Greater mobile device-prompted phone pickups are associated with daily parent stress.

AIM: No studies have examined notifications as they relate to parent stress. We aimed to examine associations between objective daily mobile device notifications and pickups with daily parenting stress. METHODS: This was a within- and between-subjects, cross-sectional study that took place from 2020 to 2021. The study occurred during the coronavirus disease of 2019 pandemic. Data were collected in a low-contact home visit. This study included 62 parents of 62 children aged 48-71 months. Parents downloaded a passive sensing app on their Android mobile devices collecting data on duration, device notifications and device pickups. Parents completed an end-of-day stress survey for 4 days. We used random effects models to examine the variation of daily stress with smartphone duration, notification frequency, pickup frequency and device-initiated pickups, adjusting for covariates. RESULTS: Parents were on average 37.3 years old (SD ± 5.7) and were predominantly mothers (82.3%). On average, parents received 293 daily notifications and picked up their phones 93 times. Duration of smartphone use and notification frequency were not associated with daily stress. Device-initiated pickups were associated with daily parent stress. CONCLUSION: When notifications prompted parents to pick up their phones more often, parents experienced greater stress.

Online Signature Biometrics for Mobile Devices.

This paper addresses issues concerning biometric authentication based on handwritten signatures. Our research aimed to check whether a handwritten signature acquired with a mobile device can effectively verify a user's identity. We present a novel online signature verification method using coordinates of points and pressure values at each point collected with a mobile device. Convolutional neural networks are used for signature verification. In this paper, three neural network models are investigated, i.e., two self-made light SigNet and SigNetExt models and the VGG-16 model commonly used in image processing. The convolutional neural networks aim to determine whether the acquired signature sample matches the class declared by the signer. Thus, the scenario of closed set verification is performed. The effectiveness of our method was tested on signatures acquired with mobile phones. We used the subset of the multimodal database, MobiBits, that was captured using a custom-made application and consists of samples acquired from 53 people of diverse ages. The experimental results on accurate data demonstrate that developed architectures of deep neural networks can be successfully used for online handwritten signature verification. We achieved an equal error rate (EER) of 0.63% for random forgeries and 6.66% for skilled forgeries.

Mobile audiometry for hearing threshold assessment: A systematic review and meta-analysis.

OBJECTIVES: Technological advancements in mobile audiometry (MA) have enabled hearing assessment using tablets and smartphones. This systematic review (PROSPERO ID: CRD42021274761) aimed to identify MA options available to health providers, assess their accuracy in measuring hearing thresholds, and explore factors that might influence their accuracy. DESIGN AND SETTING: A systematic search of online databases including PubMed, Embase, Cochrane, Evidence Search and Dynamed was conducted on 13th December 2021, and repeated on 30th October 2022, using appropriate Medical Subject Headings (MeSH) terms. Eligible studies reported the use of MA to determine hearing thresholds and compared results to conventional pure-tone audiometry (CA). Studies investigating MA for hearing screening (i.e. reporting just pass/fail) were ineligible for inclusion. Two authors independently reviewed studies, extracted data, and assessed methodological quality and risk of bias using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. PARTICIPANTS: Adults and children, with and without diagnosis of hearing impairment. MAIN OUTCOME MEASURES: A meta-analysis was performed to obtain the mean difference between thresholds measured using MA and CA in dB HL. RESULTS: Searches returned 858 articles. After systematic review, 17 articles including 1032 participants were analysed. The most used software application was ShoeboxTM (6/17) followed by Hearing TestTM (3/17), then HearTestTM (2/17). Tablet computers were used in ten studies, smartphones in six, and a computer in one. The mean difference between MA and CA thresholds was 1.36 dB (95% CI, 0.07-2.66, p = 0.04). Significant differences between mobile audiometry (MA) and conventional audiometry (CA) thresholds were observed in thresholds measured at 500Hz, in children, when MA was conducted in a sound booth, and when MA was self-administered. However, these differences did not exceed the clinically significant threshold of 10 decibels (dB). Included studies exhibited high levels of heterogeneity, high risk of bias and low concerns about applicability. CONCLUSIONS: MA compares favourably to CA in measuring hearing thresholds and has role in providing access to hearing assessment in situations where CA is not available or feasible. Future studies should prioritize the integration of pure-tone threshold assessment with additional tests, such as Speech Recognition and Digits-in-Noise, for a more rounded evaluation of hearing ability, assesses acceptability and feasibility, and the cost-effectiveness of MA in non-specialist settings.

Event-related driver stress detection with smartphones in an urban environment: a naturalistic driving study.

Driving in urban areas can be challenging and encounter acute stress. To detect driver stress, collecting data on real roads without interfering the driver is preferred. A smartphone-based data collection protocol was developed to support a naturalistic driving study. Sixty-one participants drove on predetermined real road routes, and driving information as well as physiological, psychological, and facial data were collected. The algorithm identified potentially stressful events based on the collected data. Participants classified these events as low, medium, or highly stressful events by watching recorded videos after the experiment. These events were then used to train prediction models. The best model achieved an accuracy of 92.5% in classifying low/medium/highly stressful events. The contribution of physiological, psychological, and facial expression indices and individual profile information was evaluated. The method can be applied to visualise the geographical distribution of stressors, monitor driver behaviour, and help drivers regulate their driving habits.

The data collection protocol for driving on real roads and the stressful event identification method could potentially be applied for in-vehicle driver status monitoring and stress intervention.

Prevalence and Associated Factors of Nomophobia Amongst the General Population in Makkah Province and Al-Madinah Province, Saudi Arabia: An Analytical Cross-Sectional Study.

Background: Nomophobia is a public health issue that involves the fear of being without a mobile phone. The study aimed to estimate the prevalence of nomophobia and its relation to psychological factors, including depression and insomnia, among the general population in Makkah Province and Al-Madinah Province, Saudi Arabia. Methods: This analytical cross-sectional study was conducted and data were obtained through a self-administered online questionnaire using the Patient Health Questionnaire-2 (PHQ-2) for depression, the Nomophobia Questionnaire (NMP-Q), and Insomnia Severity Index (ISI). Results: A total of 1022 participants completed the questionnaire. The prevalence of nomophobia was 96.7%. Moderate nomophobia was prevalent (47.8%). Based on the PHQ-2, possible depression was identified in 47.3% of the respondents. 37.1% had sub-threshold insomnia. In terms of personal psychiatric history, the most common mental disorders in the participants included generalized anxiety disorder (9.9%) and major depressive disorder (9.7%). 61.6% of them used mobile devices for more than four hours per day. Conclusion: Nomophobia is prevalent in the Makkah and Al-Madinah provinces in Saudi Arabia. The risk of nomophobia was significantly higher for participants who spent more hours using mobile devices, those with possible depression, and those having irritable bowel syndrome.

Adolescents' interactive electronic device use, sleep and mental health: a systematic review of prospective studies.

Optimal sleep, both in terms of duration and quality, is important for adolescent health. However, young people's sleeping habits have worsened over recent years. Access to and use of interactive electronic devices (e.g., smartphones, tablets, portable gaming devices) and social media have become deep-rooted elements of adolescents' lives and are associated with poor sleep. Additionally, there is evidence of increases in poor mental health and well-being disorders in adolescents; further linked to poor sleep. This review aimed to summarise the longitudinal and experimental evidence of the impact of device use on adolescents' sleep and subsequent mental health. Nine electronic bibliographical databases were searched for this narrative systematic review in October 2022. Of 5779 identified unique records, 28 studies were selected for inclusion. A total of 26 studies examined the direct link between device use and sleep outcomes, and four reported the indirect link between device use and mental health, with sleep as a mediator. The methodological quality of the studies was generally poor. Results demonstrated that adverse implications of device use (i.e., overuse, problematic use, telepressure, and cyber-victimisation) impacted sleep quality and duration; however, relationships with other types of device use were unclear. A small but consistent body of evidence showed sleep mediates the relationship between device use and mental health and well-being in adolescents. Increasing our understanding of the complexities of device use, sleep, and mental health in adolescents are important contributions to the development of future interventions and guidelines to prevent or increase resilience to cyber-bullying and ensure adequate sleep.

Self-management system for postpartum women with hypertension disorders: an eHealth application intervention study.

BACKGROUND: Hypertension disorders are relatively common in pregnant women and often persist in the postpartum period. Few studies are available regarding the self-management of postpartum hypertension via the eHealth system. This study aimed to develop a self-management eHealth system for women with postpartum hypertension during the postpartum period. METHODS: We adopted a multi-platform system for this research, not only for use on the web interface but also on smartphones. The proposed system possessed three features: (1) the population was limited to postnatal women with hypertension; (2) a self-care record, which allowed postnatal women to keep track of their blood pressure, pulse, weight, medication record, exercise record, and risk factor assessment; and (3) through this system, nurse-midwives could keep track of postnatal women's health status maintaining the complete record and could communicate directly with the users if their health monitor values reach beyond normal range. RESULTS: Thirty-nine postnatal women with postpartum hypertension were recruited to the study. A survey to evaluate the usability and satisfaction of the proposed e-health application system was completed by these women. The usability rate of the system reached 92.4% (46.2% satisfied and 46.2% strongly satisfied), which suggested that the system was helpful to the users. The satisfaction rate of the system reached 94.9% (43.6% satisfied and 51.3% strongly satisfied), which suggested that the system was acceptable to the users. CONCLUSION: This proposed system has been developed completely with user experience and professional advice from experts. Postnatal women could gain important postpartum-related knowledge and access their related health records and other information easily via their smartphones or computers. During the postpartum period, an eHealth application system can effectively assist women with hypertension to manage their blood pressure and related postnatal healthcare issues.

The role of the California tier system in controlling population mobility during the COVID-19 pandemic.

BACKGROUND: Policies to restrict population mobility are a commonly used strategy to limit the transmission of contagious diseases. Among measures implemented during the COVID-19 pandemic were dynamic stay-at-home orders informed by real-time, regional-level data. California was the first state in the U.S. to implement this novel approach; however, the effectiveness of California's four-tier system on population mobility has not been quantified. METHODS: Utilizing data from mobile devices and county-level demographic data, we evaluated the impact of policy changes on population mobility and explored whether demographic characteristics explained variability in responsiveness to policy changes. For each California county, we calculated the proportion of people staying home and the average number of daily trips taken per 100 persons, across different trip distances and compared this to pre-COVID-19 levels. RESULTS: We found that overall mobility decreased when counties moved to a more restrictive tier and increased when moving to a less restrictive tier, as the policy intended. When placed in a more restrictive tier, the greatest decrease in mobility was observed for shorter and medium-range trips, while there was an unexpected increase in the longer trips. The mobility response varied by geographic region, as well as county-level median income, gross domestic product, economic, social, and educational contexts, the prevalence of farms, and recent election results. CONCLUSIONS: This analysis provides evidence of the effectiveness of the tier-based system in decreasing overall population mobility to ultimately reduce COVID-19 transmission. Results demonstrate that socio-political demographic indicators drive important variability in such patterns across counties.

Willingness to Use Mobile Health Devices in the Post-COVID-19 Era: Nationwide Cross-sectional Study in China.

BACKGROUND: Despite the increased development and use of mobile health (mHealth) devices during the COVID-19 pandemic, there is little knowledge of willingness of the Chinese people to use mHealth devices and the key factors associated with their use in the post-COVID-19 era. Therefore, a more comprehensive and multiangle investigation is required. OBJECTIVE: We aimed to probe Chinese attitudes regarding the use of mHealth and analyze possible associations between the attitude of willingness to use mHealth devices and some factors based on the socioecological model. METHODS: A survey was conducted using quota sampling to recruit participants from 148 cities in China between June 20 and August 31, 2022. Data from the survey were analyzed using multiple stepwise regression to examine the factors associated with willingness to use mHealth devices. Standardized regression coefficients (ß) and 95% CIs were calculated using multiple stepwise regression. RESULTS: The survey contained a collection of 21,916 questionnaires and 21,897 were valid questionnaires, with a 99.91% effective response rate. The median score of willingness to use mHealth in the post-COVID-19 era was 70 points on a scale from 0 to 100. Multiple stepwise regression results showed that the female gender (ß=.03, 95% CI 1.04-2.35), openness personality trait (ß=.05, 95% CI 0.53-0.96), higher household per capita monthly income (ß=.03, 95% CI 0.77-2.24), and commercial and multiple insurance (ß=.04, 95% CI 1.77-3.47) were factors associated with the willingness to use mHealth devices. In addition, people with high scores of health literacy (ß=.13, 95% CI 0.53-0.68), self-reported health rating (ß=.22, 95% CI 0.24-0.27), social support (ß=.08, 95% CI 0.40-0.61), family health (ß=.03, 95% CI 0.03-0.16), neighbor relations (ß=.12, 95% CI 2.09-2.63), and family social status (ß=.07, 95% CI 1.19-1.69) were more likely to use mHealth devices. CONCLUSIONS: On the basis of the theoretical framework of socioecological model, this study identified factors specifically associated with willingness of the Chinese people to use mHealth devices in the post-COVID-19 era. These findings provide reference information for the research, development, promotion, and application of future mHealth devices.

The Use of Mobile Sensors by Children: A Review of Two Decades of Environmental Education Projects.

Over the past twenty years, the use of electronic mobile sensors by children and youngsters has played a significant role in environmental education projects in Portugal. This paper describes a research synthesis of a set of case studies (environmental education projects) on the use of sensors as epistemic mediators, evidencing the technological, environmental, social, and didactical dimensions of environmental education projects over the last two decades in Portugal. The triggers of the identified changes include: (i) the evolution of sensors, information and communication platforms, and mobile devices; (ii) the increasing relevance of environmental citizenship and participation; (iii) the recognition of the role of multisensory situated information and quantitative information in environmental citizenship; (iv) the cause-effect relation between didactical strategies and environmental education goals; (v) the potential of sensory and epistemic learners' practices in the environment to produce learning outcomes and new knowledge. To support the use of senses and sensors in environmental education projects, the SEAM model was created based on the developed research synthesis.

A Survey on Quantitative Risk Estimation Approaches for Secure and Usable User Authentication on Smartphones.

Mobile user authentication acts as the first line of defense, establishing confidence in the claimed identity of a mobile user, which it typically does as a precondition to allowing access to resources in a mobile device. NIST states that password schemes and/or biometrics comprise the most conventional user authentication mechanisms for mobile devices. Nevertheless, recent studies point out that nowadays password-based user authentication is imposing several limitations in terms of security and usability; thus, it is no longer considered secure and convenient for the mobile users. These limitations stress the need for the development and implementation of more secure and usable user authentication methods. Alternatively, biometric-based user authentication has gained attention as a promising solution for enhancing mobile security without sacrificing usability. This category encompasses methods that utilize human physical traits (physiological biometrics) or unconscious behaviors (behavioral biometrics). In particular, risk-based continuous user authentication, relying on behavioral biometrics, appears to have the potential to increase the reliability of authentication without sacrificing usability. In this context, we firstly present fundamentals on risk-based continuous user authentication, relying on behavioral biometrics on mobile devices. Additionally, we present an extensive overview of existing quantitative risk estimation approaches (QREA) found in the literature. We do so not only for risk-based user authentication on mobile devices, but also for other security applications such as user authentication in web/cloud services, intrusion detection systems, etc., that could be possibly adopted in risk-based continuous user authentication solutions for smartphones. The target of this study is to provide a foundation for organizing research efforts toward the design and development of proper quantitative risk estimation approaches for the development of risk-based continuous user authentication solutions for smartphones. The reviewed quantitative risk estimation approaches have been divided into the following five main categories: (i) probabilistic approaches, (ii) machine learning-based approaches, (iii) fuzzy logic models, (iv) non-graph-based models, and (v) Monte Carlo simulation models. Our main findings are summarized in the table in the end of the manuscript.

User Experiences of Well-Being Chatbots.

OBJECTIVE: The current paper conducted two parallel studies to explore user experiences of well-being conversational agents (CAs) and identify important features for engagement. BACKGROUND: Students transitioning into university life take on greater responsibility, yet tend to sacrifice healthy behaviors to strive for academic and financial gain. Additionally, students faced an unprecedented pandemic, leading to remote courses and reduced access to healthcare services. One tool designed to improve healthcare accessibility is well-being CAs. CAs have addressed mental health support in the general population but have yet to address physical well-being support and accessibility to those in disadvantaged socio-economic backgrounds where healthcare access is further limited. METHOD: Study One comprised a thematic analysis of mental health applications featuring CAs from the public forum, Reddit. Study Two explored emerging usability themes of an SMS-based CA designed to improve accessibility to well-being services alongside a commercially available CA, Woebot. RESULTS: Study One identified several themes, including accessibility and availability, communication style, and anthropomorphism as important features. Study Two identified themes such as user response modality, perceived CA role, question specificity, and conversation flow control as critical for user engagement. CONCLUSION: Various themes emerged from individuals' experiences regarding CA features, functionality, and responses. The mixed experiences relevant to the communication and conversational styles between the CA and the user suggest varied motivations for using CAs for mental and physical well-being. APPLICATION: Practical recommendations to encourage continued use include providing dynamic response modalities, anthropomorphizing the chatbot, and calibrating expectations early.

Cross-sectional associations between early mobile device usage and problematic behaviors among school-aged children in the Hokkaido Study on Environment and Children's Health.

BACKGROUND: Concerns have been raised about the adverse health impacts of mobile device usage. The objective of this cross-sectional study was to examine the association between a child's age at the first use of a mobile device and the duration of use as well as associated behavioral problems among school-aged children. METHODS: This study focused on children aged 7-17 years participating in the Hokkaido Study on Environment and Children's Health. Between October 2020 and October 2021, the participants (n = 3,021) completed a mobile device use-related questionnaire and the strengths and difficulties questionnaire (SDQ). According to the SDQ score (normal or borderline/high), the outcome variable was behavioral problems. The independent variable was child's age at first use of a mobile device and the duration of use. Covariates included the child's age at the time of survey, sex, sleep problems, internet addiction, health-related quality of life, and history of developmental concerns assessed at health checkups. Logistic regression analysis was performed for all children; the analysis was stratified based on the elementary, junior high, and senior high school levels. RESULTS: According to the SDQ, children who were younger at their first use of a mobile device and used a mobile device for a longer duration represented more problematic behaviors. This association was more pronounced among elementary school children. Moreover, subscale SDQ analysis showed that hyperactivity, and peer and emotional problems among elementary school children, emotional problems among junior high school children, and conduct problems among senior high school children were related to early and long usage of mobile devices. CONCLUSIONS: Elementary school children are more sensitive to mobile device usage than older children, and early use of mobile devices may exacerbate emotional instability and oppositional behaviors in teenagers. Longitudinal follow-up studies are needed to clarify whether these problems disappear with age.

Diabetic retinopathy screening in the emerging era of artificial intelligence.

Diabetic retinopathy is a frequent complication in diabetes and a leading cause of visual impairment. Regular eye screening is imperative to detect sight-threatening stages of diabetic retinopathy such as proliferative diabetic retinopathy and diabetic macular oedema in order to treat these before irreversible visual loss occurs. Screening is cost-effective and has been implemented in various countries in Europe and elsewhere. Along with optimised diabetes care, this has substantially reduced the risk of visual loss. Nevertheless, the growing number of patients with diabetes poses an increasing burden on healthcare systems and automated solutions are needed to alleviate the task of screening and improve diagnostic accuracy. Deep learning by convolutional neural networks is an optimised branch of artificial intelligence that is particularly well suited to automated image analysis. Pivotal studies have demonstrated high sensitivity and specificity for classifying advanced stages of diabetic retinopathy and identifying diabetic macular oedema in optical coherence tomography scans. Based on this, different algorithms have obtained regulatory approval for clinical use and have recently been implemented to some extent in a few countries. Handheld mobile devices are another promising option for self-monitoring, but so far they have not demonstrated comparable image quality to that of fundus photography using non-portable retinal cameras, which is the gold standard for diabetic retinopathy screening. Such technology has the potential to be integrated in telemedicine-based screening programmes, enabling self-captured retinal images to be transferred virtually to reading centres for analysis and planning of further steps. While emerging technologies have shown a lot of promise, clinical implementation has been sparse. Legal obstacles and difficulties in software integration may partly explain this, but it may also indicate that existing algorithms may not necessarily integrate well with national screening initiatives, which often differ substantially between countries.

Diagnostic yield and limitations of in-hospital documentation in patients with epilepsy.

OBJECTIVE: To determine the diagnostic yield of in-hospital video-electroencephalography (EEG) monitoring to document seizures in patients with epilepsy. METHODS: Retrospective analysis of electronic seizure documentation at the University Hospital Freiburg (UKF) and at King's College London (KCL). Statistical assessment of the role of the duration of monitoring, and subanalyses on presurgical patient groups and patients undergoing reduction of antiseizure medication. RESULTS: Of more than 4800 patients with epilepsy undergoing in-hospital recordings at the two institutions since 2005, seizures with documented for 43% (KCL) and 73% (UKF).. Duration of monitoring was highly significantly associated with seizure recordings (p < .0001), and presurgical patients as well as patients with drug reduction had a significantly higher diagnostic yield (p < .0001). Recordings with a duration of >5 days lead to additional new seizure documentation in only less than 10% of patients. SIGNIFICANCE: There is a need for the development of new ambulatory monitoring strategies to document seizures for diagnostic and monitoring purposes for a relevant subgroup of patients with epilepsy in whom in-hospital monitoring fails to document seizures.

Impact of tablet use on young children's inhibitory control and error monitoring.

The purpose of this study was to investigate the cognitive impacts of tablet use on young children's inhibitory control and error monitoring. A total of 70 children (35 boys) aged 3.5 to 5 years completed an age-appropriate go/no-go task and were then randomly assigned to a technology group or a comparison group. In the technology group, children completed a cooking task on a tablet for 15 min. In the comparison group, children completed a similarly structured cooking task with toys for the same length of time. Children then completed the go/no-go task again. Compared with children in the comparison group, children in the technology group demonstrated poorer inhibitory control as evidenced by lower accuracy on no-go trials after the cooking task. However, both groups displayed post-error reaction time slowing. Collectively, these results suggest that brief tablet use can impose selective impairment on young children's cognitive abilities for a short period of time following use.

Remote after-care using smartphones: A feasibility study of monitoring children's pain with automated SMS messaging.

BACKGROUND: Monitoring children's recovery postoperatively is important for routine care, research, and quality improvement. Although telephone follow-up is common, it is also time-consuming and intrusive for families. Using SMS messaging to communicate with families regarding their child's recovery has the potential to address these concerns. While a previous survey at our institution indicated that parents were willing to communicate with the hospital by SMS, data on response rates for SMS-based postoperative data collection is limited, particularly in pediatric populations. AIMS: We conducted a feasibility study with 50 completed pain profiles obtained from patients at Perth Children's Hospital to examine response rates. METHODS: We collected and classified daily average pain (0-10 parent proxy score) on each day after tonsillectomy until pain-free for two consecutive days. RESULTS: We enrolled 62 participants and recorded 50 (81%) completed pain profiles, with 711 (97.9%) of 726 requests for a pain score receiving a response. Two families (3%) opted out of the trial, and 10 (16%) were lost to follow-up. Responses received were classified automatically in 92% of cases. No negative feedback was received, with a median (range) satisfaction score of 5 on a 5-point Likert scale (1 = very unhappy, 5 = very happy). CONCLUSIONS: This methodology is likely to generalize well to other simple clinical questions and produce good response rates in further similar studies. We expect SMS messaging to permit expanded longitudinal data collection and broader investigation into patient recovery than previously feasible using telephone follow-up at our institution.

Development Technologies for the Monitoring of Six-Minute Walk Test: A Systematic Review.

In the pandemic time, the monitoring of the progression of some diseases is affected and rehabilitation is more complicated. Remote monitoring may help solve this problem using mobile devices that embed low-cost sensors, which can help measure different physical parameters. Many tests can be applied remotely, one of which is the six-minute walk test (6MWT). The 6MWT is a sub-maximal exercise test that assesses aerobic capacity and endurance, allowing early detection of emerging medical conditions with changes. This paper presents a systematic review of the use of sensors to measure the different physical parameters during the performance of 6MWT, focusing on various diseases, sensors, and implemented methodologies. It was performed with the PRISMA methodology, where the search was conducted in different databases, including IEEE Xplore, ACM Digital Library, ScienceDirect, and PubMed Central. After filtering the papers related to 6MWT and sensors, we selected 31 papers that were analyzed in more detail. Our analysis discovered that the measurements of 6MWT are primarily performed with inertial and magnetic sensors. Likewise, most research studies related to this test focus on multiple sclerosis and pulmonary diseases.

Utilizing Smartphones for Approachable IoT Education in K-12.

Distributed computing, computer networking, and the Internet of Things (IoT) are all around us, yet only computer science and engineering majors learn the technologies that enable our modern lives. This paper introduces PhoneIoT, a mobile app that makes it possible to teach some of the basic concepts of distributed computation and networked sensing to novices. PhoneIoT turns mobile phones and tablets into IoT devices and makes it possible to create highly engaging projects through NetsBlox, an open-source block-based programming environment focused on teaching distributed computing at the high school level. PhoneIoT lets NetsBlox programs-running in the browser on the student's computer-access available sensors. Since phones have touchscreens, PhoneIoT also allows building a Graphical User Interface (GUI) remotely from NetsBlox, which can be set to trigger custom code written by the student via NetsBlox's message system. This approach enables students to create quite advanced distributed projects, such as turning their phone into a game controller or tracking their exercise on top of an interactive Google Maps background with just a few blocks of code.

An Extended Review Concerning the Relevance of Deep Learning and Privacy Techniques for Data-Driven Soft Sensors.

The continuously increasing number of mobile devices actively being used in the world amounted to approximately 6.8 billion by 2022. Consequently, this implies a substantial increase in the amount of personal data collected, transported, processed, and stored. The authors of this paper designed and implemented an integrated personal health data management system, which considers data-driven software and hardware sensors, comprehensive data privacy techniques, and machine-learning-based algorithmic models. It was determined that there are very few relevant and complete surveys concerning this specific problem. Therefore, the current scientific research was considered, and this paper comprehensively analyzes the importance of deep learning techniques that are applied to the overall management of data collected by data-driven soft sensors. This survey considers aspects that are related to demographics, health and body parameters, and human activity and behaviour pattern detection. Additionally, the relatively complex problem of designing and implementing data privacy mechanisms, while ensuring efficient data access, is also discussed, and the relevant metrics are presented. The paper concludes by presenting the most important open research questions and challenges. The paper provides a comprehensive and thorough scientific literature survey, which is useful for any researcher or practitioner in the scope of data-driven soft sensors and privacy techniques, in relation to the relevant machine-learning-based models.