Understanding disease through remote monitoring technology:A mobile health perspective on disease and diagnosis in three conditions: stress, epilepsy, and COVID-19

Mobile systems and wearable technology have developed substantially over the last decade and provide a unique long-term and continuous insight and monitoring into medical condi- tions in health research. The opportunities afforded by mobile health in access, scale, and round-the-clock recording are counterbalanced by pronounced issues in areas like participant engagement, labelling, and dataset size. Throughout this thesis the different aspects of an mHealth study are addressed, from software development and study design to data collection and analysis. Three medically relevant fields are investigated: detection of stress from physiological signals, seizure detection in epilepsy and the characterisation and monitoring of COVID-19 through mobile health techniques.The first two analytical chapters of the thesis focus on models for acute stress and epileptic seizure detection, two conditions with autonomic and physiological manifestations. Firstly, a multi-modal machine learning pipeline is developed targetting focal and general motor seizures in patients with epilepsy. The heterogenity and inter-individual differences present in this study motivated the investigation of methods to personalise models with relatively little data. I subsequently consider meta-learning for few-shot model personalisation within acute stress classification, finding increased performance compared to standard methods.As the COVID-19 pandemic gripped the world the work of this thesis reoriented around using mHealth to understand the disease. Firstly, the study design and software development of Covid Collab, a crowdsourced, remote-enrollment COVID-19 study, are examined. Within these chapters, the patterns of participant enrolment and adherence in Covid Col- lab are also considered. Adherence could impact scientific interpretations if not properly accounted for. While basic drop-out and percent completion are often considered, a more dynamic view of a participant’s behaviour can also be important. A hidden Markov model approach is used to compare participant engagement over time.Secondly, the long-term effects of COVID are investigated through data collected in the Covid Collab study, giving insight into prevalence, risk factors, and symptom manifestation with respect to wearable-recorded physiological signals. Long-term and historical data accessed retrospectively facilitated the findings of significant correlations between development of long-COVID and mHealth-derived fitness and behaviour

A neurocomputational account of self-other distinction: from cell to society

Human social systems are unique in the animal kingdom. Social norms, constructed at a higher level of organisation, influence individuals across vast spatiotemporal scales. Characterising the neurocomputational processes that enable the emergence of these social systems could inform holistic models of human cognition and mental illness. Social neuroscience has shown that the processing of ‘social’ information demands many of the same computations as those involved in reasoning about inanimate objects in ‘non-social’ contexts. However, for people to reason about each other’s mental states, the brain must be able to distinguish between one mind and another. This ability, to attribute a mental state to a specific agent, has long been studied by philosophers under the guise of ‘meta-representation’. Empathy research has taken strides in describing the neural correlates of representing another person’s affective or bodily state, as distinct from one’s own. However, Self-Other distinction in beliefs, and hence meta-representation, has not figured in formal models of cognitive neuroscience. Here, I introduce a novel behavioural paradigm, which acts as a computational assay for Self-Other distinction in a cognitive domain. The experiments in this thesis combine computational modelling with magnetoencephalography and functional magnetic resonance imaging to explore how basic units of computation, predictions and prediction errors, are selectively attributed to Self and Other, when subjects have to simulate another agent’s learning process. I find that these low-level learning signals encode information about agent identity. Furthermore, the fidelity of this encoding is susceptible to experience-dependent plasticity, and predicts the presence of subclinical psychopathological traits. The results suggest that the neural signals generating an internal model of the world contain information, not only about ‘what’ is out there, but also about ‘who’ the model belongs to. That this agent-specificity is learnable highlights potential computational failure modes in mental illnesses with an altered sense of Self

Information technologies for pain management

Millions of people around the world suffer from pain, acute or chronic and this raises the importance of its screening, assessment and treatment. The importance of pain is attested by the fact that it is considered the fifth vital sign for indicating basic bodily functions, health and quality of life, together with the four other vital signs: blood pressure, body temperature, pulse rate and respiratory rate. However, while these four signals represent an objective physical parameter, the occurrence of pain expresses an emotional status that happens inside the mind of each individual and therefore, is highly subjective that makes difficult its management and evaluation. For this reason, the self-report of pain is considered the most accurate pain assessment method wherein patients should be asked to periodically rate their pain severity and related symptoms. Thus, in the last years computerised systems based on mobile and web technologies are becoming increasingly used to enable patients to report their pain which lead to the development of electronic pain diaries (ED). This approach may provide to health care professionals (HCP) and patients the ability to interact with the system anywhere and at anytime thoroughly changes the coordinates of time and place and offers invaluable opportunities to the healthcare delivery. However, most of these systems were designed to interact directly to patients without presence of a healthcare professional or without evidence of reliability and accuracy. In fact, the observation of the existing systems revealed lack of integration with mobile devices, limited use of web-based interfaces and reduced interaction with patients in terms of obtaining and viewing information. In addition, the reliability and accuracy of computerised systems for pain management are rarely proved or their effects on HCP and patients outcomes remain understudied. This thesis is focused on technology for pain management and aims to propose a monitoring system which includes ubiquitous interfaces specifically oriented to either patients or HCP using mobile devices and Internet so as to allow decisions based on the knowledge obtained from the analysis of the collected data. With the interoperability and cloud computing technologies in mind this system uses web services (WS) to manage data which are stored in a Personal Health Record (PHR). A Randomised Controlled Trial (RCT) was implemented so as to determine the effectiveness of the proposed computerised monitoring system. The six weeks RCT evidenced the advantages provided by the ubiquitous access to HCP and patients so as to they were able to interact with the system anywhere and at anytime using WS to send and receive data. In addition, the collected data were stored in a PHR which offers integrity and security as well as permanent on line accessibility to both patients and HCP. The study evidenced not only that the majority of participants recommend the system, but also that they recognize it suitability for pain management without the requirement of advanced skills or experienced users. Furthermore, the system enabled the definition and management of patient-oriented treatments with reduced therapist time. The study also revealed that the guidance of HCP at the beginning of the monitoring is crucial to patients' satisfaction and experience stemming from the usage of the system as evidenced by the high correlation between the recommendation of the application, and it suitability to improve pain management and to provide medical information. There were no significant differences regarding to improvements in the quality of pain treatment between intervention group and control group. Based on the data collected during the RCT a clinical decision support system (CDSS) was developed so as to offer capabilities of tailored alarms, reports, and clinical guidance. This CDSS, called Patient Oriented Method of Pain Evaluation System (POMPES), is based on the combination of several statistical models (one-way ANOVA, Kruskal-Wallis and Tukey-Kramer) with an imputation model based on linear regression. This system resulted in fully accuracy related to decisions suggested by the system compared with the medical diagnosis, and therefore, revealed it suitability to manage the pain. At last, based on the aerospace systems capability to deal with different complex data sources with varied complexities and accuracies, an innovative model was proposed. This model is characterized by a qualitative analysis stemming from the data fusion method combined with a quantitative model based on the comparison of the standard deviation together with the values of mathematical expectations. This model aimed to compare the effects of technological and pen-and-paper systems when applied to different dimension of pain, such as: pain intensity, anxiety, catastrophizing, depression, disability and interference. It was observed that pen-and-paper and technology produced equivalent effects in anxiety, depression, interference and pain intensity. On the contrary, technology evidenced favourable effects in terms of catastrophizing and disability. The proposed method revealed to be suitable, intelligible, easy to implement and low time and resources consuming. Further work is needed to evaluate the proposed system to follow up participants for longer periods of time which includes a complementary RCT encompassing patients with chronic pain symptoms. Finally, additional studies should be addressed to determine the economic effects not only to patients but also to the healthcare system

Digital Interventions for Depression : Predictors and Moderators of Treatment Adherence and Outcomes

Background. Depression is the leading cause of disability worldwide. Although evidence-based treatments exist, less than one-in-five people in high-income countries and less than one-in-twenty-seven in low-income countries receive treatment, giving rise to a treatment gap in mental healthcare. Digital interventions have been proposed as a solution to address the treatment gap. As an increasing number of public and private healthcare providers adopt digital interventions to meet the growing demand for treatment, the current thesis set out to examine the latest evidence-base for digital depression interventions and the extent to which new technologies may be used to identify at-risk individuals. Methods. Study 1 assessed the efficacy of digital interventions for the treatment of depressive symptoms based on the largest meta-analysis of digital depression interventions conducted to-date. Databases were searched for RCTs of a computer-, internet-, or smartphone-based interventions for depression versus an active or passive control condition. Participants were individuals with elevated symptoms of depression at baseline. Using a random-effects multilevel metaregression model, we examined effect size of treatment versus control (Hedges’ g) and explored moderators of treatment outcome. Study II was a secondary analysis of data from two RCTs (N=253) of a digital intervention for the prevention and treatment of major depression. Using logistic regression, we first examined participant characteristics as potential predictors of intervention dropout. We then assessed to what extent dropout could be predicted following completion of the first module using a combination of participant characteristics and intervention usage data. Dropout was defined as completing less than six modules. Study III was an observational study of N = 60 adults (ages 24–68) who owned an Apple iPhone and Oura Ring. A smartphone app (Delphi) continuously monitored participants’ location and smartphone usage behavior over a 4- week period. The Oura Ring provided measures of activity, sleep and heart rate variability (HRV). Participants were prompted to report their daily mood and self-reported measures of depression, anxiety and stress were collected at baseline, midpoint and the end of the study using the DASS-21. Multilevel regression models were used to predict the association between smartphone and wearable data and mental health scores. Study IV was a secondary analysis of data from Study III in which we compared the accuracy of five supervised machine learning algorithms in the classification of individuals with normal versus above normal symptoms of depression, as defined by the DASS-21. Results. A systematic search of the literature in Study I identified 83 trials (N = 15,530). The overall effect size of digital interventions versus all controls was g = .52. Significantly lower effect sizes were found in studies conducted in real-world settings (effectiveness trials; g = .30) versus laboratory settings (efficacy trials; g = .59). Significantly higher effect sizes were found in interventions that involved human therapeutic guidance (g = .63) compared with unguided, self- help interventions (g = .34). Additionally, we found significant differences in effect size depending on the type of control used (WLC: g = .70; attention: g = .36; TAU: g = .31). No significant difference in outcomes was found between human-guided digital interventions and face-to-face therapy, although the number of studies was low. In Study II we found that lower level of education (OR=3.33) and both lower and higher age (a quadratic effect; age: OR=0.62, age^2: OR=1.55) were significantly associated with higher risk of dropout. In the analysis that aimed to predict dropout following completion of the first module, lower and higher age (age: OR=0.61, age^2: OR=1.58), medium versus high social support (OR=3.40) and a higher number of days to module completion (OR=1.05) predicted higher risk of dropout, whilst a self-reported negative event in the previous week was associated with lower risk of dropout (OR=0.22). In Study III, we found a significant negative association between the variability of locations visited and symptoms of depression (b = −0.21, p = 0.037) and significant positive associations between total sleep time and depression (b = 0.24, p = 0.023) and time in bed and depression (b = 0.26, p = 0.020). Additionally, we found that wake after sleep onset significantly predicted symptoms of anxiety (b = 0.23, p = 0.035). Study IV revealed that a Support Vector Machine using only sensor-based predictors had an accuracy of 75.90% and an Area Under the Curve of 74.89%, whilst an XGBoost model that combined mood and sensor data as predictors classified participants as belonging to the group with normal or above normal levels of depressive symptoms with an accuracy of 81.43% and an Area Under the Curve of 82.31%. Conclusion. The current thesis provided evidence of the efficacy of digital interventions for the treatment of depression in a variety of populations. Importantly, we provided the first meta-analytic evidence that digital interventions are effective in routine healthcare settings, but only when accompanied by human guidance. Notwithstanding, adherence to digital interventions remains a major challenge with little more than 25% of patients completing the full intervention on average in real-world settings. Finally, we demonstrated that data from smartphone and wearable devices may provide valuable sources of data in predicting symptoms of depression, thereby helping to identify at-risk individuals.Tausta. Masennus on maailmanlaajuisesti keskeisimpiä toimintakykyä alentavia tekijöitä. Vaikka masennuksen hoitoon on kehitetty näyttöön perustuvia hoitomuotoja, hoidon tarjonta ei kohtaa kysyntää: korkean tulotason maissa vain viidennes hoitoa tarvitsevista saa hoitoa, ja matalan tulotason maissa hoitoa saavien osuus on vielä selkeästi alhaisempi. Hoidon saatavuusongelman ratkaisuksi on ehdotettu digitaalisia hoitomuotoja, ja digitaalisten masennushoitojen käyttö yleistyykin sekä julkisissa että yksityisissä hoitokonteksteissa. Tässä tutkimuksessa selvitettiin digitaalisten masennushoitojen tehokkuutta ja teknologiasovellusten käyttöä masennuksen riskiryhmien varhaisen tunnistamisen välineenä. Menetelmät. Ensimmäinen osatutkimus tarkasteli masennusoireiden hoidossa käytettävien digitaalisten hoitojen tehokkuutta. Tutkimuksessa toteutettiin tähän mennessä kattavin meta-analyysi satunnaistettuihin koeasetelmiin perustuvista masennusinterventiotutkimuksista, joissa hoitomuotona oli digitaalinen ohjelma ja aktiivinen tai passiivinen kontrollitilanne. Digitaaliset hoidot olivat internetissä tai muulla digitaalisella alustalla toteutettuja hoitoja (esimerkiksi tietokone- tai älypuhelinperustaisia hoitoja). Analyyseissä käytettiin monitasometaregressiomallinnusta, joka estimoi efektikoon koeryhmälle verrattuna kontrolliryhmään (Hedgesin g). Lisäksi tarkasteltiin digitaalisten hoitojen tehokkuuteen mahdollisesti vaikuttavia muokkaavia tekijöitä. Toisessa osatutkimuksessa selvitettiin digitaalisen hoidon keskeyttämistä ennakoivia tekijöitä kahden satunnaistetun vertailututkimuksen aineistossa (N=253) logistisilla regressiomalleilla. Tutkimuksessa tarkasteltiin yksilöllisten ominaisuuksien yhteyttä digitaalisen hoidon keskeyttämistodennäköisyyteen ja lisäksi sitä, miten yksilölliset ominaisuudet ja osallistujan käyttäytyminen digitaalisella alustalla ennustivat keskeyttämistodennäköisyyttä osallistujien suoritettua hoidon ensimmäisen moduulin. Kolmannessa osatutkimuksessa selvitettiin, voidaanko älylaitteilla kerätyillä käyttäytymiseen ja hyvinvointiin liittyvillä tiedoilla ennustaa mielenterveysoireilua. Tutkimuksen aineistona 60 24–68-vuotiasta aikuista, joita seurattiin Applen iPhone-sovelluksen (Delphi) ja Oura-sormuksen avulla neljän viikon ajan. Kerätty aineisto sisälsi osallistujien sijaintia ja puhelimen käyttöä koskevat tiedot sekä aktiivisuuden, unen ja syketaajuuden vaihtelun mittaukset ja päivittäin raportoidun mielialan. Masennus-, ahdistus- ja stressioireet mitattiin osallistujilta tutkimuksen alussa, puolivälissä ja lopussa itseraportointikyselyillä (DASS-21). Kerätyn aineiston ja päivittäin raportoidun mielialan yhteyttä masennus-, ahdistus- ja stressioireiluun tutkittiin monitasoregressiomalleilla. Neljäs osatutkimus toteutettiin samassa aineistossa kuin kolmas osatutkimus. Siinä vertailtiin viiden ohjatun koneoppimisalgoritmin tarkkuutta luokitella osallistujat masentuneiden ja terveiden luokkiin. Tulokset. Systemaattisen kirjallisuushaun perusteella ensimmäisen tutkimuksen meta-analyysiin sisällytettiin 83 tutkimusta (N=15,530). Digitaalisen intervention efektikoko kaikkiin kontrollitilanteisiin verrattuna oli g = 0.52. Kun digitaalinen hoito toteutettiin koeolosuhteiden ulkopuolella (ns. todellisessa elämässä), olivat efektikoot huomattavasti pienempiä (vaikuttavuus g = 0.30) kuin koeolosuhteissa havaitut (tehokkuus g = 0.59). Efektikoot olivat suurempia hoidoissa, joihin liittyi ohjaava ihmiskontakti (esimerkiksi terapeutti) (g = 0.63) verrattuna hoitoihin, joihin ei liittynyt ihmiskontaktia (g = 0.34). Efektikoot erosivat merkitsevästi myös kontrollitilanteesta riippuen (WLC: g = .70; attention: g = .36; TAU: g = .31). Ihmiskontaktin sisältävän digitaalisen hoidon havaittiin olevan yhtä tehokasta kuin kasvokkain tapahtuvan hoidon, joskin tutkimuksia tämän arvioimiseksi oli vain vähän. Toisessa osatutkimuksessa havaittiin, että matalampi koulutustaso (OR=3.33) sekä keskimääräistä(?) matalampi ja korkeampi ikä (kvadraattinen yhteys, ikä: OR=0.62, ikä^2: OR=1.55) ennustivat suurempaa todennäköisyyttä keskeyttää digitaalinen hoito. Niillä osallistujilla, jotka olivat suorittaneet hoidon ensimmäisen moduulin, keskeyttämistä ennustivat ikä (ikä: OR=0.61, ikä^2: OR=1.58), vähäisempi sosiaalinen tuki (OR=3.40) ja meneillään olevassa moduulissa jäljellä olevien päivien määrä (OR=1.05). Itseraportoitu ikävä tapahtuma edellisen viikon aikana oli puolestaan yhteydessä matalampaan keskeyttämistodennäköisyyteen (OR=0.22). Kolmannessa osatutkimuksessa havaittiin, että vähäisempi maantieteellinen liikkuvuus (b = −0.21, p = 0.037) ja suurempi unen (b = 0.24, p = 0.023) ja sängyssä vietetyn ajan määrä (b = 0.26, p = 0.020) olivat yhteydessä korkeampiin masennusoirepisteisiin. Lisäksi havaittiin yhteys nukahtamisen jälkeisen heräämisen ja ahdistuneisuusoireiden välillä (b = 0.23, p = 0.035). Neljäs tutkimus osoitti, että sensoripohjaisia ennustajia käyttävistä algoritmeista Support Vector Machine luokitteli ihmiset masennusoirepistemäärän perusteella oikein masentuneisiin ja terveisiin 75.90% tarkkuudella (käyrän alle jäävä pinta-ala (AUC) = 74.89%). Sensoripohjaisia ja päivittäisiin mielialamittauksiin perustuvia ennustajia yhdistävän XGBoost-algoritmin tarkkuus oli 81.43% (AUC = 82.31%). Johtopäätös. Tämä väitöskirjatutkimus tuotti uutta tietoa digitaalisten masennushoitojen tehokkuudesta. Tutkimuksessa esitettiin ensimmäinen kattava meta-analyysi, joka osoitti, että digitaaliset hoidot voivat olla tehokkaita psykiatrisen hoidon välineitä, mikäli digitaaliseen hoitoon sisältyy ohjaava ihmiskontakti. Digitaalisten hoitojen laajamittaisen käytön suurin haaste liittyy yhä hoitoon sitoutumiseen; keskimäärin vain joka neljäs potilas suorittaa hoidon loppuun. Tutkimustulosten mukaan kannettavien ja puettavien älylaitteiden avulla voidaan kerätä arvokasta tietoa, jonka avulla ennakoida masennusoireilua ja siten varhain tunnistaa erityisessä riskissä olevat

The development of a risk index for depression (RID)

&nbsp;This thesis developed a novel methodology for a flexible and modular Risk Index for Depression (RID) that blended data mining and machine learning techniques with traditional statistical techniques. This RID shows great potential for future clinical use.<br /

Prevention and Management of Frailty

It is important to prevent and manage the frailty of the elderly because their muscle strength and physical activity decrease in old age, making them prone to falling, depression, and social isolation. In the end, they need to be admitted to a hospital or a nursing home. When successful aging fails and motor ability declines due to illness, malnutrition, or reduced activity, frailty eventually occurs. Once frailty occurs, people with frailty do not have the power to exercise or the power to move. The functions of the heart and muscles are deteriorated more rapidly when they are not used. Consequently, frailty goes through a vicious cycle. As one’s physical fitness is deteriorated, the person has less power to exercise, poorer cognitive functions, and inferior nutrition intake. Consequently, the whole body of the person deteriorates. Therefore, in addition to observational studies to identify risk factors for preventing aging, various intervention studies have been conducted to develop exercise programs and apply them to communities, hospitals, and nursing homes for helping the elderly maintain healthy lives. Until now, most aging studies have focused on physical frailty. However, social frailty and cognitive frailty affect senile health negatively just as much as physical frailty. Nevertheless, little is known about social frailty and cognitive frailty. This special issue includes original experimental studies, reviews, systematic reviews, and meta-analysis studies on the prevention of senescence (physical senescence, cognitive senescence, social senescence), high-risk group detection, differentiation, and intervention

Improved Alzheimer’s disease detection by MRI using multimodal machine learning algorithms

Dementia is one of the huge medical problems that have challenged the public health sector around the world. Moreover, it generally occurred in older adults (age &gt; 60). Shockingly, there are no legitimate drugs to fix this sickness, and once in a while it will directly influence individual memory abilities and diminish the human capacity to perform day by day exercises. Many health experts and computing scientists were performing research works on this issue for the most recent twenty years. All things considered, there is an immediate requirement for finding the relative characteristics that can figure out the identification of dementia. The motive behind the works presented in this thesis is to propose the sophisticated supervised machine learning model in the prediction and classification of AD in elder people. For that, we conducted different experiments on open access brain image information including demographic MRI data of 373 scan sessions of 150 patients. In the first two works, we applied single ML models called support vectors and pruned decision trees for the prediction of dementia on the same dataset. In the first experiment with SVM, we achieved 70% of the prediction accuracy of late-stage dementia. Classification of true dementia subjects (precision) is calculated as 75%. Similarly, in the second experiment with J48 pruned decision trees, the accuracy was improved to the value of 88.73%. Classification of true dementia cases with this model was comprehensively done and achieved 92.4% of precision. To enhance this work, rather than single modelling we employed multi-modelling approaches. In the comparative analysis of the machine learning study, we applied the feature reduction technique called principal component analysis. This approach identifies the high correlated features in the dataset that are closely associated with dementia type. By doing the simultaneous application of three models such as KNN, LR, and SVM, it has been possible to identify an ideal model for the classification of dementia subjects. When compared with support vectors, KNN and LR models comprehensively classified AD subjects with 97.6% and 98.3% of accuracy respectively. These values are relatively higher than the previous experiments. However, because of the AD severity in older adults, it should be mandatory to not leave true AD positives. For the classification of true AD subjects among total subjects, we enhanced the model accuracy by introducing three independent experiments. In this work, we incorporated two new models called Naïve Bayes and Artificial Neural Networks along support vectors and KNN. In the first experiment, models were independently developed with manual feature selection. The experimental outcome suggested that KNN 3 is the optimal model solution because of 91.32% of classification accuracy. In the second experiment, the same models were tested with limited features (with high correlation). SVM was produced a high 96.12% of classification accuracy and NB produced a 98.21% classification rate of true AD subjects. Ultimately, in the third experiment, we mixed these four models and created a new model called hybrid type modelling. Hybrid model performance is validated AU-ROC curve value which is 0.991 (i.e., 99.1% of classification accuracy) has achieved. All these experimental results suggested that the ensemble modelling approach with wrapping is an optimal solution in the classification of AD subjects

Effects of Diversity and Neuropsychological Performance in an NFL Cohort

Objective: The aim of this study was to examine the effect of ethnicity on neuropsychological test performance by comparing scores of white and black former NFL athletes on each subtest of the WMS. Participants and Methods: Data was derived from a de-identified database in South Florida consisting of 63 former NFL white (n=28, 44.4%) and black (n=35, 55.6%) athletes (Mage= 50.38; SD= 11.57). Participants completed the following subtests of the WMS: Logical Memory I and II, Verbal Paired Associates I and II, and Visual Reproduction I and II. Results: A One-Way ANOVA yielded significant effect between ethnicity and performance on several subtests from the WMS-IV. Black athletes had significantly lower scores compared to white athletes on Logical Memory II: F(1,61) = 4.667, p= .035, Verbal Paired Associates I: F(1,61) = 4.536, p = .037, Verbal Paired Associates: II F(1,61) = 4.677, p = .034, and Visual Reproduction I: F(1,61) = 6.562, p = .013. Conclusions: Results suggest significant differences exist between white and black athletes on neuropsychological test performance, necessitating the need for proper normative samples for each ethnic group. It is possible the differences found can be explained by the psychometric properties of the assessment and possibility of a non-representative sample for minorities, or simply individual differences. Previous literature has found white individuals to outperform African-Americans on verbal and non-verbal cognitive tasks after controlling for socioeconomic and other demographic variables (Manly & Jacobs, 2002). This highlights the need for future investigators to identify cultural factors and evaluate how ethnicity specifically plays a role on neuropsychological test performance. Notably, differences between ethnic groups can have significant implications when evaluating a sample of former athletes for cognitive impairment, as these results suggest retired NFL minorities may be more impaired compared to retired NFL white athletes