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. 2022 Oct 20;17(10):e0276326. doi: 10.1371/journal.pone.0276326

Factors impacting adherence to an exercise-based physical therapy program for individuals with low back pain

Bahar Shahidi 1,*, Jennifer Padwal 1, Euyhyun Lee 2, Ronghui Xu 2,3,4, Sarah Northway 1,5, Lissa Taitano 5, Tiffany Wu 5, Kamshad Raiszadeh 5
Editor: Ravi Shankar Yerragonda Reddy6
PMCID: PMC9584523  PMID: 36264988

Abstract

Background/Objective

Exercise-based rehabilitation is a conservative management approach for individuals with low back pain. However, adherence rates for conservative management are often low and the reasons for this are not well described. The objective of this study was to evaluate predictors of adherence and patient-reported reasons for non-adherence after ceasing a supervised exercise-based rehabilitation program in individuals with low back pain.

Design

Retrospective observational study.

Methods

Data was retrospectively analyzed from 5 rehabilitation clinics utilizing a standardized exercise-based rehabilitation program. Baseline demographics, diagnosis and symptom specific features, visit number, and discontinuation profiles were quantified for 2,243 patients who underwent the program.

Results

Forty-three percent (43%) of participants were adherent to the program, with the majority (31.7%) discontinuing treatment prior to completion due to logistic and accessibility issues. Another 13.2% discontinued prior to the prescribed duration due to clinically significant improvements in pain and/or disability without formal discharge evaluation, whereas 8.3% did not continue due to lack of improvement. Finally, 6.0% were discharged for related and unrelated medical reasons including surgery. Individuals diagnosed with disc pathology were most likely to be adherent to the program.

Limitations

This study was a retrospective chart review with missing data for some variables. Future studies with a prospective design would increase quality of evidence.

Conclusions

The majority of individuals prescribed an in-clinic exercise-based rehabilitation program are non-adherent. Patient diagnosis was the most important predictor of adherence. For those who were not adherent, important barriers include personal issues, insufficient insurance authorization and lack of geographic accessibility.

Introduction

Low back pain (LBP) is a debilitating and costly condition affecting 65–85% of the United States population during their lifetime [13]. Although acute LBP is thought to be self-limiting in the short term, with most symptoms resolving within 3 months of onset, recurrences and progression to chronic LBP are observed in 24–80% of cases [4]. Improving strength and stability of the trunk musculature through therapeutic exercise is a common physical rehabilitation goal in this population, and is thought to improve functional outcomes by both facilitating hypertrophy of the supporting paraspinal musculature and decreasing or preventing commonly observed maladaptive physiological changes such as muscle atrophy and fatty infiltration [59]. However, despite the observation of short-term improvements in response to standard rehabilitation programs, these improvements often do not persist in the long term. One possible reason for this is that the most commonly prescribed exercise doses and durations may be insufficient to induce physiological change in the affected tissues [1013]. Indeed, most studies demonstrating exercise-induced changes in the form of muscle growth required treatment durations longer than are typically prescribed: an average of 16 weeks [10, 14]. Further, when implemented at these durations, they resulted in not only short-term, but also long term improvements in functional outcomes [12, 15] in addition to reductions in healthcare resource utilization by 87% after 1 year [11, 16, 17]. Despite evidence supporting longer treatment durations, the number of visits utilized for exercise-based rehabilitation remain lower, averaging 8–12 visits over a 6–8 week period [18].

One reason for the discrepancy in treatment volume is that adherence, or attendance to supervised treatment, for sustained rehabilitation programs in clinical practice settings is often low, with rates varying as widely as 15–87% [19, 20]. A systematic review [21] of literature published between 1998 and 2014 on adherence to therapeutic exercise interventions yielded only 3 studies including patients with LBP [2224]. Additionally, literature investigating adherence for supervised exercise programs lasting longer than 6 weeks is absent in this population, and of those reporting shorter-term adherence rates (<6 weeks), the largest sample size reported was 170 participants, with broad ranges of non-adherence (51–87%) [25, 26]. Adherence for these studies was based on self-reported time spent performing a home exercise program, making comparisons to supervised rehabilitation difficult, although adherence rates are also similarly wide (15–70%) [19, 20]. Wide ranges of adherence have also been observed in other musculoskeletal conditions such as hip or knee osteoarthritis, with levels ranging between 26–52% [27, 28].

These low adherence rates often go unrecognized in the literature given that many clinical trials likely over-represent adherence due to selection bias and resources being allocated to patient follow-up and retention as compared to normal clinical practice. Additionally, logistic limitations such as lack of insurance coverage and accessibility restrictions have been shown to affect trial enrollment and may reduce selection of populations with restricted resources [29, 30]. Indeed, the lack of geographic accessibility and concern over financial burden have increasingly been shown to disproportionately impact individuals of low socioeconomic status, resulting in widening gaps in healthcare disparities [31]. High medical comorbidity and medical safety is also a consideration in individuals with back pain, as this has been shown to influence clinical outcomes and ability to return to function possibly due to inability to safely tolerate an exercise-based program [32]. Conversely, publication bias against pragmatic trials with high levels of loss to follow up and poor resolution for evaluating clinical efficacy contributes to underreporting of the prevalence of low adherence in these populations and settings [33]. Indeed, a recent Cochrane Review reported that of 381 studies of exercise-based rehabilitation programs for chronic pain conditions, adherence could not be assessed in any review, and healthcare use/attendance was not reported in any of the reviews [34]. As such, it is important to provide data on adherence in typical clinical practice settings, and to evaluate the factors that contribute to the low rates observed given that it is potentially a key barrier to achieving optimal therapeutic efficacy [35, 36].

The purpose of this investigation is to evaluate adherence for a standardized 20 visit (10 weeks) supervised in-clinic exercise-based rehabilitation program taking place in an outpatient physical therapy setting in a large group of patients with LBP. Furthermore, factors predicting high adherence as well as reasons for becoming non-adherent are explored. These data will provide key insight into improving care accessibility and will help identify targets for optimizing patient retention and treatment outcomes.

Methods

Patient characteristics

This project was approved by the local ethical review board (Western IRB #1180578) and a waiver of consent was obtained due to the nature of the de-identified data. Patient data was collected retrospectively from 5 outpatient physical therapy clinics in the greater San Diego area for patients who received treatment at one or more of these clinics between November 2015 and June 2017. Patient characteristics data that have been demonstrated to be impactful in clinical outcomes were collected. Specifically, age [37], sex [37, 38], body mass index (BMI), baseline medication usage [39], smoking history [38], diabetes [38], low back pain-specific diagnosis [37], pain visual analogue scale (VAS) [4], low-back pain related disability [40] from the Oswestry Disability Index (ODI), and quality of life from the EuroQol-5D (EQ5D) questionnaire have been shown to influence outcomes and were extracted. Baseline isometric lumbar extension strength (in ft*lbs) was collected during a maximum voluntary contraction (MVC) measured on a MedX isokinetic dynamometer (Baseline Exercise). Low back pain diagnoses were categorized based on ICD-9 codes associated with the following conditions: nonspecific LBP, degenerative disc disease/disc herniation, lumbar stenosis, and spondylolisthesis. These diagnostic categories are consistent with previous literature in large systematic reviews and Cochrane databases [10, 41, 42]. Based on previously described diagnostic criteria, patients were categorized in the lumbar stenosis category if they had 1) neurogenic claudication and/or radicular leg symptoms, or 2) confirmatory cross-sectional imaging showing lumbar spinal stenosis at one or more levels [43]. Patients were categorized as having degenerative spondylolisthesis if they had one or more of the following: 1) neurogenic claudication or radicular leg pain with associated neurological signs, 2) spinal stenosis seen on cross-sectional imaging, or 3) degenerative spondylolisthesis of any grade seen on standing lateral radiographs. Patients were categorized as having disc disease/herniation if they had 1) lumbar radiculopathy and a disc herniation or pathology at a corresponding level and laterality as verified on imaging if available [44, 45]. Patients that did not have a specific diagnosis associated with supportive imaging or clear symptomology were categorized as having nonspecific LBP.

Exercise protocol

The rehabilitation protocol consisted of a standardized high intensity rehabilitation exercise program prescribed and supervised by licensed physical therapists as previously described in detail [46]. Briefly, the program consisted of a recommendation of 20 visits at 2 visits/week (a duration of 10 weeks), lasting approximately 45 minutes including lumbar extension resistance exercises performed on a MedX isokinetic dynamometer machine. Exercise dose was prescribed based on a Maximum Voluntary Contraction (MVC) and targeted 60–80% of that maximal effort for 15–20 repetitions. Exercise was advanced in subsequent visits by 5–10% of the exercise load once the patient was able to perform >20 repetitions. If they were able to reach >10 repetitions but <20 repetitions, their exercise load remained the same at their next visit. If they were unable to reach 10 repetitions, their exercise load was decreased 5–10% at their next visit.

Measurement of adherence

The total number of visits completed was used as a measure of adherence to the prescribed program. Because the instructions provided upon prescription of the program recommended that patients complete at least 75% of the prescribed 20 visits, patients were classified as “completers” if they completed at least 16 of the 20 prescribed visits or were formally discharged due to symptom resolution. Patients who completed 15 or fewer visits and were not successfully discharged from the program were considered “non-completers”. These guidelines were primarily based on the concept that muscle hypertrophy changes require at least 6–7 weeks of consistent resistance training to elicit physiological adaptation [47]. Patients who did not complete the program as prescribed were provided a discharge questionnaire indicating their reason for discontinuation of care. Reasons for discharge were categorized based on the most commonly observed reasons provided by the patient, and included a) logistic issues (personal issues, lack of sufficient insurance coverage/authorization, lack of geographic accessibility), b) medical discharge (related or unrelated health issues or progression to surgery), c) clinically important improvement in pain (>2/10 improvement on the numeric pain rating scale) [48] and/or disability (>10/100 improvement on the Oswestry Disability Index) [49] without official discharge, or d) lack of improvement/did not like the program.

Statistical analysis

Continuous and categorical variables were summarized as mean (SD) and count (percentage), respectively. In order to evaluate patient factors that predicted adherence, univariate logistic regressions were performed with program completion as a binary dependent variable to evaluate the significance of each predictor of interest. Independent predictors included age, sex, BMI, smoking history (yes or no), diabetes (yes or no), presence of radicular symptoms (yes or no), frequency of narcotic usage (None, <1/day, 1-2/day, 3+/day), frequency of Non-Steroidal Anti-inflammatory Drug (NSAID) usage (none, <1/day, 1-2/day, 3+/day), and LBP diagnosis (nonspecific, disc herniation, spondylolisthesis, stenosis). For the narcotic and NSAID use variables, non-use (None) was considered the reference category, and for the variable of LBP diagnosis, non-specific LBP was considered the reference category. Predictors with a univariate p-value of <0.2 were entered into a multivariable model. Subsequently, a multivariable logistic regression model was built using these variables to evaluate whether there were specific patient characteristics that predicted adherence while adjusting for other variables. Missing data were handled using pairwise deletion (patients were only removed from analysis only for multivariate, but not univariate analyses), with no replacement (no imputation was utilized to fill missing values).

Reasons for non-adherence based on patient charts, provider correspondence, and discharge questionnaires, were categorized and reported as count (percentage) as a function of the total number of participants included in the study (adherent and non-adherent). All statistical analyses were performed in R (V.3.6.1, R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria).

Results

Patient characteristics

Of the initial sample of 2,749 subjects, 573 patients were excluded; 2 patients were excluded due to having documented ages of <0 or >100, 67 patients were excluded due to having a documented BMI that was non-physiological and likely entered in error, 218 patients had a primary diagnosis that did not fall into the preidentified diagnostic categories (e.g. fracture following trauma, scoliosis), and 288 patients were missing one or more data points related to individual visits. After these exclusions, data for the remaining 2,243 patients (81.5%) were analyzed. Overall, 958 (42.7%) participants completed at least 16 of the 20 prescribed treatment sessions and were considered adherent. The average (SD) number of visits for individuals who were adherent was 17 (5) visits, and the average number of visits for individuals who were considered non-adherent was 6 (4) visits (Table 1).

Table 1. Patient characteristics.

Data are means (SD) unless otherwise indicated.

Mean (SD) or % Non-adherent Adherent Overall
Age (years) 54.02 (17.23) 55.06 (16.55) 54.50 (16.92)
Sex, M/F (%) 44.8/55.2 42.7/57.3 43.1/56.9
BMI (kg/m2) 27.64 (5.28) 27.76 (5.44) 27.70 (5.36)
Smoking History, no/yes (%) 93.1/6.9 93.5/6.5 93.1/6.9
Diabetes Diagnosis, no/yes (%) 90.9/9.1 90.0/10.0 90.3/9.7
Radiculopathy Diagnosis, no/yes (%) 43.0/57.0 40.7/59.3 41.9/58.1
Baseline Narcotic Usage (%)
    None 60.5 61.0 60.6
    <1/day 14.5 15.6 14.8
    1-2/day 14.8 14.1 14.8
    3+/day 10.2 9.3 9.9
Baseline NSAID Usage (%)
    None 44.3 41.0 43.1
    <1/day 19.7 20.9 20.1
    1-2/day 23.3 25.4 24.1
    3+/day 12.8 12.7 12.7
Primary Diagnoses (%)
    Nonspecific LBP 62.3 56.0 58.7
    Disc herniation 14.3 19.7 16.8
    Spondylosis/spondylolisthesis 6.3 6.4 6.9
     Stenosis 17.1 17.9 17.5
Baseline VAS (mm) 54.39 (21.83) 52.47 (21.66) 53.56 (21.77)
Baseline ODI (%) 29.52 (15.79) 28.30 (15.34) 28.96 (15.59)
Baseline EQ5D (points) 0.71 (0.14) 0.72 (0.13) 0.72 (0.14)
Baseline Exercise (ft*lb) 816.30 (476.91) 838.11 (470.50) 827.53 (473.64)
Number of Visits 6.28 (4.40) 17.55 (4.54) 11.15 (7.15)

SD: Standard Deviation; M: Male; F: Female; NSAID: Nonsteroidal Anti-inflammatory Drug; LBP: Low Back Pain; VAS: Visual Analogue Scale; ODI: Oswestry Disability Index; EQ5D: EuroQol-5 Dimension; ft*lb: foot-pound.

Predictors of adherence

Of the 13 predictor variables, age, primary diagnosis, baseline VAS, baseline ODI, and baseline EQ5D scores demonstrated p-values of <0.2 in univariate analyses (Table 2). When these resulting variables were used to build the multivariate logistic regression model, primary diagnosis remained as a significant predictor of adherence (p = 0.02), with individuals with a primary diagnosis of disc pathology being the most likely to be adherent to the program (OR (95% CI) 1.47 (1.16,1.86), p = 0.002) as compared to patients with nonspecific low back pain (reference group), spondylolisthesis, or stenosis (Table 3).

Table 2. Univariate logistic regression with the program completion as the dependent variable.

Variables with asterisks were included in the multivariable model.

Odds Ratio 95% CI X2 value p-value
Age (years)* 1.00 (0.99, 1.01) 0.13
Female sex 1.09 (0.93, 1.28) 0.30
BMI (kg/m2) 1.00 (0.99, 1.02) 0.58
Smoking History (yes/no) 0.93 (0.67, 1.28) 0.65
Diabetes (yes/no) 1.10 (0.84, 1.45) 0.48
Radiculopathy (yes/no) 1.10 (0.94, 1.29) 0.24
Baseline Narcotic Usage: Reference = none X2 = 1.19 0.76
    <1/day 1.07 (0.85, 1.35) 0.57
     1-2/day 0.95 (0.75, 1.2) 0.64
    3+/day 0.91 (0.69, 1.20) 0.50
Baseline NSAID Usage: Reference = none X2 = 3.03 0.39
    <1/day 1.15 (0.92, 1.42) 0.22
    1-2/day 1.18 (0.96, 1.44) 0.12
    3+/day 1.07 (0.823 1.38) 0.60
Primary Diagnosis:* Reference = Nonspecific LBP X2 = 14.94 0.002
    Disc herniation* 1.54 (1.23, 1.92) < 0.001
Spondylosis/spondylolisthesis 1.12 (0.80, 1.56) 0.51
    Stenosis 1.17 (0.94, 1.46) 0.15
Baseline VAS (points)* 0.99 (0.992, 0.999) 0.037
Baseline ODI (points)* 0.99 (0.99, 1.00) 0.06
Baseline EQ5D (points)* 1.66 (0.93, 2.97) 0.09
Baseline Exercise (ft*lbs) 1 (1.00, 1.00) 0.29

BMI: Body Mass Index; NSAID: Nonsteroidal Anti-inflammatory Drug; LBP: Low Back Pain; VAS: Visual Analogue Scale; ODI: Oswestry Disability Index; EQ5D: EuroQol-5 Dimension

Table 3. Multivariable logistic regression analysis results with the program completion as the dependent variable.

(N = 2181; 954 adherent). Significant p-values are bolded.

Odds Ratio 95% CI X2 value p-value
(Intercept) - - - 0.34
Age (years) 1.00 (0.99, 1.01) 0.46
Primary Diagnosis: Reference = nonspecific LBP X2 = 9.72 0.02
    Disc Herniation 1.47 (1.16, 1.86) 0.002
    Spondylolisthesis 1.08 (0.75, 1.54) 0.68
    Stenosis 1.16 (0.92, 1.47) 0.21
Baseline VAS (mm) 0.99 (0.99, 1.00) 0.19
Baseline ODI (%) 0.99 (0.99, 1.01) 0.64
Baseline EQ5D (points) 1.27 (0.54, 2.99) 0.59

CI: Confidence Interval; LBP: Low Back Pain; VAS: Visual Analogue Scale; ODI: Oswestry Disability Index; EQ5D: EuroQol-5 Dimension

Factors contributing to non-adherence

From the total cohort, 1,285 participants were considered non-adherent. Of those, 710 (31.7%) were unable to continue treatment due to logistic reasons. The most common logistical reason for discontinuing treatment was personal issues (457 (20.3%)), followed by lack of accessibility due to no insurance authorization (168 (7.4%)) and lack of geographic accessibility (85 (3.7%)). Two hundred ninety-six (13.2%) participants experienced clinically significant improvements in pain and/or disability prior to the 16th visit but did not undergo formal discharge evaluation, and 188 (8.4%) participants reported that they discontinued specifically because they felt no improvement or did not like the program. One hundred thirty-five participants (6.0%) were discharged prior to completion of treatment due to medical reasons. Of the patients who were medically discharged, 99(4.4%) were discharged due to unrelated medical problems (e.g. other unrelated surgery, cardiac issues), 21(0.9%) were discharged by their referring physician for additional work-up or alternative therapy, and 15(0.7%) were discharged to continue with spinal surgery for their condition.

Discussion

This is the largest study to our knowledge to report adherence levels in a long term (>8 weeks) supervised exercise-based rehabilitation program in a cohort of individuals with LBP, and to demonstrate diagnosis-specific differences in adherence within the patient population. Additionally, this is the only study to investigate reasons for non-adherence to supervised exercise-based rehabilitation in a quantitative manner, although some data is reported on adherence to musculoskeletal physical therapy in women with LBP without specifying an exercise component [50]. We found that just under half of participants prescribed a sustained exercise-based rehabilitation program were adherent to the recommended prescription, with most non-adherent patients discontinuing due to logistic reasons. A smaller proportion of patients either discontinued early due to improved symptoms without returning for formal discharge evaluation, or conversely did not improve enough to complete the full treatment as prescribed. Less than 5% of the cohort was unable to complete the program due to medical issues, and less than 1% of the cohort crossed over to surgery during the prescribed treatment period, suggesting that unrelated or related medical issues did not substantially impact observed adherence rates in the current study. These data also highlight that an important barrier to adherence to an exercise-based rehabilitation program is lack of accessibility to care, with over 20% of non-adherent participants reporting discontinuation due to inability to obtain insurance authorization or transportation means.

In terms of diagnosis-specific predictors of adherence, we demonstrated that patients who have been diagnosed with disc herniation were 47% more likely to complete the program compared to those with non-specific LBP. These patients were also 39%, and 31% more likely to complete the program than patients diagnosed with spondylolisthesis or stenosis respectively. Although there is no prior data reporting diagnosis-specific differences in adherence levels, the natural history of improvement in disc herniation has been reported to be shorter than other more degenerative conditions [51], which may partly explain the high adherence rates in this population. Interestingly, other demographic characteristics such as age, sex, and comorbidities (i.e. smoking and diabetes) were not related to adherence, indicating that these characteristics do not limit individuals from participating in treatment in our cohort. This information can potentially inform rehabilitation clinics in identifying individuals who are at risk for being non-adherent and may encourage clinicians to consider alternative retention strategies or modified programs for individuals with specific diagnoses. Understanding barriers to adherence may also provide direction in targeted strategies for reducing patient drop out due to financial and geographical barriers. For example, providing mobile alternatives (e.g. online- or telehealth-based programs) that reduce healthcare system and patient financial burden, as well as provide flexibility in patient engagement is a feasible alternative [52]. It also indicates that insurance-based constraints on visit number may need re-evaluation relative to value of care. Finally, it will provide useful information to design future clinical trials investigating efficacy.

Barriers to adherence

There has been a recent focus on identifying barriers to adherence and accessibility to care for management of musculoskeletal and other chronic conditions, however, there continues to be a sparsity of data elucidating these issues. Qualitative studies assessing views of patients with LBP on barriers to physical activity and exercise have reported that pain, lack of time, and difficulty with integration into daily life were primary factors limiting adherence [53], with supervision and support by professionals positively influencing adherence [53, 54]. Similarly, studies using various interventions to improve adherence have supported the concept of supervised sessions and motivational strategies [21]. These data are, in part, consistent with our finding that personal factors were a large contributor to non-adherence for those who discontinued care. Another barrier to adherence that we identified was lack of financial and geographic accessibility to care, including lack of sufficient health insurance coverage for the prescribed treatment duration. Although health insurance related factors and geographic location have not been previously identified as barriers to adherence, one recent study investigating risk factors for physical therapy visit cancellations or “no-shows” reported that insurance type and clinic location were significant predictors of not showing up for physical therapy visits [55], and these two factors are repeatedly cited as barriers to participating in clinical trials research [30]. Although “no-shows” and adherence are not identical constructs, and as stated previously, clinical trials and clinical practice behave differently, these findings highlight the potential impact of socioeconomic factors on care accessibility and compliance.

Limitations

There are several limitations to this study. First, this is a retrospective study design and therefore causality cannot be inferred from these data. Additionally, there are a number of factors that have been previously reported in the literature to impact clinical outcomes (e.g. psychosocial variables) that may also influence adherence, that were not collected in this dataset. The number of patients with complete data varied for each variable in the regression-based prediction model, and therefore patients that were missing data for the variables included in the final model were not accounted for (506 patients). However, the difference in N-size between the univariate analyses and multivariate analyses was less than 20% of the full cohort. Similarly, because a large proportion of participants were not adherent, clinical outcomes such as pain levels and functional status in response to treatment were not consistently available to concurrently evaluate treatment efficacy, and the ability to connect patient characteristics with reasons for non-adherence was limited because of the anonymous nature of the response data. Despite these limitations, our levels of missing data were either equivalent to, or lower than many studies investigating adherence. For example, studies measuring session attendance reported over 50% of their outcome data missing, although these studies reported missing data for longer term follow up beyond termination of the prescribed exercise program [28]. Finally, although the investigated program was a supervised rehabilitation program, program compliance–or the actual completion of prescribed activities outside of the supervised component of the program (e.g. home exercises), is not considered or measured in this investigation. Further research is needed to distinguish the impact of adherence and compliance on clinical outcomes.

Conclusion

The majority of participants with LBP undergoing a long-term supervised exercise-based rehabilitation program designed were non-adherent using strict criterion for program completion. However, a substantial proportion of those that were considered non-adherent reported clinically important symptom and/or disability reduction. Individuals diagnosed with disc pathology were more adherent to the program than those with other diagnoses. Most individuals who initiated but did not complete the program did so due to logistical problems such as personal issues, lack of sufficient insurance authorization and geographic accessibility to a clinic. Future research is required to target effective methods for influencing these factors to improve adherence and optimize long-term treatment efficacy in this treatment setting.

Supporting information

S1 Data

(CSV)

Abbreviations

LBP

Low back pain

BMI

Body mass index

MVC

Maximum Voluntary Contraction

NSAID

Nonsteroidal anti-inflammatory drug

ODI

Oswestry Disability Index

EQ5D

EuroQol 5D

Data Availability

Data underlying the results presented in the study are available in the Supporting Information files associated with the submission.

Funding Statement

This project was funded by the American Physical Therapy Association Foundation for Physical Therapy Research Magistro Family Foundation Grant awarded to BS. The Clinical Translational Research Institute (CTRI) is partially supported by the National Institutes of Health, Grant UL1TR001442 of CTSA funding. The funders had no role in study design, data collection, analysis, decision to publish, or preparation of the manuscript. The authors have no conflicts to declare.

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Decision Letter 0

Shao-Hsien Liu

12 Jul 2022

PONE-D-22-15309Factors impacting adherence to an exercise-based physical therapy program for individuals with low back painPLOS ONE

Dear Dr. Shahidi,

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #2: Yes

Reviewer #3: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Thank you for the opportunity to review this important paper. Overall it is an important topic that is often under-examined. However, I have a few major and some minor comments that should be addressed before considering this manuscript for publication.

Major Comments:

1) It would be beneficial to define adherence in the introduction. In the introduction and the abstract, it is unclear if adherence has to do with adhering to the rehabilitation program, which includes attending appointments or adhering to the program provided by the rehab experts, such as home exercise programs or general physical activity recommendations. Part of this is the novelty of this research, most research focuses on adherence to HEP and not adherence to attending PT sessions, so the readers can get easily confused.

2) In the introduction, you mention logistic limitations, such as lack of insurance coverage. Thus it is highly recommended that insurance coverage is added to the independent predictor variables included in your model. It is also recommended that lack of geographic accessibility is incorporated into the models as they are the top factors you identified as being contributors to non-adherence.

3) Most of the discussion are the results written in a narrative format. It is recommended that the authors expand on the discussion to add insight into the "so what?"

Minor Comments:

Abstract:

1) Your objective is related to predictors of adherence and reasons for non-adherence. But your results are primarily about those who discontinued the program. Recommend combining the first four sentences and expanding on the last one, which relates more to the objectives.

2) The conclusion should relate to your results. I think re-writing your results will help with this.

Introduction:

1) Some more background information about potential factors/expanding on the following: "Logistic limitations such as lack of insurance coverage and accessibility restrictions have been shown to affect trial enrollment and may reduce selection of populations with restricted resources." This would also help justify the reason you picked the four discharge categories on top of page 8.

Methods:

1) line 116-please specify wat clinics, outpatient physical therapy clinics?

2) It would be valuable to justify your independent predictors in the introduction or in the methods. Currently, most of the predictor variables seem random. Furthermore, some of the predictors included in the model per Table 2 are not presented in the methods. Including these in the methods would decrease the appearance of the predictors being at random.

3) Since chart data is used, it would be valuable to have a section on how missingness will be/was handled.

Results:

1) Please add a footnote on abbreviations used in the table.

2) Table 1- I find it hard to believe that Baseline VAS is significant when the 95%CI includes 1. Please double-check these values.

Discussion:

1) I fully believe that this is novel and understudied, but I can't entirely agree with the second statement as a quick google search found the following article and results:

https://bmcmusculoskeletdisord.biomedcentral.com/articles/10.1186/1471-2474-11-124

“actors that significantly correlated with adherence included: age (r = 0.7, p < 0.05), initial pain intensity (r = 0.5, p < 0.05), and subjective report of improvement (r = 0.7, p < 0.01). Adherence did not correlate with the type of LBP, patient occupation, experience or nationality of the physiotherapist."

2) I would like to see references or further support regarding the conclusion written on line 259, "suggesting adherence is not affected by insufficient health capacity."

3) The information in paragraph three of the discussion strengthens the reasons why this study should be conducted but doesn't expand on the results. I recommend moving most of this information into the introduction.

4) Throughout the paper, you mention supervised sessions, but this is different from skilled physical therapy sessions. At some point, this needs to be differentiated. Are these patients receiving skilled physical therapy or participating in a supervised exercise program? I say this because many of the references you are using in the discussion are from supervised exercise programs and not skilled physical therapy, and adherence to these are expected to be different.

Reviewer #2: Reviewer Summary and General Comments

This manuscript assess demographic and specific clinical factors as predictors of adherence to an exercise-based rehabilitation program among individuals with low back pain. In addition, they assess reasons individuals were non-adherent to the program. The contributions of these findings to the literature are very important given the lack of adherence assessment within the literature, as well as lack of assessing why individuals struggle to adhere to these important and effective rehabilitation programs. However, there are a few minor points that are unclear to the reviewer and should be addressed prior to the publication of this manuscript. Please see the list below by each section:

Ethics Statement

• It is not stated within this statement whether consent was written or oral. Please clarify.

Abstract

• Check spacing after periods throughout. It looks like a single space in generally used, however within the conclusion on Line 47 there appear to be extra spaces.

Introduction

• Line 71-72 – Do you have a reference for this statement? In addition, do the authors have information as to how often recurrences and progression to chronic LBP occur?

Methods

• Line 115 – As mentioned above, can you clarify whether written/oral informed consent was obtained? Additionally, was the IRB through UCSD? If so, please include here as well.

• Line 117-118 – The dates as currently written here are a bit confusing to read, may be clearer to write out the month and then year (e.g., November 2015).

• Line 118 – Can the authors provide a rationale for using gender instead of the more appropriate term sex? Or possibly clarify individuals who identified as either male or female gender had information collected?

• Lines 126-137 – This section may benefit from numbering the criteria for each specific diagnostic category. For example: “… patients were categorized in the lumbar stenosis category if they had, 1) neurogenic claudication and/or radicular leg symptoms, or 2) confirmatory cross-sectional imaging showing lumbar spinal stenosis at one or more levels.”

• Lines 139-140 – Can the author clarify how long the rehabilitation program was within this sentence? It appears it was 10 weeks, but the exact duration is unclear.

• Although mentioned later in the manuscript, a brief explanation for how missing data were handled in the statistical analysis section should be included.

• Line 175 – Can the authors define NSAID. This appears to be the first time NSAID has been written in the paper and thus should be defined.

• Can the authors provide a rationale for not looking at adherence as a continuous measure (e.g., percent adherence), in addition to the binary measure used in this analysis?

Results/Tables

• Check alignment/spacing within each table and table legend.

• A footnote should be included for each table that should include the definition of each abbreviation used within the table.

• Table 1 – the colons used for gender, smoking history, diabetes diagnosis, and radiculopathy diagnosis are a bit confusing. Perhaps a / would be clearer to use. (e.g., 44.8/55.2)

• Table 2 – not all units are included within this table, please check to be sure they are added for each variable.

Discussion

• Line 248 – Given this is a rehabilitation program, not necessarily a supervised exercise program, this line should be adjusted to read along the lines of: “..long term supervised rehabilitation exercise program…”

• Lines 260-263 – Did the authors happen to examine the demographic breakdown of those who reported discontinuation due to inability to obtain insurance authorization or transportation means? May be something important to include depending on the results.

• Lines 291-297 – Perhaps including the authors thoughts as to why such a big difference in percent adherence was found between the two example programs mentioned may add to the impact of this paragraph.

Conclusion

• Line 336-337 – The authors should re-word, there appears to be a missing word or some re-arranging that makes this sentence unclear.

Reviewer #3: 1. In their investigation of adherence, the authors do not consider activity tolerance to the prescribed intervention as a factor in adherance. Consideration for pain rating and pain trajectory may strengthen the argument for tolerance and appropriateness of the exercise prescription provided to the study group.

2. The authors do not detail why BMI was an exclusion criteria. Elaboration of this point would help justification of the sample.

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Reviewer #1: No

Reviewer #2: Yes: Katherine Ann Collins

Reviewer #3: Yes: Jenna M. Tosto-Mancuso

**********

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Attachment

Submitted filename: DD22-15309.docx

PLoS One. 2022 Oct 20;17(10):e0276326. doi: 10.1371/journal.pone.0276326.r002

Author response to Decision Letter 0


16 Aug 2022

We appreciate the reviewers comments and suggestions. We have incorporated responses and revisions according to the reviewer requests and feel that the manuscript is greatly improved as a result. We have included our responses to the reviewer comments below in-line.

Reviewer #1: Thank you for the opportunity to review this important paper. Overall it is an important topic that is often under-examined. However, I have a few major and some minor comments that should be addressed before considering this manuscript for publication.

Major Comments:

1) It would be beneficial to define adherence in the introduction. In the introduction and the abstract, it is unclear if adherence has to do with adhering to the rehabilitation program, which includes attending appointments or adhering to the program provided by the rehab experts, such as home exercise programs or general physical activity recommendations. Part of this is the novelty of this research, most research focuses on adherence to HEP and not adherence to attending PT sessions, so the readers can get easily confused.

Thank you for this comment. Given that this is a study of a supervised in-clinic physical therapy program, we are considering adherence primarily based on attendance to those in-clinic visits. Beyond adherence through prescribed attendance, we recognize that compliance to any non-supervised components of the treatments, such as home exercises, was not addressed or monitored in this study. This definition has been clarified in the methods and the limitations related to not collecting compliance information is added as a limitation in the discussion section.

2) In the introduction, you mention logistic limitations, such as lack of insurance coverage. Thus it is highly recommended that insurance coverage is added to the independent predictor variables included in your model. It is also recommended that lack of geographic accessibility is incorporated into the models as they are the top factors you identified as being contributors to non-adherence.

Thank you for this comment. Unfortunately, we were not able to retrieve accurate insurance coverage data for this cohort of patients. One limitation to our data is that the data on reasons for discontinuation of treatment was acquired via anonymous survey response and is not able to be aligned to the origin payor data on a per-subject basis for which adherence was modeled. Because of this, we are unable to adjust/include the insurance or geographic accessibility limitations as an independent variable in the model. We have included this as a limitation in the discussion.

3) Most of the discussion are the results written in a narrative format. It is recommended that the authors expand on the discussion to add insight into the "so what?"

Thank you for this comment. We have now expanded on the discussion to include an emphasis on the clinical impact of these data, and proposed action items based on these findings.

Minor Comments:

Abstract:

1) Your objective is related to predictors of adherence and reasons for non-adherence. But your results are primarily about those who discontinued the program. Recommend combining the first four sentences and expanding on the last one, which relates more to the objectives.

Thank you. We have now redistributed the focus of the abstract to be more aligned with the results.

2) The conclusion should relate to your results. I think re-writing your results will help with this.

Thank you. We have modified the conclusion to align more closely with the results.

Introduction:

1) Some more background information about potential factors/expanding on the following: "Logistic limitations such as lack of insurance coverage and accessibility restrictions have been shown to affect trial enrollment and may reduce selection of populations with restricted resources." This would also help justify the reason you picked the four discharge categories on top of page 8.

Thank you. We have added additional background emphasizing the identification of logistic limitations as being impactful in trials and treatments, however our discharge categories were selected based on the most commonly observed reasons for non-adherence, as opposed to being selected on an a-priori basis. As such, we aimed to encompass the most similar barriers together. We did not observe large variability in reasons for non-adherence or discharge within these categories. We have clarified this in the methods section.

Methods:

1) line 116-please specify what clinics, outpatient physical therapy clinics?

Thank you. We have now specified that these treatments took place in outpatient physical therapy clinics.

2) It would be valuable to justify your independent predictors in the introduction or in the methods. Currently, most of the predictor variables seem random. Furthermore, some of the predictors included in the model per Table 2 are not presented in the methods. Including these in the methods would decrease the appearance of the predictors being at random.

These variables were based on both evidence from prior studies indicating that these variables are potentially impactful in clinical outcomes (e.g. age, BMI, smoking history, diabetes, diagnosis, pain, disability and quality of life scores), as well as more granular detail on muscular performance based on the exercise-based focus of the clinical rehabilitation program (Baseline exercise strength level). We have now included this selection rationale in the methods section along with supporting references for the impact of these variables on clinical outcomes. We acknowledge that other factors, such as psychosocial factors, have been shown to influence clinical outcomes and were not available for collection within this data set. We acknowledge this as a limitation in our study.

3) Since chart data is used, it would be valuable to have a section on how missingness will be/was handled.

Results:

Because there were some variables with missing data, we did not remove a row completely from the analysis if the row contained any missing values. Instead, we removed rows only when a variable that was used for the specific tests were missing (pairwise deletion). We did not perform imputation to fill the missing values (no replacement). As such, the multivariate regression model includes only patients with complete data (N size (2,181) now included in regression table). This is now clarified in the methods section.

1) Please add a footnote on abbreviations used in the table.

A footnote is now included for tables with abbreviations

2) Table 1- I find it hard to believe that Baseline VAS is significant when the 95%CI includes 1. Please double-check these values.

This is an artifact of rounding for the number of significant digits reported in the table; The confidence interval was extremely small for VAS (0.992-0.999). We have modified the significant digits to clarify this.

Discussion:

1) I fully believe that this is novel and understudied, but I can't entirely agree with the second statement as a quick google search found the following article and results:

https://bmcmusculoskeletdisord.biomedcentral.com/articles/10.1186/1471-2474-11-124

“actors that significantly correlated with adherence included: age (r = 0.7, p < 0.05), initial pain intensity (r = 0.5, p < 0.05), and subjective report of improvement (r = 0.7, p < 0.01). Adherence did not correlate with the type of LBP, patient occupation, experience or nationality of the physiotherapist."

Thank you for bringing this citation to our attention. We agree that this article does provide specific predictors to musculoskeletal physical therapy in a quantitative manner, however the methods do not specify whether the physical therapy was primarily focused on exercise-based modalities as is the focus of the current study. Regardless, we agree that this should be recognized and now have included this citation in the discussion.

2) I would like to see references or further support regarding the conclusion written on line 259, "suggesting adherence is not affected by insufficient health capacity."

This comment was made in reference to the observed results and was intended to communicate that a very low proportion of participants reported that they were unable to complete the program due to unrelated medical issues. Our interpretation of this is that severe medical comorbidities were not a limiting factor to program adherence in this cohort. We have clarified this in the discussion section.

3) The information in paragraph three of the discussion strengthens the reasons why this study should be conducted but doesn't expand on the results. I recommend moving most of this information into the introduction.

Thank you. We have moved this section to the introduction and abridged for conciseness.

4) Throughout the paper, you mention supervised sessions, but this is different from skilled physical therapy sessions. At some point, this needs to be differentiated. Are these patients receiving skilled physical therapy or participating in a supervised exercise program? I say this because many of the references you are using in the discussion are from supervised exercise programs and not skilled physical therapy, and adherence to these are expected to be different.

The rehabilitation program evaluated in this study was an exercise-based physical therapy program administered by a licensed physical therapist. This has been clarified in the methods section.

Reviewer #2: Reviewer Summary and General Comments

This manuscript assess demographic and specific clinical factors as predictors of adherence to an exercise-based rehabilitation program among individuals with low back pain. In addition, they assess reasons individuals were non-adherent to the program. The contributions of these findings to the literature are very important given the lack of adherence assessment within the literature, as well as lack of assessing why individuals struggle to adhere to these important and effective rehabilitation programs. However, there are a few minor points that are unclear to the reviewer and should be addressed prior to the publication of this manuscript. Please see the list below by each section:

Ethics Statement

• It is not stated within this statement whether consent was written or oral. Please clarify.

We have now clarified that a waiver of consent was obtained due to the retrospective and de-identified nature of the data.

Abstract

• Check spacing after periods throughout. It looks like a single space in generally used, however within the conclusion on Line 47 there appear to be extra spaces.

Thank you. We have double checked and corrected inconsistent spacing throughout.

Introduction

• Line 71-72 – Do you have a reference for this statement? In addition, do the authors have information as to how often recurrences and progression to chronic LBP occur?

We have now included additional references to support this statement as well as additional information on prevalence of recurrence and LBP progression.

Methods

• Line 115 – As mentioned above, can you clarify whether written/oral informed consent was obtained? Additionally, was the IRB through UCSD? If so, please include here as well.

Thank you. We have now clarified the location and modality of consent in the methods section.

• Line 117-118 – The dates as currently written here are a bit confusing to read, may be clearer to write out the month and then year (e.g., November 2015).

Thank you, we have modified the dates to improve clarity.

• Line 118 – Can the authors provide a rationale for using gender instead of the more appropriate term sex? Or possibly clarify individuals who identified as either male or female gender had information collected?

We did not collect non-binary gender or gender identity information, and as such have changed our terminology to refer to sex instead of gender.

• Lines 126-137 – This section may benefit from numbering the criteria for each specific diagnostic category. For example: “… patients were categorized in the lumbar stenosis category if they had, 1) neurogenic claudication and/or radicular leg symptoms, or 2) confirmatory cross-sectional imaging showing lumbar spinal stenosis at one or more levels.”

Thank you, we have now organized our diagnostic categories using numbering as suggested.

• Lines 139-140 – Can the author clarify how long the rehabilitation program was within this sentence? It appears it was 10 weeks, but the exact duration is unclear.

The prescribed program was 20 visits, with a frequency of 2 visits per week (10 weeks). This is indicated in the Exercise Protocol subsection of the methods.

• Although mentioned later in the manuscript, a brief explanation for how missing data were handled in the statistical analysis section should be included.

We have now included that we used pairwise deletion to handle missing data in the statistical analysis section.

• Line 175 – Can the authors define NSAID. This appears to be the first time NSAID has been written in the paper and thus should be defined.

Thank you. NSAIDs refer to Non-Steroidal Anti-inflammatory Drugs. We have now defined this acronym in the text.

• Can the authors provide a rationale for not looking at adherence as a continuous measure (e.g., percent adherence), in addition to the binary measure used in this analysis?

Because the treatment was a prescribed program that was based on the minimum estimated training prescription required to elicit physiological adaptation of muscle (6-7 weeks at 2 times/week), we based our adherence measure according to this recommended prescription. Analytically, it is feasible to treat adherence as a continuous measure, however the impact of adherence on programs of a longer duration that are sufficient to elicit physiological adaptation of muscle was a primary focus (and novelty) of this experiment. However, we acknowledge that this approach may be useful in understanding key timepoints at which patients may be dropping out of the program and may be of interest in future studies.

Results/Tables

• Check alignment/spacing within each table and table legend.

Thank you, we have now double checked and corrected for spacing inconsistencies within the tables/legends.

• A footnote should be included for each table that should include the definition of each abbreviation used within the table.

We have now included footnotes with abbreviation definitions in the tables.

• Table 1 – the colons used for gender, smoking history, diabetes diagnosis, and radiculopathy diagnosis are a bit confusing. Perhaps a / would be clearer to use. (e.g., 44.8/55.2)

We have modified the table to clarify these data as suggested.

• Table 2 – not all units are included within this table, please check to be sure they are added for each variable.

We have now included units for all variables in Table 2.

Discussion

• Line 248 – Given this is a rehabilitation program, not necessarily a supervised exercise program, this line should be adjusted to read along the lines of: “..long term supervised rehabilitation exercise program…”

Thank you. We have modified this statement to clarify that this is a supervised exercise-based rehabilitation program.

• Lines 260-263 – Did the authors happen to examine the demographic breakdown of those who reported discontinuation due to inability to obtain insurance authorization or transportation means? May be something important to include depending on the results.

We agree that the demographic breakdown of those who reported different logistical considerations associated with their discontinuation is interesting, however, because in our data reasons for discontinuation were anonymously collected, we are not able to connect the patient characteristics to the reasons for non-adherence. As such, we are unable to determine if there is an association between demographic features and insurance authorization/transportation.

• Lines 291-297 – Perhaps including the authors thoughts as to why such a big difference in percent adherence was found between the two example programs mentioned may add to the impact of this paragraph.

Thank you for this suggestion. As this paragraph has been substantially modified and moved to the introduction as recommended by reviewer 1, the context of the information here has been shifted to focus on gaps in the literature as opposed to interpretation of differences in adherence rates across modalities of therapy. We did not expand on this concept in order to improve flow and logic of the introduction in its modified form.

Conclusion

• Line 336-337 – The authors should re-word, there appears to be a missing word or some re-arranging that makes this sentence unclear.

Thank you. We have attempted to clarify the conclusion based on this and the previous reviewers’ comments.

Reviewer #3: 1. In their investigation of adherence, the authors do not consider activity tolerance to the prescribed intervention as a factor in adherence. Consideration for pain rating and pain trajectory may strengthen the argument for tolerance and appropriateness of the exercise prescription provided to the study group.

We agree that a potential mismatch between the prescribed program and the activity tolerance of the individual may impact adherence. Although we do not have specific data on activity tolerance in this sample, the program was supervised by a licensed physical therapist that is trained to identify appropriate prescription of exercise intensity based on the tolerance and physiological capacity of the individual in the context of their individual pathology and symptomology. However, we agree that patients with high pain levels and trajectories may be differentially adherent, and we have a manuscript in preparation evaluating the clinical outcomes (including pain trajectories) of patients participating in this program. Although we did not observe significant differences in back strength at baseline between groups, we did observe that the baseline pain scores for individuals who did not complete the program were significantly higher than those who did (2-point difference). However, this difference is 10 times less than that considered to be clinically significant (20-point difference). As such, it is unlikely that differences in pain were a significant driver for lack of adherence. Interestingly we observed, through a coarse evaluation of pain change per visit over the duration of attended treatments, that those who were not adherent actually had larger improvements in symptoms on a per-visit basis compared to those who were adherent. This may suggest that some patients who were not adherent experienced rapid recovery and ceased attending due to improvement in symptoms. This is also supported by our data demonstrating that a proportion of non-adherent patients experienced improvements, but never underwent formal discharge. Because we are concurrently preparing a manuscript evaluating clinical outcome trajectories and associated predictors, we have not included a detailed discussion of these observations in the current submission.

2. The authors do not detail why BMI was an exclusion criteria. Elaboration of this point would help justification of the sample.

Only patients with BMI values that were observed to be non-physiological for the age-range studied were excluded from the analysis. Based on the numeric values observed for those excluded, we hypothesize that these patients had incorrectly entered BMI data and therefore those values were not retained in analysis. We have clarified this in the patient characteristics section.

Attachment

Submitted filename: PLOSOne responses to reviewers_RX.docx

Decision Letter 1

Ravi Shankar Yerragonda Reddy

5 Oct 2022

Factors impacting adherence to an exercise-based physical therapy program for individuals with low back pain

PONE-D-22-15309R1

Dear Dr. Bahar Shahidi,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Reviewers' comments:

Reviewer's Responses to Questions

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Reviewer #2: All comments have been addressed

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Reviewer #2: Yes

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Reviewer #2: Yes

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Acceptance letter

Ravi Shankar Yerragonda Reddy

13 Oct 2022

PONE-D-22-15309R1

Factors impacting adherence to an exercise-based physical therapy program for individuals with low back pain

Dear Dr. Shahidi:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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on behalf of

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    Submitted filename: PLOSOne responses to reviewers_RX.docx

    Data Availability Statement

    Data underlying the results presented in the study are available in the Supporting Information files associated with the submission.


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