Time Metrics Research Articles

Abstract WP38: Nurse-Driven Emergency Evaluation Improves Timely Acute Stroke Response

Background: Stroke is associated with high rates of death and disability; prompt and effective interventions are imperative. Depending on emergency department (ED) physicians to initiate stroke codes may delay critical time targets, especially for patients arriving by private vehicle (POV). This study examines existing stroke care protocols in the ED of a Comprehensive Stroke Center and evaluates a new nurse-driven acute stroke response protocol. Purpose: The primary goal of this initiative was to improve outcomes for patients arriving by POV, by reducing triage delays and accelerating time-sensitive acute stroke interventions. Methods: This retrospective study implemented a nurse-led stroke activation process, in lieu of the conventional physician-led method. Nurses received comprehensive training in intervention goals and identification of stroke symptoms and last known-well times. The nurse-led model targeted patients presenting to the ED with a minimum of 2 stroke symptoms within 6 hours of symptom onset—an assessment tool, incorporating the FAST algorithm (Face, Arm, Speech, Time) and key National Institutes of Health Stroke Scale (NIHSS) exam components, enhanced symptom specificity. Critical intervention times were evaluated for improvement. Results: ED POV arrival patients with acute stroke symptoms were studied from 11/7/21-7/31/23; the nurse-driven process was implemented on 11/7/22. Pre-intervention review included 247 patients with 216 patients in the post-intervention review. Median door-to-activation times fell from 17 to 10 minutes, door-to-CT from 24 to 19 minutes, and door-to-needle times from 49 to 43.5 minutes. Median NIHSS scores in tPA-treated patients were compared pre- and post-intervention. In the physician-led group, initial median NIHSS scores were 6, reducing to 1 at discharge. The nurse-led group showed an initial median NIHSS score of 5, also reducing to 1 at discharge. Conclusions: The nurse-driven method effectively shortened critical time metrics in the stroke code process, while maintaining optimal patient outcomes. A paradigm shift towards ED triage nurse-led stroke code activation for acute stroke may lead to higher quality acute stroke care.

Smart Resource Allocation in Mobile Cloud Next-Generation Network (NGN) Orchestration with Context-Aware Data and Machine Learning for the Cost Optimization of Microservice Applications

Mobile cloud computing (MCC) provides resources to users to handle smart mobile applications. In MCC, task scheduling is the solution for mobile users’ context-aware computation resource-rich applications. Most existing approaches have achieved a moderate service reliability rate due to a lack of instance-centric resource estimations and task offloading, a statistical NP-hard problem. The current intelligent scheduling process cannot address NP-hard problems due to traditional task offloading approaches. To address this problem, the authors design an efficient context-aware service offloading approach based on instance-centric measurements. The revised machine learning model/algorithm employs task adaptation to make decisions regarding task offloading. The proposed MCVS scheduling algorithm predicts the usage rates of individual microservices for a practical task scheduling scheme, considering mobile device time, cost, network, location, and central processing unit (CPU) power to train data. One notable feature of the microservice software architecture is its capacity to facilitate the scalability, flexibility, and independent deployment of individual components. A series of simulation results show the efficiency of the proposed technique based on offloading, CPU usage, and execution time metrics. The experimental results efficiently show the learning rate in training and testing in comparison with existing approaches, showing efficient training and task offloading phases. The proposed system has lower costs and uses less energy to offload microservices in MCC. Graphical results are presented to define the effectiveness of the proposed model. For a service arrival rate of 80%, the proposed model achieves an average 4.5% service offloading rate and 0.18% CPU usage rate compared with state-of-the-art approaches. The proposed method demonstrates efficiency in terms of cost and energy savings for microservice offloading in mobile cloud computing (MCC).

A Partitioned CAM Architecture with FPGA Acceleration for Binary Descriptor Matching

An efficient architecture for image descriptor matching that uses a partitioned content-addressable memory (CAM)-based approach is proposed. CAM is frequently used in high-speed content-matching applications. However, due to its lack of functionality to support approximate matching, conventional CAM is not directly useful for image descriptor matching. Our modifications improve the CAM architecture to support approximate content matching for selecting image matches with local binary descriptors. Matches are based on Hamming distances computed for all possible pairs of binary descriptors extracted from two images. We demonstrate an FPGA-based implementation of our CAM-based descriptor-matching unit to illustrate the high matching speed of our design. The time complexity of our modified CAM method for binary descriptor matching is O(n). Our method performs binary descriptor matching at a rate of one descriptor per clock cycle at a frequency of 102 MHz. The resource utilization and timing metrics of several experiments are reported to demonstrate the efficacy and scalability of our design.

A Convolutional Neural Network approach for image-based anomaly detection in smart agriculture

The recent technological advances and their applications to agriculture provide leverage for the new paradigm of smart agriculture. Remote sensing applications can help optimise resources, making agriculture more ecological, increasing productivity and helping farmers to anticipate events that could not otherwise be avoided. Considering that losses caused by anomalies such as diseases, weeds and pests account for 20–40 % of overall agricultural productivity, a successful research effort in this area would be a breakthrough for agriculture. In this paper, we propose a methodology with which to discover and classify anomalies in images of crops, taken from a wide range of distances, using different Convolutional Neural Network architectures. This methodology also deals with several difficulties that usually appear in this kind of problems, such as class imbalance, the insufficient and small variety of images, overtraining or lack of models generalisation. We have implemented four convolutional neural network architectures in a high-performance computing environment, and propose a methodology based on data augmentation with the addition of Gaussian noise to the images to solve the above problems. Our approach was tested using two well-established open datasets that are unalike: DeepWeeds, which provides a classification of 8 weed species native to Australia using images that were taken at a distance of 1 m, and Agriculture-Vision, which classifies 6 types of crop anomalies using multispectral satellite imagery. Our methodology attained accuracies of 98 % and 95.3% respectively, improving the state-of-the-art by several points. In order to ease reproducibility and model selection, we have provided a comparison in terms of computational time and other metrics, thus enabling the choice between architectures to be made according to the resources available. The complete code is available in an open repository in order to encourage reproducibility and promote scientific advances in sustainable agriculture.

Optimizing emergency department imaging utilization for pulmonary emboli: A study on the effects of IV contrast rationing

PurposeTo study the impact of a contrast mitigation protocol on imaging utilization for pulmonary embolism (PE) in the emergency department (ED). Material and methodsMedical records of ED patients with suspected PE who underwent CT pulmonary angiography (CTPA) or ventilation-perfusion (VQ) scans were analyzed in control (3/15/22–4/15/22) and test (5/15/22–6/15/22) periods. The test period included a contrast mitigation protocol due to a global iodinated contrast shortage (05/2022–06/2022). Out of 610 scans, 28 were excluded for non-PE indications. Patient demographics, time metrics, and imaging reports were recorded. ResultsAmong 11,019 ED visits, there were 582 imaging events for suspected PE. The test period exhibited a significantly lower imaging rate of 4.16 % compared to 6.54 % in the control period (p < 0.001). CTPA usage decreased by 47.73 %, while VQ scan usage increased by 775.00 % during the test period. Test period positivity rate was 0.82 %, with CTPA at 0.58 % (1/173) and VQ scan at 1.43 % (1/70). In the control period, the positivity rate was 0.29 %, with CTPA at 0.30 % (1/331) and VQ scan at 0.00 % (0/8). Previous hospitalization history was significantly higher in the test period (70/243 vs. 39/339, p < 0.001). The positivity rates between the two periods showed no significant difference (p = 0.57). There were no significant differences in ED length of stay and image acquisition times. ConclusionThe contrast mitigation protocol reduced CTPA use, increased VQ scans, and maintained positivity rates and image acquisition times. However, concerns persist about unnecessary imaging and low positivity rates, necessitating further research to optimize PE diagnostic algorithms.

A 0.05 m Change in Inertial Measurement Unit Placement Alters Time and Frequency Domain Metrics during Running.

Inertial measurement units (IMUs) provide exciting opportunities to collect large volumes of running biomechanics data in the real world. IMU signals may, however, be affected by variation in the initial IMU placement or movement of the IMU during use. To quantify the effect that changing an IMU's location has on running data, a reference IMU was 'correctly' placed on the shank, pelvis, or sacrum of 74 participants. A second IMU was 'misplaced' 0.05 m away, simulating a 'worst-case' misplacement or movement. Participants ran over-ground while data were simultaneously recorded from the reference and misplaced IMUs. Differences were captured as root mean square errors (RMSEs) and differences in the absolute peak magnitudes and timings. RMSEs were ≤1 g and ~1 rad/s for all axes and misplacement conditions while mean differences in the peak magnitude and timing reached up to 2.45 g, 2.48 rad/s, and 9.68 ms (depending on the axis and direction of misplacement). To quantify the downstream effects of these differences, initial and terminal contact times and vertical ground reaction forces were derived from both the reference and misplaced IMU. Mean differences reached up to -10.08 ms for contact times and 95.06 N for forces. Finally, the behavior in the frequency domain revealed high coherence between the reference and misplaced IMUs (particularly at frequencies ≤~10 Hz). All differences tended to be exaggerated when data were analyzed using a wearable coordinate system instead of a segment coordinate system. Overall, these results highlight the potential errors that IMU placement and movement can introduce to running biomechanics data.

Impacts of Deep Brain Stimulation of the Globus Pallidus Internus on Swallowing: A Retrospective, Cross-Sectional Study.

Deep brain stimulation (DBS) is a common treatment for motor symptoms of Parkinson disease (PD), a condition associated with increased risk of dysphagia. The effect of DBS on swallowing function has not been comprehensively evaluated using gold-standard imaging techniques, particularly for globus pallidus internus (GPi) DBS. The objective of this retrospective, cross-sectional study was to identify differences in swallowing safety and timing kinematics among PD subjects with and without GPi DBS. We investigated the effects of unilateral and bilateral GPi DBS as well as the relationship between swallowing safety and DBS stimulation parameters, using retrospective analysis of videofluoroscopy recordings (71 recordings from 36 subjects) from electronic medical records. Outcomes were analyzed by surgical status (pre-surgical, unilateral DBS, bilateral DBS). The primary outcome was percent of thin-liquid bolus trials rated as unsafe, with Penetration-Aspiration Scale scores of 3 or higher. Secondary analyses included swallowing timing measures, relationships between swallowing safety and DBS stimulation parameters, and Dynamic Imaging Grade of Swallowing Toxicity ratings. Most subjects swallowed all boluses safely (19/29 in the pre-surgical, 16/26 in the unilateral DBS, and 10/16 in the bilateral DBS conditions). Swallowing safety impairment did not differ among stimulation groups. There was no main effect of stimulation condition on timing metrics, though main effects were found for sex and bolus type. Stimulation parameters were not correlated with swallowing safety. Swallowing efficiency and overall impairment did not differ among conditions. These results provide evidence that GPi DBS does not affect pharyngeal swallowing function. Further, prospective, investigations are needed.

Timing synchronization of LACO-OFDM under non-linear distortions using ELM

Layered asymmetrically-clipped optical orthogonal frequency division multiplexing (LACO-OFDM) is a modulation technique for visible light communication (VLC) that provides power and spectral efficiencies. The LACO-OFDM technique utilizes all the available subcarriers and will have a relatively higher peak-to-average power ratio (PAPR). Hence, the modulated samples are prone to distortions caused by the non-linear devices and diffused channels, resulting in reduced accuracy of delay offset estimation for timing synchronization. In this paper, we propose a method to improve the timing synchronization accuracy of LACO-OFDM modulated systems using extreme learning machines (ELM). The ELM is trained offline to identify the delay offset using the timing metric (TM) as a feature identification problem. Among all types of neural networks that are used as classifiers and non-linear estimators, the ELMs are the least complex and require significantly less computational capacity. The simulations are performed to evaluate the accuracy of delay offset estimation in the presence of unknown non-linear effects of high-powered light-emitting diodes (LEDs) and diffused channel characteristics. The results show that the probability of error (PoE) of delay offset estimation and bit error rate (BER) is significantly reduced for scenarios like different frame lengths, channel coefficients, and unknown LED parameters in contrast to conventional methods. The proposed approach attains a PoE of 10−1 at signal-to-noise ratios (SNR)s of 4 dB and 6 dB with various TM, surpassing conventional methods which only achieve this beyond 15 dB. Moreover, the PoE remains below 10−2 for SNRs exceeding 7 dB and 10 dB in the proposed method, while conventional methods require SNRs above 25 dB to achieve comparable results.

Endovascular treatment for basilar artery occlusion: whether the "weekend effect" affects time metrics and clinical outcomes at a comprehensive stroke center.

This study aimed to evaluate whether the "weekend effect" would affect the time metrics and the prognosis of acute ischemic stroke (AIS) patients who underwent endovascular treatment (EVT) due to basilar artery occlusion (BAO). Clinical data of AIS patients who underwent EVT due to BAO between December 2019 and July 2023 were retrospectively analyzed. At the time when the patients were admitted, the study population was divided into the weekdays daytime group and weekends nighttime group. In the subgroup analysis, the study cohort was divided into four groups: the weekdays daytime group, weekdays nighttime group, weekend daytime group, and weekend nighttime group. A good outcome was defined as a modified Rankin Scale score of ≤3 at 90 days after EVT. Time metrics [e.g. onset-to-door time (ODT) and door-to-puncture time (DPT)] and clinical outcomes were compared using appropriate statistical methods. A total of 111 patients (88 male patients, mean age, 67.7 ± 11.7 years) were included. Of these, 37 patients were treated during weekdays daytime, while 74 patients were treated during nights or weekends. There were no statistically significant differences in ODT (P = 0.136), DPT (P = 0.931), and also clinical outcomes (P = 0.826) between the two groups. Similarly, we found no significant differences in the time metrics and clinical outcomes among the four sub-groups (all P > 0.05). This study did not reveal any influence of the "weekend effect" on the time metrics and clinical outcomes in AIS patients who underwent EVT due to BAO at a comprehensive stroke center.

Electronic Health Record Usage Patterns Across Surgical Subspecialties.

This study aimed to utilize metrics from physician action logs to analyze surgeon clinical, volume, electronic health record (EHR) efficiency, EHR proficiency, and workload outside scheduled time as impacted by physician characteristics such as years of experience, gender, subspecialty, academic title, and administrative title. We selected 30 metrics from Epic Signal, an analytic tool in Epic that extracts metrics related to clinician documentation. Metrics measuring appointments, messages, and scheduled hours per day were used as a correlate for volume. EHR efficiency, and proficiency were measured by scores built into Epic Signal. Metrics measuring time spent in the EHR outside working hours were used as a correlate for documentation burden. We analyzed these metrics among surgeons at our institution across 4 months and correlated them with physician characteristics. Analysis of 133 surgeons showed that, when stratified by gender, female surgeons had significantly higher EHR metrics for time per day, time per appointment, and documentation burden, and significantly lower EHR metrics for efficiency when compared to male surgeons. When stratified by experience, surgeons with 0 to 5 years of experience had significantly lower EHR metrics for volume, time per day, efficiency, and proficiency when compared to surgeons with 6 to 10 and more than 10 years of experience. On multivariate analysis, having over 10 years of experience was an independent predictor of more appointments per day, greater proficiency, and spending less time per completed message. Female gender was an independent predictor of spending more time in notes per appointment and time spent in the EHR outside working hours. The burden associated with volume, proficiency, efficiency, and workload outside scheduled time related to EHR use varies by gender and years of experience in our cohort of surgeons. Evaluation of physician action logs could help identify those at higher risk of burnout due to burdensome medical documentation.

Evaluating acute medical service performance against assessment time metrics: the Society for Acute Medicine Benchmarking Audit 2023 (SAMBA23).

Performance within acute medicine services is impacted by ongoing pressures on acute care services. Data from the Society for Acute Medicine Benchmarking Audit 2023 (SAMBA23), was used to assess performance of acute medicine services compared to key clinical quality indicators, comparing performance by initial assessment location. Data was analysed for 8213 unplanned attendances across 161 hospitals. Comparing by initial assessment location, performance against the clinical quality indicators was unchanged from 2022. Only 29% of daytime arrivals assessed within the Emergency Department received consultant review within target times. Delays were seen in transfer between acute care locations. 29% of patients requiring admission were not admitted to the AMU. There is ongoing variation in acute medical service performance nationally, with significant delays in patient access to appropriate assessment locations.

Examining the Use of Scott’s Formula and Link Expiration Time Metric for Vehicular Clustering

Examining the Use of Scott’s Formula and Link Expiration Time Metric for Vehicular Clustering

Streamlined workflow including nurse recognition of conjugate gaze deviation for reduced door-to-puncture time in endovascular thrombectomy: A retrospective study

BackgroundEndovascular thrombectomy is recognized as a pivotal treatment for acute ischemic stroke due to large vessel occlusion. Prolonged door-to-puncture time correlates with decreased patient independence after acute ischemic stroke. This study aimed to assess whether a streamlined workflow, including nurse recognition of conjugate gaze deviation, could reduce door-to-puncture time in endovascular thrombectomy. MethodsThis study retrospectively reviewed patients with acute ischemic stroke who underwent endovascular thrombectomy between March 2017 and March 2022 and compared a previous workflow with a streamlined workflow implemented in April 2019. In the streamlined workflow, nurses recognized conjugate gaze deviation to identify patients with large vessel occlusions and played a more active role in reducing the door-to-puncture time. We compared time metrics and outcomes, including recanalization status, parenchymal hemorrhage type 2, and favorable outcomes (modified Rankin Scale score 0–2) at three months between the previous and streamlined workflow groups. ResultsAfter the application of the streamlined workflow, the door-to-puncture time was reduced from 76 min to 68 min (p = 0.014), and the number of patients with a door-to-puncture time of less than 60 min increased (15% vs. 36%, p = 0.002). Outcomes including modified thrombolysis in cerebral infarction ≥ 2b (73% vs. 71%, p = 1.000), parenchymal hemorrhage type 2 (7% vs. 2%, p = 0.281), and favorable outcome (33% vs. 34%, p = 1.000) were comparable between the two groups. ConclusionNurse recognition of conjugate gaze deviation contributed to an 8-minute reduction in the door-to-puncture time, demonstrating the potential benefits of an organized workflow in acute ischemic stroke.

Application and Implications of Artificial Intelligence Technology in Basketball Coaching Decision Making

Abstract The proliferation of artificial intelligence (AI) technologies is significantly enhancing the precision and efficacy of decision-making in basketball. This study focuses on players from the men’s basketball team at School A, employing AI to model their sports postures, collect athletic performance data, and analyze physiological metrics in real time. By utilizing fuzzy gray correlation analysis, the research aims to identify the factors influencing basketball game outcomes, thereby informing tactical decisions for coaches. Moreover, this paper compares the proposed analysis method with other correlation techniques to evaluate its relative superiority. The findings indicate that post-training, the serum levels of all ten athletes exceeded 400 U/L. Yet, the creatine kinase (CK) levels remained below 6.8 mmol/L, and the urine specific gravity surpassed 1.05, suggesting that the training intensity was high but within the athletes’ physiological tolerances. With a correlation coefficient of 0.4, the study confirms a positive impact of game outcomes on the coach’s tactical adaptations. This underscores the utility of integrating AI systems into basketball to facilitate timely and effective decision-making by coaches.

Application of conjugate gradient least squares method in solving magnetic moments of magnetic targets based on magnetic dipole array model

In order to solve the problem of magnetic targets magnetic field modeling, a magnetic dipole array model was established, a modeling inversion equation system was constructed, and the degree of sickness of the solved equation system under different numbers of magnetic dipoles was quantitatively analyzed based on the coefficient matrix condition number. In order to solve the problem of the pathological system of magnetic field modeling equations, a regularization method based on the conjugate gradient least squares method was designed to invert the magnetic moment parameters of the magnetic dipole. In order to analyze the applicability of the regularization method in magnetic field modeling inversion, the magnetic moment solving accuracy, the magnetic moment solving robustness, calculation time, and other metrics are defined. A detailed test of the ship model was designed, and the magnetic field passing characteristics of the two types of ship models at different positive and horizontal conditions at two depths were measured. Under the condition of no interference and interference, the conjugate gradient least squares method is used to invert the magnetic field model, and the numerical test analysis shows that the conjugate gradient least squares method has higher applicability than the generalized inverse solution method and the stepwise regression method. Under the condition of interference, the relative error of magnetic field fitting of the array model with 15 magnetic dipoles is 0.1537, and the relative error of magnetic field extrapolation is 0.0868. The method proposed has the advantages of high accuracy and strong robustness in solving the magnetic moment of the magnetic dipole array model.

Touching the Ground: Evaluating the Effectiveness of Data Physicalizations for Spatial Data Analysis Tasks.

Inspired by recent advances in digital fabrication, artists and scientists have demonstrated that physical data encodings (i.e., data physicalizations) can increase engagement with data, foster collaboration, and in some cases, improve data legibility and analysis relative to digital alternatives. However, prior empirical studies have only investigated abstract data encoded in physical form (e.g., laser cut bar charts) and not continuously sampled spatial data fields relevant to climate and medical science (e.g., heights, temperatures, densities, and velocities sampled on a spatial grid). This paper presents the design and results of the first study to characterize human performance in 3D spatial data analysis tasks across analogous physical and digital visualizations. Participants analyzed continuous spatial elevation data with three visualization modalities: (1) 2D digital visualization; (2) perspective-tracked, stereoscopic "fishtank" virtual reality; and (3) 3D printed data physicalization. Their tasks included tracing paths downhill, looking up spatial locations and comparing their relative heights, and identifying and reporting the minimum and maximum heights within certain spatial regions. As hypothesized, in most cases, participants performed the tasks just as well or better in the physical modality (based on time and error metrics). Additional results include an analysis of open-ended feedback from participants and discussion of implications for further research on the value of data physicalization. All data and supplemental materials are available at https://osf.io/7xdq4/.

Research on incentive mechanisms for anti-heterogeneous federated learning based on reputation and contribution

&lt;abstract&gt; &lt;p&gt;An optimization algorithm for federated learning, equipped with an incentive mechanism, is introduced to tackle the challenges of excessive iterations, prolonged training durations, and suboptimal efficiency encountered during model training within the federated learning framework. Initially, the algorithm establishes reputation values that are tied to both time and model loss metrics. This foundation enables the creation of incentive mechanisms aimed at rewarding honest nodes while penalizing malicious ones. Subsequently, a bidirectional selection mechanism anchored in blockchain technology is developed, allowing smart contracts to enroll nodes with high reputations in training sessions, thus filtering out malicious clients and enhancing local training efficiency. Furthermore, the integration of the Earth Mover's Distance (EMD) mechanism serves to lessen the impact of non-IID (non-Independent and Identically Distributed) data on the global model, leading to a reduction in the frequency of model training cycles and an improvement in model accuracy. Experimental results confirm that this approach maintains high model accuracy in non-IID data settings, outperforming traditional federated learning algorithms.&lt;/p&gt; &lt;/abstract&gt;

Evaluating Physical Stress across Task Difficulty Levels in Augmented Reality-Assisted Industrial Maintenance

Many studies have revealed the benefits of augmented reality (AR) to enhance productivity and fulfill a quality function in industry. Yet, the potential adverse effects of AR, such as physical stress, have not been extensively studied. This study investigates the effect of task difficulty (simple versus complex maintenance tasks) on the overall performance and physical stress of individuals while interacting with multimedia instructions (i.e., AR via HoloLens versus. paper-based guidance). Twenty-eight healthy male participants with a mean age of 32.12 years (standard deviation of 2.45 years) were randomly assigned to two groups: AR-based group and paper-based group. The metrics of total maintenance time, NASA-TLX scores, and heart rate variability (HRV) were used to evaluate overall performance, perceived workload, and physical stress, respectively. The findings suggest that AR-based instruction for a complex task significantly reduced both total maintenance time (by 14.94%) and physical stress in comparison with paper-based instruction. Paper-based instruction was found to destabilize heart rhythm, increase heart rate, and cause a shift in autonomic balance due to heightened sympathetic nerve activation. This study concludes that AR-based instruction is more efficient and less stressful than paper-based instruction, indicating that the AR system may offer independent benefits in reducing physical workloads.

Acute care bundles should be used for patients with intracerebral haemorrhage: An expert consensus statement.

Intracerebral haemorrhage (ICH) is the most devastating form of stroke and a major cause of disability. Clinical trials of individual therapies have failed to definitively establish a specific beneficial treatment. However, clinical trials of introducing care bundles, with multiple therapies provided in parallel, appear to clearly reduce morbidity and mortality. Currently, not enough patients receive these interventions in the acute phase. We convened an expert group to discuss best practices in ICH and to develop recommendations for bundled care that can be delivered in all settings that treat acute ICH, with a focus on European healthcare systems. In this consensus paper, we argue for widespread implementation of formalised care bundles in ICH, including specific metrics for time to treatment and criteria for the consideration of neurosurgical therapy. There is an extraordinary opportunity to improve clinical care and clinical outcomes in this devastating disease. Substantial evidence already exists for a range of therapies that can and should be implemented now.

REVOLUTIONIZING COGNITIVE ASSESSMENT: THE POWER OF DIGITAL BIOMARKERS AND ECOLOGICALLY VALID METHODS

Abstract Digital technologies have allowed neuropsychologists to utilize process-based approaches to assess cognitive function and change. Traditional methods of neuropsychological assessment involve obtaining single scores (often composites) to represent cognitive function. However, modern process-based techniques involve the use of digital data collection to acquire insights into both the manner and reasoning behind an individual’s performance. The present study will share insights into the task development and validation of active and passive digital cognitive assessments from two longitudinal studies of aging: (1) a study of non-dementia participants (N=322), ultra-brief mobile cognitive tests were administered up to six times per day for two weeks; and (2) a neuro-exergaming study (N=31) enrolling patients with MCI and their co-residing study partner, where participants used a study-provided tablet to complete a neuro-exergaming protocol and digital cognitive assessments (e.g., digital trailmaking and eStroop). We analyzed both traditional and process-based metrics in two studies, including mean response time and patterns of inaccuracy. Study one found significant correlations between MCI status and metrics for survey completion time, working memory, and processing speed. Study two found weak to moderate correlations between cognitive function measures from digital and paper-based neuropsychology. Our findings suggest that remote self-administered digital measures can detect subtle cognitive changes. The event-level data, which is collected through a process-based approach, has enormous untapped potential for discovering and confirming novel digital biomarkers of cognitive health and progression that can be monitored beyond clinical settings.