Problematic use of the internet, smartphones, and social media among medical students and relationship with depression: An exploratory study.

BACKGROUND: Students in sub-Saharan African countries experienced online classes for the first time during the COVID-19 pandemic. For some individuals, greater online engagement can lead to online dependency, which can be associated with depression. The present study explored the association between problematic use of the internet, social media, and smartphones with depression symptoms among Ugandan medical students. METHODS: A pilot study was conducted among 269 medical students at a Ugandan public university. Using a survey, data were collected regarding socio-demographic factors, lifestyle, online use behaviors, smartphone addiction, social media addiction, and internet addiction. Hierarchical linear regression models were performed to explore the associations of different forms of online addiction with depression symptom severity. RESULTS: The findings indicated that 16.73% of the medical students had moderate to severe depression symptoms. The prevalence of being at risk of (i) smartphone addiction was 45.72%, (ii) social media addiction was 74.34%, and (iii) internet addiction use was 8.55%. Online use behaviors (e.g., average hours spent online, types of social media platforms used, the purpose for internet use) and online-related addictions (to smartphones, social media, and the internet) predicted approximately 8% and 10% of the severity of depression symptoms, respectively. However, over the past two weeks, life stressors had the highest predictability for depression (35.9%). The final model predicted a total of 51.9% variance for depression symptoms. In the final model, romantic relationship problems (ß = 2.30, S.E = 0.58; p<0.01) and academic performance problems (ß = 1.76, S.E = 0.60; p<0.01) over the past two weeks; and increased internet addiction severity (ß = 0.05, S.E = 0.02; p<0.01) was associated with significantly increased depression symptom severity, whereas Twitter use was associated with reduced depression symptom severity (ß = 1.88, S.E = 0.57; p<0.05). CONCLUSION: Despite life stressors being the largest predictor of depression symptom score severity, problematic online use also contributed significantly. Therefore, it is recommended that medical students' mental health care services consider digital wellbeing and its relationship with problematic online use as part of a more holistic depression prevention and resilience program.

Smartphone Technology to Remotely Measure Postural Sway during Double- and Single-Leg Squats in Adults with Femoroacetabular Impingement and Those with No Hip Pain.

BACKGROUND: The COVID-19 pandemic has accelerated the demand for utilising telehealth as a major mode of healthcare delivery, with increasing interest in the use of tele-platforms for remote patient assessment. In this context, the use of smartphone technology to measure squat performance in people with and without femoroacetabular impingement (FAI) syndrome has not been reported yet. We developed a novel smartphone application, the TelePhysio app, which allows the clinician to remotely connect to the patient's device and measure their squat performance in real time using the smartphone inertial sensors. The aim of this study was to investigate the association and test-retest reliability of the TelePhysio app in measuring postural sway performance during a double-leg (DLS) and single-leg (SLS) squat task. In addition, the study investigated the ability of TelePhysio to detect differences in DLS and SLS performance between people with FAI and without hip pain. METHODS: A total of 30 healthy (nfemales = 12) young adults and 10 adults (nfemales = 2) with diagnosed FAI syndrome participated in the study. Healthy participants performed DLS and SLS on force plates in our laboratory, and remotely in their homes using the TelePhysio smartphone application. Sway measurements were compared using the centre of pressure (CoP) and smartphone inertial sensor data. A total of 10 participants with FAI (nfemales = 2) performed the squat assessments remotely. Four sway measurements in each axis (x, y, and z) were computed from the TelePhysio inertial sensors: (1) average acceleration magnitude from the mean (aam), (2) root-mean-square acceleration (rms), (3) range acceleration (r), and (4) approximate entropy (apen), with lower values indicating that the movement is more regular, repetitive, and predictable. Differences in TelePhysio squat sway data were compared between DLS and SLS, and between healthy and FAI adults, using analysis of variance with significance set at 0.05. RESULTS: The TelePhysio aam measurements on the x- and y-axes had significant large correlations with the CoP measurements (r = 0.56 and r = 0.71, respectively). The TelePhysio aam measurements demonstrated moderate to substantial between-session reliability values of 0.73 (95% CI 0.62-0.81), 0.85 (95% CI 0.79-0.91), and 0.73 (95% CI 0.62-0.82) for aamx, aamy, and aamz, respectively. The DLS of the FAI participants showed significantly lower aam and apen values in the medio-lateral direction compared to the healthy DLS, healthy SLS, and FAI SLS groups (aam = 0.13, 0.19, 0.29, and 0.29, respectively; and apen = 0.33, 0.45, 0.52, and 0.48, respectively). In the anterior-posterior direction, healthy DLS showed significantly greater aam values compared to the healthy SLS, FAI DLS, and FAI SLS groups (1.26, 0.61, 0.68, and 0.35, respectively). CONCLUSIONS: The TelePhysio app is a valid and reliable method of measuring postural control during DLS and SLS tasks. The application is capable of distinguishing performance levels between DLS and SLS tasks, and between healthy and FAI young adults. The DLS task is sufficient to distinguish the level of performance between healthy and FAI adults. This study validates the use of smartphone technology as a tele-assessment clinical tool for remote squat assessment.

Discrepancies Between Self-reported and Objectively Measured Smartphone Screen Time: Before and During Lockdown.

Screen time shows higher health risks compared to other types of sedentary behaviors. A lockdown may simultaneously increase screen time, reduce physical activity (PA), and change time perception. Our goal was to compare self-reported against objectively measured smartphone screen time (SST) in a sample of active and inactive Portuguese adults before and during a social lockdown. This study was a cross-sectional analysis with 211 Portuguese adults (57.8% males), aged 25.2 ± 8.5 years, from two cohorts, one before the social lockdown and the other during the lockdown. SST was self-reported (SR-SST) and objectively measured using a smartphone (OM-SST). PA was self-reported. Linear regressions were performed to determine the association between SR-SST and OM-SST. A Bland and Altman analysis was used to assess agreement. Independent T-tests were performed for comparisons between cohorts and paired sample T-tests for comparisons within each cohort. The cohort assessed during the lockdown showed a higher SST than the cohort assessed before the lockdown (OM-SST; p < 0.001 and SR-SST; p = 0.009). Before the lockdown, there was no difference between SR-SST and OM-SST (p = 0.100). However, during the social lockdown, although the agreement between SR-SST and OM-SST was good (ICC = 0.72), participants systematically underestimated their SST by ~ 71 min/day (p < 0.001), and this underestimation was higher in inactive participants (~ 85 min/day) than in active individuals (~ 49 min/day). The general population needs to be aware of the benefits of limiting screen time, especially during periods of societal modifications, such as a generalized lockdown. There was a tendency to underestimate SST, meaning a lack of awareness of the actual time spent in this potentially deleterious behavior. This underestimation was more pronounced during the lockdown period and for the inactive participants, thus posing a greater health risk. The findings from this investigation entail relevant information for policy makers to delineate strategies for reducing population screen time from a preventive health perspective.

Advancements in portable instruments based on affinity-capture-migration and affinity-capture-separation for use in clinical testing and life science applications.

The shift from testing at centralized diagnostic laboratories to remote locations is being driven by the development of point-of-care (POC) instruments and represents a transformative moment in medicine. POC instruments address the need for rapid results that can inform faster therapeutic decisions and interventions. These instruments are especially valuable in the field, such as in an ambulance, or in remote and rural locations. The development of telehealth, enabled by advancements in digital technologies like smartphones and cloud computing, is also aiding in this evolution, allowing medical professionals to provide care remotely, potentially reducing healthcare costs and improving patient longevity. One notable POC device is the lateral flow immunoassay (LFIA), which played a major role in addressing the COVID-19 pandemic due to its ease of use, rapid analysis time, and low cost. However, LFIA tests exhibit relatively low analytical sensitivity and provide semi-quantitative information, indicating either a positive, negative, or inconclusive result, which can be attributed to its one-dimensional format. Immunoaffinity capillary electrophoresis (IACE), on the other hand, offers a two-dimensional format that includes an affinity-capture step of one or more matrix constituents followed by release and electrophoretic separation. The method provides greater analytical sensitivity, and quantitative information, thereby reducing the rate of false positives, false negatives, and inconclusive results. Combining LFIA and IACE technologies can thus provide an effective and economical solution for screening, confirming results, and monitoring patient progress, representing a key strategy in advancing diagnostics in healthcare.

Smartphone-Based Screening for Cardiovascular Diseases: A Trend?

Abstract Cardiovascular diseases are the leading cause of death in the world. People living in vulnerable and poor places such as slums, rural areas and remote locations have difficulty in accessing medical care and diagnostic tests. In addition, given the COVID-19 pandemic, we are witnessing an increase in the use of telemedicine and non-invasive tools for monitoring vital signs. These questions motivate us to write this point of view and to describe some of the main innovations used for non-invasive screening of heart diseases. Smartphones are widely used by the population and are perfect tools for screening cardiovascular diseases. They are equipped with camera, flashlight, microphone, processor, and internet connection, which allow optical, electrical, and acoustic analysis of cardiovascular phenomena. Thus, when using signal processing and artificial intelligence approaches, smartphones may have predictive power for cardiovascular diseases. Here we present different smartphone approaches to analyze signals obtained from various methods including photoplethysmography, phonocardiograph, and electrocardiography to estimate heart rate, blood pressure, oxygen saturation (SpO2), heart murmurs and electrical conduction. Our objective is to present innovations in non-invasive diagnostics using the smartphone and to reflect on these trending approaches. These could help to improve health access and the screening of cardiovascular diseases for millions of people, particularly those living in needy areas.

Use of a novel smartphone-based application tool for enrolment and randomisation in pre-hospital clinical trials.

The effective recruitment and randomisation of patients in pre-hospital clinical trials presents unique challenges. Owing to the time critical nature of many pre-hospital emergencies and limited resourcing, the use of traditional methods of randomisation that may include centralised telephone or web-based systems are often not practicable or feasible. Previous technological limitations have necessitated that pre-hospital trialists strike a compromise between implementing pragmatic, deliverable study designs, with robust enrolment and randomisation methodologies. In this commentary piece, we present a novel smartphone-based solution that has the potential to align pre-hospital clinical trial recruitment processes to that of best-in-practice in-hospital and ambulatory care based studies.

mCrutch: A Novel m-Health Approach Supporting Continuity of Care.

This paper reports the architecture of a low-cost smart crutches system for mobile health applications. The prototype is based on a set of sensorized crutches connected to a custom Android application. Crutches were instrumented with a 6-axis inertial measurement unit, a uniaxial load cell, WiFi connectivity, and a microcontroller for data collection and processing. Crutch orientation and applied force were calibrated with a motion capture system and a force platform. Data are processed and visualized in real-time on the Android smartphone and are stored on the local memory for further offline analysis. The prototype's architecture is reported along with the post-calibration accuracy for estimating crutch orientation (5° RMSE in dynamic conditions) and applied force (10 N RMSE). The system is a mobile-health platform enabling the design and development of real-time biofeedback applications and continuity of care scenarios, such as telemonitoring and telerehabilitation.

The associations between screen time and mental health in adolescents: a systematic review.

BACKGROUND: Adolescents have extensive use of screens and, they have common complains related to mental health. Here a systematic review was done to understand the association between screen time and adolescent's mental health. METHOD: This review was conducted in compliance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses - PRISMA. An update search was performed in January 2023 with the following keywords: "screen time," "adolescent," and "mental health" on PubMed, PsycINFO and Scopus databases. RESULTS: 50 articles were included, most have found associations between screen exposure and mental health in adolescents. The most used device by adolescents was the smartphone and the use on weekdays was associated with diminished mental well-being. Social media use was negatively associated with mental well-being and, in girls, associated at higher risk for depression. CONCLUSION: Excessive screen time in adolescents seems associated with mental health problems. Given the profusion and disparity of the results, additional studies are needed to clarify elements such as the screen content or the interaction of adolescents with different screen devices. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42022302817.

Smartphone-based diagnostics for biosensing infectious human pathogens.

The widespread usage of smartphones has made accessing vast troves of data easier for everyone. Smartphones are powerful, handy, and easy to operate, making them a valuable tool for improving public health through diagnostics. When combined with other devices and sensors, smartphones have shown potential for detecting, visualizing, collecting, and transferring data, enabling rapid disease diagnosis. In resource-limited settings, the user-friendly operating system of smartphones allows them to function as a point-of-care platform for healthcare and disease diagnosis. Herein, we critically reviewed the smartphone-based biosensors for the diagnosis and detection of diseases caused by infectious human pathogens, such as deadly viruses, bacteria, and fungi. These biosensors use several analytical sensing methods, including microscopic imaging, instrumental interface, colorimetric, fluorescence, and electrochemical biosensors. We have discussed the diverse diagnosis strategies and analytical performances of smartphone-based detection systems in identifying infectious human pathogens, along with future perspectives.

Treating depression with a smartphone-delivered self-help cognitive behavioral therapy for insomnia: study protocol for a parallel group randomized controlled trial.

BACKGROUND: Depression is a major public health concern. Emerging research has shown that cognitive behavioral therapy for insomnia (CBT-I) is effective in treating individuals with comorbid insomnia and depression. Traditional face-to-face CBT-I encounters many obstacles related to feasibility, accessibility, and help-seeking stigma. CBT-I delivered via smartphone application could be a potential solution. This paper reports a protocol designed to evaluate the efficacy of a self-help smartphone-based CBT-I, using a waitlist group as control, for people with major depression and insomnia. METHODS: A two-arm parallel randomized controlled trial is conducted in a target sample of 285 non-suicidal Hong Kong Chinese older than 17 years of age with major depression and insomnia. Participants complete an online rapid screening, followed by a telephone diagnostic interview. Those who meet the eligibility criteria are randomized in a ratio of 1:1 to receive either CBT-I immediately or to a waitlist control condition. The CBT-I consists of six weekly modules and is delivered through a smartphone application proACT-S. This smartphone app has been pilot tested and revamped to improve user experience. An online randomized algorithm is used to perform randomization to ensure allocation concealment. The primary outcomes are changes over the measurement points in sleep quality, insomnia severity, and depression severity. The secondary outcomes include changes over the measurement points in anxiety, subjective health, treatment expectancy, and acceptability of treatment. Assessments are administered at baseline, post-intervention, and 6-week follow-up. The recruitment is completed. Important adverse events, if any, are documented. Multilevel linear mixed model based on intention-to-treat principle will be conducted to examine the efficacy of the CBT-I intervention. DISCUSSION: It is expected that proACT-S is an efficacious brief sleep-focused self-help treatment for people with major depression and insomnia. If proven efficacious, due to its self-help nature, proACT-S may be applicable as a community-based early intervention, thereby reducing the burden of the public healthcare system in Hong Kong. TRIAL REGISTRATION: ClinicalTrials.gov NCT04228146 . Retrospectively registered on 14 January 2020.

Can social isolation caused by physical distance in people with psychosis be overcome through a Phone Pal?

The current pandemic has forced many people into self-isolation and to practice social distancing. When people are physically isolated and distant from each other, technology may play a fundamental role by enabling social connection and reducing feelings of loneliness caused by this prolonged social isolation. In response to the COVID-19 pandemic, many mental health services worldwide have had to shift their routine face-to-face outpatient appointments to remote telepsychiatry encounters. The increased pressure on mental health services highlights the importance of community-led health-promotion interventions, which can contribute to preventing mental illness or their relapses, and to reduce the burden on health services. Patients with psychosis are particularly socially isolated, have sedentary lifestyles, and commonly face stigma and discrimination from the general population. At the same time, patients with psychosis value technology, are interested in, use and own smart-phones to digitally connect, and are satisfied with their use. Thus, among psychosocial interventions, a helpful resource may be "Phone Pal," a complex intervention which facilitates remote communication between volunteers and socially isolated patients with psychosis through different smart-phone tools. While "Phone Pal" has been originally developed for people with psychosis, it may also be useful to the wider population, helping to overcome the social isolation caused by physical distancing, particularly in these times of widespread isolation. "Phone Pal" may be a potential public health resource for society, providing important support to those that may need it the most, and possibly benefit most from it.

Daily relationships between job insecurity and emotional labor amid COVID-19: Mediation of ego depletion and moderation of off-job control and work-related smartphone use.

The economic recession in the service sector during the COVID-19 pandemic has jeopardized service employees' job security. While the daily fluctuations of perceived job insecurity may have implications for service employees' emotional labor, the day-to-day relationship between these two variables and their mediating and moderating mechanisms in the pandemic context remain unknown. To fill this gap, our research examined the day-level relationship between job insecurity perceptions, ego depletion, and emotional labor, as well as the moderating effects of overnight off-job control and work-related smartphone use. To assess these relationships, we conducted two daily studies during the COVID-19 pandemic. In study 1 (March-April 2020), 135 service employees responded to morning and evening online surveys for five workdays. In study 2 (June 2022), which administered morning and evening online surveys to 90 flight attendants for five workdays, work-related COVID-19 exposure risk was controlled in the analyses. The results of the two studies demonstrated that on a day when service employees perceived a high level of job insecurity, they felt ego-depleted, which, in turn, was associated with decreased deep acting and increased surface acting. Post hoc findings indicated a significant three-way interaction between off-job control, off-job work-related smartphone use, and daily job insecurity, such that the job insecurity-ego depletion-emotional labor was most pronounced when off-job control was low and off-job work-related smartphone use was high. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Mobile Phone Data: A Survey of Techniques, Features, and Applications.

Due to the rapid growth in the use of smartphones, the digital traces (e.g., mobile phone data, call detail records) left by the use of these devices have been widely employed to assess and predict human communication behaviors and mobility patterns in various disciplines and domains, such as urban sensing, epidemiology, public transportation, data protection, and criminology. These digital traces provide significant spatiotemporal (geospatial and time-related) data, revealing people's mobility patterns as well as communication (incoming and outgoing calls) data, revealing people's social networks and interactions. Thus, service providers collect smartphone data by recording the details of every user activity or interaction (e.g., making a phone call, sending a text message, or accessing the internet) done using a smartphone and storing these details on their databases. This paper surveys different methods and approaches for assessing and predicting human communication behaviors and mobility patterns from mobile phone data and differentiates them in terms of their strengths and weaknesses. It also gives information about spatial, temporal, and call characteristics that have been extracted from mobile phone data and used to model how people communicate and move. We survey mobile phone data research published between 2013 and 2021 from eight main databases, namely, the ACM Digital Library, IEEE Xplore, MDPI, SAGE, Science Direct, Scopus, SpringerLink, and Web of Science. Based on our inclusion and exclusion criteria, 148 studies were selected.

Efficacy of distance training program for cardiopulmonary resuscitation utilizing smartphone application and home delivery system.

AIM OF THE STUDY: Community cardiopulmonary resuscitation (CPR) education is important for laypersons. However, during the COVID-19 pandemic, with social distancing, conventional face-to-face CPR training was unavailable. We developed a distance learning CPR training course (HEROS-Remote) using a smartphone application that monitors real-time chest compression quality and a home delivery collection system for mannikins. This study aimed to evaluate the efficacy of the HEROS-Remote course by comparing chest compression quality with that of conventional CPR training. METHODS: We applied layperson CPR education with HEROS-Remote and conventional education in Seoul during the COVID-19 pandemic. Both groups underwent a 2-min post-training chest compression test, and we tested non-inferiority. Chest compression depth, rate, complete recoil, and composite chest compression score was measured. Trainees completed a satisfaction survey on CPR education and delivery. The primary outcome was the mean chest compression depth. RESULTS: A total of 180 trainees were enrolled, with 90 assigned to each training group. Chest compression depth of HEROS-Remote training showed non-inferiority to that of conventional training (67.4 vs. 67.8, p = 0.78), as well as composite chest compression score (92.7 vs. 95.5, p = 0.16). The proportions of adequate chest compression depth, chest compression rate, and chest compressions with complete chest recoil were similar in both training sessions. In the HEROS-Remote training, 90% of the trainees were satisfied with CPR training, and 96% were satisfied with the delivery and found it convenient. CONCLUSION: HEROS-Remote training was non-inferior to conventional CPR training in terms of chest compression quality. Distance learning CPR training using a smartphone application and mannikin delivery had high user satisfaction and was logistically feasible.

SARS-CoV-2 viral RNA detection using the novel CoVradar device associated with the CoVreader smartphone app.

The COVID-19 pandemic has highlighted the need for innovative approaches to its diagnosis. Here we present CoVradar, a novel and simple colorimetric method that combines nucleic acid analysis with dynamic chemical labeling (DCL) technology and the Spin-Tube device to detect SARS-CoV-2 RNA in saliva samples. The assay includes a fragmentation step to increase the number of RNA templates for analysis, using abasic peptide nucleic acid probes (DGL probes) immobilized to nylon membranes in a specific dot pattern to capture RNA fragments. Duplexes are formed by labeling complementary RNA fragments with biotinylated SMART bases, which act as templates for DCL. Signals are generated by recognizing biotin with streptavidin alkaline phosphatase and incubating with a chromogenic substrate to produce a blue precipitate. CoVradar results are analysed by CoVreader, a smartphone-based image processing system that can display and interpret the blotch pattern. CoVradar and CoVreader provide a unique molecular assay capable of detecting SARS-CoV-2 viral RNA without the need for extraction, preamplification, or pre-labeling steps, offering advantages in terms of time (∼3 h/test), cost (∼€1/test manufacturing cost) and simplicity (does not require large equipment). This solution is also promising for developing assays for other infectious diseases.

Evaluating the optimised font size and viewing time of online learning in young children: a multicentre cross-sectional study.

OBJECTIVES: Near viewing distance (VD) and longer viewing times are associated with myopia. This study aimed to identify the font size and viewing time that guarantee the appropriate VD and pixels per degree (PPD) for children's online learning. DESIGN: This cross-sectional study comprised two experiments. In experiment A, participants read text in five font sizes on three backlit displays (a personal computer, a smartphone and a tablet), an E-ink display and paper for 5 min per font size. In experiment B, participants watched videos for 30 min on three backlit displays. SETTING: The Peking University People's Hospital in Beijing (China) and the School of Ophthalmology and Optometry, Wenzhou Medical University (Zhejiang Province, China). PARTICIPANTS: Thirty-five participants completed experiment A. Ten of them participated in experiment B. PRIMARY AND SECONDARY OUTCOME MEASURES: VDs were measured by Clouclip. The corresponding PPD was calculated. RESULTS: In experiment A, font size and display type significantly affected VD (F(4840)=149.44, p<0.001, ES (Effect size)=0.77; F(4840), p<0.001, ES=0.37). VDs were >33 cm for all five font sizes on the PC, the tablet and paper and for 18-pt on the smartphone and 16-pt on E-ink. PPD for 16-pt on the PC, 14-pt on the tablet and all five font sizes on the phone were >60. In experiment B, VD increased over the four previous 5 min periods but decreased slightly on tablets and PCs in the fifth 5 min period. PPD was >60. CONCLUSION: Children demonstrated different VDs and PPDs based on font size and display type. To ensure a 33 cm VD and 60 PPD, the minimum font size for online reading should be 18-pt on smartphones, 16-pt on PCs and E-ink, 10.5-pt on tablets and 9-pt on paper. More attention should be given to children's VD with continuous video viewing of more than 25 min. TRIAL REGISTRATION NUMBER: ChiCTR2100049584.

Smartphone integrated handheld (SPEED) digital polymerase chain reaction device.

We demonstrate a smartphone integrated handheld (SPEED) digital polymerase chain reaction (dPCR) device for point-of-care application. The device has dimensions of ≈100 × 200 × 35 mm3 and a weight of ≈400 g. It can perform 45 PCR cycles in ≈49 min. The device also features integrated, miniaturized modules for thermal cycling, image taking, and wireless data communication. These functions are controlled by self-developed Android-based applications. The only consumable is the developed silicon-based dPCR chip, which has the potential to be recycled. The device's precision and accuracy are comparable with commercial dPCR machines. We have verified the SPEED dPCR prototype's utility in the testing of severe acute respiratory syndrome coronavirus 2, the detection of cancer-associated gene sequences, and the confirmations of Down syndrome diagnoses. Due to its low upfront capital investment, as well as its nominal running cost, we envision that the SPEED dPCR device will help to perform cancer screenings and non-invasive prenatal tests for the general population. It will also aid in the timely identification and monitoring of infectious disease testing, thereby expediting alerts with respect to potential emerging pandemics.

Sample-to-answer platform for the clinical evaluation of COVID-19 using a deep learning-assisted smartphone-based assay.

Since many lateral flow assays (LFA) are tested daily, the improvement in accuracy can greatly impact individual patient care and public health. However, current self-testing for COVID-19 detection suffers from low accuracy, mainly due to the LFA sensitivity and reading ambiguities. Here, we present deep learning-assisted smartphone-based LFA (SMARTAI-LFA) diagnostics to provide accurate decisions with higher sensitivity. Combining clinical data learning and two-step algorithms enables a cradle-free on-site assay with higher accuracy than the untrained individuals and human experts via blind tests of clinical data (n = 1500). We acquired 98% accuracy across 135 smartphone application-based clinical tests with different users/smartphones. Furthermore, with more low-titer tests, we observed that the accuracy of SMARTAI-LFA was maintained at over 99% while there was a significant decrease in human accuracy, indicating the reliable performance of SMARTAI-LFA. We envision a smartphone-based SMARTAI-LFA that allows continuously enhanced performance by adding clinical tests and satisfies the new criterion for digitalized real-time diagnostics.

Loop-Mediated Isothermal Amplification on Paper Microfluidic Chips for Highly Sensitive and Specific Zika Virus Detection Using Smartphone.

Zika virus (ZIKV) infection may cause serious birth defects and is a critical concern for women of child-bearing age in affected regions. A simple, portable, and easy-to-use ZIKV detection method would enable point-of-care testing, which may aid in prevention of the spread of the virus. Herein, we describe a reverse transcription isothermal loop-mediated amplification (RT-LAMP) method that detects the presence of ZIKV RNA in complex samples (e.g., blood, urine, and tap water). Phenol red is the colorimetric indicator of successful amplification. Color changes based on the amplified RT-LAMP product from the presence of viral target are monitored using a smartphone camera under ambient light conditions. A single viral RNA molecule per µL can be detected in as quickly as 15 min using this method with 100% sensitivity and 100% specificity in blood and tap water, while 100% sensitivity and 67% specificity in urine. This platform can also be used to identify other viruses including SARS-CoV-2 and improve the current state of field-based diagnostics.