Development features and study characteristics of mobile health apps in the management of chronic conditions:A systematic review of randomised trials

COVID-19 pandemic challenges have accelerated the reliance on digital health fuelling the expanded incorporation of mobile apps into healthcare services, particularly for the management of long-term conditions such as chronic diseases (CDs). However, the impact of health apps on outcomes for CD remains unclear, potentially owing to both the poor adoption of formal development standards in the design process and the methodological quality of studies. A systematic search of randomised trials was performed on Medline, ScienceDirect, the Cochrane Library and Scopus to provide a comprehensive outlook and review the impact of health apps on CD. We identified 69 studies on diabetes (n = 29), cardiovascular diseases (n = 13), chronic respiratory diseases (n = 13), cancer (n = 10) or their combinations (n = 4). The apps rarely adopted developmental factors in the design stage, with only around one-third of studies reporting user or healthcare professional engagement. Apps differed significantly in content, with a median of eight behaviour change techniques adopted, most frequently pertaining to the 'Feedback and monitoring' (91%) and 'Shaping knowledge' (72%) categories. As for the study methodologies, all studies adopted a traditional randomised control trial (RCT) design, with relatively short follow-ups and limited sample sizes. Findings were not significant for the majority of studies across all CD, with most RCTs revealing a high risk of bias. To support the adoption of apps for CD management, this review reinforces the need for more robust development and appropriate study characteristics to sustain evidence generation and elucidate whether study results reflect the true benefits of apps or a biased estimate due to unsuitable designs

Implementation model of an integrated blockchain and IOT system to healthcare ecosystem

Mestrado em Gestão de Sistemas de InformaçãoNo cenário de transformação digital em que estão inseridos todos os setores de atividade, para melhorar a eficiência, a produtividade e reduzir o tempo e os custos, é necessário investir em novas tecnologias. Novas tecnologias como Internet of Things (IoT) e Blockchain são desenvolvidas para melhorar a eficiência de processamento, a criação de oportunidades de negócios, a regulamentação de requisitos, a segurança e transparência e descentralização de informações, e provavelmente serão as próximas tecnologias disruptivas que transformaram os diversos setores de atividade. Por sua vez, o setor saúde tem enfrentado dificuldades com o surgimento de novas doenças e precisa se transformar e se reinventar para manter sua legitimidade e continuar cumprindo suas obrigações para com os cidadãos. A implementação de novas tecnologias acaba sendo uma das abordagens mais eficazes para aumentar a eficiência, segurança, gerenciamento, análise de big data e performance dos dados. Devido a isso, este projeto propõe um modelo de framework Blockchain e IOT aplicada a saúde. A implementação engloba a criação de um aplicativo (i.e., pacientes) e um site (i.e., médicos, hospitais, farmácias, saúde publica), os dados partilhados pelos usuários são armazenados no blockchain conectado ao aplicativo e o acesso ao Blockchain é liberado por smartcontracts. O objetivo do modelo proposto é que os dados sejam descentralizados e possibilita o acesso a todos os conectados ao blockchain. E para não infringir a proteção dos dados pessoais dos pacientes, foi tomado o cuidado de que o usuário paciente seja o “proprietário” de todos os seus dados e compartilhe-os com qualquer entidade de saúde que deseja. Para atingir os objetivos mencionados, foi definida uma metodologia de validação por conceito do modelo proposto. A validação do conceito do modelo foi dividida em cinco etapas, seguida da análise qualitativa das entrevistas semiestruturadas realizadas com pacientes, médicos e gestores de saúde. Como resultado da validação por conceito foi observado que a opinião de todos os entrevistados é que a implementação do modelo proposto é vantajosa e poderá contribuir com avanços no setor saúde. Portanto, uma vez que médicos e hospitais tenham acesso a mais dados de saúde dos pacientes, esses dados podem colaborar para um diagnóstico mais preciso e o ecossistema da saúde obtém avanços tecnológicos que contribuem para uma melhor gestão dos dados e combate as novas doenças.In the digital transformation scenario in which all sectors of activity are inserted, to improve efficiency, productivity and reduce time and costs, it is necessary to invest in new technologies. New technologies such as Internet of Things (IoT) and Blockchain are being developed to improve processing efficiency, the creation of business opportunities, requirements regulation, security and transparency and information decentralization, and are likely to be the next disruptive technologies that have transformed the various sectors of activity. In turn, the health sector has confronted difficulties with the emergence of new diseases and needs to transform and reinvent itself in order to maintain its legitimacy and continue to fulfill its obligations to citizens. The implementation of new technologies is one of the most effective approaches to increase efficiency, security, management, big data analysis and data performance. Because of this, this project proposes a Blockchain and IOT framework model applied to health. The implementation includes the creation of an application (ie, patients) and a website (ie, doctors, hospitals, pharmacies, public health), the data shared by users is stored on the blockchain connected to the application and access to the Blockchain is released by smart contracts. The aim of the suggested model is that the data is decentralized and allows access to all those connected to the blockchain. And in order not to infringe on the protection of patients' personal data, care has been taken that the patient user is the “owner” of all his data and shares it with any health entity he wishes. To achieve the objectives was applied a validation methodology by concept of the proposed model. The validation of the model concept was divided into five stages, followed by a qualitative analysis of the semi-structured interviews conducted with patients, doctors and health managers. As a result of the concept validation, it was observed that the opinion of all interviewees is that the implementation of the proposed model is advantageous and may contribute to advances in the health ecosystem. Therefore, once doctors and hospitals have access to more patients health data, these data can collaborate for a more accurate diagnosis and the health ecosystem obtains technological advances that contribute to better data management and to fight new diseases.info:eu-repo/semantics/publishedVersio

Assessing the Pragmatic Nature of Mobile Health Interventions Promoting Physical Activity: Systematic Review and Meta-analysis

Background: Mobile health (mHealth) apps can promote physical activity; however, the pragmatic nature (ie, how well research translates into real-world settings) of these studies is unknown. The impact of study design choices, for example, intervention duration, on intervention effect sizes is also understudied. Objective: This review and meta-analysis aims to describe the pragmatic nature of recent mHealth interventions for promoting physical activity and examine the associations between study effect size and pragmatic study design choices. Methods: The PubMed, Scopus, Web of Science, and PsycINFO databases were searched until April 2020. Studies were eligible if they incorporated apps as the primary intervention, were conducted in health promotion or preventive care settings, included a device-based physical activity outcome, and used randomized study designs. Studies were assessed using the Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) and Pragmatic-Explanatory Continuum Indicator Summary-2 (PRECIS-2) frameworks. Study effect sizes were summarized using random effect models, and meta-regression was used to examine treatment effect heterogeneity by study characteristics. Results: Overall, 3555 participants were included across 22 interventions, with sample sizes ranging from 27 to 833 (mean 161.6, SD 193.9, median 93) participants. The study populations’ mean age ranged from 10.6 to 61.5 (mean 39.6, SD 6.5) years, and the proportion of males included across all studies was 42.8% (1521/3555). Additionally, intervention lengths varied from 2 weeks to 6 months (mean 60.9, SD 34.9 days). The primary app- or device-based physical activity outcome differed among interventions: most interventions (17/22, 77%) used activity monitors or fitness trackers, whereas the rest (5/22, 23%) used app-based accelerometry measures. Data reporting across the RE-AIM framework was low (5.64/31, 18%) and varied within specific dimensions (Reach=44%; Effectiveness=52%; Adoption=3%; Implementation=10%; Maintenance=12.4%). PRECIS-2 results indicated that most study designs (14/22, 63%) were equally explanatory and pragmatic, with an overall PRECIS-2 score across all interventions of 2.93/5 (SD 0.54). The most pragmatic dimension was flexibility (adherence), with an average score of 3.73 (SD 0.92), whereas follow-up, organization, and flexibility (delivery) appeared more explanatory with means of 2.18 (SD 0.75), 2.36 (SD 1.07), and 2.41 (SD 0.72), respectively. An overall positive treatment effect was observed (Cohen d=0.29, 95% CI 0.13-0.46). Meta-regression analyses revealed that more pragmatic studies (−0.81, 95% CI −1.36 to −0.25) were associated with smaller increases in physical activity. Treatment effect sizes were homogenous across study duration, participants’ age and gender, and RE-AIM scores

Cancer patients and telenursing interventions in Italy. a systematic review

Objective: The use of digital technologies could improve patients’ quality of care, satisfaction, and health-related outcomes in cancer patients. This paper aims to explore the use of digital technologies in nursing management of cancer patients in Italy. Patients and Methods: A systematic literature review was performed. PubMed, Excerpta Medica dataBASE (Embase), Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Cochrane Library databases were consulted from September 1, 2021, to January 31, 2022. Key terms for Telenurs-ing/Telemedicine and cancer in Italy were used. The quality of each study was assessed through the Grading of Recommendations, Assessment, Development, and Evaluations method. Results: 131 articles were found and 5 were included: two randomized-clinical-trial protocols aimed to explore the impact of medication management apps on patients’ quality of life; one validation trial suggested good reliability in the therapeutic adherence of patients on chemotherapy but limited sensitivity in detecting related adverse events; two observational studies described the validation of telephone triage prehospitalization programs performed by nurses during the pandemic. Conclusions: The use of digital technologies in nursing management of cancer patients is in-frequent in Italy, however, increased during the pandemic. Further studies are needed to evaluate the impact and effectiveness of the use of digital technologies in nursing management in cancer patients

Remote assessment of disease and relapse in major depressive disorder (RADAR-MDD): recruitment, retention, and data availability in a longitudinal remote measurement study

BACKGROUND: Major Depressive Disorder (MDD) is prevalent, often chronic, and requires ongoing monitoring of symptoms to track response to treatment and identify early indicators of relapse. Remote Measurement Technologies (RMT) provide an opportunity to transform the measurement and management of MDD, via data collected from inbuilt smartphone sensors and wearable devices alongside app-based questionnaires and tasks. A key question for the field is the extent to which participants can adhere to research protocols and the completeness of data collected. We aimed to describe drop out and data completeness in a naturalistic multimodal longitudinal RMT study, in people with a history of recurrent MDD. We further aimed to determine whether those experiencing a depressive relapse at baseline contributed less complete data. METHODS: Remote Assessment of Disease and Relapse – Major Depressive Disorder (RADAR-MDD) is a multi-centre, prospective observational cohort study conducted as part of the Remote Assessment of Disease and Relapse – Central Nervous System (RADAR-CNS) program. People with a history of MDD were provided with a wrist-worn wearable device, and smartphone apps designed to: a) collect data from smartphone sensors; and b) deliver questionnaires, speech tasks, and cognitive assessments. Participants were followed-up for a minimum of 11 months and maximum of 24 months. RESULTS: Individuals with a history of MDD (n = 623) were enrolled in the study,. We report 80% completion rates for primary outcome assessments across all follow-up timepoints. 79.8% of people participated for the maximum amount of time available and 20.2% withdrew prematurely. We found no evidence of an association between the severity of depression symptoms at baseline and the availability of data. In total, 110 participants had > 50% data available across all data types. CONCLUSIONS: RADAR-MDD is the largest multimodal RMT study in the field of mental health. Here, we have shown that collecting RMT data from a clinical population is feasible. We found comparable levels of data availability in active and passive forms of data collection, demonstrating that both are feasible in this patient group. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12888-022-03753-1

Automated and semi-automated contact tracing: Protocol for a rapid review of available evidence and current challenges to inform the control of COVID-19

Abstract Introduction Traditional approaches to case-finding, case isolation, and contact tracing methods have so far proved insufficient on their own to prevent the development of local epidemics of COVID-19 in many high-income countries despite relatively advanced public health systems. As a result, many governments have resorted to widespread social distancing measures and mass quarantines (‘lock-downs’) to reduce transmission and to prevent healthcare systems from being overwhelmed. However, such measures impose heavy human and societal costs. Automated or semi-automated digital contact tracing, in conjunction with scaled-up community testing, has been proposed as a key part of exit strategies from lockdowns. However, the effectiveness of these approaches to contact tracing is unclear, and to be effective, trusted, and widely adopted such technology must overcome several challenges. Methods and analysis We will perform a rapid systematic review to assess the effectiveness of automated and semi-automated digital tools for contact tracing, and identify key considerations for successful implementation, to inform the control of COVID-19. We will search PubMed, EMBASE, EBSCO Medical COVID information portal, OVID Global Health, Cochrane Library, medRxiv, BioRxiv, and arXiv for peer-reviewed and pre-print papers on automated or semi-automated digital tools for contact tracing of COVID-19, another respiratory disease with pandemic potential (limited to SARS, MERS, or pandemic influenza), or Ebola, in human populations. Studies will be eligible if published in English between 1 January 2000 and 14 April 2020. We will synthesise study findings narratively and will consider meta-analysis if ≥ 3 suitable studies with comparable interventions and outcomes are available. Ethics and dissemination Ethical approval is not required for this review. We plan to disseminate findings via pre-print, journal publication, through social media and web-based platforms and through direct stakeholder engagement

A Context-Aware mHealth System for Online Physiological Monitoring in Remote Healthcare

Physiological or biological stress is an organism’s response to a stressor such as an environmental condition or a stimulus. The identification of physiological stress while performing the activities of daily living is an important field of health research in preventive medicine. Activities initiate a dynamic physiological response that can be used as an indicator of the overall health status. This is especially relevant to high risk groups; the assessment of the physical state of patients with cardiovascular diseases in daily activities is still very difficult. This paper presents a context-aware telemonitoring platform, IPM-mHealth, that receives vital parameters from multiple sensors for online, real-time analysis. IPM-mHealth provides the technical basis for effectively evaluating patients’ physiological conditions, whether inpatient or at home, through the relevance between physical function and daily activities. The two core modules in the platform include: 1) online activity recognition algorithms based on 3-axis acceleration sensors and 2) a knowledge-based, conditional-reasoning decision module which uses context information to improve the accuracy of determining the occurrence of a potentially dangerous abnormal heart rate. Finally, we present relevant experiments to collect cardiac information and upper-body acceleration data from the human subjects. The test results show that this platform has enormous potential for use in long-term health observation, and can help us define an optimal patient activity profile through the automatic activity analysis

Wearable continuous vital sign monitoring for deterioration detection and clinical outcomes in hospitalised patients

 Current practice uses physiological early warning scoring (EWS) systems to monitor “standard” vital signs, including heart rate (HR), respiratory rate (RR), blood pressure (BP), oxygen saturations (SpO2) and temperature, coupled with a graded response such as referral for a senior review or increasing monitoring frequency. Early detection of the deteriorating patient is a known challenge within hospital environments, as EWS is dependent on correct frequency of physiological observations tailored to specific patient needs, that can be time consuming for healthcare professionals, resulting in missed or incomplete observations. Wearable monitoring systems (WMS) may bring the potential to fill the gap in vital sign monitoring between traditional intermittent manual measurements and continuous automatic monitoring. However, evidence on the feasibility and impact of WMS implementation remains scarce. The virtual High Dependency Unit (vHDU) project was designed to develop and test the feasibility of deploying a WMS system in the hospital ward environment. This doctoral work aims to critically analyse the roadmap work of the vHDU project, containing ten publications distributed throughout 7 chapters. Chapter 1 (with 3 publications) includes a systematic review and meta-analysis identifying the lack of statistical evidence of the impact of WMS in early deterioration detection and associated clinical outcomes, highlighting the need for high-quality randomised controlled trials (RCTs). It also supports the use of WMS as a complement, and not a substitute, for standard and direct care. Chapter 2 explores clinical staff and patient perceptions of current vital sign monitoring practices, as well as their early thoughts on the use of WMS in the hospital environment through a qualitative interview study. WMS were seen positively by both clinical and patient groups as a potential tool to bridge the gap between manual observations and the traditional wired continuous automatic systems, as long as it does not add more noise to the wards nor replaces direct contact from the clinical staff. In chapter 3, the wearability of 7 commercially available wearables (monitoring HR, RR and SpO2) was assessed, advocating for the use of pulse oximeters without a fingertip probe and a small chest patch to improve worn times from the patients. Out of these, five devices were submitted to measurement accuracy testing (chapter 4, with 3 publications) under movement and controlled hypoxaemia, resulting in the validation of a chest patch (monitoring HR and RR) and proving the diagnostic accuracy of 3 pulse oximeters (monitoring pulse rate, PR and SpO2) under test. These results were timely for the final selection of the devices to be integrated in our WMS, namely vHDU system, explored in chapter 5, outlining the process for its development and rapid deployment in COVID-19 isolation wards in our local hospital during the pandemic. This work is now converging in the design of a feasibility RCT to test the impact of the vHDU system (now augmented with blood pressure and temperature monitoring, completing all 5 vital signs) versus standard care in an unbiased environment (chapter 6). This will also ascertain the feasibility for a multicentre RCT, that may in the future, contribute with the much-needed statistical evidence to my systematic review and meta-analysis research question, highlighted in chapter 1. Finally, chapter 7 includes a critical reflection of the vHDU project and overall doctoral work, as well as its contributions to the field of wearable monitoring.<p class="MsoNormal"/

Formal representation of ambulatory assessment protocols in HTML5 for human readability and computer execution

Ambulatory assessment (AA) is a research method that aims to collect longitudinal biopsychosocial data in groups of individuals. AA studies are commonly conducted via mobile devices such as smartphones. Researchers tend to communicate their AA protocols to the community in natural language by describing step-by-step procedures operating on a set of materials. However, natural language requires effort to transcribe onto and from the software systems used for data collection, and may be ambiguous, thereby making it harder to reproduce a study. Though AA protocols may also be written as code in a programming language, most programming languages are not easily read by most researchers. Thus, the quality of scientific discourse on AA stands to gain from protocol descriptions that are easy to read, yet remain formal and readily executable by computers. This paper makes the case for using the HyperText Markup Language (HTML) to achieve this. While HTML can suitably describe AA materials, it cannot describe AA procedures. To resolve this, and taking away lessons from previous efforts with protocol implementations in a system called TEMPEST, we offer a set of custom HTML5 elements that help treat HTML documents as executable programs that can both render AA materials, and effect AA procedures on computational platforms.</p

Mobile Health Technologies

Mobile Health Technologies, also known as mHealth technologies, have emerged, amongst healthcare providers, as the ultimate Technologies-of-Choice for the 21st century in delivering not only transformative change in healthcare delivery, but also critical health information to different communities of practice in integrated healthcare information systems. mHealth technologies nurture seamless platforms and pragmatic tools for managing pertinent health information across the continuum of different healthcare providers. mHealth technologies commonly utilize mobile medical devices, monitoring and wireless devices, and/or telemedicine in healthcare delivery and health research. Today, mHealth technologies provide opportunities to record and monitor conditions of patients with chronic diseases such as asthma, Chronic Obstructive Pulmonary Diseases (COPD) and diabetes mellitus. The intent of this book is to enlighten readers about the theories and applications of mHealth technologies in the healthcare domain