Sensitive visual detection of norfloxacin in water by smartphone assisted colorimetric method based on peroxidase-like active cobalt-doped Fe3O4 nanozyme.

Norfloxacin is widely used owing to its strong bactericidal effect on Gram-negative bacteria. However, the residual norfloxacin in the environment can be biomagnified via food chain and may damage the human liver and delay the bone development of minors. Present work described a reliable and sensitive smartphone colorimetric sensing system based on cobalt-doped Fe3O4 magnetic nanoparticles (Co-Fe3O4 MNPs) for the visual detection of norfloxacin. Compared with Fe3O4, Co-Fe3O4 MNPs earned more remarkably peroxidase-like activity and TMB (colorless) was rapidly oxidized to oxTMB (blue) with the presence of H2O2. Interestingly, the addition of low concentration of norfloxacin can accelerate the color reaction process of TMB, and blue deepening of the solution can be observed with the naked eye. However, after adding high concentration of norfloxacin, the activity of nanozyme was inhibited, resulting in the gradual fading of the solution. Based on this principle, a colorimetric sensor integrated with smartphone RGB mode was established. The visual sensor exhibited good linearity for norfloxacin monitoring in the range of 0.13-2.51 µmol/L and 17.5-100 µmol/L. The limit of visual detection was 0.08 µmol/L. In the actual water sample analysis, the spiked recoveries of norfloxacin were over the range of 95.7%-104.7 %. These results demonstrated that the visual sensor was a convenient and fast method for the efficient and accurate detection of norfloxacin in water, which may have broad application prospect.

Smartphone-Delivered Attentional Bias Modification Training for Mental Health: Systematic Review and Meta-Analysis.

BACKGROUND: Smartphone-delivered attentional bias modification training (ABMT) intervention has gained popularity as a remote solution for alleviating symptoms of mental health problems. However, the existing literature presents mixed results indicating both significant and insignificant effects of smartphone-delivered interventions. OBJECTIVE: This systematic review and meta-analysis aims to assess the impact of smartphone-delivered ABMT on attentional bias and symptoms of mental health problems. Specifically, we examined different design approaches and methods of administration, focusing on common mental health issues, such as anxiety and depression, and design elements, including gamification and stimulus types. METHODS: Our search spanned from 2014 to 2023 and encompassed 4 major databases: MEDLINE, PsycINFO, PubMed, and Scopus. Study selection, data extraction, and critical appraisal were performed independently by 3 authors using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. When necessary, we pooled the standardized mean difference with a 95% CI. In addition, we conducted sensitivity, subgroup, and meta-regression analyses to explore moderator variables of active and placebo ABMT interventions on reducing symptoms of mental health problems and attentional bias. RESULTS: Our review included 12 papers, involving a total of 24,503 participants, and we were able to conduct a meta-analysis on 20 different study samples from 11 papers. Active ABMT exhibited an effect size (Hedges g) of -0.18 (P=.03) in reducing symptoms of mental health problems, while the overall effect remained significant. Similarly, placebo ABMT showed an effect size of -0.38 (P=.008) in reducing symptoms of mental health problems. In addition, active ABMT (Hedges g -0.17; P=.004) had significant effects on reducing attentional bias, while placebo ABMT did not significantly alter attentional bias (Hedges g -0.04; P=.66). CONCLUSIONS: Our understanding of smartphone-delivered ABMT's potential highlights the value of both active and placebo interventions in mental health care. The insights from the moderator analysis also showed that tailoring smartphone-delivered ABMT interventions to specific threat stimuli and considering exposure duration are crucial for optimizing their efficacy. This research underscores the need for personalized approaches in ABMT to effectively reduce attentional bias and symptoms of mental health problems. TRIAL REGISTRATION: PROSPERO CRD42023460749; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=460749.

Association of electronic screen exposure with depression among women in early pregnancy: a cross-sectional study.

BACKGROUND: Previous studies indicated that excessive engagement in digital devices could lead to negative psychological impacts in general population. We aimed to determine the association of electronic screen exposure with depression among women in early pregnancy. METHODS: A cross-sectional study was conducted from June 2021 to June 2022. A total of 665 women in early pregnancy were recruited and the information included socio-demographic characteristics, screen exposure and Patient Health Questionnaire - 9 depression scale. RESULTS: Among the women in early pregnancy, the total daily smartphone viewing time was the longest (median [P25-P75], 5 [3-6] hours/day) in the three types of electronic screen exposure. The total daily smartphone viewing time (P = 0.015, OR[95%CI] = 1.09[1.11-1.18]), smartphone (P = 0.016, OR[95%CI] = 1.24[1.04-1.47]) and television viewing time (P = 0.006, OR[95%CI] = 1.35[1.09-1.67]) before nocturnal sleep were significantly associated with depression among women in early pregnancy. The thresholds calculated by receiver operator characteristic curves were 7.5 h/day, 1.5 h/day and 1.5 h/day, respectively. In addition, women with higher scores of smartphone addiction were more susceptible to depression (P<0.001, OR[95%CI] = 1.11[1.07-1.16]). The top three smartphone usages in women with depression were watching videos (22.0%), listening to music (20.9%) and playing games (16.7%). CONCLUSIONS: In conclusion, electronic screen exposure, including screen viewing time, smartphone addiction and problematic smartphone use was associated with depression among women in early pregnancy. Further studies are warranted to verify the conclusions.

Robust mosquito species identification from diverse body and wing images using deep learning.

Mosquito-borne diseases are a major global health threat. Traditional morphological or molecular methods for identifying mosquito species often require specialized expertise or expensive laboratory equipment. The use of convolutional neural networks (CNNs) to identify mosquito species based on images may offer a promising alternative, but their practical implementation often remains limited. This study explores the applicability of CNNs in classifying mosquito species. It compares the efficacy of body and wing depictions across three image collection methods: a smartphone, macro-lens attached to a smartphone and a professional stereomicroscope. The study included 796 specimens of four morphologically similar Aedes species, Aedes aegypti, Ae. albopictus, Ae. koreicus and Ae. japonicus japonicus. The findings of this study indicate that CNN models demonstrate superior performance in wing-based classification 87.6% (95% CI: 84.2-91.0) compared to body-based classification 78.9% (95% CI: 77.7-80.0). Nevertheless, there are notable limitations of CNNs as they perform reliably across multiple devices only when trained specifically on those devices, resulting in an average decline of mean accuracy by 14%, even with extensive image augmentation. Additionally, we also estimate the required training data volume for effective classification, noting a reduced requirement for wing-based classification compared to body-based methods. Our study underscores the viability of both body and wing classification methods for mosquito species identification while emphasizing the need to address practical constraints in developing accessible classification systems.

Predicting suicidal ideation from irregular and incomplete time series of questionnaires in a smartphone-based suicide prevention platform: a pilot study.

Over 700,000 people die by suicide annually. Collecting longitudinal fine-grained data about at-risk individuals, as they occur in the real world, can enhance our understanding of the temporal dynamics of suicide risk, leading to better identification of those in need of immediate intervention. Self-assessment questionnaires were collected over time from 89 at-risk individuals using the EMMA smartphone application. An artificial intelligence (AI) model was trained to assess current level of suicidal ideation (SI), an early indicator of the suicide risk, and to predict its progression in the following days. A key challenge was the unevenly spaced and incomplete nature of the time series data. To address this, the AI was built on a missing value imputation algorithm. The AI successfully distinguished high SI levels from low SI levels both on the current day (AUC = 0.804, F1 = 0.625, MCC = 0.459) and three days in advance (AUC = 0.769, F1 = 0.576, MCC = 0.386). Besides past SI levels, the most significant questions were related to psychological pain, well-being, agitation, emotional tension, and protective factors such as contacts with relatives and leisure activities. This represents a promising step towards early AI-based suicide risk prediction using a smartphone application.

Nanostructured NiS2-based flexible smart sensors for human respiration monitoring.

The growing demand for wearable healthcare devices has led to an urgent need for cost-effective, wireless and portable breath monitoring systems. However, it is essential to explore novel nanomaterials that combine state-of-the-art flexible sensors with high performance and sensing capabilities along with scalability and industrially acceptable processing. In this study, we demonstrate a highly efficient NiS2-based flexible capacitive sensor fabricated via a solution-processible route using a novel single-source precursor [Ni{S2P(OPr)2}2]. The developed sensor could precisely detect the human respiration rate and exhibit rapid responsiveness, exceptional sensitivity and selectivity at ambient temperatures, with an ultra-fast response and recovery. The device effectively differentiates the exhaled breath patterns including slow, fast, oral and nasal breath, as well as post-exercise breath rates. Moreover, the sensor shows outstanding bending stability, repeatability, reliable and robust sensing performance and is capable of contactless sensing. The sensor was further employed with a user-friendly wireless interface to facilitate smartphone-enabled real-time breath monitoring systems. This work opens up numerous avenues for cost-effective, sustainable and versatile sensors with potential applications for Internet of Things-based flexible and wearable electronics.This article is part of the theme issue 'Celebrating the 15th anniversary of the Royal Society Newton International Fellowship'.

Utilization of android-based paediatric assessment triangle application as an emergency detection tool for children.

Objectives: To identify the effectiveness of an android-based paediatric assessment triangle application in emergency diagnostics. METHODS: The action research study was conducted in the emergency department of a hospital under the Ministry of Defence, Indonesia, located within the Ministry of Defence Rehabilitation Centre Complex, from April to December 2020 after approval from the ethics review committee of the Faculty of Nursing, Universitas Indonesia, Indonesia, employing quantitative and qualitative methods consisting of planning, acting, observing and reflecting stages. Emergency department nurses with at least D3 nursing graduation who were able to identify emergency status in children were included. The subjects were given training on paediatric assessment triangle application before using it in their professional life. The difference was noted through pre- and post-intervention tests. Qualitative data was collected using focus group discussion and system usability scale. RESULTS: Of the 9 nurses, 5(55.6%) were males, 4(44.4%) were females, 8(88.9%) were aged 26-35 years, and 2(22.2%) had professional experience 1-2 years. The mean baseline score was 36.1±11.4, while the mean post-intervention score was 70.9±14.4. The fastest application completion time was 13 seconds, while the slowest was 52 seconds. Qualitative data led to the emergence of 4 themes: time required to complete the application; preference for connectivity with the hospital's electronic record system; assessment of children's clinical status; and, unfamiliarity with the computerised system. The mean system usability scale score was 72.22±11.35 (range: 52.5-92.5). CONCLUSIONS: Paediatric assessment triangle application could be a valid tool for identifying emergency severity in patients during the triage process.

Smartphone App for Prehospital ECG Transmission in ST-Elevation Myocardial Infarction Activation: Protocol for a Mixed Methods Study.

BACKGROUND: Timely diagnosis and treatment for ST-elevation myocardial infarction (STEMI) requires a coordinated response from multiple providers. Rapid intervention is key to reducing mortality and morbidity. Activation of the cardiac catheterization laboratory may occur through verbal communication and may also involve the secure sharing of electrocardiographic images between frontline health care providers and interventional cardiologists. To improve this response, we developed a quick, easy-to-use, privacy-compliant smartphone app, that is SMART AMI-ACS (Strategic Management of Acute Reperfusion and Therapies in Acute Myocardial Infarction Acute Coronary Syndromes), for real-time verbal communication and sharing of electrocardiographic images among health care providers in Ontario, Canada. The app further provides information about diagnosis, management, and risk calculators for patients presenting with acute coronary syndrome. OBJECTIVE: This study aims to integrate the app into workflow processes to improve communication for STEMI activation, resulting in decreased treatment times, improved patient outcomes, and reduced unnecessary catheterization laboratory activation and transfer. METHODS: Implementation of the app will be guided by the Reach, Effectiveness, Acceptability, Implementation, and Maintenance (RE-AIM) framework to measure impact. The study will use quantitative registry data already being collected through the SMART AMI project (STEMI registry), the use of the SMART AMI app, and quantitative and qualitative survey data from physicians. Survey questions will be based on the Consolidated Framework for Implementation Research. Descriptive quantitative analysis and thematic qualitative analysis of survey results will be conducted. Continuous variables will be described using either mean and SD or median and IQR values at pre- and postintervention periods by the study sites. Categorical variables, such as false activation, will be described as frequencies (percentages). For each outcome, an interrupted time series regression model will be fitted to evaluate the impact of the app. RESULTS: The primary outcomes of this study include the usability, acceptability, and functionality of the app for physicians. This will be measured using electronic surveys to identify barriers and facilitators to app use. Other key outcomes will measure the implementation of the app by reviewing the timing-of-care intervals, false "avoidable" catheterization laboratory activation rates, and uptake and use of the app by physicians. Prospective evaluation will be conducted between April 1, 2022, and March 31, 2023. However, for the timing- and accuracy-of-care outcomes, registry data will be compared from January 1, 2019, to March 31, 2023. Data analysis is expected to be completed in Fall 2024, with the completion of a paper for publication anticipated by the end of 2024. CONCLUSIONS: Smartphone technology is well integrated into clinical practice and widely accessible. The proposed solution being tested is secure and leverages the accessibility of smartphones. Emergency medicine physicians can use this app to quickly, securely, and accurately transmit information ensuring faster and more appropriate decision-making for STEMI activation. TRIAL REGISTRATION: ClinicalTrials.gov NCT05290389; https://clinicaltrials.gov/study/NCT05290389. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/55506.

Evaluation of a digital therapy programme for the treatment of primary arterial hypertension: eXPLORE - study protocol for a fully decentralised randomised controlled feasibility study.

INTRODUCTION: Hypertension is a major cause of premature death worldwide as it is an important risk factor for coronary artery disease, myocardial infarction, heart failure and stroke. Although an estimated 1.3 billion adults suffer from hypertension, less than half of them are diagnosed correctly and therefore receive sufficient treatment. Furthermore, only one fifth of those treated reach the therapy target of normotension. This significant deficit underlines the need for new therapy concepts to improve long-term health outcomes. Several studies have shown positive effects of digital health programmes in the disease management of ambulatory, long-term hypertension treatment. More research is needed to explore the abilities of digital health programmes as an innovative pathway in ambulatory healthcare.The eXPLORE study aims to evaluate the feasibility of a clinical trial on the impact of a supplementary digital therapy programme for the treatment of primary arterial hypertension. METHODS AND ANALYSES: The eXPLORE study collects data in the setting of a prospective randomised controlled trial to evaluate methodological feasibility for larger-scaled follow-up research. The study compares a digital therapy programme using a smartphone application that is based on functions and algorithms creating tasks and recommendations based on individual health data to standard care for the treatment of primary arterial hypertension. The study period is 180 days, with a 90-day in-life phase followed by a 90-day follow-up phase. Baseline and follow-up data (3 months, 6 months follow-up) of all participants included is collected via questionnaire surveys as well as self-administered blood pressure monitoring. Patient inclusion, initial data acquisition and follow-up were carried out in an innovative remote setting. The study was initiated in November 2022 and is currently ongoing. Study outcome measures are changes in mean blood pressure, health literacy and self-sufficient health behaviour. ETHICS AND DISSEMINATION: The eXPLORE study is carried out in accordance with all applicable legal regulations. Cost-effectiveness is assured by continuous evaluation and documentation over the course of the study. All health-relevant data from the eXPLORE study will be provided for analyses and publication to the investigators of LMU Hospital. The study was approved by the local ethics committee of LMU Munich (project nr.: 22-0115). TRIAL REGISTRATION: NCT05580068. PROTOCOL VERSION: 1.5, 28.08.2023.

Empowering Portable Age-Related Macular Degeneration Screening: Evaluation of a Deep Learning Algorithm for a Smartphone Fundus Camera.

OBJECTIVES: Despite global research on early detection of age-related macular degeneration (AMD), not enough is being done for large-scale screening. Automated analysis of retinal images captured via smartphone presents a potential solution; however, to our knowledge, such an artificial intelligence (AI) system has not been evaluated. The study aimed to assess the performance of an AI algorithm in detecting referable AMD on images captured on a portable fundus camera. DESIGN, SETTING: A retrospective image database from the Age-Related Eye Disease Study (AREDS) and target device was used. PARTICIPANTS: The algorithm was trained on two distinct data sets with macula-centric images: initially on 108,251 images (55% referable AMD) from AREDS and then fine-tuned on 1108 images (33% referable AMD) captured on Asian eyes using the target device. The model was designed to indicate the presence of referable AMD (intermediate and advanced AMD). Following the first training step, the test set consisted of 909 images (49% referable AMD). For the fine-tuning step, the test set consisted of 238 (34% referable AMD) images. The reference standard for the AREDS data set was fundus image grading by the central reading centre, and for the target device, it was consensus image grading by specialists. OUTCOME MEASURES: Area under receiver operating curve (AUC), sensitivity and specificity of algorithm. RESULTS: Before fine-tuning, the deep learning (DL) algorithm exhibited a test set (from AREDS) sensitivity of 93.48% (95% CI: 90.8% to 95.6%), specificity of 82.33% (95% CI: 78.6% to 85.7%) and AUC of 0.965 (95% CI:0.95 to 0.98). After fine-tuning, the DL algorithm displayed a test set (from the target device) sensitivity of 91.25% (95% CI: 82.8% to 96.4%), specificity of 84.18% (95% CI: 77.5% to 89.5%) and AUC 0.947 (95% CI: 0.911 to 0.982). CONCLUSION: The DL algorithm shows promising results in detecting referable AMD from a portable smartphone-based imaging system. This approach can potentially bring effective and affordable AMD screening to underserved areas.

Are smartphones a tool to cope with the fear of childbirth? The correlation between the fear of loss of connection and the fear of childbirth.

OBJECTIVE: With the spread of smartphones, they have become an indispensable part of life, and nomophobia (No-Mobile-Phone Phobia) has emerged. METHODS: The present research is a cross-sectional study and was conducted with 3,870 primiparous pregnant women between April and May 2022. The research data were collected using the Personal Information Form, Nomophobia Questionnaire, and Wijma Delivery Expectancy/Experience Questionnaire. RESULTS: The Wijma Delivery Expectancy/Experience Questionnaire score of the pregnant women who participated in the study was 22.3% (n=863) had a clinical fear of childbirth and 19.5% (n=753) had extreme nomophobia. Considering the correlation of the Nomophobia Questionnaire and Wijma Delivery Expectancy/Experience Questionnaire scores with other variables, it was found that the Wijma Delivery Expectancy/Experience Questionnaire scores increased with the increasing Nomophobia Questionnaire total score (p=0.000, r=236) and the Nomophobia Questionnaire total score and fear of childbirth increased with an increase in the daily phone usage time. It was also revealed that women who had smartphone applications related to fetal development had higher nomophobia levels (p=0.0001), while they had a lower fear of childbirth. CONCLUSION: This study found that one in every five pregnant women was extremely nomophobic and had a clinical fear of childbirth and that nomophobia and the fear of childbirth were correlated at the clinical level. In this regard, women should prefer face-to-face communication rather than smartphones throughout the pregnancy period.

Usability of a smartphone-compatible, confocal micro-endoscope for cervical cancer screening in resource-limited settings.

BACKGROUND: More efficient methods to detect and treat precancerous lesions of the cervix at a single visit, such as low-cost confocal microscopy, could improve early diagnosis and hence outcomes. We piloted a prototype smartphone-compatible confocal micro-endoscope (SCME) among women presenting to a public cervical cancer screening clinic in Kampala, Uganda. We describe the piloting of the SCME device at an urban clinic used by lower cadre staff. METHODS: We screened women aged 18 and 60 years, who presented for cervical cancer screening at the Kawempe National Referral Hospital Kampala, and evaluated the experience of their providers (nurses). Nurses received a 2-day training by the study doctors on how to use the SCME, which was added to the standard Visual Inspection with Acetic acid (VIA)-based cervical cancer screening. The SCME was used to take colposcopy images before and after VIA at positions 12 and 6 O'clock if VIA negative, and on precancer-suspicious lesions if VIA positive. We used questionnaires to assess the women's experiences after screening, and the experience of the nurses who operated the SCME. RESULTS: Between November 2021 and July 2022, we screened 291 women with a median age of 36 years and 65.7% were HIV positive. Of the women screened, 146 were eligible for VIA, 123 were screened with the SCME, and we obtained confocal images from 103 women. Of those screened with the SCME, 60% found it comfortable and 81% were willing to screen again with it. Confocal images from 79% of the women showed distinguishable cellular features, while images from the remaining 21% were challenging to analyze. Nurses reported a mean score of 85% regarding the SCME's usefulness to their work, 71% regarding their satisfaction and willingness to use it again, 63% in terms of ease of use, and 57% concerning the ease of learning how to operate the SCME. CONCLUSION: Our findings demonstrate the feasibility of using the SCME by lower cadre staff in low-resource settings to aid diagnosis of precancerous lesions. However, more work is needed to make it easier for providers to learn how to operate the SCME and capture high-quality confocal images.

Unleashing the role of e-word of mouth on purchase intention in select Facebook fan pages of smart phone users.

OBJECTIVE: This study aims to examine the impact of e-WOM on customer purchase intentions in Facebook fan pages using theories of trust, value co-creation and brand attitude. The present research has set out to explore this emerging domain of study and has thus developed & tested propositions which attempt to establish a relationship between e-WOM and customer's purchase intentions. A deeper understanding of this possible association is obtained by studying the mediating roles of Trust, Value Co-Creation, Brand Image and Brand Attitude. METHODOLOGY: The context for exploring this phenomenon is chosen to be the fan pages of smartphone brands on Facebook. The study involved conducting a sample survey of 490 respondents, comprising of both male and female, who belong to 5 smartphone brands Facebook fan pages-Samsung, Moto G, Lenovo, MI and ASUS are considered for the study. Out of which sample of 100 each has been targeted individually. FINDINGS: The findings suggested that e-WOM significantly predicts the purchase intentions of the customers of a specific product and considerable impacted on the purchase decision. The findings of the study also reveal that customer 's trust beliefs, perceived value co-creation, brand image and brand attitude partially mediate in between relationships of e-WOM and purchase intention. CONCLUSION: The actual presence of different types of consumer electronics brands on the social media, more prominently, the smartphones, which undoubtedly are the most ubiquitous product of this segment. In fact, this indicates that presence on social media is a well- thought organizational strategy developed by companies to gain partial control over the customer 's decision- making process by establishing a close connect with the customers for a long period. IMPLICATION: This consequence will significantly impact the decision-making process of marketers or practitioners in relation to their marketing tactics. This research also indicates that marketers could devise more effective methods for distributing marketing content through social networking sites, while corporations can cultivate favorable electronic word-of-mouth for their products or services. Through the implementation of social media marketing strategies, companies can increase their sales volume and generate higher revenue. The study examined the role of trust, virtual community participation, and desire to purchase as mediators on smartphone brand fan sites on Facebook. It was observed that these factors had a partial influence on customer purchase intention.

Empowering older adults: evaluating the impact of a smartphone education app on independent living.

Purpose: As science and technology advance, older people's ability to use smart devices in their daily living is becoming more demanding. This study addresses the increasing use of smartphones by the older adults, who often struggle with technology due to lack of competence. We developed an educational app tailored for older adults users and compare its effectiveness with existing educational videos. Methods: An app was created based on the learning characteristics of the older adults, using the ADDIE model, and compared with traditional video education. It involved six participants aged 65 or older, and convenience sampling method was used, evaluating the app and video through usability assessments and eye tracking. Quantitative and qualitative analyzes were conducted with focus groups under the researcher's control. Results: The app received higher usability scores than the video in content, motivation, and interaction. Eye tracking showed users paid more consistent attention to the app. Conclusion: The smartphone app facilitates learning for the older adults without the constraints of time and place, improving their quality of life and technology skills. Eye tracking can be instrumental in future app development for this demographic.

Developing and evaluating a mobile-based teledermatology system for skin lesion diagnosis in Iran.

Asynchronous teledermatology (TD) has shown promise in improving access to dermatological care, yet studies reveal varied methodologies and results. This study aimed to design a mobile app using the store-and-forward model, marking a pioneering effort in Iran to facilitate written documentation of patient history and skin lesion imagery for accurate diagnosis. Conducted from July 2022 to July 2023, this study involved the development and implementation of a smartphone app to collect clinical data and photographs from 89 patients at a dermatology clinic. Diagnoses made at the clinic served as the gold standard. Three dermatologists independently reviewed the app-recorded data, providing diagnoses that were compared to in-person assessments using the Kappa coefficient and agreement percentage. Lesions were predominantly located on the whole body (47%), with psoriasis (19.1%), acne vulgaris (11.2%), and eczema (10%) being the most common. The overall inter-observer Kappa value demonstrated excellent agreement at 0.856. The study's findings endorse the use of a mobile app for TD as an effective adjunct in regions with scarce dermatological services, such as Iran. To promote the broader application of TD, ongoing research is essential to further establish its reliability and practicality.

Lack of Parental Control Is Longitudinally Associated With Higher Smartphone Addiction Tendency in Young Children: A Population-Based Cohort Study.

The purpose of the current study was to examine the risk factors of young children's smartphone addiction in a longitudinal study design. Data collected from 313 participants (mean age, 4.5 ± 0.82 years; male, 49.8%) over 4 years for Kids Cohort for Understanding of Internet Addiction Risk Factors in Early Childhood were analyzed in this study. Mixed effect models were used to evaluate the influence of various variables on the repeated measures of smartphone addiction tendency in young children over time. The multi-level analysis showed that parents' lack of control over children's smartphone use (t = -4.523; 95% confidence interval [CI], -7.32, -1.72), and parents' higher smartphone addiction proneness (t = 6.340; 95% CI, 0.23, 0.440) predicted higher smartphone addiction tendency in young children. The responsibility of the parents to prevent their children from becoming addicted to smartphones should start in a very early age when they start using the smartphones.

The chain mediating role of rumination and social withdrawal in the relationship between smartphone distraction and depression among Chinese nursing students.

Smartphone distraction (SD) is closely related to depression, and the prevalence of SD among nursing students is gradually increasing. However, the potential mechanism of the effect of SD on nursing students' depression is unclear. A total of 574 nursing students were assessed using Smartphone Distraction Scale, Ruminative Response Scale, Hikikomori Questionnaire, and Patient Health Questionnaire-9. The results indicated that SD among nursing students had an impact on depression through four pathways: (1) SD was positively associated with depression (ß = 0.353, P < 0.001); (2) Rumination (ß = 0.199, 95% CI: 0.081 to 0.162) and social withdrawal (ß = 0.061, 95% CI: 0.034 to 0.091) mediated the effects of SD on depression, respectively; and (3) Rumination and social withdrawal played a chain mediating role in the effect of SD on nursing students' depression (ß = 0.027, 95% CI: 0.015 to 0.042). The negative impact of SD on nursing students' mental health should not be taken lightly. Schools and hospitals should guide nursing students to use smartphones correctly, including providing mental health education and professional psychological counselling; families could play a supervisory role and communicate regularly to understand the psychological state and learning of nursing students. These measures can help nursing students cope with stress and reduce the risk of depression.

Smartphone IMU Sensors for Human Identification through Hip Joint Angle Analysis.

Gait monitoring using hip joint angles offers a promising approach for person identification, leveraging the capabilities of smartphone inertial measurement units (IMUs). This study investigates the use of smartphone IMUs to extract hip joint angles for distinguishing individuals based on their gait patterns. The data were collected from 10 healthy subjects (8 males, 2 females) walking on a treadmill at 4 km/h for 10 min. A sensor fusion technique that combined accelerometer, gyroscope, and magnetometer data was used to derive meaningful hip joint angles. We employed various machine learning algorithms within the WEKA environment to classify subjects based on their hip joint pattern and achieved a classification accuracy of 88.9%. Our findings demonstrate the feasibility of using hip joint angles for person identification, providing a baseline for future research in gait analysis for biometric applications. This work underscores the potential of smartphone-based gait analysis in personal identification systems.

Paper-Based Microfluidic Analytical Device Patterned by Label Printer for Point-of-Care Blood Glucose and Hematocrit Detection Using 3D-Printed Smartphone Cassette.

This study presents a portable, low-cost, point-of-care (POC) system for the simultaneous detection of blood glucose and hematocrit. The system consists of a disposable origami microfluidic paper-based analytical device (µPAD) for plasma separation, filtration, and reaction functions and a 3D-printed cassette for hematocrit and blood glucose detection using a smartphone. The origami µPAD is patterned using a cost-effective label printing technique instead of the conventional wax printing method. The 3D-printed cassette incorporates an array of LED lights, which mitigates the effects of intensity variations in the ambient light and hence improves the accuracy of the blood glucose and hematocrit concentration measurements. The hematocrit concentration is determined quantitatively by measuring the distance of plasma wicking along the upper layer of the origami µPAD, which is pretreated with sodium chloride and Tween 20 to induce dehydration and aggregation of the red blood cells. The filtered plasma also penetrates to the lower layer of the origami µPAD, where it reacts with embedded colorimetric assay reagents to produce a yellowish-brown complex. A color image of the reaction complex is captured using a smartphone inserted into the 3D-printed cassette. The image is analyzed using self-written RGB software to quantify the blood glucose concentration. The calibration results indicate that the proposed detection platform provides an accurate assessment of the blood glucose level over the range of 45-630 mg/dL (R2 = 0.9958). The practical feasibility of the proposed platform is demonstrated by measuring the blood glucose and hematocrit concentrations in 13 human whole blood samples. Taking the measurements obtained from commercial glucose and hematocrit meters as a benchmark, the proposed system has a differential of no more than 6.4% for blood glucose detection and 9.1% for hematocrit detection. Overall, the results confirm that the proposed µPAD is a promising solution for cost-effective and reliable POC health monitoring.

A Digital Platform for Home-Based Exercise Prescription for Older People with Sarcopenia.

Digital therapeutics refers to smartphone applications, software, and wearable devices that provide digital solutions to improve healthcare delivery. We developed a digital platform to support the GYM (Grow Your Muscle) study, an ongoing 48-week randomized, controlled trial on reduction of sarcopenia through a home-based, app-monitored physical exercise intervention. The GYM platform consists of a smartphone application including the exercise program and video tutorials of body-weight exercises, a wearable device to monitor heart rate during training, and a website for downloading training data to remotely monitor the exercise. The aim of this paper is to describe the platform in detail and to discuss the technical issues emerging during the study and those related to usability of the smartphone application through a retrospective survey. The main technical issue concerned the API level 33 upgrade, which did not enable participants using the Android operating systems to use the wearable device. The survey revealed some problems with viewing the video tutorials and with internet or smartphone connection. On the other hand, the smartphone application was reported to be easy to use and helpful to guide home exercising. Despite the issues encountered during the study, this digital-supported physical exercise intervention could provide useful to improve muscle measures of sarcopenia.