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PloS One 2024The elderly is commonly susceptible to depression, the symptoms for which may overlap with natural aging or other illnesses, and therefore miss being captured by routine...
BACKGROUND
The elderly is commonly susceptible to depression, the symptoms for which may overlap with natural aging or other illnesses, and therefore miss being captured by routine screening questionnaires. Passive sensing data have been promoted as a tool for depressive symptoms detection though there is still limited evidence on its usage in the elderly. Therefore, this study aims to review current knowledge on the use of passive sensing data via smartphones and smartwatches in depressive symptom screening for the elderly.
METHOD
The search of literature was performed in PubMed, IEEE Xplore digital library, and PsycINFO. Literature investigating the use of passive sensing data to screen, monitor, and/or predict depressive symptoms in the elderly (aged 60 and above) via smartphones and/or wrist-worn wearables was included for initial screening. Studies in English from international journals published between January 2012 to September 2022 were included. The reviewed studies were further analyzed by a narrative analysis.
RESULTS
The majority of 21 included studies were conducted in Western countries with a few in Asia and Australia. Most studies adopted a cohort study design (n = 12), followed by cross-sectional design (n = 7) and a case-control design (n = 2). The most popular passive sensing data was related to sleep and physical activity using an actigraphy. Sleep characteristics, such as prolonged wakefulness after sleep onset, along with lower levels of physical activity, exhibited a significant association with depression. However, cohort studies expressed concerns regarding data quality stemming from incomplete follow-up and potential confounding effects.
CONCLUSION
Passive sensing data, such as sleep, and physical activity parameters should be promoted for depressive symptoms detection. However, the validity, reliability, feasibility, and privacy concerns still need further exploration.
Topics: Humans; Smartphone; Depression; Aged; Mass Screening; Wearable Electronic Devices; Sleep; Middle Aged; Exercise; Female
PubMed: 38935797
DOI: 10.1371/journal.pone.0304845 -
PloS One 2024Monitoring and improving the quality of sleep are crucial from a public health perspective. In this study, we propose a change-point detection method using diffusion...
Monitoring and improving the quality of sleep are crucial from a public health perspective. In this study, we propose a change-point detection method using diffusion maps for a more accurate detection of respiratory arrest points. Conventional change-point detection methods are limited when dealing with complex nonlinear data structures, and the proposed method overcomes these limitations. The proposed method embeds subsequence data in a low-dimensional space while considering the global and local structures of the data and uses the distance between the data as the score of the change point. Experiments using synthetic and real-world contact-free sensor data confirmed the superiority of the proposed method when dealing with noise, and it detected apnea events with greater accuracy than conventional methods. In addition to improving sleep monitoring, the proposed method can be applied in other fields, such as healthcare, manufacturing, and finance. This study will contribute to the development of advanced monitoring systems that adapt to diverse conditions while protecting privacy.
Topics: Humans; Sleep Apnea Syndromes; Polysomnography; Algorithms; Monitoring, Physiologic
PubMed: 38935677
DOI: 10.1371/journal.pone.0306139 -
Indian Journal of Public Health Oct 2023A major group of the population, especially antenatal checkup (ANC) mothers and their spouses, people admitted for surgery, and people attending STI clinics, are...
BACKGROUND
A major group of the population, especially antenatal checkup (ANC) mothers and their spouses, people admitted for surgery, and people attending STI clinics, are reluctant to pretest counseling.
OBJECTIVES
This study has been taken up to explore the barriers and possible solutions to improve the utilization of Facility based integrated counseling and testing center (F-ICTC) counseling services.
MATERIALS AND METHODS
Phase 1: In-depth interview and ranking with stakeholders from the F-ICTC center (n = 13) were conducted to identify the barriers to utilization of F-ICTC and solution for the same. Phase 2.
A
Delphi panel with experts (n = 17) was invited through mail to find out the potential solution to improve the utilization of F-ICTC counseling services.
RESULTS
Possible barriers from the stakeholders' perspectives were fear of the disease, violate the privacy, unacceptance, gender bias, fear of social stigma and discrimination, and neglect attached to the disease. At third round of Delphi experts had arrived at a consensus regarding of following possible potential solutions: 1. Those who refuse pretest counseling they should be asked to answer a set of questions(which are usually told during counseling), only those questions not answered correctly by them can be corrected, 2.conducive hospital environment, 3.zero discrimination policy, 4. group counseling for ANC mothers and patients in waiting area of the hospital,5. phone counseling for unwilling patients and relocation of testing center and health education camping.
CONCLUSION
Context-specific proactive evidence-based intervention will help in improving the proper utilization of the F-ICTC center.
Topics: Humans; Delphi Technique; Female; Counseling; Prenatal Care; Male; Pregnancy; Adult; India; Social Stigma; Interviews as Topic; Patient Acceptance of Health Care
PubMed: 38934833
DOI: 10.4103/ijph.ijph_1529_22 -
Kidney Research and Clinical Practice Jun 2024Sepsis-associated acute kidney injury (SA-AKI) is a serious complication in critically ill patients, resulting in higher mortality, morbidity, and cost. The intricate...
Sepsis-associated acute kidney injury (SA-AKI) is a serious complication in critically ill patients, resulting in higher mortality, morbidity, and cost. The intricate pathophysiology of SA-AKI requires vigilant clinical monitoring and appropriate, prompt intervention. While traditional statistical analyses have identified severe risk factors for SA-AKI, the results have been inconsistent across studies. This has led to growing interest in leveraging artificial intelligence (AI) and machine learning (ML) to predict SA-AKI better. ML can uncover complex patterns beyond human discernment by analyzing vast datasets. Supervised learning models like XGBoost and RNN-LSTM have proven remarkably accurate at predicting SA-AKI onset and subsequent mortality, often surpassing traditional risk scores. Meanwhile, unsupervised learning reveals clinically relevant sub-phenotypes among diverse SA-AKI patients, enabling more tailored care. In addition, it potentially optimizes sepsis treatment to prevent SA-AKI through continual refinement based on patient outcomes. However, utilizing AI/ML presents ethical and practical challenges regarding data privacy, algorithmic biases, and regulatory compliance. AI/ML allows early risk detection, personalized management, optimal treatment strategies, and collaborative learning for SA-AKI management. Future directions include real-time patient monitoring, simulated data generation, and predictive algorithms for timely interventions. However, a smooth transition to clinical practice demands continuous model enhancements and rigorous regulatory oversight. In this article, we outlined the conventional methods used to address SA-AKI and explore how AI and ML can be applied to diagnose and manage SA-AKI, highlighting their potential to revolutionize SA-AKI care.
PubMed: 38934028
DOI: 10.23876/j.krcp.23.298 -
Health and Human Rights Jun 2024In this study, we systematically examined the importance of human rights standards and principles for rights-based pre-exposure prophylaxis (PrEP) provision for...
In this study, we systematically examined the importance of human rights standards and principles for rights-based pre-exposure prophylaxis (PrEP) provision for marginalized adolescents. Nested within a demonstration study of PrEP provision to adolescent men who have sex with men, , and transgender women, we carried out interviews in São Paulo, Brazil with 25 adolescents, eight health providers, and six workers involved in community-based demand creation. Analysis focused on participants' narratives about aspects of human rights within service delivery, including the availability, accessibility, acceptability, and quality of services; informed decision-making; nondiscrimination; and privacy and confidentiality. Clients and service providers highlighted the importance of availing a range of services beyond PrEP and described how community outreach and social media helped promote accessibility. Acceptability centered around clients feeling heard and respected. Health workers appreciated having time to build trusting relationships with clients to ensure quality of care and support informed decision-making. Nondiscrimination was valued by all, including using clients' chosen pronouns. Privacy and confidentiality were primary concerns for clients who were not "out" about their sexuality or PrEP use; to mitigate this, health workers sought to accommodate clients' preferred channels of communication. Rights-based PrEP services can help promote engagement and retention in PrEP services, particularly for marginalized populations.
Topics: Humans; Brazil; Adolescent; Male; Pre-Exposure Prophylaxis; HIV Infections; Human Rights; Female; Health Services Accessibility; Transgender Persons; Confidentiality; Homosexuality, Male; Health Personnel; Sexual and Gender Minorities
PubMed: 38933221
DOI: No ID Found -
Sensors (Basel, Switzerland) Jun 2024The IoT has become an integral part of the technological ecosystem that we all depend on. The increase in the number of IoT devices has also brought with it security...
The IoT has become an integral part of the technological ecosystem that we all depend on. The increase in the number of IoT devices has also brought with it security concerns. Lightweight cryptography (LWC) has evolved to be a promising solution to improve the privacy and confidentiality aspect of IoT devices. The challenge is to choose the right algorithm from a plethora of choices. This work aims to compare three different LWC algorithms: AES-128, SPECK, and ASCON. The comparison is made by measuring various criteria such as execution time, memory utilization, latency, throughput, and security robustness of the algorithms in IoT boards with constrained computational capabilities and power. These metrics are crucial to determine the suitability and help in making informed decisions on choosing the right cryptographic algorithms to strike a balance between security and performance. Through the evaluation it is observed that SPECK exhibits better performance in resource-constrained IoT devices.
PubMed: 38931791
DOI: 10.3390/s24124008 -
Sensors (Basel, Switzerland) Jun 2024The security of the Industrial Internet of Things (IIoT) is of vital importance, and the Network Intrusion Detection System (NIDS) plays an indispensable role in this....
The security of the Industrial Internet of Things (IIoT) is of vital importance, and the Network Intrusion Detection System (NIDS) plays an indispensable role in this. Although there is an increasing number of studies on the use of deep learning technology to achieve network intrusion detection, the limited local data of the device may lead to poor model performance because deep learning requires large-scale datasets for training. Some solutions propose to centralize the local datasets of devices for deep learning training, but this may involve user privacy issues. To address these challenges, this study proposes a novel federated learning (FL)-based approach aimed at improving the accuracy of network intrusion detection while ensuring data privacy protection. This research combines convolutional neural networks with attention mechanisms to develop a new deep learning intrusion detection model specifically designed for the IIoT. Additionally, variational autoencoders are incorporated to enhance data privacy protection. Furthermore, an FL framework enables multiple IIoT clients to jointly train a shared intrusion detection model without sharing their raw data. This strategy significantly improves the model's detection capability while effectively addressing data privacy and security issues. To validate the effectiveness of the proposed method, a series of experiments were conducted on a real-world Internet of Things (IoT) network intrusion dataset. The experimental results demonstrate that our model and FL approach significantly improve key performance metrics such as detection accuracy, precision, and false-positive rate (FPR) compared to traditional local training methods and existing models.
PubMed: 38931786
DOI: 10.3390/s24124002 -
Sensors (Basel, Switzerland) Jun 2024Parkinson's Disease (PD) is a complex neurodegenerative disorder characterized by a spectrum of motor and non-motor symptoms, prominently featuring the freezing of gait... (Meta-Analysis)
Meta-Analysis Review
Parkinson's Disease (PD) is a complex neurodegenerative disorder characterized by a spectrum of motor and non-motor symptoms, prominently featuring the freezing of gait (FOG), which significantly impairs patients' quality of life. Despite extensive research, the precise mechanisms underlying FOG remain elusive, posing challenges for effective management and treatment. This paper presents a comprehensive meta-analysis of FOG prediction and detection methodologies, with a focus on the integration of wearable sensor technology and machine learning (ML) approaches. Through an exhaustive review of the literature, this study identifies key trends, datasets, preprocessing techniques, feature extraction methods, evaluation metrics, and comparative analyses between ML and non-ML approaches. The analysis also explores the utilization of cueing devices. The limited adoption of explainable AI (XAI) approaches in FOG prediction research represents a significant gap. Improving user acceptance and comprehension requires an understanding of the logic underlying algorithm predictions. Current FOG detection and prediction research has a number of limitations, which are identified in the discussion. These include issues with cueing devices, dataset constraints, ethical and privacy concerns, financial and accessibility restrictions, and the requirement for multidisciplinary collaboration. Future research avenues center on refining explainability, expanding and diversifying datasets, adhering to user requirements, and increasing detection and prediction accuracy. The findings contribute to advancing the understanding of FOG and offer valuable guidance for the development of more effective detection and prediction methodologies, ultimately benefiting individuals affected by PD.
Topics: Humans; Parkinson Disease; Machine Learning; Gait Disorders, Neurologic; Gait; Wearable Electronic Devices; Algorithms; Quality of Life
PubMed: 38931743
DOI: 10.3390/s24123959 -
Sensors (Basel, Switzerland) Jun 2024This work proposes an implementation of the SHA-256, the most common blockchain hash algorithm, on a field-programmable gate array (FPGA) to improve processing capacity...
This work proposes an implementation of the SHA-256, the most common blockchain hash algorithm, on a field-programmable gate array (FPGA) to improve processing capacity and power saving in Internet of Things (IoT) devices to solve security and privacy issues. This implementation presents a different approach than other papers in the literature, using clustered cores executing the SHA-256 algorithm in parallel. Details about the proposed architecture and an analysis of the resources used by the FPGA are presented. The implementation achieved a throughput of approximately 1.4 Gbps for 16 cores on a single FPGA. Furthermore, it saved dynamic power, using almost 1000 times less compared to previous works in the literature, making this proposal suitable for practical problems for IoT devices in blockchain environments. The target FPGA used was the Xilinx Virtex 6 xc6vlx240t-1ff1156.
PubMed: 38931692
DOI: 10.3390/s24123908 -
Sensors (Basel, Switzerland) Jun 2024Healthcare is undergoing a fundamental shift in which digital health tools are becoming ubiquitous, with the promise of improved outcomes, reduced costs, and greater...
Healthcare is undergoing a fundamental shift in which digital health tools are becoming ubiquitous, with the promise of improved outcomes, reduced costs, and greater efficiency. Healthcare professionals, patients, and the wider public are faced with a paradox of choice regarding technologies across multiple domains. Research is continuing to look for methods and tools to further revolutionise all aspects of health from prediction, diagnosis, treatment, and monitoring. However, despite its promise, the reality of implementing digital health tools in practice, and the scalability of innovations, remains stunted. Digital health is approaching a crossroads where we need to shift our focus away from simply looking at developing new innovations to seriously considering how we overcome the barriers that currently limit its impact. This paper summarises over 10 years of digital health experiences from a group of researchers with backgrounds in physical therapy-in order to highlight and discuss some of these key lessons-in the areas of validity, patient and public involvement, privacy, reimbursement, and interoperability. Practical learnings from this collective experience across patient cohorts are leveraged to propose a list of recommendations to enable researchers to bridge the gap between the development and implementation of digital health tools.
Topics: Humans; Biomedical Technology; Delivery of Health Care
PubMed: 38931564
DOI: 10.3390/s24123780