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Digital Rehabilitation for Acute Low Back Pain: A Prospective Longitudinal Cohort Study

Authors Costa F , Janela D , Molinos M, Moulder RG, Lains J , Bento V, Scheer J, Yanamadala V, Correia FD , Cohen SP

Received 14 April 2022

Accepted for publication 11 June 2022

Published 3 July 2022 Volume 2022:15 Pages 1873—1887


Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Alaa Abd-Elsayed

Fabíola Costa,1 Dora Janela,1 Maria Molinos,1 Robert G Moulder,2 Jorge Lains,3,4 Virgílio Bento,1 Justin Scheer,5 Vijay Yanamadala,1,6,7 Fernando Dias Correia,1,8 Steven P Cohen9,10

1SWORD Health, Inc, Clinical Research, Draper, UT, USA; 2Institute for Cognitive Science, University of Colorado Boulder, Boulder, CO, USA; 3Rovisco Pais Medical and Rehabilitation Centre, Tocha, Portugal; 4Faculty of Medicine, Coimbra University, Coimbra, Portugal; 5Department of Neurological Surgery, University of California, San Francisco, CA, USA; 6Department of Surgery, Quinnipiac University Frank H. Netter School of Medicine, Hamden, CT, USA; 7Department of Neurosurgery, Hartford Healthcare Medical Group, Westport, CT, USA; 8Neurology Department, Centro Hospitalar e Universitário do Porto, Porto, Portugal; 9Departments of Anesthesiology & Critical Care Medicine, Physical Medicine and Rehabilitation, Neurology, and Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA; 10Departments of Anesthesiology and Physical Medicine and Rehabilitation and Anesthesiology, Uniformed Services University of the Health Sciences, Bethesda, MD, USA

Correspondence: Fernando Dias Correia, 65 E Wadsworth Park Dr Ste 230, Draper, UT, 84020, USA, Tel +1 385-308-8034, Fax +1 801-206-3433, Email [email protected]

Background: Low back pain (LBP) has a lifetime prevalence of 70– 80%. Access to timely and personalized, evidence-based care is key to prevent chronic progression. Digital solutions may ease accessibility to treatment while reducing healthcare-related costs.
Purpose: We aim to report the results of a fully remote digital care program (DCP) for acute LBP.
Patients and Methods: This was an interventional, single-arm, cohort study of patients with acute LBP who received a DCP. Primary outcome was the mean change in disability (Oswestry Disability Index – ODI) after 12 weeks. Secondary outcomes included change in pain (NPRS), analgesic consumption, surgery likelihood, depression (PHQ-9), anxiety (GAD-7), fear-avoidance beliefs (FABQ-PA), work productivity (WPAI) and engagement.
Results: A total of 406 patients were enrolled in the program and of those, 332 (81.8%) completed the intervention. A significant disability reduction of 55.1% (14.93, 95% CI 13.95; 15.91) was observed, corresponding to a 76.1% responder rate (30% cut-off). Disability reduction was accompanied by significant improvements in pain (61.0%), depression (55.4%), anxiety (59.5%), productivity (65.6%), fear-avoidance beliefs (46.3%), intent to pursue surgery (59.1%), and analgesic consumption (from 35.7% at baseline to 10.8% at program end). DCP-related patient satisfaction score was 8.7/10.0 (SD 1.4).
Conclusion: This study demonstrated the utility of a multimodal DCP for patients with acute LBP. Very high adherence rates and patient satisfaction were observed, alongside significant reductions in all assessed outcomes, consistent with the growing body of evidence supporting the management of acute LBP with DCPs.

Keywords: physical therapy, telerehabilitation, digital therapy, eHealth, musculoskeletal conditions

Plain Language Summary

Low back pain (LBP) has a very high lifetime prevalence (70–80%) and is a leading cause of absenteeism. In about 65% of patients, acute episodes of LBP are not resolved after 12 months, challenging the notion that spontaneous recovery protects most individuals from long-term LBP. Therefore, preventing progression to chronic pain is a priority.

Current guidelines emphasize exercise-based treatments, combined with pain self-management strategies as the indicated approach. Major care barriers relate to access, time and travel constraints. Digital telerehabilitation programs have shown similar results to in-person care, and may solve these challenges, while improving engagement and reducing costs. These programs are still not well explored for acute LBP management.

In this study, we assessed the progress of a large group of patients going through a digital care program managed by a physical therapist. This program integrates exercise, education on back pain, and tools for mental strength and self-management. Exercises are guided through a tablet and motion trackers which provide real-time feedback during each exercise.

We report meaningful improvements in disability (55.1%), pain (61.0%), mental health (55.4–59.5%), surgery likelihood (59.1%) and productivity (65.6%), which were associated with high engagement and satisfaction levels. Importantly, individuals at higher risk (with higher initial pain) were not less likely to respond to the treatment.

This study supports the utility of digital care programs in the early stage of LBP management, to improve functionality, well-being and productivity.


Low back pain (LBP) has long been the world’s leading cause of years lived with disability1 and a leading cause of worker absenteeism.2–4 The lifetime prevalence of LBP is extremely high (70–80%),5–7 which is expected to worsen, given the rise in life expectancy and increasing rate of obesity and persistently lower levels of physical activity than our ancestors engaged in.7,8 In the United States (US), nearly 66 million adults suffer from LBP,9 which was the major contributor for the more than $134.5 billion (95% CI, $122.4-$146.9 billion) in healthcare spending for spine pain in 2016.10

Evidence shows that about 65% of patients with acute LBP will still report pain after 12 months,11 questioning the assumption that spontaneous recovery protects most individuals from long-term LBP. Preventing progression to a chronic disease state is a priority, which might be attained through individually tailored evidence-based interventions in the acute and subacute stages of LBP.12–14 Current research and guidelines place emphasis on active exercise-based treatments embedded in a biopsychosocial framework using cognitive behavioral therapy (CBT) and self-management.15–19 Such interventions can promote significant recovery at lower costs, which include reduced utilization of health-care services,20 a reduction in unnecessary imaging procedures,21,22 and fewer surgeries.23 Exercise-based treatments, combined with education have been demonstrated to reduce the risk of future episodes of LBP and facilitate return to work.24–28 However, several barriers continue to prevent widespread access to such interventions, namely a lack of available providers in some regions, which may particularly impact vulnerable populations, and constraints associated with travel and treatment time,29 which have been amplified during the COVID pandemic.30

Entirely digital interventions, consisting of programs managed remotely/asynchronously by health-care professionals using communication-based technologies, show great potential in overcoming such challenges and improving care, as reflected in the growing number of clinical trials and systematic reviews.31–35 These may be more affordable and accessible than in-person rehabilitation, while easing caregiver burden.36,37 Patient adherence and empowerment may also be maximized through these approaches.38 Most telerehabilitation studies have been focused on populations with chronic LBP,32,33,35,39–42 while acute LBP is less well-explored.34,43–45

Previously, we have demonstrated the effectiveness of tailored digital care programs (DCP) in other musculoskeletal conditions.46–52 The present study aims to assess the outcomes and engagement of a fully remote multimodal DCP integrating exercise and education, including major components of CBT, on a real-world cohort of patients with acute LBP stratified by pain level at baseline. We hypothesize that this multimodal DCP can provide significant improvement independent of the reported pain at baseline to an extent comparable to those reported in the literature for other conventional or telerehabilitation approaches.


Study Design

Single-arm, decentralized study assessed clinical and engagement-related outcomes after a multimodal digital care program (DCP), in patients with acute LBP. This study is part of a trial that was prospectively approved by the New England Institutional Review Board (number 120190313) and registered on (NCT04092946) on September 17th 2019. The study was conducted in accordance with the Declaration of Helsinki. An exploratory analysis using baseline pain as a risk stratification variable was additionally pursued to ascertain the potential impact of this parameter on observed outcomes. The home-based DCP was delivered between June 29th 2020 and November 4th 2021.


Individuals participating in health plans of employers from 44 states in the US, older than 18 years of age and reporting acute LBP (defined as pain below the costal margin and above the inferior gluteal folds less than 12 weeks in duration) were invited to apply for SWORD Health’s DCP (Draper, Utah, USA) through a dedicated website. Exclusion criteria included: (1) a health condition (eg, cardiac, respiratory) incompatible with at least 20 minutes of light to moderate exercise; (2) receiving treatment for active cancer; and (3) reporting rapidly progressive loss of strength and/or numbness in the arms/legs or unexplained change in bowel or urinary function in the previous 2 weeks.

Informed consent was obtained from all participants before study start. To prevent the risk of selection bias, consecutive participants were enrolled until the cut-off date of August 12th, 2021. This cut-off date resulted in the inclusion of 23% (92/406) participants with acute LBP already studied by Costa et al.51


The current intervention was previously described.51,52 Briefly, a 12-week telerehabilitation intervention consisting of exercise, education and CBT was delivered through a DCP, which interfaced between the patient and an assigned physical therapist (PT) who monitored the patient for the study duration. An FDA-listed class II medical device comprised two inertial motion trackers, a mobile app on a dedicated tablet, and a cloud-based portal, was made available. Personalized exercise sessions (Annex 1) were performed independently at the patients’ convenience through the tablet display (3 sessions per week were recommended). By placing trackers on the thoracic and lumbar regions through straps, the system provided real-time video and audio biofeedback on performance. A cloud-based portal enabled asynchronous and remote monitoring by the assigned PT, who adjusted the exercise program as needed. The education and CBT component, developed according to current clinical guidelines and research, included topics centered around anatomy, physiology, symptoms, evidence-based treatments, fear-avoidance, and active coping skills (including dealing with feelings of anxiety and depression). The CBT program was based on third-generation CBT techniques – mindfulness, acceptance and commitment therapy and empathy-focused therapy. Education and CBT components were delivered on a weekly basis. These were delivered through written articles, audio content and interactive modules. Bi-directional communication was ensured through a built-in secure chat within a smartphone app (at least one touchpoint each week) and video calls (at least once every 4 weeks). Participants who did not engage in any exercise session for 28 consecutive days were considered dropouts.


Outcomes were collected at baseline, 4, 8 and 12 weeks, and mean changes were calculated between baseline and 12 weeks.

Primary outcome was self-reported disability, using the Oswestry Disability Index (ODI), which has been validated for patients with acute and subacute LBP.53,54 ODI includes 10 items scored using a 5-point Likert scale (score range 0–100%), whereby higher scores correspond to greater disability.55 Secondary outcomes included the following clinical and engagement outcomes:

Pain level, using the Numerical Pain Rating Scale (NPRS), through the question: “Please rate your average pain over the last 7 days from 0 (no pain at all) to 10 (worst pain imaginable)”

Analgesic consumption: “Are you currently taking any pain medication?”

Willingness to undergo surgery: “How likely are you to have surgery to address your condition in the next 12 months?” (range 0 - not at all likely; 100 - extremely likely)

Generalized Anxiety Disorder (GAD-7) 7-item scale (range 0-21)56 to assess anxiety, and Patient Health (PHQ-9) 9-item questionnaire (range 0-27) to assess depression.57,58 A threshold equal or greater than 5 was used to identify at least mild anxiety or depression

Fear-Avoidance Beliefs Questionnaire for physical activity (FABQ-PA), which includes 4 items scored on a 7-option Likert scale (0-24)59

Work Productivity and Activity Impairment (WPAI) for general health questionnaire, evaluated employed participants to assess overall work impairment (WPAI overall: total presenteeism and absenteeism from work), presenteeism (WPAI work), absenteeism (WPAI time) and activities impairment (WPAI activity)60

Engagement: through completion of the program (considered as the retention rate); number of completed exercise sessions; time spent performing exercise sessions; and overall satisfaction (Net promoter score) through the question: “On a scale from 0 to 10, how likely is it that you would recommend this intervention to a friend or neighbor?”

Safety and Adverse Events

Patients were instructed to report pain and fatigue scores (graduated from 0 to 10) at the end of each exercise session, as well as any adverse events when they occurred. These were continuously monitored remotely by the PT.

Data Availability

All relevant data underlying the study are included in the article or available as Supplementary Material. The protocol, de-identified data and analysis codes may be provided on request to the corresponding author.

Statistical Analysis

The study population demographics and clinical data, as well as usability metrics are characterized through descriptive statistics with differences between completers and non-completers assessed through independent samples t-test, one-way ANOVA with Bonferroni post-hoc or Chi-squared test.

Latent growth curve analysis (LGCA) was used to model the trajectories of all outcome variables over time, following an intent-to-treat principle. Because higher levels of baseline pain intensity are a risk factor for chronicity and poorer outcomes,61,62 an exploratory analysis using baseline pain as a risk stratification variable was pursued. Three groups (risk groups: low, medium and high) were created based on pain levels at baseline: (i) mild (≤3), (ii) moderate (4–6), and (iii) severe (≥7).63 Missing data was dealt with full information maximum likelihood estimation.64–67 Intercept, slope and curve were determined to represent each variable trajectory. Intercept provides information on baseline values, slope represents the outcome estimated linear change over time, while curve indicates whether a leveling effect exists. Models were adjusted for covariates and fitted as random effects allowing each to vary between individuals (see structural equation and path diagram for the LGCA used in Supplementary Figure 1). A robust sandwich estimator for standard errors was used in all model estimation. Analyses were performed both for unfiltered cases and filtering for (i) >0 for surgery intent and WPAI, and (ii) ≥5 points for GAD-7 and PHQ-9. A conditional analysis was also performed to assess the influence of age, sex, and body mass index (BMI) as covariates. Model fit estimation was assessed through chi-squared test, root mean square error of approximation (RMSEA), confirmatory fit index (CFI), and standardized root mean square residual (SRMR).68,69

Logistic regression analysis was performed to identify the association of baseline variables with being a responder for pain reduction, considering a minimum clinically important difference (MCID) of 30% between baseline and treatment end.70,71

Bivariate correlations (Pearson r) were used to investigate associations between outcomes. Correlations were classified as weak until 0.24, moderate 0.25–0.49, strong 0.50–0.74 and very strong 0.75–1.0. Significance levels were set at p < 0.05 in all analyses. LGCA was coded using R (version 1.4.1717) and all other analyses were performed using SPSS (version 17.0, SPSS Inc, Chicago, Illinois, USA).


Eligibility screening was conducted for 496 participants. From these, 25 (5.0%) declined participation and 65 (13.1%) were excluded, with 406 starting the program. The study flow diagram is presented in Figure 1. Program completion rate was 81.8% (332/406).

Figure 1 Study flow diagram.

Baseline Characteristics

Participant’s baseline demographics (N = 406) are presented in Table 1. The average participant was middle-aged (mean 46.6 years (SD 11.8)) with moderate pain (mean pain score 4.50, 95% CI 4.29; 4.70) and an average disability of 14.93 (ODI) (95% CI 13.95, 15.91). Baseline clinical characteristics divided by risk subgroups are presented in Supplementary Table S1. Differences are discussed further within subgroup analyses.

Table 1 Baseline Characteristics of Study Participants (N = 406)

Comparing completers (N = 332) with non-completers (N = 74), the latter were younger (p = 0.015) at baseline (Supplementary Table S2). No significant differences were observed in terms of baseline clinical measures, including the type of pain presentation (with or without radiating pain).

Clinical Outcomes

For each outcome variable, a multiple-group LGCA was conducted to model changes in clinical outcomes over time, considering the entire cohort and then each subgroup following an intent-to-treat principle (N = 406), alongside model fit (Supplementary Tables S3 and S4, respectively). Results from the unconditional model are presented in Table 2, while the impact of covariates is presented in the conditional model (Supplementary Table S5).

Table 2 Changes in Clinical Outcomes Between Baseline and 12-Weeks: Intent-to-Treat (Unconditional Model)

Primary Outcome


Participants reported a significant reduction in ODI (p < 0.001, Supplementary Table S3), of 8.22 points (95% CI 6.93; 9.51) representing an overall change of 55.1% (Table 2, Figure 2). Females, and those with higher BMI at baseline reported higher baseline ODI levels (p < 0.001 and p = 0.005, respectively), with females recovering at a faster pace (−0.96 per week, p = 0.006) (Supplementary Table S5). Considering the recommended minimal clinically important improvement cutoff of 30% for disability,70,71 an odds ratio (OR) of 3.19 (95% CI 2.10; 5.00) was observed, corresponding to an 76.1% responder rate (p < 0.001). The OR for being a responder was not influenced by age, BMI nor mental health status at baseline (Supplementary Table S6).

Figure 2 Longitudinal changes across time for ODI and pain level. Individual trajectories are depicted in lighter lines (with darker lines meaning overlap of trajectories), while average trajectories are depicted in bold lines, with shadowing depicting 95% confidence intervals. (A) Overall ODI change; (B) ODI change by risk groups; (C) overall pain change; (D) pain change by risk groups.

Secondary Outcomes


Significant reduction was observed for pain, translating to an improvement of 61.0% at 12 weeks (mean change 2.74, 95% CI 2.38; 3.11). Females and those with higher BMI reported more pain at baseline (p = 0.002 and p = 0.005, respectively, Supplementary Table S5). Females showed a faster recovery pace compared to males (−0.15, p = 0.042). Pain reduction was strongly correlated with disability (ODI) recovery (r(117)=0.580, p < 0.001).

Analgesic Usage

One-third of the participants (35.7%, 144/403) reported analgesic usage at baseline. An overall reduction of analgesic consumption was observed, with only 10.8% of participants (12/111) still taking analgesics by study end.

Surgery Intent

Willingness to undergo surgery decreased along the study timeline at a pace of −2.42 points (SD 0.95) per week (p < 0.001), resulting in a reduction of 59.1% by end of program (Table 2). Participants who had higher BMI scores at baseline reported greater willingness to undergo surgery before the intervention (p = 0.006) but recovered at a faster pace (−0.24 per week, p = 0.013). Older participants recovered at a slower pace (0.06 per week, p = 0.049).

Mental Health and Fear-Avoidance Beliefs

Significant improvement was observed on both mental health indicators (p < 0.001), revealing a mean change of 59.5% for GAD-7 (4.93 points, 95% CI: 3.77; 6.09) and 55.4% for PHQ-9 (4.70 points, 95% CI: 3.36; 6.03) at end of program. Reduction of PHQ-9 scores was slower in participants with higher BMI (0.05 per week, p = 0.012), and was correlated with ODI recovery (r(117)=0.276, p = 0.003). Regarding fear-avoidance beliefs (FAB), a significant improvement of 46.3% (mean change 5.19, 95% CI 4.01; 6.36) was observed.

Work Productivity

Productivity recovery improved significantly by 65.6% on WPAI overall score (mean change 19.31, 95% CI 12.03; 26.58, p < 0.001), 65.4% on the WPAI work score (mean change 17.86, 95% CI 11.48; 24.25, p < 0.001) and 77.2% on WPAI activity (25.21, 95% CI 21.77; 28.65). Regarding WPAI time, 14.8% (51/345) individuals had some degree of absenteeism at baseline which was reduced by 86.1% (20.01; 95% CI 11.34; 28.67) by program end. Older participants experienced a faster recovery pace on work (−0.12, p = 0.028) and therefore on WPAI overall (−0.15, p = 0.011). Females presented with higher baseline levels of activity impairment (p = 0.031), with no effect on recovery pace. Overall productivity recovery was correlated with disability (ODI) recovery (r(94)=0.476, p < 0.001), pain reduction (r(94)=0.409 p < 0.001), lower willingness to undergo surgery (r(94)=0.363, p < 0.001) and improvement in mental health indicators: anxiety (GAD-7, r(94)=0.368, p < 0.001) and depression (PHQ-9, r(94)=0.362, p < 0.001).

Engagement and Usability-Related Outcomes

Participants performed an average of 33.2 (SD 29.2) sessions, and engagement levels were high (average 2.7 sessions a week, SD 1.3; completers: 2.8 sessions a week, SD 1.3), independent of whether individuals experienced low, medium or high pain levels at baseline (p = 0.450). Total exercise duration was 1345.5 minutes (SD 289.7). Higher levels of engagement were observed in the first weeks (3.2, SD 1.7 at 4 weeks vs 2.2, SD 1.5 after 4 weeks, p < 0.001). Each participant read on average 4.3 pieces of educational and CBT content (SD 6.9). Average satisfaction was 8.7 (SD 1.4) with 65% (251/385) of participants reporting a 9 or 10, 29% (113/385) reporting 7 or 8 and 6% (21/385) reporting 6 or less.

Sub-Group Analysis: Risk Stratification

According to the pain thresholds proposed by Miró et al,63 risk subgroups were created. Besides pain, these also differed on ODI (p < 0.001), analgesic consumption (p < 0.001), surgery intent (p = 0.011), FABQ (p < 0.001) and productivity impairment (p < 0.001), but not on mental health scores (p = 0.493 and p = 0.094, for anxiety and depression, respectively) (Supplementary Table S1). Higher risk subgroups (medium and high pain levels at baseline) had poorer clinical metrics. All subgroups had similar demographic characteristics, except for sex (p = 0.016), BMI (p = 0.029), and pain radiating to lower limb (p = 0.020), with males and those with lower BMI and without radiating pain to lower limb reporting lower pain levels at baseline. Despite the existence of referred leg pain being reported as a poorer prognostic factor,12,13,72 herein no significant improvement differences were observed between participants with or without radiating pain, with the exception of WPAI activity, with higher improvement observed in those with radiating pain (Supplementary Table S7).

A higher recovery pace was observed in the medium and high-risk subgroups for pain (Figure 2), which translated into greater mean change in these subgroups (61.2% (3.06 95% CI 2.59; 3.54) and 66.8% (5.08 95% CI 4.16; 6.01)) vs 56.9% (1.32 95% CI 1.01; 1.64) (Table 3 and Supplementary Table S4). These subgroups reached mean changes above the minimal clinically important improvement of 30%,70,71 with a higher OR observed in the high-risk subgroup (OR 7.50, 95% CI 2.12; 47.60), corresponding to an 88.2% responder rate (p < 0.001); participants within the medium-risk subgroup had an OR of 6.50 (95% CI 3.27; 14.81), corresponding to an 86.7% responder rate (p < 0.001). Higher mean changes were also observed in the medium and high-risk subgroup for ODI with a change of 8.25 (95% CI 6.26; 10.24) and 15.51 (95% CI 12.04; 18.97), respectively, vs in low-risk patients (5.08 95% CI 3.58; 6.58) (Figure 2). Greater productivity impairment recovery was observed in the high-risk subgroup compared with medium and low-risk subgroups (21.95 95% CI 12.65; 31.26 vs 10.05 95% CI 4.43; 15.67 and 5.65 95% CI 2.76; 8.54, respectively). Higher mean changes were also observed in the high-risk subgroup for surgery intention, anxiety, depression and FABQ without reaching statistical significance (Table 3). Analgesics intake decreased in all groups from 21.0% (30/143), 38.5% (72/187) and 56.8% (42/74), to 2.6% (1/39), 16.4% (9/55) and 11.8% (2/17), for low, medium and high-risk patients, respectively.

Table 3 Outcomes Changes Between Baseline and End of Program Based on Risk Subgroups: Intent-to-Treat Approach (Unconditional Model)


Main Findings

This multimodal DCP was able to promote high engagement and completion rates, which translated into clinically meaningful improvements in all outcome measures. A significant reduction in disability was observed (55.1%), with a 76.1% responder rate based on a minimal clinically important improvement of 30%.70,71 Importantly, this recovery was accompanied by improvements in pain (61.0%), depression (55.4%) and productivity (65.6% improvement). Meaningful reductions were also noted in surgery likelihood (59.1%), fear-avoidance beliefs (46.3%), anxiety (59.5%) and analgesic consumption (from 35.7% at baseline to 10.8% at program end).

Significant improvements in all LBP risk subgroups were seen after the DCP, with higher reductions in pain, ODI, analgesics intake, and productivity impairment in the high-risk subgroup, suggesting that higher risk individuals are not less likely to respond to this treatment, as has been reported previously.61

Comparison with Literature

Telerehabilitation has demonstrated similar outcomes in comparison to in-person rehabilitation for LBP.32,41,73 However, telerehabilitation studies focusing specifically on acute or sub-acute cohorts are still scarce in the literature, varying not only in the type of intervention but also in treatment duration and reported outcomes, making a direct comparison with the DCP in the present study difficult.43–45

Del Pozo et al44 conducted an RCT comparing a web-based exercise-related intervention to standard occupational care. After a nine-month regimen, an ODI reduction was observed in 37% of the intervention group vs 6.8% of the control group. Although the absolute reduction was not reported, these results seem to suggest that a web-based approach can support LBP rehabilitation. Reported disability recovery with conventional therapies ranges between 22.9% and 53.5%.74,75 Herein, an ODI change of 55.1% was observed, aligned with the highest recoveries reported, and in line with evidence showing that multimodal approaches can be better than usual care for effective acute LBP recovery.28 Disability improvements greater than reported in the present study were only observed in cohorts where pain onset started in less than 16 days or with high baseline disabilities (>20%).76,77

In a retrospective study by Huber et al involving patients with LBP, the authors did not find difference in pain reductions for acute, subacute and chronic cohorts (mean change 21.9%) following an app-based intervention including patient education, video-guided physical therapy, and mindfulness training.78 Within conventional therapy studies, interventions comprising exercises and/or CBT have reported pain reductions ranging from 28% to 79.4%.74,76,77,79,80 Herein, we observed a mean change in back pain scores of 2.74 (95% CI 2.38; 3.11), corresponding to an overall 61.0% reduction, which is higher than that reported in most studies,74,79,80 but not in some which excluded participants with low disability at baseline.76,77

Willingness to undergo surgery has been found to be one of the strongest predictors of future surgery.81,82 Herein, an overall 59.1% reduction in the willingness to undergo surgery was observed, which was higher (74%) in the high-risk subgroup. These results are consistent with the recommendation to trial conservative therapies first.83,84

The number of participants reporting analgesic intake decreased until program end. However, the lack of universally applied measures to quantify analgesic consumption precludes direct comparison to other studies.

Fear-avoidance beliefs have been associated with transition into chronic LBP.85 In this study, we observed a 46.3% improvement in FABQ-PA, higher than that reported for other CBT or exercise interventions (22.0% to 28.6% improvements).79 Moreover, significant reduction in both anxiety (59.5%) and depression (55.4%) was observed to a greater extent than that reported by Hill et al75 (15.8–23% for anxiety and 18.3–29.3% for depression, using HADS). Similarly, Jensen et al86 described an RCT that compared a multidisciplinary intervention with usual care and reported higher mental health recoveries with the former. The superior results herein reported might reflect the pertinence of having a multimodal DCP which combines PT-monitored exercise programs with education and CBT components.

High productivity improvement was observed, with a 65.6% reduction in overall WPAI, which combines improvements in both presenteeism (65.4%) and absenteeism (86.1%). Productivity recovery was positively correlated with reductions in disability, pain, surgery likelihood, anxiety and depression. These results are consistent with evidence that a multimodal biopsychosocial treatment plan can effectively increase the likelihood of return-to-work and fewer sick leave days at 12-months follow-up.17,28

In this study, a completion rate of 81.8% was obtained, in line with that reported by telerehabilitation and conventional programs tackling acute LBP (17.8–97%) with higher completion rates being reported only in studies with much smaller cohorts.44,61,75,77,78,84 Higher engagement rates were observed in the first weeks of intervention, which paralleled steeper improvements in all outcomes early on, in accordance with what has also been reported for other telerehabilitation interventions.42,87

Subgroup Analysis

The hurdles and socioeconomic burden imposed by chronic conditions have directed research towards identifying risk factors for chronicity and tailoring care accordingly (personalized medicine).72,74,75,77,83 Current recommendations are evolving88 and the argument that a large majority of patients will recover rapidly from acute LBP is debatable.11,89,90 Three distinct subgroups were created based on baseline pain levels, to determine the results of the tailored DCP across these subgroups, particularly in high-risk individuals. In line with what was reported by other authors,13,91 the high-risk subgroup in the present study presented with greater baseline disability, FABQ scores and a higher frequency of radiating pain, but also expressed higher willingness to pursue surgery, had a higher rate of analgesic intake and experienced greater productivity impairment. This suggests that subgrouping LBP patients according to pain level was suitable to identify those at higher risk.

The observed changes in outcomes were better across subgroups with higher levels of risk (medium and high) for pain, ODI, analgesic intake, PHQ-9 and productivity impairment. Pain reductions ranged from 56.9%, to 61.2% and 66.8% for low, medium and high-risk patients, respectively. Other studies that tailored care following risk stratification found improvements in the same range: from 52.8% to 75% in medium-risk and 50% to 79.4% in high-risk patients.75,77 Similar results were observed for disability, with greater improvement found in higher risk groups.75,77 Patients with worse baseline clinical outcomes might be at higher risk to transition into chronic states, and they simultaneously present a greater opportunity for improvement, if the condition is tackled appropriately. This supports the recommendation that multimodal treatment should be employed to optimize outcomes,17,18,28 and suggests that higher risk individuals are not less likely to respond to a remote DCP.

Strengths and Limitations

The strengths of this study include the novelty of the approach – amulti-component DCP managed by PTs, which combines exercises with real-time biofeedback within a biopsychosocial framework.92,93 The digital format favors accessibility, while the regular communication with the same PT may enhance adherence, thereby maximizing clinical outcomes.38,94 Other strengths include the large sample size focused on a less studied acute cohort, stratified by risk, as well as the broad set of secondary outcome measures56–60 comprising multiple domains.

The major limitation is the lack of a control group. However, considering the high accessibility of this DCP, using a “wait list” control group would not be ethical. Still, taken together, the aspects reported herein on engagement and observed outcomes, as well as the insights derived from the exploratory analysis, will help guide future RCT comparing the DCP against in-person intervention, supporting member stratification based on baseline pain levels. Other limitations include the lack of long-term follow-up to assess the persistence of results and relapse rates, and failure to assess the effect of each individual component.


This study demonstrated the utility of a multimodal DCP for patients with acute LBP across different risk groups. Very high adherence rates and patient satisfaction were observed, alongside clinically significant reductions in disability, pain, analgesic consumption, surgery intent and mental health, which in turn resulted in marked productivity recovery. These results strengthen the argument for managing acute LBP by tailoring care to specific needs and addressing its different domains to effectively reduce disability and pain and consequently mitigate the economic burden. Future RCTs comparing the DCP with in-person PT or other digital programs should include risk stratification for chronicity and longer-term follow-up assessments in order to provide further insights into recovery pathways.


ANOVA, Analysis of variance; BMI, Body mass index; CBT, Cognitive behavioral therapy; CFI, Confirmatory fit index; CI, Confidence interval; DCP, Digital care program; FABQ-PA, Fear-Avoidance Beliefs Questionnaire for physical activity; FDA, Food and Drug Administration (Federal agency); GAD-7, Generalized Anxiety Disorder 7-item questionnaire; ITT, Intent-to-treat; LBP, Low back pain; LGCA, Latent growth curve analysis; MCID, Minimal clinically important difference; NPRS, Numerical Pain Rating Scale; ODI, Oswestry Disability Index; OR, Odds ratio; PHQ-9, Patient Health 9-item questionnaire; PT, Physical therapist; RCT, Randomized controlled trial; RMSEA, Root mean square error of approximation; SRMR, Standardized root mean square residual; US or USA, United States of America; WPAI, Work Productivity and Activity Impairment questionnaire.

Data Sharing Statement

All data relevant to the study are included in the article or are available as Digital Content at Supplementary Material. Only de-identified individual participant data is provided. Further information, including the study protocol, can be found at (NCT04092946).

Ethics Approval and Informed Consent

The study was approved by the New England IRB (protocol number 120190313) and prospectively registered in, NCT04092946, 17/09/2019. This study was conducted in accordance with the approved guidelines. All patients were informed about the purpose and procedures of the study and provided informed consent.


The authors acknowledge the team of physical therapists responsible for the management of participants. The authors also acknowledge the contributions of João Tiago Silva and Quemuel Araújo in data validation (both employed at SWORD Health).

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.


This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.


Fabíola Costa, Dora Janela, Maria Molinos, Virgílio Bento, Vijay Yanamadala and Fernando Correia are employees at SWORD Health, the study sponsor. Fernando Correia, Vijay Yanamadala and Virgilio Bento also hold equity from SWORD Health. Robert Moulder, Jorge Lains, Justin Scheer and Steven P. Cohen, receives scientific advisor honorarium from SWORD Health, and do not have equity or stock option grants from SWORD Health. The authors report no other conflicts of interest in this work.


1. Vos T, Barber RM, Bell B. Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2015;386(9995):743–800. doi:10.1016/s0140-6736(15)60692-4

2. Ihlebæk C, Hansson TH, Lærum E, et al. Prevalence of low back pain and sickness absence: a “borderline” study in Norway and Sweden. Scand J Public Health. 2006;34(5):555–558. doi:10.1080/14034940600552051

3. Besen E, Young AE, Shaw WS. Returning to work following low back pain: towards a model of individual psychosocial factors. J Occup Rehabil. 2015;25(1):25–37. doi:10.1007/s10926-014-9522-9

4. Kool JP, Oesch PR, de Bie RA. Predictive tests for non-return to work in patients with chronic low back pain. Eur Spine J. 2002;11(3):258–266. doi:10.1007/s005860100335

5. Andersson GB. Epidemiological features of chronic low-back pain. Lancet. 1999;354(9178):581–585. doi:10.1016/s0140-6736(99)01312-4

6. Woolf AD, Pfleger B. Burden of major musculoskeletal conditions. Bull World Health Organ. 2003;81(9):646–656.

7. Yegian AK, Heymsfield SB, Lieberman DE. Historical body temperature records as a population-level ‘thermometer’ of physical activity in the United States. Curr Biol. 2021;31(20):R1375–R1376. doi:10.1016/j.cub.2021.09.014

8. Du Y, Liu B, Sun Y, Snetselaar LG, Wallace RB, Bao W. Trends in adherence to the physical activity guidelines for Americans for aerobic activity and time spent on sedentary behavior among US adults, 2007 to 2016. JAMA Netw Open. 2019;2(7):e197597. doi:10.1001/jamanetworkopen.2019.7597

9. United States Bone and Joint Initiative. The Burden of Musculoskeletal Diseases in the United States (BMUS) [homepage on the Internet]. Musculoskeletal conditions. Available from: Accessed May 14, 2021.

10. Singh KA, Watkins-Castillo SI. The Burden of Musculoskeletal Diseases in the United States (BMUS). United States Bone and Joint Initiative. Available from: Accessed June 21, 2022.

11. Itz CJ, Geurts JW, Van Kleef M, Nelemans P. Clinical course of non-specific low back pain: a systematic review of prospective cohort studies set in primary care. Eur J Pain. 2013;17(1):5–15. doi:10.1002/j.1532-2149.2012.00170.x

12. Cruz EB, Canhão H, Fernandes R, et al. Prognostic indicators for poor outcomes in low back pain patients consulted in primary care. PLoS One. 2020;15(3):e0229265. doi:10.1371/journal.pone.0229265

13. Hayden JA, Dunn KM, van der Windt DA, Shaw WS. What is the prognosis of back pain? Best Pract Res Clin Rheumatol. 2010;24(2):167–179. doi:10.1016/j.berh.2009.12.005

14. Gatchel RJ, Bevers K, Licciardone JC, Su J, Du Y, Brotto M. Transitioning from acute to chronic pain: an examination of different trajectories of low-back pain. Healthcare. 2018;6(2):48. doi:10.3390/healthcare6020048

15. van Wambeke P, Desomer A, Jonckheer P, Depreitere B. The Belgian national guideline on low back pain and radicular pain: key roles for rehabilitation, assessment of rehabilitation potential and the PRM specialist. Eur J Phys Rehabil Med. 2020;56(2):220–227. doi:10.23736/s1973-9087.19.05983-5

16. Stochkendahl MJ, Kjaer P, Hartvigsen J, et al. National clinical guidelines for non-surgical treatment of patients with recent onset low back pain or lumbar radiculopathy. Eur Spine J. 2018;27(1):60–75. doi:10.1007/s00586-017-5099-2

17. George SZ, Fritz JM, Silfies SP, et al. Interventions for the management of acute and chronic low back pain: revision 2021. J Orthop Sports Phys Ther. 2021;51(11):CPG1–CPG60. doi:10.2519/jospt.2021.0304

18. National Guideline Centre. National Institute for health and care excellence: clinical guidelines. In: Low Back Pain and Sciatica in Over 16s: Assessment and Management. National Institute for Health and Care Excellence (UK) Copyright © NICE; 2016:2016.

19. Gianola S, Bargeri S, Del Castillo G, et al. Effectiveness of treatments for acute and subacute mechanical non-specific low back pain: a systematic review with network meta-analysis. Br J Sports Med. 2021;56(1):41–50. doi:10.1136/bjsports-2020-103596

20. Liu X, Hanney WJ, Masaracchio M, et al. Immediate physical therapy initiation in patients with acute low back pain is associated with a reduction in downstream health care utilization and costs. Phys Ther. 2018;98(5):336–347. doi:10.1093/ptj/pzy023

21. Morgan T, Wu J, Ovchinikova L, Lindner R, Blogg S, Moorin R. A national intervention to reduce imaging for low back pain by general practitioners: a retrospective economic program evaluation using Medicare Benefits Schedule data. BMC Health Serv Res. 2019;19(1). doi:10.1186/s12913-019-4773-y

22. Sharma S, Traeger AC, Reed B, et al. Clinician and patient beliefs about diagnostic imaging for low back pain: a systematic qualitative evidence synthesis. BMJ Open. 2020;10(8):e037820. doi:10.1136/bmjopen-2020-037820

23. Chou R, Baisden J, Carragee EJ, Resnick DK, Shaffer WO, Loeser JD. Surgery for low back pain: a review of the evidence for an American Pain Society Clinical Practice Guideline. Spine. 2009;34(10):1094–1109. doi:10.1097/BRS.0b013e3181a105fc

24. Huang R, Ning J, Chuter VH, et al. Exercise alone and exercise combined with education both prevent episodes of low back pain and related absenteeism: systematic review and network meta-analysis of randomised controlled trials (RCTs) aimed at preventing back pain. Br J Sports Med. 2020;54(13):766–770. doi:10.1136/bjsports-2018-100035

25. de Campos TF, Maher CG, Fuller JT, Steffens D, Attwell S, Hancock MJ. Prevention strategies to reduce future impact of low back pain: a systematic review and meta-analysis. Br J Sports Med. 2021;55(9):468–476. doi:10.1136/bjsports-2019-101436

26. Steffens D, Maher CG, Pereira LS, et al. Prevention of low back pain: a systematic review and meta-analysis. JAMA Intern Med. 2016;176(2):199–208. doi:10.1001/jamainternmed.2015.7431

27. Madhusudhan DK, Thokala S, Hagg HK, Schoeneck AR, Pizzarello D, Bravata DM. An employer-sponsored musculoskeletal care coordination service can improve clinical outcomes and self-reported productivity. J Occup Environ Med. 2020;62(11):e651–e656. doi:10.1097/JOM.0000000000002026

28. Marin TJ, Van Eerd D, Irvin E, et al. Multidisciplinary biopsychosocial rehabilitation for subacute low back pain. Cochrane Database Syst Rev. 2017;6(6):CD002193. doi:10.1002/14651858.CD002193.pub2

29. Darnall BD, Scheman J, Davin S, et al. Pain psychology: a global needs assessment and national call to action. Pain Med. 2016;17(2):250–263. doi:10.1093/pm/pnv095

30. Stanhope J, Weinstein P. Learning from COVID-19 to improve access to physiotherapy. Aust J Prim Health. 2020;26(4):271–272. doi:10.1071/py20141

31. Nicholl BI, Sandal LF, Stochkendahl MJ, et al. Digital support interventions for the self-management of low back pain: a systematic review. J Med Internet Res. 2017;19(5):e179. doi:10.2196/jmir.7290

32. Garg S, Garg D, Turin TC, Chowdhury MF. Web-based interventions for chronic back pain: a systematic review. J Med Internet Res. 2016;18(7):e139. doi:10.2196/jmir.4932

33. Shebib R, Bailey JF, Smittenaar P, Perez DA, Mecklenburg G, Hunter S. Randomized controlled trial of a 12-week digital care program in improving low back pain. NPJ Digital Med. 2019;2(1):1. doi:10.1038/s41746-018-0076-7

34. Priebe JA, Haas KK, Moreno Sanchez LF, et al. Digital treatment of back pain versus standard of care: the cluster-randomized controlled trial, rise-uP. J Pain Res. 2020;13:1823–1838. doi:10.2147/jpr.S260761

35. Garcia LM, Birckhead BJ, Krishnamurthy P, et al. An 8-week self-administered at-home behavioral skills-based virtual reality program for chronic low back pain: double-blind, randomized, placebo-controlled trial conducted during COVID-19. J Med Internet Res. 2021;23(2):e26292. doi:10.2196/26292

36. Fiani B, Siddiqi I, Lee SC, Dhillon L. Telerehabilitation: development, application, and need for increased usage in the COVID-19 era for patients with spinal pathology. Cureus. 2020;12(9):e10563. doi:10.7759/cureus.10563

37. Tenforde AS, Hefner JE, Kodish-Wachs JE, Iaccarino MA, Paganoni S. Telehealth in physical medicine and rehabilitation: a narrative review. PM R. 2017;9(5):S51–S58. doi:10.1016/j.pmrj.2017.02.013

38. Jordan JL, Holden MA, Mason EE, Foster NE. Interventions to improve adherence to exercise for chronic musculoskeletal pain in adults. Cochrane Database Syst Rev. 2010;2010(1):CD005956. doi:10.1002/14651858.CD005956.pub2

39. Fatoye F, Gebrye T, Fatoye C, et al. The clinical and cost-effectiveness of telerehabilitation for people with nonspecific chronic low back pain: randomized controlled trial. JMIR Mhealth Uhealth. 2020;8(6):e15375. doi:10.2196/15375

40. Özden F, Sarı Z, Karaman ÖN, Aydoğmuş H. Correction to: the effect of video exercise-based telerehabilitation on clinical outcomes, expectation, satisfaction, and motivation in patients with chronic low back pain. Ir J Med Sci. 2021. doi:10.1007/s11845-021-02797-8

41. Chhabra HS, Sharma S, Verma S. Smartphone app in self-management of chronic low back pain: a randomized controlled trial. Eur Spine J. 2018;27(11):2862–2874. doi:10.1007/s00586-018-5788-5

42. Bailey JF, Agarwal V, Zheng P, et al. Digital care for chronic musculoskeletal pain: 10,000 participant longitudinal cohort study. J Med Internet Res. 2020;22(5):e18250. doi:10.2196/18250

43. Peterson S, Kuntz C, Roush J. Use of a modified treatment-based classification system for subgrouping patients with low back pain: agreement between telerehabilitation and face-to-face assessments. Physiother Theory Pract. 2019;35(11):1078–1086. doi:10.1080/09593985.2018.1470210

44. Del Pozo-Cruz B, Gusi N, Del Pozo-Cruz J, Adsuar JC, Hernandez-Mocholí M, Parraca JA. Clinical effects of a nine-month web-based intervention in subacute non-specific low back pain patients: a randomized controlled trial. Clin Rehabil. 2013;27(1):28–39. doi:10.1177/0269215512444632

45. Suri P, Rainville J, Fitzmaurice GM, et al. Acute low back pain is marked by variability: an internet-based pilot study. BMC Musculoskelet Disord. 2011;12:220. doi:10.1186/1471-2474-12-220

46. Correia FD, Molinos M, Neves C, et al. Digital rehabilitation for acute ankle sprains: prospective longitudinal cohort study. JMIR Rehabil Assist Technol. 2021;8(3):e31247. doi:10.2196/31247

47. Correia FD, Molinos M, Luís S, et al. Digitally assisted versus conventional home-based rehabilitation after arthroscopic rotator cuff repair: a randomized controlled trial. Am J Phys Med Rehabil. 2022;101(3):237–249. 9000. doi:10.1097/phm.0000000000001780

48. Correia FD, Nogueira A, Magalhães I, et al. Medium-term outcomes of digital versus conventional home-based rehabilitation after total knee arthroplasty: prospective, parallel-group feasibility study. JMIR Rehabil Assist Technol. 2019;6(1):e13111. doi:10.2196/13111

49. Correia FD, Nogueira A, Magalhaes I, et al. Home-based rehabilitation with a novel digital biofeedback system versus conventional in-person rehabilitation after total knee replacement: a feasibility study. Sci Rep. 2018;8(1):11299. doi:10.1038/s41598-018-29668-0

50. Dias Correia F, Nogueira A, Magalhaes I, et al. Digital versus conventional rehabilitation after total hip arthroplasty: a single-center, parallel-group pilot study. JMIR Rehabil Assist Technol. 2019;6(1):e14523. doi:10.2196/14523

51. Costa F, Janela D, Molinos M, et al. Telerehabilitation of acute musculoskeletal multi-disorders: prospective, single-arm, interventional study. BMC Musculoskelet Disord. 2022;23(1):29. doi:10.1186/s12891-021-04891-5

52. Janela D, Costa F, Molinos M, et al. Asynchronous and tailored digital rehabilitation of chronic shoulder pain: a prospective longitudinal cohort study. J Pain Res. 2022;15:53–66. doi:10.2147/jpr.S343308

53. Gabel CP, Cuesta-Vargas A, Qian M, et al. The Oswestry Disability Index, confirmatory factor analysis in a sample of 35,263 verifies a one-factor structure but practicality issues remain. Eur Spine J. 2017;26(8):2007–2013. doi:10.1007/s00586-017-5179-3

54. Monticone M, Baiardi P, Vanti C, et al. Responsiveness of the Oswestry Disability Index and the Roland Morris Disability Questionnaire in Italian subjects with sub-acute and chronic low back pain. Eur Spine J. 2012;21(1):122–129. doi:10.1007/s00586-011-1959-3

55. Garg A, Pathak H, Churyukanov MV, Uppin RB, Slobodin TM. Low back pain: critical assessment of various scales. Eur Spine J. 2020;29(3):503–518. doi:10.1007/s00586-019-06279-5

56. Spitzer RL, Kroenke K, Williams JB, Lowe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006;166(10):1092–1097. doi:10.1001/archinte.166.10.1092

57. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–613. doi:10.1046/j.1525-1497.2001.016009606.x

58. Bijker L, Sleijser-Koehorst MLS, Coppieters MW, Cuijpers P, Scholten-Peeters GGM. Preferred self-administered questionnaires to assess depression, anxiety and somatization in people with musculoskeletal pain – a modified delphi study. J Pain. 2020;21(3):409–417. doi:10.1016/j.jpain.2019.08.006

59. Swinkels-Meewisse EJ, Swinkels RA, Verbeek AL, Vlaeyen JW, Oostendorp RA. Psychometric properties of the Tampa Scale for kinesiophobia and the fear-avoidance beliefs questionnaire in acute low back pain. Man Ther. 2003;8(1):29–36. doi:10.1054/math.2002.0484

60. Ospina MB, Dennett L, Waye A, Jacobs P, Thompson AH. A systematic review of measurement properties of instruments assessing presenteeism. Am J Manag Care. 2015;21(2):e171–85.

61. Campbell P, Foster NE, Thomas E, Dunn KM. Prognostic indicators of low back pain in primary care: five-year prospective study. J Pain. 2013;14(8):873–883. doi:10.1016/j.jpain.2013.03.013

62. Nieminen LK, Pyysalo LM, Kankaanpää MJ. Prognostic factors for pain chronicity in low back pain: a systematic review. Pain Rep. 2021;6(1):e919. doi:10.1097/pr9.0000000000000919

63. Miró J, de la Vega R, Solé E, et al. Defining mild, moderate, and severe pain in young people with physical disabilities. Disabil Rehabil. 2017;39(11):1131–1135. doi:10.1080/09638288.2016.1185469

64. Duncan TE, Duncan SC. An introduction to latent growth curve modeling. Behav Ther. 2004;35(2):333–363. doi:10.1016/S0005-7894(04)80042-X

65. Ferrer E, Hamagami F, McArdle JJ. Modeling latent growth curves with incomplete data using different types of structural equation modeling and multilevel software. Struct Equation Model. 2004;11(3):452–483. doi:10.1016/S0005-7894(04)80042-X

66. Preacher KJ, Wichman AL, MacCallum RC, Briggs NE. Latent Growth Curve Modeling. SAGE publications, Inc; 2008.

67. Kenneth A, Bollen PJC. Latent Curve Models: A Structural Equation Perspective. Wiley Series; 2006.

68. Iacobucci D. Structural equations modeling: fit indices, sample size, and advanced topics. J Consumer Psychol. 2010;20(1):90–98. doi:10.1016/j.jcps.2009.09.003

69. Brown TA. Confirmatory Factor Analysis for Applied Research. 2 ed. The Guilford Press; 2006.

70. Gatchel RJ, Mayer TG, Choi Y, Chou R. Validation of a consensus-based minimal clinically important difference (MCID) threshold using an objective functional external anchor. Spine J. 2013;13(8):889–893. doi:10.1016/j.spinee.2013.02.015

71. Ostelo RW, Deyo RA, Stratford P, et al. Interpreting change scores for pain and functional status in low back pain: towards international consensus regarding minimal important change. Spine. 2008;33(1):90–94. doi:10.1097/BRS.0b013e31815e3a10

72. Hill JC, Dunn KM, Lewis M, et al. A primary care back pain screening tool: identifying patient subgroups for initial treatment. Arthritis Rheum. 2008;59(5):632–641. doi:10.1002/art.23563

73. Raiszadeh K, Tapicer J, Taitano L, Wu J, Shahidi B. In-clinic versus web-based multidisciplinary exercise-based rehabilitation for treatment of low back pain: prospective clinical trial in an integrated practice unit model. J Med Internet Res. 2021;23:e22548. doi:10.2196/22548

74. Foster NE, Mullis R, Hill JC, et al. Effect of stratified care for low back pain in family practice (IMPaCT Back): a prospective population-based sequential comparison. Ann Fam Med. 2014;12(2):102–111. doi:10.1370/afm.1625

75. Hill JCD, Whitehurst DG, Lewis MP, et al. Comparison of stratified primary care management for low back pain with current best practice (STarT Back): a randomised controlled trial. Lancet. 2011;378(9802):1560–1571. doi:10.1016/S0140-6736(11)60937-9

76. Fritz JM, Magel JS, McFadden M, et al. Early physical therapy vs usual care in patients with recent-onset low back pain: a randomized clinical trial. JAMA. 2015;314(14):1459–1467. doi:10.1001/jama.2015.11648

77. Magel J, Fritz JM, Greene T, Kjaer P, Marcus RL, Brennan GP. Outcomes of patients with acute low back pain stratified by the STarT back screening tool: secondary analysis of a randomized trial. Phys Ther. 2017;97(3):330–337. doi:10.2522/ptj.20160298

78. Huber S, Priebe JA, Baumann KM, Plidschun A, Schiessl C, Tolle TR. Treatment of low back pain with a digital multidisciplinary pain treatment app: short-term results. JMIR Rehabil Assist Technol. 2017;4(2):e11. doi:10.2196/rehab.9032

79. Storheim K, Brox J, Inger H, Koller A, Bø K. Intensive group training versus cognitive intervention in sub-acute low back pain: short-term results of a single-blind randomized controlled trial. J Rehabil Med. 2003;35(3):132–140. doi:10.1080/16501970310010484

80. Campello M, Ziemke G, Hiebert R, et al. Implementation of a multidisciplinary program for active duty personnel seeking care for low back pain in a U.S. Navy Medical Center: a feasibility study. Mil Med. 2012;177(9):1075–1080. doi:10.7205/milmed-d-12-00118

81. Modi CS, Veillette CJ, Gandhi R, Perruccio AV, Rampersaud YR. Factors that influence the choice to undergo surgery for shoulder and elbow conditions. Clin Orthop Relat Res. 2014;472(3):883–891. doi:10.1007/s11999-013-3357-0

82. Hawker GA, Guan J, Croxford R, et al. A prospective population-based study of the predictors of undergoing total joint arthroplasty. Arthritis Rheum. 2006;54(10):3212–3220. doi:10.1002/art.22146

83. Foster NE, Anema JR, Cherkin D, et al. Prevention and treatment of low back pain: evidence, challenges, and promising directions. Lancet. 2018;391(10137):2368–2383. doi:10.1016/s0140-6736(18)30489-6

84. Kim LH, Vail D, Azad TD, et al. Expenditures and health care utilization among adults with newly diagnosed low back and lower extremity pain. JAMA Netw Open. 2019;2(5):e193676–e193676. doi:10.1001/jamanetworkopen.2019.3676

85. Wertli MM, Rasmussen-Barr E, Weiser S, Bachmann LM, Brunner F. The role of fear avoidance beliefs as a prognostic factor for outcome in patients with nonspecific low back pain: a systematic review. Spine J. 2014;14(5):816–36.e4. doi:10.1016/j.spinee.2013.09.036

86. Jensen C, Jensen OK, Christiansen DH, Nielsen CV. One-year follow-up in employees sick-listed because of low back pain: randomized clinical trial comparing multidisciplinary and brief intervention. Spine. 2011;36(15):1180–1189. doi:10.1097/BRS.0b013e3181eba711

87. Bennell KL, Marshall CJ, Dobson F, Kasza J, Lonsdale C, Hinman RS. Does a web-based exercise programming system improve home exercise adherence for people with musculoskeletal conditions?: a randomized controlled trial. Am J Phys Med Rehabil. 2019;98(10):850–858. doi:10.1097/phm.0000000000001204

88. Oliveira CB, Maher CG, Pinto RZ, et al. Clinical practice guidelines for the management of non-specific low back pain in primary care: an updated overview. Eur Spine J. 2018;27(11):2791–2803. doi:10.1007/s00586-018-5673-2

89. Stevans JM, Delitto A, Khoja SS, et al. Risk factors associated with transition from acute to chronic low back pain in US patients seeking primary care. JAMA Netw Open. 2021;4(2):e2037371. doi:10.1001/jamanetworkopen.2020.37371

90. Mehling WE, Gopisetty V, Bartmess E, et al. The prognosis of acute low back pain in primary care in the United States: a 2-year prospective cohort study. Spine. 2012;37(8):678–684. doi:10.1097/BRS.0b013e318230ab20

91. Konstantinou K, Dunn KM, Ogollah R, Vogel S, Hay EM. Characteristics of patients with low back and leg pain seeking treatment in primary care: baseline results from the ATLAS cohort study. BMC Musculoskelet Disord. 2015;16:332. doi:10.1186/s12891-015-0787-8

92. Keefe FJ, Main CJ, George SZ. Advancing psychologically informed practice for patients with persistent musculoskeletal pain: promise, pitfalls, and solutions. Phys Ther. 2018;98(5):398–407. doi:10.1093/ptj/pzy024

93. Lin I, Wiles L, Waller R, et al. What does best practice care for musculoskeletal pain look like? Eleven consistent recommendations from high-quality clinical practice guidelines: systematic review. Br J Sports Med. 2020;54(2):79–86. doi:10.1136/bjsports-2018-099878

94. Slade SC, Patel S, Underwood M, Keating JL. What are patient beliefs and perceptions about exercise for nonspecific chronic low back pain? A systematic review of qualitative studies. Clin J Pain. 2014;30(11):995–1005. doi:10.1097/ajp.0000000000000044

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