RESUMEN
BACKGROUND AND OBJECTIVE: The assessment of dependence in older adults currently requires a manual collection of data taken from questionnaires. This process is time consuming for the clinicians and intrudes the daily life of the elderly. This paper aims to semi-automate the acquisition and analysis of health data to assess and predict the dependence in older adults while executing one instrumental activity of daily living (IADL). METHODS: In a mobile-health (m-health) scenario, we analyze whether the acquisition of data through wearables during the performance of IADLs, and with the help of machine learning techniques could replace the traditional questionnaires to evaluate dependence. To that end, we collected data from wearables, while older adults do the shopping activity. A trial supervisor (TS) labelled the different shopping stages (SS) in the collected data. We performed data pre-processing techniques over those SS and analyzed them with three machine learning algorithms: k-Nearest Neighbors (k-NN), Random Forest (RF) and Support Vector Machines (SVM). RESULTS: Our results confirm that it is possible to replace the traditional questionnaires with wearable data. In particular, the best learning algorithm we tried reported an accuracy of 97% in the assessment of dependence. We tuned the hyperparameters of this algorithm and used embedded feature selection technique to get the best performance with a subset of only 10 features out of the initial 85. This model considers only features extracted from four sensors of a single wearable: accelerometer, heart rate, electrodermal activity and temperature. Although these features are not observational, our current proposal is semi-automatic, because it needs a TS labelling the SS (with a smartphone application). In the future, this labelling process could be automatic as well. CONCLUSIONS: Our method can semi-automatically assess the dependence, without disturbing daily activities of elderly people. This method can save clinicians' time in the evaluation of dependence in older adults and reduce healthcare costs.
Asunto(s)
Telemedicina , Dispositivos Electrónicos Vestibles , Anciano , Algoritmos , Humanos , Aprendizaje Automático , Máquina de Vectores de SoporteRESUMEN
BACKGROUND: Currently, measurement tools to assess patient-reported outcomes for drug dependence are limited in their latent trait to adapt to the needs of individual patients while also maintaining comparability of scores across patients. Purpose/Objectives: To develop an item bank for computer adaptive testing (CAT) to measure severity of drug dependence. Methods: There were four phases: (1) review the literature of drug dependence measurement; (2) formulate an item list to be assessed by experts; (3) pretest our item list in two substance dependence treatment centers; and (4) field-test and conduct psychometric performance analysis with the final item bank. Additionally, based on our response data, a CAT simulation was used to validate the item bank, Drug Dependence CAT (DD-CAT). Results: The final drug dependence item bank - with a unidimensional configuration - contained 56 items with good item-fit, high discrimination, no differential item functioning, and covered all symptoms of diagnostic criteria for drug dependence. These results revealed that the final item bank was of good quality. Additionally, the results of a simulation CAT procedure with real response data indicated that the DD-CAT item bank exhibited acceptable and reasonable test reliability, content validity, and criterion-related validity. Conclusions/Importance: The proposed item bank for DD-CAT contained acceptable reliability and validity, and exhibited a shorter but efficient assessment of drug dependence. These psychometric properties can result in shorter test times, less information loss, and a reduction in the testing burden of patients.