PurposeA literature review is presented that aims to summarize and compare current methods to evaluate sleep.MethodsCurrent sleep assessment methods have been classified according to different criteria; e.g., objective (polysomnography, actigraphy…) vs. subjective (sleep questionnaires, diaries…), contact vs. contactless devices, and need for medical assistance vs. self-assessment. A comparison of validation studies is carried out for each method, identifying their sensitivity and specificity reported in the literature. Finally, the state of the market has also been reviewed with respect to customers’ opinions about current sleep apps.ResultsA taxonomy that classifies the sleep detection methods. A description of each method that includes the tendencies of their underlying technologies analyzed in accordance with the literature. A comparison in terms of precision of existing validation studies and reports.DiscussionIn order of accuracy, sleep detection methods may be arranged as follows: Questionnaire < Sleep diary < Contactless devices < Contact devices < PolysomnographyA literature review suggests that current subjective methods present a sensitivity between 73% and 97.7%, while their specificity ranges in the interval 50%–96%. Objective methods such as actigraphy present a sensibility higher than 90%. However, their specificity is low compared to their sensitivity, being one of the limitations of such technology. Moreover, there are other factors, such as the patient’s perception of her or his sleep, that can be provided only by subjective methods. Therefore, sleep detection methods should be combined to produce a synergy between objective and subjective methods. The review of the market indicates the most valued sleep apps, but it also identifies problems and gaps, e.g., many hardware devices have not been validated and (especially software apps) should be studied before their clinical use.

Sleep assessment is a fundamental part of health evaluation. In fact, many diseases (such as obesity, diabetes, or hypertension, as well as psychiatric, neurological, and cardiovascular diseases) produce sleep disorders that are often used as indicators, diagnosis (symptoms), or even as predictors (eg, for depression) of health. For this reason, many efforts have been devoted to designing methods to control and report on sleep quality. Two of the most used sleep assessment tools are sleep questionnaires and sleep diaries. Both methods have a very low cost are easy to administer do not require a sleep centre (unlike, eg, polysomnography), and can be self-administered. Most important, as it has been shown in recent studies, their accuracy is relatively high. In this survey, we systematically review and compare these tools. We examine the evolution of sleep questionnaires and diaries over time, and compare their structure and usage. We also review the validation studies and comparatives performed in previous studies. This allows us to compare the relative sensitivities and specificities of these methods. Modern sleep diaries come in the form of an app. Therefore, we also present the most advanced and used apps, and discuss their advantages over classical paper diaries.

This article surveys previous work on program slicing-based techniques. For each technique, we describe its features, its main applications, and a common example of slicing using such a technique. After discussing each technique separately, all of them are compared in order to clarify and establish the relations between them. This comparison gives rise to a classification of techniques which can help to guide future research directions in this field.

Algorithmic debugging is a debugging technique that has been extended to practically all programming paradigms. Roughly speaking, the technique constructs an internal representation of all (sub)computations performed during the execution of a buggy program; and then, it asks the programmer about the correctness of such computations. The answers of the programmer guide the search for the bug until it is isolated by discarding correct parts of the program. After twenty years of research in algorithmic debugging many different techniques have appeared to improve the original proposal. Surprisingly, no study exists that joins together all these techniques and compares their advantages and their performance. This article presents a study that compares all current algorithmic debugging techniques and analyzes their differences and their costs. The research identifies the dimensions on which each strategy relies. This information allows us to combine the strong points of different strategies.

This work presents DDJ, an algorithmic debugger for Java. The main advantage of DDJ with respect to previous algorithmic debuggers is its scalability. DDJ has a new architecture based on the use of cache memories that allows it to scale both in time and memory. In addition, it includes new techniques that allow the debugger to start the debugging session even before the execution tree has been produced. We present the new architecture, and describe the main features of this debugger together with a usage scenario.