Commercial Smartphone-Based Devices and Smart Applications for Personalized Healthcare Monitoring and Management

Iodide based electrochemical gold quantification method for lateral flow assays

The lateral flow assay (LFA) is an ideal technology for at-home medical diagnostic tests due to its ease of use, cost-effectiveness, and rapid results. Despite these advantages, only few LFAs, such as the pregnancy and COVID-19 tests, have been translated from the laboratory to the homes of patients. To date, the medical applicability of LFAs is limited by the fact that they only provide yes/no answers unless combined with optical readers that are too expensive for at-home applications. Furthermore, LFAs are unable to compete with the state-of-the-art technologies in centralized laboratories in terms of detection limits. To address those shortcomings, we have developed an electrochemical readout procedure to enable quantitative and sensitive LFAs. This technique is based on a voltage-triggered in-situ dissolution of gold nanoparticles, the conventional label used to visualize target-specific signals on the test line in LFAs. Following the dissolution, the amount of gold is measured by electroplating onto an electrode and subsequent electrochemical quantification of the deposited gold. The measured current has a low noise, which achieves superior detection limits compared to optical techniques where background light scattering is limiting the readout performance. In addition, the hardware for the readout was developed to demonstrate translatability towards low-cost electronics.

Saliva assay: a call for methodological standardization

The oral cavity provides an ideal environment for microorganisms, including bacteria, viruses, and fungi, to flourish. Increasing attention has been focused on the connection between the oral microbiome and both oral and systemic diseases, spurring active research into the collection and analysis of specimens for healthcare purposes. Among the various methods for analyzing the oral microbiome, saliva analysis is especially prominent. Saliva samples, which can be collected non-invasively, provide information on the systemic health and oral microbiome composition of an individual. This review was performed to evaluate the current state of the relevant research through an examination of the literature and to suggest an appropriate assay method for investigating the oral microbiome. We analyzed articles published in English in SCI(E) journals after January 1, 2000, ultimately selecting 53 articles for review. Articles were identified through keyword searches in the PubMed, Embase, Cochrane, Web of Science, and CINAHL databases. Three experienced researchers conducted full-text assessments following title and abstract screening to select appropriate papers. Subsequently, they organized and analyzed the desired data. Our review revealed that most studies utilized unstimulated saliva samples for oral microbiome analysis. Of the 53 studies examined, 29 identified relationships between the oral microbiome and various diseases, such as oral disease, Behçet disease, cancer, and oral lichen planus. However, the studies employed diverse methods of collection and analysis, which compromised the reliability and accuracy of the findings. To address the limitations caused by methodological inconsistencies, a standardized saliva assay should be established.

Effectiveness of Gerontechnology Empowerment Program on Awareness and Use of Mobile Apps Among Older Adults for Instrumental Activities of Daily Living: Protocol for a Cluster Randomized Controlled Trial

BACKGROUND

Instrumental activities of daily living (iADLs) are crucial for older adults to live independently. Health care and technological advancements will increase the older adult population and life expectancy globally. Difficulties with iADLs impact older adults' quality of life. Mobile apps can assist older adults, but many require help due to limited awareness. Lack of awareness is a barrier to app use. Existing literature mainly covers health care and app design, needing more focus on iADL apps for older adults.

OBJECTIVE

The study objectives encompass 2 main aspects: first, to evaluate the awareness, use, and factors influencing the use of apps among older adults for iADLs; and second, to create and assess the effectiveness of a gerontechnology empowerment program (GEP) for older adults on the awareness and use of apps for iADLs.

METHODS

This research uses a quantitative approach divided into 2 distinct phases. In phase 1, we conduct a descriptive survey to assess the level of awareness and use of mobile apps for iADLs and identify the factors that influence the use of such apps among older adults. To ensure clarity and comprehension among participants, we provide them with a subject information sheet in both Kannada and English. The data collected during this phase enable us to gain insights into awareness levels, use patterns, and factors that shape older adults' use of apps for iADLs. The results serve as the foundation for designing the GEP. In phase 2, a cluster randomization method will be used to select older adults aged 60 to 75 years in Udupi district, Karnataka, India, who are active smartphone users. These participants will be divided into 2 groups: the experimental and the control groups. The experimental group will join the GEP. The sample size for phase 1 is 554, and phase 2 is 50. To assess the effectiveness of this program, we will measure the outcomes before and after its implementation using the same assessment tools used in phase 1.

RESULTS

This study is funded by the Indian Council of Medical Research (Adhoc/193/2022/SBHSR on November 18, 2022). Phase 1 data collection is expected to be completed by November 2023, and phase 2 is scheduled to commence in the upcoming months. Phase 1 and 2 findings will be analyzed and discussed in the main paper, which we intend to submit to a high-quality peer-reviewed journal for publication. The research protocol, informed consent forms, and associated documentation received approval from institutional ethics committees (214/2020).

CONCLUSIONS

Upon the successful testing of the GEP, it can be recommended that welfare departments encourage older adults to use mobile apps for iADLs and establish training programs to provide support to older adults in using these apps.

TRIAL REGISTRATION

Clinical Trials Registry - India CTRI/2020/09/027977; https://ctri.nic.in/Clinicaltrials/pmaindet2.php?EncHid=NDUxMzM=&Enc=&userName=027977.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/53587.

Exploring the feasibility of an artificial intelligence based clinical decision support system for cutaneous melanoma detection in primary care - a mixed method study

Objective: Skin examination to detect cutaneous melanomas is commonly performed in primary care. In recent years, clinical decision support systems (CDSS) based on artificial intelligence (AI) have been introduced within several diagnostic fields.Setting: This study employs a variety of qualitative and quantitative methodologies to investigate the feasibility of an AI-based CDSS to detect cutaneous melanoma in primary care.Subjects and Design: Fifteen primary care physicians (PCPs) underwent near-live simulations using the CDSS on a simulated patient, and subsequent individual semi-structured interviews were explored with a hybrid thematic analysis approach. Additionally, twenty-five PCPs performed a reader study (diagnostic assessment on the basis of image interpretation) of 18 dermoscopic images, both with and without help from AI, investigating the value of adding AI support to a PCPs decision. Perceived instrument usability was rated on the System Usability Scale (SUS).Results: From the interviews, the importance of trust in the CDSS emerged as a central concern. Scientific evidence supporting sufficient diagnostic accuracy of the CDSS was expressed as an important factor that could increase trust. Access to AI decision support when evaluating dermoscopic images proved valuable as it formally increased the physician's diagnostic accuracy. A mean SUS score of 84.8, corresponding to 'good' usability, was measured.Conclusion: AI-based CDSS might play an important future role in cutaneous melanoma diagnostics, provided sufficient evidence of diagnostic accuracy and usability supporting its trustworthiness among the users.

Effects of mHealth-Based Lifestyle Interventions on Gestational Diabetes Mellitus in Pregnant Women With Overweight and Obesity: Systematic Review and Meta-Analysis

BACKGROUND

The increasing incidence of gestational diabetes mellitus (GDM) is a global health problem that is more likely to occur in pregnant women with overweight or obesity. Adhering to a healthy lifestyle is associated with a reduced risk of GDM. With the development of IT, mobile health (mHealth) interventions have become widely available in health care. However, there are no definitive conclusions on the effectiveness of mHealth-based lifestyle interventions in preventing GDM.

OBJECTIVE

This study aims to evaluate the impact of mHealth-based lifestyle interventions on GDM and other pregnancy outcomes in pregnant women with overweight or obesity.

METHODS

A systematic literature search was conducted in 5 English databases (MEDLINE, Embase, Web of Science, CENTRAL, and CINAHL) and 4 Chinese databases (CBM, CNKI, Vip, and Wanfang) to identify randomized controlled trials (RCTs) on the effectiveness of mHealth-based interventions for GDM from inception to January 10, 2023. In total, 2 authors independently screened the studies and extracted the data. The quality of the included studies was examined using the Cochrane risk-of-bias tool. Data synthesis was conducted using Review Manager (version 5.4; The Cochrane Collaboration).

RESULTS

A total of 16 RCTs with 7351 participants were included in this study. The included studies were published between 2014 and 2021 and were conducted in China, the United States, Australia, New Zealand, the United Kingdom, Ireland, and Norway. The sample sizes of the studies ranged from 75 to 2202, and the duration of the mHealth-based lifestyle interventions ranged from 4 to 28 weeks. Compared with usual care, mHealth-based lifestyle interventions significantly reduced the incidence of GDM (odds ratio [OR] 0.74, 95% CI 0.56-0.96; P=.03; I2=65%), preterm birth (OR 0.65, 95% CI 0.48-0.87; P=.004; I2=25%), macrosomia (OR 0.59, 95% CI 0.40-0.87; P=.008; I2=59%), and gestational weight gain (mean difference=-1.12 kg, 95% CI -1.44 to -0.80; P<.001; I2=43%). The subgroup analysis showed that interventions delivered via apps (OR 0.55, 95% CI 0.37-0.83; P=.004; I2=44%), provided by obstetricians (OR 0.69, 95% CI 0.51-0.93; P=.02; I2=60%), and targeted at Asian populations (OR 0.44, 95% CI 0.34-0.58; P<.001; I2=0%) and that used the International Association of Diabetes and Pregnancy Study Groups diagnostic criteria (OR 0.58, 95% CI 0.39-0.86; P=.007; I2=69%) showed a statistically significant reduction in the risk of GDM.

CONCLUSIONS

mHealth-based lifestyle interventions had a favorable impact on the prevention of GDM in pregnant women with overweight and obesity. Future studies need to further explore the potential of mHealth-based interventions for GDM through better design and more rigorous large-scale RCTs.

TRIAL REGISTRATION

PROSPERO International Prospective Register of Systematic Reviews CRD42021286995; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=286995.

Telerehabilitation for a Non-specific Low Back Pain: A Case Report

One of the most common conditions that affect activities of daily living and make them significantly more difficult to perform is low back pain (LBP). As a result, it is essential to treat LBP at an early stage. Particularly in geographically remote areas where there is a shortage of medical professionals and a lack of rehabilitation services, telerehabilitation is considered a potential alternative. Hence, this case report represents the impact of telerehabilitation on LBP in a 32-year-old female corporate worker who presented to the out-patient department of physiotherapy with the chief complaints of LBP for the last three months with difficulty in performing activities, and being unable to sit for prolonged period of time. The physiotherapeutic rehabilitation was virtually administered through online sessions through the cloud-based application as the patient was not able to visit the outpatient department on a regular basis. Post-intervention results demonstrated increased range of motion and flexibility, reduced pain, increased muscle strength, reduced disability and kinesiophobia, and improved quality of life. Hence, it can be concluded that telerehabilitation offers a novel solution to increase access to rehabilitation services.

A label-free electrochemical aptasensor based on Bi-Sb alloy materials for potential POCT of HER-2

As a prognostic biomarker for breast cancer, human epidermal growth factor receptor 2 (HER-2) is of crucial diagnostic value. Here, a label-free electrochemical aptasensor was established for the ultrasensitive detection of HER-2 using a modified electrode of Bi-Sb alloy materials (Bi-Sb AMs). The performance of the aptasensor was enhanced greatly due to the introduction of Bi-Sb alloy materials (Bi-Sb AMs) with high conductivity. Furthermore, by integrating the aptasensor with the Sensit Smart U-disk electrochemical analyzer, the point-of-care testing (POCT) for HER-2 was realized. Under the optimal experimental parameters, the POCT analyzer showed a wide linear response from 0.01 pg mL-1 to 100 ng mL-1, with a low detection limit (LOD) of 5.96 fg mL-1 for the detection of HER-2. The presented POCT analyzer exhibited good specificity, stability, and reproducibility. Benefiting from the simple operation and rapid testing, the developed analyzer will have potential application in the prognostic diagnosis and treatment of breast cancer.

In-situ preparation of norepinephrine-functionalized silver nanoparticles and application for colorimetric detection of tacrolimus in plasma samples

Tacrolimus (Tac) is a well-documented immunosuppressive agent for the prevention of graft-vs-host diseases in several types of organ transplants. The narrow therapeutic window and the individual-variable pharmacokinetics of Tac demonstrate the importance of regular therapeutic drug monitoring (TDM) as an imperative concept for its oral medication regimens. A simple, one-step, selective, and sensitive colorimetric platform is fabricated for the determination of Tac by surface modification of the silver nanoparticles (AgNPs) via norepinephrine (NE) molecules. The attachment of NE and Tac induces the aggregation of the AgNPs, which is observed by color distinction (yellow to brown) and a noteworthy shifting of the absorption peak in the visible region. The fabricated nanoprobe can detect Tac concentrations in plasma samples in two linear ranges from 2 ng/mL to 70 ng/mL and 70 ng/mL to 1000 ng/mL with R2 > 0.99. The limit of detection (LOD) was calculated as low as 0.1 ng/mL. The developed method was applied for the determination of Tac in patient's plasma samples under Tac medication therapy.

Eye-tracking paradigms for the assessment of mild cognitive impairment: a systematic review

Mild cognitive impairment (MCI), representing the 'transitional zone' between normal cognition and dementia, has become a novel topic in clinical research. Although early detection is crucial, it remains logistically challenging at the same time. While traditional pen-and-paper tests require in-depth training to ensure standardized administration and accurate interpretation of findings, significant technological advancements are leading to the development of procedures for the early detection of Alzheimer's disease (AD) and facilitating the diagnostic process. Some of the diagnostic protocols, however, show significant limitations that hamper their widespread adoption. Concerns about the social and economic implications of the increasing incidence of AD underline the need for reliable, non-invasive, cost-effective, and timely cognitive scoring methodologies. For instance, modern clinical studies report significant oculomotor impairments among patients with MCI, who perform poorly in visual paired-comparison tasks by ascribing less attentional resources to novel stimuli. To accelerate the Global Action Plan on the Public Health Response to Dementia 2017-2025, this work provides an overview of research on saccadic and exploratory eye-movement deficits among older adults with MCI. The review protocol was drafted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Electronic databases were systematically searched to identify peer-reviewed articles published between 2017 and 2022 that examined visual processing in older adults with MCI and reported gaze parameters as potential biomarkers. Moreover, following the contemporary trend for remote healthcare technologies, we reviewed studies that implemented non-commercial eye-tracking instrumentation in order to detect information processing impairments among the MCI population. Based on the gathered literature, eye-tracking-based paradigms may ameliorate the screening limitations of traditional cognitive assessments and contribute to early AD detection. However, in order to translate the findings pertaining to abnormal gaze behavior into clinical applications, it is imperative to conduct longitudinal investigations in both laboratory-based and ecologically valid settings.

Digital Transformation in Toxicology: Improving Communication and Efficiency in Risk Assessment

Toxicology is undergoing a digital revolution, with mobile apps, sensors, artificial intelligence (AI), and machine learning enabling better record-keeping, data analysis, and risk assessment. Additionally, computational toxicology and digital risk assessment have led to more accurate predictions of chemical hazards, reducing the burden of laboratory studies. Blockchain technology is emerging as a promising approach to increase transparency, particularly in the management and processing of genomic data related with food safety. Robotics, smart agriculture, and smart food and feedstock offer new opportunities for collecting, analyzing, and evaluating data, while wearable devices can predict toxicity and monitor health-related issues. The review article focuses on the potential of digital technologies to improve risk assessment and public health in the field of toxicology. By examining key topics such as blockchain technology, smoking toxicology, wearable sensors, and food security, this article provides an overview of how digitalization is influencing toxicology. As well as highlighting future directions for research, this article demonstrates how emerging technologies can enhance risk assessment communication and efficiency. The integration of digital technologies has revolutionized toxicology and has great potential for improving risk assessment and promoting public health.

The Effect of Submaximal Exercise on Jugular Venous Pulse Assessed by a Wearable Cervical Plethysmography System

The jugular venous pulse (JVP) is a one of the crucial parameters of efficient cardiovascular function. Nowadays, limited data are available regarding the response of JVP to exercise because of its complex and/or invasive assessment procedure. The aim of the present work is to test the feasibility of a non-invasive JVP plethysmography system to monitor different submaximal exercise condition. Twenty (20) healthy subjects (13M/7F mean age 25 ± 3, BMI 21 ± 2) underwent cervical strain-gauge plethysmography, acquired synchronously with the electrocardiogram, while they were carrying out different activities: stand supine, upright, and during the execution of aerobic exercise (2 km walking test) and leg-press machine exercise (submaximal 6 RM test). Peaks a and x of the JVP waveform were investigated since they reflect the volume of cardiac filling. To this aim, the Δax parameter was introduced, representing the amplitude differences between a and x peaks. Significant differences in the values of a, x, and Δax were found between static and exercise conditions (p < 0.0001, p < 0.0001, p < 0.0001), respectively. Particularly, the Δax value for the leg press was approximately three times higher than the supine, and during walking was even nine times higher. The exercise monitoring by means of the novel JVP plethysmography system is feasible during submaximal exercise, and it provides additional parameters on cardiac filling and cerebral venous drainage to the widely used heartbeat rate value.

Transforming Rapid Diagnostic Tests for Precision Public Health: Open Guidelines for Manufacturers and Users

BACKGROUND

Precision public health (PPH) can maximize impact by targeting surveillance and interventions by temporal, spatial, and epidemiological characteristics. Although rapid diagnostic tests (RDTs) have enabled ubiquitous point-of-care testing in low-resource settings, their impact has been less than anticipated, owing in part to lack of features to streamline data capture and analysis.

OBJECTIVE

We aimed to transform the RDT into a tool for PPH by defining information and data axioms and an information utilization index (IUI); identifying design features to maximize the IUI; and producing open guidelines (OGs) for modular RDT features that enable links with digital health tools to create an RDT-OG system.

METHODS

We reviewed published papers and conducted a survey with experts or users of RDTs in the sectors of technology, manufacturing, and deployment to define features and axioms for information utilization. We developed an IUI, ranging from 0% to 100%, and calculated this index for 33 World Health Organization-prequalified RDTs. RDT-OG specifications were developed to maximize the IUI; the feasibility and specifications were assessed through developing malaria and COVID-19 RDTs based on OGs for use in Kenya and Indonesia.

RESULTS

The survey respondents (n=33) included 16 researchers, 7 technologists, 3 manufacturers, 2 doctors or nurses, and 5 other users. They were most concerned about the proper use of RDTs (30/33, 91%), their interpretation (28/33, 85%), and reliability (26/33, 79%), and were confident that smartphone-based RDT readers could address some reliability concerns (28/33, 85%), and that readers were more important for complex or multiplex RDTs (33/33, 100%). The IUI of prequalified RDTs ranged from 13% to 75% (median 33%). In contrast, the IUI for an RDT-OG prototype was 91%. The RDT open guideline system that was developed was shown to be feasible by (1) creating a reference RDT-OG prototype; (2) implementing its features and capabilities on a smartphone RDT reader, cloud information system, and Fast Healthcare Interoperability Resources; and (3) analyzing the potential public health impact of RDT-OG integration with laboratory, surveillance, and vital statistics systems.

CONCLUSIONS

Policy makers and manufacturers can define, adopt, and synergize with RDT-OGs and digital health initiatives. The RDT-OG approach could enable real-time diagnostic and epidemiological monitoring with adaptive interventions to facilitate control or elimination of current and emerging diseases through PPH.

Twelve Threats of Precision Livestock Farming (PLF) for Animal Welfare

Research and development of Precision Livestock Farming (PLF) is booming, partly due to hopes and claims regarding the benefits of PLF for animal welfare. These claims remain largely unproven, however, as only few PLF technologies focusing on animal welfare have been commercialized and adopted in practice. The prevailing enthusiasm and optimism about PLF innovations may be clouding the perception of possible threats that PLF may pose to farm animal welfare. Without claiming to be exhaustive, this paper lists 12 potential threats grouped into four categories: direct harm, indirect harm via the end-user, via changes to housing and management, and via ethical stagnation or degradation. PLF can directly harm the animals because of (1) technical failures, (2) harmful effects of exposure, adaptation or wearing of hardware components, (3) inaccurate predictions and decisions due to poor external validation, and (4) lack of uptake of the most meaningful indicators for animal welfare. PLF may create indirect effects on animal welfare if the farmer or stockperson (5) becomes under- or over-reliant on PLF technology, (6) spends less (quality) time with the animals, and (7) loses animal-oriented husbandry skills. PLF may also compromise the interests of the animals by creating transformations in animal farming so that the housing and management are (8) adapted to optimize PLF performance or (9) become more industrialized. Finally, PLF may affect the moral status of farm animals in society by leading to (10) increased speciesism, (11) further animal instrumentalization, and (12) increased animal consumption and harm. For the direct threats, possibilities for prevention and remedies are suggested. As the direction and magnitude of the more indirect threats are harder to predict or prevent, they are more difficult to address. In order to maximize the potential of PLF for improving animal welfare, the potential threats as well as the opportunities should be acknowledged, monitored and addressed.

The use of mobile devices in oculoplastic and oral and maxillofacial surgery: A systematic review

BACKGROUND

The use of smartphones in the United States has more than doubled since 2011. Mobile phone applications have versatile functions in ophthalmology, otolaryngology, and plastic surgery, such as increasing patient engagement in treatment, decreasing no-shows to appointments, and providing patient education. They also provide practical advantages to the clinician, including supplementing an otoscope, laryngoscope, or ophthalmoscope. Their use in oral and maxillofacial surgery (OMFS) and oculoplastic surgery has shown effectiveness for a variety of applications, such as for photography and medical reference. Research suggests that smartphones may improve clinical outcomes and efficiency.

OBJECTIVE

The goal of this study is to provide a comprehensive and up-to-date systematic review of the available literature investigating mobile phone use in oculoplastic surgery and OMFS.

METHODS

A query of terms relevant to oculoplastic surgery and OMFS was conducted using the databases PubMed, CINAHL, Web of Science, and PsychINFO to identify studies meeting inclusion criteria that investigated the implementation, efficacy, and outcomes of mobile device use in oculoplastic surgery and OMFS. A qualitative summary and discussion of the literature was subsequently synthesized.

RESULTS

Out of a total of 289 articles reviewed, 171 were removed due to duplication across the four databases. Of the 118 studies remaining, 19 of them were included within the final qualitative review after screening the abstracts and full text for relevance. The articles were published between 2005 and 2020 from 7 different countries. Review of the relevant articles showed three settings in which mobile devices were used: communication between providers, communication between providers and patients, and as surgical supplementation. The literature review included use of mobile device photography for quality improvement, visual representation of procedures for patient education, and improved communication amongst providers and patients in the setting of oculoplastics and OMFS.

CONCLUSION

Mobile device use has become ubiquitous across cultures worldwide. The literature suggests that mobile phone use in oculoplastic surgery and OMFS may improve clinical practice in multiple settings. However, further investigation is necessary to quantify the clinical benefits of mobile device use in oculoplastic and oral and maxillofacial surgery.

Smartphone-microfluidic fluorescence imaging system for studying islet physiology

Smartphone technology has been recently applied for biomedical image acquisition and data analysis due to its high-quality imaging capability, and flexibility to customize multi-purpose apps. In this work, we developed and characterized a smartphone-microfluidic fluorescence imaging system for studying the physiology of pancreatic islets. We further evaluated the system capability by performing real-time fluorescence imaging on mouse islets labeled with either chemical fluorescence dyes or genetically encoded fluorescent protein indicators (GEFPIs). Our results showed that the system was capable of analyzing key beta-cell insulin stimulator-release coupling factors in response to various stimuli with high-resolution dynamics. Furthermore, the integration of a microfluidics allowed high-resolution detection of insulin secretion at single islet level. When compared to conventional fluorescence microscopes and macro islet perifusion apparatus, the system has the advantages of low cost, portable, and easy to operate. With all of these features, we envision that this smartphone-microfluidic fluorescence imaging system can be applied to study islet physiology and clinical applications.

Molecular Diagnostic System Using Engineered Fusion Protein-Conjugated Magnetic Nanoparticles

To effectively control the spread of new infectious diseases, there is a need for highly sensitive diagnostic methods to detect viral nucleic acids rapidly. This study outlines a universal and simple detection strategy that uses magnetic nanoparticles (MNPs) and a novel MagR-MazE fusion protein for molecular diagnostics to facilitate sensitive detection. This study has engineered a novel MNP conjugate that can be generated easily, without using many chemical reagents. The technique is a nucleic acid detection method, using MagR-MazE fusion protein-conjugated MNPs, where the results can be visualized with the naked eye, regardless of the oligonucleotide sequences of the target in the lateral flow assay. This method could sensitively detect polymerase chain reaction (PCR) products of 16S ribosomal RNA (rRNA) and the 2019-nCoV-N-positive control gene in 5 min. It shows a low limit of detection (LoD) of 0.013 ng/μL for dsDNA. It is simpler and more rapid, sensitive, and versatile than other techniques, making it suitable for point-of-care testing. The proposed detection system and MNP conjugation strategy using a fusion protein can be widely applied to various fields requiring rapid on-site diagnosis.

Exploring the factors influencing adoption of health-care wearables among generation Z consumers in India

Purpose

The purpose of this study is to identify the major factors influencing the adoption of health-care wearables in generation Z (Gen Z) customers in India. A conceptual framework using push pull and mooring (PPM) adoption theory was developed.

Design/methodology/approach

Data was collected from 208 Gen Z customers based on 5 constructs related to the adoption of health-care wearables. Confirmatory factor analysis and structural equation modelling was used to analyse the responses. The mediation paths were analysed using bootstrapping method and examination of the standardized direct and indirect effects in the model.

Findings

The study results indicated that the antecedent factors consisted of push (real-time health information availability), pull (normative environment) and mooring (decision self-efficacy) factors. The mooring factor (MOOR) was related to the push factor but not the pull factor. The MOOR, in turn, was related to the switching intention of Gen Z customers for health wearables adoption.

Research limitations/implications

The research study extended the literature related to the PPM theory in the context of the adoption of health wearables among Gen Z customers in India.

Practical implications

The study outcome would enable managers working in health wearable organizations to understand consumer behaviour towards health wearables.

Social implications

The use of health wearables among Gen Z individuals would lead to future generations adopting a healthy lifestyle resulting in an effective workforce and better economy.

Originality/value

This was one of the few studies which have explored the PPM theory to explore the factors for the adoption of health wearables among Gen Z customers in India.

A Cross-Disciplinary View of Testing and Bioinformatic Analysis of SARS-CoV-2 and Other Human Respiratory Viruses in Pandemic Settings

The SARS-Coronavirus-2 (SARS-CoV-2) infectious disease, COVID-19, has spread rapidly, resulting in a global pandemic with significant mortality. The combination of early diagnosis via rapid screening, contact tracing, social distancing and quarantine has helped to control the pandemic. The absence of real time response and diagnosis is a crucial technology shortfall and is a key reason why current contact tracing methods are inadequate to control spread. In contrast, current information technology combined with a new generation of near-real time tests offers consumer-engaged smartphone-based "lab-in-a-phone" internet-of-things (IoT) connected devices that provide increased pandemic monitoring. This review brings together key aspects required to create an entire global diagnostic ecosystem. Cross-disciplinary understanding and integration of both mechanisms and technologies for effective detection, incidence mapping and disease containment in near real-time is summarized. Available measures to monitor and/or sterilize surfaces, next-generation laboratory and smartphone-based diagnostic approaches can be brought together and networked for instant global monitoring that informs Public Health policy. Cloud-based analysis enabling real-time mapping will enable future pandemic control, drive the suppression and elimination of disease spread, saving millions of lives globally. A new paradigm is introduced - scaled and multiple diagnostics for mapping and spreading of a pandemic rather than traditional accumulation of individual measurements. This can do away with the need for ultra-precise and ultra-accurate analysis by taking mass measurements that can relax tolerances and build resilience through networked analytics and informatics, the basis for novel swarm diagnostics. These include addressing ethical standards, local, national and international collaborative engagement, multidisciplinary and analytical measurements and standards, and data handling and storage protocols.

Willingness of Chinese Men Who Have Sex With Men to Use Smartphone-Based Electronic Readers for HIV Self-testing: Web-Based Cross-sectional Study

BACKGROUND

The need for strategies to encourage user-initiated reporting of results after HIV self-testing (HIVST) persists. Smartphone-based electronic readers (SERs) have been shown capable of reading diagnostics results accurately in point-of-care diagnostics and could bridge the current gaps between HIVST and linkage to care.

OBJECTIVE

Our study aimed to assess the willingness of Chinese men who have sex with men (MSM) in the Jiangsu province to use an SER for HIVST through a web-based cross-sectional study.

METHODS

From February to April 2020, we conducted a convenience web-based survey among Chinese MSM by using a pretested structured questionnaire. Survey items were adapted from previous HIVST feasibility studies and modified as required. Prior to answering reader-related questions, participants watched a video showcasing a prototype SER. Statistical analysis included descriptive analysis, chi-squared test, and multivariable logistic regression. P values less than .05 were deemed statistically significant.

RESULTS

Of 692 participants, 369 (53.3%) were aged 26-40 years, 456 (65.9%) had ever self-tested for HIV, and 493 (71.2%) were willing to use an SER for HIVST. Approximately 98% (483/493) of the willing participants, 85.3% (459/538) of ever self-tested and never self-tested, and 40% (46/115) of unwilling participants reported that SERs would increase their HIVST frequency. Engaging in unprotected anal intercourse with regular partners compared to consistently using condoms (adjusted odds ratio [AOR] 3.04, 95% CI 1.19-7.74) increased the odds of willingness to use an SER for HIVST. Participants who had ever considered HIVST at home with a partner right before sex compared to those who had not (AOR 2.99, 95% CI 1.13-7.90) were also more willing to use an SER for HIVST. Playing receptive roles during anal intercourse compared to playing insertive roles (AOR 0.05, 95% CI 0.02-0.14) was associated with decreased odds of being willing to use an SER for HIVST. The majority of the participants (447/608, 73.5%) preferred to purchase readers from local Centers of Disease Control and Prevention offices and 51.2% (311/608) of the participants were willing to pay less than US $4.70 for a reader device.

CONCLUSIONS

The majority of the Chinese MSM, especially those with high sexual risk behaviors, were willing to use an SER for HIVST. Many MSM were also willing to self-test more frequently for HIV with an SER. Further research is needed to ascertain the diagnostic and real-time data-capturing capacity of prototype SERs during HIVST.

Mobile 5P-Medicine Approach for Cardiovascular Patients

Medicine is heading towards personalized care based on individual situations and conditions. With smartphones and increasingly miniaturized wearable devices, the sensors available on these devices can perform long-term continuous monitoring of several user health-related parameters, making them a powerful tool for a new medicine approach for these patients. Our proposed system, described in this article, aims to develop innovative solutions based on artificial intelligence techniques to empower patients with cardiovascular disease. These solutions will realize a novel 5P (Predictive, Preventive, Participatory, Personalized, and Precision) medicine approach by providing patients with personalized plans for treatment and increasing their ability for self-monitoring. Such capabilities will be derived by learning algorithms from physiological data and behavioral information, collected using wearables and smart devices worn by patients with health conditions. Further, developing an innovative system of smart algorithms will also focus on providing monitoring techniques, predicting extreme events, generating alarms with varying health parameters, and offering opportunities to maintain active engagement of patients in the healthcare process by promoting the adoption of healthy behaviors and well-being outcomes. The multiple features of this future system will increase the quality of life for cardiovascular diseases patients and provide seamless contact with a healthcare professional.

Can empirical mode decomposition improve heartbeat detection in fetal phonocardiography signals?

BACKGROUND AND OBJECTIVE

A fetal phonocardiography signal can be hard to interpret and classify due to various sources of additive noise in the womb, spanning from fetal movement to maternal heart sounds. Nevertheless, the non-invasive nature of the method makes it potentially suitable for long-term monitoring of fetal health, especially since it can be implemented on ubiquitous devices such as smartphones. We have employed empirical mode decomposition for the extraction of intrinsic mode functions that would enable the utilization of additional characteristics from the signal.

METHODS

Fetal heart recordings from 7 pregnant women in the 3rd trimester or pregnancy were taken in parallel with a measurement microphone and a portable Doppler device. Signal peaks positions from the Doppler were taken as the locations of S1 heart sounds and subsequently used as classification labels for the microphone signal. After employing a moving window approach for segmentation, more than 7600 observations were stored in the final dataset. The 135 extracted features consisted of typical audio temporal and spectral characteristics, each taken from separate sets of audio signals and intrinsic mode functions. We have used a number of metrics and methods to validate the usability of features, including univariate analysis of feature ranking and importance. Furthermore, we have used machine learning to train a number of classifiers to validate the usability of features based on intrinsic mode functions, taking prediction accuracy as the comparison metric.

RESULTS

Features extracted from intrinsic mode functions combined with audio features significantly improve accuracy in comparison to using only audio features. The improvements of detection accuracy obtained with a selected set of combined features spanned from 3.8% to even 10.3% based on the employed classifier.

CONCLUSIONS

We have utilized empirical mode decomposition as a method of extracting features relevant for fetal heartbeat classification. The results show consistent improvements in detection accuracy when these characteristics are added to a set of conventional audio features. This implies substantial benefits of applying empirical mode decomposition and lays the groundwork for future research on fetal heartbeat detection.

Enzyme embedded microfluidic paper-based analytic device (μPAD): a comprehensive review

Low-cost paper-based analytical devices are the latest generation of portable lab-on-chip designs that offers an innovative platform for the on/off-site analysis (biosensing) of target analytes, especially in rural and remote areas. Recently, microfluidic paper-based analytical devices (μPADs) have attained significant recognition owing to their exciting fundamental features such as: ease of fabrication, rapid operation, and precise interpretations. The incorporation of enzymes with paper-based analytical devices significantly improves analytical performance while exhibiting excellent chemical and storage stability. In addition to that, these devices are highly compact, portable, easy-to-use, and do not require any additional sophisticated equipment for the detection and quantification of target analytes. This review provides a holistic insight into design, fabrication, and enzyme immobilization strategies for the development of enzyme-μPADs, which enables them to be widely implemented for in-field analysis. It also highlights the recent application of enzyme-μPADs in the area of: biomedical, food safety, and environmental monitoring while exploring the mechanisms of detection involved. Further, in order to improve the accuracy of analysis, researchers have designed a smartphone-based scanning tool for multi-variant point-of-care devices, which is summarized in the latter part of the review. Finally, the future perspectives and outlook of major challenges associated with enzyme-μPADs are discussed with their possible solutions. The development of enzyme integrated μPADs will open a new avenue as an exceptional analytical tool to explore various applications.HIGHLIGHTSEnzyme embedded paper-based analytical devices are a revolution in the field of biosensing.The design, fabrication, and enzyme immobilization on μPADs have been comprehensively discussed.The application of enzyme-μPADs food safety, environmental monitoring, and clinical diagnostic have been reviewed.Smartphones can be used as an on-site, user-friendly, and compact next-gen scanning tool for biosensing.

Label-Free Protein Analysis Using Liquid Chromatography with Gravimetric Detection

The detection and analysis of proteins in a label-free manner under native solution conditions is an increasingly important objective in analytical bioscience platform development. Common approaches to detect native proteins in solution often require specific labels to enhance sensitivity. Dry mass sensing approaches, by contrast, using mechanical resonators, can operate in a label-free manner and offer attractive sensitivity. However, such approaches typically suffer from a lack of analyte selectivity as the interface between standard protein separation techniques and micro-resonator platforms is often constrained by qualitative mechanical sensor performance in the liquid phase. Here, we describe a strategy that overcomes this limitation by coupling liquid chromatography with a quartz crystal microbalance (QCM) platform by using a microfluidic spray dryer. We explore a strategy which allows first to separate a protein mixture in a physiological buffer solution using size exclusion chromatography, permitting specific protein fractions to be selected, desalted, and subsequently spray-dried onto the QCM for absolute mass analysis. By establishing a continuous flow interface between the chromatography column and the spray device via a flow splitter, simultaneous protein mass detection and sample fractionation is achieved, with sensitivity down to a 100 μg/mL limit of detection. This approach for quantitative label-free protein mixture analysis offers the potential for detection of protein species under physiological conditions.

How Do Machines Learn? Artificial Intelligence as a New Era in Medicine

With an increased number of medical data generated every day, there is a strong need for reliable, automated evaluation tools. With high hopes and expectations, machine learning has the potential to revolutionize many fields of medicine, helping to make faster and more correct decisions and improving current standards of treatment. Today, machines can analyze, learn, communicate, and understand processed data and are used in health care increasingly. This review explains different models and the general process of machine learning and training the algorithms. Furthermore, it summarizes the most useful machine learning applications and tools in different branches of medicine and health care (radiology, pathology, pharmacology, infectious diseases, personalized decision making, and many others). The review also addresses the futuristic prospects and threats of applying artificial intelligence as an advanced, automated medicine tool.

Advances of ECG Sensors from Hardware, Software and Format Interoperability Perspectives

It is well-known that cardiovascular disease is one of the major causes of death worldwide nowadays. Electrocardiogram (ECG) sensor is one of the tools commonly used by cardiologists to diagnose and detect signs of heart disease with their patients. Since fast, prompt and accurate interpretation and decision is important in saving the life of patients from sudden heart attack or cardiac arrest, many innovations have been made to ECG sensors. However, the use of traditional ECG sensors is still prevalent in the clinical settings of many medical institutions. This article provides a comprehensive survey on ECG sensors from hardware, software and data format interoperability perspectives. The hardware perspective outlines a general hardware architecture of an ECG sensor along with the description of its hardware components. The software perspective describes various techniques (denoising, machine learning, deep learning, and privacy preservation) and other computer paradigms used in the software development and deployment for ECG sensors. Finally, the format interoperability perspective offers a detailed taxonomy of current ECG formats and the relationship among these formats. The intention is to help researchers towards the development of modern ECG sensors that are suitable and approved for adoption in real clinical settings.

Smart materials for point-of-care testing: From sample extraction to analyte sensing and readout signal generator

The last decade has seen a surge of technical developments in the field on point-of-care testing (POCT). While these developments are extremely diverse, the common aim is to implement improved methods for quick, reliable and inexpensive diagnosis of patients within the clinical setting. While examples of successful introduction and use of POCT techniques are growing, further developments are still necessary to create POCT devices with better portability, usability and performance. Advances in smart materials emerge as potentially valuable know-hows to provide a competitive edge to the development of next generation POCT devices. This review describes the key advantages of adopting smart material-based technologies at different analytical stages of a POCT platform. Under these analytical stages which involves sample pre-treatment, analyte sensing and readout signal generator, several concepts and approaches from contemporary research work in using smart material-based technologies will be the major focus in this review. Lastly, challenges and potential outlook in implementing materials technologies from the application point of view for POCT will be discussed.

Biocompatible near-infrared quantum dots delivered to the skin by microneedle patches record vaccination

Accurate medical recordkeeping is a major challenge in many low-resource settings where well-maintained centralized databases do not exist, contributing to 1.5 million vaccine-preventable deaths annually. Here, we present an approach to encode medical history on a patient using the spatial distribution of biocompatible, near-infrared quantum dots (NIR QDs) in the dermis. QDs are invisible to the naked eye yet detectable when exposed to NIR light. QDs with a copper indium selenide core and aluminum-doped zinc sulfide shell were tuned to emit in the NIR spectrum by controlling stoichiometry and shelling time. The formulation showing the greatest resistance to photobleaching after simulated sunlight exposure (5-year equivalence) through pigmented human skin was encapsulated in microparticles for use in vivo. In parallel, microneedle geometry was optimized in silico and validated ex vivo using porcine and synthetic human skin. QD-containing microparticles were then embedded in dissolvable microneedles and administered to rats with or without a vaccine. Longitudinal in vivo imaging using a smartphone adapted to detect NIR light demonstrated that microneedle-delivered QD patterns remained bright and could be accurately identified using a machine learning algorithm 9 months after application. In addition, codelivery with inactivated poliovirus vaccine produced neutralizing antibody titers above the threshold considered protective. These findings suggest that intradermal QDs can be used to reliably encode information and can be delivered with a vaccine, which may be particularly valuable in the developing world and open up new avenues for decentralized data storage and biosensing.

A Novel Mobile App Intervention to Reduce Dental Anxiety in Infant Patients

Introduction: Anxiety and fear are feelings routinely associated with the dental experience, especially in childhood and adolescence. These have been prominent in the world for many years and are currently considered a public health issue. Since the young population is an assiduous user of virtual inputs, it was sought to develop a mobile application (app) with the function of reducing the levels of dental anxiety in children before dental treatment and evaluate its effectiveness. Materials and Methods: An app, entitled "Hello, Dentist!", containing dental anticipation videos, was developed by a multidisciplinary team and evaluated. This app was tested in two children groups (dental and school settings), aged 6-10 years. Children and caregivers answered sociodemographic questions. In addition, to measure children's dental anxiety, they completed the Children's Fear Survey Schedule before and after app utilization. Control groups were utilized. A total of 100 children were included in this study. Results: Anxiety levels were statistically lower in the groups using the app when compared with their control groups (p < 0.05). The effectiveness in reducing anxiety was greater when the initial anxiety values were higher, as seen in the dental environment group (p = 0.029). Dental procedures previously experienced, as well as the environment (dental or school) in which children were inserted, were factors that influenced their initial anxiety levels. Conclusions: The creation and utilization of an app to reduce dental anxiety are feasible and effective. The app developed decreased children's anxiety, especially in the dental setting, by anticipating dental situation.

Binary CorNET: Accelerator for HR Estimation From Wrist-PPG

Research on heart rate (HR) estimation using wrist-worn photoplethysmography (PPG) sensors have progressed rapidly owing to the prominence of commercial sensing modules, used widely for lifestyle monitoring. Reported methodologies have been fairly successful in mitigating the effect of motion artifacts (MA) in ambulatory environment for HR estimation. Recently, a learning framework, CorNET, employing two-layer convolution neural networks (CNN) and two-layer long short-term network (LSTM) was successfully reported for estimating HR from MA-induced PPG signals. However, such a network topology with large number of parameters presents a challenge, towards low-complexity hardware implementation aimed at on-node processing. In this paper, we demonstrate a fully binarized network (bCorNET) topology and its corresponding algorithm-to-architecture mapping and energy-efficient implementation for HR estimation. The proposed framework achieves a MAE of 6.67 ± 5.49 bpm when evaluated on 22 IEEE SPC subjects. The design, synthesized with ST65 nm technology library achieving 3 GOPS @ 1 MHz, consumes 56.1 μJ per window with occupied 1634K NAND2 equivalent cell area and had a latency of 32 ms when estimating HR every 2 s from PPG signals.

A primer on artificial intelligence for the paediatric cardiologist

The combination of pediatric cardiology being both a perceptual and a cognitive subspecialty demands a complex decision-making model which makes artificial intelligence a particularly attractive technology with great potential. The prototypical artificial intelligence system would autonomously impute patient data into a collaborative database that stores, syncs, interprets and ultimately classifies the patient's profile to specific disease phenotypes to compare against a large aggregate of shared peer health data and outcomes, the current medical body of literature and ongoing trials to offer morbidity and mortality prediction, drug therapy options targeted to each patient's genetic profile, tailored surgical plans and recommendations for timing of sequential imaging. The focus of this review paper is to offer a primer on artificial intelligence and paediatric cardiology by briefly discussing the history of artificial intelligence in medicine, modern and future applications in adult and paediatric cardiology across selected concentrations, and current barriers to implementation of these technologies.

Smartphone-based spectral imaging otoscope: System development and preliminary study for evaluation of its potential as a mobile diagnostic tool

We develop a novel smartphone-based spectral imaging otoscope for telemedicine and examine its capability for the mobile diagnosis of middle ear diseases. The device was applied to perform spectral imaging and analysis of an ear-mimicking phantom and a normal and abnormal tympanic membrane for evaluation of its potential for the mobile diagnosis. Spectral classified images were obtained via online spectral analysis in a remote server. The phantom experimental results showed that it allowed us to distinguish four different fluids located behind a semitransparent membrane. Also, in the spectral classified images of normal ears (n = 3) and an ear with chronic otitis media (n = 1), the normal and abnormal regions in each ear could be quantitatively distinguished with high contrast. These preliminary results thus suggested that it might have the potentials for providing quantitative information for the mobile diagnosis of various middle ear diseases.

Remote blood pressure monitoring and behavioral intensification for stroke: A randomized controlled feasibility trial

Measuring blood pressure (BP) at home and remote monitoring can improve the patient’s adherence to BP control and vascular outcomes. This study evaluated the feasibility of a trial regarding the effects of an intensive mobile BP management strategy versus usual care in acute ischemic stroke patients. A feasibility-testing, randomized, open-labeled controlled trial was conducted. Remote BP measurement, data transmission, storage, and centralized monitoring system were organized through a Bluetooth-equipped sphygmomanometer paired to the participants’ smartphones. Participants were randomized equally into intensive management (behavioral intensification to measure BP at home by texting, direct telephone call, or breakthrough visit) and control (usual care) groups. The primary feasibility outcomes were: 1) recruitment time for the pre-specified number of participants, 2) retention of participants, 3) frequency of breakthrough visit calls, 4) response to breakthrough visit call, and 5) proportions satisfying BP measurement criteria. Sixty participants were randomly assigned to the intensive management (n = 31) and control (n = 29) groups, of which 57 participants were included in the primary analysis with comparable baseline characteristics. Recruitment time from the first to the last participant was 350 days, and 95% of randomized participants completed the final visit (intensive, 94%; control, 98%). Eight breakthrough visit calls were made to 7 participants (23%), with complete and immediate responses within 3 ± 4 days. The median of half-day blocks fulfilling the BP measurement criteria per patient were 91% in the intensive group and 83% in the control group (difference, 12.2; 95% confidence interval, 2.2–22.2). No adverse events related to the trial procedures were reported. The intensive monitoring, including remote BP measurement, data transfer, and centralized monitoring system, engaged with behavioral intensification was feasible if the patients complied with the intervention. However, the device utilized would need further improvement prior to a large trial.

Mobile Health Applications for Prenatal Assessment and Monitoring

BACKGROUND

A mobile health application is an exciting, fast-paced domain that is likely to improve prenatal care.

METHODS

In this narrative review, we summarise the use of mobile health applications in this setting with a special emphasis on both the benefits of remote monitoring devices and the potential pitfalls of their use, highlighting the need for robust regulations and guidelines before their widespread introduction into prenatal care.

RESULTS

Remote monitoring devices for four areas of prenatal care are reported: (1) cardio-tocography; (2) blood glucose levels; (3) blood pressure; and (4) prenatal ultrasound. The majority of publications are pilot projects on remote consultation, education, coaching, screening, monitoring and selective booking, mostly reporting potential medical and/or economic benefits by mobile health applications over conventional care for very specific situations, indications and locations, but not always generalizable.

CONCLUSIONS

Despite the potential advantages of these devices, some caution must be taken when implementing this technology into routine daily practice. To date, the majority of published research on mobile health in the prenatal setting consists of observational studies and there is a need for high-quality randomized controlled trials to confirm the reported clinical and economic benefits as well as the safety of this technology. There is also a need for guidance and governance on the development and validation of new apps and devices and for the implementation of mobile health technology into healthcare systems in both high and low-income settings. Finally, digital communication technologies offer perspectives towards exploration and development of the very new domain of tele-pharmacology.

Patient preferences for visualization of longitudinal patient-reported outcomes data

OBJECTIVE

The study sought to design symptom reports of longitudinal patient-reported outcomes data that are understandable and meaningful to end users.

MATERIALS AND METHODS

We completed a 2-phase iterative design and evaluation process. In phase I, we developed symptom reports and refined them according to expert input. End users then completed a survey containing demographics, a measure of health literacy, and items to assess visualization preferences and comprehension of reports. We then collected participants' perspectives on reports through semistructured interviews and modified them accordingly. In phase II, refined reports were evaluated in a survey that included demographics, validated measures of health and graph literacy, and items to assess preferences and comprehension of reports. Surveys were administered using a think-aloud protocol.

RESULTS

Fifty-five English- and Spanish-speaking end users, 89.1% of whom had limited health literacy, participated. In phase I, experts recommended improvements and 20 end users evaluated reports. From the feedback received, we added emojis, changed date and font formats, and simplified the y-axis scale of reports. In phase II, 35 end users evaluated refined designs, of whom 94.3% preferred reports with emojis, the favorite being a bar graph combined with emojis, which also promoted comprehension. In both phases, participants literally interpreted reports and provided suggestions for future visualizations.

CONCLUSIONS

A bar graph combined with emojis was participants' preferred format and the one that promoted comprehension. Target end users must be included in visualization design to identify literal interpretations of images and ensure final products are meaningful.

Novel Data Mining Methodology for Healthcare Applied to a New Model to Diagnose Metabolic Syndrome without a Blood Test

Metabolic Syndrome (MetS) is a cluster of risk factors that increase the likelihood of heart disease and diabetes mellitus. It is crucial to get diagnosed with time to take preventive measures, especially for patients in locations without proper access to laboratories and medical consultations. This work presented a new methodology to diagnose diseases using data mining that documents all the phases thoroughly for further improvement of the resulting models. We used the methodology to create a new model to diagnose the syndrome without using biochemical variables. We compared similar classification models, using their reported variables and previously obtained data from a study in Colombia. We built a new model and compared it to previous models using the holdout, and random subsampling validation methods to get performance evaluation indicators between the models. Our resulting ANN model used three hidden layers and only Hip Circumference, dichotomous Waist Circumference, and dichotomous blood pressure variables. It gave an Area Under Curve (AUC) of 87.75% by the IDF and 85.12% by HMS MetS diagnosis criteria, higher than previous models. Thanks to our new methodology, diagnosis models can be thoroughly documented for appropriate future comparisons, thus benefiting the diagnosis of the studied diseases.

A Vendor-Independent Mobile Health Monitoring Platform for Digital Health Studies: Development and Usability Study

Background

Medical smartphone apps and mobile health devices are rapidly entering mainstream use because of the rising number of smartphone users. Consequently, a large amount of consumer-generated data is being collected. Technological advances in innovative sensory systems have enabled data connectivity and aggregation to become cornerstones in developing workable solutions for remote monitoring systems in clinical practice. However, few systems are currently available to handle such data, especially for clinical use.

Objective

The aim of this study was to develop and implement the digital health research platform for mobile health (DHARMA) that combines data saved in different formats from a variety of sources into a single integrated digital platform suitable for mobile remote monitoring studies.

Methods

DHARMA comprises a smartphone app, a Web-based platform, and custom middleware and has been developed to collect, store, process, and visualize data from different vendor-specific sensors. The middleware is a component-based system with independent building blocks for user authentication, study and patient administration, data handling, questionnaire management, patient files, and reporting.

Results

A prototype version of the research platform has been tested and deployed in multiple clinical studies. In this study, we used the platform for the follow-up of pregnant women at risk of developing pre-eclampsia. The patients’ blood pressure, weight, and activity were semi-automatically captured at home using different devices. DHARMA automatically collected and stored data from each source and enabled data processing for the end users in terms of study-specific parameters, thresholds, and visualization.

Conclusions

The increasing use of mobile health apps and connected medical devices is leading to a large amount of data for collection. There has been limited investment in handling and aggregating data from different sources for use in academic and clinical research focusing on remote monitoring studies. In this study, we created a modular mobile health research platform to collect and integrate data from a variety of third-party devices in several patient populations. The functionality of the platform was demonstrated in a real-life setting among women with high-risk pregnancies.

Commercial Devices-Based System Designed to Improve the Treatment Adherence of Hypertensive Patients

This paper presents an intelligent system designed to increase the treatment adherence of hypertensive patients. The architecture was developed to allow communication among patients, physicians, and families to determine each patient's rate assertion of medication intake time and their self-monitoring of blood pressure. Concerning the medication schedule, the system is designed to follow a predefined prescription, adapting itself to undesired events, such as mistakenly taking medication or forgetting to take medication on time. When covering the blood pressure measurement, it incorporates best medical practices, registering the actual values in recommended frequency and form, trying to avoid the known "white-coat effect." We assume that taking medicine precisely and measuring blood pressure correctly may lead to good adherence to the treatment. The system uses commercial consumer electronic devices and can be replicated in any home equipped with a standard personal computer and Internet access. The resulting architecture has four layers. The first is responsible for adding electronic devices that typically exist in today's homes to the system. The second is a preprocessing layer that filters the data generated from the patient's behavior. The third is a reasoning layer that decides how to act based on the patient's activities observed. Finally, the fourth layer creates messages that should drive the reactions of all involved actors. The reasoning layer takes into consideration the patient's schedule and medication-taking activity data and uses implicit algorithms based on the J48, RepTree, and RandomTree decision tree models to infer the adherence. The algorithms were first adjusted using one academic machine learning and data mining tool. The system communicates with users through smartphones (anytime and anywhere) and smart TVs (in the patient's home) by using the 3G/4G and WiFi infrastructure. It interacts automatically through social networks with doctors and relatives when changes or mistakes in medication intake and blood pressure mean values are detected. By associating the blood pressure data with the history of medication intake, our system can indicate the treatment adherence and help patients to achieve better treatment results. Comparisons with similar research were made, highlighting our findings.

Internet of Things Architectures, Technologies, Applications, Challenges, and Future Directions for Enhanced Living Environments and Healthcare Systems: A Review

Internet of Things (IoT) is an evolution of the Internet and has been gaining increased attention from researchers in both academic and industrial environments. Successive technological enhancements make the development of intelligent systems with a high capacity for communication and data collection possible, providing several opportunities for numerous IoT applications, particularly healthcare systems. Despite all the advantages, there are still several open issues that represent the main challenges for IoT, e.g., accessibility, portability, interoperability, information security, and privacy. IoT provides important characteristics to healthcare systems, such as availability, mobility, and scalability, that offer an architectural basis for numerous high technological healthcare applications, such as real-time patient monitoring, environmental and indoor quality monitoring, and ubiquitous and pervasive information access that benefits health professionals and patients. The constant scientific innovations make it possible to develop IoT devices through countless services for sensing, data fusing, and logging capabilities that lead to several advancements for enhanced living environments (ELEs). This paper reviews the current state of the art on IoT architectures for ELEs and healthcare systems, with a focus on the technologies, applications, challenges, opportunities, open-source platforms, and operating systems. Furthermore, this document synthesizes the existing body of knowledge and identifies common threads and gaps that open up new significant and challenging future research directions.

Image registration method for mobile-device-based multispectral optical diagnostics for buildings

The recent advances in mobile device hardware and software introduced new opportunities to perform more advanced computation and measurements for buildings. Multispectral optical imaging is a widely recognized technique for building diagnostics, as it is non-destructive, quick, and provides rich spatial-spectral information about the different surfaces for decisions related to building energy performance. However, such implementations require an accurate image fusion. The purpose of this study is to introduce a practical and robust image registration method for mobile-device-based multispectral imaging analysis for building diagnostics. Considering the complicated building geometries including walls, floors, ceilings, roofs, windows, and doors, a new approach based on planar homography is chosen for determining the feature points that are required to perform image fusion between different images. The results obtained are compared against the other available methods, which show that the current method provides multispectral images enhanced with accurate qualitative information, as long as the constraints are satisfied. The results are compared with the state-of-the-art methods. The possible impact of the multispectral imaging to the future of building diagnostics is also discussed.

Towards Lateral Flow Quantitative Assays: Detection Approaches

Point-of-care (POC) or bedside analysis is a global trend in modern diagnostics. Progress in POC testing has largely been provided by advanced manufacturing technology for lateral flow (immunochromatographic) test strips. They are widely used to rapidly and easily control a variety of biomarkers of infectious diseases and metabolic and functional disorders, as well as in consumer protection and environmental monitoring. However, traditional lateral flow tests rely on visual assessment and qualitative conclusion, which limit the objectivity and information output of the assays. Therefore, there is a need for approaches that retain the advantages of lateral flow assays and provide reliable quantitative information about the content of a target compound in a sample mixture. This review describes the main options for detecting, processing, and interpreting immunochromatographic analysis results. The possibilities of modern portable detectors that register colored, fluorescent, magnetic, and conductive labels are discussed. Prospects for further development in this direction are also examined.

Lighting Up Biosensors: Now and the Decade To Come

Optical biosensors are defined as portable optical devices that use biorecognition molecules to interrogate a sample for the presence of a target. The capabilities of optical biosensors have expanded rapidly with advances in miniature optical components and molecular engineering. Biosensors to meet the needs in health and environmental monitoring and food safety have become commercially available, with many more in the pipeline. We review the innovative approaches to overcoming existing hurdles to practical biosensor designs and explore potential areas for future breakthroughs in optical biosensor technology.

Disposable Sensors in Diagnostics, Food, and Environmental Monitoring

Disposable sensors are low-cost and easy-to-use sensing devices intended for short-term or rapid single-point measurements. The growing demand for fast, accessible, and reliable information in a vastly connected world makes disposable sensors increasingly important. The areas of application for such devices are numerous, ranging from pharmaceutical, agricultural, environmental, forensic, and food sciences to wearables and clinical diagnostics, especially in resource-limited settings. The capabilities of disposable sensors can extend beyond measuring traditional physical quantities (for example, temperature or pressure); they can provide critical chemical and biological information (chemo- and biosensors) that can be digitized and made available to users and centralized/decentralized facilities for data storage, remotely. These features could pave the way for new classes of low-cost systems for health, food, and environmental monitoring that can democratize sensing across the globe. Here, a brief insight into the materials and basics of sensors (methods of transduction, molecular recognition, and amplification) is provided followed by a comprehensive and critical overview of the disposable sensors currently used for medical diagnostics, food, and environmental analysis. Finally, views on how the field of disposable sensing devices will continue its evolution are discussed, including the future trends, challenges, and opportunities.

Web-Based Eligibility Quizzes to Verify Opioid Use and County Residence Among Rural Young Adults: Eligibility Screening Results from a Feasibility Study

Background

Web-based methods can be used to collect data from hidden populations, including people who use drugs (PWUD). These methods might be especially advantageous among PWUD in rural areas, where transportation barriers are prevalent, stigma may heighten concerns about confidentiality, and internet access is improving. However, Web-based research with PWUD can be challenging, especially in verifying eligibility. Administering quizzes to verify residential and substance use eligibility could prove valuable in online research among PWUD, yet the utility of this approach is currently unknown.

Objective

This study describes the implementation of online eligibility quizzes about the local community to verify residence in the target study area along with drug dose, appearance, and price to verify opioid misuse.

Methods

To be eligible, individuals had to live in 1 of 5 eastern Kentucky counties, report using opioids to get high in the past 30 days, and be 18 to 35 years old. Participants recruited from August 2017 to July 2018 were asked questions about their opioid use followed by a quiz about drug dose, appearance, and price to verify substance use eligibility. Residential eligibility was verified with 5-question quizzes assessing knowledge of the county where they reported living. Questions tested knowledge about towns, festivals, and landmarks; local school mascots and colors; and presence of certain retail stores, restaurants, and facilities (eg, jails). A subsample that reported using opioids in the past 24 hours was randomly selected to complete urine drug testing (UDT). Nonparametric tests were performed to explore differences across demographic subgroups.

Results

Of the 410 entries assessed for eligibility, 39.3% (161/410) were ineligible as they reported no substance use, being outside the age range, or living outside the study area. Of the remaining 249 who met the eligibility criteria based on age, residency, and opioid misuse, 94.0% (234/249) passed the eligibility quizzes. Among those who passed the heroin quiz, 99.4% (167/168) recognized the image of powdered heroin, 94.6% (159/168) answered the cap size (ie, the purchase unit) question correctly, and 97.0% (163/168) answered the street price question correctly. Among those who passed the drug quiz for prescription opioids, 95% (36/38) answered the dose question correctly, and 82% (31/38) selected the correct image. In a random sample of participants who completed UDT within 3 days of their online screening, 74% (25/34) tested positive for an opioid.

Conclusions

This study demonstrated the utility of using online eligibility screening quizzes to verify opioid misuse and residence. Participants accurately recognized heroin and prescription opioid doses, prices, and images and correctly answered questions about features of their county. Online quizzes to screen and enroll PWUD hold promise for future research as an alternative to more time- and resource-intensive approaches that could offset the advantages of Web-based methods.

Comparative Efficacy of Clinic-Based and Telerehabilitation Application of Mckenzie Therapy in Chronic Low-Back Pain

Studies on validation of telerehabilitation as an effective platform to help manage as well as reduce burden of care for Low-Back Pain (LBP) are sparse. This study compared the effects of Telerehabilitation-Based McKenzie Therapy (TBMT) and Clinic-Based McKenzie Therapy (CBMT) among patients with LBP. Forty-seven consenting patients with chronic LBP who demonstrated 'directional preference' for McKenzie Extension Protocol (MEP) completed this quasi experimental study. The participants were assigned into either the CBMT or TBMT group using block permuted randomization. Participants in the CBMT and TBMT groups received MEP involving a specific sequence of lumbosacral repeated movements in extension aimed to centralize, decrease, or abolish symptoms, thrice weekly for eight weeks. TBMT is a comparable version of CBMT performed in the home with the assistance of a mobile phone app. Outcomes were assessed at the 4th and 8th weeks of the study in terms of Pain Intensity (PI), Back Extensors Muscles' Endurance (BEME), Activity Limitation (AL), Participation Restriction (PR), and General Health Status (GHS). Data were analyzed using descriptive and inferential statistics. Alpha level was set at p< 0.05. Within-group comparison across baseline, 4^th and 8^th weeks indicate that both CBMT and TBMT had significant effects on PI (p=0.001), BEME (p=0.001), AL (p=0.001), PR (p=0.001) and GHS (p=0.001) respectively. However, there were no significant differences (p>0.05) in the treatment effects between TBMT and CBMT, except for 'vitality' (p=0.011) scale in the GHS where TBMT led to significantly higher mean score. Mobile-app platform of the McKenzie extension protocol has comparable clinical outcomes with the traditional clinic-based McKenzie Therapy, and thus is an effective supplementary platform for care of patients with low-back pain.

Relevance of Activity Tracking With Mobile Devices in the Relationship Between Physical Activity Levels and Satisfaction With Physical Fitness in Older Adults: Representative Survey

Background

Physical activity has been shown to positively affect many aspects of life, and the positive relationship between physical activity levels and health is well established. Recently, research on the interrelationship between physical activity levels and subjective experiences has gained attention. However, the underlying mechanisms that link physical activity levels with subjective experiences of physical fitness have not been sufficiently explained.

Objective

This study aimed to explore the role of physical activity tracking (PAT) in the relationship between physical activity levels and satisfaction with physical fitness in older adults. It is hypothesized that higher levels of physical activity are associated with a higher satisfaction with physical fitness in older adults and that this positive association is stronger for older people who use mobile devices for PAT.

Methods

As part of this study, 1013 participants aged 50 years or older and living in Switzerland were interviewed via computer-assisted telephone interviews. Bivariate and multivariate analyses were applied. The interaction effects between physical activity levels and PAT were evaluated using multiple linear regression analysis.

Results

Descriptive analyses showed that 719 participants used at least 1 mobile device and that 136 out of 719 mobile device users (18.9%) used mobile devices for PAT. In the multivariate regression analysis, frequent physical activity was found to have a positive effect on satisfaction with physical fitness (beta=.24, P<.001). A significant interaction effect between physical activity levels and PAT (beta=.30, P=.03) provides some first evidence that the positive effects of physical activity on satisfaction with physical fitness can be enhanced by PAT.

Conclusions

The results indicate the potential of PAT to enhance the physical fitness of older adults. However, the results also raise new issues in this context. Recommendations for further research and practice include the acquisition of longitudinal data, a more detailed observation of durations of use, and the development of devices for PAT considering health psychology and gerontology theories.