Transforming Veteran Rehabilitation Care: Learnings from a Remote Digital Approach for Musculoskeletal Pain.
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SUBMITTER: Areias AC
PROVIDER: S-EPMC11311802 | biostudies-literature | 2024 Jul
REPOSITORIES: biostudies-literature
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Transforming Veteran Rehabilitation Care: Learnings from a Remote Digital Approach for Musculoskeletal Pain.
Areias Anabela C AC Doverspike Dan D Brostek Daniel F DF Janela Dora D Erwin Michael S MS Pinter John M JM Ficke James R JR Costa Fabíola F
Healthcare (Basel, Switzerland) 20240731 15
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 am ...[more]
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Project description:Elbow musculoskeletal pain (EP) is a major cause of disability. Telerehabilitation has shown great potential in mitigating musculoskeletal pain conditions, but EP is less explored. This single-arm interventional study investigates clinical outcomes and engagement levels of a completely remote multimodal digital care program (DCP) in patients with EP. The DCP consisted of exercise, education, and cognitive-behavioral therapy for 8 weeks. Primary outcome: disability change (through the Quick Disabilities of the Arm, Shoulder, and Hand questionnaire (QuickDASH), treatment response cut-offs: 12.0-point reduction and 30% change). Secondary outcomes: pain, analgesic intake, surgery intent, mental health, fear-avoidance beliefs, work productivity, and patient engagement. Of the 132 individuals that started the DCP, 112 (84.8%) completed the intervention. Significant improvements were observed in QuickDASH with an average reduction of 48.7% (11.9, 95% CI 9.8; 14.0), with 75.3% of participants reporting ≥30% change and 47.7% reporting ≥12.0 points. Disability change was accompanied by reductions in pain (53.1%), surgery intent (57.5%), anxiety (59.8%), depression (68.9%), fear-avoidance beliefs (34.2%), and productivity impairment (72.3%). Engagement (3.5 (SD 1.4) sessions per week) and satisfaction 8.5/10 (SD 1.6) were high. The significant improvement observed in clinical outcomes, alongside high engagement, and satisfaction suggests patient acceptance of this care delivery mode.
| S-EPMC9367806 | biostudies-literature
Project description:BACKGROUND:Chronic musculoskeletal pain has a vast global prevalence and economic burden. Conservative therapies are universally recommended but require patient engagement and self-management to be effective. OBJECTIVE:This study aimed to evaluate the efficacy of a 12-week digital care program (DCP) in a large population of patients with chronic knee and back pain. METHODS:A longitudinal observational study was conducted using a remote DCP available through a mobile app. Subjects participated in a 12-week multimodal DCP incorporating education, sensor-guided exercise therapy (ET), and behavioral health support with 1-on-1 remote health coaching. The primary outcome was pain measured by the visual analog scale (VAS). Secondary measures included engagement levels, program completion, program satisfaction, condition-specific pain measures, depression, anxiety, and work productivity. RESULTS:A total of 10,264 adults with either knee (n=3796) or low back (n=6468) pain for at least three months were included in the study. Participants experienced a 68.45% average improvement in VAS pain between baseline intake and 12 weeks. In all, 73.04% (7497/10,264) participants completed the DCP into the final month. In total, 78.60% (5893/7497) of program completers (7144/10,264, 69.60% of all participants) achieved minimally important change in pain. Furthermore, the number of ET sessions and coaching interactions were both positively associated with improvement in pain, suggesting that the amount of engagement influenced outcomes. Secondary outcomes included a 57.9% and 58.3% decrease in depression and anxiety scores, respectively, and 61.5% improvement in work productivity. Finally, 3 distinct clusters of pain response trajectories were identified, which could be predicted with a mean 76% accuracy using baseline measures. CONCLUSIONS:These results support the efficacy and scalability of a DCP for chronic low back and knee pain in a large, diverse, real-world population. Participants demonstrated high completion and engagement rates and a significant positive relationship between engagement and pain reduction was identified, a finding that has not been previously demonstrated in a DCP. Furthermore, the large sample size allowed for the identification of distinct pain response subgroups, which may prove beneficial in predicting recovery and tailoring future interventions. This is the first longitudinal digital health study to analyze pain outcomes in a sample of this magnitude, and it supports the prospect for DCPs to serve the overwhelming number of musculoskeletal pain sufferers worldwide.
| S-EPMC7248800 | biostudies-literature
Project description: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.
| S-EPMC11615557 | biostudies-literature
Project description:BackgroundMusculoskeletal (MSK) conditions are the number one cause of disability worldwide. Digital care programs (DCPs) for MSK pain management have arisen as alternative care delivery models to circumvent challenges in accessibility of conventional therapy. Despite the potential of DCPs to reduce inequities in accessing care, the outcomes of such interventions in rural and urban populations have yet to be studied.ObjectiveThe aim of this study was to assess the impact of urban or rural residency on engagement and clinical outcomes after a multimodal DCP for MSK pain.MethodsThis study consists of an ad hoc analysis of a decentralized single-arm investigation into engagement and clinical-related outcomes after a multimodal DCP in patients with MSK conditions. Patients were coded according to their zip codes to a specific rural-urban commuting area code and grouped into rural and urban cohorts. Changes in their engagement and clinical outcomes from baseline to program end were assessed. Latent growth curve analysis was performed to estimate change trajectories adjusting for the following covariates: age, gender, BMI, employment status, and pain acuity. Outcomes included engagement, self-reported pain, and the results of the Generalized Anxiety Disorder 7-item, Patient Health Questionnaire 9-item, and Work Productivity and Activity Impairment scales. A minimum clinically important difference (MCID) of 30% was considered for pain.ResultsPatients with urban and rural residency across the United States participated in the program (n=9992). A 73.8% (7378/9992) completion rate was observed. Both groups reported high satisfaction scores and similar engagement with exercise sessions, with rural residents showing higher engagement with educational content (P<.001) and higher program completion rates (P=.02). All groups showed a significant improvement in all clinical outcomes, including pain, mental health, and work productivity, without statistically significant intergroup differences. The percentage of patients meeting the MCID was similar in both groups (urban: 67.1%, rural: 68.3%; P=.30).ConclusionsThis study advocates for the utility of a DCP in improving access to MSK care in urban and rural areas alike, showcasing its potential to promote health equity. High engagement, satisfaction, and completion rates were noted in both groups, as well as significant improvements in clinical outcomes.Trial registrationClinicalTrials.gov NCT04092946; https://clinicaltrials.gov/ct2/show/NCT04092946.
| S-EPMC10132051 | biostudies-literature
Project description:BackgroundMusculoskeletal (MSK) pain disproportionately affects people from different ethnic backgrounds through higher burden and less access to care. Digital care programs (DCPs) can improve access and help reduce inequities. However, the outcomes of such programs based on race and ethnicity have yet to be studied.ObjectiveWe aimed to assess the impact of race and ethnicity on engagement and outcomes in a multimodal DCP for MSK pain.MethodsThis was an ad hoc analysis of an ongoing decentralized single-arm investigation into engagement and clinical-related outcomes after a multimodal DCP in patients with MSK conditions. Patients were stratified by self-reported racial and ethnic group, and their engagement and outcome changes between baseline and 12 weeks were compared using latent growth curve analysis. Outcomes included program engagement (number of sessions), self-reported pain scores, likelihood of surgery, Generalized Anxiety Disorder 7-item scale, Patient Health Questionnaire 9-item, and Work Productivity and Activity Impairment. A minimum clinically important difference (MCID) of 30% was calculated for pain, and multivariable logistic regression was performed to evaluate race as an independent predictor of meeting the MCID.ResultsA total of 6949 patients completed the program: 65.5% (4554/6949) of them were non-Hispanic White, 10.8% (749/6949) were Black, 9.7% (673/6949) were Asian, 9.2% (636/6949) were Hispanic, and 4.8% (337/6949) were of other racial or ethnic backgrounds. The population studied was diverse and followed the proportions of the US population. All groups reported high engagement and satisfaction, with Hispanic and Black patients ranking first among satisfaction despite lower engagement. Black patients had a higher likelihood to drop out (odds ratio [OR] 1.19, 95% CI 1.01-1.40, P=.04) than non-Hispanic White patients. Hispanic and Black patients reported the highest level of pain, surgical intent, work productivity, and impairment in activities of daily living at baseline. All race groups showed a significant improvement in all outcomes, with Black and Hispanic patients reporting the greatest improvements in clinical outcomes. Hispanic patients also had the highest response rate for pain (75.8%) and a higher OR of meeting the pain MCID (OR 1.74, 95% CI 1.24-2.45, P=.001), when compared with non-Hispanic White patients, independent of age, BMI, sex, therapy type, education level, and employment status. No differences in mental health outcomes were found between race and ethnic groups.ConclusionsThis study advocates for the utility of a DCP in improving access to MSK care and promoting health equity. Engagement and satisfaction rates were high in all the groups. Black and Hispanic patients had higher MSK burden at baseline and lower engagement but also reported higher improvements, with Hispanic patients presenting a higher likelihood of pain improvement.
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Project description:Digital interventions have emerged as a solution for time and geographical barriers, however, their potential to target other social determinants of health is largely unexplored. In this post-hoc analysis, we report the impact of social deprivation on engagement and clinical outcomes after a completely remote multimodal musculoskeletal (MSK) digital care program managed by a culturally-sensitive clinical team. Patients were stratified in five categories according to their social deprivation index, and cross-referenced with their race/ethnicity, rurality and distance to healthcare facilities. From a total of 12,062 patients from all U.S. states, 8569 completed the program. Higher social deprivation was associated with greater baseline disease burden. We observed that all categories reported pain improvements (ranging from -2.0 95%CI -2.1, -1.9 to -2.1 95%CI -2.3, -1.9, p < 0.001) without intergroup differences in mean changes or responder rates (from 59.9% (420/701) to 66.6% (780/1172), p = 0.067), alongside reduction in analgesic consumption. We observed significant improvements in mental health and productivity across all categories, with productivity and non-work-related functional recovery being greater within the most deprived group. Engagement was high but varied slightly across categories. Together these findings highlight the importance of a patient-centered digital care program as a tool to address health inequities in musculoskeletal pain management. The idea of investigating social deprivation within a digital program provides a foundation for future work in this field to identify areas of improvement.
| S-EPMC10564877 | biostudies-literature
Project description:BackgroundAging 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).ObjectiveThis 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.MethodsWe 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.ResultsOf 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).ConclusionsOlder 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.
| S-EPMC10466151 | biostudies-literature
Project description:Musculoskeletal disorders (MSDs) are highly prevalent, painful, and costly disorders. The MSD Cohort was created to characterize variation in pain, comorbidities, treatment, and outcomes among patients with MSD receiving Veterans Health Administration care across demographic groups, geographic regions, and facilities. We searched electronic health records to identify patients treated in Veterans Health Administration who had ICD-9-CM codes for diagnoses including, but not limited to, joint, back, and neck disorders, and osteoarthritis. Cohort inclusion criteria were 2 or more outpatient visits occurring within 18 months of one another or one inpatient visit with an MSD diagnosis between 2000 and 2011. The first diagnosis is the index date. Pain intensity numeric rating scale (NRS) scores, comorbid medical and mental health diagnoses, pain-related treatments, and other characteristics were collected retrospectively and prospectively. The cohort included 5,237,763 patients; their mean age was 59, 6% were women, 15% identified as black, and 18% reported severe pain (NRS ≥ 7) on the index date. Nontraumatic joint disorder (27%), back disorder (25%), and osteoarthritis (21%) were the most common MSD diagnoses. Patients entering the cohort in recent years had more concurrent MSD diagnoses and higher NRS scores. The MSD Cohort is a rich resource for collaborative pain-relevant health service research.
| S-EPMC4949131 | biostudies-literature
Project description:An international conference titled "Transforming Health Care in Remote Communities" was held at the Chateau Lacombe Hotel in Edmonton, Canada, April 28-30, 2016. The event was organized by the University of Alberta's School of Public Health, in partnership with the Institute for Circumpolar Health Research in Yellowknife, Northwest Territories, and the Qaujigiartiit Health Research Centre in Iqaluit, Nunavut. There were 150 registrants from 7 countries: Canada (7 provinces and 3 territories), United States, Denmark, Iceland, Norway, Sweden, and Australia. They included representatives of academic institutions, health care agencies, government ministries, community organizations, and private industry. The Conference focused on developing solutions to address health care in remote regions. It enabled new networks to be established and existing ones consolidated.
| S-EPMC4989899 | biostudies-other
Project description:IntroductionWith the growing prevalence of value-based contracts, health systems are incentivized to consider population approaches to service delivery, particularly for chronic conditions like depression. To this end, UW Medicine implemented the Depression-Population Approach to Health (PATH) program in primary care (PC) as part of a system-wide Center for Medicare and Medicaid Innovation (CMMI) quality improvement (QI) initiative.AimTo examine the feasibility of a pilot PATH program and its impact on clinical and process-of-care outcomes.SettingA large, diverse, geographically disparate academic health system in Western Washington State including 28 PC clinics across five networks.Program descriptionThe PATH program was a population-level, centralized, measurement-based care intervention that utilized a clinician to provide remote monitoring of treatment progress via chart review and facilitate patient engagement when appropriate. The primary goals of the program were to improve care engagement and increase follow-up PHQ-9 assessments for patients with depression and elevated initial PHQ-9 scores.Program evaluationWe employed a prospective, observational study design, including commercially insured adult patients with new depression diagnoses and elevated initial PHQ-9 scores. The pilot intervention group, consisting of accountable care network (ACN) self-enrollees (N = 262), was compared with a similar commercially insured cohort (N = 2527) using difference-in-differences analyses adjusted for patient comorbidities, initial PHQ-9 score, and time trends. The PATH program was associated with three times the odds of PHQ-9 follow-up (OR 3.28, 95% CI 1.79-5.99), twice the odds of a follow-up PC clinic visit (OR 1.74, 95% CI 0.99-3.08), and twice the odds of treatment response, defined as reduction in PHQ-9 score by ≥ 50% (OR 2.02, 95% CI 0.97-4.21).DiscussionOur results demonstrate that a centralized, remote care management initiative is both feasible and effective for large academic health systems aiming to improve depression outcome ascertainment, treatment engagement, and clinical care.
| S-EPMC7878605 | biostudies-literature