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This paper describes a multiagent architecture integrated system designed to supervise infrastructures in solar farms. The system enables monitoring the environment by means of sensor networks that are in charge of collecting data. It is designed using a hybrid model composed of an inference engine and an ontology. The former makes the system intelligent, while the latter structures knowledge. We have also developed a tool to configure and use the multiagent system in a simple and intuitive way. [ABSTRACT FROM AUTHOR]

Background: Fatigue is the most common symptom in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and long COVID, impacting patients' quality of life; however, there is currently a lack of evidence-based context-aware tools for fatigue self-management in these populations., Objective: This study aimed to (1) address fatigue in ME/CFS and long COVID through the development of digital mobile health solutions for self-management, (2) predict perceived fatigue severity using real-time data, and (3) assess the feasibility and potential benefits of personalized digital mobile health solutions., Methods: The MyFatigue project adopts a patient-centered approach within the participatory health informatics domain. Patient representatives will be actively involved in decision-making processes. This study combines inductive and deductive research approaches, using qualitative studies to generate new knowledge and quantitative methods to test hypotheses regarding the relationship between factors like physical activity, sleep behaviors, and perceived fatigue in ME/CFS and long COVID. Co-design methods will be used to develop a personalized digital solution for fatigue self-management based on the generated knowledge. Finally, a pilot study will evaluate the feasibility, acceptance, and potential benefits of the digital health solution., Results: The MyFatigue project opened to enrollment in November 2023. Initial results are expected to be published by the end of 2024., Conclusions: This study protocol holds the potential to expand understanding, create personalized self-management approaches, engage stakeholders, and ultimately improve the well-being of individuals with ME/CFS and long COVID., International Registered Report Identifier (irrid): PRR1-10.2196/50157., (©Enrique Dorronzoro-Zubiete, Jesús Castro-Marrero, Jorge Ropero, José Luis Sevillano-Ramos, María Dolores Hernández, Ramon Sanmartin Sentañes, Jose Alegre-Martin, Patricia Launois-Obregón, Isabel Martin-Garrido, Asuncion Luque Budia, Juan R Lacalle-Remigio, Luis Béjar Prado, Octavio Rivera Romero. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 12.04.2024.)

Background: Health care services are undergoing a digital transformation in which the Participatory Health Informatics field has a key role. Within this field, studies aimed to assess the quality of digital tools, including mHealth apps, are conducted. Privacy is one dimension of the quality of an mHealth app. Privacy consists of several components, including organizational, technical, and legal safeguards. Within legal safeguards, giving transparent information to the users on how their data are handled is crucial. This information is usually disclosed to users through the privacy policy document. Assessing the quality of a privacy policy is a complex task and several scales supporting this process have been proposed in the literature. However, these scales are heterogeneous and even not very objective. In our previous study, we proposed a checklist of items guiding the assessment of the quality of an mHealth app privacy policy, based on the General Data Protection Regulation., Objective: To refine the robustness of our General Data Protection Regulation-based privacy scale to assess the quality of an mHealth app privacy policy, to identify new items, and to assign weights for every item in the scale., Methods: A two-round modified eDelphi study was conducted involving a privacy expert panel., Results: After the Delphi process, all the items in the scale were considered "important" or "very important" (4 and 5 in a 5-point Likert scale, respectively) by most of the experts. One of the original items was suggested to be reworded, while eight tentative items were suggested. Only two of them were finally added after Round 2. Eleven of the 16 items in the scale were considered "very important" (weight of 1), while the other 5 were considered "important" (weight of 0.5)., Conclusion: The Benjumea privacy scale is a new robust tool to assess the quality of an mHealth app privacy policy, providing a deeper and complementary analysis to other scales. Also, this robust scale provides a guideline for the development of high-quality privacy policies of mHealth apps., Competing Interests: None declared., (The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/).)

Introduction: Mental health is one of the major global concerns in the field of healthcare. The emergence of digital solutions is proving to be a great aid for individuals suffering from mental health disorders. These solutions are particularly useful and effective when they are personalized. The objective of this paper is to understand the personalization factors and the methods that have been used to collect information to personalize the digital mental health solutions., Methods: This paper builds on a previous review that analyzed the personalization of digital solutions in mHealth, and expands on the extracted information for the specific case of mental health., Results: Ten mental health digital solutions have been analyzed. The paper focuses on targeted conditions, personalization factors and the methods used for collecting personalization factors., Discussion: The analyzed mental health digital solutions cover a wide range of health conditions. It is remarkable that most articles do not explicitly mention the factors used to personalize the solution. Among the solutions that mention them, there is a great diversity of factors utilized, such as age, gender, user preferences, and subjective behavior. The authors point out the methods for obtaining data to personalize the solutions, including in-app questionnaires, self-reports, and usage data of the solutions., Conclusions: The analysis of current mental health digital solutions emphasizes the need to create guidelines for designing personalized digital solutions for mental health.

Introduction: Mobile health, or mHealth, is based on mobile information and communication technologies and provides solutions for empowering individuals to participate in healthcare. Personalisation techniques have been used to increase user engagement and adherence to interventions delivered as mHealth solutions. This study aims to explore the current state of personalisation in mHealth, including its current trends and implementation., Materials and Methods: We conducted a review following PRISMA guidelines. Four databases (PubMed, ACM Digital Library, IEEE Xplore, and APA PsycInfo) were searched for studies on mHealth solutions that integrate personalisation. The retrieved papers were assessed for eligibility and useful information regarding integrated personalisation techniques., Results: Out of the 1,139 retrieved studies, 62 were included in the narrative synthesis. Research interest in the personalisation of mHealth solutions has increased since 2020. mHealth solutions were mainly applied to endocrine, nutritional, and metabolic diseases; mental, behavioural, or neurodevelopmental diseases; or the promotion of healthy lifestyle behaviours. Its main purposes are to support disease self-management and promote healthy lifestyle behaviours. Mobile applications are the most prevalent technological solution. Although several design models, such as user-centred and patient-centred designs, were used, no specific frameworks or models for personalisation were followed. These solutions rely on behaviour change theories, use gamification or motivational messages, and personalise the content rather than functionality. A broad range of data is used for personalisation purposes. There is a lack of studies assessing the efficacy of these solutions; therefore, further evidence is needed., Discussion: Personalisation in mHealth has not been well researched. Although several techniques have been integrated, the effects of using a combination of personalisation techniques remain unclear. Although personalisation is considered a persuasive strategy, many mHealth solutions do not employ it., Conclusions: Open research questions concern guidelines for successful personalisation techniques in mHealth, design frameworks, and comprehensive studies on the effects and interactions among multiple personalisation techniques., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Circulating acylethanolamides (NAEs) are bioactive signaling molecules that modulate multiple homeostatic functions including mood and hedonic responses. Variations in their plasma concentrations are associated with substance use disorders (SUD) and recent studies suggest that psychotropic medication might influence its circulating levels, limiting its use as a clinical biomarker of addiction. In addition, they might have a role as mediators of the pharmacological effects of psychotropic drugs. Thus, in mild depression, the response to selective serotonin reuptake inhibitor-type antidepressants (SSRI) is associated with a marked increase in circulating NAEs. To further investigate if antidepressants are able to modify the plasma concentration of NAEs in SUD patients, we analyzed the circulating levels of NAEs in 333 abstinent and 175 healthy controls on the basis of the treatment with SSRI antidepressants. As described previously, SUD patients display higher concentrations of NAEs than those measured in a control population. This increase was not further modified by antidepressant therapy. Only marginal increases in palmitoylethanolamide (PEA), oleoylethanolamide (OEA), or docosatetraenoyl-ethanolamide (DEA) were found, and the net effect was very small. Thus, our study shows that treatment with SSRI-type antidepressants does not modify the clinical utility of monitoring enhanced NAE production as biomarkers of SUD. In addition, the possibility that a blunted NAE response to antidepressant therapy might be related to the loss of efficacy of SSRIs in dual depression emerges as an attractive hypothesis that needs to be addressed in future studies.

Plasma acylethanolamides (NAEs), including the endocannabinoid anandamide (AEA), have been proposed as circulating biomarkers of substance use disorders. However, the concentration of these lipid transmitters might be influenced by the use of drugs prescribed for either the treatment of addiction or the associated psychiatric co-morbidities such as psychosis. As an example, neuroleptics, used for attenuation of psychotic symptoms and sedation, might theoretically interfere with the monoamine-mediated production of NAEs, obstructing the interpretation of plasma NAEs as clinical biomarkers. To solve the lack of information on the impact of neuroleptics on the concentration of NAEs, we evaluated the concentrations of NAEs in a control group and compared them to those present in (a) substance use disorders (SUD) patients that are not prescribed with neuroleptics, and (b) SUD patients (both alcohol use disorder and cocaine use disorder patients) using neuroleptics. The results demonstrate that SUD patients exhibited greater concentrations of NAEs than the control population, affecting all species with the exception of stearoylethanolamide (SEA) and palmitoleoylethanolamide (POEA). Neuroleptic treatment enhanced the concentrations of NAEs, especially those of AEA, linoleoylethanolamide (LEA), and oleoylethanolamide (OEA). This effect of neuroleptic treatment was observed independently of the drug addiction that motivated the demand for treatment (either alcohol or cocaine). This study remarks the need to control the current use of psychotropic medication as a potential confounding variable when considering the use of NAEs as biomarkers in SUD.

Background: Cancer patients are increasingly using mobile health (mHealth) apps to take control of their health. Many studies have explored their efficiency, content, usability, and adherence; however, these apps have created a new set of privacy challenges, as they store personal and sensitive data., Objective: The purpose of this study was to refine and evaluate a scale based on the General Data Protection Regulation and assess the fairness of privacy policies of mHealth apps., Methods: Based on the experience gained from our previous work, we redefined some of the items and scores of our privacy scale. Using the new version of our scale, we conducted a case study in which we analyzed the privacy policies of cancer Android apps. A systematic search of cancer mobile apps was performed in the Spanish version of the Google Play website., Results: The redefinition of certain items reduced discrepancies between reviewers. Thus, use of the scale was made easier, not only for the reviewers but also for any other potential users of our scale. Assessment of the privacy policies revealed that 29% (9/31) of the apps included in the study did not have a privacy policy, 32% (10/31) had a score over 50 out of a maximum of 100 points, and 39% (12/31) scored fewer than 50 points., Conclusions: In this paper, we present a scale for the assessment of mHealth apps that is an improved version of our previous scale with adjusted scores. The results showed a lack of fairness in the mHealth app privacy policies that we examined, and the scale provides developers with a tool to evaluate their privacy policies., (©Jaime Benjumea, Jorge Ropero, Octavio Rivera-Romero, Enrique Dorronzoro-Zubiete, Alejandro Carrasco. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 28.07.2020.)

Background: Privacy has always been a concern, especially in the health domain. The proliferation of mobile health (mHealth) apps has led to a large amount of sensitive data being generated. Some authors have performed privacy assessments of mHealth apps. They have evaluated diverse privacy components; however, different authors have used different criteria for their assessments., Objective: This scoping review aims to understand how privacy is assessed for mHealth apps, focusing on the components, scales, criteria, and scoring methods used. A simple taxonomy to categorize the privacy assessments of mHealth apps based on component evaluation is also proposed., Methods: We followed the methodology defined by Arksey and O'Malley to conduct a scoping review. Included studies were categorized based on the privacy component, which was assessed using the proposed taxonomy., Results: The database searches retrieved a total of 710 citations-24 of them met the defined selection criteria, and data were extracted from them. Even though the inclusion criteria considered articles published since 2009, all the studies that were ultimately included were published from 2014 onward. Although 12 papers out of 24 (50%) analyzed only privacy, 8 (33%) analyzed both privacy and security. Moreover, 4 papers (17%) analyzed full apps, with privacy being just part of the assessment. The evaluation criteria used by authors were heterogeneous and were based on their experience, the literature, and/or existing legal frameworks. Regarding the set of items used for the assessments, each article defined a different one. Items included app permissions, analysis of the destination, analysis of the content of communications, study of the privacy policy, use of remote storage, and existence of a password to access the app, among many others. Most of the included studies provided a scoring method that enables the comparison of privacy among apps., Conclusions: The privacy assessment of mHealth apps is a complex task, as the criteria used by different authors for their evaluations are very heterogeneous. Although some studies about privacy assessment have been conducted, a very large set of items to evaluate privacy has been used up until now. In-app information and privacy policies are primarily utilized by the scientific community to extract privacy information from mHealth apps. The creation of a scale based on more objective criteria is a desirable step forward for privacy assessment in the future., (©Jaime Benjumea, Jorge Ropero, Octavio Rivera-Romero, Enrique Dorronzoro-Zubiete, Alejandro Carrasco. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 02.07.2020.)

A nondestructive and faster methodology to quantify mechanical properties of polypropylene (PP) pellets, obtained from an industrial plant, was developed with Raman spectroscopy. Raman spectra data were obtained from several types of samples such as homopolymer PP, random ethylene-propylene copolymer, and impact ethylene-propylene copolymer. Multivariate calibration models were developed by relating the changes in the Raman spectra to mechanical properties determined by ASTM tests (Young's traction modulus, tensile strength at yield, elongation at yield on traction, and flexural modulus at 1% secant). Several strategies were evaluated to build robust models including the use of preprocessing methods (baseline correction, vector normalization, de-trending, and standard normal variate), selecting the best subset of wavelengths to model property response and discarding irrelevant variables by applying genetic algorithm (GA). Linear multivariable models were investigated such as partial least square regression (PLS) and PLS with genetic algorithm (GA-PLS) while nonlinear models were implemented with artificial neural network (ANN) preceded by GA (GA-ANN). The best multivariate calibration models were obtained when a combination of genetic algorithms and artificial neural network were used on Raman spectral data with relative standard errors (%RSE) from 0.17 to 0.41 for training and 0.42 to 0.88% validation data sets., (© The Author(s) 2016.)

Objective: To describe the anatomical characteristics and vascularization of the biaxial hair free scrotal flap (BAES-flap) and to detail its surgical application to reconstruction of the more complex urethral strictures., Methods: We performed macro and micro anatomical dissections of the scrotum in 15 cryopreserved cadavers for the study of the arterial microvascularization of the BAES flap, and this anatomical knowledge has been implemented with the aim to improve the anterior and posterior urethra reconstructive surgical technique. For scrotal skin conditioning we performed definitive hair removal with the alexandrite laser., Results: The BAES flap, thanks to its rich biaxial vascularization, its anatomical disposition over the urethral axis, and the suitable characteristics of hair free scrotal skin, has allowed us to perform successful one-step urethral reconstruction in complex cases such as panurethral disease, multioperated hypospadias, failed urethroplasties and obliterative stenosis., Conclusions: Detailed study of scrotal skin arterial vascularization is essential to design reliable and versatile genital skin flaps that result appropriate for the most complex reconstructive urethral surgery. The BAES scrotal flap complies with these requirements offering the patient a one step reconstructive option with a very satisfactory surgical experience over more than 20 years.