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Background: Digital therapeutics (DTx) in the form of mobile health (mHealth) self-management programs have demonstrated effectiveness in reducing disease activity across various diseases, including fibromyalgia and arthritis. However, the content of online self-management programs varies widely, making them difficult to compare. Aim: This study aims to employ generative artificial intelligence (AI)-based knowledge graphs and network analysis to categorize and structure mHealth content at the example of a fibromyalgia self-management program. Methods: A multimodal mHealth online self-management program targeting fibromyalgia and post-viral fibromyalgia-like syndromes was developed. In addition to general content, the program was customized to address specific features and digital personas identified through hierarchical agglomerative clustering applied to a cohort of 202 patients with chronic musculoskeletal pain syndromes undergoing multimodal assessment. Text files consisting of 22,150 words divided into 24 modules were used as the input data. Two generative AI web applications, ChatGPT-4 (OpenAI) and Infranodus (Nodus Labs), were used to create knowledge graphs and perform text network analysis, including 3D visualization. A sentiment analysis of 129 patient feedback entries was performed. Results: The ChatGPT-generated knowledge graph model provided a simple visual overview with five primary edges: "Mental health challenges", "Stress and its impact", "Immune system function", "Long COVID and fibromyalgia" and "Pain management and therapeutic approaches". The 3D visualization provided a more complex knowledge graph, with the term "pain" appearing as the central edge, closely connecting with "sleep", "body", and "stress". Topical cluster analysis identified categories such as "chronic pain management", "sleep hygiene", "immune system function", "cognitive therapy", "healthy eating", "emotional development", "fibromyalgia causes", and "deep relaxation". Gap analysis highlighted missing links, such as between "negative behavior" and "systemic inflammation". Retro-engineering of the self-management program showed significant conceptual similarities between the knowledge graph and the original text analysis. Sentiment analysis of free text patient comments revealed that most relevant topics were addressed by the online program, with the exception of social contacts. Conclusion: Generative AI tools for text network analysis can effectively structure and illustrate DTx content. Knowledge graphs are valuable for increasing the transparency of self-management programs, developing new conceptual frameworks, and incorporating feedback loops. [ABSTRACT FROM AUTHOR]

A sustainable strategy is required to help older people maintain their dental health as this population increases and is expected to increase, in addition to the high expense and numerous challenges that prevent them from receiving continuous dental treatment. Through oral health education, mobile health (mHealth) technology may help prevent and manage oral diseases. This study aims to summarise the use of mHealth in oral health care and analyse the facilitators and barriers to adopting mHealth among older people. A systematic review was conducted using the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to search for articles on Web of Science, Scopus, PubMed, Cochrane Library, Google Scholar, SAGE, Wiley online library, and ScienceDirect databases from January 2014 to March 2024. Integrating mobile health (mHealth) into oral health care offers significant potential to enhance care for older people. However, its adoption is hindered by barriers such as technological literacy and accessibility. Overcoming these challenges through user-cantered design and supportive measures is crucial. This review highlights the transformative promise of mHealth for older adults' oral health, calling for continued research and practical efforts to maximize its benefits. [ABSTRACT FROM AUTHOR]

The 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. [ABSTRACT FROM AUTHOR]

BackgroundPatients are increasingly turning to the Internet for health information. Numerous online symptom checkers and digital triage tools are currently available to the general public in an effort to meet this need, simultaneously acting as a demand management strategy to aid the overburdened health care system. The implementation of these services requires an evidence-based approach, warranting a review of the available literature on this rapidly evolving topic.ObjectiveThis scoping review aims to provide an overview of the current state of the art and identify research gaps through an analysis of the strengths and weaknesses of the presently available literature.MethodsA systematic search strategy was formed and applied to six databases: Cochrane library, NICE, DARE, NIHR, Pubmed, and Web of Science. Data extraction was performed by two researchers according to a pre-established data charting methodology allowing for a thematic analysis of the results.ResultsA total of 10,250 articles were identified, and 28 publications were found eligible for inclusion. Users of these tools are often younger, female, more highly educated and technologically literate, potentially impacting digital divide and health equity. Triage algorithms remain risk-averse, which causes challenges for their accuracy. Recent evolutions in algorithms have varying degrees of success. Results on impact are highly variable, with potential effects on demand, accessibility of care, health literacy and syndromic surveillance. Both patients and healthcare providers are generally positive about the technology and seem amenable to the advice given, but there are still improvements to be made toward a more patient-centered approach. The significant heterogeneity across studies and triage systems remains the primary challenge for the field, limiting transferability of findings.ConclusionCurrent evidence included in this review is characterized by significant variability in study design and outcomes, highlighting the significant challenges for future research.An evolution toward more homogeneous methodologies, studies tailored to the intended setting, regulation and standardization of evaluations, and a patient-centered approach could benefit the field.

BackgroundDeveloping culturally appropriate, scalable interventions to meet the growing needs for stroke rehabilitation is a significant problem of public health concern. Therefore, systematic development and evaluation of a scalable, inclusive, technology-driven solution for community-based stroke care are of immense public health importance in India. ReWin is a digital therapeutics platform that was developed systematically. This study aimed to evaluate its feasibility and acceptability in an Indian context.ObjectivesPhase–1: To pilot the intervention for identifying operational issues and finalize the intervention. Phase–2: To assess the feasibility and acceptability of ReWin intervention in an Indian context.MethodsDesign: Mixed-methods research design. Setting: Participant's home and rehabilitation centers. Participants were selected from rehabilitation centers in South India. Participants: Ten stroke survivors and their caregivers, as well as four rehabilitation service providers were recruited for phase 1. Thirty stroke survivors who were treated and discharged from the hospital, and their caregivers as well as 10 rehabilitation service providers were recruited for Phase 2. Intervention: ReWin a digital therapeutic platform with the provider and patient app for the rehabilitation of physical disabilities following stroke was piloted. Process: Evaluation of the intervention was completed in two phases. In the first phase, the preliminary intervention was field-tested with 10 stroke survivors and four rehabilitation service providers for 2 weeks. In the second phase, the finalized intervention was provided to a further 30 stroke survivors to be used in their homes with support from their carers as well as to 10 rehabilitation service providers for 4 weeks. Outcome measures: Primary outcomes: (1) operational difficulties in using the ReWin intervention; (2) feasibility and acceptability of the ReWin intervention in an Indian setting.ResultsField-testing identified operational difficulties related to 1. Therapeutic content; 2. Format; 3. Navigation; 4. Connectivity, 5. Video-streaming, 6. Language; and 7. Comprehensibility of the animated content. The intervention was reviewed, revised and finalized before pilot testing. Findings from the pilot testing showed that the ReWin intervention was feasible and acceptable. About 76% of the participants had used ReWin for more than half of the intervention period of 4 weeks. Ninety percentage of the stroke care providers and about 60% of the stroke survivors and caregivers felt that the content of ReWin was very relevant to the needs of the stroke survivors. Forty percentage of the stroke survivors and caregivers rated ReWin intervention as excellent. Another 45% of the stroke survivors and caregivers as well as 90% of the stroke care providers rated ReWin intervention as very good based on its overall credibility, usability, and user-friendliness.ConclusionsReWin has all the essential components to connect care providers and consumers not just for stroke rehabilitation but for several other health conditions with the use of several other technological features that support rehabilitation of persons with disabilities and strengthen rehabilitation in health systems worldwide. It is critical to amalgamate ReWin and other evidence-based interventions for rehabilitation to innovate scalable solutions and promote universal health coverage for stroke care worldwide.

BackgroundUnilateral (Hemiplegic) cerebral palsy (UCP) causes weakness and stiffness affecting one sided of the body, often impacting activities of daily living. Upper limb therapy at effective intensity is not accessible to most.AimTo determine stakeholder views on design of an approach using wrist-worn devices and a smartphone application to encourage use of the affected upper limb for children with hemiplegia.MethodFour participatory design workshops and one young people's advisory group workshop incorporating views of five young people with hemiplegia, 13 typically developing peers aged 8–18 years, four parents, three occupational therapists, one teacher and two paediatricians. Two special educational needs co-ordinators were consulted separately. Peers were included to explore a study design whereby each child with hemiplegia would have a participating “buddy”. Topics included views on an acceptable wrist-worn device and smartphone application, participant age range, involvement of a buddy, and barriers to using the technology in a school setting. Ethical/welfare considerations included data security, and potential risks around providing smartphones to young children.ResultsChildren wanted a comfortable, conventional-appearing wristband incorporating a watch face and a secure, well-fitting strap. They were prepared to wear a band on each wrist. They wanted support with explaining the study to schoolteachers. Most schools restricted smartphone use during the school day: the study design accommodated this. Children agreed with a game as reward but had different views on an acceptable game; direct access to feedback data was preferred by some. Parents commented on the lack of access to upper limb therapy for children with UCP; therapists concurred. The proposed participant age range was widened based on feedback. Typically developing children were prepared to be buddies to help a friend with CP. Stakeholders were reassured by data security explanations and plans to provide internet safety information to participants.ConclusionThe participatory design process informed plans for the proof-of-concept stage of the study, hopefully leading to an approach that will be fun, easy to integrate into everyday life, and have the capacity to increase use of the affected arm and hand.

Mental health smartphone apps could increase the safety and self-management of patients at risk of suicide, but it is still unclear whether it is feasible to integrate such apps into routine mental healthcare. This study reports on the feasibility of using a safety planning app (BackUp) and a self-monitoring app (mEMA) as components of the routine treatment of depressed outpatients with suicidal ideation. Clinicians were trained in working with both of the apps, and they invited their eligible patients with suicidal ideation for study participation. Patients used the apps for 3 months and discussed these with their clinician during treatment. Patients completed assessments at baseline (T0), 4 weeks (T1) and post-test (T2, 12 weeks after baseline). Both patients and clinicians also participated in telephone interviews. Feasibility was assessed in terms of usability (score > 70 on System Usability Scale, SUS), acceptability (score > 20 on Client Satisfaction Questionnaire-8, CSQ-8), and uptake (sufficient rates of component completion and app usage in treatment). The sample included 17 adult outpatients (52.9% male, age range 20-50 years) diagnosed with a depressive disorder and suicidal ideation at baseline. BackUp was rated by patients at above the cut-off scores for usability (SUS mean score at T1 75.63 and at T2 77.71) and acceptability (CSQ-8 mean score at T1 23.42 and at T2 23.50). mEMA was similarly rated (SUS mean score at T1 75.83 and at T2 76.25; CSQ-8 mean score at T1 23.92 and at T2 22.75). Telephone interviews with patients and clinicians confirmed the usability and acceptability. The uptake criteria were not met. Our findings suggest that mobile safety planning and mobile self-monitoring can be considered acceptable and usable as treatment components for depressed suicidal outpatients, but the integration of apps into routine treatment needs to be further explored. [ABSTRACT FROM AUTHOR]

IntroductionAfrica has one of the world's highest populations of young people. In addition, Africa has one of the highest proportions of young people facing the worst health challenges. Although previous scholars have reported that young people were using mobile phones to fill in the gaps in accessing reproductive health services, among other health services, there was little comprehensive research on the perception of young people in Tamale, Ghana, on the use of mobile phones to access reproductive health services. This study analyzed the perceptions on mobile phone use to access reproductive health services among young people in Tamale, Ghana.MethodsThe research used a quantitative method design from a target population of 72,706 young people from selected peri-urban, low-income, middle income and high-income residential areas in Tamale Metropolis, Ghana. The sample size used was 397 young people. Participants were selected using a stratified multistage sampling strategy. Descriptive statistics were used to analyse the data.ResultsA total of 86% of the respondents agree that the use of mobile phones helps to overcome cultural challenges that young people in Tamale encounter in accessing reproductive health care. Also, 84.6% of the respondents agree that the use of mobile phones helps them to overcome inadequate access to reproductive health information and services. The use of mobile phones helps to overcome the negative attitude of health providers toward young people in need of reproductive health services was agreed by most of the respondents [strongly agree (35.4%) and agree (49.4%)].ConclusionThis study informed highly positive perceptions and attitudes toward the use of mobile phones to access Reproductive Health Services in Tamale, Ghana. There is, therefore the need for the health sector to reform its mode of prescriptions of medication, consultation, and service delivery to leverage on the advantages that mHealth presents.

Mental health smartphone apps could increase the safety and self-management of patients at risk of suicide, but it is still unclear whether it is feasible to integrate such apps into routine mental healthcare. This study reports on the feasibility of using a safety planning app (BackUp) and a self-monitoring app (mEMA) as components of the routine treatment of depressed outpatients with suicidal ideation. Clinicians were trained in working with both of the apps, and they invited their eligible patients with suicidal ideation for study participation. Patients used the apps for 3 months and discussed these with their clinician during treatment. Patients completed assessments at baseline (T0), 4 weeks (T1) and post-test (T2, 12 weeks after baseline). Both patients and clinicians also participated in telephone interviews. Feasibility was assessed in terms of usability (score > 70 on System Usability Scale, SUS), acceptability (score > 20 on Client Satisfaction Questionnaire-8, CSQ-8), and uptake (sufficient rates of component completion and app usage in treatment). The sample included 17 adult outpatients (52.9% male, age range 20–50 years) diagnosed with a depressive disorder and suicidal ideation at baseline. BackUp was rated by patients at above the cut-off scores for usability (SUS mean score at T1 75.63 and at T2 77.71) and acceptability (CSQ-8 mean score at T1 23.42 and at T2 23.50). mEMA was similarly rated (SUS mean score at T1 75.83 and at T2 76.25; CSQ-8 mean score at T1 23.92 and at T2 22.75). Telephone interviews with patients and clinicians confirmed the usability and acceptability. The uptake criteria were not met. Our findings suggest that mobile safety planning and mobile self-monitoring can be considered acceptable and usable as treatment components for depressed suicidal outpatients, but the integration of apps into routine treatment needs to be further explored.

Although US tobacco use trends show overall improvement, social disadvantage continues to drive significant disparities. Traditional tobacco cessation interventions and public policy initiatives have failed to equitably benefit socially-disadvantaged populations. Advancements in mobile digital technologies have created new opportunities to develop resource-efficient mobile health (mHealth) interventions that, relative to traditional approaches, have greater reach while still maintaining comparable or greater efficacy. Their potential for affordability, scalability, and efficiency gives mHealth tobacco cessation interventions potential as tools to help redress tobacco use disparities. We discuss our perspectives on the state of the science surrounding mHealth tobacco cessation interventions for use by socially-disadvantaged populations. In doing so, we outline existing models of health disparities and social determinants of health (SDOH) and discuss potential ways that mHealth interventions might be optimized to offset or address the impact of social determinants of tobacco use. Because smokers from socially-disadvantaged backgrounds face multi-level barriers that can dynamically heighten the risks of tobacco use, we discuss cutting-edge mHealth interventions that adapt dynamically based on context. We also consider complications and pitfalls that could emerge when designing, evaluating, and implementing mHealth tobacco cessation interventions for socially-disadvantaged populations. Altogether, this perspective article provides a conceptual foundation for optimizing mHealth tobacco cessation interventions for the socially-disadvantaged populations in greatest need.

IntroductionDigital diagnostic decision support tools promise to accelerate diagnosis and increase health care efficiency in rheumatology. Rheumatic? is an online tool developed by specialists in rheumatology and general medicine together with patients and patient organizations. It calculates a risk score for several rheumatic diseases. We ran a pilot study retrospectively testing Rheumatic? for its ability to differentiate symptoms from existing or emerging immune-mediated rheumatic diseases from other rheumatic and musculoskeletal complaints and disorders in patients visiting rheumatology clinics.Materials and MethodsThe performance of Rheumatic? was tested using in three university rheumatology centers: (A) patients at Risk for RA (Karolinska Institutet, n = 50 individuals with musculoskeletal complaints and anti-citrullinated protein antibody positivity) (B) patients with early joint swelling [dataset B (Erlangen) n = 52]. (C) Patients with early arthritis where the clinician considered it likely to be of auto-immune origin [dataset C (Leiden) n = 73]. In dataset A we tested whether Rheumatic? could predict the development of arthritis. In dataset B and C we tested whether Rheumatic? could predict the development of an immune-mediated rheumatic diseases. We examined the discriminative power of the total score with the Wilcoxon rank test and the area-under-the-receiver-operating-characteristic curve (AUC-ROC). Next, we calculated the test characteristics for these patients passing the first or second expert-based Rheumatic? scoring threshold.ResultsThe total test scores differentiated between: (A) Individuals developing arthritis or not, median 245 vs. 163, P < 0.0001, AUC-ROC = 75.3; (B) patients with an immune-mediated arthritic disease or not median 191 vs. 107, P < 0.0001, AUC-ROC = 79.0; but less patients with an immune-mediated arthritic disease or not amongst those where the clinician already considered an immune mediated disease most likely (median 262 vs. 212, P < 0.0001, AUC-ROC = 53.6). Threshold-1 (advising to visit primary care doctor) was highly specific in dataset A and B (0.72, 0.87, and 0.23, respectively) and sensitive (0.67, 0.61, and 0.67). Threshold-2 (advising to visit rheumatologic care) was very specific in all three centers but not very sensitive: specificity of 1.0, 0.96, and 0.91, sensitivity 0.05, 0.07, 0.14 in dataset A, B, and C, respectively.ConclusionRheumatic? is a web-based patient-centered multilingual diagnostic tool capable of differentiating immune-mediated rheumatic conditions from other musculoskeletal problems. The current scoring system needs to be further optimized.

BackgroundSalt reduction is a cost-effective, and rather challenging public health strategy for controlling chronic diseases. The AppSalt program is a school-based multi-component mobile health (mhealth) salt reduction program designed to tackle the high salt intake in China. This mixed-methods process evaluation was conducted to investigate the implementation of this program across sites, identify factors associated with the implementation, and collect evidence to optimize the intervention design for future scale-up.MethodsMixed methods were used sequentially to collect data regarding five process evaluation dimensions: fidelity, dose delivered, dose received, reach, and context. Quantitative data were collected during the intervention process. Participation rate of intervention activities was calculated and compared across cities. The quantitative data was used for the selection of representative intervention participants for the qualitative interviews. Qualitative data were collected in face-to-face semi-structured interviews with purposively selected students (n = 33), adult family members (n = 33), teachers (n = 9), heads of schools (n = 9), key informants from local health, and education departments (n = 8). Thematic analysis technique was applied to analyze the interview transcripts using NVivo. The qualitative data were triangulated with the quantitative data during the interpretation phase.ResultsThe total number of families recruited for the intervention was 1,124. The overall retention rate of the AppSalt program was 97%. The intervention was implemented to a high level of fidelity against the protocol. About 80% of intervention participants completed all the app-based salt reduction courses, with a significant difference across the three cities (Shijiazhuang: 95%; Luzhou: 73%; Yueyang: 64%). The smartphone app in this program was perceived as a feasible and engaging health education tool by most intervention participants and key stakeholders. Through the interviews with participants and key stakeholders, we identified some barriers to implementing this program at primary schools, including the left-behind children who usually live with their grandparents and have limited access of smartphones; perceived adverse effects of smartphones on children (e.g., eyesight damage); and overlooked health education curriculum at Chinese primary schools.ConclusionThis process evaluation demonstrated the feasibility and acceptability of using smartphone applications delivered through the education system to engage families in China to reduce excessive salt intake.Clinical Trial RegistrationThe AppSalt study was registered at www.chictr.org.cn, identifier: ChiCTR1800017553. The date of registration is August 3, 2018.

Mobile health (mHealth) interventions are ubiquitous and effective treatment options for obesity. There is a widespread assumption that the mHealth interventions will be equally effective in other locations. In an initial test of this assumption, this retrospective study assesses weight loss and engagement with an mHealth behavior change weight loss intervention developed in the United States (US) in four English-speaking regions: the US, Australia and New Zealand (AU/NZ), Canada (CA), and the United Kingdom and Ireland (UK/IE). Data for 18,459 participants were extracted from the database of Noom's Healthy Weight Program. Self-reported weight was collected every week until program end (week 16). Engagement was measured using user-logged and automatically recorded actions. Linear mixed models were used to evaluate change in weight over time, and ANOVAs evaluated differences in engagement. In all regions, 27.2–33.2% of participants achieved at least 5% weight loss by week 16, with an average of 3–3.7% weight loss. Linear mixed models revealed similar weight outcomes in each region compared to the US, with a few differences. Engagement, however, significantly differed across regions (P < 0.001 on 5 of 6 factors). Depending on the level of engagement, the rate of weight loss over time differed for AU/NZ and UK/IE compared to the US. Our findings have important implications for the use and understanding of digital weight loss interventions worldwide. Future research should investigate the determinants of cross-country engagement differences and their long-term effects on intervention outcomes.

A major challenge faced by mobile health (mHealth) is identifying an evaluation technique which provides a rigorous evaluation while capturing the unique characteristics of the intervention. This study investigates traditional and emerging methods of mHealth evaluation, identifying existing gaps. This research is a useful first step toward developing an evaluation technique which will facilitate implementation and enable mHealth to reach its potential in accelerating socio-economic development, particularly in Low and Middle Income countries (LMICs). [ABSTRACT FROM AUTHOR]

In Latin America, cardiovascular disease (CVD) mortality rates will increase by an estimated 145% from 1990 to 2020. Several challenges related to social strains, inadequate public health infrastructure, and underfinanced healthcare systems make cardiometabolic conditions and non-communicable diseases (NCDs) difficult to prevent and control. On the other hand, the region has high mobile phone coverage, making mobile health (mHealth) particularly attractive to complement and improve strategies toward prevention and control of these conditions in low- and middle-income countries. In this article, we describe the experiences of three Centers of Excellence for prevention and control of NCDs sponsored by the National Heart, Lung, and Blood Institute with mHealth interventions to address cardiometabolic conditions and other NCDs in Argentina, Guatemala, and Peru. The nine studies described involved the design and implementation of complex interventions targeting providers, patients and the public. The rationale, design of the interventions, and evaluation of processes and outcomes of each of these studies are described, together with barriers and enabling factors associated with their implementation. [ABSTRACT FROM AUTHOR]

Objectives: Gay, bisexual and other men who have sex with men (GBMSM) and trans and gender diverse (TGD) people are disproportionately affected by poorer sexual health outcomes compared to heterosexual populations. We aimed to explore the preferences of GBMSM and TGD for using eHealth for sexual health (eSexualHealth)., Methods: We distributed an anonymous online survey from April to August 2021among the lesbian, gay, bisexual, transgender, intersex, queer and other people of diverse sexuality or gender (LGBTIQA+) community in Australia. The survey collected data on sociodemographic characteristics and sexual behaviors, their preferences for app/website functions and preferred HIV and sexually transmitted infection (STI) testing reminders. We used descriptive statistics to summarize the characteristics of the study population. Free-text responses were thematically analyzed., Results: Of 466 participants included, most identified as cisgender males (92.7%). The median age was 48 [interquartile range (IQR): 37-56]. For accessing sexual health-related information, 160 (34.6%) would use either a website or an app, 165 (32.7%) would prefer a website, 119 (25.8%) would prefer an app, and 33 (7.1%) would not use either platform. There was no significant difference between GBMSM and TGD people. Participants were most interested in information about STI clinics, HIV/STI hotspots, and sexual health education. Participants stressed the need for privacy and anonymity when using eHealth. Regarding reminders to test for HIV/STIs, receiving regular SMS was most popular (112/293, 38.2%), followed by regular emails (55/293 18.8%) and a reminder function on their phone (48/293, 16.4%)., Conclusion: Our study suggests a promising future for eHealth among GBMSM and TGD people. Sexual health is still a stigmatized area, and eHealth may circumvent barriers this population faces., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Abraham, Chow, Fairley, Lee, Kong, Mao, Goller, Medland, Bavinton, Sudarto, Joksic, Wong, Phillips and Ong.)

Cardiovascular disease (CVD) is a major issue to public health. It contributes 41% to the Chinese death rate each year. This huge loss encouraged us to develop a Wearable Efficient teleCARdiology systEm (WE-CARE) for early warning and prevention of CVD risks in real time. WE-CARE is expected to work 24/7 online for mobile health (mHealth) applications. Unfortunately, this purpose is often disrupted in system experiments and clinical trials, even if related enabling technologies work properly. This phenomenon is rooted in the overload issue of complex Electrocardiogram (ECG) data in terms of system integration. In this study, our main objective is to get a system light-loading technology to enable mHealth with a benchmarked ECG anomaly recognition rate. To achieve this objective, we propose an approach to purify clinical features from ECG raw data based on manifold learning, called the Manifold-based ECG-feature Purification algorithm. Our clinical trials verify that our proposal can detect anomalies with a recognition rate of up to 94% which is highly valuable in daily public health-risk alert applications based on clinical criteria. Most importantly, the experiment results demonstrate that the WE-CARE system enabled by our proposal can enhance system reliability by at least two times and reduce false negative rates to 0.76%, and extend the battery life by 40.54%, in the system integration level. [ABSTRACT FROM PUBLISHER]