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Low back pain is one of the most common health problems and a main cause of disability, which imposes a great burden on patients. Mobile health (mHealth) affects many aspects of people’s lives, and it has progressed rapidly, showing promise as an effective intervention for patients with low back pain. However, the efficacy of mHealth interventions for patients with low back pain remains unclear; thus, further exploration is necessary.
The purpose of this study was to evaluate the efficacy of mHealth interventions in patients with low back pain compared to usual care.
This was a systematic review and meta-analysis of randomized controlled trials designed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analysis) statement standard. We searched for studies published in English before October 2020 in the PubMed, EMBASE, Web of Science, and Cochrane Library databases. Two researchers independently scanned the literature, extracted data, and assessed the methodological quality of the included studies. Bias risks were assessed using the Cochrane Collaboration tool. We used RevMan 5.4 software to perform the meta-analysis.
A total of 9 studies with 792 participants met the inclusion criteria. The simultaneous use of mHealth and usual care showed a better reduction in pain intensity than usual care alone, as measured by the numeric rating scale (mean difference [MD] –0.85, 95% CI –1.29 to –0.40;
The use of simultaneous mHealth and usual care interventions has better efficacy than usual care alone in reducing pain intensity and disability in patients with low back pain. Moreover, the results of subgroup analysis revealed that mHealth using telephone calls might play a positive role in improving pain intensity and disability in patients with low back pain.
Low back pain is one of the most common health problems worldwide, and is a main cause of disability according to the latest Global Burden of Disease Study [
The term mobile health (mHealth) refers to the use of mobile devices such as mobile phones, patient monitoring equipment, and other wireless devices to provide medical support and health management [
At present, few meta-analyses have been performed on the efficacy of mHealth for patients with low back pain, and therefore the ability to provide more effective or accurate clinical advice is limited. In light of the various advantages of mHealth that are different from usual care and the current global status of low back pain, we performed a meta-analysis to clarify the efficacy of mHealth for patients with low back pain.
Data were retrieved from the PubMed, Embase, Web of Science, and Cochrane Library databases. We searched for studies in English published until October 2020. The key search strings consisted of two concepts: mHealth and low back pain (
Search 1: (“mobile application” OR “telemedicine” OR “text messaging” OR “mobile phone” OR “smartphone” OR “social media” OR “internet”)/exp
Search 2: (mobile OR “portable software application” OR tele* OR mhealth OR ehealth OR “e health” OR “mhealth” OR ?phone* OR text* OR “short message” OR sms OR app OR apps OR digital* OR web* OR internet* OR ?media OR wireless OR computer OR video* OR bluetooth OR blog* OR online OR electronic OR “mp3 player” OR “mp4 player” OR wechat OR whatsapp OR twitter OR “virtual reality” OR “interactive voice response” OR facebook OR networking): title/abstract/keywords
Search 3: Search 1 OR Search 2
Search 4: “low back pain”/exp OR “backache”/exp
Search 5: (“low back pain” OR “low back ache” OR “low backache” OR “lower back pain” OR “back disorder” OR backache OR “back pain” OR lumbago OR dorsalgia OR coccyx OR coccydynia OR sciatica OR ischialgia OR spondylosis): title/abstract/keywords
Search 6: Search 4 OR Search 5
Search 7: Search 3 AND Search 6
The following inclusion criteria were used: (1) the study design was a randomized controlled trial (RCT); (2) mHealth (eg, mobile phone, computer, motion sensor biofeedback, and network-based game consoles) and usual care (eg, exercise and/or advice) were used simultaneously in the experimental group, and usual care or usual care and placebo were used in the control group; (3) participants were confirmed to have low back pain; and (4) the outcomes were measured using the numeric rating scale (NRS) and/or Roland-Morris Disability Questionnaire (RMDQ), with data expressed as mean (SD). Two researchers selected the studies independently in accordance with the above criteria.
Studies were excluded from the meta-analysis if: (1) not all of the participants were diagnosed with low back pain; (2) the study population included pregnant women or patients recovering after spinal surgery; or (3) email had been used as an intervention for office workers in the workplace. The latter criterion was based on previous studies [
The required data were extracted independently by two researchers and crosschecked to avoid potential data extraction errors. Disagreements during the extraction process were resolved by seeking the opinion of a third researcher. The extracted information included the first author’s name, year of publication, sample size, age, sex ratio, and participants’ scores on the NRS and RMDQ. The final postintervention data with the longest follow-up time was used in our analysis if the study (ie, [
We assigned participants who used telephone calls, internet/email, mobile phones, or other mHealth methods and usual care at the same time to the mHealth experimental group, and those who used usual care alone to the control group. RevMan 5.4 software was used for the meta-analysis, with the mean difference (MD), SD, and 95% CI as the statistics of interest. The overall pooled effect estimate was assessed using
Given reports of positive effects of telephone calls on patients’ self-management and compliance [
The risk of bias was assessed using the Cochrane Collaboration Tool for Assessing Risk of Bias in Randomised Trials [
A total of 17,670 studies were identified during the initial examination. After all studies were screened and filtered using the inclusion and exclusion criteria, 9 studies were included in the final meta-analysis. These studies comprised 792 participants, 407 of whom were allocated to the mHealth group and 385 were allocated to the usual care group, as shown in the flowchart in
Flowchart of the screening and selection of studies.
Features of the included studies and outcome data are summarized in
Pain and disability scores at baseline and after the interventions.
Study | Pain intensity: NRSa (score range 0-10), mean (SD) | Disability: RMDQb (score range 0-24), mean (SD) | ||||||||
|
mHealthc group | Control group | mHealth group | Control group | ||||||
|
Baseline | After intervention | Baseline | After intervention | Baseline | After intervention | Baseline | After intervention | ||
Amorim et al [ |
5.3 (1.9) | 3.8 (2.4) | 5.1 (1.4) | 4.0 (3.4) | 8.9 (5.4) | 5.7 (5.3) | 9.0 (6.1) | 6.0 (5.7) | ||
Bernardelli et al [ |
NAd | NA | NA | NA | 6.3 (4.4) | 3.8 (3.9) | 6.4 (4.9) | 4.3 (4.2) | ||
Chhabra et al [ |
7.3 (1.9 | 3.3 (1.7) | 6.6 (2.1) | 3.2 (2.7) | NA | NA | NA | NA | ||
Damush et al [ |
NA | NA | NA | NA | 14.7 (6.7) | 9.1 (6.8) | 13.9 (6.8) | 11.3 (8.1) | ||
Geraghty et al [ |
Ⅰ: 4.0 (2.6); Ⅱ:4.5 (2.6) | Ⅰ: 3.6 (2.5); Ⅱ: 3.1 (2.3) | 3.6 (3.1) | 4.0 (2.5) | Ⅰ: 6.6 (4.6); Ⅱ: 7.7 (4.7) | Ⅰ: 5.8 (4.5); Ⅱ: 5.1 (5.1) | 6.8 (4.9) | 6.3 (5.1) | ||
Kent et al [ |
NA | NA | NA | NA | 11.8 (8.8) | 7.2 (2.6) | 11.3 (7.0) | 11.0 (1.3) | ||
Monteiro-Junior et al [ |
6.5 (1.1) | 1.7 (1.9) | 6.6 (1.2) | 1.4 (2.9) | NA | NA | NA | NA | ||
Petrozzi et al [ |
5.1 (1.8) | 3.0 (2.1) | 4.9 (2.0) | 4.0 (2.1) | 9.9 (4.2) | 4.2 (3.7) | 9.9 (4.7) | 5.3 (5.1) | ||
Yang et al [ |
NA | NA | NA | NA | 6.00 (3.74) | 4.40 (3.05) | 12.00 (3.61) | 11.70 (5.69) |
aNRS: numeric rating scale.
bRMDQ: Rolland-Morris Disability Questionnaire.
c mHealth: mobile health.
dNA: not applicable (not assessed).
The Cochrane Collaboration Risk of Bias Tool was used to assess the risk of bias of the 9 included studies. Seven studies used computer-generated random numbers [
The overall risk of bias was relatively low, but performance bias was relatively high, as 3 of the studies used a single-blinded method [
Risk assessment of bias in the included studies.
Compared with usual care, the simultaneous interventions of mHealth and usual care were more effective in reducing pain, as indicated by the NRS scores of 404 participants in 5 studies (MD –0.85, 95% CI –1.29 to –0.40; I2=9%;
Forest plot of the efficacy of mobile health and traditional health interventions in reducing pain intensity.
Compared with usual care, the simultaneous interventions of mHealth and usual care had a larger effect on reducing disability in patients with low back pain, as indicated by the RMDQ scores of 885 participants in 8 studies (MD –1.54, 95% CI –2.35 to –0.73; I2 =31%;
Forest plot of the efficacy of mobile health and traditional health interventions on disability.
We evaluated pain intensity, as measured by the NRS, to examine the efficacy of mHealth using telephone calls. We performed a subgroup analysis of participants in the mHealth experimental group that used telephone calls and those who were in an intervention group that did not use telephone calls. A difference was found between the telephone group and nontelephone group, although it was not statistically significant (I2=48.5%,
Efficacy of telephone calls use in reducing pain intensity.
We evaluated disability, as measured by the RMDQ, to examine the efficacy of different types of mHealth. We performed a subgroup analysis of participants in the mHealth experimental group that used telephone calls, did not use telephone calls, or used a more sensitive feedback intervention. The analysis indicated a significant difference between the telephone calls group, the more sensitive feedback intervention group, and the nontelephone group (I2=69.3%,
Efficacy of telephone use and the more sensitive feedback intervention in reducing disability.
A sensitivity analysis was performed using one-by-one elimination of studies that reported the outcomes of the NRS and the RMDQ. No significant change was found in the outcomes, indicating that the results were stable. According to the Cochrane Group, a funnel plot to detect publication bias is not recommended when fewer than 9 studies are included in a meta-analysis [
This meta-analysis of 9 studies with 792 patients revealed a significant positive effect of the simultaneous interventions of mHealth and usual care compared with usual care only on patients with low back pain. The mHealth intervention performed significantly better than usual care on measures of pain intensity (MD –0.85, 95% CI –1.29 to –0.40; I2=9%,
Our study demonstrates the importance of telephone calls in mHealth. We believe that telephone calls may be one of the main effective types of mHealth with great positive effects on patients in reducing pain and disability. As one of the mobile medical methods, telephone calls might be superior to other types of mHealth for the following reasons. First, according to Simblett et al [
To our knowledge, subgroup analyses of the efficacy of telephone calls have not been performed to date. Since few studies have examined the impact of mHealth on patients with low back pain, there are no related articles for comparison. Only a systematic review and meta-analysis of 5 articles performed by Du et al [
Establishing clinical relevance is the key to whether mHealth can be used in patients with low back pain. Yet, small effects (–0.85) are observed at the group level for pain intensity when compared to the control group, which do not meet the minimal clinically important difference criterion of –1.77 [
The objective of this study was to examine the influence of mHealth interventions on the pain intensity and disability of patients with low back pain. Our investigation highlights differences between the intervention of usual care alone and the simultaneous use of usual care and mHealth. Compared with using usual care alone, the intervention of telephone calls had a significant beneficial effect on patients’ disability. These findings are expected to provide guidance for clinical decisions and contribute to this field.
Our study has several limitations. First, this meta-analysis may be biased if the literature search failed to identify all trials reporting on differences between mHealth and usual care or if the selection criteria for including trials were applied in a subjective manner. To reduce these risks, we performed thorough searches across multiple literature databases and clinical trial databases, and used explicit criteria for study selection and data extraction and analysis. Second, mHealth may have specific effects that vary by the type of low back pain. That is, to better evaluate the efficacy, and save human resources and time costs, passive sensing in mHealth may be more suitable for chronic low back pain, whereas active sensing may be more suitable for acute low back pain, which can be administered multiple times a day to capture short-term variations in responses [
The results of this meta-analysis suggest that the simultaneous interventions of mHealth and usual care, compared with usual care alone, are significantly better for reducing pain intensity and disability in patients with low back pain. The use of telephone calls or more sensitive feedback interventions may further increase the positive effects of these simultaneous interventions on the disability of patients with low back pain. The wider use of mHealth may contribute significantly to the population of patients with low back pain. Therefore, the simultaneous interventions of mHealth and usual care may be a promising method worth considering.
Characteristics of the included studies.
mean difference
mobile health
numeric rating scale
randomized controlled trial
Roland-Morris Disability Questionnaire
This study was funded by the Joint Funds for the Innovation of Science and Technology, Fujian Province (grant number 2018Y9060), the Natural Science Foundation of Fujian Province, China (2018Y0037), and the Fujian Provincial Health Technology Project, China (2019-CX-19).
JZ initiated the study. ZF and JQ selected the studies for inclusion in the meta-analysis. SJ and ZZ performed the quality assessments of the included studies. TW and ML performed the data extraction and analyses, and MC and CC drafted the first version of the manuscript. WC advised on data analysis. JZ, TW, and MC critically reviewed and revised the manuscript. All authors made a substantial contribution to the concept and design of the study, interpretation of the data, and review of the manuscript.
None declared.