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.

Digital Therapeutic Care Apps With Decision-Support Interventions for People With Low Back Pain in Germany: Cost-Effectiveness Analysis.

BACKGROUND: Digital therapeutic care apps provide a new effective and scalable approach for people with nonspecific low back pain (LBP). Digital therapeutic care apps are also driven by personalized decision-support interventions that support the user in self-managing LBP, and may induce prolonged behavior change to reduce the frequency and intensity of pain episodes. However, these therapeutic apps are associated with high attrition rates, and the initial prescription cost is higher than that of face-to-face physiotherapy. In Germany, digital therapeutic care apps are now being reimbursed by statutory health insurance; however, price targets and cost-driving factors for the formation of the reimbursement rate remain unexplored. OBJECTIVE: The aim of this study was to evaluate the cost-effectiveness of a digital therapeutic care app compared to treatment as usual (TAU) in Germany. We further aimed to explore under which circumstances the reimbursement rate could be modified to consider value-based pricing. METHODS: We developed a state-transition Markov model based on a best-practice analysis of prior LBP-related decision-analytic models, and evaluated the cost utility of a digital therapeutic care app compared to TAU in Germany. Based on a 3-year time horizon, we simulated the incremental cost and quality-adjusted life years (QALYs) for people with nonacute LBP from the societal perspective. In the deterministic sensitivity and scenario analyses, we focused on diverging attrition rates and app cost to assess our model's robustness and conditions for changing the reimbursement rate. All costs are reported in Euro (€1=US $1.12). RESULTS: Our base case results indicated that the digital therapeutic care strategy led to an incremental cost of €121.59, but also generated 0.0221 additional QALYs compared to the TAU strategy, with an estimated incremental cost-effectiveness ratio (ICER) of €5486 per QALY. The sensitivity analysis revealed that the reimbursement rate and the capability of digital therapeutic care to prevent reoccurring LBP episodes have a significant impact on the ICER. At the same time, the other parameters remained unaffected and thus supported the robustness of our model. In the scenario analysis, the different model time horizons and attrition rates strongly influenced the economic outcome. Reducing the cost of the app to €99 per 3 months or decreasing the app's attrition rate resulted in digital therapeutic care being significantly less costly with more generated QALYs, and is thus considered to be the dominant strategy over TAU. CONCLUSIONS: The current reimbursement rate for a digital therapeutic care app in the statutory health insurance can be considered a cost-effective measure compared to TAU. The app's attrition rate and effect on the patient's prolonged behavior change essentially influence the settlement of an appropriate reimbursement rate. Future value-based pricing targets should focus on additional outcome parameters besides pain intensity and functional disability by including attrition rates and the app's long-term effect on quality of life.

Digital Therapeutic Care and Decision Support Interventions for People With Low Back Pain: Systematic Review.

BACKGROUND: Low back pain (LBP) is the leading cause of worldwide years lost because of disability, with a tremendous economic burden for health care systems. Digital therapeutic care (DTC) programs provide a scalable, universally accessible, and low-cost approach to the multidisciplinary treatment of LBP. Moreover, novel decision support interventions such as personalized feedback messages, push notifications, and data-driven activity recommendations amplify DTC by guiding the user through the program while aiming to increase overall engagement and sustainable behavior change. OBJECTIVE: This systematic review aims to synthesize recent scientific literature on the impact of DTC apps for people with LBP and outline the implementation of add-on decision support interventions, including their effect on user retention and attrition rates. METHODS: We searched bibliographic databases, including MEDLINE, Cochrane Library, Web of Science, and the Physiotherapy Evidence Database, from March 1, 2016, to October 15, 2020, in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and conducted this review based on related previously published systematic reviews. Besides randomized controlled trials (RCTs), we also included study designs with the evidence level of at least a retrospective comparative study. This enables the consideration of real-world user-generated data and provides information regarding the adoption and effectiveness of DTC apps in a real-life setting. For the appraisal of the risk of bias, we used the Risk of Bias 2 Tool and the Risk of Bias in Non-Randomized Studies of Interventions Tool for the RCTs and nonrandomized trials, respectively. The included studies were narratively synthesized regarding primary and secondary outcome measures, DTC components, applied decision support interventions, user retention, and attrition rates. RESULTS: We retrieved 1388 citations, of which 12 studies are included in this review. Of the 12 studies, 6 (50%) were RCTs and 6 (50%) were nonrandomized trials. In all included studies, lower pain levels and increased functionality compared with baseline values were observed in the DTC intervention group. A between-group comparison revealed significant improvements in pain and functionality levels in 67% (4/6) of the RCTs. The study population was mostly homogeneous, with predominantly female, young to middle-aged participants of normal to moderate weight. The methodological quality assessment revealed moderate to high risks of biases, especially in the nonrandomized trials. CONCLUSIONS: This systematic review demonstrates the benefits of DTC for people with LBP. There is also evidence that decision support interventions benefit overall engagement with the app and increase participants' ability to self-manage their recovery process. Finally, including retrospective evaluation studies of real-world user-generated data in future systematic reviews of digital health intervention trials can reveal new insights into the benefits, challenges, and real-life adoption of DTC programs.

Economic impact of clinical decision support interventions based on electronic health records.

BACKGROUND: Unnecessary healthcare utilization, non-adherence to current clinical guidelines, or insufficient personalized care are perpetual challenges and remain potential major cost-drivers for healthcare systems around the world. Implementing decision support systems into clinical care is promised to improve quality of care and thereby yield substantial effects on reducing healthcare expenditure. In this article, we evaluate the economic impact of clinical decision support (CDS) interventions based on electronic health records (EHR). METHODS: We searched for studies published after 2014 using MEDLINE, CENTRAL, WEB OF SCIENCE, EBSCO, and TUFTS CEA registry databases that encompass an economic evaluation or consider cost outcome measures of EHR based CDS interventions. Thereupon, we identified best practice application areas and categorized the investigated interventions according to an existing taxonomy of front-end CDS tools. RESULTS AND DISCUSSION: Twenty-seven studies are investigated in this review. Of those, twenty-two studies indicate a reduction of healthcare expenditure after implementing an EHR based CDS system, especially towards prevalent application areas, such as unnecessary laboratory testing, duplicate order entry, efficient transfusion practice, or reduction of antibiotic prescriptions. On the contrary, order facilitators and undiscovered malfunctions revealed to be threats and could lead to new cost drivers in healthcare. While high upfront and maintenance costs of CDS systems are a worldwide implementation barrier, most studies do not consider implementation cost. Finally, four included economic evaluation studies report mixed monetary outcome results and thus highlight the importance of further high-quality economic evaluations for these CDS systems. CONCLUSION: Current research studies lack consideration of comparative cost-outcome metrics as well as detailed cost components in their analyses. Nonetheless, the positive economic impact of EHR based CDS interventions is highly promising, especially with regard to reducing waste in healthcare.

Predictable real-time constraints reveal anticipatory strategies of coupled planning in a sequential pick and place task.

Planning a sequence of two motor elements is much more than concatenating two independent movements. However, very little is known about the cognitive strategies that are used to perform fluent sequences for intentional object manipulation. In this series of studies, the participants' task was to reach for and pick to place a wooden cylinder to set it on a place pad of three different diameters, which served to modify terminal accuracy constraints. Participants were required to perform the sequences (1) at their preferred speed or (2) as fast as possible. Action kinematics were recorded with the Qualisys motion-capture system in order to implement a real-time protocol to get participants to engage in a true interactive relation. Results revealed that with low internal constraints (at preferred speed), low coupling between the two elements of the motor sequence was observed, suggesting a step-by-step planning strategy. Under high constraints (at fastest speed), an important terminal accuracy effect back propagated to modify early kinematic parameters of the first element, suggesting strong coupling of the parameters in an encapsulated planning strategy. In Studies 2 and 3, we further manipulated instructions and timing constraints to confirm the importance of time and predictability of external information for coupled planning. These findings overall sustain the hypothesis that coupled planning can take place in a pick and place task when anticipatory strategies are possible. This mode of action planning may be the key reason why motor intention can be read through the observation of micro variations in body kinematics.

Overactivation of the Pupillary Response to Emotional Information in Short- and Long-Term Alcohol Abstinent Patients.

AIMS: The aim of the study was to compare emotional information processing in patients with severe alcohol use disorder in short-term abstinence (<1 month) and long-term abstinence (at least 6 months to 9 years) with control participants. METHODS: We studied the variation in pupil diameter during the presentation of pictures of human interactions associated with positive, negative or neutral valences. RESULTS: Overall, the results of the short-term abstinent group revealed greater pupil dilation regardless of the valence of the pictures while the pupillary response of long-term abstainers did not differ from the control group. More specifically, according to each valence, the pupil response to neutral pictures was greater for both patient groups than for controls. For the long-term abstainers, a negative correlation was found between the length of abstinence and the pupillary response to emotional stimuli. CONCLUSION: In long-term abstainers group, the high activation by neutral stimuli suggests however some difficulties in the processing of nonemotional stimuli, considered emotional ones and may constitute a potential relapse factor or the maintenance of addiction.

Real-Time Motion Capture Toolbox (RTMocap): an open-source code for recording 3-D motion kinematics to study action-effect anticipations during motor and social interactions.

We present here a toolbox for the real-time motion capture of biological movements that runs in the cross-platform MATLAB environment (The MathWorks, Inc., Natick, MA). It provides instantaneous processing of the 3-D movement coordinates of up to 20 markers at a single instant. Available functions include (1) the setting of reference positions, areas, and trajectories of interest; (2) recording of the 3-D coordinates for each marker over the trial duration; and (3) the detection of events to use as triggers for external reinforcers (e.g., lights, sounds, or odors). Through fast online communication between the hardware controller and RTMocap, automatic trial selection is possible by means of either a preset or an adaptive criterion. Rapid preprocessing of signals is also provided, which includes artifact rejection, filtering, spline interpolation, and averaging. A key example is detailed, and three typical variations are developed (1) to provide a clear understanding of the importance of real-time control for 3-D motion in cognitive sciences and (2) to present users with simple lines of code that can be used as starting points for customizing experiments using the simple MATLAB syntax. RTMocap is freely available (http://sites.google.com/site/RTMocap/) under the GNU public license for noncommercial use and open-source development, together with sample data and extensive documentation.

Individual differences in reading social intentions from motor deviants.

As social animals, it is crucial to understand others' intention. But is it possible to detect social intention in two actions that have the exact same motor goal? In the present study, we presented participants with video clips of an individual reaching for and grasping an object to either use it (personal trial) or to give his partner the opportunity to use it (social trial). In Experiment 1, the ability of naïve participants to classify correctly social trials through simple observation of short video clips was tested. In addition, detection levels were analyzed as a function of individual scores in psychological questionnaires of motor imagery, visual imagery, and social cognition. Results revealed that the between-participant heterogeneity in the ability to distinguish social from personal actions was predicted by the social skill abilities. A second experiment was then conducted to assess what predictive mechanism could contribute to the detection of social intention. Video clips were sliced and normalized to control for either the reaction times (RTs) or/and the movement times (MTs) of the grasping action. Tested in a second group of participants, results showed that the detection of social intention relies on the variation of both RT and MT that are implicitly perceived in the grasping action. The ability to use implicitly these motor deviants for action-outcome understanding would be the key to intuitive social interaction.

Effects of social intention on movement kinematics in cooperative actions.

Optimal control models of biological movements are used to account for those internal variables that constrain voluntary goal-directed actions. They, however, do not take into account external environmental constraints as those associated to social intention. We investigated here the effects of the social context on kinematic characteristics of sequential actions consisting in placing an object on an initial pad (preparatory action) before reaching and grasping as fast as possible the object to move it to another location (main action). Reach-to-grasp actions were performed either in an isolated condition or in the presence of a partner (audience effect), located in the near or far space (effect of shared reachable space), and who could intervene on the object in a systematic fashion (effect of social intention effect) or not (effect of social uncertainty). Results showed an absence of audience effect but nevertheless an influence of the social context both on the main and the preparatory actions. In particular, a "localized" effect of shared reachable space was observed on the main action, which was smoother when performed within the reachable space of the partner. Furthermore, a "global" effect of social uncertainty was observed on both actions with faster and jerkier movements. Finally, social intention affected the preparatory action with higher wrist displacements and slower movements when the object was placed for the partner rather than placed for self-use. Overall, these results demonstrate specific effects of action space, social uncertainty and social intention on the planning of reach-to-grasp actions, in particular on the preparatory action, which was performed with no specific execution constraint. These findings underline the importance of considering the social context in optimal models of action control for human-robot interactions, in particular when focusing on the implementation of motor parameters required to afford intuitive interactions.