AIM:To compare healthcare utilization and spending among women enrolled in an employer-sponsored, artificial intelligence (AI) structured pelvic care program with those receiving usual in-person care for pelvic floor dysfunction (PFD) in routine clinical settings. METHODS:This retrospective payor-perspective economic evaluation used exact and propensity score-matched cohorts derived from a third-party U.S. nationwide claims database from July 2022 to May 2025. Eligible participants were adult females with a pelvic-related condition, at least 24 months of continuous health-insurance coverage, and a minimum of one pelvic claim in the prior year. Intervention group (IG) comprised women who participated in the AI pelvic care program (consisting of biofeedback-mediated pelvic floor muscle training asynchronously monitored by a physical therapist specialized in pelvic health). Comparator group (CG) included women who sought a medical or physical therapy evaluation visit for PFD. Self-reported clinical outcomes available for the IG were assessed using latent-basis growth analysis. RESULTS:The matched cohort included 602 women (301 per group). Relative to CG, IG patients had substantially lower healthcare spending over 12 months, with mean gross per-person pelvic-related savings of $3,082.4 (95% CI $1,270.2 to $4,894.7, p<.001). Savings were primarily associated with fewer surgical procedures (per-person difference of $2,534.2; 95% CI $831.2 to $4,237.2, p=.004), with differences also noted in medical office visits and imaging utilization. IG participants demonstrated significant improvements in pelvic floor symptom burden, work productivity, and mental health. LIMITATIONS:Claims-based analyses cannot exclude unmeasured confounding, misclassification, or selection bias. The one-year follow-up limits assessment of long-term economic impact. CONCLUSIONS:Participation in this AI pelvic care program was associated with markedly lower healthcare utilization and spending compared with usual care, largely linked to fewer surgical interventions. These findings highlight the potential of accessible, guideline-concordant AI pelvic care to lessen healthcare spending associated with PFD and inform payor-oriented care delivery models.

BackgroundRace/ethnicity and gender concordance between patients and providers is a potential strategy to improve health care interventions. In digital health, where human interactions occur both synchronously and asynchronously, the effect of concordance between patients and providers is unknown. ObjectiveThis study aimed to evaluate the impact of race/ethnicity or gender concordance between patients and physical therapists (PTs) in engagement and the clinical outcomes following a digital care program (DCP) in patients with musculoskeletal (MSK) conditions. MethodsThis secondary analysis of 2 prospective longitudinal studies (originally focused on assessing the acceptance, engagement, and clinical outcomes after a remote DCP) examined the impact of both race/ethnicity concordance and gender concordance between patients and PTs on outcomes for a digital intervention for MSK conditions. Outcomes included engagement (measured by the completion rate and communication, assessed by text interactions), satisfaction, and clinical outcomes (response rate, ie, percentage of patients achieving at least a minimal clinically important change in pain, measured by the Numerical Pain Rating Scale [NPRS]; anxiety, measured by the Generalized Anxiety Disorder 7-item scale [GAD-7]; depression, measured by the Patient Health Questionnaire 9-item [PHQ-9]; and daily activity impairment, measured by the Work Productivity and Activity Impairment [WPAI] questionnaire). ResultsOf 71,201 patients, 63.9% (n=45,507) were matched with their PT in terms of race/ethnicity, while 61.2% (n=43,560) were matched for gender. Concordant dyads showed a higher completion rate among White (adjusted odds ratio [aOR] 1.11, 95% CI 1.05-1.19, P<.001) and Hispanic (aOR 1.27, 95% CI 1.08-1.54, P=.009) groups, as well as women (aOR 1.10, 95% CI 1.06-1.18, P<.001), when compared to discordant dyads. High and similar levels of interaction between patients and PTs were observed across race/ethnicity and gender dyads, except for Asian concordant dyads (adjusted β coefficient 5.32, 95% CI 3.28-7.36, P<.001). Concordance did not affect satisfaction, with high values (>8.52, 95% CI 8.27-8.77) reported across all dyads. Response rates for pain, anxiety, and daily activity impairment were unaffected by race/ethnicity concordance. An exception was observed for depression, with White patients reporting a higher response rate when matched with PTs from other races/ethnicities (aOR 1.20, 95% CI 1.02-1.39, P=.02). In terms of gender, men had a slightly higher pain response rate in discordant dyads (aOR 1.08, 95% CI 1.01-1.15, P=.03) and a higher depression response rate in concordant dyads (aOR 1.23, 95% CI 1.05-1.47, P=.01). ConclusionsRace/ethnicity and gender concordance between patients and PTs does not translate into higher satisfaction or improvement for most clinical outcomes, aside from a positive effect on treatment completion. These results highlight the importance of other PT characteristics, in addition to race/ethnicity or gender concordance, suggesting the potential benefit of experience, languages spoken, and cultural safety training as ways to optimize care. Trial RegistrationClinicalTrials.gov NCT04092946, NCT05417685; https://clinicaltrials.gov/study/NCT05417685, https://clinicaltrials.gov/study/NCT04092946

IMPORTANCE:Psychological factors are associated with chronic spinal pain, yet their mediating role in postrehabilitation recovery remains poorly understood, particularly in fully remote digital care. Most research has focused on baseline predictors, with few studies evaluating psychological mediators and moderators. OBJECTIVE:The objective of this study was to investigate whether changes in fear avoidance beliefs, depression, and anxiety mediate pain outcome following a digital care program (DCP) for chronic spinal conditions and whether these effects vary by Body Mass Index (BMI), self-reported gender, and socioeconomic status. DESIGN:This was an ad hoc analysis of a real-world registry of patients undergoing a DCP. SETTING:The setting was a fully remote DCP delivered across the United States. PARTICIPANTS:The participants were adults who had chronic spinal musculoskeletal pain (N = 14,818) and who accessed the DCP via employer-sponsored health plans. INTERVENTION:The DCP consisted of exercise, education, and behavior change, managed asynchronously by physical therapists. MAIN OUTCOMES AND MEASURES:The final pain score (11-point numeric pain rating scale) was the primary outcome. Candidate mediators were changes in fear avoidance beliefs, depression, and anxiety. Confounding was mitigated through demographic and clinical covariates. Moderation was tested for BMI, self-reported gender, and socioeconomic deprivation. Structural equation modeling was used. RESULTS:Improvements in fear avoidance beliefs (β = -0.10, SE = 0.00), depression (β = -0.05, SE = 0.01), and anxiety (β = -0.04, SE = 0.01) significantly mediated lower final pain scores after adjustment for confounding. The mediating effect of fear avoidance was especially pronounced among patients with severe obesity. Self-reported gender and socioeconomic status did not show moderating effects. The model's explained variance was 30%. CONCLUSIONS AND RELEVANCE:Changes in fear avoidance beliefs, depression, and anxiety play a central role in pain recovery following digital rehabilitation. Fear avoidance mediation was particularly strong in individuals with severe obesity, highlighting the need for targeted psychological support in this subgroup. The findings emphasize the pertinence of systematically screening, monitoring, and addressing psychological factors in remote care, contributing to understanding how digital rehabilitation promotes recovery.

OBJECTIVE:To evaluate whether baseline disability, work impairment, and job occupation predicted post-pain levels of a digital care program (DCP) for chronic spinal pain. DESIGN:Ad hoc analysis of a real-world clinical registry of patients undergoing a DCP. SETTING:DCP delivered remotely across the United States. SUBJECTS:Adults with chronic spinal pain (N = 13 330) enrolled in a DCP through employer-sponsored health plans. METHODS:Predictors included baseline disability (Oswestry Disability Index or Neck Disability Index), work impairment (Work Productivity and Activity Impairment questionnaire-WPAI), and occupation (job type group). Primary outcome was the last pain score reported during the intervention (11-point Numeric Pain Rating Scale). Structural equation modeling was used, adjusted for demographic and clinical covariates. Moderation analysis assessed whether effects varied by pain location (neck vs low back). RESULTS:Baseline disability and occupation significantly predicted post-treatment pain. Greater disability was associated with higher last pain scores (β = 0.30, SE 0.02, P<.001). Business-related occupations were non-significantly different from trade, transportation, and utilities, but showed higher last pain score than those in goods-producing (β=-0.18, SE 0.07, P=.015) and healthcare/education (β=-0.14, SE 0.04, P=.001) jobs. WPAI Overall and WPAI Activity were not significant predictors after adjustment. Predictor effects were consistent across spinal locations. Final model explained 21.3% of variance. CONCLUSIONS:Baseline disability and occupation were predictors of outcomes post-digital rehabilitation for chronic spinal pain, while work impairment was non-significant. Integrating these factors into routine screening may enhance predictive accuracy, patient communication, and facilitate personalized care pathways. These results encourage confirmatory studies to reinforce these findings. TRIAL REGISTRATION:ClinicalTrials.gov, NCT05417685. Registered on June 14, 2022; https://clinicaltrials.gov/study/NCT05417685.

Background Aging is closely associated with an increased prevalence of musculoskeletal conditions. Digital musculoskeletal care interventions emerged to deliver timely and proper rehabilitation; however, older adults frequently face specific barriers and concerns with digital care programs (DCPs). Objective This study aims to investigate whether known barriers and concerns of older adults impacted their participation in or engagement with a DCP or the observed clinical outcomes in comparison with younger individuals. Methods We conducted a secondary analysis of a single-arm investigation assessing the recovery of patients with musculoskeletal conditions following a DCP for up to 12 weeks. Patients were categorized according to age: ≤44 years old (young adults), 45-64 years old (middle-aged adults), and ≥65 years old (older adults). DCP access and engagement were evaluated by assessing starting proportions, completion rates, ability to perform exercises autonomously, assistance requests, communication with their physical therapist, and program satisfaction. Clinical outcomes included change between baseline and program end for pain (including response rate to a minimal clinically important difference of 30%), analgesic usage, mental health, work productivity, and non–work-related activity impairment. Results Of 16,229 patients, 12,082 started the program: 38.3% (n=4629) were young adults, 55.7% (n=6726) were middle-aged adults, and 6% (n=727) were older adults. Older patients were more likely to start the intervention and to complete the program compared to young adults (odds ratio [OR] 1.72, 95% CI 1.45-2.06; P<.001 and OR 2.40, 95% CI 1.97-2.92; P<.001, respectively) and middle-aged adults (OR 1.22, 95% CI 1.03-1.45; P=.03 and OR 1.38, 95% CI 1.14-1.68; P=.001, respectively). Whereas older patients requested more technical assistance and exhibited a slower learning curve in exercise performance, their engagement was higher, as reflected by higher adherence to both exercise and education pieces. Older patients interacted more with the physical therapist (mean 12.6, SD 18.4 vs mean 10.7, SD 14.7 of young adults) and showed higher satisfaction scores (mean 8.7, SD 1.9). Significant improvements were observed in all clinical outcomes and were similar between groups, including pain response rates (young adults: 949/1516, 62.6%; middle-aged adults: 1848/2834, 65.2%; and older adults: 241/387, 62.3%; P=.17). Conclusions Older adults showed high adherence, engagement, and satisfaction with the DCP, which were greater than in their younger counterparts, together with significant clinical improvements in all studied outcomes. This suggests DCPs can successfully address and overcome some of the barriers surrounding the participation and adequacy of digital models in the older adult population.

Whether the mode of delivery of first-line conservative musculoskeletal (MSK) care influences downstream opioid use remains unclear. We conducted an observational retrospective cohort study using a U.S. administrative claims database to evaluate opioid prescribing among opioid-naive individuals with new-onset MSK conditions initiating AI-enabled, digitally delivered care versus in-person physical therapy (N = 30,900). Using inverse probability weighting and complementary analytic approaches, we assessed opioid initiation and prescribing patterns over 12 months under both conventional and more stringent definitions of opioid exposure. Under the stringent definition, opioid initiation was lower among individuals receiving digitally delivered care (1.8% vs 3.2%; adjusted OR 0.54, 95% CI 0.49; 0.59; p < 0.001), with fewer prescriptions per 100 patients (5.7 vs 11.4). Compared with conventional definitions, differences in prescribing intensity and profile were attenuated. These findings suggest that care delivery pathways may influence opioid prescribing and support scalable, digitally delivered care models in opioid stewardship.

OBJECTIVE:To evaluate health care utilization and spending of individuals enrolled in a musculoskeletal (MSK) digital care program (DCP), compared with those who initiated in-person physical therapy. DESIGN:Economic retrospective analysis of comparative matched-group cost-savings from a payer perspective. SETTING:Health care claims data from a commercial platform, through a deidentified US data set spanning March 2022 to October 2024. PARTICIPANTS:Adults (≥18y) with an MSK-related index event between March and October 2023 (N=4,366). The intervention group enrolled in the DCP, and the comparator group initiated MSK-related in-person physical therapy. INTERVENTIONS:The DCP included biofeedback-mediated exercise, education, and behavior change, asynchronously managed by a physical therapist. In-person physical therapy was detected through physical therapy evaluation claims. MAIN OUTCOME MEASURES:Total health care and MSK-related utilization and costs 12-months preindex and postindex were compared between groups. Comparability was ensured through exact and propensity matching. Subgroup analysis stratified by acuity was performed. Intervention group self-reported clinical outcomes were evaluated. RESULTS:Matched groups included 2183 patients each. The digital intervention was associated with annual per-person savings of $2025.7 in MSK care (95% CI, 1362.0-2689.4; P<.001) and $2369.5 in total health care (95% CI, 1305.4-3433.7) versus comparator group. These were mainly driven by surgery avoidance, imaging, and medical office visits, with generally consistent patterns across acute and chronic MSK conditions. The DCP executed a larger number of sessions and had significant clinical improvements and an estimated $518.1 (95% CI, 480.5-555.7) in productivity-related savings per patient at program-end. CONCLUSIONS:This study showcases that a fully remote, digital MSK program is able to drive significantly lower health care utilization and spending, mainly by avoiding care escalation into invasive, high-cost interventions. The results highlight the potential of digital-first models to scale value-based MSK care.

Background:Despite compelling evidence identifying psychological predictive factors in in-person rehabilitation, their validity in remote digital care settings remains unknown. Objective:To assess whether fear-avoidance beliefs, depression, and anxiety predict pain outcomes after a digital care program (DCP) for chronic musculoskeletal pain (CMP). Methods:This ad hoc analysis of a decentralized interventional investigation included patients with CMP who underwent a DCP integrating exercise, education, and behavioral change. Pain outcomes were assessed using 4 measures: last pain score, relative pain change, achievement of postintervention mild pain, and pain response (≥30% change or last pain score ≤3). Predictors included baseline scores of Fear-avoidance Beliefs Questionnaire related to Physical Activity, Patient Health 9-item Questionnaire, and Generalized Anxiety Disorder 7-item scale. Structural equation models evaluated their predictive value on pain outcomes, with or without including potential demographic and clinical confounders. Results:Fear-avoidance beliefs and depression symptoms were consistent predictors across all pain outcomes after confounders adjustment. Worse outcomes were associated with higher baseline fear-avoidance beliefs (eg, last pain score: β = 0.15, SE 0.04, P < 0.001; pain response: odds ratio [OR] 0.86, 95% confidence interval [CI] 0.78; 0.96, P = 0.001) and depression levels (eg, last pain score: β = 0.14, SE 0.05, P = 0.007; pain response: OR 0.85, 95% CI 0.74; 0.98, P = 0.008). Anxiety did not significantly affect any pain outcome. Sensitivity analyses showed stronger predictive performance when psychological factors were combined with clinical characteristics. Conclusions:Fear-avoidance beliefs and depression consistently predicted pain outcomes, reinforcing their critical role in digital rehabilitation for CMP. Multimodal, tailored approaches targeting these factors may optimize recovery in remote care.

Background:The menopause transition is a significant life milestone that impacts quality of life and work performance. Among menopause-related conditions, pelvic floor dysfunctions (PFDs) affect ∼40%-50% of postmenopausal women, including urinary or fecal incontinence, genito-pelvic pain, and pelvic organ prolapse. While pelvic floor muscle training (PFMT) is the primary treatment, access barriers leave many untreated, advocating for new care delivery models. Objective:This study aims to assess the outcomes of a digital pelvic program, combining PFMT and education, in postmenopausal women with PFDs. Methods:This prospective, longitudinal study evaluated engagement, safety, and clinical outcomes of a remote digital pelvic program among postmenopausal women (n=3051) with PFDs. Education and real-time biofeedback PFMT sessions were delivered through a mobile app. The intervention was asynchronously monitored and tailored by a physical therapist specializing in pelvic health. Clinical measures assessed pelvic floor symptoms and their impact on daily life (Pelvic Floor Impact Questionnaire-short form 7, Urinary Impact Questionnaire-short form 7, Colorectal-Anal Impact Questionnaire-short form 7, and Pelvic Organ Prolapse Impact Questionnaire-short form 7), mental health, and work productivity and activity impairment. Structural equation modeling and minimal clinically important change response rates were used for analysis. Results:The digital pelvic program had a high completion rate of 77.6% (2367/3051), as well as a high engagement and satisfaction level (8.6 out of 10). The safety of the intervention was supported by the low number of adverse events reported (21/3051, 0.69%). The overall impact of pelvic floor symptoms in participants' daily lives decreased significantly (-19.55 points, 95% CI -22.22 to -16.88; P<.001; response rate of 59.5%, 95% CI 54.9%-63.9%), regardless of condition. Notably, nonwork-related activities and productivity impairment were reduced by around half at the intervention-end (-18.09, 95% CI -19.99 to -16.20 and -15.08, 95% CI -17.52 to -12.64, respectively; P<.001). Mental health also improved, with 76.1% (95% CI 60.7%-84.9%; unadjusted: 97/149, 65.1%) and 54.1% (95% CI 39%-68.5%; unadjusted: 70/155, 45.2%) of participants with moderate to severe symptomatology achieving the minimal clinically important change for anxiety and depression, respectively. Recovery was generally not influenced by the higher baseline symptoms' burden in individuals with younger age, high BMI, social deprivation, and residence in urban areas, except for pelvic health symptoms where lower BMI levels (P=.02) and higher social deprivation (P=.04) were associated with a steeper recovery. Conclusions:This study demonstrates the feasibility, safety, and positive clinical outcomes of a fully remote digital pelvic program to significantly improve PFD symptoms, mental health, and work productivity in postmenopausal women while enhancing equitable access to personalized interventions that empower women to manage their condition and improve their quality of life.

Background: Obesity is a known risk factor and aggravator of musculoskeletal (MSK) conditions. The rising prevalence of obesity calls for scalable solutions to address MSK conditions in this population, given their complex clinical profile and barriers to accessing care. Purpose: To evaluate the engagement and clinical outcomes of a fully remote digital care program in patients with MSK conditions, focusing on those with and without comorbid obesity. Patients and Methods: A post-hoc analysis of a prospective, longitudinal, single-arm observational home-based study conducted between August, 2023, and August, 2024. Adults suffering from chronic MSK pain were categorized according to their body mass index (BMI) into non-obesity, obesity and severe obesity. Outcomes included completion rates, engagement, satisfaction, pain (minimal clinically important change: 30%), impairment in daily activities, and patient global impression of change (PGIC). Depending on the clinical outcomes, latent basis growth analysis and logistic regression were used. Results: Completion rates were high across all groups (77.5-85.6%), although slightly lower in the obesity groups. Fairly similar engagement was observed with both exercise sessions and the educational content (1.9-2.2 exercise sessions per week; 8.10-9.31 educational content videos watched). Obesity groups interacted more with the physical therapists than the non-obesity group (severe obesity: 24.6 (SD 10.1); obesity: 23.2 (SD 10.46) vs non-obesity: 22.4 (SD 9.8), P < 0.001). Despite higher baseline risk and clinical impairment in the obesity groups, all groups showed significant pain reductions, with pain responder rates ranging from 56.6 to 63.6%, slightly lower in the severe obesity group. Improvements in daily activities were significant across groups, alongside a positive PGIC (50.4-53.6%). Satisfaction was very high (>9/10) in all BMI groups. Conclusion: Despite worse baseline clinical presentations, obesity groups achieved high completion rates, engagement, and significant clinical improvements comparable to the non-obesity group, highlighting the potential of a digital program for this population.

BackgroundMusculoskeletal (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. ObjectiveThis 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. MethodsRetrospective, 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. ResultsIn 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. ConclusionsThis 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.

Background/Objectives: The rising prevalence of musculoskeletal (MSK) conditions has not been balanced by a sufficient increase in healthcare providers. Scalability challenges are being addressed through the use of artificial intelligence (AI) in some healthcare sectors, with this showing potential to also improve MSK care. Digital care programs (DCP) generate automatically collected data, thus making them ideal candidates for AI implementation into workflows, with the potential to unlock care scalability. In this study, we aimed to assess the impact of scaling care through AI in patient outcomes, engagement, satisfaction, and adverse events. Methods: Post hoc analysis of a prospective, pre-post cohort study assessing the impact on outcomes after a 2.3-fold increase in PT-to-patient ratio, supported by the implementation of a machine learning-based tool to assist physical therapists (PTs) in patient care management. The intervention group (IG) consisted of a DCP supported by an AI tool, while the comparison group (CG) consisted of the DCP alone. The primary outcome concerned the pain response rate (reaching a minimal clinically important change of 30%). Other outcomes included mental health, program engagement, satisfaction, and the adverse event rate. Results: Similar improvements in pain response were observed, regardless of the group (response rate: 64% vs. 63%; p = 0.399). Equivalent recoveries were also reported in mental health outcomes, specifically in anxiety (p = 0.928) and depression (p = 0.187). Higher completion rates were observed in the IG (79.9% (N = 19,252) vs. CG 70.1% (N = 8489); p < 0.001). Patient engagement remained consistent in both groups, as well as high satisfaction (IG: 8.76/10, SD 1.75 vs. CG: 8.60/10, SD 1.76; p = 0.021). Intervention-related adverse events were rare and even across groups (IG: 0.58% and CG 0.69%; p = 0.231). Conclusions: The study underscores the potential of scaling MSK care that is supported by AI without compromising patient outcomes, despite the increase in PT-to-patient ratios.

While musculoskeletal pain (MSP) stands as the most prevalent health condition among Veterans, timely and high-quality care is often hindered due to access barriers. Team Red, White & Blue (Team RWB), a nonprofit organization dedicated to promoting a healthier lifestyle among Veterans, aimed to assess innovative approaches to veteran care. This is a single-arm pilot study investigating the feasibility, clinical outcomes, engagement, and satisfaction of a remote multimodal digital care program among Veterans with MSP. The impact of deployment experience on outcomes was explored as a secondary aim. From 75 eligible Veterans, 61 started the program, reporting baseline pain frequently comorbid with mental distress. Program acceptance was suggested by the high completion rate (82%) and engagement levels, alongside high satisfaction (9.5/10, SD 1.0). Significant improvements were reported in all clinical outcomes: pain (1.98 points, 95%CI 0.13; 3.84, p = 0.036); mental distress, with those reporting at least moderate baseline depression ending the program with mild symptoms (8.50 points, 95%CI: 6.49; 10.51, p = 0.012); daily activity impairment (13.33 points, 95%CI 1.31; 25.34, p = 0.030). Deployed Veterans recovered similarly to their counterparts. Overall, the above results underscore the potential of a remote digital intervention to expand Veterans’ access to timely MSP care.

BackgroundLow back pain (LBP) presents with diverse manifestations, necessitating personalized treatment approaches that recognize various phenotypes within the same diagnosis, which could be achieved through precision medicine. Although prediction strategies have been explored, including those employing artificial intelligence (AI), they still lack scalability and real-time capabilities. Digital care programs (DCPs) facilitate seamless data collection through the Internet of Things and cloud storage, creating an ideal environment for developing and implementing an AI predictive tool to assist clinicians in dynamically optimizing treatment. ObjectiveThis study aims to develop an AI tool that continuously assists physical therapists in predicting an individual’s potential for achieving clinically significant pain relief by the end of the program. A secondary aim was to identify predictors of pain nonresponse to guide treatment adjustments. MethodsData collected actively (eg, demographic and clinical information) and passively in real-time (eg, range of motion, exercise performance, and socioeconomic data from public data sources) from 6125 patients enrolled in a remote digital musculoskeletal intervention program were stored in the cloud. Two machine learning techniques, recurrent neural networks (RNNs) and light gradient boosting machine (LightGBM), continuously analyzed session updates up to session 7 to predict the likelihood of achieving significant pain relief at the program end. Model performance was assessed using the area under the receiver operating characteristic curve (ROC-AUC), precision-recall curves, specificity, and sensitivity. Model explainability was assessed using SHapley Additive exPlanations values. ResultsAt each session, the model provided a prediction about the potential of being a pain responder, with performance improving over time (P<.001). By session 7, the RNN achieved an ROC-AUC of 0.70 (95% CI 0.65-0.71), and the LightGBM achieved an ROC-AUC of 0.71 (95% CI 0.67-0.72). Both models demonstrated high specificity in scenarios prioritizing high precision. The key predictive features were pain-associated domains, exercise performance, motivation, and compliance, informing continuous treatment adjustments to maximize response rates. ConclusionsThis study underscores the potential of an AI predictive tool within a DCP to enhance the management of LBP, supporting physical therapists in redirecting care pathways early and throughout the treatment course. This approach is particularly important for addressing the heterogeneous phenotypes observed in LBP. Trial RegistrationClinicalTrials.gov NCT04092946; https://clinicaltrials.gov/ct2/show/NCT04092946 and NCT05417685; https://clinicaltrials.gov/ct2/show/NCT05417685

BACKGROUND AND PURPOSE:Knowledge of the factors affecting pain intensity and pain sensitivity can inform treatment targets and strategies aimed at personalizing the intervention, conceivably increasing its positive impact on patients. Therefore, this study aimed to investigate the association between demographic factors (sex and age), body mass index (BMI), psychological factors (anxiety and depression, kinesiophobia and catastrophizing), self-reported physical activity, pain phenotype (symptoms of central sensitization, and nociceptive or neuropathic pain), history of COVID-19 and pain intensity and sensitivity in patients with chronic non-specific low back pain (LBP).METHODS:This was a cross-sectional secondary analysis with 83 participants with chronic non-specific LBP recruited from the community between August 2021 and April 2022. BMI, pain intensity (Visual Analog Scale), pain sensitivity at the lower back and at a distant point [pressure pain threshold], catastrophizing (Pain Catastrophizing Scale), kinesiophobia (Tampa Scale for Kinesiophobia), anxiety and depression (Hospital Anxiety and Depression Scale), pain phenotype (Central Sensitization Inventory and PainDetect Questionnaire), physical activity (International Physical Activity Questionnaire), and disability (Roland Morris Disability Questionnaire) were assessed. Multiple linear regression analyses with pain intensity and sensitivity as the dependent variables were used.RESULTS:The model for pain intensity explained 34% of its variance (Adjusted R2 = -0.343, p < 0.001), with depression and anxiety (p = 0.008) and disability (p = 0.035) reaching statistical significance. The model for pain sensitivity at the lower back, also explained 34% of its variance (Adjusted R2 = 0.344, p < 0.001) at the lower back with sex, BMI, and kinesiophobia reaching statistical significance (p < 0.05) and 15% of the variance at a distant body site (Adjusted R2 = 0.148, p = 0.018) with sex and BMI reaching statistical significance (p < 0.05).DISCUSSION:This study found that different factors are associated with pain intensity and pain sensitivity in individuals with LBP. Increased pain intensity was associated with higher levels of anxiety and depression and disability and increased pain sensitivity was associated with being a female, higher kinesiophobia, and lower BMI.

Female urinary incontinence (UI) is highly prevalent in the US (>60%). Pelvic floor muscle training (PFMT) represents first-line care for UI; however, access and adherence challenges urge new care delivery models. This prospective cohort study investigates the feasibility and safety of a remote digital care program (DCP) combining education and PFMT with real-time biofeedback with an average duration of 10 weeks. The primary outcome was the change in the Urinary Impact Questionnaire-short form (UIQ-7) from baseline to program-end, calculated through latent growth curve analysis (LGCA). Secondary outcomes included the impact of pelvic conditions (PFIQ-7), depression (PHQ-9), anxiety (GAD-7), productivity impairment (WPAI), intention to seek additional healthcare, engagement, and satisfaction. Of the 326 participants who started the program, 264 (81.0%) completed the intervention. Significant improvement on UIQ-7 (8.8, 95%CI 4.7; 12.9, p < 0.001) was observed, corresponding to a response rate of 57.3%, together with improvements in all other outcomes and high satisfaction (8.9/10, SD 1.8). This study shows the feasibility and safety of a completely remote DCP with biofeedback managed asynchronously by a physical therapist to reduce UI-related symptoms in a real-world setting. Together, these findings may advocate for the exploration of this care delivery option to escalate access to proper and timely UI care.

Objective: To investigate potential savings obtained from restoring productivity in employees with chronic MSK pain through a digital care program (DCP).Methods: Secondary analysis of a prospective longitudinal study assessing cumulative savings overall or across several industry sectors by analyzing changes in Work Productivity and Activities Impairment (WPAI questionnaire).Results: Employees from 50 U.S. states started the program (n = 5032). Significant improvements in productivity impairment were observed across all industries, yielding median cumulative savings from $151 (95% confidence interval [CI], 128-174) to $294 (95% CI, 286-303) per participant at treatment end. Twelve-month projections estimated median savings of $2916 (95% CI, 2861-2972). Additionally, significant improvements in non-work-related daily activities were observed.Conclusions: This study underlines the burden of MSK-related productivity loss on employers' financial balance, illustrating the importance of a DCP to assist patients to recover quality of life and succeed professionally.