ACCU3RATE: A mobile health application rating scale based on user reviews

Influence of next-generation artificial intelligence on headache research, diagnosis and treatment: the junior editorial board members' vision - part 1

Artificial intelligence (AI) is revolutionizing the field of biomedical research and treatment, leveraging machine learning (ML) and advanced algorithms to analyze extensive health and medical data more efficiently. In headache disorders, particularly migraine, AI has shown promising potential in various applications, such as understanding disease mechanisms and predicting patient responses to therapies. Implementing next-generation AI in headache research and treatment could transform the field by providing precision treatments and augmenting clinical practice, thereby improving patient and public health outcomes and reducing clinician workload. AI-powered tools, such as large language models, could facilitate automated clinical notes and faster identification of effective drug combinations in headache patients, reducing cognitive burdens and physician burnout. AI diagnostic models also could enhance diagnostic accuracy for non-headache specialists, making headache management more accessible in general medical practice. Furthermore, virtual health assistants, digital applications, and wearable devices are pivotal in migraine management, enabling symptom tracking, trigger identification, and preventive measures. AI tools also could offer stress management and pain relief solutions to headache patients through digital applications. However, considerations such as technology literacy, compatibility, privacy, and regulatory standards must be adequately addressed. Overall, AI-driven advancements in headache management hold significant potential for enhancing patient care, clinical practice and research, which should encourage the headache community to adopt AI innovations.

HyEpiSeiD: a hybrid convolutional neural network and gated recurrent unit model for epileptic seizure detection from electroencephalogram signals

Epileptic seizure (ES) detection is an active research area, that aims at patient-specific ES detection with high accuracy from electroencephalogram (EEG) signals. The early detection of seizure is crucial for timely medical intervention and prevention of further injuries of the patients. This work proposes a robust deep learning framework called HyEpiSeiD that extracts self-trained features from the pre-processed EEG signals using a hybrid combination of convolutional neural network followed by two gated recurrent unit layers and performs prediction based on those extracted features. The proposed HyEpiSeiD framework is evaluated on two public datasets, the UCI Epilepsy and Mendeley datasets. The proposed HyEpiSeiD model achieved 99.01% and 97.50% classification accuracy, respectively, outperforming most of the state-of-the-art methods in epilepsy detection domain.

Patients' Perspectives on the Data Confidentiality, Privacy, and Security of mHealth Apps: Systematic Review

BACKGROUND

Mobile health (mHealth) apps have the potential to enhance health care service delivery. However, concerns regarding patients' confidentiality, privacy, and security consistently affect the adoption of mHealth apps. Despite this, no review has comprehensively summarized the findings of studies on this subject matter.

OBJECTIVE

This systematic review aims to investigate patients' perspectives and awareness of the confidentiality, privacy, and security of the data collected through mHealth apps.

METHODS

Using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a comprehensive literature search was conducted in 3 electronic databases: PubMed, Ovid, and ScienceDirect. All the retrieved articles were screened according to specific inclusion criteria to select relevant articles published between 2014 and 2022.

RESULTS

A total of 33 articles exploring mHealth patients' perspectives and awareness of data privacy, security, and confidentiality issues and the associated factors were included in this systematic review. Thematic analyses of the retrieved data led to the synthesis of 4 themes: concerns about data privacy, confidentiality, and security; awareness; facilitators and enablers; and associated factors. Patients showed discordant and concordant perspectives regarding data privacy, security, and confidentiality, as well as suggesting approaches to improve the use of mHealth apps (facilitators), such as protection of personal data, ensuring that health status or medical conditions are not mentioned, brief training or education on data security, and assuring data confidentiality and privacy. Similarly, awareness of the subject matter differed across the studies, suggesting the need to improve patients' awareness of data security and privacy. Older patients, those with a history of experiencing data breaches, and those belonging to the higher-income class were more likely to raise concerns about the data security and privacy of mHealth apps. These concerns were not frequent among patients with higher satisfaction levels and those who perceived the data type to be less sensitive.

CONCLUSIONS

Patients expressed diverse views on mHealth apps' privacy, security, and confidentiality, with some of the issues raised affecting technology use. These findings may assist mHealth app developers and other stakeholders in improving patients' awareness and adjusting current privacy and security features in mHealth apps to enhance their adoption and use.

TRIAL REGISTRATION

PROSPERO CRD42023456658; https://tinyurl.com/ytnjtmca.

Problems and Barriers Related to the Use of mHealth Apps From the Perspective of Patients: Focus Group and Interview Study

BACKGROUND

Since fall 2020, mobile health (mHealth) apps have become an integral part of the German health care system. The belief that mHealth apps have the potential to make the health care system more efficient, close gaps in care, and improve the economic outcomes related to health is unwavering and already partially confirmed. Nevertheless, problems and barriers in the context of mHealth apps usually remain unconsidered.

OBJECTIVE

The focus groups and interviews conducted in this study aim to shed light on problems and barriers in the context of mHealth apps from the perspective of patients.

METHODS

Guided focus groups and individual interviews were conducted with patients with a disease for which an approved mHealth app was available at the time of the interviews. Participants were recruited via self-help groups. The interviews were recorded, transcribed, and subjected to a qualitative content analysis. The content analysis was based on 10 problem categories ("validity," "usability," "technology," "use and adherence," "data privacy and security," "patient-physician relationship," "knowledge and skills," "individuality," "implementation," and "costs") identified in a previously conducted scoping review. Participants were asked to fill out an additional questionnaire about their sociodemographic data and about their use of technology.

RESULTS

A total of 38 patients were interviewed in 5 focus groups (3 onsite and 2 web-based) and 5 individual web-based interviews. The additional questionnaire was completed by 32 of the participants. Patients presented with a variety of different diseases, such as arthrosis, tinnitus, depression, or lung cancer. Overall, 16% (5/32) of the participants had already been prescribed an app. During the interviews, all 10 problem categories were discussed and considered important by patients. A myriad of problem manifestations could be identified for each category. This study shows that there are relevant problems and barriers in the context of mHealth apps from the perspective of patients, which warrant further attention.

CONCLUSIONS

There are essentially 3 different areas of problems in the context of mHealth apps that could be addressed to improve care: quality of the respective mHealth app, its integration into health care, and the expandable digital literacy of patients.

Interpreting artificial intelligence models: a systematic review on the application of LIME and SHAP in Alzheimer's disease detection

Explainable artificial intelligence (XAI) has gained much interest in recent years for its ability to explain the complex decision-making process of machine learning (ML) and deep learning (DL) models. The Local Interpretable Model-agnostic Explanations (LIME) and Shaply Additive exPlanation (SHAP) frameworks have grown as popular interpretive tools for ML and DL models. This article provides a systematic review of the application of LIME and SHAP in interpreting the detection of Alzheimer's disease (AD). Adhering to PRISMA and Kitchenham's guidelines, we identified 23 relevant articles and investigated these frameworks' prospective capabilities, benefits, and challenges in depth. The results emphasise XAI's crucial role in strengthening the trustworthiness of AI-based AD predictions. This review aims to provide fundamental capabilities of LIME and SHAP XAI frameworks in enhancing fidelity within clinical decision support systems for AD prognosis.

Developing, implementing, and evaluating the visiting Neighbors' program in rural Appalachia: A quality improvement protocol

INTRODUCTION

Older adults living alone in rural areas frequently experience health declines, social isolation, and limited access to services. To address these challenges, our medical academic university supported a quality improvement project for developing and evaluating the Visiting Neighbors program in two rural Appalachian counties. Our Visiting Neighbors program trained local volunteers to visit and guide rural older adults in healthy activities. These age-appropriate activities (Mingle, Manage, and Move- 3M's) were designed to improve the functional health of older adults. The program includes four in-home visits and four follow-up telephone calls across three months.

PURPOSE

The purpose of this paper was to describe the 3M's Visiting Neighbors protocol steps guiding the quality improvement procedures relating to program development, implementation, and evaluation.

METHODS AND MATERIALS

This Visiting Neighbors study used a single-group exploratory quality improvement design. This program was tested using quality improvement standards, including collecting participant questionnaires and visit observations.

RESULTS

Older adults (> 65 years) living alone (N = 30) participants were female (79%) with a mean age of 82.96 (SD = 7.87) years. Volunteer visitor participants (N = 10) were older adult females. Two volunteer visitors implemented each visit, guided by the 3M's activities manual. All visits were verified as being consistently delivered (fidelity). Enrollment and retention data found the program was feasible to conduct. The older adult participants' total program helpfulness ratings (1 to 5) were high (M = 51.27, SD = 3.77). All volunteer visitor's program helpfulness ratings were also high (M = 51.78, SD = 3.73).

DISCUSSION

The Visiting Neighbors program consistently engaged older Appalachian adults living alone in the 3M's activities. The feasibility and fidelity of the 3M's home visits were verified. The quality improvement processes included engaging the expert advisory committee and rural county stakeholders to ensure the quality of the program development, implementation, and evaluation.

Application of Telemedicine in the Ambulance for Stroke Patients: A Systematic Review

INTRODUCTION

The use of telemedicine for the prehospital management of emergency conditions, especially stroke, is increasing day by day. Few studies have investigated the applications of telemedicine in Emergency Medical Services (EMS). A comprehensive study of the applications of this technology in stroke patients in ambulances can help to build a better understanding. Therefore, this systematic review was conducted to investigate the use of telemedicine in ambulances for stroke patients in 2023.

METHODS

A systematic search was conducted in PubMed, Cochrane, Scopus, ProQuest, Science Direct, and Web of Science from 2013 through March 1, 2023. The authors selected the articles based on keywords and criteria and reviewed them in terms of title, abstract, and full text. Finally, the articles that were related to the study aim were evaluated.

RESULTS

The initial search resulted in the extraction of 2,795 articles. After review of the articles, and applying the inclusion and exclusion criteria, seven articles were selected for the final analysis. Three (42.85%) studies were on the feasibility and intervention types. Also, randomized trials, feasibility, feasibility and prospective-observational, and feasibility and retrospective-interventional studies were each one (14.28%). Six (85.71%) of the studies were conducted in the United States. The National Institutes of Health Stroke Scale (NIHSS) and RP-Xpress were the most commonly used tools for neurological evaluations and teleconsultations.

CONCLUSION

Remote prehospital consultations, triage, and sending patient data before they go to the emergency department can be provided through telemedicine in ambulances. Neurological evaluations via telemedicine are reliable and accurate, and they are almost equal to in-person evaluations by a neurologist.

Enhancing biofeedback-driven self-guided virtual reality exposure therapy through arousal detection from multimodal data using machine learning

Virtual reality exposure therapy (VRET) is a novel intervention technique that allows individuals to experience anxiety-evoking stimuli in a safe environment, recognise specific triggers and gradually increase their exposure to perceived threats. Public-speaking anxiety (PSA) is a prevalent form of social anxiety, characterised by stressful arousal and anxiety generated when presenting to an audience. In self-guided VRET, participants can gradually increase their tolerance to exposure and reduce anxiety-induced arousal and PSA over time. However, creating such a VR environment and determining physiological indices of anxiety-induced arousal or distress is an open challenge. Environment modelling, character creation and animation, psychological state determination and the use of machine learning (ML) models for anxiety or stress detection are equally important, and multi-disciplinary expertise is required. In this work, we have explored a series of ML models with publicly available data sets (using electroencephalogram and heart rate variability) to predict arousal states. If we can detect anxiety-induced arousal, we can trigger calming activities to allow individuals to cope with and overcome distress. Here, we discuss the means of effective selection of ML models and parameters in arousal detection. We propose a pipeline to overcome the model selection problem with different parameter settings in the context of virtual reality exposure therapy. This pipeline can be extended to other domains of interest where arousal detection is crucial. Finally, we have implemented a biofeedback framework for VRET where we successfully provided feedback as a form of heart rate and brain laterality index from our acquired multimodal data for psychological intervention to overcome anxiety.

Four-way classification of Alzheimer's disease using deep Siamese convolutional neural network with triplet-loss function

Alzheimer's disease (AD) is a neurodegenerative disease that causes irreversible damage to several brain regions, including the hippocampus causing impairment in cognition, function, and behaviour. Early diagnosis of the disease will reduce the suffering of the patients and their family members. Towards this aim, in this paper, we propose a Siamese Convolutional Neural Network (SCNN) architecture that employs the triplet-loss function for the representation of input MRI images as k-dimensional embeddings. We used both pre-trained and non-pretrained CNNs to transform images into the embedding space. These embeddings are subsequently used for the 4-way classification of Alzheimer's disease. The model efficacy was tested using the ADNI and OASIS datasets which produced an accuracy of 91.83% and 93.85%, respectively. Furthermore, obtained results are compared with similar methods proposed in the literature.

Exploring the factors that affect user experience in mobile-health applications: A text-mining and machine-learning approach

Recent years have witnessed an increased demand for mobile health (mHealth) platforms owing to the COVID-19 pandemic and preference for doorstep delivery. However, factors impacting user experiences and satisfaction levels across these platforms, using customer reviews, are still largely unexplored in academic research. The empirical framework we proposed in this paper addressed this research gap by analysing unmonitored user comments for some popular mHealth platforms. Using topic-modelling techniques, we identified the impacting factors (predictors) and categorised them into two major dimensions based on strategic adoption and motivational association. Findings from our study suggest that time and money, convenience, responsiveness, and availability emerge as significant predictors for delivering a positive user experience on m-health platforms. Next, we identified substantial moderating effects of review polarity on the predictors related to brand association and hedonic motivation, such as online booking and video consultation. Further, we also identified the top predictors for successful user experience across these platforms. Recommendations from our study will benefit business managers by offering an improved service design leading to higher user satisfaction across these m-health platforms.

Detecting COVID-19 infection status from chest X-ray and CT scan via single transfer learning-driven approach

COVID-19 has caused over 528 million infected cases and over 6.25 million deaths since its outbreak in 2019. The uncontrolled transmission of the SARS-CoV-2 virus has caused human suffering and the death of uncountable people. Despite the continuous effort by the researchers and laboratories, it has been difficult to develop reliable efficient and stable vaccines to fight against the rapidly evolving virus strains. Therefore, effectively preventing the transmission in the community and globally has remained an urgent task since its outbreak. To avoid the rapid spread of infection, we first need to identify the infected individuals and isolate them. Therefore, screening computed tomography (CT scan) and X-ray can better separate the COVID-19 infected patients from others. However, one of the main challenges is to accurately identify infection from a medical image. Even experienced radiologists often have failed to do it accurately. On the other hand, deep learning algorithms can tackle this task much easier, faster, and more accurately. In this research, we adopt the transfer learning method to identify the COVID-19 patients from normal individuals when there is an inadequacy of medical image data to save time by generating reliable results promptly. Furthermore, our model can perform both X-rays and CT scan. The experimental results found that the introduced model can achieve 99.59% accuracy for X-rays and 99.95% for CT scan images. In summary, the proposed method can effectively identify COVID-19 infected patients, could be a great way which will help to classify COVID-19 patients quickly and prevent the viral transmission in the community.

Erectile dysfunction and mobile phone applications: Quality, content and adherence to European Association guidelines on male sexual dysfunction

INTRODUCTION

Nowadays numerous mobile health applications (MHA) have been developed to assist and simplify the life of patients affected by erectile dysfunction (ED), however the scientific quality and the adherence to guidelines are not yet addressed and solved.

MATERIALS AND METHODS

On 17 January 2022, we conducted a search in the Apple App Store and Google Play Store.We reviewed all mobile apps from iTunes App Store and Google Play Store for ED and evaluated different aspects as well as their usage in screening, prevention, management, and their adherence to EAU guidelines.

RESULTS

A total of 18 apps were reviewed. All apps are geared towards the patient and provide information about diagnoses and treatment of ED.

CONCLUSIONS

MHA represent an integral part of patients' lives, and apps providing services for male sexual dysfunction are constantly increasing. Despite this the overall quality is still low. Although many of these devices are useful in ED, the problems of scientific validation, content, and quality are not yet solved. Further work is needed to improve the quality of apps and developing new accessible, user designed, and high-quality apps.