Continuous Assessment of Function and Disability via Mobile Sensing: Real-World Data-Driven Feasibility Study.

BACKGROUND: Functional limitations are associated with poor clinical outcomes, higher mortality, and disability rates, especially in older adults. Continuous assessment of patients' functionality is important for clinical practice; however, traditional questionnaire-based assessment methods are very time-consuming and infrequently used. Mobile sensing offers a great range of sources that can assess function and disability daily. OBJECTIVE: This work aims to prove the feasibility of an interpretable machine learning pipeline for predicting function and disability based on the World Health Organization Disability Assessment Schedule (WHODAS) 2.0 outcomes of clinical outpatients, using passively collected digital biomarkers. METHODS: One-month-long behavioral time-series data consisting of physical and digital activity descriptor variables were summarized using statistical measures (minimum, maximum, mean, median, SD, and IQR), creating 64 features that were used for prediction. We then applied a sequential feature selection to each WHODAS 2.0 domain (cognition, mobility, self-care, getting along, life activities, and participation) in order to find the most descriptive features for each domain. Finally, we predicted the WHODAS 2.0 functional domain scores using linear regression using the best feature subsets. We reported the mean absolute errors and the mean absolute percentage errors over 4 folds as goodness-of-fit statistics to evaluate the model and allow for between-domain performance comparison. RESULTS: Our machine learning-based models for predicting patients' WHODAS functionality scores per domain achieved an average (across the 6 domains) mean absolute percentage error of 19.5%, varying between 14.86% (self-care domain) and 27.21% (life activities domain). We found that 5-19 features were sufficient for each domain, and the most relevant being the distance traveled, time spent at home, time spent walking, exercise time, and vehicle time. CONCLUSIONS: Our findings show the feasibility of using machine learning-based methods to assess functional health solely from passively sensed mobile data. The feature selection step provides a set of interpretable features for each domain, ensuring better explainability to the models' decisions-an important aspect in clinical practice.

Automatic patient functionality assessment from multimodal data using deep learning techniques - Development and feasibility evaluation.

Wearable devices and mobile sensors enable the real-time collection of an abundant source of physiological and behavioural data unobtrusively. Unlike traditional in-person evaluation or ecological momentary assessment (EMA) questionnaire-based approaches, these data sources open many possibilities in remote patient monitoring. However, defining robust models is challenging due to the data's noisy and frequently missing observations. This work proposes an attention-based Long Short-Term Memory (LSTM) neural network-based pipeline for predicting mobility impairment based on WHODAS 2.0 evaluation from such digital biomarkers. Furthermore, we addressed the missing observation problem by utilising hidden Markov models and the possibility of including information from unlabelled samples via transfer learning. We validated our approach using two wearable/mobile sensor data sets collected in the wild and socio-demographic information about the patients. Our results showed that in the WHODAS 2.0 mobility impairment prediction task, the proposed pipeline outperformed a prior baseline while additionally providing interpretability with attention heatmaps. Moreover, using a much smaller cohort via task transfer learning, the same model could learn to predict generalised anxiety severity accurately based on GAD-7 scores.

Virtual reality and speech analysis for the assessment of impulsivity and decision-making: protocol for a comparison with neuropsychological tasks and self-administered questionnaires.

INTRODUCTION: Impulsivity is present in a range of mental disorders and has been associated with suicide. Traditional measures of impulsivity have certain limitations, such as the lack of ecological validity. Virtual reality (VR) may overcome these issues. This study aims to validate the VR assessment tool 'Spheres & Shield Maze Task' and speech analysis by comparing them with traditional measures. We hypothesise that these innovative tools will be reliable and acceptable by patients, potentially improving the simultaneous assessment of impulsivity and decision-making. METHODS AND ANALYSIS: This study will be carried out at the University Hospital Fundación Jiménez Díaz (Madrid, Spain). Our sample will consist of adults divided into three groups: psychiatric outpatients with a history of suicidal thoughts and/or behaviours, psychiatric outpatients without such a history and healthy volunteers. The target sample size was established at 300 participants (100 per group). Participants will complete the Barratt Impulsiveness Scale 11; the Urgency, Premeditation, Perseverance, Sensation Seeking, Positive Urgency, Impulsive Behaviour Scale; Iowa Gambling Task; Continuous Performance Test; Stop signal Task, and Go/no-go task, three questions of emotional affect, the Spheres & Shield Maze Task and two satisfaction surveys. During these tasks, participant speech will be recorded. Construct validity of the VR environment will be calculated. We will also explore the association between VR-assessed impulsivity and history of suicidal thoughts and/or behaviour, and the association between speech and impulsivity and decision-making. ETHICS AND DISSEMINATION: This study was approved by the Ethics Committee of the University Hospital Fundación Jiménez Díaz (PIC128-21_FJD). Participants will be required to provide written informed consent. The findings will be presented in a series of manuscripts that will be submitted to peer-reviewed journals for publication. TRIAL REGISTRATION NUMBER: NCT05109845; Pre-results.