Wearables for Stress Management: A Scoping Review.

In recent years, wearable devices have been increasingly used to monitor people's health. This has helped healthcare professionals provide timely interventions to support their patients. In this study, we investigated how wearables help people manage stress. We conducted a scoping review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) standard to address this question. We searched studies in Scopus, IEEE Explore, and Pubmed databases. We included studies reporting user evaluations of wearable-based strategies, reporting their impact on health or usability outcomes. A total of 6259 studies were identified, of which 40 met the inclusion criteria. Based on our findings, we identified that 21 studies report using commercial wearable devices; the most common are smartwatches and smart bands. Thirty-one studies report significant stress reduction using different interventions and interaction modalities. Finally, we identified that the interventions are designed with the following aims: (1) to self-regulate during stress episodes, (2) to support self-regulation therapies for long-term goals, and (3) to provide stress awareness for prevention, consisting of people's ability to recall, recognize and understand their stress.

ECG Standards and Formats for Interoperability between mHealth and Healthcare Information Systems: A Scoping Review.

Interoperability is defined as the ability of a system or device to communicate between different technologies and software applications. This allows the exchange and use of data in an efficient, precise, and robust way. The present article gives researchers and healthcare information systems developers a qualitative and quantitative synthesis of the state of knowledge related to data formats and data standards proposed for mHealth devices interoperability in healthcare information systems that retrieve and store ECG data. We carry out a scoping review to answer to following questions: (1) What digital data formats or data standards have been proposed for the interoperability of electrocardiograph data between traditional healthcare information systems and mobile healthcare information systems? (2) What are the advantages and disadvantages of these data formats or data standards? The scoping review was conducted in four databases in accordance with the JBI methodology for scoping reviews, and in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). A total of 4018 studies were identified of which 30 studies met the inclusion criteria. Based on our findings, we identify four standards and nine formats for capturing and storing streaming ECG data in mobile health applications. The standards used were HL7, SCP-ECG, x73-PHD, and PDF/A. Formats include CSV, PDF-ECG, and seven XML-based formats. These are ECG-XML, HL7-XML, mPCG-XML, mECGML, JSON, SaECG, and CDA R2.

Stimulating the Involvement of Family Members in the Medication Management Activities of Older Adults Through Ambient Displays: Qualitative Study.

Little attention has been paid to how medication management technologies, designed for older adults, modify the participation of family caregivers. We developed a tablet-based ambient display that provides external cues to remind and motivate older adults to take their medications. This study aimed to understand the effect of ambient displays on the involvement of family members in the elderly's medication management. We conducted a 10-week study consisting of interviews administered weekly to nine elderly-caregivers. We identify that new involvement patterns of the family caregivers were provoked through external cues, which made them aware of older adults' medication adherence and encouraged younger relatives to help older adults.

Dataset for localization and classification of Medjool dates in digital images.

Nowadays, harvesting, sorting, and packaging fruit and vegetables are still done manually, despite the hard work this represents. The features that experts commonly use to sorting the date palm fruit are size, color, shape, and texture. Recently, it has started to design and develop artificial vision systems that consider the criteria of size, color, shape, and texture to automate these processes. However, the development of these systems is complex due to the lack of labeled datasets that facilitate the creation of models to locate, recognize and classify palm date fruit. This dataset is entitled Medjool, an image dataset of different sizes and maturity levels of Medjool dates. Researchers may use this data to develop a model for automatic location, recognition, classification, and visual counting of the Medjool dates on trays taking into account their visual features such as shape, color, size, and texture. This dataset was collected from the first-round harvest at Palmeras RQ Ranch in Mexicali, Mexico. Images acquisition was performed in natural light. The dataset comprises 2,576 annotated images in two formats, YOLO and PascalVOC format.

Supporting the Medication Adherence of Older Mexican Adults Through External Cues Provided With Ambient Displays: Feasibility Randomized Controlled Trial.

BACKGROUND: Problems with prospective memory, which refers to the ability to remember future intentions, cause deficits in basic and instrumental activities of daily living, such as taking medications. Older adults show minimal deficits when they rely on mostly preserved and relatively automatic associative retrieval processes. On the basis of this, we propose to provide external cues to support the automatic retrieval of an intended action, that is, to take medicines. To reach this end, we developed the Medication Ambient Display (MAD), a system that unobtrusively presents relevant information (unless it requires the users' attention) and uses different abstract modalities to provide external cues that enable older adults to easily take their medications on time and be aware of their medication adherence. OBJECTIVE: This study aimed to assess the adoption and effect of external cues provided through ambient displays on medication adherence in older adults. METHODS: A total of 16 older adults, who took at least three medications and had mild cognitive impairment, participated in the study. We conducted a 12-week feasibility study in which we used a mixed methods approach to collect qualitative and quantitative evidence. The study included baseline, intervention, and postintervention phases. Half of the participants were randomly allocated to the treatment group (n=8), and the other half was assigned to the control group (n=8). During the study phases, research assistants measured medication adherence weekly through the pill counting technique. RESULTS: The treatment group improved their adherence behavior from 80.9% at baseline to 95.97% using the MAD in the intervention phase. This decreased to 76.71% in the postintervention phase when the MAD was no longer being used. Using a one-way repeated measures analysis of variance and a post hoc analysis using the Tukey honestly significant difference test, we identified a significant statistical difference between the preintervention and intervention phases (P=.02) and between the intervention and postintervention phases (P=.002). In addition, the medication adherence rate of the treatment group (95.97%) was greater than that of the control group (88.18%) during the intervention phase. Our qualitative results showed that the most useful cues were the auditory reminders, followed by the stylized representations of medication adherence. We also found that the MAD's external cues not only improved older adults' medication adherence but also mediated family caregivers' involvement. CONCLUSIONS: The findings of this study demonstrate that using ambient modalities for implementing external cues is useful for drawing the attention of older adults to remind them to take medications and to provide immediate awareness on adherence behavior. TRIAL REGISTRATION: ClinicalTrials.gov NCT04289246; https://tinyurl.com/ufjcz97.

Infrastructure-Less Indoor Localization Using the Microphone, Magnetometer and Light Sensor of a Smartphone.

In this paper, we present the development of an infrastructure-less indoor location system (ILS), which relies on the use of a microphone, a magnetometer and a light sensor of a smartphone, all three of which are essentially passive sensors, relying on signals available practically in any building in the world, no matter how developed the region is. In our work, we merge the information from those sensors to estimate the user's location in an indoor environment. A multivariate model is applied to find the user's location, and we evaluate the quality of the resulting model in terms of sensitivity and specificity. Our experiments were carried out in an office environment during summer and winter, to take into account changes in light patterns, as well as changes in the Earth's magnetic field irregularities. The experimental results clearly show the benefits of using the information fusion of multiple sensors when contrasted with the use of a single source of information.

Magnetic field feature extraction and selection for indoor location estimation.

User indoor positioning has been under constant improvement especially with the availability of new sensors integrated into the modern mobile devices, which allows us to exploit not only infrastructures made for everyday use, such as WiFi, but also natural infrastructure, as is the case of natural magnetic field. In this paper we present an extension and improvement of our current indoor localization model based on the feature extraction of 46 magnetic field signal features. The extension adds a feature selection phase to our methodology, which is performed through Genetic Algorithm (GA) with the aim of optimizing the fitness of our current model. In addition, we present an evaluation of the final model in two different scenarios: home and office building. The results indicate that performing a feature selection process allows us to reduce the number of signal features of the model from 46 to 5 regardless the scenario and room location distribution. Further, we verified that reducing the number of features increases the probability of our estimator correctly detecting the user's location (sensitivity) and its capacity to detect false positives (specificity) in both scenarios.