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Musculoskeletal (MSK) conditions are the number one cause of disability worldwide. Digital care programs (DCPs) for MSK pain management have arisen as alternative care delivery models to circumvent challenges in accessibility of conventional therapy. Despite the potential of DCPs to reduce inequities in accessing care, the outcomes of such interventions in rural and urban populations have yet to be studied.
The aim of this study was to assess the impact of urban or rural residency on engagement and clinical outcomes after a multimodal DCP for MSK pain.
This study consists of an ad hoc analysis of a decentralized single-arm investigation into engagement and clinical-related outcomes after a multimodal DCP in patients with MSK conditions. Patients were coded according to their zip codes to a specific rural-urban commuting area code and grouped into rural and urban cohorts. Changes in their engagement and clinical outcomes from baseline to program end were assessed. Latent growth curve analysis was performed to estimate change trajectories adjusting for the following covariates: age, gender, BMI, employment status, and pain acuity. Outcomes included engagement, self-reported pain, and the results of the Generalized Anxiety Disorder 7-item, Patient Health Questionnaire 9-item, and Work Productivity and Activity Impairment scales. A minimum clinically important difference (MCID) of 30% was considered for pain.
Patients with urban and rural residency across the United States participated in the program (n=9992). A 73.8% (7378/9992) completion rate was observed. Both groups reported high satisfaction scores and similar engagement with exercise sessions, with rural residents showing higher engagement with educational content (
This study advocates for the utility of a DCP in improving access to MSK care in urban and rural areas alike, showcasing its potential to promote health equity. High engagement, satisfaction, and completion rates were noted in both groups, as well as significant improvements in clinical outcomes.
ClinicalTrials.gov NCT04092946; https://clinicaltrials.gov/ct2/show/NCT04092946
Musculoskeletal (MSK) conditions are highly prevalent worldwide, resulting in significant disability and suffering [
Despite the many benefits of telehealth, inequities in health other than underlying health status still exist based on age, geography, respective availability of health care facilities, and socioeconomic factors [
To our knowledge, no study has been conducted on the impact of urban or rural location on engagement and clinical outcomes following a telerehabilitation program for MSK conditions. We have previously reported a multimodal digital care program (DCP) combining exercise-based physical therapy with psychoeducational components, which provided a comprehensive approach to pain management. This program encourages patients to develop strategies and self-management skills to manage their pain and has been validated in several acute and chronic MSK conditions [
This study is an ad hoc analysis of a decentralized, single-arm investigation into clinical and engagement-related outcomes following a multimodal DCP in patients with musculoskeletal (MSK) pain conditions. The DCP was administered at the patients’ homes and delivered between March 1, 2021, and March 10, 2022.
This study is part of a trial that was prospectively registered on ClinicalTrials.gov (NCT04092946) on September 17, 2019, and approved by the New England Institutional Review Board (120190313) on June 18, 2020.
The study population included adults (≥18 years of age) who were beneficiaries of employer health plans from 50 US states and the District of Columbia. Employees and their dependents who reported either acute or chronic MSK pain in the spine, upper limbs, or lower limbs were eligible and were invited to apply to the DCP of Sword Health (located in Draper, Utah) through a dedicated website. Throughout enrollment, participants were asked to provide demographic data, including zip codes and baseline clinical information (eg, initial pain levels). Participants were informed about the study and invited to provide consent. The exclusion criteria were as follows: (1) a health condition (eg, cardiac or respiratory) not allowing a participant to engage in at least 20 minutes of light to moderate exercise, (2) being under treatment for active cancer, and (3) rapid loss of strength or numbness in the arms or legs or change in bowel or urinary function in the previous 2 weeks.
The DCP has been described previously [
Demographic data included age, BMI, patient gender, educational level, and employment status. The gender category included “man,” “woman,” “nonbinary,” “other,” and “prefer not to specify.” The employment status categories were defined as the following: full-time employed, part-time employed, or not employed. The educational levels were (1) high school or less (including technical or vocational training), (2) some college, including a bachelor’s degree, community college, or an associate degree, (3) some graduate school, including a master’s or doctoral degree, and (4) “not available” or “prefer not to answer.”
Patients were coded according to their zip codes to a specific rural-urban commuting area (RUCA) code [
Outcomes were collected at baseline and 4, 8, and 12 weeks, and mean changes were calculated between baseline and program end. Engagement and clinical outcomes are described in
Engagement and clinical outcomes in this study.
Outcome | Description |
Engagement | Measured through the following: Completion of the program (considered as the retention rate) Number of completed exercise sessions over the 12-week digital care program Weekly session frequency Time spent performing exercise sessions Articles read Interactions with the physical therapist Satisfaction, assessed 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?” |
Numerical Pain Rating Scale [ |
Assessed through the question “Please rate your average pain over the last 7 days, from 0 (no pain at all) to 10 (worst pain imaginable)”; the number of patients reaching the minimum clinically important difference of 30% between baseline and treatment end was also assessed |
Generalized Anxiety Disorder 7-item scale (range 0-21) [ |
Used to assess anxiety; higher scores are associated with worse outcomes |
Patient Health Questionnaire 9-item scale (range 0-27) [ |
Used to assess depression; higher scores are associated with worse outcomes |
WPAIa for general health questionnaire (version 2.0) [ |
Evaluated in employed participants to assess overall work impairment (WPAI overall: total presenteeism and absenteeism from work), presenteeism (WPAI work), absenteeism (WPAI time), and non–work-related activity impairment (WPAI activity); higher scores represent higher impairment. |
aWPAI: Work Productivity and Activity Impairment.
Analyses of baseline characteristics (demographics and clinical data), as well as engagement metrics, were performed using a 2-tailed, 2-sample
Latent growth curve analysis (LGCA) was used to estimate trajectories of each outcome over time [
All statistical analyses were conducted using commercially available software (SPSS version 22; IBM Corp), and the level of significance was set at
A total of 14,754 participants were screened for eligibility (
Study flow chart showing the number of participants who were excluded, included, and dropped out. RUCA: rural-urban commuting area.
Patients’ baseline demographics grouped by urban and rural areas are presented in
Baseline characteristics for urban and rural groups following an intention-to-treat analysis. Filtered cases correspond to participants who reported relevant impairment at baseline (>0 or ≥5 points). Statistically significant
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Total (n=9992) | Urban (n=8809) | Rural (n=1183) | |||
Age (years), mean (SD) | 48.55 (12.45) | 48.11 (12.37) | 51.85 (12.57) |
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<25 | 127 (1.3) | 114 (1.3) | 13 (1.1) |
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25-40 | 2753 (27.6) | 2520 (28.6) | 233 (19.7) |
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40-60 | 5279 (52.8) | 4649 (52.8) | 630 (53.3) |
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>60 | 1833 (18.3) | 1526 (17.3) | 307 (26) |
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BMI (kg/m2), mean (SD) | 29.18 (6.74) | 28.96 (6.60) | 30.83 (7.48) |
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Underweight (<18.5) | 90 (0.9) | 84 (1) | 6 (0.5) |
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Normal (18.5-25) | 2798 (28) | 2548 (28.9) | 250 (21.1) |
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Overweight (25-30) | 3373 (33.8) | 3011 (34.2) | 362 (30.6) |
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Obese (30-40) | 2957 (29.6) | 2525 (28.7) | 432 (36.5) |
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Obese grade III (>40) | 743 (7.4) | 614 (7) | 129 (10.9) |
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.12 | |||||
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Woman | 5502 (55.1) | 4818 (54.7) | 684 (57.8) |
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Man | 4457 (44.6) | 3963 (45) | 494 (41.8) |
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Nonbinary | 24 (0.2) | 19 (0.2) | 5 (0.4) |
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Other | 3 (0) | 3 (0) | 0 (0) |
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Prefer not to specify | 6 (0.1) | 6 (0.1) | 0 (0) |
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Employed full-time | 6271 (62.8) | 5616 (63.8) | 655 (55.4) |
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Employed part-time | 2348 (23.5) | 2076 (23.6) | 272 (23) |
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Not employed | 1067 (10.7) | 853 (9.7) | 214 (18.1) |
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High school or less | 866 (8.7) | 700 (7.9) | 166 (14) |
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Some college, including bachelor’s or associate degree | 4543 (45.5) | 4031 (45.8) | 512 (43.3) |
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Some graduate school, including master’s or doctoral degree | 2082 (20.8) | 1876 (21.3) | 206 (17.4) |
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Not available or prefer not to answer | 2501 (25) | 2202 (25) | 299 (25.3) |
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Acute | 2147 (21.5) | 1952 (22.2) | 195 (16.5) |
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Chronic | 7845 (78.5) | 6857 (77.8) | 988 (83.5) |
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Ankle | 422 (4.2) | 380 (4.3) | 42 (3.6) |
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Elbow | 286 (2.9) | 259 (2.9) | 27 (2.3) |
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Hip | 900 (9) | 786 (8.9) | 114 (9.6) |
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Knee | 1438 (14.4) | 1292 (14.7) | 146 (12.3) |
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Low back | 3976 (39.8) | 3441 (39.1) | 535 (45.2) |
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Neck | 936 (9.4) | 834 (9.5) | 102 (8.6) |
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Shoulder | 1632 (16.3) | 1461 (16.6) | 171 (14.5) |
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Wrist or hand | 402 (4) | 356 (4) | 46 (3.9) |
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Pain, mean (SD) | 4.83 (1.99) | 4.83 (1.99) | 4.85 (1.98) | .72 | |
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GAD-7b ≥5, n (%) | 2751 (27.5) | 2430 (27.6) | 321 (27.1) | .74 | |
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GAD-7 ≥5, mean (SD) | 8.89 (4.08) | 8.89 (4.07) | 8.89 (4.19) | .99 | |
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GAD-7, mean (SD) | 3.03 (4.35) | 3.04 (4.35) | 2.98 (4.37) | .69 | |
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PHQ-9c ≥5, n (%) | 2071 (20.7) | 1790 (20.3) | 281 (23.8) |
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PHQ-9 ≥5, mean (SD) | 9.21 (4.30) | 9.20 (4.29) | 9.31 (4.41) | .69 | |
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PHQ-9, mean (SD) | 2.37 (4.15) | 2.33 (4.11) | 2.7 (4.41) |
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WPAId overall >0, mean (SD) | 29.89 (20.11) | 29.9 (20.11) | 29.80 (20.16) | .91 | |
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WPAI overall, mean (SD) | 17.32 (21.26) | 17.23 (21.25) | 17.9 (21.39) | .34 | |
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WPAI work >0, mean (SD) | 28.63 (18.80) | 28.62 (18.77) | 28.71 (19.05) | .91 | |
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WPAI work, mean (SD) | 16.27 (20.05) | 16.16 (20.0) | 17.04 (20.35) | .21 | |
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WPAI time >0, mean (SD) | 18.08 (18.07) | 18.45 (18.45) | 15.38 (14.94) | .11 | |
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WPAI time, mean (SD) | 1.91 (8.07) | 1.94 (8.22) | 1.65 (6.82) | .31 | |
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WPAI activity >0, mean (SD) | 37.37 (22.86) | 37.33 (22.85) | 37.70 (22.91) | .65 | |
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WPAI activity, mean (SD) | 29.04 (25.46) | 28.92 (25.45) | 29.93 (25.48) | .20 | |
Medications, n (%) | 2364 (23.7) | 2062 (23.5) | 302 (25.6) | .11 |
aA total of 1.1% (114/9992) of patients were postsurgical.
bGAD-7: Generalized Anxiety Disorder 7-item scale.
cPHQ-9: Patient Health Questionnaire 9-item scale.
dWPAI: Work Productivity and Activity Impairment scale.
Patients from rural areas were significantly older than patients from urban areas (51.85, SD 12.57 years vs 48.11, SD 12.37 years, respectively;
Patients in rural areas also presented a higher prevalence of chronic pain and low back pain conditions than patients from urban areas (535/1183, 45.2% vs 3441/8809, 39.1%, respectively). In opposition, a higher prevalence of knee-related conditions was observed in patients from urban areas than from rural areas (1292/8809, 14.7% vs 146/1183, 12.3%, respectively;
Overall, similar clinical metrics were observed between patients from rural and urban areas at baseline. The statistically significant differences found were in baseline depression (rural PHQ-9 score 2.70, SD 4.41 vs urban PHQ-9 score 2.33, SD 4.22;
When comparing completers with noncompleters, the latter were younger (46.23, SD 12.61 years vs 49.38, SD 12.29 years, respectively;
Individuals from rural areas were more likely to complete the program than patients from urban areas (906/1183, 76.6% vs 6472/8809, 73.5%, respectively;
Engagement data across the groups. Statistically significant
Engagement outcomes | Urban, mean (SD) | Rural, mean (SD) | |
Sessions, n | 33.88 (32.23) | 34.18 (30.94) | .77 |
Sessions per week, n | 2.76 (1.14) | 2.78 (1.12) | .11 |
Training time, minutes | 472.02 (485.56) | 482.54 (485.96) | .48 |
Articles read, n | 2.72 (5.27) | 3.44 (6.18) |
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Interactions with physical therapist, n | 11.79 (12.54) | 12.37 (13.90) | .14 |
Average satisfaction score | 8.6 (1.7) | 8.6 (1.8) | .95 |
Mean changes in clinical outcomes for both urban and rural groups following an intent-to-treat analysis are presented in
Mean changes between baseline and program end and mean differences between groups for the studied clinical outcomes following an intent-to-treat analysis. Statistically significant
Scores | Urban | Rural | Mean difference | ||||
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Mean change (95% CI) | Mean change (95% CI) | Difference (95% CI) | ||||
Pain | 2.2 (2.2 to 2.3) |
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2.3 (2.1 to 2.5) |
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–0.1 (–0.3 to 0.2) | .62 | |
GAD-7a | 1.26 (1.16 to 1.37) |
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1.16 (0.86 to 1.47) |
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0.1 (–0.22 to 0.43) | .53 | |
GAD-7 ≥5 | 4.5 (3.7 to 5.4) |
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4.6 (4.3 to 4.9) |
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–0.1 (–1.0 to 0.8) | .85 | |
PHQ-9b | 0.93 (0.82 to 1.03) |
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1.14 (0.84 to 1.45) |
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–0.22 (–0.54 to 0.1) | .19 | |
PHQ-9 ≥5 | 4.5 (3.39 to 5.53) |
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4.9 (4.5 to 5.2) |
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–0.41 (–1.5 to 0.7) | .48 | |
WPAIc overall | 7.37 (6.65 to 8.09) |
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7.19 (5.28 to 9.11) |
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0.18 (–1.87 to 2.22) | .87 | |
WPAI overall >0 | 14.6 (11.7 to 17.4) |
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15.6 (14.5 to 16.7) |
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–1.0 (–4.1 to 2.0) | .50 | |
WPAI work | 13.73 (12.99 to 14.47) |
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13.59 (11.67 to 15.51) |
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0.15 (–1.91 to 2.2) | .89 | |
WPAI work >0 | 14.3 (11.6 to 17.0) |
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15.4 (14.3 to 16.4) |
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–1.1 (–4 to 1.8) | .46 | |
WPAI time missed | 7.14 (6.47 to 7.81) |
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6.82 (5 to 8.63) |
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0.32 (–1.62 to 2.25) | .75 | |
WPAI time missed >0 | 11.4 (6.9 to 16.0) |
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11.8 (9.9 to 13.7) |
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–0.35 (–5.2 to 4.5) | .89 | |
WPAI activity | 0.66 (0.37 to 0.96) |
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0.42 (–0.34 to 1.19) | .28 | 0.24 (–0.58 to 1.06) | .57 | |
WPAI activity >0 | 19.4 (18.6 to 20.3) |
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18.4 (16.1 to 20.7) |
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1.06 (–1.4 to 3.5) | .40 |
aGAD-7: Generalized Anxiety Disorder 7-item scale.
bPHQ-9: Patient Health Questionnaire 9-item scale.
cWPAI: Work Productivity and Activity Impairment scale.
Longitudinal changes across time using intent-to-treat analysis. A: pain level; B and C: mental health (GAD-7-and PHQ-9 scores, respectively) for cases with at least mild or moderate anxiety or depression at baseline; D-F: work productivity (WPAI overall, WPAI work, and WPAI activity scores, respectively) for cases reporting impairment at baseline. The shaded areas are the 95% CI. GAD-7: Generalized Anxiety Disorder 7-item scale; PHQ-9: Patient Health Questionnaire 9-item scale; WPAI: Work Productivity and Activity Impairment scale.
Pain levels decreased similarly in both groups from baseline to program end (
Among those who reported at least mild or moderate anxiety (GAD-7 score ≥5) at baseline, we observed a significant decrease in anxiety in both groups (urban score 4.5, 95% CI 3.7-5.4;
Productivity improvements were observed in both groups with no differences between them (
In urban areas, individuals with higher BMI reported a greater leveling effect on absenteeism improvement than those from rural areas (the difference in curve between groups was 0.14,
The multimodal DCP herein reported was able to reach all US states in both urban and rural locations and had a completion rate of 73.8% (7378/9992), which is similar to previous studies reporting the use of digital interventions for MSK pain management [
Health inequities between urban and rural populations are prevalent in the United States. [
Studies have shown that patients in rural areas of the United States may face additional difficulties in recovery due to fewer opportunities for in-person physical activity programs as a consequence of limited access to indoor facilities, limited transportation, and a lower overall health status when compared to urban patients [
In this study, engagement was similar between both rural and urban areas (eg, the number of sessions and interactions with a PT), and completion rates were higher in the rural cohort. The reasons behind these observations may be multifactorial, but one can speculate that the lack of access to alternative health care resources, as well as the provision of a Wi-Fi hotspot to those without internet, might have prompted patients from rural areas to not only engage with the exercise sessions but also to achieve higher completion rates [
Despite the worse clinical outcomes reported at baseline by those in rural communities, in line with what has been described before [
The prevalence of depression and anxiety has been reported to be higher in residents of rural areas compared to urban areas [
Despite the wide reach of telerehabilitation, many areas across the United States are still facing unmet needs. The results observed herein support the need for further research and investment in digital rehabilitation to mitigate inequities in health care access and care delivery optimization.
There are many strengths to this study, namely the novelty of investigating the urban-rural dichotomy within a digital therapy program in a large sample size from a real-world context, including patients from 50 US states and the District of Columbia, which allows for a diverse population and thus better generalizability. Another strength is the DCP itself, which uses a multimodal approach that includes exercises with real-time biofeedback, mental support, regular communication with the PT, and a digital format. All these components favor accessibility and maximize engagement and clinical outcomes, allowing us to study different aspects of the problem, from pain to mental health to productivity.
The classification of rural and urban areas is a challenging topic considering the multitude of factors that can highly influence the obtained readings. Despite the application of a recognized classification system [
This study provides important insights regarding the impact of a multimodal digital program for MSK pain management in rural and urban settings. The DCP was able to reach all areas across the United States with high completion rates in both settings. Despite the inherent health inequities between patients from rural and urban areas, similarly high satisfaction and engagement, alongside significant improvements in pain, mental health, and productivity, were observed in both groups. This showcases the potential of the DCP to mitigate inequities by improving the accessibility of MSK care independently of geographic location.
Information regarding (1) Rural-urban commuting area (RUCA) codes, (2) baseline characteristics of completers and non-completers, (3) Latent growth curve analysis (LGCA) model following intent-to-treat analysis with corresponding model fitness and conditional analysis, and (4) 12-week mean changes for the per-protocol analysis and corresponding LGCA model and model fitness.
analysis of variance
cognitive behavioral therapy
digital care program
full information maximum likelihood
Generalized Anxiety Disorder 7-item
latent growth curve analysis
minimum clinically important difference
musculoskeletal
Patient Health Questionnaire 9-item
physical therapist
rural-urban commuting area
Work Productivity and Activity Impairment
The authors acknowledge the team of physical therapists responsible for managing the participants. The authors also acknowledge the contributions of João Tiago Silva and Guilherme Freches in data validation (both employees of Sword Health). Critical revision of the manuscript for important intellectual content was done by all authors. All authors were involved with the final approval of the manuscript. The study sponsor, Sword Health, was involved in the study design, data collection, and interpretation and writing of the manuscript.
The data sets generated during or analyzed during this study are available from the corresponding author upon reasonable request.
All authors made a significant contribution to the work. FC, FDC, and JL were responsible for the study concept and design. MM acquired the data. RM performed the statistical analysis. JS, FC, ACA, DJ, MM, and FC interpreted the data. JS was responsible for drafting the work. VB was responsible for funding.
Sword Health owns the Digital Care Program and as such this program is only available to those covered by a commercial agreement with Sword Health. FC, DJ, AA, MM, FDC, VB, and VY are employees of Sword Health, the sponsor of this study. DJ, FC, FDC, VY, and VB also hold equity in Sword Health. RM is an independent scientific consultant responsible for the statistical analysis. JS and JL are independent scientific and clinical consultants who received adviser honorariums from Sword Health.