Economic Evaluation of Digital Therapeutic Care Apps for Unsupervised Treatment of Low Back Pain: Monte Carlo Simulation

BACKGROUND

Digital therapeutic care (DTC) programs are unsupervised app-based treatments that provide video exercises and educational material to patients with nonspecific low back pain during episodes of pain and functional disability. German statutory health insurance can reimburse DTC programs since 2019, but evidence on efficacy and reasonable pricing remains scarce. This paper presents a probabilistic sensitivity analysis (PSA) to evaluate the efficacy and cost-utility of a DTC app against treatment as usual (TAU) in Germany.

OBJECTIVE

The aim of this study was to perform a PSA in the form of a Monte Carlo simulation based on the deterministic base case analysis to account for model assumptions and parameter uncertainty. We also intend to explore to what extent the results in this probabilistic analysis differ from the results in the base case analysis and to what extent a shortage of outcome data concerning quality-of-life (QoL) metrics impacts the overall results.

METHODS

The PSA builds upon a state-transition Markov chain with a 4-week cycle length over a model time horizon of 3 years from a recently published deterministic cost-utility analysis. A Monte Carlo simulation with 10,000 iterations and a cohort size of 10,000 was employed to evaluate the cost-utility from a societal perspective. Quality-adjusted life years (QALYs) were derived from Veterans RAND 6-Dimension (VR-6D) and Short-Form 6-Dimension (SF-6D) single utility scores. Finally, we also simulated reducing the price for a 3-month app prescription to analyze at which price threshold DTC would result in being the dominant strategy over TAU in Germany.

RESULTS

The Monte Carlo simulation yielded on average a €135.97 (a currency exchange rate of EUR €1=US $1.069 is applicable) incremental cost and 0.004 incremental QALYs per person and year for the unsupervised DTC app strategy compared to in-person physiotherapy in Germany. The corresponding incremental cost-utility ratio (ICUR) amounts to an additional €34,315.19 per additional QALY. DTC yielded more QALYs in 54.96% of the iterations. DTC dominates TAU in 24.04% of the iterations for QALYs. Reducing the app price in the simulation from currently €239.96 to €164.61 for a 3-month prescription could yield a negative ICUR and thus make DTC the dominant strategy, even though the estimated probability of DTC being more effective than TAU is only 54.96%.

CONCLUSIONS

Decision-makers should be cautious when considering the reimbursement of DTC apps since no significant treatment effect was found, and the probability of cost-effectiveness remains below 60% even for an infinite willingness-to-pay threshold. More app-based studies involving the utilization of QoL outcome parameters are urgently needed to account for the low and limited precision of the available QoL input parameters, which are crucial to making profound recommendations concerning the cost-utility of novel apps.

Community-based participatory research application of an artificial intelligence-enhanced electrocardiogram for cardiovascular disease screening: A FAITH! Trial ancillary study

Objective

With the emergence of artificial intelligence (AI)-based health interventions, systemic racism remains a concern as these advancements are frequently developed without race-specific data analysis or validation. To evaluate the potential utility of an AI-based cardiovascular diseases (CVD) screening tool in an under-resourced African-American cohort, we reviewed the AI-enhanced electrocardiogram (ECG) data of participants enrolled in a community-based clinical trial as a proof-of-concept ancillary study for community-based screening.

Methods

Enrollees completed cardiovascular testing including standard 12-lead ECG and a limited echocardiogram (TTE). All ECGs were analyzed using previously published institution-based AI algorithms. AI-ECG predictions were generated for age, sex, and decreased left ventricular ejection fraction (LVEF). Diagnostic accuracy of the AI-ECG for decreased LVEF and sex was quantified using area under the receiver operating characteristic curve (AUC). Correlation between actual age and AI-ECG predicted age was assessed using Pearson correlation coefficients.

Results

Fifty-four participants completed both an ECG and TTE (mean age 55 years [range 31-87 years]; 66.7% female). All participants were in sinus rhythm, and the median LVEF of the cohort was 60-65%. The AI-ECG for decreased LVEF demonstrated excellent performance with an AUC of 0.892 (95% confidence interval [CI] 0.708-1); sensitivity=50% (95% CI 9.5-90.5%; n=1/2) and specificity=96% (95% CI 86.8-98.9%; n=49/51). The AI-ECG for participant sex demonstrated similar performance with AUC of 0.944 (95% CI 0.891-0.998); sensitivity=100% (95% CI 82.4-100.0%; n=18/18) and specificity=77.8% (95% CI 61.9-88.3%; n=28/36). The AI-ECG predicted mean age was 55 years (range 26.9-72.6 years) with a strong correlation to actual age (R=0.769; p<0.001).

Conclusion

Our analyses of previously developed AI-ECG algorithms for prediction of age, sex, and decreased LVEF demonstrated reliable performance in this community-based, African-American cohort. This novel, community-centric delivery of AI could provide valuable screening resources and appropriate referrals for early detection of highly-morbid CVD for under-resourced patient populations.

Consumer-Led Screening for Atrial Fibrillation: A Report From the mAFA-II Trial Long-Term Extension Cohort

BACKGROUND

There are limited data on mobile health detection of prevalent atrial fibrillation (AF) and its related risk factors over time.

OBJECTIVES

This study aimed to report the trends on prevalent AF detection over time and risk factors, with a consumer-led photoplethysmography screening approach.

METHODS

3,499,461 subjects aged over 18 years, who use smart devices (Huawei Technologies Co.) were enrolled between October 26, 2018, and December 1, 2021.

RESULTS

Among 2,852,217 subjects for AF screening, 12,244 subjects (0.43%; 83.2% male, mean age 57 ± 15 years) detected AF episodes. When compared with 2018, the risk (adjusted HRs, 95% CI) for monitored prevalent AF increased significantly for subjects when monitoring started in 2020 (adjusted HR: 1.34; 95% CI: 1.27-1.40; P < .001) or in 2021 (adjusted HR: 1.67; 95% CI: 1.59-1.76; P < 0.001). Of the 961,931 subjects who screening for both AF and OSA, 18,032 (1.9%, 97.8% male, mean age 44 ±17 years) were identified as high risk for OSA, which resulted in a 1.5-fold increase (95% CI: 1.30-fold to 1.75-fold) in the prevalent AF. A total of 5,227 (53.3%, 5,227/9,797) subjects were effectively followed up, from which 4,903 (93.8%, 4,903/5,227) subjects were confirmed with the diagnosis of AF, by the mAFA Telecare Team health providers.

CONCLUSIONS

Photoplethysmography-based smart devices can facilitate screening for AF with >93% confirmation of detected AF episodes even for the low-risk general population, highlighting the increased risk for detecting prevalent AF and the need for modification of OSA that increase AF susceptibility. (Mobile Health [mHealth] Technology for Improved Screening, Patient Involvement and Optimizing Integrated Care in Atrial Fibrillation [mAFA (mAF-App) II study]; ChiCTR-OOC-17014138).

mHealth Intervention Applications for Adults Living With the Effects of Stroke: A Scoping Review

Objective

To conduct a scoping review of mobile health (mHealth) application (app) interventions to support needs of adults living with the effects of stroke reported in the literature.

Data Sources

PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Scopus were systematically searched for peer-reviewed publications. Articles were published between January 2007 and September 2020 and met predefined inclusion and exclusion criteria.

Study Selection

Articles included were written in English language, involved adults older than 18 years, and described an mHealth app specifically tested and/or developed as an intervention for someone with stroke to be used remotely and/or independently without constant provider supervision or assistance. Articles were excluded if they focused on acute management of stroke only, focused on primary prevention, were animal studies, were not an app for smartphone or tablet, and did not describe an empirical study.

Data Extraction

Two researchers independently screened titles and abstracts for inclusion. The full-text articles were then reviewed for eligibility by the research team. Data were extracted and verified by a third reviewer.

Data Synthesis

The search yielded 2123 studies and 49 were included for data extraction. The findings reveal that a global surge of studies on mHealth apps for people with stroke have emerged within the past 2 years. Most studies were developed for persons with stroke in the United States and the primary content foci included upper extremity function (31.5%); lower extremity function (5.3%); general exercise, physical activity, and/or functional mobility (23.7%); trunk control (5.3%); medical management and secondary prevention (26.3%); language and speech skills (20.5%); cognitive skills (7.9%); general disability and activities of daily living (5.3%); and home safety (2.6%). Of the included studies, a majority were preliminary in nature, with 36.7% being categorized as pilot or feasibility trials and 24.4% discussing initial design, development, and/or refinement.

Conclusions

Results from this study reveal that the number of apps specifically developed for people with stroke and described in the scientific literature are growing exponentially. The apps have widely varied content to meet the needs of persons with stroke; however, the studies are generally preliminary in nature, focusing on development, usability, and initial pilot testing. This review highlights the need for additional research and development of mHealth apps targeted for adults with stroke. Development should consider the various and complex needs of people living with the effects of chronic stroke, while large-scale trials are needed to build on the existing evidence.