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Background: Pediatric obesity treatment is based on high-intensity lifestyle counseling. However, high dropout rates and low effectiveness have been reported, even in specialized centers. Mobile health technologies have been used to overcome these limits with contrasting results. This study aims at evaluating the effectiveness of a six-month intervention with a mobile app for the treatment of pediatric obesity at 6 and 12 months of follow-up. Methods: Seventy-five patients were randomly assigned to standard care or standard care plus mobile app (2:1) using an online randomizer system. The mobile app delivered high-intensity lifestyle counseling for diet and physical activity. Results: At six months of follow-up, the M-App group showed significantly lower dropout rates compared to standard care (p = 0.01). The risk of dropout was significantly higher in controls compared to the intervention group (OR 3.86, 95% C.I. 1.39–10.42, p = 0.01). After one year, we observed lower albeit non-statistically significant dropout rates in the M-App compared to the standard care group (p = 0.24). No differences were observed in z-score BMI and percentage of BMI reduction between the two groups. Conclusions: Our findings suggest that the mobile app might help in the clinical management of children and adolescents with obesity in terms of dropout reduction.

Transgender women (TGW) in India, especially those who engage in sex work, are at high risk for HIV. Guided by the information-motivation-behavioral skills model and qualitative formative research findings, Sakhi (girlfriend), a 3-week smartphone-based pilot intervention consisting of short videos (one/week) and text messages (two/week), was implemented using a one-group pre- and post-test design to test its efficacy in promoting condom use and HIV testing among TGW (n = 50) who engage in sex work in Chennai. Changes in outcomes were assessed by conducting multivariable analyses using generalized estimating equations. Participants’ mean age was 26 years, and the mean monthly income was INR 21700 (USD 292). About one-third completed college, and 96% were HIV-negative. Significant changes in the desired direction were observed in the primary outcomes: condom use – decrease in the engagement of condomless anal sex with male partners (12% to 2%, p < 0.05) and HIV testing – increase in intentions to undergo HIV testing every 6 months (34% to 86%, p < 0.001); and in some of the secondary outcomes: decrease in alcohol use before sex, increase in intentions to use condoms consistently and increase in the well-being score. This study demonstrated the feasibility, acceptability, and preliminary efficacy of the Sakhi intervention and warrants a larger randomized trial among diverse subgroups in diverse settings.

Childhood obesity is a complex disease with multiple biological and psychosocial risk factors. Recently, novel digital programs were developed with growing evidence for their effectiveness in pediatric weight management studies. The ENDORSE platform consists of mobile applications, wearables, and serious games for the remote management of childhood obesity. The pilot studies included 50 mothers and their children aged 6–14 years and resulted in a clinically significant BMI z-score reduction over 4 to 5 months. This secondary analysis of the ENDORSE study focuses on parenting styles and psychosocial factors. Methodology: Semi-structured clinical interviews were conducted with all participating mothers pre-and post-intervention. The Parenting Styles and Dimensions Questionnaire (PSDQ) evaluated the mothers’ parenting styles. The psychosocial functioning of the participating children was assessed with the parental version of the Strengths and Difficulties Questionnaire (SDQ). The relationship between parenting styles, psychosocial parameters, and weight outcomes was investigated using a linear regression analysis. Results: Weight-related stigma at school (56%), body image concerns (66%), and difficulties in family relationships (48%) were the main concerns documented during the initial psychological interviews. According to the SDQ, there was a significant decrease in children’s conduct problems during the study’s initial phase (pre-pilot group). A decrease in maternal demandingness (i.e., strict parenting style) was associated with a decrease in BMI z-score (beta coefficient = 0.314, p-value = 0.003). Conclusion: Decreasing parental demandingness was associated with better weight outcomes, highlighting the importance of assessing parenting factors in pediatric weight management programs.

This paper proposes a new system based on the Industrial Internet of Things (IIoT) for the monitoring of Mobile Health (m-Health) of workers in the underground mining industry. The proposed architecture uses a hybrid model in data transmission. Visible Light Communication (VLC) is used for downlink because of its narrow coverage, which aids in worker positioning. Radio frequency (RF) communication technology is used to send data for primary vital signs in the uplink, which is more efficient in transmission and is a viable solution according to the problem raised. The results obtained in terms of coverage and transmission for the downlink and uplink links show the feasibility of implementing the proposed system.

The Internet of Things (IoT) has positioned itself globally as a dominant force in the technology sector. IoT, a technology based on interconnected devices, has found applications in various research areas, including healthcare. Embedded devices and wearable technologies powered by IoT have been shown to be effective in patient monitoring and management systems, with a particular focus on pregnant women. This study provides a comprehensive systematic review of the literature on IoT architectures, systems, models and devices used to monitor and manage complications during pregnancy, postpartum and neonatal care. The study identifies emerging research trends and highlights existing research challenges and gaps, offering insights to improve the well-being of pregnant women at a critical moment in their lives. The literature review and discussions presented here serve as valuable resources for stakeholders in this field and pave the way for new and effective paradigms. Additionally, we outline a future research scope discussion for the benefit of researchers and healthcare professionals.

Mobile-health (m-health) is described as the application of medical sensors and mobile computing to the healthcare provision. While 5G networks can support a variety of m-health services, applications such as telesurgery, holographic communications, and augmented/virtual reality are already emphasizing their limitations. These limitations apply to both the Quality of Service (QoS) and the Quality of Experience (QoE). However, 6G mobile networks are predicted to proliferate over the next decade in order to solve these limitations, enabling high QoS and QoE. Currently, academia and industry are concentrating their efforts on the 6G network, which is expected to be the next major game-changer in the telecom industry and will significantly impact all other related verticals. The exponential growth of m-health multimedia traffic (e.g., audio, video, and images) creates additional challenges for service providers in delivering a suitable QoE to their customers. As QoS is insufficient to represent the expectations of m-health end-users, the QoE of the services is critical. In recent years, QoE has attracted considerable attention and has established itself as a critical component of network service and operation evaluation. This article aims to provide the first thorough survey on a promising research subject that exists at the intersection of two well-established domains, i.e., QoE and m-health, and is driven by the continuing efforts to define 6G. This survey, in particular, creates a link between these two seemingly distinct domains by identifying and discussing the role of 6G in m-health applications from a QoE viewpoint. We start by exploring the vital role of QoE in m-health multimedia transmission. Moreover, we examine how m-health and QoE have evolved over the cellular network’s generations and then shed light on several critical 6G technologies that are projected to enable future m-health services and improve QoE, including reconfigurable intelligent surfaces, extended radio communications, terahertz communications, enormous ultra-reliable and low-latency communications, and blockchain. In contrast to earlier survey papers on the subject, we present an in-depth assessment of the functions of 6G in a variety of anticipated m-health applications via QoE. Multiple 6G-enabled m-health multimedia applications are reviewed, and various use cases are illustrated to demonstrate how 6G-enabled m-health applications are transforming human life. Finally, we discuss some of the intriguing research challenges associated with burgeoning multimedia m-health applications.

Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data.

Considering the increasing need for oral and dental care in the elderly, teledentistry has been proposed to improve the education of elderly patients in oral health maintenance and risk factor control, identify patients’ concerns in advance, facilitate monitoring, and save time and money. The present cross-sectional analysis of Google search data through real-time surveillance with Google Trends aimed to determine Google users’ interest in oral and dental needs and teledentistry applications in the elderly, and to compare search volumes before and after the COVID-19 outbreak. Extracted CVS data were qualitatively analyzed. Pearson and Spearman correlation analyses were performed between searches for “elderly” and “teledentistry”, and all the oral and dental needs and teledentistry applications. The Mann–Whitney U test compared search volumes in the 36 months before and after the beginning of the COVID-19 pandemic. Google users’ interest in the elderly and related oral and dental needs was diffusely medium–high, while teledentistry and its applications were of lower interest. Interest in teledentistry and its applications was strongly related to interest in the older population, which is consistent with the assumption that older adults represent the population segment that could benefit most from these tools. A positive correlation was also found between searches for “Elderly” and searches for almost all oral and dental needs typical of the geriatric population. Search volumes increased significantly after the outbreak of the COVID-19 pandemic. More information about teledentistry should be disseminated to increase knowledge and awareness, especially among older patients, about its indications, applications, and advantages.

Maternal health includes health during pregnancy and childbirth. Each stage during pregnancy should be a positive experience, ensuring that women and their babies reach their full potential in health and well-being. However, this cannot always be achieved. According to UNFPA (United Nations Population Fund), approximately 800 women die every day from avoidable causes related to pregnancy and childbirth, so it is important to monitor mother and fetal health throughout the pregnancy. Many wearable sensors and devices have been developed to monitor both fetal and the mother’s health and physical activities and reduce risk during pregnancy. Some wearables monitor fetal ECG or heart rate and movement, while others focus on the mother’s health and physical activities. This study presents a systematic review of these analyses. Twelve scientific articles were reviewed to address three research questions oriented to (1) sensors and method of data acquisition; (2) processing methods of the acquired data; and (3) detection of the activities or movements of the fetus or the mother. Based on these findings, we discuss how sensors can help effectively monitor maternal and fetal health during pregnancy. We have observed that most of the wearable sensors were used in a controlled environment. These sensors need more testing in free-living conditions and to be employed for continuous monitoring before being recommended for mass implementation.

Background: Pediatric obesity treatment is based on high-intensity lifestyle counseling. However, high dropout rates and low effectiveness have been reported, even in specialized centers. Mobile health technologies have been used to overcome these limits with contrasting results. This study aims at evaluating the effectiveness of a six-month intervention with a mobile app for the treatment of pediatric obesity at 6 and 12 months of follow-up., Methods: Seventy-five patients were randomly assigned to standard care or standard care plus mobile app (2:1) using an online randomizer system. The mobile app delivered high-intensity lifestyle counseling for diet and physical activity., Results: At six months of follow-up, the M-App group showed significantly lower dropout rates compared to standard care ( p = 0.01). The risk of dropout was significantly higher in controls compared to the intervention group (OR 3.86, 95% C.I. 1.39-10.42, p = 0.01). After one year, we observed lower albeit non-statistically significant dropout rates in the M-App compared to the standard care group ( p = 0.24). No differences were observed in z-score BMI and percentage of BMI reduction between the two groups., Conclusions: Our findings suggest that the mobile app might help in the clinical management of children and adolescents with obesity in terms of dropout reduction.

Background: The large health disparities among those diagnosed with schizophrenia urgently need to be addressed. These disparities are partially caused by adverse health behaviours such as smoking. Smoking cessation apps vary in efficacy across various populations, and there are concerns regarding the accessibility and usability of apps for people with schizophrenia. Objective: This review identifies and examines the feasibility of using apps for smoking cessation in people with schizophrenia. Methods: A non-systematic narrative literature review of smoking cessation apps for individuals with schizophrenia was performed. Results: Eight studies were included in this review. Conclusion: Smoking cessation apps can be acceptable and feasible, but may need to be tailored to the needs of people with schizophrenia. Key messages: (1) Smoking cessation apps could be acceptable and feasible for use in people with schizophrenia; (2) Lack of motivation was perceived as the main potential barrier with regard to people with schizophrenia engaging with smoking cessation apps; (3) In order to improve motivation of people diagnosed with schizophrenia, apps could include games, rewards, and/or social support; (4) Smoking cessation apps with a simple interface seem to be beneficial for this population; (5) Apps may need to be tailored to consider this population’s mental health needs.

Health and exercise technology may promote a healthy lifestyle during pregnancy. The objective of this cross-sectional study was to understand and involve the perspective of pregnant women as users in the design of a framework for future e-health and exercise interventions during pregnancy. Pregnant women replied to a questionnaire aimed at understanding their physical activity patterns, needs, and preferences regarding the use of mobile applications (apps). The main results showed that one-third of the women did not practice any type of exercise during pregnancy. Women preferred to exercise in a gym, outdoors, or at home. The majority already had or were currently using a fitness app, but never used any pregnancy-specific app. Most women agreed that it was important to have a specific app for pregnancy to improve knowledge about recommendations on lifestyle, have direct contact with health and exercise professionals, have social interaction with other mothers, and have guidance on preparation for childbirth and postpartum recovery. Understanding and involving the perspective of pregnant women as users will allow researchers to improve the design of a pregnancy-specific app and future e-health and exercise interventions during pregnancy. These preliminary results will lead to the development of the "active pregnancy app" focused on the promotion of an active and healthy lifestyle during pregnancy and postpartum.

In this work, we describe a new method to detect periods of eating by tracking wrist motion during everyday life. Eating uses hand-to-mouth gestures for ingestion, each of which lasts a few seconds. Previous works have detected these gestures individually and then aggregated them to identify meals. The novelty of our approach is that we analyze a much longer window (0.5–15 min) using a convolutional neural network. Longer windows can contain other gestures related to eating, such as cutting or manipulating food, preparing foods for consumption, and resting between ingestion events. The context of these other gestures can improve the detection of periods of eating. We test our methods on the public Clemson all-day dataset, which consists of 354 recordings containing 1063 eating episodes. We found that accuracy at detecting eating increased by 15% in ≥4 min windows compared to ≤15 s windows. Using a 6 min window, we detected 89% of eating episodes, with 1.7 false positives for every true positive (FP/TP). These are the best results achieved to date on this dataset.

In recent years, the promotion of healthy habits, and especially diet-oriented habits, has been one of the priority interests of our society. There are many apps created to count calories based on what we eat, or to estimate calorie consumption according to the sport we do, or to recommend recipes, but very few are capable of giving personalized recommendations. This review tries to see what studies exist and what recommendation systems are used for this purpose, over the last 5 years in the main databases. Among the results obtained, it is observed that the existing works focus on the recommendation system (usually collaborative filtering), and not so much on the description of the data or the sample analyzed; the indices used for the calculation of calories or nutrients are not specified. Therefore, it is necessary to work with open data, or well-described data, which allows the experience to be reproduced by third parties, or at least to be comparable. In recent years, the promotion of healthy habits, and especially diet-oriented habits, has been one of the priority interests of our society.

Background: The widespread adoption of mobile devices by adolescents underscores the potential to harness these tools to instill healthy habits into their daily lives. An exemplary manifestation of this initiative is the Healthy Jeart app, crafted with the explicit goal of fostering well-being., Methodology: This study, framed within an applied investigation, adopts an exploratory and descriptive approach, specifically delving into the realm of user experience analysis. The focus of this research is a preliminary examination aimed at understanding users' perceived usability of the application. To glean insights, a comprehensive questionnaire was administered to 101 teenagers, seeking their evaluations on various usability attributes. The study took place during 2022., Results: The findings reveal a considerable consensus among users regarding the evaluated usability aspects. However, the areas for improvement predominantly revolve around managing the information density, particularly for a subset of end users grappling with overwhelming content. Additionally, recommendations are put forth to streamline the confirmation process for user suggestions and comments., Conclusion: This analysis illuminates both the strengths of the app and areas ripe for refinement, paving the way for a more user-centric and efficacious Healthy Jeart application.

According to the International Diabetes Federation, in 2019, approximately 416.7 million people worldwide suffered from type 2 diabetes mellitus (T2DM). T2DM is a chronic degenerative disease of long term and slow progression. This condition requires a strict follow-up by physicians and nutritionists, combined with rigorous adherence to treatment by the patient to avoid possible complications. In this context, this paper describes the analysis, design, development, and preliminary usability assessment of a telemonitoring system focused on the monitoring, control, and remote nutritional therapy of people with T2DM. The proposed system comprises two mobile web applications, one focused on the patient and another oriented to physicians and nutritionists. The central services that our system provides to the patient and health personnel are: generate risk alerts; consult food menu options; receive recommendations; consult results of the food intake frequency questionnaire; patient history management; record anthropometry of patients, and review health education material. We carry out a preliminary usability assessment of our system based on a field study with four physicians, two nutritionists, and seven patients with T2DM. Based on the obtained results, our telemonitoring system shows a satisfactory/favorable opinion in terms of usability from the users’ perspective.

This paper presents a new wearable e-textile based system, named SWEET Sock, for biomedical signals remote monitoring. The system includes a textile sensing sock, an electronic unit for data transmission, a custom-made Android application for real-time signal visualization, and a software desktop for advanced digital signal processing. The device allows the acquisition of angular velocities of the lower limbs and plantar pressure signals, which are postprocessed to have a complete and schematic overview of patient’s clinical status, regarding gait and postural assessment. In this work, device performances are validated by evaluating the agreement between the prototype and an optoelectronic system for gait analysis on a set of free walk acquisitions. Results show good agreement between the systems in the assessment of gait cycle time and cadence, while the presence of systematic and proportional errors are pointed out for swing and stance time parameters. Worse results were obtained in the comparison of spatial metrics. The “wearability” of the system and its comfortable use make it suitable to be used in domestic environment for the continuous remote health monitoring of de-hospitalized patients but also in the ergonomic assessment of health workers, thanks to its low invasiveness.

The development of the Internet of Things (IoT) predicts several new applications, some of which are designed to be incorporated into e-health systems, and some technologies, like cloud computing and device-to-device communication (D2D), are promising for use in the support of resource-constrained devices employed in Mobile-health (m-health) and Telecare Medicine Information Systems (TMIS). In a scenario with billions of devices predicted for the IoT, it is essential to avoid performance and security problems, among others. Security is fundamental for the achievement of optimal performance regarding the sensibility of e-health shared data and, especially, the anonymity of patients and other entities, while it is also essential to consider the scarcity of bandwidth in wireless networks. This paper proposes a new mutual authentication protocol for m-health systems, which supports D2D communication, ensuring security and surpassing the performance and security of other authentication procedures reported in the literature.

Mobile health monitoring plays a central role in the future of cyber physical systems (CPS) for healthcare applications. Such monitoring systems need to process user data accurately. Unlike in other human-centered CPS, in healthcare CPS, the user functions in multiple roles all at the same time: as an operator, an actuator, the physical environment and, most importantly, the target that needs to be monitored in the process. Therefore, mobile health CPS devices face highly dynamic settings generally, and accuracy of the machine learning models the devices employ may drop dramatically every time a change in setting happens. Novel learning architecture that specifically address challenges associated with dynamic environments are therefore needed. Using active learning and transfer learning as organizing principles, we propose a collaborative multiple-expert architecture and accompanying algorithms for the design of machine learning models that autonomously adapt to a new configuration, context, or user need. Specifically, our architecture and its constituent algorithms are designed to manage heterogeneous knowledge sources or experts with varying levels of confidence and type while minimizing adaptation cost. Additionally, our framework incorporates a mechanism for collaboration among experts to enrich their knowledge, which in turn decreases both cost and uncertainty of data labeling in future steps. We evaluate the efficacy of the architecture using two publicly available human activity datasets. We attain activity recognition accuracy of over 85 % (for the first dataset) and 92 % (for the second dataset) by labeling only 15 % of unlabeled data.

The development of innovative solutions that allow the aging population to remain healthier and independent longer is essential to alleviate the burden that this increasing segment of the population supposes for the long term sustainability of the public health systems. It has been claimed that promoting physical activity could prevent functional decline. However, given the vulnerability of this population, the activity prescription requires to be tailored to the individual’s physical condition. We propose mobile Senior Fitness Test (m-SFT), a novel m-health system, that allows the health practitioner to determine the elderly physical condition by implementing a smartphone-based version of the senior fitness test (SFT). The technical reliability of m-SFT has been tested by carrying out a comparative study in seven volunteers (53–61 years) between the original SFT and the proposed m-health system obtaining high agreement (intra-class correlation coefficient (ICC) between 0.93 and 0.99). The system usability has been evaluated by 34 independent health experts (mean = 36.64 years; standard deviation = 6.26 years) by means of the System Usability Scale (SUS) obtaining an average SUS score of 84.4 out of 100. Both results point out that m-SFT is a reliable and easy to use m-health system for the evaluation of the elderly physical condition, also useful in intervention programs to follow-up the patient’s evolution.

Childhood obesity is a complex disease with multiple biological and psychosocial risk factors. Recently, novel digital programs were developed with growing evidence for their effectiveness in pediatric weight management studies. The ENDORSE platform consists of mobile applications, wearables, and serious games for the remote management of childhood obesity. The pilot studies included 50 mothers and their children aged 6-14 years and resulted in a clinically significant BMI z-score reduction over 4 to 5 months. This secondary analysis of the ENDORSE study focuses on parenting styles and psychosocial factors., Methodology: Semi-structured clinical interviews were conducted with all participating mothers pre-and post-intervention. The Parenting Styles and Dimensions Questionnaire (PSDQ) evaluated the mothers' parenting styles. The psychosocial functioning of the participating children was assessed with the parental version of the Strengths and Difficulties Questionnaire (SDQ). The relationship between parenting styles, psychosocial parameters, and weight outcomes was investigated using a linear regression analysis., Results: Weight-related stigma at school (56%), body image concerns (66%), and difficulties in family relationships (48%) were the main concerns documented during the initial psychological interviews. According to the SDQ, there was a significant decrease in children's conduct problems during the study's initial phase (pre-pilot group). A decrease in maternal demandingness (i.e., strict parenting style) was associated with a decrease in BMI z-score (beta coefficient = 0.314, p -value = 0.003)., Conclusion: Decreasing parental demandingness was associated with better weight outcomes, highlighting the importance of assessing parenting factors in pediatric weight management programs.

Nowadays, more than one billion people are in need of one or more assistive technologies, and this number is expected to increase beyond two billion by 2050. The majority of assistive technologies are supported by battery-operated devices like smartphones and wearables. This means that battery weight is an important concern in such assistive devices because it may affect negatively its ergonomics. Saving power in these assistive devices is of utmost importance for its potential twofold benefits: extend the device life and reduce the global warming aggravated by billion of these devices. Dynamic Software Product Lines (DSPLs) are a suitable technology that supports system adaptation, in this case, to reduce energy consumption at runtime, considering contextual information and the current state of the device. However, a reduction in battery consumption could negatively affect other quality of service parameters, like response time. Therefore, it is important to trade-off battery saving and these other concerns. This work illustrates how to approach the self-adaptation of smart assistive devices by means of a DSPL-based strategy that optimizes battery consumption taking into account other QoS parameters at the same time. We illustrate our proposal with a real case study: a Smart Cane that is integrated with a DSPL platform, Tanit. Experimentation shows that it is possible to make a trade-off between different quality concerns (energy consumption and relative error). The results of the experiments allow us to conclude that the Tanit approach elongates battery duration of the Smart Cane in one day (an increase of a 6% with a relative error of 1%), so we improve the user quality of experience and reduce the energy footprint with a reasonable relative error.

In light of the need for Extramural Hospital Information System (HIS) access through mobile devices outside the hospital, this research analyzes situational information security threats, including the circumstances in which a mobile device may get lost and personal data may be stolen. Moreover, the system needs to be implemented in accordance with the regulations. Based on the security threat analysis, it is proposed to use a security control module to provide a security-enabled HIS proxy module, two-way authentication module, and One-Time Password (OTP). The sending module and cryptographic technology computing module with Micro SD encryption card form a set of HIS extension system, which includes the SMS OTP method to simultaneously verify the two-way authentication mechanism of a user and the device that the user owns.

As a unique form of mobile health service (m-Health) for elderly care in China, the virtual nursing home is considered a reliable alternative to the traditional model of home-based care, but services from virtual nursing homes are infrequently used by the elderly in practice. Thus, this study aims to measure the driving factors affecting the behavioral intention to use the virtual nursing home by designing a research framework that combined the unified theory of acceptance and use of technology (UTAUT) with the technology acceptance model (TAM). Furthermore, a new variable, conformist mentality, is proposed as a moderator. In order to validate the model, a well-structured questionnaire using mature scales was used, and the linear regression analysis method was conducted on 200 valid data samples collected during a field study in Harbin, China. The results show that performance expectancy, effort expectancy, and social influence significantly affect behavioral intention. However, the effect of facilitating conditions is not significant. Moreover, performance expectancy and effort expectancy have a positive effect on attitude toward use, and attitude toward use not only affects behavioral intention but also plays a mediating role in the effect of performance expectancy and effort expectancy on behavioral intention. This study also innovatively proposes and confirms conformist mentality as a moderator to strengthen the driving effect of social influence on behavioral intention. This is the first time that conformist mentality is introduced as a moderator in a study on the behavioral perception and acceptance of virtual nursing homes among Chinese older adults. Based on these findings, this study offers theoretical contributions and management implications that are conducive to the sustainable development of virtual nursing homes, thereby making extensive contributions to this field. Additionally, it also aids in the contextual expansion of the UTAUT model.

Currently, mental stress is a major problem in our society. It is related to a wide variety of diseases and is mainly caused by daily-life factors. The use of mobile technology for healthcare purposes has dramatically increased during the last few years. In particular, for out-of-lab stress detection, a considerable number of biosignal-based methods and systems have been proposed. However, these approaches have not matured yet into applications that are reliable and useful enough to significantly improve people’s quality of life. Further research is needed. In this paper, we propose a portable system for real-time detection of stress based on multiple biosignals such as electroencephalography, electrocardiography, electromyography, and galvanic skin response. In order to validate our system, we conducted a study using a previously published and well-established methodology. In our study, ten subjects were stressed and then relaxed while their biosignals were simultaneously recorded with the portable system. The results show that our system can classify three levels of stress (stress, relax, and neutral) with a resolution of a few seconds and 86% accuracy. This suggests that the proposed system could have a relevant impact on people’s lives. It can be used to prevent stress episodes in many situations of everyday life such as work, school, and home.

Depressive disorders cover a set of disabling problems, often chronic or recurrent. They are characterized by a high level of psychiatric and somatic comorbidities and represent an important public health problem. To date, therapeutic solutions remain unsatisfactory. For some researchers, this is a sign of decisive paradigmatic failure due to the way in which disorders are conceptualized. They hypothesize that the symptoms of a categorical disorder, or of different comorbid disorders, can be interwoven in chains of interdependencies on different elements, of which it would be possible to act independently and synergistically to influence the functioning of the symptom system, rather than limiting oneself to targeting a hypothetical single underlying cause. New connected technologies make it possible to invent new observation and intervention tools allowing better phenotypic characterization of disorders and their evolution, that fit particularly well into this new “symptoms network” paradigm. Synergies are possible and desirable between these technological and epistemological innovations and can possibly help to solve some of the difficult problems people with mental disorders face in their everyday life, as we will show through a fictional case study exploring the possibilities of connected technologies in mental disorders in the near future.

There is growing evidence to support the potential benefit of e-Health interventions targeting psychosocial outcomes and/or pain-related psychological variables for chronic pain conditions, including fibromyalgia syndrome (FMS). This systematic review aims at providing an in-depth description of the available e-Health psychological and/or multicomponent interventions for patients with FMS. Searches were made in PubMed, Cochrane, Web of Science, and PsycINFO up to 15 May 2023, finally including twenty-six articles. The quality of the included articles was medium-high (average quality assessment score of 77.1%). 50% of studies were randomized controlled trials (RCTs) ( n = 13), and the majority of them focused exclusively on adult patients with FMS ( n = 23) who were predominantly female. Four categories of e-Health modalities were identified: web-based ( n = 19), mobile application (m-Health) ( n = 3), virtual reality (VR) ( n = 2), and video consulting ( n = 2). Interventions were mainly based on the cognitive behavioral therapy (CBT) approach ( n = 14) and mostly involved contact with a healthcare professional through different digital tools. Overall, a growing number of psychological and multicomponent interventions have been created and delivered using digital tools in the context of FMS, showing their potentiality for improving psychosocial outcomes and pain-related psychological variables. However, some digital tools resulted as underrepresented, and the literature on this topic appears highly heterogeneous precluding robust conclusions.

Although mobile health (m-health) has great potential to reduce the cost of medical care and improve its quality and efficiency, it is not widely accepted by consumers. In addition, there is still a lack of comprehensive insight into m-health acceptance, especially among consumers with different demographic characteristics. This study aimed to explore the factors affecting consumers' acceptance and usage behaviors of m-health and to examine whether their roles differ by demographic characteristics. A comprehensive m-health acceptance model was proposed by integrating factors from the Self-Determination Theory, Task-Technology Fit, and Technology Acceptance Model. Survey data were collected from 623 Chinese adults with at least 6 months of m-health usage experience and analyzed using structural equation modeling techniques. Multi-group analyses were performed to assess whether the model relationships were different across gender, age, and usage experience. The results indicated that relatedness and competence were significant motivational antecedents of perceived ease of use. Task-technology fit and the perceived ease of use significantly affected the perceived usefulness. The perceived ease of use and perceived usefulness were significant determinants of consumer usage behaviors of m-health and together explained 81% of its variance. Moreover, the relationships among autonomy, perceived usefulness, and usage behaviors of m-health were moderated by gender. Consumer usage behaviors of m-health were affected by factors such as self-motivation (i.e., relatedness and competence), technology perceptions (i.e., perceived ease of use and perceived usefulness), and task-technology fit. These findings provide a theoretical underpinning for future research on m-health acceptance and provide empirical evidence for practitioners to promote the better design and use of m-health for healthcare activities.

Cardiac rehabilitation is a key program which significantly reduces the mortality in at-risk patients with ischemic heart disease; however, there is a lack of accessibility to these programs in health centers. To resolve this issue, home-based programs for cardiac rehabilitation have arisen as a potential solution. In this work, we present an approach based on a new generation of wrist-worn devices which have improved the quality of heart rate sensors and applications. Real-time monitoring of rehabilitation sessions based on high-quality clinical guidelines is embedded in a wearable application. For this, a fuzzy temporal linguistic approach models the clinical protocol. An evaluation based on cases is developed by a cardiac rehabilitation team.

To date, there has been a paucity of research on optimal ways to educate and promote dietary behavioural change within athletes. Optimising athlete nutrition is fundamental to reaching peak performance and maintaining athlete wellbeing. MealLogger® is a smartphone application that incorporates the use of an image-based food record and social-media functionality to provide in-application personalised feedback to individuals or groups, peer-support, and a platform to deliver nutrition education material. This study measured the feasibility of MealLogger® within New Zealand elite male field hockey players (n = 17) aged 18–20 to increase athlete knowledge and nutrition promoting behaviours. During a six-week intervention, participants were instructed to log images of their meals three days per week and they received individualised dietetic feedback on logged meals. Weekly nutrition-education fact-sheets and videos were delivered through the application. Nutrition knowledge increased moderately from baseline (%Pre 54.7 ± 14.3; %Post 61.1 ± 11.45, p = 0.01). Participants report a highly positive experience of application use (8/10) with 82.3% attempting to make positive changes in dietary behaviours based on in-app education. All participants preferred this method to traditional methods of dietary analysis. Using image-based applications such as MealLogger® is an effective approach to monitor dietary intake and deliver education to optimise the nutritional behaviours of elite athletes.

Background: Telemedicine has the potential to revolutionize healthcare. While the development of digital health technologies for the management of patients with cardiovascular diseases has been increasingly reported, applications in vascular surgery have been far less specifically investigated. The aim of this review is to summarize applications related to telemedicine in vascular surgery, highlighting expected benefits, current limits and future directions., Methods: The MEDLINE database was searched using a combination of keywords to identify studies related to telehealth/telemedicine in three main pathologies, including aortic, peripheral artery and carotid disease. A comprehensive literature review was performed to identify the type of digital application, intended use, expected benefits, strengths and limitations., Results: Telemedicine can improve the management of patients through digital platforms allowing teleconsultation, telemonitoring or telecoaching. Intended use involved remote consultation with a vascular surgeon, applications to enhance education, self-management, follow-up or adherence to treatment or lifestyle changes., Conclusion: Telemedicine offers innovative perspectives to improve access to care in distant locations and optimize care through patients' empowerment and personalized follow-up, contributing to the development of precision medicine. Huge efforts remain necessary for its implementation in daily clinical practice and involve ethical, legal, technical, economic and cultural considerations., Competing Interests: The authors declare no conflict of interest.

Background: The large health disparities among those diagnosed with schizophrenia urgently need to be addressed. These disparities are partially caused by adverse health behaviours such as smoking. Smoking cessation apps vary in efficacy across various populations, and there are concerns regarding the accessibility and usability of apps for people with schizophrenia. Objective: This review identifies and examines the feasibility of using apps for smoking cessation in people with schizophrenia. Methods: A non-systematic narrative literature review of smoking cessation apps for individuals with schizophrenia was performed. Results: Eight studies were included in this review. Conclusion: Smoking cessation apps can be acceptable and feasible, but may need to be tailored to the needs of people with schizophrenia. Key messages: (1) Smoking cessation apps could be acceptable and feasible for use in people with schizophrenia; (2) Lack of motivation was perceived as the main potential barrier with regard to people with schizophrenia engaging with smoking cessation apps; (3) In order to improve motivation of people diagnosed with schizophrenia, apps could include games, rewards, and/or social support; (4) Smoking cessation apps with a simple interface seem to be beneficial for this population; (5) Apps may need to be tailored to consider this population's mental health needs.

Mobile-based ecological momentary assessment (mEMA) offers a novel method for dietary assessment and may reduce recall bias and participant burden. This review evaluated mEMA methodology and the feasibility, acceptability and validity as a dietary assessment method in young people. Five databases were searched from January 2008 to September 2021 for studies including healthy young people aged 16-30 years and used mEMA for obtaining dietary intake data, food consumption behaviours and/or contextual factors. Data on the method used to administer mEMA, compliance with recording and validation were extracted. A total of 46 articles from 39 independent studies were included, demonstrating a wide variation in mEMA methods. Signal-contingent prompting (timed notification to record throughout the day) was used in 26 studies, 9 used event-contingent (food consumption triggered recordings), while 4 used both. Monitoring periods varied and most studies reported a compliance rate of 80% or more. Two studies found mEMA to be burdensome and six reported mEMA as easy to use. Most studies (31/39) reported using previously validated questions. mEMA appears to be a feasible and acceptable methodology to assess dietary intake and food consumption in near real time.