporating functionality, well-being, and quality of life; however, they reported a beneficial effect of the intervention compared with the control arm (eta-square = 0.033, P=.001) [32].


Depression was reported in 3 of the 6 RCT reports [28,29,33] but only one study reported beneficial effects of the digital intervention using the Negative Mood Regulation Scale (an increase in score of 0.4 in the intervention group compared with 0.1 in the control group after 3 weeks of the intervention, P<.001) [28].

Fear Avoidance

Three studies reported fear of movement with the Fear Avoidance Belief Questionnaire (FABQ) [28,29,33], but only one reported an effect in favor of the digital intervention group [28], just for the physical activity subscale (decrease in score of 1.0 compared with an increase of 0.1 in the control group, after 3 weeks of the intervention, P<.001). One study used the TSK as a measure of fear avoidance; no between-group difference was reported [32].

Pain Catastrophizing

The PCS questionnaire was used in 2 RCT reports [28,29] but again only one study reported an effect in favor of the digital intervention compared with the waiting list control for the 3 subscales [28].

Physical Activity

Only one of the completed RCTs assessed physical activity outcomes and observed no difference in daily steps achieved between the control and intervention group [33].

Medication Use

No studies reported medication use.

Health Care Utilization

No studies reported details of health care utilization (eg, primary and secondary care visits, emergency department visits).

Health Care Costs

No studies reported on health care costs or cost-effectiveness.

Knowledge of LBP

Three of the RCT reports used participants’ knowledge of LBP as an outcome measure [32,35,37]. Simon et al [35] and Weymann et al [37] used the same self-developed questionnaire, but neither study found a difference between the digital intervention and control group. Irvine et al assessed knowledge using a self-developed questionnaire and reported an effect in favor of the intervention group, however, as a composite score of 3 different outcomes (self-efficacy, behavior intentions, and knowledge) [32].

Markers of Self-Care

In total, 14 different outcomes were identified as markers of self-care, such as the Decision Conflict Scale [35,37], Patient Activation Measure, [32] and Preparation for Decision Making Scale [35,37]. Overall, 5 of the 14 outcomes showed an effect in favor of the digital invention when compared with a control group. Of the 14 outcomes, 10 were reported in only 3 of 6 RCT reports. Of these, the studies by Simon et al [35] and Weymann et al [37] originate from the same research group, and consequently there is considerable overlap between the interventions described and outcomes assessed in both trials. Irvine et al reported an effect on 3 outcomes of self-care in favor of the digital mobile app FitBack, when compared with the control group (behavior intentions, Patient Activation Measure, and prevention helping behaviors) [32].


Four different measures of self-efficacy were reported in 4 RCT reports. The Self-Efficacy for Exercise Scale was used by Carpenter et al, who found an effect on self-efficacy in favor of the digital intervention group as compared with the waiting list control [28]. Irvine et al used a self-developed self-efficacy scale in a composite outcome score and reported a difference in favor of the digital intervention, however, again reported in a composite score [32]. The two other studies reported no benefits [29,33].

Other Outcomes

Ten outcomes could not be classified within our a priori defined outcome domains. These 10 included work-related outcomes, such as the Stanford Presenteeism Scale (SPS) [32], time off work, [32] and the Work Limitations Questionnaire (WLQ) [32], and procedural and implementation outcomes, which included issues such as feasibility [31], treatment adherence [35], as well as credibility and expectations of the intervention [31]. Four outcomes were additionally placed in an “other” category: the Chronic Pain Coping Inventory (CPCI) [29]; Participants’ Global Impression of Change (PGIC) [29]; StartBack Screen Tool [31]; and the Problematic experience of Therapy Scale [31]. For these outcomes in the completed RCTs, Irvine et al reported a between-group difference favoring the digital intervention for the SPS and WLQ in a composite score [32] and Chiauzzi et al reported between-group differences favoring the digital intervention for the CPCI and PGIC outcomes [29].

Specific Characteristics or Components of Digital Self-Management Interventions for LBP Associated With Beneficial Outcomes

Key characteristics of the digital interventions are summarized in Table 5.

Aim of Interventions

Eight of the 9 studies aimed to investigate the effectiveness of the digital intervention in relation to pain intensity, attitudes toward pain, or pain-related disability by comparison with a control group (usual care or a nondigital intervention; as summarized in Table 4) [27-29,32,33,35-37]. One study had its main objective to explore the feasibility of the digital intervention [31].

Intervention Characteristics
Format and Delivery

Seven of 9 studies assessed digital interventions that were accessed over the Internet and by use of a computer [28,29,31,33,35-37], and 2 studies assessed digital interventions, which were app based, but accessible from both computer and handheld devices (tablets or smartphones) [27,32].

Frequency, Duration of Use, and Intervention Duration

Large variation was seen in the reported frequency and duration of use of the digital interventions. Six studies reported unlimited access to the programs with no report of recommendations given regarding frequency of use [27,32,33,35-37]. Geraghty et al [31] recommended a frequency of 1 session per week; Carpenter et al [28] recommended participants complete 2 chapters of the program per week over the 3-week study period; and Chiauzzi et al [29] instructed participants to log in for sessions twice per week. In 3 studies, weekly reminders to visit the website or app were sent to participants in the intervention groups [28,32,33]. Although all studies provided participants with a recommended frequency of use, only 2 of the 9 studies reported their recommended duration of use per visit with a range of 20 min per session to 1-1.5 hours per session [28,29]. Several studies reported that they registered user data but did not give results. Intervention duration also varied greatly, with 3 RCTs lasting between 2 and 4 weeks [28,29,36], one lasting 8 weeks [32], 3 were 3-month long [31,35,37], one study was 6-month long [27], and the longest duration was reported to be of 12 months [33].

Interactive Elements

The interactive elements reported in the studies included (1) keeping a log or journal of use of the intervention [29,32]; (2) simulated dialogue between the user and the system, where the user’s answer(s) was (were) used to create individualized information [28,35,37]; (3) small exercises, such as quizzes, drag-and-drop questions [28]; (4) patient’s report of outcome data and receiving feedback in the form of revised goals, for example, goals for steps per day based on pedometer data [27,33] or graphs illustrating changes in pain intensity [27,32]; (5) targeted messages with information and motivational feedback from the system [27,31-33]; and (6) Web-based discussion forums with peers and health care professionals [33].

Table 4. Study aim, available outcomes, and main results.
StudyAimPrimary analysisSecondary outcomesMain resultControl condition

OutcomesMeasurement Times

Chiauzzi et al [29]Compare interactive self-management website for chronic LBP to standard text-based materials; hypothesized improved emotional management, coping, self-efficacy to manage pain, pain levels, and physical functioningBPI (Brief Pain Inventory)

ODQ (Oswestry Disability Questionnaire)

DASS (Depression/Anxiety and Stress Scale)

PGIC (Patient Global Impression of Change scale)
Baseline, post-intervention (4 weeks), 3 months, 6 monthsPCS (Pain Catastrophizing Scale)

FABQ (Fear Avoidance Belief Questionnaire)

Hypothesis not supported

Educational material: “A back pain guide”
No reminder emails
Simon et al [35]Whether insurees with depression or LBP experienced more favorable decision-related outcomes after using a Web-based tailored decision aid compared with non-tailored, static patient informationDCS (Decisional Conflict Scale)Baseline, post-intervention, 3 monthsPreparation for decision-making scale

Preference for participation, knowledge

Doctor facilitation

Information exchange

Decision regret

Treatment adherence
Intervention effective in short term
Follow-up data of >3 months did not suggest further effects of intervention
Same information as intervention, website, but no tailoring to the individual user

Carpenter et al [28]Efficacy of a pilot version of a Web-based CBT (cognitive behavioral therapy) intervention for chronic LBPSOPA (Survey of Pain Attitudes)Baseline, 3 weeks, 6 weeksFABQ

NMR (Negative Mood Regulation scale)



PSES (Pain Self-Efficacy Scale)

Demographics and pain assessment questionnaire
Difference in favor of the intervention group on all SOPA subscales in the SOPA questionnaire except “medical cure”

Wait list, received no care for 3 weeks, then access to website
Krein et al [33]
Krein et al [34]a
Whether a pedometer-based, Internet-mediated intervention would reduce pain-related disability and functional interference in chronic LBPRMDQ (Roland-Morris Disability Questionnaire)

SF-36 function scale

Baseline, 6 months, 12 monthsPain intensity (NRS, numerical rating scale)

Walking (steps/day)

FABQ PA (physical activity) subscale


6-min walking testb

CES-D 100b(Centre for Epidemiologic Studies Depression Scale)
No between-group difference reported at any time-points

Usual care (attending Back Class) and uploading pedometer data after receiving monthly email reminders to upload; no goal-setting or feedback received; no access to website
Irvine et al [32]Test FitBack for adults at increased risk for chronic LBP due to a recent episode of NLBP

No primary outcome stated
ODQ stated as primary outcome in trial registration
Baseline, 8 weeks, 16 weeksPain: level, frequency, intensity and duration

MPI (Multidimensional Pain Inventory Interference Scale)

Dartmouth CO-OP

Prevention-helping behaviors (self-developed)

WLQ (Work Limitations Questionnaire)

SPS (Stanford Presenteeism Scale)

PAM (Patient Activation Measures)


Behavioral intensions


SOPA (modified)

TSK (Tampa Scale of Kinesiophobia; modified)
No data available for primary outcome analysis

Usual care, emails to request completion of questionnaire

Weymann et al [37]

Dirmaier et al [30]a

Investigate effectiveness of a Web-based, tailored, fully automated intervention for patients with type-2 diabetes or chronic LBP against a standard website with identical content without tailoringKnowledge (post-intervention)

Patient empowerment (heiQ, Health Education Impact Questionnaire; 3 months)
Baseline, post-intervention, 3 monthsDCS

PDMS (Preparation for Decision Making Scale)

The tailored intervention had no effect on the total study population

Same website material as intervention but not tailored; not presented in a dialogue format; no guidance through the content
Geraghty et al [31]aExplore feasibility of providing an Internet intervention for patients with LBP in primary care, with and without physiotherapist telephone support (in addition to usual care), compared with usual care aloneFeasibility outcome

Number need to screen

Recruitment rates

Login and usage information
Baseline, 3 monthsPain: days, duration, intensity


StartBack Screen Tool



IPAQ (International Physical Activity Questionnaire)

PEI (Patient Enablement Instrument)

EQ-5D (Euro-Qol 5D)

LBP related health care use

Time off work

CEQ (Credibility and Expectancy Questionnaire)

SESE (Self-Efficacy for Exercise Scale)

PETS (Problematic Experiences of Therapy Scale)
-Usual care from their general practitioner; this may consist of education and self-management advice, including advice to stay active

Valenzuela-Pascual et al [36]aEvaluate effect of a biopsychosocial Web-based, educational intervention for chronic LBP based on pain intensity compared with normal carePain intensity (100-mm VAS [visual analogue scale] scale)Baseline, 2 weeksFABQ




-No intervention; asked to return to webpage to complete questionnaire at 2 weeks
Amorim et al [27]aInvestigate effect of a patient-centered PA intervention supported by health coaching and technology in chronic LBPCare-seeking

Pain levels (NRS)

Baseline, weekly during intervention, 6 months, 12 monthsIPAQ

Actigraph accelerometer

GAS (Goal Attainment Scale)

-Educational material same as intervention: “Make your move—Sit less, be active for life!”; advice to work toward increasing PA and achieving long-term goals

aProtocol paper, no data available.

bDifference between the protocol paper and RCT report.


Two of the 9 studies did not report any tailoring element to the content of their digital intervention [27,28]. Valenzuela-Pascual et al [36] did not specify the information they used for tailoring. Of the other 6 studies, all used some form of patient characteristics to inform tailoring, for example, Krein et al [33] used gender as a tailoring variable; Chiauzzi et al [29] used participant responses and characteristics (not further specified); Irvine et al [32] used job-type assessed by questionnaires; and Geraghty et al [31] used the extent to which LBP obstructed daily activities as a tailoring variable.

Table 5. Intervention characteristics.
StudyMode of deliveryRecommended frequencyRecommended duration of visitInteractive elementTailoring



Attrition rateg
Chiauzzi et al [29]Website2 times/week for 4 weeks, then unlimited<20 min/sessionLog of activities and content viewed during sessionsYes
Matched patient characteristics to educational content, articles, and interactive tools
4 week intervention period, access for 6 months

Attrition rate:
6 months
Simon et al [35]Website

Small information units presented in combination of text and graphics
Unlimited access but no required frequencyN/RcSimulated dialogue between user and system
Text or graphics varied based on needs of users
Ottawa Decision Support Framework
Tailoring based on ≥4 tailoring concepts, including patient characteristics and preferences
One-time use required, access for 3 months

Attrition rate:

3 months
Carpenter et al [28]Website
Text and graphic with audio narration
Animation used in educational material
Two times/week, email reminders1-1.5 hour/log-inReflective and interactive exercisesNo

3-week intervention period

Attrition rate:
3 weeks
Krein et al [33]
Krein et al[34]d
Graphical and written feedback
Motivational messages
Weekly news updates
Unlimited access with weekly reminders to upload dataN/RcPedometer data, used to create weekly PAegoals and track progress
Targeted messages
Discussion on Web-based forum with peers and health personnel
Written and graphical information as targeted messagesf
12-month intervention period

Attrition rate:
12 months
Irvine et al [32]Web app, accessible from Internet and mobile
Gain-framed text and video messages
Unlimited access, weekly reminders to visit appN/RcPain and PAeself-monitoring tool
Journal-keeping function
7- and 30-day graphs of pain
Job-type assessed by questionnaires
8-week intervention period, access for 16 weeks

Attrition rate:
8 weeks
Weymann et al [37]
Dirmaier et al [30]d


Unlimited access, designed to be used in 1 sittingN/RcSimulated dialogue between user and system

User-control to navigate site by replying to at least 3 options after each text passage

Avoidance Endurance Model
Health literacyf
Motivational Interviewing
Tunnelled design developed
3-month intervention period

Attrition rate:
3 months

Geraghty et al [31]dWebsiteOne session/weekN/RcUser selects PAe, system generates activity goals
User may navigate the content as they find best
Extent of pain obstructing daily activities
3-month intervention period
Valenzuela-Pascual et al [36]dWebsite
Changing delivery formats
Video, 2-3D animation
Unlimited accessN/RcN/Rc
(content not yet developed)
(content not yet developed)

2-week intervention period
Amorim et al [27]dApp, accessed via computer or smartphoneUnlimited access, no recommendations on frequency or durationN/RcUser reports PAelevels, pain intensity, and disability
User receives encouragement based on PAelevel

6-month intervention period

aC: control group.

bI: intervention group.

cN/R: not reported.

dProtocol paper, no data available.

ePA: physical activity.

fInformation given in the protocol but not stated in the RCT report.

gAttrition rates reported as number of completed cases in relation to the total number of participants randomized to the group.

Principal Findings

We have systematically searched and reviewed the literature pertaining to interactive, digital interventions for self-management of LBP. The effectiveness of interventions was mixed, with only 1 study reporting a positive effect on their primary outcome [28]. We found a large degree of heterogeneity regarding the description of intervention content and delivery, theoretical underpinnings, and outcomes reported, making comparison between interventions difficult. A comprehensive description of intervention development and use of theory has been recommended when reporting on RCTs of digital interventions [38]; however, such descriptions were either brief or completely lacking in the included studies. Participants were predominantly female, white, younger, and well educated, which renders the external validity of the identified studies as low.

Despite international recommendations for reporting core outcome domains (physical functioning, pain intensity, and health-related quality of life) in LBP studies [23], we identified 16 different primary outcome measures and a total of 52 outcomes covering a wide range of domains. Better consistency in choice of outcome measures was seen in the 3 RCT protocols of planned RCTs [27,31,36]. We expect that these trials will provide more useful information and data for future meta-analyses. Generally, the included studies were not able to demonstrate significant beneficial effects on either the primary or secondary outcomes and we were unable to identify specific characteristics of interventions to explain these findings. However, it may be that the most important factors related to whether an individual engages with a digital support tool were not taken into account, for example, low mood and additional physical comorbidities. Surprisingly, physical activity, which is considered one of the mainstays of the treatment of LBP [10], was only included as a key component in one study. Consequently, no evidence was presented to support effects on physical activity behavioral changes from digital self-management for LBP. This should be a matter for focus for future RCTs in this area. None of the studies showed any evidence of harm from interactive digital interventions. There was no evidence regarding cost-effectiveness of interactive digital interventions.

Strengths and Limitations

This systematic review was undertaken by a team with extensive experience in conducting such reviews. We used multiple databases, and a thorough search strategy that was designed iteratively by the research team and an information specialist to account for the 3 different dimensions of the search (back pain, digital interventions, and self-management). The methodological assessment tool used in our systematic review has been specifically developed to assess the risk of bias in RCTs [26], and its constructs are in line with the recommendations of the PRISMA statement [18]. All aspects of data extraction, quality appraisal, and data analysis were carried out independently by two researchers, with a third party available for adjudication in case of disagreements.

The primary limitation of this systematic review is the sparse literature related to our objectives. Due to the sparsity and heterogeneity of the data, a formal meta-analysis was not possible. Additionally, our search was limited to studies published in English, Danish, or Norwegian, which could be construed as a limitation, although there is increasing evidence that this is not a particular problem [39]; six papers were excluded at the abstract screening stage of this review based on language. Finally, gray literature was not included; however, given the nature of this review and that there is no suggestion of publication bias, it is unlikely that this will have any impact on the results.

Comparison With Previous Literature

To the best of our knowledge this is the first systematic review of RCTs of interactive digital interventions for self-management of LBP. However, systematic reviews of Web-based interventions for LBP (not specifically self-management) [16], nondigital self-management for LBP [13], and chronic musculoskeletal pain [40] have been published. The first review suggests that CBT-based approaches and interventions that offer Web-based support may have some effect on reducing pain-related catastrophizing and improving patient attitudes; however, study quality was relatively low and further studies were recommended [16]. Reviews of interventions targeted specifically at self-management have suggested that there is only moderate-quality evidence that self-management has small effects on pain and disability in people with LBP [13,40]. These reviews have not dismissed self-management as a treatment option for LBP, but rather suggested that further research is needed to understand the limitations of self-management and whether or how effectiveness can be increased. In addition, these reviews have suggested that future studies should extend the outcomes of interest to include aspects of self-efficacy, and also consider the impact of the duration of the intervention [13,40], increase the length of follow-up [16], and also consider the impact of such interventions on health care utilization [16]. Similar conclusions have been made in systematic reviews of digital self-management interventions in conditions like asthma [22], hypertension [19], and problematic cannabis use [41]. Tailoring digital interventions to individual patient needs has been advocated to enhance engagement [42]. Our review highlights that although 5 out of 6 of the RCT reports included some form of tailoring, there was a lack of detail on exactly what this involved and the role it played in the outcome of the RCT or in user engagement. Finally, small and very similar effects across types of interventions such as different types of exercises, manual treatment, or acupuncture for people with LBP are well recognized [43]; however, because of the enormous societal impact of LBP and LBP-related disability, these interventions may still have worthwhile effects both at the patient and population level [43,44]. In this context, digital interventions aiming to promote self-management are particularly attractive because they are easy to deliver, inexpensive, and safe.

Study Implications

The populations within the identified studies were predominantly female, white, well-educated, and middle-aged, and thus the wider applicability of digital self-management interventions remains uncertain and therefore further investigation including a broader range of participants is merited. Seven of the 9 included studies specifically aimed to address the self-management of chronic LBP, and thus the usefulness of supporting self-management for acute LBP using digital tools remains underinvestigated; any such interventions for acute LBP would possibly require different advice and support to that offered for chronic LBP, as directed in clinical guidelines [10,11]. In addition, the absence of any health economics data was surprising and certainly needs to be addressed in future studies. There were a number of areas of reporting that were identified as deficient in the majority of studies in this systematic review. This suggests that going forward greater adherence to published guidelines that have recommended increasing focus on reporting of the technical aspects of the digital intervention as well as reporting the content of the intervention and its theoretical underpinnings [38,45] would be valuable. Finally, there is growing evidence that tailoring of digital interventions may be an important ingredient for success [42], and this will be an important issue to address in future RCTs of digital interventions aimed at promoting self-management of LBP. We are aware of at least one such study currently underway [46,47].


Our review has highlighted that the published literature is extremely heterogeneous and that digital intervention studies for LBP are generally poorly described. The literature provides insufficient detail regarding target and participating populations, and intervention components, theoretical underpinnings, and the rationale for the wide variety of outcome measures used. This makes it difficult to gain a clear impression of what might work best, for whom and in what circumstances. It is clear that the existing evidence has not yet proven the wider utility of digital interventions for self-management of LBP for the population at large, a knowledge gap that future research should address by better characterizing participants and interventions in a way that would allow replication and by providing clear rationales for intervention components and outcome measure selection.


We would like to thank our librarian adviser Ingrid Ingeborg Riphagen, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU). This project has received funding from the European Union Horizon 2020 research and innovation program under grant agreement no. 689043.

Authors' Contributions

BN, LS, MS, OV, JH, PM, PK, KS, and FM contributed to the design of the study. LS and BN managed the review process. LS, MM, NS, BN, PJM, and PK were involved in one or more of the following stages of the review: screening, data extraction, quality appraisal, or resolution of conflicts. LS led on data synthesis with input from BN, FM, JH, PK, and KS. BN and LS drafted the manuscript. FM oversaw manuscript preparation.

Conflicts of Interest

None declared.

Multimedia Appendix 1

MEDLINE search strategy.

PDF File (Adobe PDF File), 285KB

Multimedia Appendix 2

Overview of all outcome measures included.

PDF File (Adobe PDF File), 374KB

  1. Hoy D, Bain C, Williams G, March L, Brooks P, Blyth F, et al. A systematic review of the global prevalence of low back pain. Arthritis Rheum 2012 Jun;64(6):2028-2037 [FREE Full text] [CrossRef] [Medline]
  2. Vos T, Flaxman AD, Naghavi M, Lozano R, Michaud C, Ezzati M, de Vaccaro, Fowkes Francis Gerry R, De Leòn Felipe Rodriguez, et al. Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012 Dec 15;380(9859):2163-2196. [CrossRef] [Medline]
  3. Hoy D, March L, Brooks P, Blyth F, Woolf A, Bain C, et al. The global burden of low back pain: estimates from the global burden of disease 2010 study. Ann Rheum Dis 2014 Jun;73(6):968-974. [CrossRef] [Medline]
  4. Walker BF, Muller R, Grant WD. Low back pain in Australian adults. health provider utilization and care seeking. J Manipulative Physiol Ther 2004 Jun;27(5):327-335. [CrossRef] [Medline]
  5. Driscoll T, Jacklyn G, Orchard J, Passmore E, Vos T, Freedman G, et al. The global burden of occupationally related low back pain: estimates from the global burden of disease 2010 study. Ann Rheum Dis 2014 Jun;73(6):975-981. [CrossRef] [Medline]
  6. Dagenais S, Caro J, Haldeman S. A systematic review of low back pain cost of illness studies in the United States and internationally. Spine J 2008;8(1):8-20. [CrossRef] [Medline]
  7. Maniadakis N, Gray A. The economic burden of back pain in the UK. Pain 2000 Jan;84(1):95-103. [Medline]
  8. Katz JN. Lumbar disc disorders and low-back pain: socioeconomic factors and consequences. J Bone Joint Surg Am 2006 Apr;88(Suppl 2):21-24. [CrossRef] [Medline]
  9. Nolet PS, Kristman VL, Côté P, Carroll LJ, Cassidy JD. Is low back pain associated with worse health-related quality of life 6 months later? Eur Spine J 2015 Mar;24(3):458-466. [CrossRef] [Medline]
  10. National Institute for HealthCare Excellence (NICE). NICE.: National Institute for Health and Care Excellence (NICE); 2016 Nov. Low back pain and sciatica in over 16s: assessment and management. NICE guideline [NG59]   URL: [accessed 2017-05-04] [WebCite Cache]
  11. The Danish Health and Medicines Authorities. SST. 2016 Jun. National clinical guideline for treatment of recent onset low back pain   URL: [accessed 2017-05-04] [WebCite Cache]
  12. Freburger JK, Holmes GM, Agans RP, Jackman AM, Darter JD, Wallace AS, et al. The rising prevalence of chronic low back pain. Arch Intern Med 2009 Feb 9;169(3):251-258. [CrossRef] [Medline]
  13. Oliveira VC, Ferreira PH, Maher CG, Pinto RZ, Refshauge KM, Ferreira ML. Effectiveness of self-management of low back pain: systematic review with meta-analysis. Arthritis Care Res (Hoboken) 2012 Nov;64(11):1739-1748 [FREE Full text] [CrossRef] [Medline]
  14. Murray E, Burns J, See TS, Lai R, Nazareth I. Interactive health communication applications for people with chronic disease. Cochrane Database Syst Rev 2005(4):CD004274. [CrossRef] [Medline]
  15. Beatty L, Lambert S. A systematic review of internet-based self-help therapeutic interventions to improve distress and disease-control among adults with chronic health conditions. Clin Psychol Rev 2013 Jun;33(4):609-622. [CrossRef] [Medline]
  16. Garg S, Garg D, Turin TC, Chowdhury MF. Web-based interventions for chronic back pain: a systematic review. J Med Internet Res 2016 Jul 26;18(7):e139 [FREE Full text] [CrossRef] [Medline]
  17. Richmond H, Hall AM, Copsey B, Hansen Z, Williamson E, Hoxey-Thomas N, et al. The effectiveness of cognitive behavioural treatment for non-specific low back pain: a systematic review and meta-analysis. PLoS One 2015;10(8):e0134192 [FREE Full text] [CrossRef] [Medline]
  18. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ 2009;339:b2535 [FREE Full text] [Medline]
  19. McLean G, Band R, Saunderson K, Hanlon P, Murray E, Little P, et al. Digital interventions to promote self-management in adults with hypertension systematic review and meta-analysis. J Hypertens 2016 Apr;34(4):600-612 [FREE Full text] [CrossRef] [Medline]
  20. McLean G, Murray E, Band R, Saunderson K, Hanlon P, Little P, et al. Digital interventions to promote self-management in adults with hypertension: protocol for systematic review and meta-analysis. JMIR Res Protoc 2015 Nov 20;4(4):e133 [FREE Full text] [CrossRef] [Medline]
  21. McLean G, Murray E, Band R, Moffat KR, Hanlon P, Bruton A, et al. Interactive digital interventions to promote self-management in adults with asthma: systematic review and meta-analysis. BMC Pulm Med 2016 May 23;16(1):83 [FREE Full text] [CrossRef] [Medline]
  22. Morrison D, Wyke S, Agur K, Cameron EJ, Docking RI, Mackenzie AM, et al. Digital asthma self-management interventions: a systematic review. J Med Internet Res 2014;16(2):e51 [FREE Full text] [CrossRef] [Medline]
  23. Chiarotto A, Deyo RA, Terwee CB, Boers M, Buchbinder R, Corbin TP, et al. Core outcome domains for clinical trials in non-specific low back pain. Eur Spine J 2015 Jun;24(6):1127-1142. [CrossRef] [Medline]
  24. Deyo RA, Battie M, Beurskens AJ, Bombardier C, Croft P, Koes B, et al. Outcome measures for low back pain research. a proposal for standardized use. Spine (Phila Pa 1976) 1998 Sep 15;23(18):2003-2013. [Medline]
  25. Deyo RA, Dworkin SF, Amtmann D, Andersson G, Borenstein D, Carragee E, et al. Report of the NIH Task Force on research standards for chronic low back pain. J Pain 2014 Jun;15(6):569-585 [FREE Full text] [CrossRef] [Medline]
  26. Higgins JP, Altman DG, Gøtzsche PC, Jüni P, Moher D, Oxman AD, Cochrane Bias Methods Group, Cochrane Statistical Methods Group. The Cochrane Collaboration's tool for assessing risk of bias in randomised trials. BMJ 2011;343:d5928 [FREE Full text] [Medline]
  27. Amorim AB, Pappas E, Simic M, Ferreira ML, Tiedemann A, Jennings M, et al. Integrating mobile health and physical activity to reduce the burden of chronic low back pain trial (IMPACT): a pilot trial protocol. BMC Musculoskelet Disord 2016;17:36 [FREE Full text] [CrossRef] [Medline]
  28. Carpenter KM, Stoner SA, Mundt JM, Stoelb B. An online self-help CBT intervention for chronic lower back pain. Clin J Pain 2012 Jan;28(1):14-22 [FREE Full text] [CrossRef] [Medline]
  29. Chiauzzi E, Pujol LA, Wood M, Bond K, Black R, Yiu E, et al. painACTION-back pain: a self-management website for people with chronic back pain. Pain Med 2010 Jul;11(7):1044-1058. [CrossRef] [Medline]
  30. Dirmaier J, Härter M, Weymann N. A tailored, dialogue-based health communication application for patients with chronic low back pain: study protocol of a randomised controlled trial. BMC Med Inform Decis Mak 2013;13:66 [FREE Full text] [CrossRef] [Medline]
  31. Geraghty AW, Stanford R, Little P, Roberts L, Foster NE, Hill JC, 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 Sep 23;5(9):e009524 [FREE Full text] [CrossRef] [Medline]
  32. Irvine AB, Russell H, Manocchia M, Mino DE, Cox GT, Morgan R, et al. Mobile-Web app to self-manage low back pain: randomized controlled trial. J Med Internet Res 2015;17(1):e1 [FREE Full text] [CrossRef] [Medline]
  33. Krein SL, Kadri R, Hughes M, Kerr EA, Piette JD, Holleman R, et al. Pedometer-based internet-mediated intervention for adults with chronic low back pain: randomized controlled trial. J Med Internet Res 2013 Aug 19;15(8):e181 [FREE Full text] [CrossRef] [Medline]
  34. Krein SL, Metreger T, Kadri R, Hughes M, Kerr EA, Piette JD, et al. Veterans walk to beat back pain: study rationale, design and protocol of a randomized trial of a pedometer-based internet mediated intervention for patients with chronic low back pain. BMC Musculoskelet Disord 2010;11:205 [FREE Full text] [CrossRef] [Medline]
  35. Simon D, Kriston L, von WA, Buchholz A, Vietor C, Hecke T, et al. Effectiveness of a web-based, individually tailored decision aid for depression or acute low back pain: a randomized controlled trial. Patient Educ Couns 2012 Jun;87(3):360-368. [CrossRef] [Medline]
  36. Valenzuela-Pascual F, Molina F, Corbi F, Blanco-Blanco J, Gil RM, Soler-Gonzalez J. The influence of a biopsychosocial educational internet-based intervention on pain, dysfunction, quality of life, and pain cognition in chronic low back pain patients in primary care: a mixed methods approach. BMC Med Inform Decis Mak 2015 Nov 23;15:97 [FREE Full text] [CrossRef] [Medline]
  37. Weymann N, Dirmaier J, von WA, Kriston L, Härter M. Effectiveness of a Web-based tailored interactive health communication application for patients with type 2 diabetes or chronic low back pain: randomized controlled trial. J Med Internet Res 2015;17(3):e53 [FREE Full text] [CrossRef] [Medline]
  38. Eysenbach G, CONSORT- E. CONSORT-EHEALTH: improving and standardizing evaluation reports of web-based and mobile health interventions. J Med Internet Res 2011;13(4):e126 [FREE Full text] [CrossRef] [Medline]
  39. Morrison A, Polisena J, Husereau D, Moulton K, Clark M, Fiander M, et al. The effect of English-language restriction on systematic review-based meta-analyses: a systematic review of empirical studies. Int J Technol Assess Health Care 2012 Apr;28(2):138-144. [CrossRef] [Medline]
  40. Taylor S, Carnes D, Homer K, Pincus T, Kahan B, Hounsome N, et al. Improving the self-management of chronic pain: COping with persistent Pain, Effectiveness Research in Self-management (COPERS). Southampton, UK: NIHR Journals Library; Sep 2016.
  41. Hoch E, Preuss UW, Ferri M, Simon R. Digital interventions for problematic Cannabis users in non-clinical settings: findings from a systematic review and meta-analysis. Eur Addict Res 2016;22(5):233-242 [FREE Full text] [CrossRef] [Medline]
  42. O'Connor S, Hanlon P, O'Donnell CA, Garcia S, Glanville J, Mair FS. Understanding factors affecting patient and public engagement and recruitment to digital health interventions: a systematic review of qualitative studies. BMC Med Inform Decis Mak 2016 Sep 15;16(1):120 [FREE Full text] [CrossRef] [Medline]
  43. Artus M, van der Windt DA, Jordan KP, Hay EM. Low back pain symptoms show a similar pattern of improvement following a wide range of primary care treatments: a systematic review of randomized clinical trials. Rheumatology (Oxford) 2010 Dec;49(12):2346-2356. [CrossRef] [Medline]
  44. Hancock MJ, Hill JC. Are small effects for back pain interventions really surprising? J Orthop Sports Phys Ther 2016 May;46(5):317-319. [CrossRef] [Medline]
  45. Agarwal S, LeFevre AE, Lee J, L'Engle K, Mehl G, Sinha C, WHO mHealth Technical Evidence Review Group. Guidelines for reporting of health interventions using mobile phones: mobile health (mHealth) evidence reporting and assessment (mERA) checklist. BMJ 2016;352:i1174. [Medline]
  46. Nicholl B, Bach K, Aamodt A, Mork P, Mair F. selfBACK: a decision support system for self-management of low back pain. 2016 Presented at: North American Primary Care Research Group Annual Meeting; November 11-15, 2016; Colorado Springs.
  47. Mork PJ. Selfback. 2016. A decision support system for self-management of non-specific low back pain   URL:

BPI: Brief Pain Inventory
CBT: cognitive behavioral therapy
CDSR: Cochrane Database of Systematic Reviews
CENTRAL: Cochrane Central Register of Controlled Trials
CEQ: Credibility and Expectancy Questionnaire
CES-D-100: Centre for Epidemiologic Studies Depression Scale
CINAHL: Cumulative Index to Nursing and Allied Health Literature
CO-OP: Primary Care Cooperative Information Project
CPCI: Chronic Pain Coping Inventory
DARE: Database of Abstracts of Reviews of Effects
DASS: Depression/Anxiety and Stress Scale
DCS: Decisional Conflict Scale
DoPHER: Database of Promoting Health Effectiveness Reviews
FABQ: Fear Avoidance Belief Questionnaire
GAS: Goal Attainment Scale
heiQ: Health Education Impact Questionnaire
HTA: Health Technology Assessment
IPAQ: International Physical Activity Questionnaire
LBP: low back pain
MPI: Multidimensional Pain Inventory Interference Scale
NMR: Negative Mood Regulation scale
NRS: Numerical Rating Scale
NTNU: Norwegian University of Science and Technology
ODQ: Oswestry Disability Questionnaire
ODI: Oswestry Disability Index
PA: physical activity
PAM: Patient Activation Measures
PCS: Pain Catastrophizing Scale
PDMS: Preparation for Decision Making Scale
PEI: Patient Enablement Instrument
PETS: Problematic Experiences of Therapy Scale
PGIC: Patient Global Impression of Change
PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses
PSES: Pain Self-Efficacy Scale
RCT: randomized controlled trial
RMDQ: Roland-Morris Disability Questionnaire
SD: standard deviation
SES: socioeconomic status
SOPA: Survey of Pain Attitudes
SPS: Stanford Presenteeism Scale
TSK: Tampa Scale of Kinesiophobia
TROPHI: Trials Register of Promoting Health Interventions
TSK: Tampa Scale of Kinesiophobia
VAS: visual analogue scale
WLQ: Work Limitations Questionnaire

Edited by G Eysenbach; submitted 09.01.17; peer-reviewed by S Peterson, A Geraghty, K Morton; comments to author 29.01.17; revised version received 13.03.17; accepted 18.03.17; published 21.05.17


©Barbara I Nicholl, Louise F Sandal, Mette J Stochkendahl, Marianne McCallum, Nithya Suresh, Ottar Vasseljen, Jan Hartvigsen, Paul J Mork, Per Kjaer, Karen Søgaard, Frances S Mair. Originally published in the Journal of Medical Internet Research (, 21.05.2017.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (, 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, as well as this copyright and license information must be included.