Personalized Health Monitoring System for Managing Well-Being in Rural Areas

Development of a Dog Health Score Using an Artificial Intelligence Disease Prediction Algorithm Based on Multifaceted Data

Detecting aberrant behaviors in dogs or observing emotional interactions between a dog and its owner may serve as indicators of potential canine diseases. However, dog owners typically struggle to assess or predict the health status of their pets independently. Consequently, there is a demand for a methodology enabling owners to evaluate their dogs' health based on everyday behavioral data. To address this need, we gathered individual canine data, including three months of standard daily activities (such as scratching, licking, swallowing, and sleeping), to train an AI model. This model identifies abnormal behaviors and quantifies each behavior as a numerical score, termed the "Health Score". This score is categorized into ten levels, where a higher score indicates a healthier state. Scores below 5 warrant medical consultation, while those above 5 are deemed healthy. We validated the baseline value of the Health Score against veterinarian diagnoses, achieving an 87.5% concordance rate. This validation confirms the reliability of the Health Score, which assesses canine health through daily activity monitoring, and is expected to significantly benefit dog owners who face challenges in determining the health status of their pets.

Effectiveness of support from community health workers on the sustained use of a wearable monitoring device among community-dwelling older adults: A randomized trial protocol

BACKGROUND

Wearable monitoring devices, such as smartwatches and fitness bands, are health technologies for enhancing self-care management among community-dwelling older adults. While the evidence suggests that these devices can promote health, older adults often struggle to use them over the long term. Community health workers can effectively motivate older adults to change their health behaviors. This study proposes an intervention involving community health workers as peer supporters to promote sustained daily use of wearable monitoring devices among community-dwelling older adults.

METHODS

The intervention group in this randomized controlled trial will receive the Live with Wearable Monitoring Device program from trained community health workers with the support of a nurse and social workers through a one-time home visit and regular phone calls. The control group will receive only the wearable monitoring device. Data will be collected at baseline, 1 month, 3 months, and 6 months.

DISCUSSION

Merely providing older adults with wearable monitoring devices may not lead to the realization of the potential health benefits of these devices, as long-term usage can be challenging. The results of this trial can provide evidence for a new approach to enhancing self-management and community healthcare among community-dwelling older adults, ultimately improving their health outcomes.

IMPACT

Wearable monitoring devices not only enable real-time monitoring of vital signs, but can also support tailored messaging and facilitate virtual communication between users and healthcare professionals. Despite considerable health benefits, there is evidence showing that older adults largely stop using them after a few months. This study is the first to use a peer support approach to help older adults incorporate a wearable monitoring device in their daily routines in conjunction with goal setting and regular reminders. This will boost the self-care ability of the older adults, allowing them to continue physically functioning in the community.

TRIAL REGISTRATION

This study was prospectively registered at clinicaltrials.gov (identifier: NCT05269303). Registration date: 24/2/2022.

Real-Time Evaluation of Time-Domain Pulse Rate Variability Parameters in Different Postures and Breathing Patterns Using Wireless Photoplethysmography Sensor: Towards Remote Healthcare in Low-Resource Communities

Photoplethysmography (PPG) signals have been widely used in evaluating cardiovascular biomarkers, however, there is a lack of in-depth understanding of the remote usage of this technology and its viability for underdeveloped countries. This study aims to quantitatively evaluate the performance of a low-cost wireless PPG device in detecting ultra-short-term time-domain pulse rate variability (PRV) parameters in different postures and breathing patterns. A total of 30 healthy subjects were recruited. ECG and PPG signals were simultaneously recorded in 3 min using miniaturized wearable sensors. Four heart rate variability (HRV) and PRV parameters were extracted from ECG and PPG signals, respectively, and compared using analysis of variance (ANOVA) or Scheirer-Ray-Hare test with post hoc analysis. In addition, the data loss was calculated as the percentage of missing sampling points. Posture did not present statistical differences across the PRV parameters but a statistical difference between indicators was found. Strong variation was found for the RMSSD indicator in the standing posture. The sitting position in both breathing patterns demonstrated the lowest data loss (1.0 ± 0.6 and 1.0 ± 0.7) and the lowest percentage of different factors for all indicators. The usage of commercial PPG and BLE devices can allow the reliable extraction of the PPG signal and PRV indicators in real time.

Feasibility of an Individualized mHealth Nutrition (iNutrition) Intervention for Post-Discharged Gastric Cancer Patients Following Gastrectomy: A Randomized Controlled Pilot Trial

(1) Background: A major challenge for post-discharged gastric cancer patients following gastrectomy is the impact of the anatomy change on decreased oral intake, nutritional status, and, ultimately, quality of life. The purpose of this study is to examine the feasibility and preliminary effects of an individualized mHealth nutrition (iNutrition) intervention in post-discharged gastric cancer patients following gastrectomy. (2) Methods: A mixed-method feasibility study with a parallel randomized controlled design was conducted. Patients were randomly assigned to either the iNutrition intervention group (n = 12) or the control group (n = 12). Participants completed measures at baseline (T0), four (T1), and twelve weeks (T2) post-randomization. (3) Results: Recruitment (33%) and retention (87.5%) rates along with high adherence and acceptability supported the feasibility of the iNutrition intervention for post-discharged gastric cancer patients following gastrectomy, echoed by the qualitative findings. The iNutrition intervention significantly improved participants' nutritional behavior (p = 0.005), energy intake (p = 0.038), compliance with energy requirements (p = 0.006), and compliance with protein requirements (p = 0.008). (4) Conclusions: The iNutrition intervention is feasible and potentially benefits post-discharged gastric cancer patients following gastrectomy. A larger trial is required to establish the efficacy of this approach. Trial Registration: 19 October 2022 Chinese Clinical Trial Registry, ChiCTR2200064807.

Current Status and Future Perspectives on the Internet of Things in Oncology

The Internet of Things (IoT) has penetrated many aspects of everyday human life. The use of IoT in healthcare has been expanding over the past few years. In this review, we highlighted the current applications of IoT in the medical literature, along with the challenges and opportunities. IoT use mainly involves sensors and wearables, with potential applications in improving the quality of life, personal health monitoring, and diagnosis of diseases. Our literature review highlights that the current main application studied in the literature is physical activity tracking. In addition, we discuss the current technologies that would help IoT-enabled devices achieve safe, quick, and meaningful data transfer. These technologies include machine learning/artificial intelligence, 5G, and blockchain. Data on current IoT-enabled devices are still limited, and future research should address these devices' effect on patients' outcomes and the methods by which their integration in healthcare will avoid increasing costs.

How does digital life influence the health service use among rural residents? Evidence from China

BACKGROUND

The Healthy China strategy is an important development objective of the 14th Five-Year Plan and Vision 2035 in China, while health service use in rural China has been a weak link in this strategy.

OBJECTIVE

Nowadays, people's health service use will be influenced by digital technology due to the arrival of the Digital Age, and that is the reason why our interest is to discuss the effect of digital life on health service use among rural residents.

METHODS

We use the data from the China Family Panel Studies (CFPS) 2020 to examine the effect of digital life on health service use among rural residents, and we use Instrumental Variables method to control the endogenous problem and use KHB model to discuss the mechanism of this effect.

RESULTS

It was found that digital life has increased the health service use among rural residents significantly, and this result has been verified by robust test and Instrumental Variables method. Besides, digital life can increase health service use through the information channel effect and the health literacy effect indirectly. Moreover, digital life has a more significant impact on the residents with low social capital, low physical capital and low social trust, which represents the inclusivity of digital life.

CONCLUSION

The results of our paper will be helpful to examine the effect of the digital policy on promoting the health service use in rural China, and our findings will provide evidence of how to use digital life to enhance health service use among rural residents. Based on this, the government should take measures to eliminate the digital divide between urban and rural areas by promoting the level of digital life among rural residents, paying more attention to the digital literacy development among them, and forging ahead toward the great goal of the Healthy China under the Digital Age.

Lessons for Vietnam on the use of digital technologies to support patient-centred care in low- and middle-income countries in the Asia Pacific Region: A scoping review (Preprint)

BACKGROUND

A rapidly aging population, a shifting disease burden and the ongoing threat of infectious disease outbreaks pose major concerns for Vietnam's health care system. Health disparities are evident in many parts of the country, especially in rural areas, and the population faces inequitable access to patient-centered health care. Vietnam must therefore explore and implement advanced solutions to the provision of patient-centered care, with a view to reducing pressures on the health care system simultaneously. The use of digital health technologies (DHTs) may be one of these solutions.

OBJECTIVE

This study aimed to identify the application of DHTs to support the provision of patient-centered care in low- and middle-income countries in the Asia-Pacific region (APR) and to draw lessons for Vietnam.

METHODS

A scoping review was undertaken. Systematic searches of 7 databases were conducted in January 2022 to identify publications on DHTs and patient-centered care in the APR. Thematic analysis was conducted, and DHTs were classified using the National Institute for Health and Care Excellence evidence standards framework for DHTs (tiers A, B, and C). Reporting was in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines.

RESULTS

Of the 264 publications identified, 45 (17%) met the inclusion criteria. The majority of the DHTs were classified as tier C (15/33, 45%), followed by tier B (14/33, 42%) and tier A (4/33, 12%). At an individual level, DHTs increased accessibility of health care and health-related information, supported individuals in self-management, and led to improvements in clinical and quality-of-life outcomes. At a systems level, DHTs supported patient-centered outcomes by increasing efficiency, reducing strain on health care resources, and supporting patient-centered clinical practice. The most frequently reported enablers for the use of DHTs for patient-centered care included alignment of DHTs with users' individual needs, ease of use, availability of direct support from health care professionals, provision of technical support as well as user education and training, appropriate governance of privacy and security, and cross-sectorial collaboration. Common barriers included low user literacy and digital literacy, limited user access to DHT infrastructure, and a lack of policies and protocols to guide the implementation and use of DHTs.

CONCLUSIONS

The use of DHTs is a viable option to increase equitable access to quality, patient-centered care across Vietnam and simultaneously reduce pressures on the health care system. Vietnam can take advantage of the lessons learned by other low- and middle-income countries in the APR when developing a national road map to digital health transformation. Recommendations that Vietnamese policy makers may consider include emphasizing stakeholder engagement, strengthening digital literacy, supporting the improvement of DHT infrastructure, increasing cross-sectorial collaboration, strengthening governance of cybersecurity, and leading the way in DHT uptake.

Hurdles to developing and scaling remote patients' health management tools and systems: a scoping review

BACKGROUND

Despite all the excitement and hype generated regarding the expected transformative impact of digital technology on the healthcare industry, traditional healthcare systems around the world have largely remained unchanged and resultant improvements in developed countries are slower than anticipated. One area which was expected to significantly improve the quality of and access to primary healthcare services in particular is remote patient monitoring and management. Based on a combination of rapid advances in body sensors and information and communication technologies (ICT), it was hoped that remote patient management tools and systems (RPMTSs) would significantly reduce the care burden on traditional healthcare systems as well as health-related costs. However, the uptake or adoption of above systems has been extremely slow and their roll out has not yet properly taken off especially in developing countries where they ought to have made the greatest positive impact.

AIM

The aim of the study was to assess whether or not recent, relevant literature would support the development of in-community, design, deployment and implementation framework based on three factors thought to be important drivers and levers of RPMTS's adoption and scalability.

METHODS

A rapid, scoping review conducted on relevant articles obtained from PubMed, MEDLINE, PMC and Cochrane databases and grey literature on Google and published between 2012 and May 2020, by combining a number of relevant search terms and phrases.

RESULTS

Most RPMTSs are targeted at and focused on a single disease, do not extensively involve patients and clinicians in their early planning and design phases, are not designed to best serve a specific catchment area and are mainly directed at post-hospital, disease management settings. This may be leading to a situation where patients, potential patients and clinicians simply do not make use of these tools, leading to low adoption and scalability thereof.

CONCLUSION

The development of a user-centred, context-dependent, customizable design and deployment framework could potentially increase the adoption and scalability of RPMTSs, if such framework addressed a combination of diseases, prevalent in a given specific catchment area, especially in developing countries with limited financial resources.

Effects of technology-based interventions on dietary intake or anthropometrics among adolescents and adults in South Asia - A systematic review of intervention studies

INTRODUCTION

Mobile technology has been increasingly used as part of dietary interventions, but the effects of such interventions have not been systematically evaluated in the South Asian context. The systematic review aimed to determine the effects of technology-based interventions on dietary intake or anthropometrics among adolescents and adults in South Asia.

METHODS

Five electronic databases were searched (PubMed, Scopus, Web of Science, Global Health Library and Health Technology Assessment). Studies published in English between 1st January 2011 and 31st December 2021were included. Interventions that evaluated the effects of dietary interventions using technology on dietary outcomes and anthropometrics in adolescents or adults in the age group of 13-44 years (or a broader age group) from South Asia were eligible for inclusion. The risk of bias was assessed using the Cochrane Risk-of-bias 2 tool and ROBINS-I tool. A narrative synthesis was conducted.

RESULTS

Twenty-one studies met the inclusion criteria (20,667 participants). Eleven of the 17 randomised controlled trials (RCTs) had a high overall risk of bias. The four non-randomised intervention studies had a serious or critical overall risk of bias. When including studies with low risk or some concern for bias, the interventions had a beneficial effect on at least one dietary outcome in four of the six RCTs that measured changes in diet, and no effect on the anthropometric outcomes in the six RCTs that measured changes in anthropometric outcomes.

DISCUSSION

Technology-based dietary interventions have had some positive effects on dietary intake, but no effects on anthropometry in South Asia. More evidence is needed as the overall risk of bias was high in a majority of the studies.

Privacy Preservation in Patient Information Exchange Systems Based on Blockchain: System Design Study

Background

With the increasing sophistication of the medical industry, various advanced medical services such as medical artificial intelligence, telemedicine, and personalized health care services have emerged. The demand for medical data is also rapidly increasing today because advanced medical services use medical data such as user data and electronic medical records (EMRs) to provide services. As a result, health care institutions and medical practitioners are researching various mechanisms and tools to feed medical data into their systems seamlessly. However, medical data contain sensitive personal information of patients. Therefore, ensuring security while meeting the demand for medical data is a very important problem in the information age for which a solution is required.

Objective

Our goal is to design a blockchain-based decentralized patient information exchange (PIE) system that can safely and efficiently share EMRs. The proposed system preserves patients’ privacy in the EMRs through a medical information exchange process that includes data encryption and access control.

Methods

We propose a blockchain-based EMR-sharing system that allows patients to manage their EMRs scattered across multiple hospitals and share them with other users. Our PIE system protects the patient’s EMR from security threats such as counterfeiting and privacy attacks during data sharing. In addition, it provides scalability by using distributed data-sharing methods to quickly share an EMR, regardless of its size or type. We implemented simulation models using Hyperledger Fabric, an open source blockchain framework.

Results

We performed a simulation of the EMR-sharing process and compared it with previous works on blockchain-based medical systems to check the proposed system’s performance. During the simulation, we found that it takes an average of 0.01014 (SD 0.0028) seconds to download 1 MB of EMR in our proposed PIE system. Moreover, it has been confirmed that data can be freely shared with other users regardless of the size or format of the data to be transmitted through the distributed data-sharing technique using the InterPlanetary File System. We conducted a security analysis to check whether the proposed security mechanism can effectively protect users of the EMR-sharing system from security threats such as data forgery or unauthorized access, and we found that the distributed ledger structure and re-encryption–based data encryption method can effectively protect users’ EMRs from forgery and privacy leak threats and provide data integrity.

Conclusions

Blockchain is a distributed ledger technology that provides data integrity to enable patient-centered health information exchange and access control. PIE systems integrate and manage fragmented patient EMRs through blockchain and protect users from security threats during the data exchange process among users. To increase safety and efficiency in the EMR-sharing process, we used access control using security levels, data encryption based on re-encryption, and a distributed data-sharing scheme.

Personalized Lifestyle Intervention and Functional Evaluation Health Outcomes SurvEy: Presentation of the LIFEHOUSE Study Using N-of-One Tent-Umbrella-Bucket Design

The working definition of health is often the simple absence of diagnosed disease. This common standard is limiting given that changes in functional health status represent early warning signs of impending health declines. Longitudinal assessment of functional health status may foster prevention of disease occurrence and modify disease progression. The LIFEHOUSE (Lifestyle Intervention and Functional Evaluation-Health Outcomes SurvEy) longitudinal research project explores the impact of personalized lifestyle medicine approaches on functional health determinants. Utilizing an adaptive tent-umbrella-bucket design, the LIFEHOUSE study follows the functional health outcomes of adult participants recruited from a self-insured employee population. Participants were each allocated to the tent of an all-inclusive N-of-one case series. After assessing medical history, nutritional physical exam, baseline functional status (utilizing validated tools to measure metabolic, physical, cognitive, emotional and behavioral functional capacity), serum biomarkers, and genomic and microbiome markers, participants were assigned to applicable umbrellas and buckets. Personalized health programs were developed and implemented using systems biology formalism and functional medicine clinical approaches. The comprehensive database (currently 369 analyzable participants) will yield novel interdisciplinary big-health data and facilitate topological analyses focusing on the interactome among each participant's genomics, microbiome, diet, lifestyle and environment.

Digital Health and Care Study on Elderly Monitoring

Sustainable technologies are being increasingly used in various areas of human life. While they have a multitude of benefits, they are especially useful in health monitoring, especially for certain groups of people, such as the elderly. However, there are still several issues that need to be addressed before its use becomes widespread. This work aims to clarify the aspects that are of great importance for increasing the acceptance of the use of this type of technology in the elderly. In addition, we aim to clarify whether the technologies that are already available are able to ensure acceptable accuracy and whether they could replace some of the manual approaches that are currently being used. A two-week study with people 65 years of age and over was conducted to address the questions posed here, and the results were evaluated. It was demonstrated that simplicity of use and automatic functioning play a crucial role. It was also concluded that technology cannot yet completely replace traditional methods such as questionnaires in some areas. Although the technologies that were tested were classified as being “easy to use”, the elderly population in the current study indicated that they were not sure that they would use these technologies regularly in the long term because the added value is not always clear, among other issues. Therefore, awareness-raising must take place in parallel with the development of technologies and services.

Patent Landscape of Automated Systems for Personalized Health Management (ASHM): Features, Shortcomings, and Implications for Developing an Optimal ASHM

The current struggle of national health care systems against global epidemic of non-communicable diseases (NCD) is both clinically ineffective and cost ineffective. On the other hand, rapid development of systems biology, P4 medicine and new digital and communication technologies are good prerequisites for creating an affordable and scalable automated system for personalized health management (ASHM). The current practice of ASHM is better represented in patent literature (36 relevant documents found in Google Patents and USPTO) than in scientific papers (17 documents found in PubMed and Google Scholar). However, only a small fraction of publications disclose a complete self-sufficient system. Problems that authors of ASHM aim to address, methodological approaches, and the most important technical solutions are reviewed and discussed along with shortcomings and limitations. Technical solutions for ASHM currently commercialized or described in literature generally fail to enable practicable, scalable and affordable automated and individualized screening, monitoring, prevention and correction of human health conditions. They also fail to provide a decision support system to patients that would help effectively prevent major NCD and their complications, be accessible and cost effective, consider individual lifestyle factors and involve patients in management of their individual health. Based on analysis of the literature, models of health and care, we propose conceptual framework for developing an ASHM that would be free from the mentioned problems.

Trends and gaps in precision health research: a scoping review

OBJECTIVE

To determine progress and gaps in global precision health research, examining whether precision health studies integrate multiple types of information for health promotion or restoration.

DESIGN

Scoping review.

DATA SOURCES

Searches in Medline (OVID), PsycINFO (OVID), Embase, Scopus, Web of Science and grey literature (Google Scholar) were carried out in June 2020.

ELIGIBILITY CRITERIA

Studies should describe original precision health research; involve human participants, datasets or samples; and collect health-related information. Reviews, editorial articles, conference abstracts or posters, dissertations and articles not published in English were excluded.

DATA EXTRACTION AND SYNTHESIS

The following data were extracted in independent duplicate: author details, study objectives, technology developed, study design, health conditions addressed, precision health focus, data collected for personalisation, participant characteristics and sentence defining 'precision health'. Quantitative and qualitative data were summarised narratively in text and presented in tables and graphs.

RESULTS

After screening 8053 articles, 225 studies were reviewed. Almost half (105/225, 46.7%) of the studies focused on developing an intervention, primarily digital health promotion tools (80/225, 35.6%). Only 28.9% (65/225) of the studies used at least four types of participant data for tailoring, with personalisation usually based on behavioural (108/225, 48%), sociodemographic (100/225, 44.4%) and/or clinical (98/225, 43.6%) information. Participant median age was 48 years old (IQR 28-61), and the top three health conditions addressed were metabolic disorders (35/225, 15.6%), cardiovascular disease (29/225, 12.9%) and cancer (26/225, 11.6%). Only 68% of the studies (153/225) reported participants' gender, 38.7% (87/225) provided participants' race/ethnicity, and 20.4% (46/225) included people from socioeconomically disadvantaged backgrounds. More than 57% of the articles (130/225) have authors from only one discipline.

CONCLUSIONS

Although there is a growing number of precision health studies that test or develop interventions, there is a significant gap in the integration of multiple data types, systematic intervention assessment using randomised controlled trials and reporting of participant gender and ethnicity. Greater interdisciplinary collaboration is needed to gather multiple data types; collectively analyse big and complex data; and provide interventions that restore, maintain and/or promote good health for all, from birth to old age.

A qualitative study of clinician perceptions regarding the potential role for digital health interventions for the management of COPD

Effective self-management of chronic obstructive pulmonary disease (COPD) can lead to increased patient control and reduced health care costs. However, both patients and healthcare professionals encounter significant challenges. Digital health interventions, such as smart oximeters and COPD self-management applications, promise to enhance the management of COPD, yet, there is little evidence to support their use and user-experience issues are still common. Understanding the needs of healthcare professionals is central for increasing adoption and engagement with digital health interventions but little is known about their perceptions of digital health interventions in COPD. This paper explored the perceptions of healthcare professionals regarding the potential role for DHI in the management of COPD. Snowball sampling was used to recruit the participants (n = 32). Each participant underwent a semi-structured interview. Using NVivo 12 software, thematic analysis was completed. Healthcare professionals perceive digital health interventions providing several potential benefits to the management of COPD including the capture of patient status indicators during the interappointment period, providing new patient data to support the consultation process and perceived digital health interventions as a potential means to improve patient engagement. The findings offer new insights regarding potential future use-cases for digital health interventions in COPD, which can help ease user-experience issues as they align with the needs of healthcare professionals.

Internet of things-based cloud computing platform for analyzing the physical health condition

BACKGROUND

Health monitoring is important for early disease diagnosis and will reduce the discomfort and treatment expenses, which is very relevant in terms of prevention. The early diagnosis and treatment of multiple conditions will improve solutions to the patient's healthcare radically. A concept model for the real-time patient tracking system is the primary goal of the method. The Internet of things (IoT) has made health systems accessible for programs based on the value of patient health.

OBJECTIVE

In this paper, the IoT-based cloud computing for patient health monitoring framework (IoT-CCPHM), has been proposed for effective monitoring of the patients.

METHOD

The emerging connected sensors and IoT devices monitor and test the cardiac speed, oxygen saturation percentage, body temperature, and patient's eye movement. The collected data are used in the cloud database to evaluate the patient's health, and the effects of all measures are stored. The IoT-CCPHM maintains that the medical record is processed in the cloud servers.

RESULTS

The experimental results show that patient health monitoring is a reliable way to improve health effectively.

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

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

E-health literacy and associated factors among chronic patients in a low-income country: a cross-sectional survey

Background

Chronic patients persistently seek for health information on the internet for medication information seeking, nutrition, disease management, information regarding disease preventive actions and so on. Consumers ability to search, find, appraise and use health information from the internet is known as eHealth literacy skill. eHealth literacy is a congregate set of six basic skills (traditional literacy, health literacy, information literacy, scientific literacy, media literacy and computer literacy). The aim of this study was to assess eHealth literacy level and associated factors among internet user chronic patients in North-west Ethiopia.

Methods

Institutional based cross-sectional study design was conducted. Stratified sampling technique was used to select 423 study participants among chronic patients. The eHealth literacy scale (eHEALS) was used for data collection. The eHEALS is a validated eight-item Likert scaled questionnaire used to asses self-reported capability of eHealth consumers to find, appraise, and use health related information from the internet to solve health problems. Statistical Package for Social science version 20 was used for data entry and further analysis. Multivariable logistic regression was used to examine the association between the eHealth literacy skill and associated factors. Significance was obtained at 95% CI and p < 0.05.

Result

In total, 423 study subjects were approached and included in the study from February to May, 2019. The response rate to the survey was 95.3%. The majority of respondents 268 (66.3%) were males and mean age was 35.58 ± 14.8 years. The multivariable logistic regression model indicated that participants with higher education (at least having the diploma) are more likely to possess high eHealth literacy skill with Adjusted Odds Ratio (AOR): 3.48, 95% CI (1.54, 7.87). similarly, being government employee AOR: 1.71, 95% CI (1.11, 2.68), being urban resident AOR: 1.37, 95% CI (0.54, 3.49), perceived good health status AOR: 3.97, 95% CI (1.38, 11.38), having higher income AOR: 4.44, 95% CI (1.32, 14.86), Daily internet use AOR: 2.96, 95% CI (1.08, 6.76), having good knowledge about the availability and importance of online resources AOR: 3.12, 95% CI (1.61, 5.3), having positive attitude toward online resources AOR: 2.94, 95% CI (1.07, 3.52) and higher level of computer literacy AOR: 3.81, 95% CI (2.19, 6.61) were the predictors positively associated with higher eHealth literacy level.

Conclusion

Besides the mounting indication of efficacy, the present data confirm that internet use and eHealth literacy level of chronic patients in this setting is relatively low which clearly implicate that there is a need to fill the skill gap in eHealth literacy among chronic patients which might help them in finding and evaluating relevant online sources for their health-related decisions.

Remote heart rate monitoring - Assessment of the Facereader rPPg by Noldus

Remote photoplethysmography (rPPG) allows contactless monitoring of human cardiac activity through a video camera. In this study, we assessed the accuracy and precision for heart rate measurements of the only consumer product available on the market, namely the FacereaderTM rPPG by Noldus, with respect to a gold standard electrocardiograph. Twenty-four healthy participants were asked to sit in front of a computer screen and alternate two periods of rest with two stress tests (i.e. Go/No-Go task), while their heart rate was simultaneously acquired for 20 minutes using the ECG criterion measure and the FacereaderTM rPPG. Results show that the FacereaderTM rPPG tends to overestimate lower heart rates and underestimate higher heart rates compared to the ECG. The Facereader™ rPPG revealed a mean bias of 9.8 bpm, the 95% limits of agreement (LoA) ranged from almost -30 up to +50 bpm. These results suggest that whilst the rPPG FacereaderTM technology has potential for contactless heart rate monitoring, its predictions are inaccurate for higher heart rates, with unacceptable precision across the entire range, rendering its estimates unreliable for monitoring individuals.

A hierarchical, scalable architecture for a real-time monitoring system for an electrocardiography, using context-aware computing

Heart failure is one of the most common cause of death in the world. The real-time health monitoring system with the advent of the Internet of things has attracted growing attention in the health care industry, which can help reducing the death rate of heart failure. Despite the recent success of these efforts, there have been some limitations, such as response time, scalability, latency and fault tolerance. To address these issues, in this paper, we propose a hierarchical architecture with four layers to develop health care systems. In the proposed model, the vital signs of a patient are measured by means of a body sensor network and sent to a smart health care system. Each of these layers is related to a certain level of heart failure. Therefore, in the proposed model, simple and low-risk heart failure can be detected quickly before it reaches to a dangerous level. Empirical results confirm a significant improvement in terms of response time and scalability in comparison with the state of the art techniques.

Construction of medical equipment-based doctor health monitoring system

The health status of doctors has been overlooked by the society and even the doctors themselves, especially those doctors who work long hours. Their attention is always on patients, so they are more likely to ignore their own health problems. Therefore, in this paper, we propose a medical equipment-based doctor health monitoring system (hereinafter referred to as Doc-care). Doc-care can be used as a private health manager for doctors, and doctors can monitor their health indicators in real time while using medical equipment to aid diagnosis and treatment. When the doctor's health status is neglected, Doc-care can protect the doctor's health; combining with the convolutional neural network method to detect and grade the doctor's health indicators, to assess the doctor's real-time health status. After referring to the doctor's past health data in the cloud server, giving appropriate advice and predictions about the doctor's health status.

The Effectiveness and Safety of Utilizing Mobile Phone-Based Programs for Rehabilitation After Lumbar Spinal Surgery: Multicenter, Prospective Randomized Controlled Trial

Background

Rehabilitation is crucial for postoperative patients with low back pain (LBP). However, the implementation of traditional clinic-based programs is limited in developing countries, such as China, because of the maldistribution of medical resources. Mobile phone–based programs may be a potential substitute for those who have no access to traditional rehabilitation.

Objective

The aim of this study was to examine the efficacy of mobile phone–based rehabilitation systems in patients who underwent lumbar spinal surgery.

Methods

Patients who accepted spinal surgeries were recruited and randomized into 2 groups of rehabilitation treatments: (1) a mobile phone–based eHealth (electronic health) program (EH) or (2) usual care treatment (UC). The primary outcomes were (1) function and pain status assessed by the Oswestry Disability Index (ODI) and (2) the visual analog scale (VAS). Secondary outcomes were (1) general mental health and (2) quality of life (Likert scales, EuroQol-5 Dimension health questionnaire, and 36-item Short-Form Health Survey). All the patients were assessed preoperatively and then at 3, 6, 12, and 24 months postoperatively.

Results

A total of 168 of the 863 eligible patients were included and randomized in this study. Our analysis showed that the improvement of primary outcomes in the EH group was superior to the UC group at 24 months postoperatively (ODI mean 7.02, SD 3.10, P<.05; VAS mean 7.59, SD 3.42, P<.05). No significant difference of primary outcomes was found at other time points.

A subgroup analysis showed that the improvements of the primary outcomes were more significant in those who completed 6 or more training sessions each week throughout the trial (the highest compliance group) compared with the UC group at 6 months (ODI mean 17.94, SD 5.24, P<.05; VAS mean 19.56, SD 5.27, P<.05), 12 months (ODI mean 13.39, SD 5.32, P<.05; VAS mean 14.35, SD 5.23, P<.05), and 24 months (ODI mean 18.80, SD 5.22, P<.05; VAS mean 21.56, SD 5.28, P<.05).

Conclusions

This research demonstrated that a mobile phone–based telerehabilitation system is effective in self-managed rehabilitation for postoperative patients with LBP. The effectiveness of eHealth was more evident in participants with higher compliance. Future research should focus on improving patients’ compliance.

Trial Registration

Chinese Clinical Trial Registry ChiCTR-TRC-13003314; http://www.chictr.org.cn/showproj.aspx?proj=6245 (Archived by WebCite at http://www.webcitation.org/766RAIDNc)

A new look at population health through the lenses of cognitive, functional and social disability clustering in eastern DR Congo: a community-based cross-sectional study

BACKGROUND

The importance of viewing health from a broader perspective than the mere presence or absence of disease is critical at primary healthcare level. However, there is scanty evidence-based stratification of population health using other criteria than morbidity-related indicators in developing countries. We propose a novel stratification of population health based on cognitive, functional and social disability and its covariates at primary healthcare level in DR Congo.

METHOD

We conducted a community-based cross-sectional study in adults with diabetes or hypertension, mother-infant pairs with child malnutrition, their informal caregivers and randomly selected neighbours in rural and sub-urban health zones in South-Kivu Province, DR Congo. We used the WHO Disability Assessment Schedule 2.0 (WHODAS) to measure functional, cognitive and social disability. The study outcome was health status clustering derived from a principal component analysis with hierarchical clustering around the WHODAS domains scores. We calculated adjusted odds ratios (AOR) using mixed-effects ordinal logistic regression.

RESULTS

Of the 1609 respondents, 1266 had WHODAS data and an average age of 48.3 (SD: 18.7) years. Three hierarchical clusters were identified: 9.2% of the respondents were in cluster 3 of high dependency, 21.1% in cluster 2 of moderate dependency and 69.7% in cluster 1 of minor dependency. Associated factors with higher disability clustering were being a patient compared to being a neighbour (AOR: 3.44; 95% CI: 1.93-6.15), residency in rural Walungu health zone compared to semi-urban Bagira health zone (4.67; 2.07-10.58), female (2.1; 1.25-2.94), older (1.05; 1.04-1.07), poorest (2.60; 1.22-5.56), having had an acute illness 30 days prior to the interview (2.11; 1.24-3.58), and presenting with either diabetes or hypertension (2.73; 1.64-4.53) or both (6.37; 2.67-15.17). Factors associated with lower disability clustering were being informally employed (0.36; 0.17-0.78) or a petty trader/farmer (0.44; 0.22-0.85).

CONCLUSION

Health clustering derived from WHODAS domains has the potential to suitably classify individuals based on the level of health needs and dependency. It may be a powerful lever for targeting appropriate healthcare service provision and setting priorities based on vulnerability rather than solely presence of disease.

Deriving High Performance Alerts from Reduced Sensor Data for Timely Intervention in Acute Hypotensive Episodes

Alerting critical health conditions ahead of time leads to reduced mortality rates. Recently wirelessly enabled medical sensors have become pervasive in both hospital and ambulatory settings. These sensors pour out voluminous data that are generally not amenable to direct interpretation. For this data to be practically useful for patients, they must be translatable into alerts that enable doctors to intervene in a timely fashion. In this paper we present a novel three-step technique to derive high performance alerts from voluminous sensor data: A data reduction algorithm that takes into account the medical condition at personalized patient level and thereby converts raw multi-sensor data to patient and disease specific severity representation, which we call as the Personalized Health Motifs (PHM). The PHMs are then modulated by criticality factors derived from interventional time and severity frequency to yield a Criticality Measure Index (CMI). In the final step we generate alerts whenever the CMI crosses patientdisease-specific thresholds. We consider one medical condition called Acute Hypotensive Episode (AHE). We evaluate the performance of our CMI derived alerts using 7,200 minutes of data from the MIMIC II [7] database. We show that the CMI generates valid alerts up to 180 minutes prior to onset of AHE with accuracy, specificity, and sensitivity of 0.76, 1.0 and 0.67 respectively, outperforming alerts from raw data.

Data-Driven Methods for Advancing Precision Oncology

PURPOSE OF REVIEW

This article discusses the advances, methods, challenges, and future directions of data-driven methods in advancing precision oncology for biomedical research, drug discovery, clinical research, and practice.

RECENT FINDINGS

Precision oncology provides individually tailored cancer treatment by considering an individual's genetic makeup, clinical, environmental, social, and lifestyle information. Challenges include voluminous, heterogeneous, and disparate data generated by different technologies with multiple modalities such as Omics, electronic health records, clinical registries and repositories, medical imaging, demographics, wearables, and sensors. Statistical and machine learning methods have been continuously adapting to the ever-increasing size and complexity of data. Precision Oncology supportive analytics have improved turnaround time in biomarker discovery and time-to-application of new and repurposed drugs. Precision oncology additionally seeks to identify target patient populations based on genomic alterations that are sensitive or resistant to conventional or experimental treatments. Predictive models have been developed for cancer progression and survivorship, drug sensitivity and resistance, and identification of the most suitable combination treatments for individual patient scenarios. In the future, clinical decision support systems need to be revamped to better incorporate knowledge from precision oncology, thus enabling clinical practitioners to provide precision cancer care.

SUMMARY

Open Omics datasets, machine learning algorithms, and predictive models have enabled the advancement of precision oncology. Clinical decision support systems with integrated electronic health record and Omics data are needed to provide data-driven recommendations to assist clinicians in disease prevention, early identification, and individualized treatment. Additionally, as cancer is a constantly evolving disorder, clinical decision systems will need to be continually updated based on more recent knowledge and datasets.