Dataset Information

Economic Evaluation of the Dr. Bart Application in Individuals With Knee and/or Hip Osteoarthritis.

ABSTRACT:

Objective

To evaluate the cost-utility and cost-effectiveness of the dr. Bart app compared to usual care in people with osteoarthritis (OA) of the knees and hips, applying a health care payer perspective.

Methods

This economic evaluation was conducted alongside a 6-month randomized controlled trial that included 427 participants. The dr. Bart app is a stand-alone eHealth application that invites users to select pre-formulated goals (i.e., "tiny habits") and triggers for a healthier lifestyle. Self-reported outcome measures were health care costs, quality-adjusted life years (QALYs) according to the EuroQol 5-dimension 3-level (EQ-5D-3L) descriptive system, the EuroQol visual analog scale (QALY VAS), patient activation measure 13 (PAM-13), and 5 subscales of the Knee Injury and Osteoarthritis Outcome Score/Hip Disability and Osteoarthritis Outcome Score. Missing data were multiply imputed, and bootstrapping was used to estimate statistical uncertainty.

Results

The mean ± SD age of the study participants was 62.1 ± 7.3 years, and the majority of participants were female (72%). Health care costs were lower in the intervention group compared to the group who received usual care (€-22 [95% confidence interval €-36, -3]). For QALY and QALY VAS, the probability of the dr. Bart app being cost-effective compared to usual care was 0.71 and 0.67, respectively, at a willingness-to-pay (WTP) of €10,000 and 0.64 and 0.56, respectively, at a WTP of €80.000. For self-management behavior, symptoms, pain, and activities of daily living, the probability that the dr. Bart app was cost-effective was >0.82, and the probability that the dr. Bart app was cost-effective in the areas of activities and quality of life was <0.40, regardless of WTP thresholds.

Conclusion

This economic evaluation showed that costs were lower for the dr. Bart app group compared to the group who received usual care. Given the noninvasive nature of the intervention and the moderate probability of it being cost-effective for the majority of outcomes, the dr. Bart app has the potential to serve as a tool to provide education and goal setting in OA and its treatment options.

SUBMITTER: Pelle T

PROVIDER: S-EPMC9314956 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundSelf-management is of paramount importance in the non-surgical treatment of knee/hip osteoarthritis (OA). Modern technologies offer the possibility of 24/7 self-management support. We developed an e-self-management application (dr. Bart app) for people with knee/hip OA. The aim of this study was to document the use and usability of the dr. Bart app and its relation with health care utilisation and clinical outcomes in people with knee/hip OA.MethodsFor this study we used backend data for the first 26 weeks of use by the intervention group (N = 214) of an RCT examining the effectiveness of the dr. Bart app. A central element of the dr. Bart app is that it proposes a selection of 72 preformulated goals for health behaviours based on the 'tiny habits method' (e.g. after lunch I rise 12 times from my chair to train my leg muscles). The usability of the app was measured using the System Usability Scale questionnaire (SUS), on a scale of 0-100. To assess the association between the intensity of use of the app and health care utilisation (i.e., consultations in primary or secondary health care) and clinical outcomes (i.e., self-management behaviour, physical activity, health-related quality of life, illness perceptions, symptoms, pain, activities of daily living) we calculated Spearman rank correlation coefficients.ResultsOf the 214 participants, 171 (80%) logged in at least once with 151 (71%) choosing at least one goal and 114 (53%) completing at least one goal during the 26 weeks. Of those who chose at least one goal, 56 participants (37%) continued to log in for up to 26 weeks, 12 (8%) continued to select new goals from the offered goals and 37 (25%) continued to complete goals. Preformulated goals in the themes of physical activity (e.g., performing an exercise from the exercises library in the app) and nutrition (e.g., 'eat two pieces of fruit today') were found to be most popular with users. The mean usability scores (standard deviation) at the three and six month follow-ups were 65.9 (16.9) and 64.5 (17.5), respectively. The vast majority of associations between the intensity of use of the dr. Bart app and target outcomes were weak at ρ < (-) 0.25.ConclusionsMore than one-third of people with knee/hip OA who started using the app, continued to use it up to 26 weeks, though usability could be improved. Patients appear to have preferences for goals related to physical activity and nutrition, rather than for goals related to vitality and education. We found weak/no associations between the intensity of use of the dr. Bart app and health care utilisation and clinical outcomes.Trial registration(21 September 2017): Dutch Trial Register (Trial Number NTR6693/NL6505 ).

Project description:Reduced external knee adduction moments in the second half of stance after total hip replacement have been reported in hip osteoarthritis patients. This reduction is thought to shift the load from the medial to the lateral knee compartment and as such increase the risk for knee osteoarthritis. The knee adduction moment is a surrogate for the load distribution between the medial and lateral compartments of the knee and not a valid measure for the tibiofemoral contact forces which are the result of externally applied forces and muscle forces. The purpose of this study was to investigate whether the distribution of the tibiofemoral contact forces over the knee compartments in unilateral hip osteoarthritis patients 1 year after receiving a primary total hip replacement differs from healthy controls. Musculoskeletal modeling on gait was performed in OpenSim using the detailed knee model of Lerner et al. (2015) for 19 patients as well as for 15 healthy controls of similar age. Knee adduction moments were calculated by the inverse dynamics analysis, medial and lateral tibiofemoral contact forces with the joint reaction force analysis. Moments and contact forces of patients and controls were compared using Statistical Parametric Mapping two-sample t-tests. Knee adduction moments and medial tibiofemoral contact forces of both the ipsi- and contralateral leg were not significantly different compared to healthy controls. The contralateral leg showed 14% higher medial tibiofemoral contact forces compared to the ipsilateral (operated) leg during the second half of stance. During the first half of stance, the lateral tibiofemoral contact force of the contralateral leg was 39% lower and the ratio 32% lower compared to healthy controls. In contrast, during the second half of stance the forces were significantly higher (39 and 26%, respectively) compared to healthy controls. The higher ratio indicates a changed distribution whereas the increased lateral tibiofemoral contact forces indicate a higher lateral knee joint loading in the contralateral leg in OA patients after total hip replacement (THR). Musculoskeletal modeling using a detailed knee model can be useful to detect differences in the load distribution between the medial and lateral knee compartment which cannot be verified with the knee adduction moment.

Project description:BackgroundMeasuring and modifying movement-related joint loading is integral to the management of lower extremity osteoarthritis (OA). Although traditional approaches rely on measurements made within the laboratory or clinical environments, inertial sensors provide an opportunity to quantify these outcomes in patients' natural environments, providing greater ecological validity and opportunities to develop large data sets of movement data for the development of OA interventions.ObjectiveThis narrative review aimed to discuss and summarize recent developments in the use of inertial sensors for assessing movement during daily activities in individuals with hip and knee OA and to identify how this may translate to improved remote health care for this population.MethodsA literature search was performed in November 2018 and repeated in July 2019 and March 2021 using the PubMed and Embase databases for publications on inertial sensors in hip and knee OA published in English within the previous 5 years. The search terms encompassed both OA and wearable sensors. Duplicate studies, systematic reviews, conference abstracts, and study protocols were also excluded. One reviewer screened the search result titles by removing irrelevant studies, and 2 reviewers screened study abstracts to identify studies using inertial sensors as the main sensing technology and a primary outcome related to movement quality. In addition, after the March 2021 search, 2 reviewers rescreened all previously included studies to confirm their relevance to this review.ResultsFrom the search process, 43 studies were determined to be relevant and subsequently included in this review. Inertial sensors have been successfully implemented for assessing the presence and severity of OA (n=11), assessing disease progression risk and providing feedback for gait retraining (n=7), and remotely monitoring intervention outcomes and identifying potential responders and nonresponders to interventions (n=14). In addition, studies have validated the use of inertial sensors for these applications (n=8) and analyzed the optimal sensor placement combinations and data input analysis for measuring different metrics of interest (n=3). These studies show promise for remote health care monitoring and intervention delivery in hip and knee OA, but many studies have focused on walking rather than a range of activities of daily living and have been performed in small samples (<100 participants) and in a laboratory rather than in a real-world environment.ConclusionsInertial sensors show promise for remote monitoring, risk assessment, and intervention delivery in individuals with hip and knee OA. Future opportunities remain to validate these sensors in real-world settings across a range of activities of daily living and to optimize sensor placement and data analysis approaches.