A Research on the Classification and Applicability of the Mobile Health Applications

Negotiating pricing and payment terms for insurance covered mHealth apps: a qualitative content analysis and taxonomy development based on a German experience

BACKGROUND

Germany was the first country worldwide to offer mobile digital health applications (mHealth apps, "DiGA") on prescription with full cost coverage by statutory health insurances. Especially statutory health insurances criticize the current pricing and payment regulations in Germany due to "free and non-transparent" pricing in the first year and lack of cost use evidence. The study consists of two parts: The first part evaluates interests of digital health application providers and statutory health insurances in Germany to identify overlaps and divergences of interests. The second part includes the development of a comprehensive pricing and payment taxonomy for reimbursable mHealth apps in general.

METHODS

Both parts of the study used the input from 16 expert interviews with representatives of digital health application providers and statutory health insurances in Germany. In part one the authors conducted a qualitative content analysis and in part two they followed the taxonomy development process according to Nickerson et al. (2013).

RESULTS

A value based care model is expected to bring the greatest benefit for patients while statutory health insurances welcome the idea of usage based pricing. The final pricing and payment taxonomy consists of four design and negotiation steps (price finding, payment prerequisites, payment modalities, composition of negotiation board).

CONCLUSIONS

As healthcare resources are scarce and thus need to be optimally allocated, it is important to implement pricing and payment terms for reimbursable mHealth apps that result in the greatest benefit for patients. To the best of the authors' knowledge, there has been no structured study yet that examines alternative pricing strategies for reimbursable mHealth apps.The developed pricing and payment taxonomy for reimbursable mHealth apps serves as planning and decision basis for developers, health policy makers and payers internationally.

Gamified Mobile App (MobERAS) for Telemonitoring Patients in the Postoperative Period Based on the Enhanced Recovery after Surgery Program: Development and Validation Study

BACKGROUND

Digital technology and gamified apps can be useful in the health care context. Gamification uses technology to influence users' actions and motivations through experiences that resemble games. Patient adherence to the enhanced recovery after surgery (ERAS) program is crucial for achieving early recovery after surgery and continuous monitoring is essential for obtaining good results.

OBJECTIVE

This study aimed to describe the development and validation of a mobile app for enhanced recovery after surgery (MobERAS), a gamified mobile health app for telemonitoring patients in the postoperative period based on the ERAS program, and to evaluate its functionality and usability and the experience of patients, health care professionals, and computer professionals with its use.

METHODS

We developed MobERAS for postoperative telemonitoring, with active participation of patients in the process, and offering availability of real-time information for the health team. The app development process included idealization, interdisciplinary team formation, potential needs assessment, and product deployment. Usability tests were conducted throughout the development process with improvements, technical adjustments, and updates. After finalization, comprehensive verification tests were performed. The parameters evaluated are those that can influence the length of hospital stay, such as nausea, vomiting, pain scales, return to normal gastrointestinal function, and thromboembolic events. MobERAS was designed to be downloaded by users on their phones, tablets, or other mobile devices and to provide postoperative data. The app has a GPS that monitors the patient's walking time and distance and is connected to a virtual database that stores the collected data.

RESULTS

Women undergoing medium and major gynecologic oncologic surgeries were included. We included 65 patients with an average age of 53.2 (SD 7.4, range 18-85) years. The time of use ranged from 23.4 to 70 hours (mean 45.1, SD 19.2 hours). Regarding adherence to the use of MobERAS, the mean fill rate was 56.3% (SD 12.1%, range 41.7%-100%), and ambulation data were obtained for 60 (92.3%) of the 65 patients. The researcher had access to the data filled out by the patients in real time. There was good acceptance of the use of MobERAS by the patients, with good evaluation of the app's usability. MobERAS was easy to use and considered attractive because of its gamified design. The app was rated as good or very good in all items by health care professionals (n=20) and professionals specializing in technological innovation (n=10).

CONCLUSIONS

MobERAS is easy to use, safe, well accepted by patients, and well evaluated by experts. It can be of great use in clinical surgical practice and an important tool for greater engagement of patients and health care professionals with the ERAS program.

Value of a quality label and European healthcare professionals' willingness to recommend health apps: An experimental vignette study

This study aimed to evaluate healthcare professionals' (HCPs') willingness to recommend health apps presented with versus without the CEN-ISO/TS 82304-2 health app quality label. The study was an experimental vignette study describing 12 short hypothetical scenarios, with Label (absent vs present) as a between and Type of App (prevention vs self-monitoring vs healthcare) and Patient Socioeconomic Status (low vs high) as within-subjects factors. The main outcome measure was HCPs' willingness to recommend apps. A total of 116 HCPs took part in the study. A significant main effect of the label was found. Further, HCPs were most willing to recommend self-management apps and more willing to recommend apps to high as opposed to low SES patients. However, the effect of the label did not differ between apps or according to patients' SES. Results confirm that the quality label has potential for increasing willingness to changing HCPs' recommendation behavior.

Effect of telehealth interventions on anxiety and depression in cancer patients: A systematic review and meta-analysis of randomized controlled trials

INTRODUCTION

Cancer patients not only experience a variety of problems associated with the treatment of their disease but also a range of psychological problems such as anxiety and depression, which may lead to reduced adherence to treatment and a lower quality of life for cancer patients. Telehealth interventions are widely used for cancer patients, and their effectiveness in improving anxiety and depression in cancer patients is variable and still contradictory.

METHODS

Embase, Pubmed, Web of Science, PsycINFO, CINAHL Complete, and the Cochrane Central Register of Controlled Trials were searched from inception to 19 April 2022. Data synthesis was conducted using STATA 15.0, and scores for anxiety and depression were calculated using standardized mean differences and 95% confidence intervals.

RESULTS

A total of 13125 cancer patients from 68 randomized controlled trials were included in the systematic evaluation. The meta-analysis showed that the telehealth intervention had a significant effect on anxiety (standardized mean differences = -0.40, 95% confidence intervals: -0.6 to 0.2, p < 0.001) and depression (standardized mean differences = -0.48, 95% confidence intervals: -0.67 to 0.28, p < 0.001) in patients with cancer.

DISCUSSION

Telehealth interventions significantly improved anxiety and depression levels in cancer patients compared to traditional care interventions. Breast cancer patients most often received telehealth interventions; electronic device-based and application-based telehealth interventions were more effective than online interventions; short-term interventions were more effective than medium-term and long-term interventions, and different outcome measurement tools led to different intervention outcomes. More high-quality research is needed to explore the effects of telehealth interventions.

Artificial Intelligence and Healthcare: A Journey through History, Present Innovations, and Future Possibilities

Artificial intelligence (AI) has emerged as a powerful tool in healthcare significantly impacting practices from diagnostics to treatment delivery and patient management. This article examines the progress of AI in healthcare, starting from the field's inception in the 1960s to present-day innovative applications in areas such as precision medicine, robotic surgery, and drug development. In addition, the impact of the COVID-19 pandemic on the acceleration of the use of AI in technologies such as telemedicine and chatbots to enhance accessibility and improve medical education is also explored. Looking forward, the paper speculates on the promising future of AI in healthcare while critically addressing the ethical and societal considerations that accompany the integration of AI technologies. Furthermore, the potential to mitigate health disparities and the ethical implications surrounding data usage and patient privacy are discussed, emphasizing the need for evolving guidelines to govern AI's application in healthcare.

Integration of digital health applications into the German healthcare system: development of "The DiGA-Care Path"

Introduction

Since 2019 people who have insured in the German statutory health insurance are entitled to use certified apps called the Digitale Gesundheitsanwendungen [Digital Health Applications (DiGAs)]. The prerequisite for this is that an app certified as DiGA and suitable for their diagnosis exists. The DiGA can then either be prescribed by a physician or psychotherapist or requested by the patient from the statutory health insurance fund. Given the novelty of this type of healthcare, the implementation of a DiGA should be closely monitored to identify potential weaknesses and achieve quality improvements. To enable an analysis of the supply of DiGAs step-by-step, we aimed to create the DiGA-Care Path.

Methods

We conducted three steps to create the DiGA-Care Path. First, a knowledge base was created based on a structured literature research matched with knowledge gathered from the superordinate research project "QuaSiApps" funded by the German Federal Joint Committee. Second, we aimed to create an "ideal-typical" DiGA-Care Path using a flowchart. Third, based on the first path, a final path was developed using the graphical modeling language "Event-Driven Process Chain."

Results

The DiGA-Care Path was developed to depict the supply of DiGAs in Germany. The final path is constituted by a "main path" as well as a corresponding "sub-path". While the "main path" focuses more on the supply environment in which a DiGA is used, the "sub-path" depicts the supply delivered by the DiGA itself. Besides the process itself, the paths include relevant actors to indicate responsibilities for individual process steps.

Discussion

The DiGA-Care Path helps to analyze the current supply of DiGAs step-by-step. Thereby, each step can be investigated in detail to identify problems and to detect further steps where quality improvements can be enabled. Depending on the perspective, focused either on the supply environment, or the supply delivered by the DiGA itself, the "main path" or the "sub-path" can be used, respectively. Besides the potential of the DiGA-Care Path to improve the current supply of DiGAs, it can help as an orientation for international policymakers or further stakeholders either to develop their own integration of apps into healthcare systems or for international manufacturers to consider entering the German market.

Mobile applications in adolescent psychotherapy during the COVID-19 pandemic: a systematic review

Background

To bridge the gap in adolescent psychotherapy created by the increasing need for mental health interventions and the limited possibilities of in-person treatment during the pandemic, many health care providers opted to offer online mental health care programs. As a result, the number of mental health apps available in app stores experienced a sharp increase during the COVID-19 pandemic.

Objective

The aim of the current review is to provide an overview of feasibility and effectiveness studies testing mobile applications in adolescent psychotherapy during the peak phase of the COVID-19 pandemic.

Methods

We conducted a literature search in Pubmed, PsychInfo, Google Scholar, OpenSIGLE and OpenGREY for papers published from June 2020 to June 2023. Studies were included if they evaluated app-based interventions intended for psychotherapeutic treatment and targeted adolescents between 12 and 27 years of age with symptoms of psychological disorders. The quality of each study was assessed using the Systematic Assessment of Quality in Observational Research (SAQOR). Effectiveness outcomes were analyzed by vote counting and calculating a binomial probability test.

Results

The search yielded 31 relevant studies that examined 27 different apps with a total of 1,578 adolescent participants. Nine articles were primary effectiveness studies and 22 focused on feasibility measures as primary outcome. There was evidence that mental health apps influenced adolescents' psychotherapy, with 83% of the studies with effectiveness outcomes favoring the intervention (p = 0.002). Sixty-one percent of the included studies were rated at low or very low quality.

Conclusions

The pandemic has given apps a firm and important role in healthcare that will probably continue to expand in the future. To ensure that mental health apps are truly effective and beneficial for adolescents' psychotherapy, we need a standardized measurement of quality features of mental health apps and higher quality app evaluation studies.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=406455, PROSPERO International Prospective Register of Systematic Reviews [CRD42023406455].

A survey of the adoption and perception of mobile health applications among community pharmacists in Malaysia

OBJECTIVES

To determine the adoption and perception of mobile health (mHealth) applications among community pharmacists in Malaysia.

METHODS

A cross-sectional survey using a self-administered questionnaire was conducted with 300 community pharmacists in the Klang Valley, Malaysia using a stratified sampling approach. The questionnaire consisted of 36 questions with three sections: demographic data, adoption of mHealth applications and perception towards mHealth applications. Descriptive and inferential tests as well as exploratory factor analysis were used to analyse the data.

KEY FINDINGS

Adoption of mHealth applications by community pharmacists for both professional and personal use was relatively high at 79.7%. Utilised mHealth applications were primarily from the medical references category, while applications for patient monitoring, personal care and fitness were used to a lesser degree. Among mHealth application users, only 65.7% recommended them to their patients. Overall perception towards mHealth applications was positive, but perception towards the benefits and favour of mHealth applications for their patients was lower. This was corroborated by the factor analysis, which identified four main factors explaining 59.9% of variance in the dataset. These factors were perception towards use in their own professional practice, perception on benefits and use in their patients, perception on specific features of mHealth applications, and reliability of mHealth applications.

CONCLUSIONS

Adoption of mHealth applications among community pharmacists in Malaysia is high. Community pharmacists are more likely to use mHealth applications professionally and personally but less likely to recommend them to patients due to less favourable perceptions on how patients will benefit from mHealth applications.

Unlocking the microbial studies through computational approaches: how far have we reached?

The metagenomics approach accelerated the study of genetic information from uncultured microbes and complex microbial communities. In silico research also facilitated an understanding of protein-DNA interactions, protein-protein interactions, docking between proteins and phyto/biochemicals for drug design, and modeling of the 3D structure of proteins. These in silico approaches provided insight into analyzing pathogenic and nonpathogenic strains that helped in the identification of probable genes for vaccines and antimicrobial agents and comparing whole-genome sequences to microbial evolution. Artificial intelligence, more precisely machine learning (ML) and deep learning (DL), has proven to be a promising approach in the field of microbiology to handle, analyze, and utilize large data that are generated through nucleic acid sequencing and proteomics. This enabled the understanding of the functional and taxonomic diversity of microorganisms. ML and DL have been used in the prediction and forecasting of diseases and applied to trace environmental contaminants and environmental quality. This review presents an in-depth analysis of the recent application of silico approaches in microbial genomics, proteomics, functional diversity, vaccine development, and drug design.

Using a Hybrid Neural Network and a Regularized Extreme Learning Machine for Human Activity Recognition with Smartphone and Smartwatch

Mobile health (mHealth) utilizes mobile devices, mobile communication techniques, and the Internet of Things (IoT) to improve not only traditional telemedicine and monitoring and alerting systems, but also fitness and medical information awareness in daily life. In the last decade, human activity recognition (HAR) has been extensively studied because of the strong correlation between people's activities and their physical and mental health. HAR can also be used to care for elderly people in their daily lives. This study proposes an HAR system for classifying 18 types of physical activity using data from sensors embedded in smartphones and smartwatches. The recognition process consists of two parts: feature extraction and HAR. To extract features, a hybrid structure consisting of a convolutional neural network (CNN) and a bidirectional gated recurrent unit GRU (BiGRU) was used. For activity recognition, a single-hidden-layer feedforward neural network (SLFN) with a regularized extreme machine learning (RELM) algorithm was used. The experimental results show an average precision of 98.3%, recall of 98.4%, an F₁-score of 98.4%, and accuracy of 98.3%, which results are superior to those of existing schemes.

Usability of a mobile application for health professionals in home care services: a user-centered approach

The use of mobile devices by healthcare professionals has led to rapid growth in the development of mobile healthcare applications designed to improve healthcare services. This study was conducted to assess the acceptability and usability of a mobile application for health professionals in relation to their work in hospitalization at home. A mixed methods approach was used. Acceptance, included the satisfaction of the professionals, attitudes toward using the application, and intention or willingness to continue using the application. Usability tests were performed in laboratory analyzing five controlled clinical tasks, and the interaction of the participants with the mobile application was based on the six basic facial expressions published by the American Psychological Association. Perceived satisfaction was assessed using the computer system usability questionnaire. Thirty-two participants completed the task scenarios and questionnaire. More than 90 per cent of participants were able to complete the tasks with only some difficult with vital signs. Satisfaction had a score of 6.18/7 (SD: 0.76), and recommendation of the mobile application had a score of 6.21/7 (SD: 0.81). This study showed a significant usability and acceptability of this mobile application, in terms of effectiveness, efficiency, and satisfaction.

Understanding Use Intention of mHealth Applications Based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT-2) Model in China

The COVID-19 pandemic has significantly impacted the healthcare industry, especially public health resources and resource allocation. With the change in people's lifestyles and increased demand for medical and health care in the post-pandemic era, the Internet and home healthcare have rapidly developed. As an essential part of Internet healthcare, mobile health (mHealth) applications help to fundamentally address the lack of medical resources and meet people's healthcare needs. In this mixed-method study, we conducted in-depth interviews with 20 users in China (mean age = 26.13, SD = 2.80, all born in China) during the pandemic, based on the unified theory of acceptance and use of technology 2 (UTAUT-2) mode, and identified four dimensions of user needs in mHealth scenarios: convenience, control, trust, and emotionality. Based on the interview results, we adjusted the independent variables, deleted the hedonic motivation and the habit, and added the perceived trust and perceived risk as the variables. Using a structural equation model (SEM), we designed the questionnaire according to the qualitative results and collected data from 371 participants (above 18 years old, 43.9% male) online to examine the interrelationships these variables. The results show that performance expectancy (β = 0.40, p < 0.001), effort expectancy (β = 0.40, p < 0.001), social influence (β = 0.14, p < 0.05), facilitating condition (β = 0.15, p < 0.001), and perceived trust (β = 0.31, p < 0.001) had positive effects on use intention. Perceived risk (β = -0.31, p < 0.001) harmed use intention, and price value (β = 0.10, p > 0.5) had no significant effects on use intention. Finally, we discussed design and development guidelines that can enhance user experience of mHealth applications. This research combines the actual needs and the main factors affecting the use intention of users, solves the problems of low satisfaction of user experience, and provides better strategic suggestions for developing mHealth applications in the future.

The DIY IVF cycle-harnessing the power of deeptech to bring ART to the masses

The emergence of telehealth including telemedicine, at-home testing, and mobile health applications has enabled patients to self-manage their reproductive care, especially during the COVID-19 pandemic. Reproduction is rapidly changing and embracing deeptech initiatives that can improve outcomes and facilitate personalized fertility solutions in the near future. This so-called DIY IVF informed by deeptech and moderated by femtech not only holds a tremendous amount of promise, but also challenges and possible pitfalls. This review discusses the current status of deeptech and femtech for IVF care in a post-Roe v. Wade environment.

Factors Associated With the Acceptance of an eHealth App for Electronic Health Record Sharing System: Population-Based Study

BACKGROUND

In the second stage of the Electronic Health Record Sharing System (eHRSS) development, a mobile app (eHealth app) was launched to further enhance collaborative care among the public sector, the private sector, the community, and the caregivers.

OBJECTIVE

This study aims to investigate the factors associated with the downloading and utilization of the app, as well as the awareness, perception, and future improvement of the app.

METHODS

We collected 2110 surveys; respondents were stratified into 3 groups according to their status of enrollment in the eHRSS. The primary outcome measure was the downloading and acceptance of the eHealth app. We collected the data on social economics factors, variables of the Technology Acceptance Model and Theory of Planned Behavior. Any factors identified as significant in the univariate analysis (P<.20) will be included in a subsequent multivariable regression analysis model. All P values ≤.05 will be considered statistically significant in multiple logistic regression analysis. The structural equation modeling was performed to identify interactions among the variables.

RESULTS

The respondents had an overall high satisfaction rate and a positive attitude toward continuing to adopt and recommend the app. However, the satisfaction rate among respondents who have downloaded but not adopted the app was relatively lower, and few of them perceived that the downloading and acceptance processes are difficult. A high proportion of current users expressed a positive attitude about continuing to adopt and recommend the app to friends, colleagues, and family members. The behavioral intention strongly predicted the acceptance of the eHealth app (β=.89; P<.001). Attitude (β=.30; P<.001) and perceived norm; β=.37; P<.001) played important roles in determining behavioral intention, which could predict the downloading and acceptance of the eHealth app (β=.14; P<.001).

CONCLUSIONS

Despite the high satisfaction rate among the respondents, privacy concerns and perceived difficulties in adopting the app were the major challenges of promoting eHealth. Further promotion could be made through doctors and publicity. For future improvement, comprehensive health records and tailored health information should be included.

My Real Avatar has a Doctor Appointment in the Wepital: A System for Persistent, Efficient, and Ubiquitous Medical Care

COVID-19 created a great deal of personal, social, and economic anxiety in the USA and across the globe and exposed the inadequacy of traditional medical systems in handling large-scale emergencies. While telemedicine and virtual visits have become popular as a result, they end once a visit is over, hence lacking data persistence and continuity in caring for patients. Using the design science research approach with support from the theory of affordances, this paper proposes the design of a medical system (called wepital) in which patients receive care through their real avatars, enabling hospitals and other medical centers to provide immediate care that can continue for as long as a patient needs it. Real avatars are digital representations of patients that embody their real-time vital signs and health information. We have created a functional prototype to demonstrate how the proposed design can work. To assess the usability of the design, we have used the prototype in an experiment to provide medical advice to patient volunteers. Based on a theory-based conceptual model, we collected survey data after the experiment to identify factors contributing to the success of such a system, as measured by patient satisfaction. We report the factors that significantly contribute to the patients’ satisfaction. As part of the application and policy implications of our work, we propose a nationwide system that could supplement and expand the capacity of medical systems at the national or even global level.

The Effect of Mobile Health (m-Health) Education Based on WANTER Application on Knowledge, Attitude, and Practice (KAP) Regarding Anemia among Female Students in a Rural Area of Indonesia

Female students, as adolescent girls, are more at risk of anemia because of high nutritional requirements. Health education through mobile applications influences the knowledge, attitude, and practice (KAP) of anemia in adolescent girls. Based on our previous study, several adolescents experienced anemia. This study aims to analyze the effect of health education through mobile applications, namely the WANTER application, on increasing KAP. This study was quasi-experimental with a pretest−posttest design; the sample was divided into an intervention (WANTER application) and a control group (booklet). The study was conducted in a rural area of Indonesia. There were 162 adolescent girls included in the intervention group and as many as 115 adolescent girls in the control group. Our study showed that adolescents’ knowledge and attitude increased significantly in three months after the intervention of WANTER and booklets toward preventing anemia with p < 0.001; however, there was no significant difference in KAP between the control and intervention groups. In addition, there was no improvement in practice, either in the control or intervention groups. Knowledge, attitudes, and practices to prevent anemia need to be continuously improved. Health education through appropriate media for adolescents is very important to make interventions more effective.

Systematic Literature Review of Text Messaging Interventions to Promote Medication Adherence Among People With Serious Mental Illness

OBJECTIVE

Mobile health tools are feasible options to encourage behavior change among patients with serious mental illness. Mobile health tools vary widely, both in platforms used and content delivered. This literature review assessed the use of text messaging interventions to promote medication adherence among patients with serious mental illness.

METHODS

A systematic literature review using PRISMA guidelines examined short message service (SMS) text messaging interventions promoting medication adherence to people with a serious mental illness diagnosis. Databases included PubMed, Cochrane, CINAHL, and PsycINFO. Data extraction included demographic information, participant diagnoses, intervention components, medication class, adherence measures, research design, and study outcomes. Study quality was also assessed.

RESULTS

Of 114 full-text articles screened, 10 articles were selected from nine unique interventions (N=937 people with serious mental illness). Study durations ranged from 30 days to 18 months, with frequency of SMS ranging from twice weekly to 12 times daily. Of the nine unique trials, most reported using an automated server to deliver SMS messages (N=7), two-way SMS capabilities (N=6), customized message content or timing (N=7), and additional components (e.g., provider contact, educational content, and monetary rewards) (N=7). Seven of the 10 articles reported statistically significant improvement in medication adherence and in at least one clinical outcome.

CONCLUSIONS

Evidence to date indicates that text messaging interventions are feasible and appear to improve medication adherence and clinical outcomes among patients with serious mental illness. Future research should assess implementation approaches and how to scale up efforts in nonresearch settings.

Development, usability and quality evaluation of the resilient mobile application for women with breast cancer

Introduction

Mental health problems as a consequence of cancer lower the quality of life of cancer patients. Despite increasing studies of breast cancer-focused mobile health applications (m-Health apps), there is less research on breast cancer patients' quality of life or well-being. The purpose of this study is to develop and evaluate the usability and quality of an educational m-Health app aimed at improving the resilience of breast cancer in women.

Methods

This study was conducted in four phases. It included extracting the requirements of the app through the nominal group technique. Based on these results, an m-Health app was developed and evaluated in terms of usability and quality by two scales, System Usability Scale and Mobile App Rating Scale questionnaires, respectively. Finally, the role of patients' age and educational backgrounds in the use of the app was assessed. The relationship between learnability and usability of the app was measured by the T-Test.

Results

The app was developed with three user interfaces. Its usability developed from the patient's point of view scored a remarkable score of 83.20 with a 95% confidence interval. This value was too indicative of high satisfaction with the usefulness and the possibility of recommending it to other cancer survivors. The results of the quality evaluation from an expert's point of view showed that this app had good functionality. Evaluation of the role of demographic information in the use of the app showed that it can be used for all age groups with different levels of education. The app did not differ significantly between learnability and usability.

Conclusion

The development of m-Health apps, based on usability principles that are suitable for all age groups with different levels of education, is welcomed by cancer patients.

Monitoring of Cardiovascular Diseases: An Analysis of the Mobile Applications Available in the Google Play Store

Cardiovascular diseases have always been here, but there has been an increase in their numbers over time. Even though there are in the digital world a few applications to help with this kind of problem, there are not enough to fulfill the needs of the patients. This study reviews mobile applications that allow patients to monitor and report cardiovascular diseases. It presents a review of 14 mobile applications that were free to download in Portugal and classified and compared according to their characteristics. The selection criteria combined the following keywords: “patient”, “cardiac/or heart”, “report”, and (“tracking” or “monitoring”). Based on the analysis, we point out the errors of the applications and present some solutions. To finish, we investigated how mobile applications can help patients track and self-report cardiovascular diseases.

Understanding the Drivers of Ghanaian Citizens' Adoption Intentions of Mobile Health Services

Mobile health (m-health) application development and diffusion in developing countries have always been a challenge; therefore, research that seeks to provide an elucidation of the drivers of m-Health adoption is vital. Mobile health information systems and applications can contribute to the delivery of a good healthcare system. This study examined the factors influencing citizens' adoption of mobile health services. The Technology Acceptance Model (TAM) was used as the research underpinning for this study, while the data gathered were analyzed with SmartPLS through the use of the structural equation modeling technique. The results showed that perceived usefulness and ease of use were both significant predictors of the behavioral intention to use and recommend the adoption of mobile health services. Also, perceived risk was negative but significant in predicting the intention to use and recommend adoption. Mobile self-efficacy was found to significantly determine the behavioral intention to use, intention to recommend, perceived usefulness, and perceived ease of use of mobile health services. Besides, word-of-mouth showed a positive impact on both the intention to use and recommend. Contrary to expectations, the intention to use had no significant impact on the recommendation intention. The theoretical and practical implications of these findings are thoroughly examined.

Persuasive Technology in an mHealth App Designed for Pelvic Floor Muscle Training Among Women: Systematic Review

BACKGROUND

Pelvic floor muscle training (PFMT) is one of the first-line treatments for stress urinary incontinence among pregnant women. Mobile health (mHealth) technology is potentially effective for delivering PFMT to pregnant women. Persuasive technology in the development of such mobile apps may facilitate behavior change by improving adherence to the exercises. The Capability, Opportunity, and Motivation-Behavior (COM-B) model is potentially useful in selecting the appropriate interventions to be incorporated into the apps.

OBJECTIVE

This review of mHealth apps for PFMT aims to describe the principles of persuasion used for each app and to propose mHealth app design features based on the COM-B model.

METHODS

A systematic literature search was conducted to answer three main research questions: what are the available mHealth apps for PFMT in the published literature, what persuasive strategies were used in their studies how were they mapped to the COM-B model, and how effective were the selected persuasive strategies for PFMT adherence? We searched PubMed, CINAHL, Web of Science, Scopus, and local Malaysian databases such as MyCite and MyMedR for articles reporting mHealth apps used for the delivery of PFMT. We included original articles reporting experimental and cross-sectional studies, including pilot or feasibility trials. Systematic and narrative reviews were excluded. Narrative and thematic syntheses were conducted on the eligible articles based on the research questions. The Cochrane risk of bias tool and the Risk of Bias Assessment Tool for Non-randomized Studies were used to assess study bias.

RESULTS

Of the 169 records from the initial search, 10 (5.9%) articles meeting the selection criteria were included in this review. There were 8 mHealth apps designed for the delivery of PFMT. The Tät, which used 3 categories of persuasive system design, improved PFMT adherence and was cost-effective. Only 1 app, the iBall app, used all categories of persuasive system design, by including social support such as "competition" in its design. The Diário Saúde app was the only app developed using operant conditioning. All apps incorporated Tailoring and Expertise as part of their PSD strategies. Only 3 apps, the Diário Saúde, Tät, and Pen Yi Kang demonstrated improved PFMT adherence.

CONCLUSIONS

Persuasive technology used in mobile apps may target desired behavior change more effectively. The persuasive system design can be mapped to the COM-B model to explain its effectiveness on behaviour change outcomes.

A large-scale group decision making method to select the ideal mobile health application for the hospital

Mobile health, which is not limited by time and space, can effectively alleviate the imbalance of medical resources. Currently, more and more hospitals begin to pay attention to online medical care and actively expand their mobile channels. Among of which, the cooperation with the third-party platform is an effective way to expand the online services of most hospitals. With the increasing number of mobile health applications (mHealth apps), it is difficult to select the ideal application. Most of the existing studies on mHealth app selection are conducted from the perspective of users who have health needs, which is insufficient. The views of multiple stakeholders should be taken into account. mHealth app selection can be regarded as a large-scale group decision making (LSGDM) problem. In this paper, a hybrid LSGDM method is proposed to select the mHealth app with the highest user satisfaction. First, the weights of criteria are obtained based on quality function deployment and 2-additive measure. Furthermore, a consensus model that considers cooperative and non-cooperative behaviors of decision makers is applied to select the ideal mHealth app. Finally, an illustrative example is implemented to exhibit the utility and validity of the proposed model.

Telehealth and Artificial Intelligence Insights into Healthcare during the COVID-19 Pandemic

Soon after the coronavirus disease 2019 pandemic was proclaimed, digital health services were widely adopted to respond to this public health emergency, including comprehensive monitoring technologies, telehealth, creative diagnostic, and therapeutic decision-making methods. The World Health Organization suggested that artificial intelligence might be a valuable way of dealing with the crisis. Artificial intelligence is an essential technology of the fourth industrial revolution that is a critical nonmedical intervention for overcoming the present global health crisis, developing next-generation pandemic preparation, and regaining resilience. While artificial intelligence has much potential, it raises fundamental privacy, transparency, and safety concerns. This study seeks to address these issues and looks forward to an intelligent healthcare future based on best practices and lessons learned by employing telehealth and artificial intelligence during the COVID-19 pandemic.

The Barriers and Facilitators to the Use of Lifestyle Apps: A Systematic Review of Qualitative Studies

BACKGROUND

Mobile-health applications are revolutionising the way healthcare is being delivered. However, current research focusses on apps aimed at monitoring of conditions rather than the prevention of disease. Healthcare apps that prevent disease can be classified as lifestyle apps (LAs) and encompass mindfulness, exercise, and diet apps. In order for widespread implementation of these apps, perspectives of the user must be taken into consideration. Therefore, this systematic literature review identifies the barriers and facilitators to the use of LAs from a user's perspective.

OBJECTIVE

To both identify the facilitators to the use of LAs from a user perspective as well as identify the barriers to the use of LAs from a user perspective.

METHODS

A systematic literature review was conducted following PRISMA guidelines. Qualitative articles focussed on a healthy non-diseased population were obtained. Two independent researchers coded the articles, and themes were identified.

RESULTS

Our results found that there were five barriers and five facilitators to app use. The facilitators included (1) motivational aspects to the user, (2) effective marketing and communication, (3) user-centred design and content, (4) humanising technology, and (5) accessibility. The five barriers identified were (1) a non-conducive, (2) poor marketing and branding, (3) controlling and invasive, (4) disengaging content, and (5) inaccessibility.

CONCLUSIONS

By overcoming the barriers of LAs and encouraging the facilitators found, users are more likely to engage with this method of health promotion. Future research must be conducted on the barriers and facilitators to development and distribution of apps in order for LAs to be implemented in widespread healthcare practice.

Identification and Validation of Pathogenic Genes in Sepsis and Associated Diseases by Integrated Bioinformatics Approach

Sepsis is a clinical syndrome with high mortality and morbidity rates. In sepsis, the abrupt release of cytokines by the innate immune system may cause multiorgan failure, leading to septic shock and associated complications. In the presence of a number of systemic disorders, such as sepsis, infections, diabetes, and systemic lupus erythematosus (SLE), cardiorenal syndrome (CRS) type 5 is defined by concomitant cardiac and renal dysfunctions Thus, our study suggests that certain mRNAs and unexplored pathways may pave a way to unravel critical therapeutic targets in three debilitating and interrelated illnesses, namely, sepsis, SLE, and CRS. Sepsis, SLE, and CRS are closely interrelated complex diseases likely sharing an overlapping pathogenesis caused by erroneous gene network activities. We sought to identify the shared gene networks and the key genes for sepsis, SLE, and CRS by completing an integrative analysis. Initially, 868 DEGs were identified in 16 GSE datasets. Based on degree centrality, 27 hub genes were revealed. The gProfiler webtool was used to perform functional annotations and enriched molecular pathway analyses. Finally, core hub genes (EGR1, MMP9, and CD44) were validated using RT-PCR analysis. Our comprehensive multiplex network approach to hub gene discovery is effective, as evidenced by the findings. This work provides a novel research path for a new research direction in multi-omics biological data analysis.

Literature Review on Artificial Intelligence Methods for Glaucoma Screening, Segmentation, and Classification

Artificial intelligence techniques are now being applied in different medical solutions ranging from disease screening to activity recognition and computer-aided diagnosis. The combination of computer science methods and medical knowledge facilitates and improves the accuracy of the different processes and tools. Inspired by these advances, this paper performs a literature review focused on state-of-the-art glaucoma screening, segmentation, and classification based on images of the papilla and excavation using deep learning techniques. These techniques have been shown to have high sensitivity and specificity in glaucoma screening based on papilla and excavation images. The automatic segmentation of the contours of the optic disc and the excavation then allows the identification and assessment of the glaucomatous disease's progression. As a result, we verified whether deep learning techniques may be helpful in performing accurate and low-cost measurements related to glaucoma, which may promote patient empowerment and help medical doctors better monitor patients.

Mobile app requirements for patients with rare liver diseases: A single center survey for the ERN RARE-LIVER‬‬‬

BACKGROUND

More patient data are needed to improve research on rare liver diseases. Mobile health apps enable an exhaustive data collection. Therefore, the European Reference Network on Hepatological diseases (ERN RARE-LIVER) intends to implement an app for patients with rare liver diseases communicating with a patient registry, but little is known about which features patients and their healthcare providers regard as being useful.

AIMS

This study aimed to investigate how an app for rare liver diseases would be accepted, and to find out which features are considered useful.

METHODS

An anonymous survey was conducted on adult patients with rare liver diseases at a single academic, tertiary care outpatient-service. Additionally, medical experts of the ERN working group on autoimmune hepatitis were invited to participate in an online survey.

RESULTS

In total, the responses from 100 patients with autoimmune (n = 90) or other rare (n = 10) liver diseases and 32 experts were analyzed. Patients were convinced to use a disease specific app (80%) and expected some benefit to their health (78%) but responses differed significantly between younger and older patients (93% vs. 62%, p < 0.001; 88% vs. 64%, p < 0.01). Comparing patients' and experts' feedback, patients more often expected a simplified healthcare pathway (e.g. 89% vs. 59% (p < 0.001) wanted access to one's own medical records), while healthcare providers saw the benefit mainly in improving compliance and treatment outcome (e.g. 93% vs. 31% (p < 0.001) and 70% vs. 21% (p < 0.001) expected the app to reduce mistakes in taking medication and improve quality of life, respectively).

CONCLUSION

Our results underline the great desire for disease-specific apps but also the need to involve patients and healthcare providers in the development of such apps in order to achieve long-term use and, thereby, improvements of patient care and research. The results of this study will help tremendously to implement the first cross-country app that communicates with an ERN patient registry.

A Multidimensional Analysis of Released COVID-19 Location-Based Mobile Applications

The spread of coronavirus disease (COVID-19) has triggered a series of responses worldwide ranging from traveling restrictions and shelter-in-place orders to lockdowns, contact tracing, social distancing, and other mitigation measures. To assist with contact tracing and ensure the safety of citizens, a significant number of mobile applications has been developed, utilizing geospatial information and proximity sensing. We perform a thorough research on seven digital databases (Appbrain, e-Health Hub, GDPRhub, “fs0c131y”, News Sites, Appstore, and Google Play), identifying a total of 160 apps regarding COVID-19 related to our research questions. The aim of this research is to identify the main categories of apps and analyze their functions based on a proposed framework of by mapping aspects that affect their functionalities regarding Services, Technology, Societal & Business, and Legal aspects. As the world comes to the new normal, the utilization of these apps might become more essential for more mobile users and developers. The new encryption protocols that are established are also in favor of this argument. Future work can utilize our framework to further examine the development, design, and adoption of such mobile applications.

The Impact of a Gameful Breathing Training Visualization on Intrinsic Experiential Value, Perceived Effectiveness, and Engagement Intentions: Between-Subject Online Experiment

Background

Slow-paced breathing has been shown to be positively associated with psychological and physiological health. In practice, however, there is little long-term engagement with breathing training, as shown by the usage statistics of breathing training apps. New research suggests that gameful smartphone-delivered breathing training may address this challenge.

Objective

This study assesses the impact of breathing training, guided by a gameful visualization, on perceived experiential and instrumental values and the intention to engage in such training.

Methods

A between-subject online experiment with 170 participants was conducted, and one-way multiple analysis of variance and two-tailed t test analyses were used to test for any difference in intrinsic experiential value, perceived effectiveness, and the intention to engage in either a breathing training with a gameful or a nongameful guidance visualization. Moreover, prior experience in gaming and meditation practices were assessed as moderator variables for a preliminary analysis.

Results

The intrinsic experiential value for the gameful visualization was found to be significantly higher compared to the nongameful visualization (P=.001), but there was no difference in either perceived effectiveness (P=.50) or the intention to engage (P=.44). The preliminary analysis of the influence of meditation and gaming experience on the outcomes indicates that people with more meditation experience yielded higher intrinsic experiential values from using the gameful visualization than people with no or little meditation experience (P=.03). This analysis did not find any additional evidence of gaming time or meditation experience impacting the outcomes.

Conclusions

The gameful visualization was found to increase the intrinsic experiential value of the breathing training without decreasing the perceived effectiveness. However, there were no differences in intentions to engage in both breathing training conditions. Furthermore, gaming and meditation experiences seem to have no or only a small positive moderating effect on the relationship between the gameful visualization and the intrinsic experiential value. Future longitudinal field studies are required to assess the impact of gameful breathing training on actual behavior, that is, long-term engagement and outcomes.

Cardiac rehabilitation for coronary artery disease: latest updates

PURPOSE OF REVIEW

To summarize recent innovations in cardiac rehabilitation and provide a view towards the future of cardiac rehabilitation as it adjusts to the pressures of a global pandemic.

RECENT FINDINGS

Although cardiac rehabilitation has been shown to result in a mortality benefit, research continues to enumerate the benefits of cardiac rehabilitation to patient function and quality of life in a growing range of cardiovascular diseases. In addition, new methodologies and new models of cardiac rehabilitation have emerged with the goal of increasing patient referral and participation.

SUMMARY

Cardiac rehabilitation continues to evolve and adapt to serve a growing and diversifying number of patients with cardiovascular disease with the goal of both decreasing mortality and improving patient function.

Integrating Digital Technologies and Public Health to Fight Covid-19 Pandemic: Key Technologies, Applications, Challenges and Outlook of Digital Healthcare

Integration of digital technologies and public health (or digital healthcare) helps us to fight the Coronavirus Disease 2019 (COVID-19) pandemic, which is the biggest public health crisis humanity has faced since the 1918 Influenza Pandemic. In order to better understand the digital healthcare, this work conducted a systematic and comprehensive review of digital healthcare, with the purpose of helping us combat the COVID-19 pandemic. This paper covers the background information and research overview of digital healthcare, summarizes its applications and challenges in the COVID-19 pandemic, and finally puts forward the prospects of digital healthcare. First, main concepts, key development processes, and common application scenarios of integrating digital technologies and digital healthcare were offered in the part of background information. Second, the bibliometric techniques were used to analyze the research output, geographic distribution, discipline distribution, collaboration network, and hot topics of digital healthcare before and after COVID-19 pandemic. We found that the COVID-19 pandemic has greatly accelerated research on the integration of digital technologies and healthcare. Third, application cases of China, EU and U.S using digital technologies to fight the COVID-19 pandemic were collected and analyzed. Among these digital technologies, big data, artificial intelligence, cloud computing, 5G are most effective weapons to combat the COVID-19 pandemic. Applications cases show that these technologies play an irreplaceable role in controlling the spread of the COVID-19. By comparing the application cases in these three regions, we contend that the key to China's success in avoiding the second wave of COVID-19 pandemic is to integrate digital technologies and public health on a large scale without hesitation. Fourth, the application challenges of digital technologies in the public health field are summarized. These challenges mainly come from four aspects: data delays, data fragmentation, privacy security, and data security vulnerabilities. Finally, this study provides the future application prospects of digital healthcare. In addition, we also provide policy recommendations for other countries that use digital technology to combat COVID-19.

Changes in Internet Use Over Time Among Individuals with Traumatic Spinal Cord Injury

OBJECTIVE

To investigate the changes in total internet and mobile internet use over time and determine how demographic characteristics are related to changes in internet and mobile internet use among individuals with spinal cord injury (SCI).

DESIGN

Cross-sectional analysis of multicenter cohort study.

SETTING

National SCI Database.

PARTICIPANTS

Persons with traumatic SCI with follow-up data collected between 2012 and 2018 (n=13,622).

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURE

Proportion of sample reporting internet use at all or through a mobile device over time and specifically in 2018.

RESULTS

The proportion of internet users increased from 77.7% in 2012 to 88.1% in 2018. Older participants (p<0.001), those with lower annual income (p<0.001), less education (p<0.001), non-White race or Hispanic ethnicity (p<0.001), motor incomplete tetraplegia (p=0.004), or males (p=0.035) were less likely to use internet from 2012 to 2018. By 2018, there were no longer differences in internet use based upon race/ethnicity (p=0.290) or sex (p=0.066). Mobile internet use increased each year (52.4% to 87.7% of internet users from 2012 to 2018), with a participant being 13.7 times more likely to use mobile internet in 2018 than 2012. Older age (p<0.001), income less than $50,000 (p<0.001), high school diploma or less (p=0.011), or non-Hispanic White race/ethnicity (p=0.001) were associated with less mobile internet use over time. By 2018 there were no differences in mobile internet use by education (p=0.430) and only participants with incomes greater than $75,000/year had greater odds of mobile internet use (p=0.016).

CONCLUSIONS

Disparities associated with internet access are decreasing likely due to mobile device use. Increased internet access offers an important opportunity to provide educational and training materials to frequently overlooked groups of individuals with SCI.

Effect of telehealth interventions on quality of life in cancer survivors: A systematic review and meta-analysis of randomized controlled trials

BACKGROUND

With advances in cancer disease diagnosis and treatment, the trends of cancer survival continue to increase, but cancer survivors usually experience disease- or treatment-related problems (including both physiological and psychological problems) and poorer quality of life. Various types of telehealth interventions have been widespread in the field of medical care and have been shown to be cost-effective, to have high levels of patient satisfaction, and to have high acceptability among health professionals. Currently, there is no definite conclusion about the effectiveness of telehealth interventions on cancer survivors' quality of life.

OBJECTIVES

To evaluate the effects of telehealth interventions on cancer survivors' quality of life and compare the effectiveness of different types.

DESIGN

A systematic review and meta-analysis.

METHODS

A systematic literature search was conducted in six databases (MEDLINE, Embase, the Cochrane Central Register of Controlled trials, CINAHL, PsycINFO, and Web of Science) to identify relevant studies from inception to 14 April 2021. Two reviewers independently screened studies and extracted the data. The Cochrane risk-of-bias tool was used to evaluate the quality of the included studies. Data synthesis was conducted in Review Manager (Version 5.3), and the quality of life scores were calculated by using the standard mean difference (SMD) and 95% confidence intervals (CIs). Sensitivity analysis and subgroup analysis were also conducted.

RESULTS

Twenty-eight randomized controlled trials (RCTs) published from 2002 to 2020 were included. Meta-analysis revealed significant effects of telehealth interventions on cancer survivors' quality of life (SMD = 0.24, 95% CI: 0.14-0.34, P < 0.00001). Subgroup analysis showed that the most effective method was application-based intervention (SMD = 0.41, 95% CI: 0.17-0.66) and the short-term telehealth intervention was more effective than other durations of intervention (SMD = 0.28, 95% CI: 0.06-0.50). The effects on breast cancer survivors' quality of life were greater than those on the other types of cancer survivors (SMD = 0.30, 95% CI: 0.10-0.51). Sensitivity analysis indicated that the pooled results were robust and reliable.

CONCLUSION

Telehealth interventions are effective and alternative methods for improving quality of life among cancer survivors. The most effective approach was application-based intervention, the most common approach was website-based intervention, and in terms of intervention durations, the short-term telehealth intervention was the most effective. Most telehealth interventions included breast cancer survivors. More large, well-designed RCTs are needed to confirm the effects of telehealth interventions on quality of life in cancer survivors.

A Systematic Investigation of Models for Color Image Processing in Wound Size Estimation

In recent years, research in tracking and assessing wound severity using computerized image processing has increased. With the emergence of mobile devices, powerful functionalities and processing capabilities have provided multiple non-invasive wound evaluation opportunities in both clinical and non-clinical settings. With current imaging technologies, objective and reliable techniques provide qualitative information that can be further processed to provide quantitative information on the size, structure, and color characteristics of wounds. These efficient image analysis algorithms help determine the injury features and the progress of healing in a short time. This paper presents a systematic investigation of articles that specifically address the measurement of wounds’ sizes with image processing techniques, promoting the connection between computer science and health. Of the 208 studies identified by searching electronic databases, 20 were included in the review. From the perspective of image processing color models, the most dominant model was the hue, saturation, and value (HSV) color space. We proposed that a method for measuring the wound area must implement different stages, including conversion to grayscale for further implementation of the threshold and a segmentation method to measure the wound area as the number of pixels for further conversion to metric units. Regarding devices, mobile technology is shown to have reached the level of reliable accuracy.

Psychometric properties of a self-assessment questionnaire concerning symptoms and impairment in urinary tract infections: the UTI-SIQ-8

OBJECTIVES

To validate the urinary tract infection-Symptom and Impairment Questionnaire (UTI-SIQ-8), a questionnaire that consists of four items to assess the symptom severity for dysuria, urgency, frequenc, and low abdominal pain and four items to assess the resulting impairment of activity by UTIs.

DESIGN

Prospective observation study.

SETTING

German primary care practices.

PARTICIPANTS

An unselected population of women with UTI. Women could participate online via a web application for smartphones, smartwatches and tablets or use a paper-and-pencil version.

MAIN OUTCOMES

Psychometric properties of the UTI-SIQ-8 regarding reliability, validity and sensitivity to change by using factor analysis and multilevel and network analysis.

RESULTS

Data from 120 women with a total of 769 symptom reports across 7 days of measurement were analysed. The majority of the participating patients (87/120) used the web application via smartphones or other devices. The reliability of the UTI-SIQ-8 was high, with Cronbach's alpha of .86 at intake; convergent and discriminant validity was satisfactory. Intraclass correlation demonstrated high sensitivity to change, with 68% of the total variance being due to time differences. These daily changes in an individual's symptoms moved parallel with daily changes in the EQ-5D-5L (b=1.68, SE=0.12, p<0.001) and the visual analogue scale (b=0.03, SE=0.003, p<0.001), also highlighting convergent validity with respect to daily changes in symptom severity.

CONCLUSIONS

The present findings support the UTI-SIQ-8 questionnaire as an economic, reliable and valid instrument for the assessment of symptom severity and symptom change in women with uncomplicated UTI. The web application helped patients to report symptoms on a daily basis. These findings may encourage primary care physicians to use the UTI-SIQ-8 in their daily practice and researchers to apply it to studies involving patients with uncomplicated UTI.

Literature on Applied Machine Learning in Metagenomic Classification: A Scoping Review

Applied machine learning in bioinformatics is growing as computer science slowly invades all research spheres. With the arrival of modern next-generation DNA sequencing algorithms, metagenomics is becoming an increasingly interesting research field as it finds countless practical applications exploiting the vast amounts of generated data. This study aims to scope the scientific literature in the field of metagenomic classification in the time interval 2008-2019 and provide an evolutionary timeline of data processing and machine learning in this field. This study follows the scoping review methodology and PRISMA guidelines to identify and process the available literature. Natural Language Processing (NLP) is deployed to ensure efficient and exhaustive search of the literary corpus of three large digital libraries: IEEE, PubMed, and Springer. The search is based on keywords and properties looked up using the digital libraries' search engines. The scoping review results reveal an increasing number of research papers related to metagenomic classification over the past decade. The research is mainly focused on metagenomic classifiers, identifying scope specific metrics for model evaluation, data set sanitization, and dimensionality reduction. Out of all of these subproblems, data preprocessing is the least researched with considerable potential for improvement.

A Case Study on the Development of a Data Privacy Management Solution Based on Patient Information

Data on diagnosis of infection in the general population are strategic for different applications in the public and private spheres. Among them, the data related to symptoms and people displacement stand out, mainly considering highly contagious diseases. This data is sensitive and requires data privacy initiatives to enable its large-scale use. The search for population-monitoring strategies aims at social tracking, supporting the surveillance of contagions to respond to the confrontation with COVID-19. There are several data privacy issues in environments where IoT devices are used for monitoring hospital processes. In this research, we compare works related to the subject of privacy in the health area. To this end, this research proposes a taxonomy to support the requirements necessary to control patient data privacy in a hospital environment. According to the tests and comparisons made between the variables compared, the application obtained results that contribute to the scenarios applied. In this sense, we modeled and implemented an application. By the end, a mobile application was developed to analyze the privacy and security constraints with COVID-19.

Data acquisition of timed-up and go test with older adults: accelerometer, magnetometer, electrocardiography and electroencephalography sensors' data

We present a dataset related to the acquisition of different sensors data during the performance of the Timed-Up and Go test with the mobile device positioned in a waistband for the acquisition of accelerometer and magnetometer data, and a BITalino device positioned in a chest band for the acquisition of Electrocardiography and Electroencephalography for further processing. The data acquired from the BITalino device is acquired simultaneously by a Bluetooth connection with the same mobile application. The data was acquired in five institutions, including Centro Comunitário das Lameiras, Lar Nossa Senhora de Fátima, Centro Comunitário das Minas da Panasqueira, Lar da Misericórdia da Santa Casa da Misericórdia do Fundão, and Lar da Aldeia de Joanes da Santa Casa da Misericórdia do Fundão from Fundão and Covilhã municipalities (Portugal). This article describes the data acquired from a several subjects from the different institutions for the acquisition of accelerometer and magnetometer data, where each person performed the Timed-Up and Go test three times, where each output from the sensors was acquired with a sampling rate of 100 Hz. Related to the data acquired by the sensors connected to the BITalino device, 31 persons performed the different experiments related to the Timed-Up and Go Test. Following the data acquired from Electroencephalography and Electrocardiography sensors, only the data acquired from 14 individuals was considered valid. The data acquired by a BITalino device has a sampling rate of 100 Hz. These data can be reused for testing machine learning methods for the evaluation of the performance of the Timed-Up and Go test with older adults.

Mobile Applications for Training Plan Using Android Devices: A Systematic Review and a Taxonomy Proposal

Fitness and physical exercise are preferred in the pursuit of healthier and active lifestyles. The number of mobile applications aiming to replace or complement a personal trainer is increasing. However, this also raises questions about the reliability, integrity, and even safety of the information provided by such applications. In this study, we review mobile applications that serve as virtual personal trainers. We present a systematic review of 36 related mobile applications, updated between 2017 and 2020, classifying them according to their characteristics. The selection criteria considers the following combination of keywords: “workout”, “personal trainer”, “physical activity”, “fitness”, “gymnasium”, and “daily plan”. Based on the analysis of the identified mobile applications, we propose a new taxonomy and present detailed guidelines on creating mobile applications for personalised workouts. Finally, we investigated how can mobile applications promote health and well-being of users and whether the identified applications are used in any scientific studies.

Identification of Diseases Based on the Use of Inertial Sensors: A Systematic Review

Inertial sensors are commonly embedded in several devices, including smartphones, and other specific devices. This type of sensors may be used for different purposes, including the recognition of different diseases. Several studies are focused on the use of accelerometer signals for the automatic recognition of different diseases, and it may empower the different treatments with the use of less invasive and painful techniques for patients. This paper aims to provide a systematic review of the studies available in the literature for the automatic recognition of different diseases by exploiting accelerometer sensors. The most reliably detectable disease using accelerometer sensors, available in 54% of the analyzed studies, is the Parkinson’s disease. The machine learning methods implemented for the automatic recognition of Parkinson’s disease reported an accuracy of 94%. The recognition of other diseases is investigated in a few other papers, and it appears to be the target of further analysis in the future.

Circular Economy for Clothes Using Web and Mobile Technologies—A Systematic Review and a Taxonomy Proposal

Nowadays, it is possible to buy clothing using online platforms, either by accessing online brand stores, general online stores or circular economy platforms. This paper presents a study on mobile applications that support online commerce for clothing, focusing on the review of the mobile applications with features that are characteristic of the circular economy paradigm. Findings include the fact that almost all the mobile applications analysed have pictures illustrative of the clothes and accessories that are available for trading as well as their brief description. Furthermore, this paper presents a study of various scientific articles about the circular economy of clothes and how it can be beneficial to the future of the environment. It is a junction with a Web platform for its growth and its disclosure. The paper builds conclusions upon the assumption that the circular economy is a growing business that is part of a sustainable development where the main goal is to reduce the environmental impact. The paper proposes the analysis of an innovative taxonomy of mobile applications about the circular economy.