Predicting cognitive scores from wearable-based digital physiological features using machine learning: data from a clinical trial in mild cognitive impairment.

BACKGROUND: Continuous assessment and remote monitoring of cognitive function in individuals with mild cognitive impairment (MCI) enables tracking therapeutic effects and modifying treatment to achieve better clinical outcomes. While standardized neuropsychological tests are inconvenient for this purpose, wearable sensor technology collecting physiological and behavioral data looks promising to provide proxy measures of cognitive function. The objective of this study was to evaluate the predictive ability of digital physiological features, based on sensor data from wrist-worn wearables, in determining neuropsychological test scores in individuals with MCI. METHODS: We used the dataset collected from a 10-week single-arm clinical trial in older adults (50-70 years old) diagnosed with amnestic MCI (N = 30) who received a digitally delivered multidomain therapeutic intervention. Cognitive performance was assessed before and after the intervention using the Neuropsychological Test Battery (NTB) from which composite scores were calculated (executive function, processing speed, immediate memory, delayed memory and global cognition). The Empatica E4, a wrist-wearable medical-grade device, was used to collect physiological data including blood volume pulse, electrodermal activity, and skin temperature. We processed sensors' data and extracted a range of physiological features. We used interpolated NTB scores for 10-day intervals to test predictability of scores over short periods and to leverage the maximum of wearable data available. In addition, we used individually centered data which represents deviations from personal baselines. Supervised machine learning was used to train models predicting NTB scores from digital physiological features and demographics. Performance was evaluated using "leave-one-subject-out" and "leave-one-interval-out" cross-validation. RESULTS: The final sample included 96 aggregated data intervals from 17 individuals. In total, 106 digital physiological features were extracted. We found that physiological features, especially measures of heart rate variability, correlated most strongly to the executive function compared to other cognitive composites. The model predicted the actual executive function scores with correlation r = 0.69 and intra-individual changes in executive function scores with r = 0.61. CONCLUSIONS: Our findings demonstrated that wearable-based physiological measures, primarily HRV, have potential to be used for the continuous assessments of cognitive function in individuals with MCI.

Real-world walking behaviors are associated with early-stage heart failure: a Project Baseline Health Study.

BACKGROUND: Data collected via wearables may complement in-clinic assessments to monitor subclinical heart failure (HF). OBJECTIVES: Evaluate the association of sensor-based digital walking measures with HF stage and characterize their correlation with in-clinic measures of physical performance, cardiac function and participant reported outcomes (PROs) in individuals with early HF. METHODS: The analyzable cohort included participants from the Project Baseline Health Study (PBHS) with HF stage 0, A, or B, or adaptive remodeling phenotype (without risk factors but with mild echocardiographic change, termed RF-/ECHO+) (based on available first-visit in-clinic test and echocardiogram results) and with sufficient sensor data. We computed daily values per participant for 18 digital walking measures, comparing HF subgroups vs stage 0 using multinomial logistic regression and characterizing associations with in-clinic measures and PROs with Spearman's correlation coefficients, adjusting all analyses for confounders. RESULTS: In the analyzable cohort (N=1265; 50.6% of the PBHS cohort), one standard deviation decreases in 17/18 walking measures were associated with greater likelihood for stage-B HF (multivariable-adjusted odds ratios [ORs] vs stage 0 ranging from 1.18-2.10), or A (ORs vs stage 0, 1.07-1.45), and lower likelihood for RF-/ECHO+ (ORs vs stage 0, 0.80-0.93). Peak 30-minute pace demonstrated the strongest associations with stage B (OR vs stage 0=2.10; 95% CI:1.74-2.53) and A (OR vs stage 0=1.43; 95% CI:1.23-1.66). Decreases in 13/18 measures were associated with greater likelihood for stage-B HF vs stage A. Strength of correlation with physical performance tests, echocardiographic cardiac-remodeling and dysfunction indices and PROs was greatest in stage B, then A, and lowest for 0. CONCLUSIONS: Digital measures of walking captured by wearable sensors could complement clinic-based testing to identify and monitor pre-symptomatic HF.

Digitally-enabled, person-centred care (PCC) in allergen immunotherapy: An ARIA-EAACI Position Paper.

In rhinitis and asthma, several mHealth apps have been developed but only a few have been validated. However, these apps have a high potential for improving person-centred care (PCC), especially in allergen immunotherapy (AIT). They can provide support in AIT initiation by selecting the appropriate patient and allergen shared decision-making. They can also help in (i) the evaluation of (early) efficacy, (ii) early and late stopping rules and (iii) the evaluation of (carried-over) efficacy after cessation of the treatment course. Future perspectives have been formulated in the first report of a joint task force (TF)-Allergic Rhinitis and Its Impact on Asthma (ARIA) and the European Academy of Allergy and Clinical Immunology (EAACI)-on digital biomarkers. The TF on AIT now aims to (i) outline the potential of the clinical applications of mHealth solutions, (ii) express their current limitations, (iii) make proposals regarding further developments for both clinical practice and scientific purpose and (iv) suggest which of the tools might best comply with the purpose of digitally-enabled PCC in AIT.

Naturalistic assessment of reaction time variability in older adults at risk for Alzheimer's disease.

OBJECTIVE: Maintaining attention underlies many aspects of cognition and becomes compromised early in neurodegenerative diseases like Alzheimer's disease (AD). The consistency of maintaining attention can be measured with reaction time (RT) variability. Previous work has focused on measuring such fluctuations during in-clinic testing, but recent developments in remote, smartphone-based cognitive assessments can allow one to test if these fluctuations in attention are evident in naturalistic settings and if they are sensitive to traditional clinical and cognitive markers of AD. METHOD: Three hundred and seventy older adults (aged 75.8 +/- 5.8 years) completed a week of remote daily testing on the Ambulatory Research in Cognition (ARC) smartphone platform and also completed clinical, genetic, and conventional in-clinic cognitive assessments. RT variability was assessed in a brief (20-40 seconds) processing speed task using two different measures of variability, the Coefficient of Variation (CoV) and the Root Mean Squared Successive Difference (RMSSD) of RTs on correct trials. RESULTS: Symptomatic participants showed greater variability compared to cognitively normal participants. When restricted to cognitively normal participants, APOE ε4 carriers exhibited greater variability than noncarriers. Both CoV and RMSSD showed significant, and similar, correlations with several in-clinic cognitive composites. Finally, both RT variability measures significantly mediated the relationship between APOE ε4 status and several in-clinic cognition composites. CONCLUSIONS: Attentional fluctuations over 20-40 seconds assessed in daily life, are sensitive to clinical status and genetic risk for AD. RT variability appears to be an important predictor of cognitive deficits during the preclinical disease stage.

Data Analytics in Physical Activity Studies With Accelerometers: Scoping Review.

BACKGROUND: Monitoring free-living physical activity (PA) through wearable devices enables the real-time assessment of activity features associated with health outcomes and provision of treatment recommendations and adjustments. The conclusions of studies on PA and health depend crucially on reliable statistical analyses of digital data. Data analytics, however, are challenging due to the various metrics adopted for measuring PA, different aims of studies, and complex temporal variations within variables. The application, interpretation, and appropriateness of these analytical tools have yet to be summarized. OBJECTIVE: This research aimed to review studies that used analytical methods for analyzing PA monitored by accelerometers. Specifically, this review addressed three questions: (1) What metrics are used to describe an individual's free-living daily PA? (2) What are the current analytical tools for analyzing PA data, particularly under the aims of classification, association with health outcomes, and prediction of health events? and (3) What challenges exist in the analyses, and what recommendations for future research are suggested regarding the use of statistical methods in various research tasks? METHODS: This scoping review was conducted following an existing framework to map research studies by exploring the information about PA. Three databases, PubMed, IEEE Xplore, and the ACM Digital Library, were searched in February 2024 to identify related publications. Eligible articles were classification, association, or prediction studies involving human PA monitored through wearable accelerometers. RESULTS: After screening 1312 articles, 428 (32.62%) eligible studies were identified and categorized into at least 1 of the following 3 thematic categories: classification (75/428, 17.5%), association (342/428, 79.9%), and prediction (32/428, 7.5%). Most articles (414/428, 96.7%) derived PA variables from 3D acceleration, rather than 1D acceleration. All eligible articles (428/428, 100%) considered PA metrics represented in the time domain, while a small fraction (16/428, 3.7%) also considered PA metrics in the frequency domain. The number of studies evaluating the influence of PA on health conditions has increased greatly. Among the studies in our review, regression-type models were the most prevalent (373/428, 87.1%). The machine learning approach for classification research is also gaining popularity (32/75, 43%). In addition to summary statistics of PA, several recent studies used tools to incorporate PA trajectories and account for temporal patterns, including longitudinal data analysis with repeated PA measurements and functional data analysis with PA as a continuum for time-varying association (68/428, 15.9%). CONCLUSIONS: Summary metrics can quickly provide descriptions of the strength, frequency, and duration of individuals' overall PA. When the distribution and profile of PA need to be evaluated or detected, considering PA metrics as longitudinal or functional data can provide detailed information and improve the understanding of the role PA plays in health. Depending on the research goal, appropriate analytical tools can ensure the reliability of the scientific findings.

A Decision-Making Algorithm for Remote Digital Assessments of Alzheimer's Disease.

INTRODUCTION: Remote digital assessments (RDAs) such as voice recording, video and motor sensors, olfactory, hearing, and vision screenings are now starting to be employed to complement classical biomarker and clinical evidence to identify patients in the early AD stages. Choosing which RDA can be proposed to individual patients is not trivial and often time-consuming. This position paper presents a decision-making algorithm for using RDA during teleconsultations in memory clinic settings. METHOD: The algorithm was developed by an expert panel following the Delphi methodology. RESULTS: The decision-making algorithm is structured as a series of yes-no questions. The resulting questionnaire is freely available online. DISCUSSION: We suggest that the use of screening questionnaires in the context of memory clinics may help accelerating the adoption of RDA in everyday clinical practice.

A New Wrist-Worn Tool Supporting the Diagnosis of Parkinsonian Motor Syndromes.

To date, clinical expert opinion is the gold standard diagnostic technique for Parkinson's disease (PD), and continuous monitoring is a promising candidate marker. This study assesses the feasibility and performance of a new wearable tool for supporting the diagnosis of Parkinsonian motor syndromes. The proposed method is based on the use of a wrist-worn measuring system, the execution of a passive, continuous recording session, and a computation of two digital biomarkers (i.e., motor activity and rest tremor index). Based on the execution of some motor tests, a second step is provided for the confirmation of the results of passive recording. In this study, fifty-nine early PD patients and forty-one healthy controls were recruited. The results of this study show that: (a) motor activity was higher in controls than in PD with slight tremors at rest and did not significantly differ between controls and PD with mild-to-moderate tremor rest; (b) the tremor index was smaller in controls than in PD with mild-to-moderate tremor rest and did not significantly differ between controls and PD patients with slight tremor rest; (c) the combination of the said two motor parameters improved the performances in differentiating controls from PD. These preliminary findings demonstrate that the combination of said two digital biomarkers allowed us to differentiate controls from early PD.

Wearable Devices and Digital Biomarkers for Optimizing Training Tolerances and Athlete Performance: A Case Study of a National Collegiate Athletic Association Division III Soccer Team over a One-Year Period.

Wearable devices in sports have been used at the professional and higher collegiate levels, but not much research has been conducted at lower collegiate division levels. The objective of this retrospective study was to gather big data using the Catapult wearable technology, develop an algorithm for musculoskeletal modeling, and longitudinally determine the workloads of male college soccer (football) athletes at the Division III (DIII) level over the course of a 12-week season. The results showed that over the course of a season, (1) the average match workload (432 ± 47.7) was 1.5× greater than the average training workload (252.9 ± 23.3) for all positions, (2) the forward position showed the lowest workloads throughout the season, and (3) the highest mean workload was in week 8 (370.1 ± 177.2), while the lowest was in week 4 (219.1 ± 26.4). These results provide the impetus to enable the interoperability of data gathered from wearable devices into data management systems for optimizing performance and health.

Overarm Training Tolerance: A Pilot Study on the Use of Muscle Oxygen Saturation as a Biomarker.

Ulnar collateral ligament (UCL) tears occur due to the prolonged exposure and overworking of joint stresses, resulting in decreased strength in the flexion and extension of the elbow. Current rehabilitation approaches for UCL tears involve subjective assessments (pain scales) and objective measures such as monitoring joint angles and range of motion. The main goal of this study is to find out if using wearable near-infrared spectroscopy technology can help measure digital biomarkers like muscle oxygen levels and heart rate. These measurements could then be applied to athletes who have been injured. Specifically, measuring muscle oxygen levels will help us understand how well the muscles are using oxygen. This can indicate improvements in how the muscles are healing and growing new blood vessels after reconstructive surgery. Previous research studies demonstrated that there remains an unmet clinical need to measure biomarkers to provide continuous, internal data on muscle physiology during the rehabilitation process. This study's findings can benefit team physicians, sports scientists, athletic trainers, and athletes in the identification of biomarkers to assist in clinical decisions for optimizing training regimens for athletes that perform overarm movements; the research suggests pathways for possible earlier detection, and thus earlier intervention for injury prevention.

Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review.

Respiratory diseases represent a significant global burden, necessitating efficient diagnostic methods for timely intervention. Digital biomarkers based on audio, acoustics, and sound from the upper and lower respiratory system, as well as the voice, have emerged as valuable indicators of respiratory functionality. Recent advancements in machine learning (ML) algorithms offer promising avenues for the identification and diagnosis of respiratory diseases through the analysis and processing of such audio-based biomarkers. An ever-increasing number of studies employ ML techniques to extract meaningful information from audio biomarkers. Beyond disease identification, these studies explore diverse aspects such as the recognition of cough sounds amidst environmental noise, the analysis of respiratory sounds to detect respiratory symptoms like wheezes and crackles, as well as the analysis of the voice/speech for the evaluation of human voice abnormalities. To provide a more in-depth analysis, this review examines 75 relevant audio analysis studies across three distinct areas of concern based on respiratory diseases' symptoms: (a) cough detection, (b) lower respiratory symptoms identification, and (c) diagnostics from the voice and speech. Furthermore, publicly available datasets commonly utilized in this domain are presented. It is observed that research trends are influenced by the pandemic, with a surge in studies on COVID-19 diagnosis, mobile data acquisition, and remote diagnosis systems.

Wearable Sensor-Based Assessments for Remotely Screening Early-Stage Parkinson's Disease.

Prevalence estimates of Parkinson's disease (PD)-the fastest-growing neurodegenerative disease-are generally underestimated due to issues surrounding diagnostic accuracy, symptomatic undiagnosed cases, suboptimal prodromal monitoring, and limited screening access. Remotely monitored wearable devices and sensors provide precise, objective, and frequent measures of motor and non-motor symptoms. Here, we used consumer-grade wearable device and sensor data from the WATCH-PD study to develop a PD screening tool aimed at eliminating the gap between patient symptoms and diagnosis. Early-stage PD patients (n = 82) and age-matched comparison participants (n = 50) completed a multidomain assessment battery during a one-year longitudinal multicenter study. Using disease- and behavior-relevant feature engineering and multivariate machine learning modeling of early-stage PD status, we developed a highly accurate (92.3%), sensitive (90.0%), and specific (100%) random forest classification model (AUC = 0.92) that performed well across environmental and platform contexts. These findings provide robust support for further exploration of consumer-grade wearable devices and sensors for global population-wide PD screening and surveillance.

Toward digitally screening and profiling AD: A GAMLSS approach of MemTrax in China.

PURPOSES: To establish a normative range of MemTrax (MTx) metrics in the Chinese population. METHODS: The correct response percentage (MTx-%C) and mean response time (MTx-RT) were obtained and the composite scores (MTx-Cp) calculated. Generalized additive models for location, shape and scale (GAMLSS) were applied to create percentile curves and evaluate goodness of fit, and the speed-accuracy trade-off was investigated. RESULTS: 26,633 subjects, including 13,771 (51.71%) men participated in this study. Age- and education-specific percentiles of the metrics were generated. Q tests and worm plots indicated adequate fit for models of MTx-RT and MTx-Cp. Models of MTx-%C for the low and intermediate education fit acceptably, but not well enough for a high level of education. A significant speed-accuracy trade-off was observed for MTx-%C from 72 to 94. CONCLUSIONS: GAMLSS is a reliable method to generate smoothed age- and education-specific percentile curves of MTx metrics, which may be adopted for mass screening and follow-ups addressing Alzheimer's disease or other cognitive diseases. HIGHLIGHTS: GAMLSS was applied to establish nonlinear percentile curves of cognitive decline. Subjects with a high level of education demonstrate a later onset and slower decline of cognition. Speed-accuracy trade-off effects were observed in a subgroup with moderate accuracy. MemTrax can be used as a mass-screen instrument for active cognition health management advice.

Wearable technologies for health research: Opportunities, limitations, and practical and conceptual considerations.

One of the most notable limitations of laboratory-based health research is its inability to continuously monitor health-relevant physiological processes as individuals go about their daily lives. As a result, we have generated large amounts of data with unknown generalizability to real-world situations and also created a schism between where data are collected (i.e., in the lab) and where we need to intervene to prevent disease (i.e., in the field). Devices using noninvasive wearable technology are changing all of this, however, with their ability to provide high-frequency assessments of peoples' ever-changing physiological states in daily life in a manner that is relatively noninvasive, affordable, and scalable. Here, we discuss critical points that every researcher should keep in mind when using these wearables in research, spanning device and metric decisions, hardware and software selection, and data quality and sampling rate issues, using research on stress and health as an example throughout. We also address usability and participant acceptability issues, and how wearable "digital biomarker" and behavioral data can be integrated to enhance basic science and intervention studies. Finally, we summarize 10 key questions that should be addressed to make every wearable study as strong as possible. Collectively, keeping these points in mind can improve our ability to study the psychobiology of human health, and to intervene, precisely where it matters most: in peoples' daily lives.

Unsupervised high-frequency smartphone-based cognitive assessments are reliable, valid, and feasible in older adults at risk for Alzheimer's disease.

OBJECTIVE: Smartphones have the potential for capturing subtle changes in cognition that characterize preclinical Alzheimer's disease (AD) in older adults. The Ambulatory Research in Cognition (ARC) smartphone application is based on principles from ecological momentary assessment (EMA) and administers brief tests of associative memory, processing speed, and working memory up to 4 times per day over 7 consecutive days. ARC was designed to be administered unsupervised using participants' personal devices in their everyday environments. METHODS: We evaluated the reliability and validity of ARC in a sample of 268 cognitively normal older adults (ages 65-97 years) and 22 individuals with very mild dementia (ages 61-88 years). Participants completed at least one 7-day cycle of ARC testing and conventional cognitive assessments; most also completed cerebrospinal fluid, amyloid and tau positron emission tomography, and structural magnetic resonance imaging studies. RESULTS: First, ARC tasks were reliable as between-person reliability across the 7-day cycle and test-retest reliabilities at 6-month and 1-year follow-ups all exceeded 0.85. Second, ARC demonstrated construct validity as evidenced by correlations with conventional cognitive measures (r = 0.53 between composite scores). Third, ARC measures correlated with AD biomarker burden at baseline to a similar degree as conventional cognitive measures. Finally, the intensive 7-day cycle indicated that ARC was feasible (86.50% approached chose to enroll), well tolerated (80.42% adherence, 4.83% dropout), and was rated favorably by older adult participants. CONCLUSIONS: Overall, the results suggest that ARC is reliable and valid and represents a feasible tool for assessing cognitive changes associated with the earliest stages of AD.

Digital health technologies and major depressive disorder.

There is an urgent need to improve the clinical management of major depressive disorder (MDD), which has become increasingly prevalent over the past two decades. Several gaps and challenges in the awareness, detection, treatment, and monitoring of MDD remain to be addressed. Digital health technologies have demonstrated utility in relation to various health conditions, including MDD. Factors related to the COVID-19 pandemic have accelerated the development of telemedicine, mobile medical apps, and virtual reality apps and have continued to introduce new possibilities across mental health care. Growing access to and acceptance of digital health technologies present opportunities to expand the scope of care and to close gaps in the management of MDD. Digital health technology is rapidly evolving the options for nonclinical support and clinical care for patients with MDD. Iterative efforts to validate and optimize such digital health technologies, including digital therapeutics and digital biomarkers, continue to improve access to and quality of personalized detection, treatment, and monitoring of MDD. The aim of this review is to highlight the existing gaps and challenges in depression management and discuss the current and future landscape of digital health technology as it applies to the challenges faced by patients with MDD and their healthcare providers.

Effectiveness of a scalable, remotely delivered stepped-care intervention to reduce symptoms of psychological distress among Polish migrant workers in the Netherlands: study protocol for the RESPOND randomised controlled trial.

BACKGROUND: The COVID-19 pandemic has negatively affected the mental health of international migrant workers (IMWs). IMWs experience multiple barriers to accessing mental health care. Two scalable interventions developed by the World Health Organization (WHO) were adapted to address some of these barriers: Doing What Matters in times of stress (DWM), a guided self-help web application, and Problem Management Plus (PM +), a brief facilitator-led program to enhance coping skills. This study examines whether DWM and PM + remotely delivered as a stepped-care programme (DWM/PM +) is effective and cost-effective in reducing psychological distress, among Polish migrant workers with psychological distress living in the Netherlands. METHODS: The stepped-care DWM/PM + intervention will be tested in a two-arm, parallel-group, randomized controlled trial (RCT) among adult Polish migrant workers with self-reported psychological distress (Kessler Psychological Distress Scale; K10 > 15.9). Participants (n = 212) will be randomized into either the intervention group that receives DWM/PM + with psychological first aid (PFA) and care-as-usual (enhanced care-as-usual or eCAU), or into the control group that receives PFA and eCAU-only (1:1 allocation ratio). Baseline, 1-week post-DWM (week 7), 1-week post-PM + (week 13), and follow-up (week 21) self-reported assessments will be conducted. The primary outcome is psychological distress, assessed with the Patient Health Questionnaire Anxiety and Depression Scale (PHQ-ADS). Secondary outcomes are self-reported symptoms of depression, anxiety, posttraumatic stress disorder (PTSD), resilience, quality of life, and cost-effectiveness. In a process evaluation, stakeholders' views on barriers and facilitators to the implementation of DWM/PM + will be evaluated. DISCUSSION: To our knowledge, this is one of the first RCTs that combines two scalable, psychosocial WHO interventions into a stepped-care programme for migrant populations. If proven to be effective, this may bridge the mental health treatment gap IMWs experience. TRIAL REGISTRATION: Dutch trial register NL9630, 20/07/2021, https://www.onderzoekmetmensen.nl/en/trial/27052.

Machine Learning and the Digital Measurement of Psychological Health.

Since its inception, the discipline of psychology has utilized empirical epistemology and mathematical methodologies to infer psychological functioning from direct observation. As new challenges and technological opportunities emerge, scientists are once again challenged to define measurement paradigms for psychological health and illness that solve novel problems and capitalize on new technological opportunities. In this review, we discuss the theoretical foundations of and scientific advances in remote sensor technology and machine learning models as they are applied to quantify psychological functioning, draw clinical inferences, and chart new directions in treatment.

Neuroeducation and Mathematics: The Formation of New Educational Practices.

Recent research in educational neuroscience has established the correlation between the way the human brain works and the process of perceiving and learning mathematical concepts. In this chapter, a research approach is proposed, based on the principles of educational neuroscience, and focuses on the way students deal with new knowledge in mathematics. Initially, using neuroscientific techniques and a multidimensional approach to new knowledge, data will be collected from students. By collecting neurophysiological measurements and analyzing the data, an attempt will be made to formulate learning paths for a better understanding of fractional concepts, based on the needs of each student.

Using Biomarkers for Cognitive Enhancement and Evaluation in Mobile Applications.

Clinicians are increasingly using biomarkers to diagnose and monitor cognitive conditions such as mild cognitive impairment, Alzheimer's disease, and dementia. Biomarkers are classified into two main categories based on their clinical goal: disease-associated biomarkers and drug-related biomarkers. In the case of disease-associated biomarkers, neuroimaging biomarkers are used to predict and validate Alzheimer's disease at any of its stages including mild cognitive impairment. The use of mobile and wearable devices to collect data about a person's daily activities and behaviors has led to the emergence of a new type of biomarker known as digital biomarkers. This type of data provides a digital reflection of a person's function in the context of everyday life and can be used to monitor and track changes in an individual's health and behaviors over time. The use of biomarkers in mobile applications for cognitive enhancement and evaluation can provide valuable insights into an individual's cognitive health and can help to optimize treatment and prevention strategies.

Making Pre-screening for Alzheimer's Disease (AD) and Postoperative Delirium Among Post-Acute COVID-19 Syndrome (PACS) a National Priority: The Deep Neuro Study.

SARS-CoV-2 effects on cognition are a vibrant area of active research. Many researchers suggest that COVID-19 patients with severe symptoms leading to hospitalization sustain significant neurodegenerative injury, such as encephalopathy and poor discharge disposition. However, despite some post-acute COVID-19 syndrome (PACS) case series that have described elevated neurodegenerative biomarkers, no studies have been identified that directly compared levels to those in mild cognitive impairment, non-PACS postoperative delirium patients after major non-emergent surgery, or preclinical Alzheimer's disease (AD) patients that have clinical evidence of Alzheimer's without symptoms. According to recent estimates, there may be 416 million people globally on the AD continuum, which include approximately 315 million people with preclinical AD. In light of all the above, a more effective application of digital biomarker and explainable artificial intelligence methodologies that explored amyloid beta, neuronal, axonal, and glial markers in relation to neurological complications in-hospital or later outcomes could significantly assist progress in the field. Easy and scalable subjects' risk stratification is of utmost importance, yet current international collaboration initiatives are still challenging due to the limited explainability and accuracy to identify individuals at risk or in the earliest stages that might be candidates for future clinical trials. In this open letter, we propose the administration of selected digital biomarkers previously discovered and validated in other EU-funded studies to become a routine assessment for non-PACS preoperative cognitive impairment, PACS neurological complications in-hospital, or later PACS and non-PACS improvement in cognition after surgery. The open letter also includes an economic analysis of the implications for such national-level initiatives. Similar collaboration initiatives could have existing pre-diagnostic detection and progression prediction solutions pre-screen the stage before and around diagnosis, enabling new disease manifestation mapping and pushing the field into unchartered territory.