This paper is in the following e-collection/theme issue:

Web-based and Mobile Health Interventions (4037) mHealth for Rehabilitation (354) E-Health / Health Services Research and New Models of Care (723) Surgery, Trauma and Surgical Sciences (204) Technology in Physiotherapy (192) Innovations in Exercise Rehabilitation (286) Disabilities (158) Digital Pain Assessment and Management (234) Telerehabilitation (219)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/82573, first published .
Digital Versus In-Person Physical Therapy in Adults With Musculoskeletal Conditions: Retrospective Matched-Cohort Analysis of Surgery and Low-Value Surgical Rates

Digital Versus In-Person Physical Therapy in Adults With Musculoskeletal Conditions: Retrospective Matched-Cohort Analysis of Surgery and Low-Value Surgical Rates

Digital Versus In-Person Physical Therapy in Adults With Musculoskeletal Conditions: Retrospective Matched-Cohort Analysis of Surgery and Low-Value Surgical Rates

Authors of this article:

Beatriz Domingues1 Author Orcid Image ;   Ana P Pereira1 Author Orcid Image ;   Akshat Pradhan1 Author Orcid Image ;   Catarina Zidde1 Author Orcid Image ;   Dora Janela1 Author Orcid Image ;   Carolina Marramaque1 Author Orcid Image ;   Virgílio Bento1 Author Orcid Image ;   Vijay Yanamadala1, 2, 3 Author Orcid Image ;   Steven P Cohen4, 5 Author Orcid Image ;   Luke Belz1 Author Orcid Image ;   Kevin Wang1 Author Orcid Image ;   Fernando Dias Correia1 Author Orcid Image ;   Fabíola Costa1 Author Orcid Image
Article Authors Cited by Tweetations Metrics

    Original Paper

    1Clinical Research & Dev, Sword Health, New York, NY, United States

    2Department of Surgery, Frank H Netter School of Medicine, Quinnipiac University, Hamden, CT, United States

    3Department of Neurosurgery, Hartford Healthcare Medical Group, Westport, CT, United States

    4Anesthesiology Department, Northwestern University Feinberg School of Medicine, Chicago, IL, United States

    5Departments of Anesthesiology and Physical Medicine and Rehabilitation and Anesthesiology, Walter Reed National Military Medical Center, Uniformed Services University of the Health Sciences, Bethesda, MD, United States

    Corresponding Author:

    Fabíola Costa, PhD

    Clinical Research & Dev

    Sword Health

    169 Madison Ave

    New York, NY, STE 15501

    United States

    Phone: 1 385 308 8034

    Email: f.costa@swordhealth.com


    Background: Musculoskeletal (MSK) disorders are leading causes of disability worldwide, with clinical guidelines recommending physical therapy–based interventions. Digital MSK programs offer an alternative to address logistical and socioeconomic barriers to regular in-person care. However, evidence comparing surgical use between digital and in-person physical therapy remains limited, particularly for low-value procedures.

    Objective: This study aimed to evaluate the 12-month incidence of MSK surgery and low-value surgical procedures among participants initiating a multimodal Digital Care Program (DCP) versus a matched-cohort initiating in-person physical therapy.

    Methods: Retrospective, matched-cohort study, using exact and propensity matching, with a Health Insurance Portability and Accountability Act (HIPAA)–deidentified US nationwide merged claims dataset (July 2022-February 2025). Eligible adults had spine, knee, hip, or shoulder conditions, ≥24 months uninterrupted health insurance coverage to an employer-sponsored DCP, and no MSK surgery in the prior year. The intervention group (IG) participated in a DCP combining exercise, education, and cognitive behavioral therapy, with real-time biofeedback and remote physical therapist oversight. The comparator group (CG) initiated in-person physical therapy, identified from a third-party claims database, using relevant MSK ICD-10 (International Statistical Classification of Diseases, Tenth Revision) codes as primary diagnosis. The primary outcome was the incidence of any MSK surgery within 12 months; the secondary outcome was the incidence of low-value surgery based on Choosing Wisely–aligned definitions. Cohort characteristics were compared using t test and chi-square test. Risk ratios (RRs) were calculated overall and by pain site, age group, and Social Deprivation Index.

    Results: In a matched cohort of 4190 individuals, predominantly middle-aged (~52 years old) women (1335/2095, 63.7%) with spinal pain (1123/2095, 53.6%), participation in the digital program was linked to a 58% (95% CI 49-66) lower relative risk of surgery at 12 months compared to those initiating in-person physical therapy (RR 0.42, 95% CI 0.34-0.52; E-value=4.19 [lower CI 3.29]). For surgeries categorized as low-value, IG was associated with 82% (95% CI 68-90) lower relative risk (RR 0.17, 95% CI 0.09-0.31; E-value=11.24 [lower CI 5.91]). Overall MSK surgical trends were consistent across pain sites, with greatest relative differences for knee (IG: 40/414 9.7% vs CG: 122/414, 29.5%; RR 0.26; 95% CI 0.17-0.38) followed by hip (19/203, 9.4% vs 42/203, 20.7%; RR 0.40; 95% CI 0.22-0.71). Lower surgery incidences in the IG (overall and low-value) were found across all socioeconomic and age strata.

    Conclusions: This real-world study demonstrated, for the first time, that participation in a digital MSK program was associated with substantially lower incidences of both overall and low-value surgeries compared to those who opted for in-person physical therapy among commercially-insured adults. These findings suggest that digital MSK programs can mitigate access barriers, promote adherence to guideline-concordant care, and reduce unnecessary procedures, including among underserved populations.

    J Med Internet Res 2025;27:e82573

    doi:10.2196/82573

    Keywords

    eHealth; low-value care; pain; surgery rate; telerehabilitation 


    Musculoskeletal (MSK) disorders are among the most widespread health problems, being a leading contributor to years lived with disability worldwide [1-4]. In the United States, roughly half of adults live with an MSK condition [2,5], whose economic burden reaches an estimated US $980 billion annually, impacting patients, payers, and society as a whole [6].

    MSK surgery rates have risen over the past decades in the United States. For example, incidences of elective lumbar spinal fusion rose markedly between 2002 and 2014, with an average annual increase of about 12% [4,7,8]. Similarly, shoulder arthroplasties increased substantially between 2011 and 2017, showing an average annual increase of approximately 13% [9]. More recently, from 2013 to 2022, total knee and hip arthroplasties continued to expand, rising by roughly 6%-8% per year [10,11]. These upward trajectories have raised significant concerns about overtreatment and the rising prevalence of low-value surgical procedures—defined as those offering no net clinical benefit in certain scenarios, especially when nonsurgical care has not been exhausted [12-18]. Such procedures may expose patients to unnecessary risks and prolonged recovery, without providing better outcomes than nonoperative interventions in several conditions [4,19-29].

    Previous studies have reported that guideline-concordant physical therapy can promote better clinical outcomes (as greater functional improvement [30,31]), reduce health care resources use (eg, 28% lower risk of receiving advanced imaging [32]) and downstream MSK surgery (eg, 53% lower risk of receiving lumbar surgery [32]), with clinical guidelines uniformly endorsing physical therapy approaches, including exercise, education, and behavior change, as first-line care for most MSK presentations [33-38]. Yet, real-world uptake remains suboptimal [39,40], reflecting systemic barriers. The number of physical therapists varies greatly from state to state in the United States, ranging from 35 to 119 therapists per 100,000 individuals in 2021 [41]. Moreover, the distribution of clinicians is uneven across regions, leaving rural areas dependent on telehealth or traveling providers and limiting consistent access to regular in-person care, often delaying or preventing guideline-concordant treatment [42-47]. Additionally, modern lifestyles [43,45] and limited health literacy [46,48] may shape patients’ perspectives of surgery as a quicker or more definitive solution, even when it poses greater risks and limited benefits [19-22].

    Digital care programs have emerged as a transformative strategy to improve adherence to evidence-based care, given their convenience [49-53]. By enabling easier access to care, it has the potential to curb avoidable surgeries [4,20,32,54,55]. We have previously shown, through randomized controlled trials, that a multimodal digital program—combining exercise, education, and cognitive behavioral therapy—can improve pain, function, and mental health, as effectively as in-person rehabilitation [56,57].

    Despite growing evidence supporting the clinical effectiveness of digital MSK programs [50,52,58], there is limited evidence of their impact on surgical use, particularly regarding the reduction of low-value procedures. To address this gap, this study aims to compare 1-year incidences of MSK surgery among patients with spine, knee, hip, or shoulder conditions that initiated a Digital Care Program (DCP) and those initiating in-person physical therapy (primary aim), and to assess differences in the proportion of low-value surgeries between groups (secondary aim), in a commercially insured population.


    Study Design

    This was a retrospective matched cohort study comparing surgery incidences during 12 months between patients in a DCP (intervention group [IG]) to patients eligible for the DCP who opted for in-person physical therapy (comparator group [CG]) among a population of individuals enrolled in employer-sponsored commercial insurance plans that offered the DCP as a benefit. The comparison cohort was obtained using Health Insurance Portability and Accountability Act (HIPAA)–compliant, deidentified medical claims data sourced from a large third-party platform (PurpleLab) across the US health care claims spanned July 2022 to February 2025. Reporting of this study followed the RECORD (REporting of studies Conducted using Observational Routinely-collected Data) guidelines (Table S1 in Multimedia Appendix 1) [59].

    Study Population

    Adults (≥18 years) who had an MSK-related index event from July 2023 to February 2024, residing in the United States (including all states) and with conditions related to spine, knee, hip, or shoulder, were included only if they had ≥12 months of uninterrupted health insurance coverage before and after their individual index event date. The MSK index event corresponded either to the date of enrollment in the DCP for the IG (obtained from DCP data collected through an observational clinical trial, refer to “Data Sources”) or to the date of the in-person MSK physical therapy evaluation visit in the CG (identified in claims, refer to “Comparator Group”). Targeted MSK conditions, reflecting conditions addressable by the DCP (including joint and soft tissue conditions, such as tendinopathies and arthropathies, affecting the spine, knee, hip, or shoulder), were identified through claims with relevant ICD-10 (International Statistical Classification of Diseases, Tenth Revision) diagnostic codes (Table S2 in Multimedia Appendix 1). To ensure comparability and interpretability of outcomes, patients were required to have attended at least 1 physical therapy treatment session and have at least 1 MSK-coded medical claim in the 12 months preceding their index date. This criterion (1) established a preindex baseline for MSK care-seeking, use, and spending; (2) enabled matching and covariate adjustment using preindex characteristics; and (3) reduced misclassification of isolated or incidental presentations as index episodes. All patients with any MSK surgery in the 12-month preindex period (identified in medical claims through CPT [Current Procedural Terminology] and HCPCS [Healthcare Common Procedure Coding System] codes) were excluded. Additional exclusion criteria included patients with cancer, dementia, or conditions impairing the ability to follow motor instructions, pregnancy or childbirth (identified through ICD-10 codes; Table S3 in Multimedia Appendix 1), and those with outlier total annual medical expenses detected through claims data (>US $500,000) during the full study period. These criteria were applied consistently to both the intervention (DCP) and comparison (in-person physical therapy) groups.

    Intervention Group

    The DCP, a health benefit offered through 140 employers at no cost to patients, consisted of exercise, education, and cognitive behavioral therapy [56,57,60]. Enrollment was self-initiated by patients through a secure website, where a structured onboarding form was completed with demographics, clinical information, and red flags screening. Upon onboarding, each patient was assigned a Doctor of Physical Therapy who conducted a synchronous videocall assessment, in which eligibility was confirmed. The physical therapist subsequently prescribed and asynchronously monitored an individualized, guideline-based care plan [33-35,61]. Care was delivered via a US Food and Drug Administration (FDA)–listed Class II medical device, consisting of a tablet with motion-tracking technology for real-time audiovisual biofeedback during exercise. A companion smartphone app enabled 2-way communication and access to educational content, and interactive cognitive behavioral therapy modules [62,63].

    Comparator Group

    The CG included individuals drawn from the eligible claims database who never enrolled in the DCP and had a claim with an MSK-related ICD-10 code (Table S2 in Multimedia Appendix 1) as the primary diagnosis, categorized under physical therapy evaluation according to the Restructured BETOS Classification System (RBCS) [64] (CPT codes: 97161, 97162, 97163, or 97164), during the study period.

    Data Sources

    Claims data for both groups were obtained from PurpleLab (schematic flow depicted in Figure S1 in Multimedia Appendix 1; summary of PurpleLab data source structure and data elements available are described in Text S1). A list of all available variables per group can be found in Table S4 in Multimedia Appendix 1, encompassing: (1) deidentified patient demographics (age, gender, geographic region, and Social Deprivation Index [SDI]); and (2) administrative medical claims data. Medical claims data are generated in the United States when health care providers submit standardized billing information to payers. These claims include fields such as diagnosis (ICD-10 codes), health care procedure codes (CPT and HCPCS), service dates, and place of service.

    Data obtained from Sword Health included longitudinal clinical data and engagement metrics of patients in the IG. Given the retrospective nature of the study, clinical data were not available for individuals in the CG, as they did not enroll in the DCP. Nevertheless, CG engagement was assessed via claims.

    Before analysis, claims data preparation procedures were applied to ensure accuracy and consistency of code classifications and service groupings, following recommendations (Text S2 in Multimedia Appendix 1).

    Matching

    Patients in the IG and CG were matched through both exact and propensity-matched scoring (Table 1; MatchIt package in R [version 4.2.2; R Foundation for Statistical Computing]), using 1:1 nearest-neighbor matching without replacement. A broad range of variables, ranging from demographic characteristics to MSK condition severity proxies, were applied. Propensity scores were estimated using a generalized linear model that included the covariates listed under “Propensity” (Table 1). Variable-specific calipers were used to restrict the allowable difference in propensity scores for matching. Balance between matched groups was assessed using standardized mean differences, with values of <0.10 considered indicative of acceptable balance (Figure S2 in Multimedia Appendix 1).

    Table 1. Variables used for patients matching between the intervention group (DCP) and the comparison group (in-person physical therapy) in a retrospective claims-based matched-cohort analysis of patients with musculoskeletal conditions in the United States (July 2022-February 2025), specified separately for each applied method (ie, propensity score and exact matching).
    VariablesTypeDescriptionRationale for inclusion
    Exact matching

    		Age, sex, and geographic regionTo account for differences in care access, cost structures, and treatment patterns.


    		Age (years)Categorical18-29; 30-39; 40-49; 50-59; 60-69; 70-79


    		SexCategoricalMale; Female


    		Geographic regionCategoricalMidwest; Northeast; South; West

    		MSKapain site and acuityTo reflect condition type and severity, influencing care needs.


    		MSK pain siteCategoricalPrimary body region affected by the MSK condition, grouped by ICD-10b codes for low back, neck, knee, hip or shoulder)


    		AcuityCategoricalShort-term recent; Short-term ongoing; Chronic. Short-term recent corresponds to patients with a single claim for the pain site of the index event up to its date; Short-term ongoing refers to patients with at least 2 related claims, with the first occurring less than 12 weeks prior to the index date; Chronic reflects episodes lasting more than 12 weeks with 2 or more related claims [65]

    		Number of MSK conditions in the past 3 months,
    physical therapy visits, and imaging visits    		To account for prior MSK-related health care use, ongoing care pathways, and patient preferences in treatment-seeking behavior.


    		Number of MSK conditions in the past 3 monthsCategorical1; 2+


    		Physical therapy visitsCategorical0; 1+


    		Imaging visitsCategorical0; 1+
    Propensity

    		Elixhauser Comorbidity Index [66,67]ContinuousScore calculated based on ICD-10 codesTo control for overall health status and chronic disease complexity.

    		Preindex total MSK-related health care spendingContinuousTotal health care costs related to MSK conditions. All costs were standardized using the Centers for Medicare & Medicaid Services Fee Schedules and expressed in 2024 US dollarsTo reflect care intensity and pain burden, which are predictive of future health care use.

    		Social Deprivation Index [63,68]Categorical0-20; 21-40; 41-60; 61-80; 81-100To reflect the impact of social determinants of health.

    aMSK: musculoskeletal.

    bICD-10: International Statistical Classification of Diseases, Tenth Revision.

    Outcomes

    Comparing Digital to In-Person Physical Therapy

    The primary outcome of this study was the incidence of MSK surgery in the 12 months after initiating the digital MSK program (IG) or in-person physical therapy (CG). CPT and HCPCS codes were used to identify MSK surgeries in the medical claims (Table S5 in Multimedia Appendix 1). Visits occurring on the same service date for a beneficiary were treated as a single event. Analyses were run for the entire cohort and stratified by pain area (spine, knee, hip, or shoulder).

    Evaluating Potential Low-Value Care

    The study evaluated low-value surgeries previously defined [12,14-16,18], and aligned with Choosing Wisely recommendations [69]. It included clinically diverse, claims-based indicators of low-value care (the coding algorithm for each surgery indicator and the underlying evidence base are detailed in Table S6 in Multimedia Appendix 1). To ensure alignment with robust evidence, these indicators were reviewed against recommendations from the best available research and current clinical practice guidelines [34-38]. The targeted surgeries, included spinal fusion, knee procedures (meniscectomy for degenerative joint disease and arthroplasty for osteoarthritis), hip arthroplasty for osteoarthritis, and shoulder procedures (rotator cuff repair, replacement for osteoarthritis, arthroscopic release or manipulation for frozen shoulder, arthroscopy or open distal clavicle resection for impingement syndrome or arthritis, acromioplasty or acromiotomy for rotator cuff disorders, and labral repair). In order to identify if a given surgery was low value or not, a stringent set of inclusion and exclusion criteria had to be met (Table S6 in Multimedia Appendix 1). Particularly, in the case of conditions for which conservative should be first-line (knee arthroplasty for knee osteoarthritis, hip arthroplasty for hip osteoarthritis, rotator cuff repair for partial tears, shoulder replacement for osteoarthritis, arthroscopic release or manipulation under anesthesia for frozen shoulder, shoulder arthroscopy or distal clavicle resection for rotator cuff disorders or arthritis, and shoulder labral repair for labral tears), these would only be considered low value if no physical therapy was used throughout the 6 months period before surgery date. As such, in the IG group, the 6-month preperiod was screened for both DCP session logs and physical therapy medical claims, while in the CG group, the screening was run through physical therapy medical claims (Table S6 in Multimedia Appendix 1). The 6-month criterion was aligned with international benchmarks on orthopedic surgical waiting times, as the reported median waiting time was approximately 172 days for hip replacement and 119 days for knee replacement [70].

    Engagement in Digital and In-Person Physical Therapy

    Engagement was evaluated over the 12-month postindex period for patients receiving either digital or in-person physical therapy, based on physical therapy visit claims in the CG and claims and logged participation in the IG.

    Assessing Digital Intervention Clinical Outcomes

    Leveraging the availability of clinical outcomes for the IG: Numerical Pain Rating Scale (NPRS), Work Productivity and Activity Impairment (WPAI) scale, and satisfaction data, these were analyzed to support the clinical interpretation of findings. Clinical outcomes were collected at baseline, 9th, 19th, and 24th sessions in the IG (Table S7 in Multimedia Appendix 1).

    Statistical Analysis

    Baseline characteristics of matched cohorts were descriptively reported and compared through independent t test and chi-square test, with a statistical significance level of .05. Surgery incidences were computed as the number of patients who underwent surgery divided by the total sample size per group over the 12-month follow-up period. MSK surgery counts within 12 months postindex date were assessed. To evaluate the association between DCP participation and surgical use (including low-value procedures), risk ratios (RRs) were computed directly from the observed incidence proportions, and 95% CIs were estimated using the Wald method. E-values were calculated to assess the robustness of the observed associations to potential unmeasured confounding, using the formula proposed by VanderWeele and Ding [71,72]. For protective associations (RR<1), the reciprocal of the RR was used. E-values were computed for both the point estimate and the CI limit closest to the null. Absolute risk reduction and number needed to treat (NNT) were calculated to quantify the magnitude of the effect. For metrics stratified by pain sites (spine, knee, hip, and shoulder), pain site assignment was defined as the primary diagnosis code (ICD-10) associated with the claim corresponding to the index event after matching and duplicate removal, ensuring that each patient was assigned to a single pain site group and included in the study only once.

    To explore potential heterogeneity in treatment effects, subgroup analyses were conducted by SDI categories (C1: 0-25; C2: 26-50; C3: 51-75; C4: 76-100) and by age group (18-39, 40-59, ≥60 years). Within each stratum, surgery incidences and RRs were estimated.

    Clinical outcomes among IG patients were evaluated under an intention-to-treat framework using latent-basis growth analysis (LBGA) [73], a longitudinal modeling technique that accommodates nonlinear change, handles missing data via full-information maximum likelihood, and provides model fit [74,75]. A robust sandwich estimator was used for SEs. Analyses were also performed for patients with baseline clinically-relevant scores: WPAI-overall>0, WPAI-activities>0. Pain response rate was calculated using 30% or 2-points improvement in NPRS as the Minimal Clinically Important Change (MCIC) [76].

    Sensitivity Analyses

    To assess the robustness of findings to unmeasured confounding and model misspecification, we conducted a sensitivity analysis using augmented inverse probability weighting (AIPW). The AIPW analysis included the same covariates used in the main matching procedure (Table 1). Propensity scores were estimated for all eligible individuals (before matching) using logistic regression, and these scores were applied within the AIPW framework to obtain doubly robust estimates of the average treatment effect. The outcome model used logistic regression for the binary end points (any surgery and low-value surgery). AIPW combines outcome regression with inverse-probability weighting to produce valid inference if either model is correctly specified. Results are reported as adjusted RRs (95% CIs).

    As an additional bias assessment, a negative control analysis comparing rates of non-MSK elective procedures (cataract surgery and colonoscopy) between groups was conducted to detect residual confounding or systematic error, as these outcomes should not plausibly be influenced by MSK care.

    To evaluate the robustness of the 6-month preoperative observation window criterion to define low-value procedures (refer to “Evaluating Potential Low-Value Care”), a sensitivity analysis applying alternative look-back windows of 3, 9, and 12 months prior to surgery was conducted. For each window, the total number of low-value surgeries, both overall and stratified by pain site, was calculated.

    All statistical analyses were conducted using R (version 4.2.2; R Foundation for Statistical Computing) and commercially available software (SPSS version 22; IBM Corp). This significance level was set at .05 for all 2-sided hypothesis tests.

    Ethical Considerations

    This research was conducted in accordance with the Declaration of Helsinki and all applicable ethical guidelines and regulations. The IG (digital MSK group) was created under a prospective observational study that was reviewed and approved by the Advarra Institutional Review Board (Pro00063337) with electronic informed consent provided by all included participants, which included authorization for future secondary analyses. For the retrospective claims-based analysis, all claims data were HIPAA-deidentified and tokenized by PurpleLab using Datavant Tokens prior to transfer to the study team. Sword Health applied the same Datavant tokenization process to its member eligibility and enrollment data, enabling secure linkage between datasets while maintaining full deidentification. Both data streams remained anonymous throughout analysis, and the study team had no access to any identifiable information. Data privacy compliance and reidentification risk mitigation were verified by Promeed Inc [77] through expert review to ensure adherence to HIPAA standards. Importantly, no individual can be identified from the manuscript or underlying data. Since no identifiable private information was accessed by the researchers, the regulatory definition of human subjects research [78-80] was not met; therefore, institutional review board review or additional informed consent was not required. Participants were not offered any form of compensation.


    Baseline Characteristics

    From 569,857 individuals included in the dataset, the eligible population included 3443 individuals in the IG and 14,589 in the CG (study cohort flowchart in Figure 1). The matched cohort yielded a final analytical sample of 4190 participants.

    Baseline demographic and clinical characteristics were balanced between the IG and CG (N=2095 each) after matching, with no statistically significant differences observed (see exact P values in Table 2; Figure S2 in Multimedia Appendix 1; Table 2). Both groups were predominantly female (1335/2095, 63.79%), and their mean ages differed by –0.3 years (SMD=–0.02, 95% CI –0.09 to 0.04). Preindex MSK-related costs mean difference was US $78.3 (SMD=0.06, 95% CI –0.00 to 0.12). Baseline geographic region and pain site distributions were equivalent between cohorts, as a reflection of the exact matching procedure. Most patients resided in the Midwest US region (1751/2095, 83.6%), consistent with the geographic distribution of individuals eligible for the DCP. Spine pain was the most common site (1123/2095, 53.6%), followed by knee (414/2095, 19.8%), shoulder (355/2095, 16.9%), and hip (203/2095, 9.7%; Table 2).

    ‎
    Figure 1. Study cohorts and flowchart.
    Table 2. Baseline characteristics of the intervention group (Digital Care Program) and the comparison group (in-person physical therapy) in a retrospective claims-based matched-cohort analysis of patients with musculoskeletal conditions in the United States (July 2022-February 2025).
    Baseline characteristicsIntervention group (N=2095)Comparator group (N=2095)P value
    Demographic characteristics

    		Sex, n (%)>.99


    		Female1335 (63.7)1335 (63.7)


    		Male760 (36.3)760 (36.3)

    		Age (years), mean (SD)52.3 (10.5)52.0 (10.9).44

    		Age categories (years), n (%)>.99


    		18-2975 (3.6)75 (3.6)


    		30-39190 (9.1)190 (9.1)


    		40-49465 (22.2)465 (22.2)


    		50-59786 (37.5)786 (37.5)


    		60-69559 (26.7)559 (26.7)


    		70-7920 (1.0)20 (1.0)

    		Geographic US region, n (%)>.99


    		Midwest1751 (83.6)1751 (83.6)


    		South233 (11.1)233 (11.1)


    		West97 (4.6)97 (4.6)


    		Northeast14 (0.7)14 (0.7)

    		Social Deprivation Indexa, n (%).09


    		C1: 0-25822 (39.2)851 (40.6)


    		C2: 26-50567 (27.1)570 (27.2)


    		C3: 51-75414 (19.8)436 (20.8)


    		C4: 76-100292 (13.9)238 (11.4)
    Clinical data

    		Pain site, n (%)>.99


    		Spine1123 (53.6)1123 (53.6)


    		Knee414 (19.8)414 (19.8)


    		Hip203 (9.7)203 (9.7)


    		Shoulder355 (16.9)355 (16.9)

    		Acuityb, n (%)>.99


    		Short-term recent462 (22.1)462 (22.2)


    		Short-term ongoing957 (45.6)957 (45.7)


    		Chronic676 (32.3)676 (32.3)

    		Number of patients with concurrent MSKc conditions 3 months preindex (yes/no), n (%)612 (29.2)612 (29.2)>.99

    		Weighted Elixhauser Comorbidity Indexd, n (%).65


    		<0696 (33.2)692 (33.1)


    		01194 (56.8)1217 (58.1)


    		1-4137 (6.5)118 (5.6)


    		≥568 (3.2)68 (3.2)
    Preindex care

    		MSK-related health care use, n (%)


    		Physical therapy (yes/no)897 (42.8)897 (42.8)>.99


    		Imaging (yes/no)1377 (65.6)1377 (65.6)>.99

    		MSK-related costs (US $), mean (SD)1166.4 (1326.5)1244.6 (1338.1).06

    		TCOCe (US $), mean (SD)5805.8 (11,320.5)6330.8 (12,404.7).11

    aHigher Social deprivation index values correspond to higher level of social deprivation [63,68,81].

    bShort-term recent corresponds to patients with a single claim for the pain site of the index event up to its date; Short-term ongoing refers to patients with at least 2 related claims, with the first occurring less than 12 weeks prior to the index date; chronic reflects episodes lasting more than 12 weeks with 2 or more related claims [65].

    cMSK: musculoskeletal.

    dHigher Elixhauser scores indicate higher comorbidity burden.

    eTCOC: total all-cause costs of care.

    Comparing Surgeries in Digital and In-Person Physical Therapy

    Over 12 months, patients in the digital MSK program had 58.3% lower surgery incidence than those who received in-person physical therapy: 5.5% (115/2095) underwent MSK surgery in the IG versus 13.2% (276/2095) in the CG (Table 3). This corresponds to a 58% (95% CI 49-66) lower relative risk of surgery (RR 0.42, 95% CI 0.34-0.52; Table 3; E-value=4.19 [lower CI 3.29]), consistent with results from the AIPW sensitivity analysis (refer to “Sensitivity Analyses” subsection). Estimated absolute risk reduction was 7.7 percentage points (95% CI 6.0-9.4), meaning that for every 13 patients undergoing the DCP, one surgery is prevented within 1 year (NNT=13; 95% CI 11-17).

    Surgery incidences were consistent and significantly lower in the IG than the CG across all pain sites. The largest difference was observed for knee surgeries (40/414, 9.7% vs 122/414, 29.5%; RR 0.26, 95% CI 0.17-0.38), followed by hip (19/203, 9.4% vs 42/203, 20.7%; RR 0.40, 95% CI 0.22-0.71), shoulder (19/355, 5.4% vs 38/355, 10.7%; RR 0.47, 95% CI 0.27-0.84), and spine (37/1123, 3.3% vs 74/1123, 6.6%; RR 0.48, 95% CI 0.32-0.72; Table 3).

    Among patients who underwent surgery in the postindex period, 7.0% (8/115) of the IG had 2 or more surgeries, versus 15.9% (44/276) of the CG (P=.03). Overall, the IG had 62.5% (205/328) fewer surgeries compared to the CG, with the most substantial differences occurring in the knee (111/154, 72.1%) and hip (27/46, 58.7%) regions (Figure 2; Table S8 in Multimedia Appendix 1). Figure 2 presents the relative difference, in percentage, in surgery counts between the IG and CG; arrows denote the direction and magnitude of reduction.

    Table 3. Comparison of 1-year incidence of surgeries and low-value surgeries between the intervention group (Digital Care Program) and the comparison group (in-person physical therapy) in a retrospective claims-based matched-cohort analysis of patients with musculoskeletal conditions in the United States (July 2022-February 2025). Results are presented overall and stratified by pain site.a
    Pain site and outcomeIGbCGcRRd (95% CI)
    Overall (N=2095), n (%)

    		Any surgery115 (5.5)276 (13.2)0.42 (0.34-0.52)

    		Low-value surgery13 (0.6)74 (3.5)0.17 (0.09-0.31)
    Spine (n=1123), n (%)

    		Any surgery37 (3.3)74 (6.6)0.48 (0.32-0.72)

    		Low-value surgery9 (0.8)37 (3.3)0.24 (0.11-0.49)
    Knee (n=414), n (%)

    		Any surgery40 (9.7)122 (29.5)0.26 (0.17-0.38)

    		Low-value surgery2 (0.5)23 (5.5)0.08 (0.02-0.35)
    Hip (n=203), n (%)

    		Any surgery19 (9.4)42 (20.7)0.40 (0.22-0.71)

    		Low-value surgery2 (1.0)10 (4.9)0.19 (0.04-0.89)
    Shoulder (n=355), n (%)

    		Any surgery19 (5.4)38 (10.7)0.47 (0.27-0.84)

    		Low-value surgery0 (0.0)4 (1.1)—e

    an represents the number of patients in the cohort who had surgery, and N represents the total number of patients in that cohort.

    bIG: Intervention group.

    cCG: Comparator group.

    dRR: risk ratio.

    eNot available.

    ‎
    Figure 2. Postindex surgery use at 1 year (proportions) between the intervention group (Digital Care Program) and the comparison group (in-person physical therapy) in a retrospective claims-based matched-cohort analysis of patients with musculoskeletal conditions in the United States (July 2022-February 2025). Percentages inside the bars and the arrow represent the relative surgery differences obtained in the intervention group compared to the comparison group. Results are shown overall and stratified by pain site.

    Comparing Low-Value Surgeries in Digital and in-Person Physical Therapy

    Incidents of surgeries identified as low-value among patients in the DCP were lower by 82.4% in comparison to CG (Table 3). Overall, 0.6% (13/2095) of patients in the DCP underwent a low-value surgery, compared to 3.5% (74/2095) in the CG, corresponding to an 83% (95% CI 68-90) lower risk of surgery (RR 0.17; 95% CI 0.09-0.31; Table 3; E-value=11.24 [lower CI 5.91]), which was consistent with AIPW results (refer to “Sensitivity Analyses” subsection). This represents an absolute risk reduction of 2.9 percentage points (95% CI 2.1-3.8), corresponding to an NNT of 34 (95% CI 27-49) to prevent one low-value surgery.

    Similar to the overall trend, low-value surgery incidences across pain sites were consistently lower in the IG than CG for spine (0.8%, 9/1123 vs 3.3%, 37/1123, RR 0.24; 95% CI 0.11-0.49), knee (0.5%, 2/414 vs 5.5%, 23/414; RR 0.08; 95% CI 0.02-0.35), and hip (1.0%, 2/203 vs 4.9%, 10/203; RR 0.19; 95% CI 0.04-0.89). In the shoulder cohort, no patient in the IG had low-value surgeries, compared to 4 in the CG (Table 3).

    Among patients who underwent low-value surgery, 5 individuals in the CG had 2 or more surgeries, compared to zero individuals in the IG (P>.99). Overall, IG had 84.0% (68/81) fewer low-value surgeries compared to CG, with the largest differences observed in the shoulder (4/4, 100.0%) and knee (23/25, 92.0%) cohorts (Figure 2; Table S8 in Multimedia Appendix 1).

    Sensitivity Analyses

    Results from the AIPW analysis were consistent with the matched analysis, showing an overall RR 0.45 (95% CI 0.35-0.57) and a low-value RR 0.19 (95% CI 0.10-0.35; Table S9 in Multimedia Appendix 1), confirming the robustness of the findings.

    Negative control analyses comparing rates of cataract and colonoscopy procedures (outcomes not plausibly affected by MSK care) showed no significant differences between groups before or after the intervention, supporting the absence of residual systematic bias (see exact P values in Table S10 in Multimedia Appendix 1).

    Sensitivity analyses varying the preoperative observation window (3, 9, and 12 months) yielded consistent results, showing similarly lower counts of surgeries in the IG (Table S11 in Multimedia Appendix 1).

    Subgroup Analyses by SDI and Age

    Surgery incidences in the IG were consistently lower across all social deprivation levels, with relative differences starting around 50% among more deprived groups (SDI C1-C4: 49.2%-70.0%; RRs ranged from 0.30, 95% CI 0.19-0.47 to 0.51, 95% CI 0.37-0.70; Table S12 and Figure S3 in Multimedia Appendix 1). Moreover, a significantly lower likelihood of receiving low-value care was observed in the IG than CG across SDI categories, with differences ranging from 84.9% to 88.4% in the most deprived categories (SDI C2-C4, RRs ranging from 0.12, 95% CI 0.01-0.94 to 0.15, 95% CI 0.05-0.50), compared with a difference of 75.0% in the least deprived group (SDI C1, RR: 0.25, 95% CI 0.11-0.57; Table S12 in Multimedia Appendix 1).

    Age-stratified analyses also showed a significant benefit toward for the IG at a similar level across age groups (18-39 years: RR 0.42, 95% CI 0.19-0.94; 40-59 years: RR 0.42, 95% CI 0.31-0.56; ≥60 years: RR 0.42, 95% CI 0.30-0.57), with relative differences in low-value surgeries all exceeding 70.4% (18-39 years: no low-value surgeries observed in the IG; 40-59 years: RR 0.13, 95% CI 0.05-0.32; ≥60 years: RR 0.30, 95% CI 0.14-0.65; Table S12 in Multimedia Appendix 1; Figure S3 in Multimedia Appendix 1).

    Engagement in Digital and In-Person Physical Therapy

    The IG performed 47,526 physical therapy sessions, including 42,067 digital sessions, over the 12-month postindex period, corresponding to a mean of 22.7 (SD 31.6, median 14, IQR 9-25) sessions. All patients in the CG initiated in-person physical therapy care, collectively completed 19,047 sessions corresponding on average to 9.2 (SD 9.3, median 6, IQR 3-12) sessions over the 12-month postindex period.

    Digital Intervention Clinical Outcomes

    To support the clinical interpretation of the surgery incidence in the IG, clinical outcomes were evaluated through the available DCP data. Patients experienced a significant reduction in pain by program-end (mean change: –2.22 points, 95% CI –2.35 to –2.08; P<.001), with 63.8% (1036/1626) achieving clinically meaningful relief, alongside high satisfaction (mean 8.7/10, SD 1.8). Further information is available in Table S13 in Multimedia Appendix 1.


    Principal Results

    This study compared 1-year incidences of MSK surgeries between patients with spine, knee, hip, or shoulder conditions who initiated a DCP versus those who initiated in-person physical therapy. Following our primary aim, this study demonstrated that participation in the DCP was associated with a significantly lower incidence of surgery at 1 year. Regarding our secondary aim, the DCP group also demonstrated a substantially lower incidence of surgeries classified as low-value based on clinical guidelines. The robustness of these results was supported by several bias assessment sensitivity analyses, including AIPW, negative control analysis, and E-value estimation. In this large, real-world, matched cohort of 4190 commercially insured adults, surgery incidence was 58% lower overall and 82% lower for low-value procedures in the DCP group compared with in-person physical therapy. Results were consistent across pain sites, socioeconomic, and age strata. These findings underscore the potential of digitally delivered multimodal MSK interventions to reduce low-value surgical use at the population level.

    Comparison With Prior Work

    Evidence on the role of digital programs in avoiding surgery is limited [82,83]. Herein, we used a nationwide sample, diverse in terms of demographics, socioeconomic status, and pain sites, comparable to the US population with MSK conditions [2,40], to evaluate the impact of a digital care solution in promoting access and adherence to guideline-concordant care.

    Extending prior work that contrasted physical therapy with the absence or delay of care [32,39], this study underscores the potential impact of alternative delivery modality on downstream outcomes. Alongside lower surgical incidences, the percentage of patients who underwent multiple surgeries was lower in the IG. Literature suggests that undergoing an MSK surgery can increase the likelihood of additional surgical interventions [84]. The DCP cohort experienced a 58% (95% CI 49-66) reduction in the risk of undergoing MSK surgery, consistent across pain sites, in alignment with prior literature [82,83], with the largest relative benefit in knee-related conditions and the smallest for spinal disorders. The smaller difference in spine surgery rates is likely attributable to less standardized and more variable selection of surgical candidates than for other orthopedic surgeries, and more subjective outcomes than many other surgeries [85-87].

    From available data, it was observed that the total number of sessions completed in the IG exceeded the observed in the CG, whose average engagement is aligned with prior claims studies for in-person therapy [88]. While session duration may differ between digital and in-person settings, the marked engagement gap highlights the greater convenience and compliance associated with remote interventions. These results suggest that the DCP is capable of facilitating more sustained engagement, potentially being an inherent component of its real-world effectiveness. The obstacles associated with in-person visits, including scheduling, transportation, and caregiving responsibilities, may explain high rates of no-shows [42,46] or dropouts [43,56,57]. Nevertheless, the mechanistic assessment of the observed results is outside the scope of this study, given its retrospective observational design, which precludes causal inference. Higher adherence has been shown to produce better outcomes [89], which in turn reduces the chances of care escalation [20,49]. In a previous study, 31% of participants initially willing to consider joint replacement no longer considered surgery after completing a multimodal digital osteoarthritis program, with this shift strongly associated with reduced pain and improved function at follow-up [90]. The findings of this study are aligned with the literature as patients in the DCP experienced clinically meaningful improvements in pain and function consistent with benchmarks observed in prior randomized trials [55,56].

    Low-value MSK procedures—such as meniscectomy in degenerative knee disease or spinal fusion in the absence of instability or red flags—are costly, carry substantial procedural risks, and often do not improve outcomes compared to conservative care [19-22]. In this study, patients in the DCP had 83% (95% CI 68-90) lower risk of low-value procedures compared to CG, with E-values deeming these estimations robust to unmeasured confounding. This trend remained stable even when addressing different prior conservative care evaluation windows. Among those seeking care for spine pain, a high proportion of those who followed a surgical path underwent low-value care, in accordance with literature [14,91], which was less frequent among the IG. Reducing low-value surgery aligns with national value-based care priorities [69,92], aiming to reduce the overuse of medical procedures that yield little benefit or cause harm—a problem that has persisted despite recent policy initiatives [12,13,15]. Scalable digital programs may represent a practical and sustainable solution to achieve this goal, particularly among vulnerable populations.

    The subgroup analyses revealed consistently lower MSK surgery incidences in the IG across social deprivation and age groups. Studies have shown the impact social determinants of health have on both care use and surgical outcomes [93-95]. Herein, relatively low-value surgery incidence differences among those more deprived underscore the potential of the DCP to promote equitable outcomes for diverse populations. The results suggest that when persistent barriers to access and care compliance are overcome, patients from more disadvantaged backgrounds can achieve benefits comparable to those from less deprived settings [63].

    These findings highlight the potential of digital care models as modifiers of surgical decision-making. By combining convenience, enhanced adherence, and delivery of evidence-based, patient-centered care, digital programs can empower individuals to actively manage their condition—fostering confidence and autonomy that could delay or avert surgical interventions. These implications are relevant for payers and health systems seeking scalable strategies to reduce unnecessary procedures and promote recovery—particularly in remote or underserved populations where access to in-person MSK treatment remains limited. This study offers a framework for evaluating digital health interventions as tools to increase care value.

    Limitations and Future Directions

    This retrospective study has inherent limitations. Although we applied exact and propensity score matching and several sensitivity analyses, including AIPW and negative control analysis, to mitigate bias, residual confounding from unmeasured factors cannot be excluded. Albeit claims-based analyses have the advantage of objectively capturing in a real-world context the health care use through standardized coding at large scale, these analyses are dependent on coding accuracy and lack clinical granularities (disease severity, imaging findings, time from symptoms onset to physical therapy initiation, patient preferences, and provider decision-making). Specifically for low-value care, claims may not capture all clinically appropriate indications—a common limitation in low-value care studies [14,15,18]. To mitigate this risk, we have applied a standardized data validation protocol, used the most specific service definitions available, used validated criteria from prior research, incorporated conditions severity, health care use and patients’ preferences in treatment-seeking behavior proxies for matching, conducted a thorough literature review and consulted a panel of expert clinicians to minimize misclassification.

    The predominance of participants residing in the Midwest may limit the generalizability of our findings, as regional differences in MSK surgery practice patterns [96] and access to employer-sponsored DCP may not reflect the national landscape. Consistent findings across subgroups, sensitivity analyses, and analytic approaches increase confidence, but the 12-month follow-up cannot distinguish surgeries averted from those deferred beyond the observation window. The spine category included both low back and neck cases; however, we acknowledge that the overall pathways of care differ between these regions, including surgical indications and physical therapy regimens.

    Future studies should assess the mechanisms underlying the observed results, namely through randomized controlled trials. Qualitative studies exploring patient decision-making may shed light on behavioral mechanisms contributing to lower surgery rates. Additionally, future studies should assess the generalizability of findings across different geographic populations, insurance types, and care settings, as well as longer evaluation timeframes. Cost-effectiveness analyses are also warranted, given the potentially substantial implications for payer budgets and value-based purchasing.

    Conclusions

    This study demonstrated, for the first time, within a large, real-world, commercially insured cohort, that providing access to digital MSK care was associated with substantially lower incidences of surgical procedures, including those deemed as low-value. Effects were consistent across pain sites, age groups, and socioeconomic strata.

    These findings have important real-world implications, showing that increasing access and convenience to physical therapy through the form of digital programs can promote treatment compliance and lower health care use, namely preventable surgical care. By supporting more equitable and value-based care delivery, such programs may represent a scalable and alternative model for MSK and resource management in routine clinical practice.

    Acknowledgments

    The authors acknowledge the team of physical therapists responsible for managing the participants. During the writing of this work, the authors used ChatGPT-4o exclusively to support copyediting and grammar checking. The output of this tool was reviewed and edited by the authors, ensuring full responsibility for the content of the publication.

    Funding

    This work was supported by Sword Health Inc and also developed within the scope of project no. 62—“Responsible AI,” financed by European Funds, namely the Recovery and Resilience Plan—“Componente 5: Agendas Mobilizadoras para a Inovação Empresarial,” included in the NextGenerationEU funding program.

    Data Availability

    The datasets generated or analyzed during this study are not publicly available due to contractual obligations to PurpleLab but deidentified data may be available from the corresponding author on reasonable request.

    Authors' Contributions

    Conceptualization: FC (lead), FDC (supporting), LB (equal), KW (equal)

    Data curation: BD (equal), APP (equal), AP (equal)

    Formal analysis: BD (equal), APP (equal), CZ (supporting)

    Methodology: KW (equal), FC (equal), DJ (supporting)

    Resources: VB (lead)

    Supervision: FC (lead)

    Validation: BD (equal), APP (equal), FC (supporting)

    Visualization: BD (equal), APP (equal)

    Writing – original draft: BD (equal), APP (equal), DJ (equal), FC (equal)

    Writing – review & editing: All authors (equal)

    Conflicts of Interest

    The authors declare the following competing interests: BD, APP, AP, CZ, DJ, CM, VY, LB, KW, FDC, and FC are employees of Sword Health, the sponsor of this study. VB, VY, and FDC also hold equity in Sword Health, and VB is the CEO of the same company. SPC is an independent scientific and clinical consultant who received an adviser honorarium from Sword Health.

    Multimedia Appendix 1

    Additional figures, tables, and text.

    DOCX File , 2241 KB
    1. GBD 2021 Diseases and Injuries Collaborators. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2133-2161. [FREE Full text] [CrossRef] [Medline]
    2. United States Bone and Joint Initiative. The Burden of Musculoskeletal Diseases in the United States (BMUS). 3rd edition. Rosemont, IL. ORS; 2014.
    3. Blyth FM, Briggs AM, Schneider CH, Hoy DG, March LM. The global burden of musculoskeletal pain-where to from here? Am J Public Health. 2019;109(1):35-40. [CrossRef] [Medline]
    4. Chang D, Lui A, Matsoyan A, Safaee MM, Aryan H, Ames C. Comparative review of the socioeconomic burden of lower back pain in the United States and globally. Neurospine. 2024;21(2):487-501. [FREE Full text] [CrossRef] [Medline]
    5. Briggs AM, Woolf AD, Dreinhöfer K, Homb N, Hoy DG, Kopansky-Giles D, et al. Reducing the global burden of musculoskeletal conditions. Bull World Health Organ. 2018;96(5):366-368. [FREE Full text] [CrossRef] [Medline]
    6. Dieleman JL, Beauchamp M, Crosby SW, DeJarnatt D, Johnson EK, Lescinsky H, et al. Tracking US health care spending by health condition and county. JAMA. 2025;333(12):1051-1061. [CrossRef] [Medline]
    7. Martin B, Mirza SK, Spina N, Spiker WR, Lawrence B, Brodke DS. Trends in lumbar fusion procedure rates and associated hospital costs for degenerative spinal diseases in the United States, 2004 to 2015. Spine (Phila Pa 1976). 2019;44(5):369-376. [CrossRef] [Medline]
    8. Deng H, Yue JK, Ordaz A, Suen CG, Sing DC. Elective lumbar fusion in the United States: national trends in inpatient complications and cost from 2002-2014. J Neurosurg Sci. 2021;65(5):503-512. [CrossRef] [Medline]
    9. Wagner ER, Farley KX, Higgins I, Wilson JM, Daly CA, Gottschalk MB. The incidence of shoulder arthroplasty: rise and future projections compared with hip and knee arthroplasty. J Shoulder Elbow Surg. 2020;29(12):2601-2609. [CrossRef] [Medline]
    10. Singh JA, Yu S, Chen L, Cleveland JD. Rates of total joint replacement in the United States: future projections to 2020-2040 using the national inpatient sample. J Rheumatol. 2019;46(9):1134-1140. [CrossRef] [Medline]
    11. Jones CM, Potluri AS, Federico VP, Nie JW, Forlenza EM, Serino J, et al. Trends in medicare arthroplasty procedure volume: projecting from 2025 to 2040. J Arthroplasty. 2025;40(11):2781-2790.e1. [CrossRef] [Medline]
    12. Mafi JN, Reid RO, Baseman LH, Hickey S, Totten M, Agniel D, et al. Trends in low-value health service use and spending in the US medicare fee-for-service program, 2014-2018. JAMA Netw Open. 2021;4(2):e2037328. [FREE Full text] [CrossRef] [Medline]
    13. Park S, Jung J, Burke RE, Larson EB. Trends in use of low-value care in traditional fee-for-service medicare and medicare advantage. JAMA Netw Open. 2021;4(3):e211762. [FREE Full text] [CrossRef] [Medline]
    14. Chalmers K, Smith P, Garber J, Gopinath V, Brownlee S, Schwartz AL, et al. Assessment of overuse of medical tests and treatments at US hospitals using medicare claims. JAMA Netw Open. 2021;4(4):e218075. [FREE Full text] [CrossRef] [Medline]
    15. Ganguli I, Morden NE, Yang CW, Crawford M, Colla CH. Low-value care at the actionable level of individual health systems. JAMA Intern Med. 2021;181(11):1490-1500. [FREE Full text] [CrossRef] [Medline]
    16. Riddle DL, Jiranek WA, Hayes CW. Use of a validated algorithm to judge the appropriateness of total knee arthroplasty in the United States: a multicenter longitudinal cohort study. Arthritis Rheumatol. 2014;66(8):2134-2143. [FREE Full text] [CrossRef] [Medline]
    17. Yanamadala V, Kim Y, Buchlak QD, Wright AK, Babington J, Friedman A, et al. Multidisciplinary evaluation leads to the decreased utilization of lumbar spine fusion: an observational cohort pilot study. Spine (Phila Pa 1976). 2017;42(17):E1016-E1023. [CrossRef] [Medline]
    18. Segal JB, Sen AP, Glanzberg-Krainin E, Hutfless S. Factors associated with overuse of health care within US health systems: a cross-sectional analysis of medicare beneficiaries from 2016 to 2018. JAMA Health Forum. 2022;3(1):e214543. [FREE Full text] [CrossRef] [Medline]
    19. Blom AW, Donovan RL, Beswick AD, Whitehouse MR, Kunutsor SK. Common elective orthopaedic procedures and their clinical effectiveness: umbrella review of level 1 evidence. BMJ. 2021;374:n1511. [FREE Full text] [CrossRef] [Medline]
    20. Mannion AF, Brox JI, Fairbank JC. Comparison of spinal fusion and nonoperative treatment in patients with chronic low back pain: long-term follow-up of three randomized controlled trials. Spine J. 2013;13(11):1438-1448. [CrossRef] [Medline]
    21. Ketola S, Lehtinen JT, Arnala I. Arthroscopic decompression not recommended in the treatment of rotator cuff tendinopathy: a final review of a randomised controlled trial at a minimum follow-up of ten years. Bone Joint J. 2017;99-B(6):799-805. [CrossRef] [Medline]
    22. Solsona-Carcas D, Trenado-Molina J, Buesa-Estéllez A, López-Royo MP, Bellosta-López P. Physical therapist interventions versus or combined with surgical treatment in nontraumatic degenerative meniscal pathology: a systematic review and network meta-analysis. Phys Ther. 2024;104(5):pzae007. [CrossRef] [Medline]
    23. Minetama M, Kawakami M, Teraguchi M, Enyo Y, Nakagawa M, Yamamoto Y, et al. Supervised physical therapy versus surgery for patients with lumbar spinal stenosis: a propensity score-matched analysis. BMC Musculoskelet Disord. 2022;23(1):658. [FREE Full text] [CrossRef] [Medline]
    24. Migliorini F, Oliva F, Eschweiler J, Cuozzo F, Hildebrand F, Maffulli N. No evidence in support of arthroscopic partial meniscectomy in adults with degenerative and nonobstructive meniscal symptoms: a level I evidence-based systematic review. Knee Surg Sports Traumatol Arthrosc. 2023;31(5):1733-1743. [FREE Full text] [CrossRef] [Medline]
    25. Abram SGF, Hopewell S, Monk AP, Bayliss LE, Beard DJ, Price AJ. Arthroscopic partial meniscectomy for meniscal tears of the knee: a systematic review and meta-analysis. Br J Sports Med. 2020;54(11):652-663. [CrossRef] [Medline]
    26. Craig R, Goodier H, Singh JA, Hopewell S, Rees JL. Shoulder replacement surgery for osteoarthritis and rotator cuff tear arthropathy. Cochrane Database Syst Rev. 2020;4(4):CD012879. [FREE Full text] [CrossRef] [Medline]
    27. Lähdeoja T, Karjalainen T, Jokihaara J, Salamh P, Kavaja L, Agarwal A, et al. Subacromial decompression surgery for adults with shoulder pain: a systematic review with meta-analysis. Br J Sports Med. 2020;54(11):665-673. [CrossRef] [Medline]
    28. Karjalainen T, Jain NB, Heikkinen J, Johnston RV, Page CM, Buchbinder R. Surgery for rotator cuff tears. Cochrane Database Syst Rev. 2019;12(12):CD013502. [FREE Full text] [CrossRef] [Medline]
    29. Nazari G, MacDermid JC, Bryant D, Athwal GS. The effectiveness of surgical vs conservative interventions on pain and function in patients with shoulder impingement syndrome. A systematic review and meta-analysis. PLoS One. 2019;14(5):e0216961. [FREE Full text] [CrossRef] [Medline]
    30. Stewart CJ, Bravata DM, Nelson MT, Datta E, Behal R. Clinical and economic outcomes associated with musculoskeletal care in an integrated advanced primary care model: controlled cohort analysis. J Med Internet Res. 2025;27:e76794. [FREE Full text] [CrossRef] [Medline]
    31. Rodeghero J, Wang Y, Flynn T, Cleland JA, Wainner RS, Whitman JM. The impact of physical therapy residency or fellowship education on clinical outcomes for patients with musculoskeletal conditions. J Orthop Sports Phys Ther. 2015;45(2):86-96. [CrossRef] [Medline]
    32. Magel J, Kim J, Fritz JM, Freburger JK. Time between an emergency department visit and initiation of physical therapist intervention: health care utilization and costs. Phys Ther. 2020;100(10):1782-1792. [FREE Full text] [CrossRef] [Medline]
    33. Qaseem A, Wilt TJ, McLean RM, Forciea MA, Clinical Guidelines Committee of the American College of Physicians, Denberg TD, et al. Noninvasive treatments for acute, subacute, and chronic low back pain: a clinical practice guideline from the American college of physicians. Ann Intern Med. 2017;166(7):514-530. [FREE Full text] [CrossRef] [Medline]
    34. National Guideline Centre (UK). Low Back Pain and Sciatica in Over 16s: Assessment and Management. London, United Kingdom. National Institute for Health and Care Excellence UK (NICE); 2016.
    35. National Guideline Centre (UK). Osteoarthritis in Over 16s: Diagnosis and Management. London, United Kingdom. National Institute for Health and Care Excellence UK (NICE); 2022.
    36. Siemieniuk RAC, Harris IA, Agoritsas T, Poolman RW, Brignardello-Petersen R, Van de Velde S, et al. Arthroscopic surgery for degenerative knee arthritis and meniscal tears: a clinical practice guideline. BMJ. 2017;357:j1982. [FREE Full text] [CrossRef] [Medline]
    37. Dyer J-O, Doiron-Cadrin P, Lafrance S, Roy J-S, Frémont P, Dionne CE, et al. Diagnosing, managing, and supporting return to work of adults with rotator cuff disorders: clinical practice guideline methods. J Orthop Sports Phys Ther. 2022;52(10):665-674. [CrossRef] [Medline]
    38. Management of Rotator Cuff Injuries. Rosemont, Illinois. American Academy of Orthopaedic Surgeons; 2019.
    39. Childs JD, Fritz JM, Wu SS, Flynn TW, Wainner RS, Robertson EK, et al. Implications of early and guideline adherent physical therapy for low back pain on utilization and costs. BMC Health Serv Res. 2015;15:150. [FREE Full text] [CrossRef] [Medline]
    40. Feldman DE, Carlesso LC, Nahin RL. Management of patients with a musculoskeletal pain condition that is likely chronic: results from a national cross sectional survey. J Pain. 2020;21(7-8):869-880. [FREE Full text] [CrossRef] [Medline]
    41. A Physical Therapy Profile: Demographics of the Profession, 2021-22. Anaheim, California. American Physical Therapy Association; 2023.
    42. Bhavsar NA, Doerfler SM, Giczewska A, Alhanti B, Lutz A, Thigpen CA, et al. Prevalence and predictors of no-shows to physical therapy for musculoskeletal conditions. PLoS One. 2021;16(5):e0251336. [FREE Full text] [CrossRef] [Medline]
    43. Shahidi B, Padwal J, Lee E, Xu R, Northway S, Taitano L, et al. Factors impacting adherence to an exercise-based physical therapy program for individuals with low back pain. PLoS One. 2022;17(10):e0276326. [FREE Full text] [CrossRef] [Medline]
    44. Carvalho E, Bettger JP, Goode AP. Insurance coverage, costs, and barriers to care for outpatient musculoskeletal therapy and rehabilitation services. N C Med J. 2017;78(5):312-314. [FREE Full text] [CrossRef] [Medline]
    45. Leatherwood W, Torres A, Hidalgo Perea S, Paulus M. Demographic and diagnostic factors in physical therapy attendance. Cureus. 2024;16(3):e55908. [FREE Full text] [CrossRef] [Medline]
    46. Thomas AC, Shaver SN, Young JL, Cook CE. Reasons for patient no-shows and drop-offs after initial evaluation in physical therapy outpatient care: a qualitative study. Musculoskelet Sci Pract. 2025;77:103326. [CrossRef] [Medline]
    47. Baker MB, Liu EC, Bully MA, Hsieh A, Nozari A, Tuler M, et al. Overcoming barriers: A comprehensive review of chronic pain management and accessibility challenges in rural America. Healthcare (Basel). 2024;12(17):1765. [FREE Full text] [CrossRef] [Medline]
    48. Sharpe J, Martin BI, Fritz JM, Newman MG, Magel J, Vanneman ME, et al. Identifying patients who access musculoskeletal physical therapy: a retrospective cohort analysis. Fam Pract. 2021;38(3):203-209. [FREE Full text] [CrossRef] [Medline]
    49. Suhr M, Keese M. The role of virtual physical therapy in the management of musculoskeletal patients: current practices and future implications. Curr Rev Musculoskelet Med. 2025;18(8):289-301. [CrossRef] [Medline]
    50. Molina-Garcia P, Mora-Traverso M, Prieto-Moreno R, Díaz-Vásquez A, Antony B, Ariza-Vega P. Effectiveness and cost-effectiveness of telerehabilitation for musculoskeletal disorders: a systematic review and meta-analysis. Ann Phys Rehabil Med. 2024;67(1):101791. [FREE Full text] [CrossRef] [Medline]
    51. Hawley-Hague H, Lasrado R, Martinez E, Stanmore E, Tyson S. A scoping review of the feasibility, acceptability, and effects of physiotherapy delivered remotely. Disabil Rehabil. 2023;45(23):3961-3977. [FREE Full text] [CrossRef] [Medline]
    52. Bargeri S, Castellini G, Vitale JA, Guida S, Banfi G, Gianola S, et al. Effectiveness of telemedicine for musculoskeletal disorders: umbrella review. J Med Internet Res. 2024;26:e50090. [FREE Full text] [CrossRef] [Medline]
    53. Shi JL, Sit RW. Impact of 25 years of mobile health tools for pain management in patients with chronic musculoskeletal pain: systematic review. J Med Internet Res. 2024;26:e59358. [FREE Full text] [CrossRef] [Medline]
    54. Fritz JM, Childs JD, Wainner RS, Flynn TW. Primary care referral of patients with low back pain to physical therapy: impact on future health care utilization and costs. Spine (Phila Pa 1976). 2012;37(25):2114-2121. [CrossRef] [Medline]
    55. Ojha HA, Wyrsta NJ, Davenport TE, Egan WE, Gellhorn AC. Timing of physical therapy initiation for nonsurgical management of musculoskeletal disorders and effects on patient outcomes: a systematic review. J Orthop Sports Phys Ther. 2016;46(2):56-70. [CrossRef] [Medline]
    56. Cui D, Janela D, Costa F, Molinos M, Areias AC, Moulder RG, et al. Randomized-controlled trial assessing a digital care program versus conventional physiotherapy for chronic low back pain. NPJ Digit Med. 2023;6(1):121. [FREE Full text] [CrossRef] [Medline]
    57. Pak SS, Janela D, Freitas N, Costa F, Moulder R, Molinos M, et al. Comparing digital to conventional physical therapy for chronic shoulder pain: randomized controlled trial. J Med Internet Res. 2023;25:e49236. [FREE Full text] [CrossRef] [Medline]
    58. Seron P, Oliveros M-J, Gutierrez-Arias R, Fuentes-Aspe R, Torres-Castro RC, Merino-Osorio C, et al. Effectiveness of telerehabilitation in physical therapy: a rapid overview. Phys Ther. 2021;101(6):pzab053. [FREE Full text] [CrossRef] [Medline]
    59. Benchimol EI, Smeeth L, Guttmann A, Harron K, Moher D, Petersen I, et al. RECORD Working Committee. The REporting of studies conducted using observational Routinely-collected health Data (RECORD) statement. PLoS Med. 2015;12(10):e1001885. [FREE Full text] [CrossRef] [Medline]
    60. Areias AC, Costa F, Janela D, Molinos M, Moulder RG, Lains J, et al. Impact on productivity impairment of a digital care program for chronic low back pain: a prospective longitudinal cohort study. Musculoskelet Sci Pract. 2023;63:102709. [FREE Full text] [CrossRef] [Medline]
    61. Kolasinski SL, Neogi T, Hochberg MC, Oatis C, Guyatt G, Block J, et al. et al. 2019 American College of Rheumatology/Arthritis foundation guideline for the management of osteoarthritis of the hand, hip, and knee. Arthritis Care Res (Hoboken). 2020;72(2):149-162. [FREE Full text] [CrossRef] [Medline]
    62. Areias AC, Janela D, Molinos M, Moulder RG, Bento V, Yanamadala V, et al. Managing musculoskeletal pain in older adults through a digital care solution: secondary analysis of a prospective clinical study. JMIR Rehabil Assist Technol. 2023;10:e49673. [FREE Full text] [CrossRef] [Medline]
    63. Areias AC, Molinos M, Moulder RG, Janela D, Scheer JK, Bento V, et al. The potential of a multimodal digital care program in addressing healthcare inequities in musculoskeletal pain management. NPJ Digit Med. 2023;6(1):188. [FREE Full text] [CrossRef] [Medline]
    64. Restructured BETOS Classification System. Centers for Medicare & Medicaid Services. URL: https:/​/data.​cms.gov/​provider-summary-by-type-of-service/​provider-service-classifications/​restructured-betos-classification-system [accessed 2025-04-29]
    65. Treede R, Rief W, Barke A, Aziz Q, Bennett MI, Benoliel R, et al. A classification of chronic pain for ICD-11. Pain. 2015;156(6):1003-1007. [FREE Full text] [CrossRef] [Medline]
    66. Austin S, Wong Y-N, Uzzo RG, Beck JR, Egleston BL. Why summary comorbidity measures such as the charlson comorbidity index and elixhauser score work. Med Care. 2015;53(9):e65-e72. [FREE Full text] [CrossRef] [Medline]
    67. Prommik P, Tootsi K, Saluse T, Strauss E, Kolk H, Märtson A. Simple excel and ICD-10 based dataset calculator for the charlson and elixhauser comorbidity indices. BMC Med Res Methodol. 2022;22(1):4. [FREE Full text] [CrossRef] [Medline]
    68. Social deprivation index (SDI). Robert Graham Center. 2018. URL: https://www.graham-center.org/maps-data-tools/social-deprivation-index.html [accessed 2025-07-03]
    69. Orthopaedics. Choosing Wisely Canada. 2024. URL: https://choosingwiselycanada.org/recommendation/orthopaedics [accessed 2025-08-06]
    70. Health at a Glance 2023: OECD Indicators. Paris, France. OECD Publishing; 2023.
    71. AI Agents to streamline compliance and unlock HIPAA-regulated data. Integral Technologies. URL: https://www.useintegral.com/ [accessed 2025-10-24]
    72. Guidance regarding methods for de-identification of protected health information in accordance with the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule. U.S. Department of Health and Human Services. 2025. URL: https://www.hhs.gov/hipaa/for-professionals/special-topics/de-identification/index.html [accessed 2025-10-24]
    73. Human Subject Regulations Decision Charts: 2018 Requirements. U.S. Department of Health and Human Services. 2020. URL: https://www.hhs.gov/ohrp/regulations-and-policy/decision-charts-2018/index.html [accessed 2025-10-24]
    74. 45 CFR 46. U.S. Department of Health and Human Services. 2023. URL: https://www.hhs.gov/ohrp/regulations-and-policy/regulations/45-cfr-46/index.html [accessed 2025-10-24]
    75. VanderWeele TJ, Ding P. Sensitivity analysis in observational research: introducing the E-value. Ann Intern Med. 2017;167(4):268-274. [CrossRef] [Medline]
    76. Mathur M, Ding P, Riddell CA, VanderWeele TJ. Web site and R package for computing E-values. Epidemiology. 2018;29(5):e45-e47. [FREE Full text] [CrossRef] [Medline]
    77. McNeish D. Relaxing the proportionality assumption in latent basis models for nonlinear growth. Struct Equ Modeling. 2019;27(5):817-824. [CrossRef]
    78. Xiao J, Bulut O. Evaluating the performances of missing data handling methods in ability estimation from sparse data. Educ Psychol Meas. 2020;80(5):932-954. [FREE Full text] [CrossRef] [Medline]
    79. Pfaffel A, Schober B, Spiel C. A comparison of three approaches to correct for direct and indirect range restrictions: a simulation study. Pract Assess Res Eval. 2016;21(6):1-15. [CrossRef]
    80. Dworkin R, Turk DC, Wyrwich KW, Beaton D, Cleeland CS, Farrar JT, et al. et al. Interpreting the clinical importance of treatment outcomes in chronic pain clinical trials: IMMPACT recommendations. J Pain. 2008;9(2):105-121. [FREE Full text] [CrossRef] [Medline]
    81. Cotton A, Salerno PRVO, Deo SV, Virani S, Nasir K, Neeland I, et al. The association between county-level premature cardiovascular mortality related to cardio-kidney-metabolic disease and the social determinants of health in the US. Sci Rep. 2024;14(1):24984. [FREE Full text] [CrossRef] [Medline]
    82. Yadav S, Gold LS, Zaidi QH, Hwang R, Lu L, Wang G. Spinal fusion surgery use among adults with low back pain enrolled in a digital musculoskeletal program: an observational study. BMC Musculoskelet Disord. 2024;25(1):520. [FREE Full text] [CrossRef] [Medline]
    83. Lu L, Gold LS, Koenig KM, Lee JH, Wang G. Digital musculoskeletal program is associated with decreased joint replacement rates. Am J Manag Care. 2024;30(4):e103-e108. [FREE Full text] [CrossRef] [Medline]
    84. Kim J, Ryu H, Kim T. Early reoperation rates and its risk factors after instrumented spinal fusion surgery for degenerative spinal disease: a nationwide cohort study of 65,355 patients. J Clin Med. 2022;11(12):3338. [FREE Full text] [CrossRef] [Medline]
    85. Birkmeyer JD, Reames BN, McCulloch P, Carr AJ, Campbell WB, Wennberg JE. Understanding of regional variation in the use of surgery. Lancet. 2013;382(9898):1121-1129. [FREE Full text] [CrossRef] [Medline]
    86. Moore ML, Singh R, McQueen K, Doan MK, Dodd H, Makovicka JL, et al. Workforce trends in spinal surgery: geographic distribution and primary specialty characteristics from 2012 to 2017. World Neurosurg. 2021;156:e392-e397. [CrossRef] [Medline]
    87. Brinjikji W, Luetmer P, Comstock B, Bresnahan B, Chen L, Deyo R, et al. Systematic literature review of imaging features of spinal degeneration in asymptomatic populations. AJNR Am J Neuroradiol. 2015;36(4):811-816. [FREE Full text] [CrossRef] [Medline]
    88. Marrache M, Prasad N, Margalit A, Nayar SK, Best MJ, Fritz JM, et al. Initial presentation for acute low back pain: is early physical therapy associated with healthcare utilization and spending? A retrospective review of a National Database. BMC Health Serv Res. 2022;22(1):851. [FREE Full text] [CrossRef] [Medline]
    89. Jones MD, Hansford HJ, Bastianon A, Gibbs MT, Gilanyi YL, Foster NE, et al. Exercise adherence is associated with improvements in pain intensity and functional limitations in adults with chronic non-specific low back pain: a secondary analysis of a Cochrane review. J Physiother. 2025;71(2):91-99. [FREE Full text] [CrossRef] [Medline]
    90. Cronström A, Nero H, Dahlberg LE. Factors associated with patients' willingness to consider joint surgery after completion of a digital osteoarthritis treatment program: a prospective cohort study. Arthritis Care Res (Hoboken). 2019;71(9):1194-1201. [FREE Full text] [CrossRef] [Medline]
    91. Saltychev M, Eskola M, Laimi K. Lumbar fusion compared with conservative treatment in patients with chronic low back pain: a meta-analysis. Int J Rehabil Res. 2014;37(1):2-8. [CrossRef] [Medline]
    92. Agency for Healthcare Research and Quality (AHRQ). EvidenceNOW. URL: https://www.ahrq.gov/evidencenow/index.html [accessed 2025-07-24]
    93. Ganguli I, Mackwood MB, Yang CW, Crawford M, Mulligan KL, O'Malley AJ, et al. Racial differences in low value care among older adult Medicare patients in US health systems: retrospective cohort study. BMJ. 2023;383:e074908. [FREE Full text] [CrossRef] [Medline]
    94. Mota REM, Tarricone R, Ciani O, Bridges JF, Drummond M. Determinants of demand for total hip and knee arthroplasty: a systematic literature review. BMC Health Serv Res. 2012;12:225. [FREE Full text] [CrossRef] [Medline]
    95. Hood CM, Gennuso KP, Swain GR, Catlin BB. County health rankings: relationships between determinant factors and health outcomes. Am J Prev Med. 2016;50(2):129-135. [CrossRef] [Medline]
    96. Jain NB, Peterson E, Ayers GD, Song A, Kuhn JE. US geographical variation in rates of shoulder and knee arthroscopy and association with orthopedist density. JAMA Netw Open. 2019;2(12):e1917315. [FREE Full text] [CrossRef] [Medline]

    ‎
    AIPW: Augmented Inverse Probability Weighting
    CG: comparator group
    CPT: Current Procedural Terminology
    DCP: Digital Care Program
    FDA: US Food and Drug Administration
    HCPCS: Healthcare Common Procedure Coding System
    HIPAA: Health Insurance Portability and Accountability Act
    ICD-10: International Statistical Classification of Diseases, Tenth Revision
    IG: intervention group
    LBGA: latent-basis growth analysis
    MCIC: Minimal Clinically Important Change
    MSK: musculoskeletal
    NNT: number needed to treat
    NPRS: Numerical Pain Rating Scale
    RBCS: Restructured BETOS Classification System
    RECORD: REporting of studies Conducted using Observational Routinely-collected Data
    RR: risk ratio
    SDI: Social Deprivation Index
    WPAI: Work Productivity and Activity Impairment

    Edited by S Brini; submitted 18.Aug.2025; peer-reviewed by L Baumbach, W-J He; comments to author 29.Sep.2025; accepted 14.Nov.2025; published 17.Dec.2025.

    Copyright

    ©Beatriz Domingues, Ana P Pereira, Akshat Pradhan, Catarina Zidde, Dora Janela, Carolina Marramaque, Virgílio Bento, Vijay Yanamadala, Steven P Cohen, Luke Belz, Kevin Wang, Fernando Dias Correia, Fabíola Costa. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 17.Dec.2025.

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