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.

Understanding what aspects of Parkinson's disease matter most to patients and families.

Understanding what matters to people with Parkinson's and their family is essential to derive relevant clinical outcome measures and guide clinical care. The purpose of this study was to explore what is important to people with Parkinson's disease vs. family over time. A qualitative content-analysis of online survey data collected by Parkinson's UK was conducted to identify types and frequencies of important symptoms and impacts of Parkinson's for people with the disease vs. family of people with Parkinson's. Independent T-tests were used to identify significance of between group differences for patients vs. family at < 2, 2-5, 6-10, 11-20, > 20-year durations. ANOVA was used to assess for within group differences by disease duration. We found that symptom priority changed significantly over time with longer disease duration. Tremor was reported less often later on, whereas mobility, dyskinesias, gait and speech/communication symptoms gained priority. In general, patients identified movement-related symptoms (e.g., walking, bradykinesia) as the most bothersome at all durations while family more strongly prioritized the physical and psychosocial impacts of disease (e.g., mobility, safety, interpersonal interactions, independence, and family impact). We conclude that important differences exist between family and patient perspectives of what matters and change over time with longer duration of disease.

Using a smartwatch and smartphone to assess early Parkinson's disease in the WATCH-PD study over 12 months.

Digital measures may provide objective, sensitive, real-world measures of disease progression in Parkinson's disease (PD). However, multicenter longitudinal assessments of such measures are few. We recently demonstrated that baseline assessments of gait, tremor, finger tapping, and speech from a commercially available smartwatch, smartphone, and research-grade wearable sensors differed significantly between 82 individuals with early, untreated PD and 50 age-matched controls. Here, we evaluated the longitudinal change in these assessments over 12 months in a multicenter observational study using a generalized additive model, which permitted flexible modeling of at-home data. All measurements were included until participants started medications for PD. Over one year, individuals with early PD experienced significant declines in several measures of gait, an increase in the proportion of day with tremor, modest changes in speech, and few changes in psychomotor function. As measured by the smartwatch, the average (SD) arm swing in-clinic decreased from 25.9 (15.3) degrees at baseline to 19.9 degrees (13.7) at month 12 (P = 0.004). The proportion of awake time an individual with early PD had tremor increased from 19.3% (18.0%) to 25.6% (21.4%; P < 0.001). Activity, as measured by the number of steps taken per day, decreased from 3052 (1306) steps per day to 2331 (2010; P = 0.16), but this analysis was restricted to 10 participants due to the exclusion of those that had started PD medications and lost the data. The change of these digital measures over 12 months was generally larger than the corresponding change in individual items on the Movement Disorder Society-Unified Parkinson's Disease Rating Scale but not greater than the change in the overall scale. Successful implementation of digital measures in future clinical trials will require improvements in study conduct, especially data capture. Nonetheless, gait and tremor measures derived from a commercially available smartwatch and smartphone hold promise for assessing the efficacy of therapeutics in early PD.

Using Wearable Sensors and Machine Learning to Assess Upper Limb Function in Huntington's Disease.

Huntington's disease (HD), like many other neurological disorders, affects both lower and upper limb function that is typically assessed in the clinic - providing a snapshot of disease symptoms. Wearable sensors enable the collection of real-world data that can complement such clinical assessments and provide a more comprehensive insight into disease symptoms. In this context, almost all studies are focused on assessing lower limb function via monitoring of gait, physical activity and ambulation. In this study, we monitor upper limb function during activities of daily living in individuals with HD (n = 16), prodromal HD (pHD, n = 7), and controls (CTR, n = 16) using a wrist-worn wearable sensor, called PAMSys ULM, over seven days. The participants were highly compliant in wearing the sensor with an average daily compliance of 99% (100% for HD, 98% for pHD, and 99% for CTR). Goal-directed movements (GDM) of the hand were detected using a deep learning model, and kinematic features of each GDM were estimated. The collected data was used to predict disease groups (i.e., HD, pHD, and CTR) and clinical scores using a combination of statistical and machine learning-based models. Significant differences in GDM features were observed between the groups. HD participants performed fewer GDMs with long duration (> 7.5 seconds) compared to CTR (p-val = 0.021, d = -0.86). In velocity and acceleration metrics, the highest effect size feature was the entropy of the velocity zero-crossing length segments (HD vs CTR p-val <0.001, d = -1.67; HD vs pHD p-val = 0.043, d=-0.98; CTR vs pHD p-val = 0.046, d=0.96). In addition, this same variable showed a strongest correlation with clinical scores. Classification models achieved good performance in distinguishing HD, pHD and CTR individuals with a balanced accuracy of 67% and a 0.72 recall for the HD group, while regression models accurately predicted clinical scores. Notably the explained variance for the upper extremity function subdomain scale of Unified Huntington's Disease Rating Scale (UHDRS) was the highest, with the model capturing 60% of the variance. Our findings suggest the potential of wearables and machine learning for early identification of phenoconversion, remote monitoring in HD, and evaluating new treatments efficacy in clinical trials and medicine.

Improved measurement of disease progression in people living with early Parkinson's disease using digital health technologies.

BACKGROUND: Digital health technologies show promise for improving the measurement of Parkinson's disease in clinical research and trials. However, it is not clear whether digital measures demonstrate enhanced sensitivity to disease progression compared to traditional measurement approaches. METHODS: To this end, we develop a wearable sensor-based digital algorithm for deriving features of upper and lower-body bradykinesia and evaluate the sensitivity of digital measures to 1-year longitudinal progression using data from the WATCH-PD study, a multicenter, observational digital assessment study in participants with early, untreated Parkinson's disease. In total, 82 early, untreated Parkinson's disease participants and 50 age-matched controls were recruited and took part in a variety of motor tasks over the course of a 12-month period while wearing body-worn inertial sensors. We establish clinical validity of sensor-based digital measures by investigating convergent validity with appropriate clinical constructs, known groups validity by distinguishing patients from healthy volunteers, and test-retest reliability by comparing measurements between visits. RESULTS: We demonstrate clinical validity of the digital measures, and importantly, superior sensitivity of digital measures for distinguishing 1-year longitudinal change in early-stage PD relative to corresponding clinical constructs. CONCLUSIONS: Our results demonstrate the potential of digital health technologies to enhance sensitivity to disease progression relative to existing measurement standards and may constitute the basis for use as drug development tools in clinical research.

Parkinson's disease can impact a person's ability to move, which can result in slow or rigid movements. Wearable sensors can be used to measure these symptoms and could be particularly useful to detect changes early in the course of the disease when symptoms may be subtle. We developed a wearable sensor-based method to measure movement in people with early Parkinson's disease that uses wrist and foot-worn sensors. Our results demonstrate that our sensor-based measurements can accurately quantify progressive changes in movement function. Such measurements may allow researchers to more accurately evaluate how well treatments designed to slow the course of Parkinson's disease are working in the future.

Dry-Cleaning Chemicals and a Cluster of Parkinson's Disease and Cancer: A Retrospective Investigation.

BACKGROUND: Environmental exposure to trichloroethylene (TCE), a carcinogenic dry-cleaning chemical, may be linked to Parkinson's disease (PD). OBJECTIVE: The objective of this study was to determine whether PD and cancer were elevated among attorneys who worked near a contaminated site. METHODS: We surveyed and evaluated attorneys with possible exposure and assessed a comparison group. RESULTS: Seventy-nine of 82 attorneys (96.3%; mean [SD] age: 69.5 [11.4] years; 89.9% men) completed at least one phase of the study. For comparison, 75 lawyers (64.9 [10.2] years; 65.3% men) underwent clinical evaluations. Four (5.1%) of them who worked near the polluted site reported PD, more than expected based on age and sex (1.7%; P = 0.01) but not significantly higher than the comparison group (n = 1 [1.3%]; P = 0.37). Fifteen (19.0%), compared to four in the comparison group (5.3%; P = 0.049), had a TCE-related cancer. CONCLUSIONS: In a retrospective study, diagnoses of PD and TCE-related cancers appeared to be elevated among attorneys who worked next to a contaminated dry-cleaning site. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Digital assessment of speech in Huntington disease.

Background: Speech changes are an early symptom of Huntington disease (HD) and may occur prior to other motor and cognitive symptoms. Assessment of HD commonly uses clinician-rated outcome measures, which can be limited by observer variability and episodic administration. Speech symptoms are well suited for evaluation by digital measures which can enable sensitive, frequent, passive, and remote administration. Methods: We collected audio recordings using an external microphone of 36 (18 HD, 7 prodromal HD, and 11 control) participants completing passage reading, counting forward, and counting backwards speech tasks. Motor and cognitive assessments were also administered. Features including pausing, pitch, and accuracy were automatically extracted from recordings using the BioDigit Speech software and compared between the three groups. Speech features were also analyzed by the Unified Huntington Disease Rating Scale (UHDRS) dysarthria score. Random forest machine learning models were implemented to predict clinical status and clinical scores from speech features. Results: Significant differences in pausing, intelligibility, and accuracy features were observed between HD, prodromal HD, and control groups for the passage reading task (e.g., p < 0.001 with Cohen'd = -2 between HD and control groups for pause ratio). A few parameters were significantly different between the HD and control groups for the counting forward and backwards speech tasks. A random forest classifier predicted clinical status from speech tasks with a balanced accuracy of 73% and an AUC of 0.92. Random forest regressors predicted clinical outcomes from speech features with mean absolute error ranging from 2.43-9.64 for UHDRS total functional capacity, motor and dysarthria scores, and explained variance ranging from 14 to 65%. Montreal Cognitive Assessment scores were predicted with mean absolute error of 2.3 and explained variance of 30%. Conclusion: Speech data have the potential to be a valuable digital measure of HD progression, and can also enable remote, frequent disease assessment in prodromal HD and HD. Clinical status and disease severity were predicted from extracted speech features using random forest machine learning models. Speech measurements could be leveraged as sensitive marker of clinical onset and disease progression in future clinical trials.

Revisiting Snodgrass and Vanderwart in photograph form: The Keele Photo Stimulus Set (KPSS).

Over the last 40 years, object recognition studies have moved from using simple line drawings, to more detailed illustrations, to more ecologically valid photographic representations. Researchers now have access to various stimuli sets, however, existing sets lack the ability to independently manipulate item format, as the concepts depicted are unique to the set they derive from. To enable such comparisons, Rossion and Pourtois (2004) revisited Snodgrass and Vanderwart's (1980) line drawings and digitally re-drew the objects, adding texture and shading. In the current study, we took this further and created a set of stimuli that showcase the same objects in photographic form. We selected six photographs of each object (three color/three grayscale) and collected normative data and RTs. Naming accuracy and agreement was high for all photographs and appeared to steadily increase with format distinctiveness. In contrast to previous data patterns for drawings, naming agreement (H values) did not differ between grey and color photographs, nor did familiarity ratings. However, grey photographs received significantly lower mental imagery agreement and visual complexity scores than color photographs. This suggests that, in comparison to drawings, the ecological nature of photographs may facilitate deeper critical evaluation of whether they offer a good match to a mental representation. Color may therefore play a more vital role in photographs than in drawings, aiding participants in judging the match with their mental representation. This new photographic stimulus set and corresponding normative data provide valuable materials for a wide range of experimental studies of object recognition.

Towards interpretable speech biomarkers: exploring MFCCs.

While speech biomarkers of disease have attracted increased interest in recent years, a challenge is that features derived from signal processing or machine learning approaches may lack clinical interpretability. As an example, Mel frequency cepstral coefficients (MFCCs) have been identified in several studies as a useful marker of disease, but are regarded as uninterpretable. Here we explore correlations between MFCC coefficients and more interpretable speech biomarkers. In particular we quantify the MFCC2 endpoint, which can be interpreted as a weighted ratio of low- to high-frequency energy, a concept which has been previously linked to disease-induced voice changes. By exploring MFCC2 in several datasets, we show how its sensitivity to disease can be increased by adjusting computation parameters.

Using AI to measure Parkinson's disease severity at home.

We present an artificial intelligence (AI) system to remotely assess the motor performance of individuals with Parkinson's disease (PD). In our study, 250 global participants performed a standardized motor task involving finger-tapping in front of a webcam. To establish the severity of Parkinsonian symptoms based on the finger-tapping task, three expert neurologists independently rated the recorded videos on a scale of 0-4, following the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS). The inter-rater reliability was excellent, with an intra-class correlation coefficient (ICC) of 0.88. We developed computer algorithms to obtain objective measurements that align with the MDS-UPDRS guideline and are strongly correlated with the neurologists' ratings. Our machine learning model trained on these measures outperformed two MDS-UPDRS certified raters, with a mean absolute error (MAE) of 0.58 points compared to the raters' average MAE of 0.83 points. However, the model performed slightly worse than the expert neurologists (0.53 MAE). The methodology can be replicated for similar motor tasks, providing the possibility of evaluating individuals with PD and other movement disorders remotely, objectively, and in areas with limited access to neurological care.

What over 1,000,000 participants tell us about online research protocols.

With the ever-increasing adoption of tools for online research, for the first time we have visibility on macro-level trends in research that were previously unattainable. However, until now this data has been siloed within company databases and unavailable to researchers. Between them, the online study creation and hosting tool Gorilla Experiment Builder and the recruitment platform Prolific hold metadata gleaned from millions of participants and over half a million studies. We analyzed a subset of this data (over 1 million participants and half a million studies) to reveal critical information about the current state of the online research landscape that researchers can use to inform their own study planning and execution. We analyzed this data to discover basic benchmarking statistics about online research that all researchers conducting their work online may be interested to know. In doing so, we identified insights related to: the typical study length, average completion rates within studies, the most frequent sample sizes, the most popular participant filters, and gross participant activity levels. We present this data in the hope that it can be used to inform research choices going forward and provide a snapshot of the current state of online research.

Relative Meaningfulness and Impacts of Symptoms in People with Early-Stage Parkinson's Disease.

BACKGROUND: Patient perspectives on meaningful symptoms and impacts in early Parkinson's disease (PD) are lacking and are urgently needed to clarify priority areas for monitoring, management, and new therapies. OBJECTIVE: To examine experiences of people with early-stage PD, systematically describe meaningful symptoms and impacts, and determine which are most bothersome or important. METHODS: Forty adults with early PD who participated in a study evaluating smartwatch and smartphone digital measures (WATCH-PD study) completed online interviews with symptom mapping to hierarchically delineate symptoms and impacts of disease from "Most bothersome" to "Not present," and to identify which of these were viewed as most important and why. Individual symptom maps were coded for types, frequencies, and bothersomeness of symptoms and their impacts, with thematic analysis of narratives to explore perceptions. RESULTS: The three most bothersome and important symptoms were tremor, fine motor difficulties, and slow movements. Symptoms had the greatest impact on sleep, job functioning, exercise, communication, relationships, and self-concept- commonly expressed as a sense of being limited by PD. Thematically, most bothersome symptoms were those that were personally limiting with broadest negative impact on well-being and activities. However, symptoms could be important to patients even when not present or limiting (e.g., speech, cognition). CONCLUSION: Meaningful symptoms of early PD can include symptoms that are present or anticipated future symptoms that are important to the individual. Systematic assessment of meaningful symptoms should aim to assess the extent to which symptoms are personally important, present, bothersome, and limiting.

Mapping Relevance of Digital Measures to Meaningful Symptoms and Impacts in Early Parkinson's Disease.

BACKGROUND: Adoption of new digital measures for clinical trials and practice has been hindered by lack of actionable qualitative data demonstrating relevance of these metrics to people with Parkinson's disease. OBJECTIVE: This study evaluated of relevance of WATCH-PD digital measures to monitoring meaningful symptoms and impacts of early Parkinson's disease from the patient perspective. METHODS: Participants with early Parkinson's disease (N = 40) completed surveys and 1:1 online-interviews. Interviews combined: 1) symptom mapping to delineate meaningful symptoms/impacts of disease, 2) cognitive interviewing to assess content validity of digital measures, and 3) mapping of digital measures back to personal symptoms to assess relevance from the patient perspective. Content analysis and descriptive techniques were used to analyze data. RESULTS: Participants perceived mapping as deeply engaging, with 39/40 reporting improved ability to communicate important symptoms and relevance of measures. Most measures (9/10) were rated relevant by both cognitive interviewing (70-92.5%) and mapping (80-100%). Two measures related to actively bothersome symptoms for more than 80% of participants (Tremor, Shape rotation). Tasks were generally deemed relevant if they met three participant context criteria: 1) understanding what the task measured, 2) believing it targeted an important symptom of PD (past, present, or future), and 3) believing the task was a good test of that important symptom. Participants did not require that a task relate to active symptoms or "real" life to be relevant. CONCLUSION: Digital measures of tremor and hand dexterity were rated most relevant in early PD. Use of mapping enabled precise quantification of qualitative data for more rigorous evaluation of new measures.

Using a smartwatch and smartphone to assess early Parkinson's disease in the WATCH-PD study.

Digital health technologies can provide continuous monitoring and objective, real-world measures of Parkinson's disease (PD), but have primarily been evaluated in small, single-site studies. In this 12-month, multicenter observational study, we evaluated whether a smartwatch and smartphone application could measure features of early PD. 82 individuals with early, untreated PD and 50 age-matched controls wore research-grade sensors, a smartwatch, and a smartphone while performing standardized assessments in the clinic. At home, participants wore the smartwatch for seven days after each clinic visit and completed motor, speech and cognitive tasks on the smartphone every other week. Features derived from the devices, particularly arm swing, the proportion of time with tremor, and finger tapping, differed significantly between individuals with early PD and age-matched controls and had variable correlation with traditional assessments. Longitudinal assessments will inform the value of these digital measures for use in future clinical trials.

WormWatch: Park soil surveillance reveals extensive Toxocara contamination across the UK and Ireland.

BACKGROUND: Toxocarosis is a globally distributed zoonotic disease, but sources of infection are not well documented over large geographical scales. To determine levels of environmental contamination, soil from 142 parks and recreational areas across the UK and Ireland was assessed for the presence of Toxocara. METHODS: Toxocara ova (eggs) were isolated from soil samples by sieving and flotation and then enumerated. Individual eggs were isolated and imaged, and a subset was characterised by species-specific PCR and Sanger sequencing. RESULTS: Characteristic Toxocara-type eggs were found in 86.6% of parks, with an average of 2.1 eggs per 50 g of topsoil. Representative eggs were confirmed as Toxocara canis by Sanger sequencing, with many eggs containing developed larvae, hence being viable and potentially infective. Positive samples were more common, and egg density was higher, in parks with greater perceived levels of dog fouling. LIMITATIONS: Samples were collected at a single timepoint and with limited spatial mapping within parks. Further study is needed to discern spatiotemporal differences within parks and recreational areas. CONCLUSION: Toxocara is widespread in soil in public parks, indicating a need for further efforts to reduce egg shedding from pet dogs. Standardised methods and large-scale surveys are required to evaluate risk factors for egg presence and the impact of interventions.

Monitoring gait at home with radio waves in Parkinson's disease: A marker of severity, progression, and medication response.

Parkinson's disease (PD) is the fastest-growing neurological disease in the world. A key challenge in PD is tracking disease severity, progression, and medication response. Existing methods are semisubjective and require visiting the clinic. In this work, we demonstrate an effective approach for assessing PD severity, progression, and medication response at home, in an objective manner. We used a radio device located in the background of the home. The device detected and analyzed the radio waves that bounce off people's bodies and inferred their movements and gait speed. We continuously monitored 50 participants, with and without PD, in their homes for up to 1 year. We collected over 200,000 gait speed measurements. Cross-sectional analysis of the data shows that at-home gait speed strongly correlates with gold-standard PD assessments, as evaluated by the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part III subscore and total score. At-home gait speed also provides a more sensitive marker for tracking disease progression over time than the widely used MDS-UPDRS. Further, the monitored gait speed was able to capture symptom fluctuations in response to medications and their impact on patients' daily functioning. Our study shows the feasibility of continuous, objective, sensitive, and passive assessment of PD at home and hence has the potential of improving clinical care and drug clinical trials.

Identifying and characterising sources of variability in digital outcome measures in Parkinson's disease.

Smartphones and wearables are widely recognised as the foundation for novel Digital Health Technologies (DHTs) for the clinical assessment of Parkinson's disease. Yet, only limited progress has been made towards their regulatory acceptability as effective drug development tools. A key barrier in achieving this goal relates to the influence of a wide range of sources of variability (SoVs) introduced by measurement processes incorporating DHTs, on their ability to detect relevant changes to PD. This paper introduces a conceptual framework to assist clinical research teams investigating a specific Concept of Interest within a particular Context of Use, to identify, characterise, and when possible, mitigate the influence of SoVs. We illustrate how this conceptual framework can be applied in practice through specific examples, including two data-driven case studies.

Metadata Framework to Support Deployment of Digital Health Technologies in Clinical Trials in Parkinson's Disease.

Sensor data from digital health technologies (DHTs) used in clinical trials provides a valuable source of information, because of the possibility to combine datasets from different studies, to combine it with other data types, and to reuse it multiple times for various purposes. To date, there exist no standards for capturing or storing DHT biosensor data applicable across modalities and disease areas, and which can also capture the clinical trial and environment-specific aspects, so-called metadata. In this perspectives paper, we propose a metadata framework that divides the DHT metadata into metadata that is independent of the therapeutic area or clinical trial design (concept of interest and context of use), and metadata that is dependent on these factors. We demonstrate how this framework can be applied to data collected with different types of DHTs deployed in the WATCH-PD clinical study of Parkinson's disease. This framework provides a means to pre-specify and therefore standardize aspects of the use of DHTs, promoting comparability of DHTs across future studies.

Epithelial-Mesenchymal Transition (EMT) as a Therapeutic Target.

Metastasis is the spread of cancer cells from the primary tumour to distant sites and organs throughout the body. It is the primary cause of cancer morbidity and mortality, and is estimated to account for 90% of cancer-related deaths. During the initial steps of the metastatic cascade, epithelial cancer cells undergo an epithelial-mesenchymal transition (EMT), and as a result become migratory and invasive mesenchymal-like cells while acquiring cancer stem cell properties and therapy resistance. As EMT is involved in such a broad range of processes associated with malignant transformation, it has become an increasingly interesting target for the development of novel therapeutic strategies. Anti-EMT therapeutic strategies could potentially not only prevent the invasion and dissemination of cancer cells, and as such prevent the formation of metastatic lesions, but also attenuate cancer stemness and increase the effectiveness of more classical chemotherapeutics. In this review, we give an overview about the pros and cons of therapies targeting EMT and discuss some already existing candidate drug targets and high-throughput screening tools to identify novel anti-EMT compounds.

Remote smartphone monitoring of Parkinson's disease and individual response to therapy.

Remote health assessments that gather real-world data (RWD) outside clinic settings require a clear understanding of appropriate methods for data collection, quality assessment, analysis and interpretation. Here we examine the performance and limitations of smartphones in collecting RWD in the remote mPower observational study of Parkinson's disease (PD). Within the first 6 months of study commencement, 960 participants had enrolled and performed at least five self-administered active PD symptom assessments (speeded tapping, gait/balance, phonation or memory). Task performance, especially speeded tapping, was predictive of self-reported PD status (area under the receiver operating characteristic curve (AUC) = 0.8) and correlated with in-clinic evaluation of disease severity (r = 0.71; P < 1.8 × 10-6) when compared with motor Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Although remote assessment requires careful consideration for accurate interpretation of RWD, our results support the use of smartphones and wearables in objective and personalized disease assessments.