mHealth System Research Articles

PASSO Project: Design of a Smart System Using Biofeedback to Train People with Parkinson’s Disease

This paper describes the design process of a smart system monitoring and training the gait and posture of people with Parkinson’s disease. The project aimed to develop an innovative mHealth system and validate it in a laboratory context. The designed solution is supposed to help people in postponing the rise of the most impairing symptoms and prolong autonomy. The project was divided into three main design cycles regarding respectively the design and test of sensory signals, the smart wearable devices transmitting them, and the system interface. The multidisciplinary team involved in the project followed a User-Centered methodology, involving users in the design process to improve the system’s usability and accessibility. The adopted methodological strategy led to satisfactory results and proved to be particularly suitable for multidisciplinary design processes involving both human and technological factors related to the development of smart systems targeted to niche users.

A semi-automatic mHealth system using wearable devices for identifying pain-related parameters in elderly individuals

BackgroundMobile health systems integrating wearable devices are emerging as promising tools for registering pain-related factors. However, their application in populations with chronic conditions has been underexplored. ObjectiveTo design a semi-automatic mobile health system with wearable devices for evaluating the potential predictive relationship of pain qualities and thresholds with heart rate variability, skin conductance, perceived stress, and stress vulnerability in individuals with preclinical chronic pain conditions such as suspected rheumatic disease. MethodsA multicenter, observational, cross-sectional study was conducted with 67 elderly participants. Predicted variables were pain qualities and pain thresholds, assessed with the McGill Pain Questionnaire and a pressure algometer, respectively. Predictor variables were heart rate variability, skin conductance, perceived stress, and stress vulnerability. Multiple linear regression analyses were conducted to examine the influence of the predictor variables on the pain dimensions. ResultsThe multiple linear regression analysis revealed that the predictor variables significantly accounted for 27% of the variability in the affective domain, 14% in the miscellaneous domain, 15% in the total pain rating index, 10% in the number of words chosen, 14% in the present pain intensity, and 16% in the Visual Analog Scale scores. ConclusionThe study found significant predictive values of heart rate variability, skin conductance, perceived stress, and stress vulnerability in relation to pain qualities and thresholds in the elderly population with suspected rheumatic disease. The comprehensive integration of physiological and psychological stress measures into pain assessment of elderly individuals with preclinical chronic pain conditions could be promising for developing new preventive strategies.

Electronic Health System Integration Framework for Secure M-Health Services: A Case of University of Nairobi Hospital

The purpose of this article sought to design a secure framework that can be used in M-Health systems development. The researcher used the integrated information theory as a framework for enforcing system security as a holistic approach. To actualize this study, objectives that were meant to guide in carrying out the research were: To evaluate the significance of Confidentiality, Integrity and availability on the security of M-health systems and to develop a framework for secure integration of M-Health systems. The researcher used University of Nairobi Hospital because of ease of accessibility and financial resources available to conduct the research. The study adopted a cross section survey design methodology that included a sample size of 44 ICT personnel and users of University Health System at the University of Nairobi Hospital. Data collection methods were observation, conducting interviews and filling questionnaires that were administered to the target population in the University Hospital. The target population were handed the questionnaires and had them filled. The filled in questionnaires were then picked later from the respondents. SPSS version 23 was used for data analysis, then presented in frequency tables, bar charts, pie charts and standard deviation.

A digital health solution for long-term cardiac monitoring supporting a value-based stroke care path

Abstract Introduction Long-term continuous cardiac monitoring in high-risk populations increases the chances of detecting atrial fibrillation and related arrhythmias[1]. In-hospital ECG monitoring is usually too brief to detect cardiac risk factors, while the availability and usability of ambulatory ECG is often lacking. Digital health tools are an attractive option for user-friendly, accurate, scalable mid-to-long-term cardiac risk monitoring[2]. These tools can also act as a driver to organize personalized care around the patient but that requires innovation in the reimbursement system. Recently Belgium established the mHealthBelgium validation pyramid[3], a 3-stage framework for reimbursement of digital health tools. Methods We used our remote monitoring platform to build a digital health solution for cardiac monitoring that provides direct diagnostic value and supports the care path for patients with cryptogenic stroke or transient ischemic attack. Our CE-certified solution has a cardiac chest patch to record ECG and motion data and a docking station at the patient’s home to securely send data to the cloud where data analytics is done. Our annotation flow detects atrial fibrillation and other stroke-relevant risk factors, reducing data burden for healthcare providers (HCP). HCPs interact with the raw and annotated data through a dashboard that generates reports and integrates with medical record systems. On-demand logistics allows hospitals to monitor as many patients as needed without delay and a pay-per-use model allows flexible monitoring periods and obviates the need for up-front device purchase. Results Since January 2022, more than 529 patients have been monitored with a median recording duration of 14 days±12 days. Approximately 205,500 hours of continuous ECG data was collected and 13421 potential risk factors were annotated, including 7227 potential atrial events (fibrillation or flutter) in 374 patients (>70%). In 45% of cases (n = 242), one or more events were confirmed by HCPs as clinically relevant. Our solution has progressed through the first two stages of the mHealth system by demonstrating CE-certification and interoperability with the government eHealth platform. For the final stage (M3), we have submitted one of the first dossiers in Belgium for improving the stroke care path with a more fit-for-purpose technology and coupling reimbursement to this specific use case instead of fee-for-service coding. Our proposal includes a real-world outcome data study. Conclusion We validate the importance and utility of remote monitoring of cardiac risk factors within the stroke care path. Retrospective results show that in 45% of cases, clinically relevant events were detected indicating that meaningful remote patient data is compatible with HCP workflows. The solution is progressing through a new care path-based reimbursement system that serves as a model for organizing value-based care around the patient's needs.Care path with remote patient monitoringReduced time to diagnosis with RPM

Computerized analysis of hypomimia and hypokinetic dysarthria for improved diagnosis of Parkinson's disease

Background and ObjectiveAn aging society requires easy-to-use approaches for diagnosis and monitoring of neurodegenerative disorders, such as Parkinson's disease (PD), so that clinicians can effectively adjust a treatment policy and improve patients' quality of life. Current methods of PD diagnosis and monitoring usually require the patients to come to a hospital, where they undergo several neurological and neuropsychological examinations. These examinations are usually time-consuming, expensive, and performed just a few times per year. Hence, this study explores the possibility of fusing computerized analysis of hypomimia and hypokinetic dysarthria (two motor symptoms manifested in the majority of PD patients) with the goal of proposing a new methodology of PD diagnosis that could be easily integrated into mHealth systems. MethodsWe enrolled 73 PD patients and 46 age- and gender-matched healthy controls, who performed several speech/voice tasks while recorded by a microphone and a camera. Acoustic signals were parametrized in the fields of phonation, articulation and prosody. Video recordings of a face were analyzed in terms of facial landmarks movement. Both modalities were consequently modeled by the XGBoost algorithm. ResultsThe acoustic analysis enabled diagnosis of PD with 77% balanced accuracy, while in the case of the facial analysis, we observed 81% balanced accuracy. The fusion of both modalities increased the balanced accuracy to 83% (88% sensitivity and 78% specificity). The most informative speech exercise in the multimodality system turned out to be a tongue twister. Additionally, we identified muscle movements that are characteristic of hypomimia. ConclusionsThe introduced methodology, which is based on the myriad of speech exercises likewise audio and video modality, allows for the detection of PD with an accuracy of up to 83%. The speech exercise - tongue twisters occurred to be the most valuable from the clinical point of view. Additionally, the clinical interpretation of the created models is illustrated. The presented computer-supported methodology could serve as an extra tool for neurologists in PD detection and the proposed potential solution of mHealth will facilitate the patient's and doctor's life.

The Vinyasa Tool for mHealth solutions: supporting human-centered design in nascent digital health ecosystems.

mHealth systems have been deployed widely in Lower- and Middle-Income countries (LMICs) for health system strengthening, requiring considerable resource allocation. However, most solutions have not achieved scale or sustainability. Poor usability and failure to address perceived needs are among the principal reasons mHealth systems fail to achieve acceptance and adoption by healthcare workers. A human-centered design approach to improve mHealth system use requires an exploration of users' perceptions of mHealth systems including the environmental, user-related, and technological aspects of a system. At present, there is a dearth of contextually intelligent tools available to mHealth developers that can guide such exploration prior to full-scale development and deployment. To develop a tool to aid optimization of mHealth solutions in LMICs to facilitate human-centered design, and consequently, successful adoption. We collated findings and themes from key qualitative studies on mHealth deployment in LMICs. We then used the Informatics Stack framework by Lehmann (2017) to label, sort and collate findings and themes into a list of questions which explore the environment, users, artifacts, information governance, and interoperability of mHealth systems deployed in LMICs. We developed the Vinyasa Tool - to aid qualitative research about the need and usability of mHealth solutions in LMICs. The tool is a guide for focus group discussions and key informant interviews with community-based healthcare workers and primary care medical personnel who use or are expected to use proposed mHealth solutions. The tool consists of 71 questions organized in 11 sections which unpack and explore multiple aspects of mHealth systems from the perspectives of its users. These include the wider world and organization in which an mHealth solution is deployed; the roles, functions, workflow and adoption behavior of a system's users; the security, privacy and interoperability afforded by a system; and the artifacts of an information system - the data, information, knowledge, algorithms and technology which constitute the system. The tool can be deployed in whole or in part, depending on the context of study. The Vinyasa Tool is the first such comprehensive qualitative research instrument incorporating questions contextualized to the LMIC setting. We expect it will find wide application among mHealth developers, health system administrators and researchers developing and deploying mHealth tools for use by patients, providers, and administrators. The tool is expected to guide users toward human centered design with the goal of improving relevance and usability, and therefore, adoption.

Deep reinforcement learning‐based precise prediction model for Smart M‐Health system

AbstractThe use of Android mobile phones and other wireless technology in the field of healthcare infers to mHealth (mobile health). Modern Science and technological developments have paved way for better and more sophisticated solutions towards Smart mHealth systems and preventive Smart Healthcare services for disease—treatment, surveillance, management of chronic disease and tracking epidemic outbreaks. Thus, the mHealth data can be collected from various end users, deposited in repository, perform analysis and suitable Smart Healthcare services can be made accessible to end users, anytime, anywhere, over the internet. This can be achieved by incorporating the methodologies of advanced Soft Computing methodologies, data communication, cloud storage and cloud computing, big data analysis, artificial intelligence, computer communication/networking and other engineering techniques. However, the analysis of such huge volumes of data and to provide precise Smart Healthcare services is a million‐dollar question. This research article exposes a Deep Reinforcement Learning model for precisely predicting the disease and offer precise Smart m‐Healthcare services to the end users. This research provides an intensive experimental analysis and investigation using synthetic health parameters that were simulated using various mHealth sensors. The dataset includes 15 varieties of mHealth metrics with a total dataset size of 285.

Chatbot-assisted therapy for patients with methamphetamine use disorder: a preliminary randomized controlled trial.

Methamphetamine (MA) use disorder is associated with a large public health burden. Despite the therapeutic effects of psychosocial interventions based on current evidence, finding an approach to retain patients in treatment remains a real-world challenge. The rapid development of mobile health (mHealth) systems suggests the potential to provide real-time personalized care at any time and from any location, minimize barriers to treatment, maximize use, and promote the dissemination of accessible therapeutic tools in at-risk populations. Our study aimed to investigate the feasibility and effectiveness of chatbots for the treatment of MA use disorder. The inclusion criteria were (a) a diagnosis of MA use disorder as defined by the DSM-5, (b) age between 18 and 65 years, (c) no acute exacerbation of severe mental illness during the initial assessment, such as schizophrenia or bipolar I disorder, (d) willingness to participate in standard outpatient treatment for ≥ 6 months, and (e) an Android phone. Participants were randomly allocated to either a chatbot-assisted therapy via smartphone (CAT) group or a control group following simple randomization procedures (computerized random numbers) without blinding. All participants were followed up for 6 months. Treatment retention and monthly urine test results were analyzed as outcome measures. Participants' satisfaction with CAT was also assessed. In total, 50 and 49 participants were allocated to the CAT and control groups, respectively. There were no significant differences in retention time between the two treatment groups (df = 1, p = 0.099). The CAT group had fewer MA-positive urine samples than the control group (19.5% vs. 29.6%, F = 9.116, p = 0.003). The proportion of MA-positive urine samples was positively correlated with the frequency of MA use (r = 0.323, p = 0.001), severity of MA use disorder (r = 0.364, p < 0.001), and polysubstance use (r = 0.212, p = 0.035), and negatively correlated with readiness to change (r = -0.330, p = 0.001). Totally 55 participants completed the study at the 6-month follow-up and 60% reported relative satisfaction. Participants in this study had favorable acceptance and generally positive outcomes, which indicates that chatbot is feasible for treating people who use MA.

Nonlinear, Multicomponent Physical Exercise with Heart Rate Variability-Guided Prescription in Women with Breast Cancer during Treatment: Feasibility and Preliminary Results (ATOPE Study).

The purpose of this study was to examine the feasibility, safety, adherence, and preliminary efficacy of the ATOPE program during radiotherapy (RT) or chemotherapy (CT) for women with breast cancer. This single-blind, pretest-posttest feasibility study included 38 women with breast cancer at the beginning of their treatment. The ATOPE program consisted of 12 to 18 sessions of a multimodal physical exercise program, prescribed based on daily heart rate variability (HRV) and clinimetric assessments using the ATOPE+ mHealth system. Overall health was assessed with quality of life, autonomous balance, and body composition, whereas health-related fitness (HRF) was measured through functional capacity, physical activity levels, and upper and lower limb strength. The rates of recruitment, retention, and adherence were 52.35%, 73.68%, and 84.37%, respectively, and the satisfaction rating was 9.2 out of a possible 10 points. The perceived health status change score was 3.83 points, scored on a - 5 to 5 point scale. No adverse effects were found. Compliance results showed that the ATOPE+ mHealth system was used on 73.38% of the days, and the Fitbit bracelet (Google, Mountain View, CA, USA) was used on 84.91% of the days. Women stayed physically active 55% of days. Regarding preliminary results, for overall health, the percentage of body fat in the RT group decreased by 1.93%, whereas it increased by 5.03% in the CT group. Lower limb strength increased in the RT group, specifically knee extensor isometric strength (6.07%), isokinetic knee flexors 180°/s (1.53%) and isokinetic knee extensors 300°/s (4.53%), in contrast with the reductions found in the CT group (11.07%, 18.67%, and 14.89%, respectively). The ATOPE program, through nonlinear prescription based on daily monitoring with the ATOPE+ mHealth system, is feasible and safe for application during breast cancer treatment. The results suggest that overall health can be maintained or even improved regarding most variables. This study focused on the feasibility, safety, and completion of a physical therapist-led program at early diagnosis for adults with breast cancer. The multimodal, supervised, tailored, nonlinear physical exercise program is feasible and safe, showed a good completion rate, and was able to prevent the quality-of-life deficits that are often triggered by systemic breast cancer treatment. This study highlights the importance of daily morning assessments using the ATOPE+ mHealth system in patients with breast cancer to prescribe nonlinear physical exercise.

Abstract 22: Collaboratively Adapting a Telebehavioral Mobile Platform to Enhance Patient-Provider Communication Regarding Cervical Cancer Prevention Efforts in Bluefields, Nicaragua

Abstract Purpose: Nicaragua has the highest rate of cervical cancer in Central America. Cervical cancer screening is available nationally, however substantial barriers to screening engagement persist. Azulado is a telebehavioral, patient-centered mobile platform that was culturally, linguistically, and regionally created to specifically address the cervical cancer screening and treatment needs of women in Bluefields, Nicaragua, on the Caribbean coast. The app was successfully piloted in summer 2021. In summer 2022, the research team explored healthcare provider perspectives of Azulado, collaborating with providers to design an accompanying portal for the app. Methods: The team conducted a mixed-methods, community-based participatory investigation to elicit healthcare provider perspectives to inform the design of a provider portal. Focus groups and individual interviews were conducted with providers at healthcare facilities throughout Bluefields. A tablet-based survey adapted from mHealth App Usability Questionnaire (MAUQ) and System Usability Scale (SUS) was administered. The team conducted thematic analysis and descriptive statistics. Results: Three provider focus groups (n=8) and multiple individual interviews (n=18) were conducted in six unique healthcare settings in Bluefields. Twenty participants completed the survey (15 women and 5 men). Provider years of experience ranged from 1 to 37 years (average 17.0 years, median 16.5 years). Quantitative findings demonstrated that when asked if they agreed that the app would be useful for patient health and well-being, the average response was 4.89 (SD 0.32) on a Likert scale (0= strongly disagree, 5= strongly agree). The average response for the statement “I would recommend this app” was 4.68 (SD 0.58). Emergent themes included the importance of inclusivity and representation of different regional ethnicities and situations, clarity and functionality of the app, emotional support by community or provider, ability to input clinic visit reminders, and ability to anonymously and confidentially communicate with patients. Conclusion: The study demonstrated feasibility and acceptability of the Azulado patient-provider interface. Providers gave suggestions for how to improve patient education and facilitate communication between patients and clinics. Future studies will include trialing the updated app with patients and determining its effectiveness in improving prevention and treatment efforts in Bluefields. Citation Format: Maria Geba, Katherine Hall, Anneda Rong, Michelet McLean Estrada, Rebecca Dillingham, Emma McKim Mitchell. Collaboratively Adapting a Telebehavioral Mobile Platform to Enhance Patient-Provider Communication Regarding Cervical Cancer Prevention Efforts in Bluefields, Nicaragua [abstract]. In: Proceedings of the 11th Annual Symposium on Global Cancer Research; Closing the Research-to-Implementation Gap; 2023 Apr 4-6. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(6_Suppl):Abstract nr 22.

Effects of a Mobile-Health Exercise Intervention on Body Composition, Vascular Function, and Autonomic Nervous System Function in Obese Women: A Randomized Controlled Trial.

The present study verified the effect of an m-health exercise intervention using a 12-week exercise program on body composition, vascular function, and the ANS. Thirty obese adult women participants were randomized (n = 15 each) into the experimental (EXP) group, those who performed mobile-health (m-health) exercises using a wearable device (Charge 4, Fitbit, USA) and AI-fit web page, or the control (CON) group, those who continued their daily activities as before. Muscle function, cardiorespiratory endurance, and flexibility were assessed during the exercise program using the AI-fit web page and wearable device. The EXP group participated in exercise interventions using the m-health system for 12 weeks, while the CON group was encouraged to maintain their normal daily routines. Body composition, vascular function, and autonomic nervous system (ANS) were evaluated before and after the intervention. Significant decreases were noted in fat mass (Post - Pre: -1.47 kg; p < 0.001) and percent body fat (Post - Pre: -2.11%; p < 0.05). Flow-mediated dilatation (Post - Pre: 2.63%; p < 0.001) was significantly increased, and brachial-ankle pulse wave velocity (Post - Pre: -91.49 cm·sec-1; p < 0.01) was significantly decreased. RMSSD (Post - Pre: 10.43 ms; p < 0.01), NN50 (Post-Pre: 24.04; p < 0.05), pNN50 (Post - Pre: 7.70%; p < 0.05) and HF (Post-Pre: 179.60 ms2; p < 0.05) increased significantly. In conclusion, m-health exercise interventions using AI fit and wearable devices are effective in preventing obesity and improving vascular function, and ANS.

Self-Assessment of Having COVID-19 With the Corona Check mHealth App

At the beginning of the COVID-19 pandemic, with a lack of knowledge about the novel virus and a lack of widely available tests, getting first feedback about being infected was not easy. To support all citizens in this respect, we developed the mobile health app Corona Check. Based on a self-reported questionnaire about symptoms and contact history, users get first feedback about a possible corona infection and advice on what to do. We developed Corona Check based on our existing software framework and released the app on Google Play and the Apple App Store on April 4, 2020. Until October 30, 2021, we collected 51,323 assessments from 35,118 users with explicit agreement of the users that their anonymized data may be used for research purposes. For 70.6% of the assessments, the users additionally shared their coarse geolocation with us. To the best of our knowledge, we are the first to report about such a large-scale study in this context of COVID-19 mHealth systems. Although users from some countries reported more symptoms on average than users from other countries, we did not find any statistically significant differences between symptom distributions (regarding country, age, and sex). Overall, the Corona Check app provided easily accessible information on corona symptoms and showed the potential to help overburdened corona telephone hotlines, especially during the beginning of the pandemic. Corona Check thus was able to support fighting the spread of the novel coronavirus. mHealth apps further prove to be valuable tools for longitudinal health data collection.

Data quality assessment of mHealth systems: strengths, potential limitations and opportunities for improvement; Case of Yendanafe system in Neno District, Malawi

Data quality assessment of mHealth systems: strengths, potential limitations and opportunities for improvement; Case of Yendanafe system in Neno District, Malawi

Implementation of the PANDA mHealth system for providing a positive pregnancy experience in Tanzania

Implementation of the PANDA mHealth system for providing a positive pregnancy experience in Tanzania

Exploring the Relationship Between Privacy and Utility in Mobile Health: Algorithm Development and Validation via Simulations of Federated Learning, Differential Privacy, and External Attacks.

Although evidence supporting the feasibility of large-scale mobile health (mHealth) systems continues to grow, privacy protection remains an important implementation challenge. The potential scale of publicly available mHealth applications and the sensitive nature of the data involved will inevitably attract unwanted attention from adversarial actors seeking to compromise user privacy. Although privacy-preserving technologies such as federated learning (FL) and differential privacy (DP) offer strong theoretical guarantees, it is not clear how such technologies actually perform under real-world conditions. Using data from the University of Michigan Intern Health Study (IHS), we assessed the privacy protection capabilities of FL and DP against the trade-offs in the associated model's accuracy and training time. Using a simulated external attack on a target mHealth system, we aimed to measure the effectiveness of such an attack under various levels of privacy protection on the target system and measure the costs to the target system's performance associated with the chosen levels of privacy protection. A neural network classifier that attempts to predict IHS participant daily mood ecological momentary assessment score from sensor data served as our target system. An external attacker attempted to identify participants whose average mood ecological momentary assessment score is lower than the global average. The attack followed techniques in the literature, given the relevant assumptions about the abilities of the attacker. For measuring attack effectiveness, we collected attack success metrics (area under the curve [AUC], positive predictive value, and sensitivity), and for measuring privacy costs, we calculated the target model training time and measured the model utility metrics. Both sets of metrics are reported under varying degrees of privacy protection on the target. We found that FL alone does not provide adequate protection against the privacy attack proposed above, where the attacker's AUC in determining which participants exhibit lower than average mood is over 0.90 in the worst-case scenario. However, under the highest level of DP tested in this study, the attacker's AUC fell to approximately 0.59 with only a 10% point decrease in the target's R2 and a 43% increase in model training time. Attack positive predictive value and sensitivity followed similar trends. Finally, we showed that participants in the IHS most likely to require strong privacy protection are also most at risk from this particular privacy attack and subsequently stand to benefit the most from these privacy-preserving technologies. Our results demonstrated both the necessity of proactive privacy protection research and the feasibility of the current FL and DP methods implemented in a real mHealth scenario. Our simulation methods characterized the privacy-utility trade-off in our mHealth setup using highly interpretable metrics, providing a framework for future research into privacy-preserving technologies in data-driven health and medical applications.

Accessibility Study of mHealth Systems Based on The Internet of Things (IoT)

The Internet of Things (IoT) has become part of people's daily life, allowing physical and digital contact. The rise of mobile devices and scientific and technological advances in health have led to breakthroughs in meeting consumer needs. mHealth refers to using mobile devices to improve healthcare services, increase medical care, and reduce costs. Mobile cloud computing (MCC) lets users bypass mobile device limits on processing, storage, and battery life. A body area network uses implanted wireless sensors to remotely monitor patients (WBAN). These networks collect and distribute data for disease diagnosis and prevention. This mix of technologies allows hospitals and clinics new ways to treat and monitor patients. This research examines IoT availability in mHealth. The analysed architecture includes wireless sensors to monitor patients, an intra-BAN, a mobile device with a battery and communication interfaces, an extra-BAN with Wi-Fi and 4G connectivity, and a cloud environment to store data. Hierarchical models were developed using RBD and continuous-time Markov chains (CTMC). Each component's MTTF (mean time to failure) and MTTR (mean time to repair) values are used to quantify system or section availability. Experiments were conducted to test mHealth availability parameters. Intra-BAN, Zigbee, or Bluetooth protocols have no meaningful impact on system availability. For households, two small routers in the extra-BAN are more effective than one large router. Finally, backup batteries and power banks boost availability. The offered models can help developers and maintainers scale mHealth systems based on service needs.&#x0D;

MHealth technology identifies visual impairment in young children.

mHealth technology identifies visual impairment in young children.

Getting Connected to M-Health Technologies through a Meta-Analysis.

The demand for mobile e-health technologies (m-health) continues with constant growth, stimulating the technological advancement of such devices. However, the customer needs to perceive the utility of these devices to incorporate them into their daily lives. Hence, this study aims to identify users' perceptions regarding the acceptance of m-health technologies based on a synthesis of meta-analysis studies on the subject in the literature. Using the relations and constructs proposed in the UTAUT2 (Unified Theory of Acceptance and Use of Technology 2) technology acceptance model, the methodological approach utilized a meta-analysis to raise the effect of the main factors on the Behavioral Intention to Use m-health technologies. Furthermore, the model proposed also estimated the moderation effect of gender, age, and timeline variables on the UTAUT2 relations. In total, the meta-analysis utilized 84 different articles, which presented 376 estimations based on a sample of 31,609 respondents. The results indicate an overall compilation of the relations, as well as the primary factors and moderating variables that determine users' acceptance of the studied m-health systems.

AI based Dynamic Prediction Model for Mobile Health Application System

In recent decades, mobile health (m-health) applications have gained significant attention in the healthcare sector due to their increased support during critical cases like cardiac disease, spinal cord problems, and brain injuries. Also, m-health services are considered more valuable, mainly where facilities are deficient. In addition, it supports wired and advanced wireless technologies for data transmission and communication. In this work, an AI-based deep learning model is implemented to predict healthcare data, where the data handling is performed to improve the prediction performance. It includes the working modules of data collection, normalization, AI-based classification, and decision-making. Here, the m-health data are obtained from the smart devices through the service providers, which comprises the health information related to blood pressure, heart rate, glucose level, etc. The main contribution of this paper is to accurately predict Cardio Vascular Disease (CVD) from the patient dataset using the AI-based m-health system. After obtaining the data, preprocessing can be performed for noise reduction and normalization because prediction performance highly depends on data quality. Consequently, We use the Gorilla Troop Optimization Algorithm (GTOA) to select the most relevant functions for classifier training and testing. Classify his CVD type according to a selected set of features using bidirectional long-term memory (Bi-LSTM). Moreover, the proposed AI-based prediction model’s performance is validated and compared using different measures.

MHealth Intervention for Improving Pain, Quality of Life, and Functional Disability in Patients With Chronic Pain: Systematic Review

Chronic pain (CP) is 1 of the leading causes of disability worldwide and represents a significant burden on individual, social, and economic aspects. Potential tools, such as mobile health (mHealth) systems, are emerging for the self-management of patients with CP. A systematic review was conducted to analyze the effects of mHealth interventions on CP management, based on pain intensity, quality of life (QoL), and functional disability assessment, compared to conventional treatment or nonintervention. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines were followed to conduct a systematic review of randomized controlled trials (RCTs) published in PubMed, Web of Science, Scopus, and Physiotherapy Evidence Database (PEDro) databases from February to March 2022. No filters were used. The eligibility criteria were RCTs of adults (≥18 years old) with CP, intervened with mHealth systems based on mobile apps for monitoring pain and health-related outcomes, for pain and behavioral self-management, and for performing therapeutic approaches, compared to conventional treatments (physical, occupational, and psychological therapies; usual medical care; and education) or nonintervention, reporting pain intensity, QoL, and functional disability. The methodological quality and risk of bias (RoB) were assessed using the Checklist for Measuring Quality, the Oxford Centre for Evidence-Based Medicine Levels of Evidence, and the Cochrane RoB 2.0 tool. In total, 22 RCTs, involving 2641 patients with different CP conditions listed in the International Classification of Diseases 11th Revision (ICD-11), including chronic low back pain (CLBP), chronic musculoskeletal pain (CMSP), chronic neck pain (CNP), unspecified CP, chronic pelvic pain (CPP), fibromyalgia (FM), interstitial cystitis/bladder pain syndrome (IC/BPS), irritable bowel syndrome (IBS), and osteoarthritis (OA). A total of 23 mHealth systems were used to conduct a variety of CP self-management strategies, among which monitoring pain and symptoms and home-based exercise programs were the most used. Beneficial effects of the use of mHealth systems in reducing pain intensity (CNP, FM, IC/BPS, and OA), QoL (CLBP, CNP, IBS, and OA), and functional disability (CLBP, CMSP, CNP, and OA) were found. Most of the included studies (18/22, 82%) reported medium methodological quality and were considered as highly recommendable; in addition, 7/22 (32%) studies had a low RoB, 10/22 (45%) had some concerns, and 5/22 (23%) had a high RoB. The use of mHealth systems indicated positive effects for pain intensity in CNP, FM, IC/BPS, and OA; for QoL in CLBP, CNP, IBS, and OA; and for functional disability in CLBP, CMSP, CNP, and OA. Thus, mHealth seems to be an alternative to improving pain-related outcomes and QoL and could be part of multimodal strategies for CP self-management. High-quality studies are needed to merge the evidence and recommendations of the use of mHealth systems for CP management. PROSPERO International Prospective Register of Systematic Reviews CRD42022315808; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=315808.