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Studies show that executive functions and motor development are associated with each other and with learning ability. A more technological lifestyle combined with digital culture should be considered a viable alternative to stimulate children's development. Therefore, this study aimed to present a systematic mapping of the literature involving executive functions, motor development, and the use of digital games in intervention programs for elementary school children from 6 to 11 years old. Four databases were researched: PubMed, Scielo, Science Direct, and SCOPUS, including publications between 2012 and March 2021. The initial results indicated 4881 records. After the selection process, 15 manuscripts that presented the central theme of the study were selected. The main results indicated that intervention strategies are rather heterogeneous. Most of the studies demonstrated efficient results after intervention protocols, many of them were conducted in Europe, and 46% occurred in a school environment. No research was identified involving technological solutions using executive functions, motor development, and digital games in an integrated manner. Hence, this constitutes a field of future scientific research.

Anxiety and stress are common emotional responses for human beings, but their chronic manifestation can lead to physical and psychological illnesses. The advancement of sensing technologies, such as Internet of Things, has contributed to the understanding and assisting events related to anxiety and stress. However, the main challenge is knowing which approaches can be used to better monitor these emotions and assist people. Based on a systematic literature review, this work analyzed studies both to determine how data is collected, and to monitor anxiety and stress levels. Two taxonomies synthesize the techniques mapped. The results indicated more emphasis on studying stress than anxiety and more focus on detecting anxiety and stress levels than on assisting the user. Among the main techniques to collect data, 62.5% of the studies used physiological data like heart data, and for data analysis techniques, 48% of the studies used Decision Trees. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is an incurable disease and a type of dementia. About 55 million people around the world have AD. However, technologies have been used to assist in the healthcare of AD, supporting physicians in the palliative care of patients. This article presents a systematic mapping study (SMS) to identify articles that use technologies to monitor patients with AD in order to show an overview of the literature, identifying gaps and research opportunities in this field. The scientific contribution of this work is to identify monitoring technologies related to AD and highlight current trends on the subject. The paper uses the term technologies as hardware infrastructure and devices or systems without considering software technologies. In addition, this article proposes a taxonomy for the domain of technologies applied to AD patients. The SMS study was conducted in six databases, including articles from 1997 to 2021. An initial search resulted in 7,781 articles. After applying filter criteria, throwing automatic selection on databases, and manual assortment, 171 articles were selected. Subsequently, a second search was performed to reduce the list of articles and filter by the specific search objective of articles focused on technologies for monitoring with tracking, resulting in 74 works. The main results obtained are: (1) a relevant number of articles (43.42%) reported solutions used in sensor-based devices; (2) several works (33.33%) have the interaction focus on Position/Distance/Proximity/Location sensor type; (3) another group of articles has a secondary focus on Emergency help (18.97%). The results indicated the need for technologies to help caregivers monitor patients, in addition to evidence of research opportunities in palliative care and support for the daily activities of AD patients. [ABSTRACT FROM AUTHOR]

Although many benefits emerge from the growing capabilities of smartphones, there are also concerns related to the long-term hyper-connected experience. Based on a systematic mapping method, this study investigates the primary factors of problematic smartphone use (PSU). Initially, this mapping considered ten academic databases, which allowed the analysis of 436 studies, and the creation of a taxonomy that categorises technology addiction topics such as the Internet, Smartphones, Video games, and Electronic devices. After the initial search and filtering, the study selected and deeply analysed 115 articles concerning the PSU influences on mental health, proposing a taxonomy to classify mental disorders and common symptoms related to PSU. The outcomes suggest that those who fear missing out on important events, females, depressed, anxious, and bored people are prone to PSU, reinforcing the importance of understanding the factors that lead a person to use smartphones in a problematic way and alternatives to help people cope with PSU. Scales such as the Smartphone Addiction Scale-Short Version (SAS-SV) and strategies such as cognitive-behavioural therapy (CBT) and limiting smartphone access are being used to handle PSU. Finally, this study presents implications and recommendations for future research in this area. [ABSTRACT FROM AUTHOR]

Noncommunicable diseases (NCDs) or chronic diseases are responsible for 41 million deaths each year, equivalent to 71% of all worldwide deaths. Many technologies are used to aid the treatment of NCDs, and data analysis has been used as an approach to improve the understanding of human behaviour related to risk factors. This study aims to distinguish how human behaviour data analysis has been applied to support the treatment and prevention of NCDs, what technologies are currently used, and what gaps are still left unexplored. We conducted a systematic mapping study to analyse academic articles published from 2010 to September 2021. A filtering process mitigated article bias by reviewing, analysing, and classifying 41 works from 12,395 collected. The main results obtained presented that 43% applied data analysis in depression, 17% applied for general NCDs, and 12% for diabetes. Whereas, machine learning represents 60% of technologies found in the articles, mobile devices 58%, and wearables 29%. This study proposes two taxonomies obtained from the analysis of the selected articles that allow systematised guides to access the knowledge produced in the study. In addition, the taxonomies link technologies used to identify human behaviour with associated NCDs. [ABSTRACT FROM AUTHOR]

Studies indicate the growth and relevance of systems that can recognize affective states in the most different areas. A tendency to more natural environments could be noticed that considering physical or physiological signs and the individual's relationship with the environment, other people, and daily activities. Therefore, what is the importance of combining ubiquitous computing, affective computing, and the contributions of context-aware information to provide more accurate and intelligent affective systems? This study presents a systematic literature mapping about the use of contextual information to identify affective states, which covered articles published between 2010 and October 2021, resulting in 1.638 studies. After applying filters, which we explain further in this article, we selected 49 works to answer a set of research questions. The results indicate that physiological data was the main parameter for recognizing affective signs (62.3%, 33/53), followed by visual data (32.1%, 17/53). The links between context and affective signs presented a more significant occurrence in the combination of contexts related to activities and physiological data (34%, 18/53). [ABSTRACT FROM AUTHOR]