mHealth in sub-Saharan Africa and Europe: A systematic review comparing the use and availability of mHealth approaches in sub-Saharan Africa and Europe

Abstract

Background

mHealth can help with healthcare service delivery for various health issues, but there's a significant gap in the availability and use of mHealth systems between sub-Saharan Africa and Europe, despite the ongoing digitalization of the global healthcare system.

Objective

This work aims to compare and investigate the use and availability of mHealth systems in sub-Saharan Africa and Europe, and identify gaps in current mHealth development and implementation in both regions.

Methods

The study adhered to the PRISMA 2020 guidelines for article search and selection to ensure an unbiased comparison between sub-Saharan Africa and Europe. Four databases (Scopus, Web of Science, IEEE Xplore, and PubMed) were used, and articles were evaluated based on predetermined criteria. Details on the mHealth system type, goal, patient type, health concern, and development stage were collected and recorded in a Microsoft Excel worksheet.

Results

The search query produced 1020 articles for sub-Saharan Africa and 2477 articles for Europe. After screening for eligibility, 86 articles for sub-Saharan Africa and 297 articles for Europe were included. To minimize bias, two reviewers conducted the article screening and data retrieval. Sub-Saharan Africa used SMS and call-based mHealth methods for consultation and diagnosis, mainly for young patients such as children and mothers, and for issues such as HIV, pregnancy, childbirth, and child care. Europe relied more on apps, sensors, and wearables for monitoring, with the elderly as the most common patient group, and the most common health issues being cardiovascular disease and heart failure.

Conclusion

Wearable technology and external sensors are heavily used in Europe, whereas they are seldom used in sub-Saharan Africa. More efforts should be made to use the mHealth system to improve health outcomes in both regions, incorporating more cutting-edge technologies like wearables internal and external sensors. Undertaking context-based studies, identifying determinants of mHealth systems use, and considering these determinants during mHealth system design could enhance mHealth availability and utilization.

Keywords

Digital health eHealth Europe healthcare mHealth sub-Saharan Africa

Introduction

The emergence of digital systems, as well as the widespread availability and use of mobile phones worldwide, present a notable opportunity for improving access and equity to healthcare services. In order to enhance the delivery of high-quality medical care, mobile health (mHealth, according to WHO) applications offer tremendous potential for technology integration into clinical services.¹ It is a crucial component of eHealth, which is the secure and affordable application of information and communication technology to serve the disciplines of health and related ones.²

The benefit of mHealth is that it can promote innovative forms of care by overcoming organizational, temporal, and geographic constraints, and it can help keep healthcare systems viable. By enhancing treatment, empowering patients, increasing adherence, providing patient-centered care, reducing medical costs, and streamlining the use of health resources, mHealth has the potential to improve the delivery of high-quality healthcare. It also increases the opportunity to deliver more consistent, coordinated, and efficient healthcare when and where it is needed and increases access to health services to isolated or underserved communities.³

Additionally, depending on the demands made by the various health conditions, mHealth can be used for many facets of healthcare, including remote monitoring, consulting, diagnosis, reminding, informing, and patient treatment. It is obvious that more than ever, mHealth may provide a solution to many of the current healthcare issues.

Despite the enormous promise and adaptability that mHealth could offer, a number of obstacles prevent various mHealth systems from being implemented, used, or scaled up in different regions or by different users. These potential obstacles include, among others, a lack of policy and institutional frameworks, low mobile phone penetration, low acceptance among healthcare professionals and patients, regulations surrounding data security, and insufficient procedures for quality evaluation.⁴

Regardless of the fact that we are in an era where the global healthcare system is being further digitalized, the availability and use of mHealth systems in developed and developing countries differ significantly. The potential benefits of mHealth in enhancing healthcare delivery and improving health outcomes are widely acknowledged. However, there remains a lack of evidence regarding their availability and utilization in both Europe and Sub-Saharan Africa (SSA). Additionally, it remains unclear whether any disparities or gaps exist in terms of their availability and use between these two regions. The main aim of this study is to review the literature focusing on differences between the availability and use of mHealth systems between Europe and SSA and to give recommendations on how mHealth can help to tackle future health issues in both regions.

The rationale for undertaking this study is to conduct an extensive and impartial evaluation of the current literature on the use and availability of mHealth systems in both Europe and SSA. This review will identify any gaps and research needs in the field, and provide a more reliable evidence base to inform decision-making and shape the design and implementation of mHealth projects. These findings can guide policymakers and stakeholders in making informed decisions about resource allocation to address these inequalities and improve overall access to mHealth technologies.

Methods

To study the use and availability of mHealth systems in SSA and Europe, a systematic review is performed based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 updated guidelines.⁵ To make an objective comparison, the process for article search and selection was done in the same way for SSA and Europe, using the same search query (except for the words Europe and SSA) in the same databases with the same inclusion and exclusion criteria. Since this study relied on a review of secondary data obtained from published articles, the need to obtain patient consent was considered unnecessary. The study did not utilize any identifiable patient information.

Search approach

Four databases were used for the literature search: Scopus, Web of Science, IEEE Xplore, and PubMed. The search query was (“Mobile health” OR mHealth OR telehealth OR telemedicine OR “Wearable devices”) AND (“Sub Saharan Africa”) AND (Use* OR Practice OR Implementation* OR Enactment OR Application OR Operation OR Employment OR Execution OR Development OR Growth OR Expansion OR Advance OR Progress OR Change).

For Europe it is the same search query, except the search term “Sub Saharan Africa” is replaced by “Europe.” For both search queries the results were filtered, such that only articles from 2000 and onwards were given. The last date each database was searched was on December 2021.

Screening process

A title and abstract screening were performed in succession by two reviewers. The inclusion and exclusion criteria used in the screening process can be seen in Table 1. All articles that discussed the proposal, development, or implementation of a specific mHealth system were included for analysis.

Table 1.

Inclusion and exclusion criteria for article selection.

Inclusion criteria	Exclusion criteria
i. mHealth is addressed as a primary issue ii. mHealth implementation and feedback are discussed in the paper iii. The mHealth approach is towards the human domain iv. Also states/describes the development, and design of mHealth v. The paper discusses on ground policy framework to use mHealth vi. The mHealth approach is directly for patients (not for educating nurses, doctors, etc.)	i. If it is about electronic medical records and data management ii. If it is about general IT and ICT technology and data collection iii. The mHealth approach is not towards the human domain (i.e. plants, livestock, etc.) iv. If the paper concentrates on providing and advocating policy framework input v. If it is about tele education for healthcare professionals vi. if it concentrates on enabling and blocking features to use mHealth vii. if it is not about sub-Saharan Africa (SSA) and Europe

Inclusion criteria

Exclusion criteria

i. mHealth is addressed as a primary issue
ii. mHealth implementation and feedback are discussed in the paper
iii. The mHealth approach is towards the human domain
iv. Also states/describes the development, and design of mHealth
v. The paper discusses on ground policy framework to use mHealth vi. The mHealth approach is directly for patients (not for educating nurses, doctors, etc.)

i. If it is about electronic medical records and data management
ii. If it is about general IT and ICT technology and data collection
iii. The mHealth approach is not towards the human domain (i.e. plants, livestock, etc.)
iv. If the paper concentrates on providing and advocating policy framework input
v. If it is about tele education for healthcare professionals
vi. if it concentrates on enabling and blocking features to use mHealth
vii. if it is not about sub-Saharan Africa (SSA) and Europe

From each of the articles, the following data was then extracted and entered in a Microsoft Excel worksheet (the full data table for SSA and Europe can be obtained as a supplementary file. In the first column, the lead author of each included article is mentioned. In the next columns the items of each of the five categories listed in Table 2 were then be marked by 0 or 1 (only the 1's are shown in the data table), for example, if an article mentioned the development of an app-based mHealth system, a 1 was allocated to the “App” column, and so on. The data was sorted by lead author name. All included articles are also referenced in the bibliography.

Table 2.

Data extracted for analysis.

Category	Item
mHealth system	App, wearable, external sensor, implanted sensors, SMS/call, videocall/Chat, and other
Goal	Monitoring, consultation/diagnosis, informing, reminder, motivation/empowerment, treatment, and other
Patient type	Any patient, child, young mother, woman, elderly
Health issue	multiple, HIV, pregnancy/birth(control)/child care, CVD/HF, cancer, chronic disease, diabetes, COPD/asthma, Alzheimer, obesity, stress/mental health, physical rehabilitation, allergy, etc.
Development stage	Proposal/ early development, test phase, in practice

Results

The search query for the SSA articles yielded a total of 1020 articles of which 831 articles remained after removal of duplicates. The search query for the Europe articles yielded a total of 2477 articles of which 1605 articles remained after the removal of duplicates. After title and abstract screening, 593 articles for SSA were excluded, leaving 238 articles for further screening. 1201 articles for Europe were excluded, leaving 404 articles for further screening. The remaining articles were then assessed for eligibility for further analysis. After eligibility screening, 86 articles for SSA and 297 articles for Europe,^6–386 were selected for analysis. The PRISMA flow diagram for both SSA and Europe can be found in Figures 1 and 2.

Figure 1.

PRISMA flow diagram for mHealth articles related to SSA.

Figure 2.

PRISMA flow diagram for mHealth articles related to Europe.

For each of the selected articles, information was extracted on five different categories: the type of mHealth system; the goal of the mHealth system; the patient; the health issue; and the development stage of the mHealth system (cf. Table 2). The complete table of data extracted from each individual article can be obtained in the research data for SSA and Europe. An overview of the results is given in Table 3. Note that within one article sometimes more than one item per category was covered—for example, when the goal of the mHealth system is monitoring and informing—such that the numbers of the items within a category do not always add up to the total number of articles.

Table 3.

Overview of results of the analysis.

	Overview of results of qualitative analysis
	SSA	Europe
Articles included	86	297
mHealth system	App 34	160
	Wearable 4	67
	External sensor 8	70
	Implanted sensor 0	4
	SMS/call 40	10
	Videocall/chat 12	40
	Other 1	19
Goal	Monitoring 26	208
	Consultation/diagnosis 35	51
	Informing 20	42
	Reminder 17	8
	Motivation/empowerment 9	39
	Treatment 2	33
	Other 0	6
Patient	Any patient 66	228
	Child 16	7
	Young mother 16	6
	Woman 6	0
	Elderly 2	65
Health issue	HIV 19	5
	Pregnancy/birth (control)/child care 14	6
	CVD/HF 14	6
	Allergy 8	1
	Cancer 6	16
	Diabetes 4	22
	Alzheimer/cognitive function 3	11
	Chronic disease 1	26
	Physical rehabilitation 1	14
	Hearing 1	5
	COPD/asthma 0	15
	Obesity 0	7
	Stress/mental health 0	16
	Vision 0	7
	Pain 0	6
	Multiple 17	71
	Other 13	46
Development stage	Proposal/early development 22	134
	Test phase 38	91
	In practice 26	73

In the following sections, the relative results of each of the five categories are presented and discussed. Again, it should be noted that percentages of items within each category could add up to more than 100% since more than one item per category is possible for one mHealth system.

Type of mHealth systems

The first category of information extracted from the articles was the kind of mHealth system that was used. The relative number of results for each kind of mHealth system is presented in Figure 3.

Figure 3.

Type of mHealth system used in sub-Saharan Africa (SSA) and Europe.

Goal of mHealth systems

The second category of information extracted from the articles was the goal of the mHealth system. The relative number of results for each type of goal is presented in Figure 4.

Figure 4.

The goal of the mHealth system used in sub-Saharan Africa (SSA) and Europe.

The mHealth system's intended patient population

The third category of information extracted from the articles was the patient the mHealth system was used for, more specifically the age and gender of the patient. The relative number of results for each type of patient is presented in Figure 5.

Figure 5.

The mHealth system's intended patient population is in sub-Saharan Africa (SSA) and Europe.

When looking at the target patients of the mHealth systems, we see that for both regions about three-quarters of all mHealth systems were not targeted at a specific type of patient. However, it must be noted that while not specifically mentioned, some of these mHealth systems were in fact clearly targeted towards a specific group of patients, for example, when the tackled health issue occurs mostly in elderly people.

If we look at the other patient groups, we see a big difference between SSA and Europe. The patients of the remaining mHealth systems in SSA are mostly children and young mothers. In Europe the patients of the remaining mHealth systems are elderly. This clearly coincides with the respective demographic age structures and corresponding health issues of both populations. SSA has a very young population with very high child mortality. Europe has a very old population where aging is a major health issue.

The big difference between the two regions—that is, mHealth systems used in SSA focus more towards consultation or diagnosis; informing and reminding, and mHealth systems used in Europe focus more towards monitoring—is quite logical if we take a look at what health issues the mHealth systems are used for.

Health issues addressed by the mHealth systems

The fourth category of information extracted from the articles was the health issue tackled by the mHealth system. The relative number of results for each type of health issue is presented in Figure 6. The most common health issues that were tackled by the mHealth systems in SSA were HIV (22,1%) and pregnancy/birth (control)/child care (16,3%), followed by CVD/HF (10.5%), allergy (9.3%), cancer (7.0%), and diabetes (4.7%). The stated health issues coincide well with the major causes of mortality and disease burden as seen when we assessed the health status of SSA. However, mainly lower respiratory infections, enteric infections, and malaria, which are major causes of mortality and disease burden, are clearly missing in the reported health issues addressed by mHealth systems. However, the major causes of child death are not addressed very well. Currently, only 16.3% of the reported mHealth approaches were targeted toward pregnancy, birth, and child care, and none of the other mHealth approaches were specifically targeted toward pneumonia, diarrheal diseases, or malaria in SSA. The most common health issue that was tackled by the mHealth systems in Europe was CVD/HF (23.2%), followed by chronic diseases (8.7%), diabetes (7.4%), and cancer (5.4%). These health issues coincide very well with the major causes of mortality and disease burden in Europe.

Figure 6.

Health issues tackled by the mHealth system in sub-Saharan Africa (SSA) and Europe.

Development stage of the mHealth systems

The fifth and final category of information extracted from the articles was the development stage of the mHealth system. The relative number of results for each type of health issue is presented in Figure 7.

Figure 7.

The development stage of the mHealth system in sub-Saharan Africa (SSA) and Europe.

When comparing the development stage, we see that on average mHealth systems in SSA are further in the development stage than in Europe. This is explained by the used mHealth technologies.

Discussion

We see that for SSA the major kind of mHealth systems that are reported are apps and SMS or call-based systems. These results are quite logical if we look at what the mHealth systems are mostly used for (the goal of the mHealth system). Notably, the adoption of wearable mHealth systems is still quite low in SSA.

In Europe, the vast majority of mHealth systems are apps, followed by wearables and external sensors. Examples of external (wearable) sensors are weight scales, heart rate sensors, smart blood pressure devices, etc. These results are again very straightforward if we look at what the goal of these mHealth systems is and what health issues they are used for.

In both regions, we also see a substantial number of videocall or chat-based mHealth systems. Many of these can be accounted to the Covid-19 pandemic, where videocalls and chat were a welcome and necessary alternative to face-to-face contact between the patient and medical personnel.

In SSA, the main goal of the mHealth systems was consultation or diagnosis followed closely by monitoring, informing, and reminding. The kind of mHealth systems used for consultation/diagnosis, informing, and reminding were mainly SMS or call-based (34.3%; 60.0%; and 100%, respectively), hence explaining their large share as seen in the previous section. Monitoring is mostly done by app-based mHealth systems (61.5%).

In Europe, we see that almost 70% of mHealth systems are used for monitoring. The main goals identified through the analysis coherence given the health issues for which the mHealth systems are used in both regions. Monitoring in Europe is mainly done by apps (55.3%), wearables (29.8%), and external sensors (28.4%). Note that the percentages add up to more than 100% because several mHealth approaches used more than one system, for example, a mHealth approach with both an app and an external sensor.

The majority of mHealth systems did not mention a specific patient, but when they did, children (18.6%), young mothers (18.6%), and women (7.0%) were the most frequent. HIV (22.1%), pregnancy/(birth) control/child care (16.3%), CVD/HF (10.5%), allergy (9.3%), cancer (7.0%), and diabetes (4.7%) were the most common health issues addressed by mHealth systems in SSA. However, it must be noted that while not specifically mentioned, some of these mHealth systems were in fact clearly targeted towards a specific group of patients, for example, when the tackled health issue occurs mostly in elderly people. If we look at the other patient groups, we see a big difference between SSA and Europe. The patients of the remaining mHealth systems in SSA are mostly children and young mothers. In Europe, the patients of the remaining mHealth systems are elderly.

The difference in target health issues of mHealth between the two regions can be partly attributed to the difference in demographic structure. SSA has a very young population with high child mortality, hence the large share of pregnancy, birth, and childcare-related mHealth approaches. Europe has an old population, where aging is a big issue, hence the large share of age-related diseases—like CVD, HF, and cancer—targeted by mHealth systems.

In SSA a large amount of the mHealth systems is SMS based. This is an older technology, which is easily implemented. In Europe, more mHealth systems are used with newer technology, such as wearables and implantable sensors. Since there is less experience and know-how on the use and implementation of those novel technologies, the development process of mHealth will take longer.

The finding that mHealth systems in SSA did not address major causes of child mortality, lower respiratory infections, enteric infections, and malaria, while European mHealth focused on health issues related to major causes of mortality and disease burden in that setting, has significant implications. This discrepancy suggests that mHealth systems may not be effectively addressing the most pressing health issues in SSA, which could result in negative consequences for human health in the region.

Conclusions

The healthcare challenges faced by SSA and Europe are not equivalent. They may differ in terms of prevalence, severity, or other factors. mHealth can be a valuable tool for overcoming these challenges. By enabling remote monitoring, diagnosis, treatment, and informing patients with a variety of diseases, mHealth can aid medical professionals. In our examination of mHealth availability and use in SSA, we discovered that SMS/call-based systems (46.5%) and apps (39.5%) were the most common. The mHealth systems’ primary objectives were consultation/diagnosis (40.7%), monitoring (30.2%), informing (23.3%), and reminding (19.8%). When looking at the target patients of the mHealth systems, we see that for both regions about three-quarters of all mHealth systems were not targeted at a specific type of patient.

In the analysis of the use and availability of mHealth systems in Europe, we found that most used mHealth systems use apps (53.7%), followed by external sensors (23.5%) and wearables (22.5%) (the percentages add up to more than 100% because some mHealth system makes use of more than one technology, e.g., an app and an external sensor). The most common goal of the mHealth systems by far was monitoring (69.8%), followed by consultation/diagnosis (17.1%) and informing (14.1%). While the majority of mHealth systems did not mention a specific patient, in the ones that did the most common were elderly (21.8%). The most common health issues that were tackled by the mHealth systems in Europe were CVD/HF (23.2%), chronic diseases (8.7%), diabetes (7.4%), cancer (5.4%), stress/mental health (5.4%) and COPD/asthma (5.0%). In our qualitative analysis, we found that other studies reported very similar results. The stated health issues addressed by mHealth coincide very well with the major causes of mortality and disease burden in Europe.

The difference in health issues addressed by mHealth between the two regions can be partly attributed to their different demographic structure. SSA has a very young population with high child mortality, hence the large share of pregnancy, birth, and childcare-related mHealth approaches. Europe has a very old population, where aging is a big issue, hence the large share of age-related diseases—like CVD, HF, and cancer—targeted by mHealth. The recent COVID-19 pandemic increased the pressure on healthcare even further with setbacks on progress in immunization coverage, malaria, tuberculosis, vaccines, and claiming the lives of front-line healthcare workers around the world. In addition to these setbacks, COVID-19 and the need for social distancing demanded the use of remote healthcare solutions in many divisions of general healthcare.

It is important to explore the potential for mHealth systems to address these health issues in SSA and ensure that mHealth systems are designed and implemented effectively in these areas. Addressing these gaps could lead to better health outcomes and a reduction in child mortality rates in SSA. Countries should develop a clear policy framework and allocate sufficient budgets to address these issues, as mHealth should not be considered a luxury but a basic necessity. It is a cost-effective way to deliver healthcare services, and it can help to fight poverty by facilitating the effective and efficient use of resources.

The healthcare challenges in different regions require unique and tailored approaches and solutions. To improve the availability and utilization of mHealth systems, it is recommended to conduct context-specific studies that identify the determinants that affect the use of these systems. These factors should then be taken into account during the design phase of mHealth systems. This approach can help address the unique healthcare challenges faced by different regions and populations and ensure that mHealth systems are designed and implemented in a way that maximizes their potential impact.

Limitations of the study

The limitation of the study is the potential impact of non-standardized data collection and reporting practices across various regions and countries. Additionally, there may be a number of mHealth implementations that have not been documented or reported in scientific literature. Given that this study is based solely on secondary sources, it is possible that some important information may be overlooked. Moreover, differences in healthcare systems and cultural variances could influence the use and implementation of mHealth technologies across different regions, further complicating the investigation. Future works should aim at exploring the potential of qualitative research methods to gain a deeper understanding of the cultural and contextual factors that impact the implementation and use of mHealth technologies and provide a more comprehensive understanding of the implementation and use of mHealth technologies in different contexts.

Supplemental Material

sj-docx-1-dhj-10.1177_20552076231180972 - Supplemental material for mHealth in sub-Saharan Africa and Europe: A systematic review comparing the use and availability of mHealth approaches in sub-Saharan Africa and Europe

Supplemental material, sj-docx-1-dhj-10.1177_20552076231180972 for mHealth in sub-Saharan Africa and Europe: A systematic review comparing the use and availability of mHealth approaches in sub-Saharan Africa and Europe by Genet Tadese Aboye, Martijn Vande Walle, Gizeaddis Lamesgin Simegn and Jean-Marie Aerts in DIGITAL HEALTH

Supplemental Material

sj-xlsx-2-dhj-10.1177_20552076231180972 - Supplemental material for mHealth in sub-Saharan Africa and Europe: A systematic review comparing the use and availability of mHealth approaches in sub-Saharan Africa and Europe

Supplemental material, sj-xlsx-2-dhj-10.1177_20552076231180972 for mHealth in sub-Saharan Africa and Europe: A systematic review comparing the use and availability of mHealth approaches in sub-Saharan Africa and Europe by Genet Tadese Aboye, Martijn Vande Walle, Gizeaddis Lamesgin Simegn and Jean-Marie Aerts in DIGITAL HEALTH

Supplemental Material

sj-xlsx-3-dhj-10.1177_20552076231180972 - Supplemental material for mHealth in sub-Saharan Africa and Europe: A systematic review comparing the use and availability of mHealth approaches in sub-Saharan Africa and Europe

Supplemental material, sj-xlsx-3-dhj-10.1177_20552076231180972 for mHealth in sub-Saharan Africa and Europe: A systematic review comparing the use and availability of mHealth approaches in sub-Saharan Africa and Europe by Genet Tadese Aboye, Martijn Vande Walle, Gizeaddis Lamesgin Simegn and Jean-Marie Aerts in DIGITAL HEALTH

Footnotes

Acknowledgements

We would like to thank all individuals who directly or indirectly contributed to this research.

Contributorship

Genet Tadese Aboye co-conceived the study with Jean-Marie Aerts and Gizeaddis Lamesgin Simegn. Genet Tadese and Martijn Vande Walle designed and implemented the search strategy. The title and abstract screenings were performed by Genet Tadese Aboye and Martijn Vande Walle. Genet Tadese Aboye and Martijn Vande Walle both contributed to the data synthesis and extraction processes. Genet Tadese Aboye and Martijn Vande Walle drafted the manuscript, which was revised by all authors. Jean-Marie Aerts and Gizeaddis Lamesgin Simegn supervised the review process and critically revised the manuscript for significant intellectual content. At all stages of the manuscript's development, all authors discussed the findings and read and approved the final version.

Data availability

The data extracted from the included studies is available and is provided as an attachment.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethical approval

None.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Guarantor

GTA

Registration and protocol

The review was not registered.

ORCID iD

Genet Tadese Aboye

References

Källander

Tibenderana

Akpogheneta

, et al. Mobile health (mHealth) approaches and lessons for increased performance and retention of community health workers in low- and middle-income countries: a review. J Med Internet Res 2013 Jan 25; 15: e17–e17. https://pubmed.ncbi.nlm.nih.gov/23353680 . doi:10.2196/jmir.2130

Hossein

. Consideration of the relationship between ICT and eHealth. J Biol Agric Healthc 2015; 2: 49–59.

Emmanouilidou

. The status of mHealth in Europe and a review of regulative challenges. In: L. R, M. M (eds) 2016 International Conference on E-Health, EH 2016 [Internet]. Kent Business School, University of Kent, Canterbury, Kent, CT2 7NZ, United Kingdom: IADIS; 2016. p.203–6. https://www-scopus-com-s.web.bisu.edu.cn/inward/record.uri?eid=2-s2.0-85040195146&partnerID=40&md5=f9f80b4d6996c02bafb120b8f9d4a114.

Messner

Probst

O’Rourke

, et al. mHealth applications: potentials, limitations, current quality and future directions. In: Baumeister H and Montag C (eds) Digital phenotyping and mobile sensing: new developments in psychoinformatics. Ulm, Germany: Ulm Univ, Clin Psychol & Psychotherapy, 2019. pp.235–248.

Page

McKenzie

Bossuyt

, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews.

Dubner

Auricchio

Steinberg

, et al.. ISHNE/EHRA expert consensus on remote monitoring of cardiovascular implantable electronic devices (CIEDs). EHJ Arrhythmias and Electrophysiology ; February 2012; 14(2). https://doi.org/10.1093/europace/eur303

Lund

Nielsen

Hemed

, et al. Mobile phones improve antenatal care attendance in Zanzibar: a cluster randomized controlled trial. BMC Pregnancy Childbirth 2014; 14.

Villamil

Landinez

Lopez

, et al. A mobile ECG system for the evaluation of cardiovascular risk. In: Hoerbst A, Hackl WO, DeKeizer N, Prokosch HU, HercigonjaSzekeres M and DeLusignan S (eds) Exploring complexity in health: an interdisciplinary systems approach. 228. Popayan, Colombia: Univ Cauca, Telemat Engn Res Grp, 2016, pp.210–214.

Caso

Del Puente

Girolimetto

, et al. Improving telemedicine and in-person management of rheumatic and autoimmune diseases, during and after COVID-19 pandemic outbreak. Definite need for more rheumatologists. Response to: ‘Can telerheumatology improve rheumatic and musculoskeletal disease ser’. Ann Rheum Dis 2020 Jul.

10.

Siedner

Santorino

Lankowski

, et al. A combination SMS and transportation reimbursement intervention to improve HIV care following abnormal CD4 test results in rural Uganda: a prospective observational cohort study. BMC Med 2015; 13.

11.

Dafoulas

. Deploying integrated homecare telemedicine services in Central Greece. In Stone CS (eds) Vol. 49, Benchmarking telemedicine: improving health security in the Balkans. Trikala, Greece: E Trikala SA Municipal Trikala, 2017. pp.65–71.

12.

Brunetti

De Gennaro

Dellegrottaglie

, et al. A regional prehospital electrocardiogram network with a single telecardiology “hub” for public emergency medical service: technical requirements, logistics, manpower, and preliminary results. Telemed e-Health 2011; 17: 727–733.

13.

Tassorelli

Jensen

Allena

, et al. The added value of an electronic monitoring and alerting system in the management of medication-overuse headache: a controlled multicentre study. Cephalalgia 2017; 37: 1115–1125.

14.

van Lonkhuizen

PJC

Vegt

NJH

Meijer

, et al. Study protocol for the development of a European eHealth platform to improve quality of life in individuals with Huntington’s disease and their partners (HD-eHelp Study): A user-centered design approach. Front Neurol 2021; 12: 719460.

15.

Bautista-Mesa

Lopez-Villegas

Peiro

, et al. Long-term cost-utility analysis of remote monitoring of older patients with pacemakers: the PONIENTE study. BMC Geriatr 2020; 20.

16.

Quirós-González

Rubio

Pulido

, et al. Healthcare outcomes in patients with HIV infection at a tertiary hospital during the COVID-19 pandemic. Enfermedades Infecc y Microbiol Clin (English ed) 2021 Aug.

17.

Ribu

Holmen

Torbjørnsen

, et al. Low-intensity self-management intervention for persons with type 2 diabetes using a mobile phone-based diabetes diary, with and without health counseling and motivational interviewing: protocol for a randomized controlled trial. JMIR Res Protoc 2013 Aug; 2: e34.

18.

Morgan

Walker

Melaas

, et al. Tele-orthopaedics: United States Army European Regional Medical Command. Stud Health Technol Inform 2012; 173: 294–296.

19.

Karari

Tittle

Penner

, et al. Evaluatinuptake, acceptability, and effectiveness of Uliza! Clinicians’ HIV hotline: a telephone consultation service vice in Kenya. Telemed e-Health 2011; 17: 420–426.

20.

Cullington

Kitterick

DeBold

, et al. Personalised long-term follow-up of cochlear implant patients using remote care, compared with those on the standard care pathway: study protocol for a feasibility randomised controlled trial. BMJ Open 2016; 6.

21.

Anderberg

Barnestein-Fonseca

Guzman-Parra

, et al. The effects of the digital platform support monitoring and reminder technology for mild dementia (SMART4MD) for people with mild cognitive impairment and their informal carers: Protocol for a pilot randomized controlled trial. JMIR Res Protoc 2019 Jun; 8: e13711.

22.

Blank

Tuikong

Misoi

, et al. Usability of implementing a tablet-based decision support and integrated record-keeping (DESIRE) tool in the nurse management of hypertension in rural Kenya [Internet]. Vol. 192, 14th World Congress on Medical and Health Informatics, MEDINFO 2013. Mount Sinai School of Medicine, New York, NY, United States: IOS Press; 2013. p.1002.

23.

Kulin

Antali

Kulin

, et al. Preclinical, multi-aspect assessment of the reliability of a photoplethysmography-based telemonitoring system to track cardiovascular status. Appl Sci 2020; 10.

24.

Georgi

Corvol

Le Bouquin Jeannès

. For a more reliable measure of wrist blood pressure using smartwatch. Telemed J e-Health 2019 Sep; 25: 862–866.

25.

Tschanz

Dorner

Holm

, et al. Using eMMA to manage medication. Computer (Long Beach Calif) 2018; 51: 18–25.

26.

Olayiwola

Udenyi

Yusuf

, et al. Leveraging electronic consultations to address severe subspecialty care access gaps in Nigeria. J Natl Med Assoc 2020 Feb; 112: 97–102.

27.

Papageorgiou

Maimaris

Ness

. A value creation analysis of implementing an intelligent active mobility system for the elderly market. In: 2019 3rd European conference on electrical engineering and computer science (EECS), 2019, pp.1–5.

28.

Bibault

J-E

Denis

Guihard

, et al. New concepts of medical consultation in oncology. Cancer Radiother 2020; 24: 751–754.

29.

Laing

Remmelzwaal

Cooper

, et al. Mobile telephone follow-up to ascertain birth outcomes in the Gambia. Telemed J e-health Off J Am Telemed Assoc 2020 Nov; 26: 1363–1367.

30.

Perings

Klein

Toft

, et al. The RIONI study rationale and design: validation of the first stored electrograms transmitted via home monitoring in patients with implantable defibrillators. Europace 2006 Apr; 8: 288–292.

31.

Schoot

Weenk

Van De Belt

, et al. A new cuffless device for measuring blood pressure: a real-life validation study. J Med Internet Res 2016; 18.

32.

Delgado

Rodriguez-Alsina

Gurgui

, et al. Enhancing accessibility through speech technologies on AAL telemedicine services for iTV. In Keyson DV, Maher ML, Streitz N, Cheok A, Augusto JC, Wichert R, et al. (eds) Vol. 7040, Ambient Intelligence. Ctr Ambient Intelligence & Accessibil Catalonia, Bellaterra 08193, Spain NR – 12 PU – Springer-Verlag Berlin PI - Berlin PA – Heidelberger PLATZ 3, D-14197 Berlin, Germany, 2011. pp.300–3008.

33.

Logie

Okumu

Hakiza

, et al. Mobile health-supported HIV self-testing strategy among urban refugee and displaced youth in Kampala, Uganda: protocol for a cluster randomized trial (Tushirikiane, supporting each other). JMIR Res Protoc 2021 Feb; 10: e26192–e26192.

34.

Faye

Bagayoko

Dicko

, et al. A teledermatology pilot programme for the management of skin diseases in primary health care centres: experiences from a resource-limited country (Mali, West Africa). Trop Med Infect Dis 2018; 3.

35.

Budai

Lengyel

Vajda

, et al. Monitoring health status of relatives—“The smoke in the chimney” approach. In: 2015 38th international conference on telecommunications and signal processing (TSP), 2015, pp.601–606.

36.

Sánchez-Tato

Senciales

Salinas

, et al. Health @ Home: a telecare system for patients with chronic heart failure. In: 2010 fifth international conference on broadband and biomedical communications, 2010, pp.1–5.

37.

Akinseinde

Badejo

Malgwi

. GRAVID: an indigenous m-health tool for smart and connected communities. In: 2016 future technologies conference, FTC 2016 [Internet]. Department of Electrical and Information Engineering, Covenant University, Ota, Nigeria: Institute of Electrical and Electronics Engineers Inc., 2017, pp.1331–1334.

38.

Ahmad

Komninos

Baillie

. Future mobile health systems: designing personal mobile applications to assist self-diagnosis. In: 22nd British HCI group annual conference on people and computers: culture, creativity, interaction, BCS HCI 2008 [Internet]. Glasgow Caledonian University, 70 Cowcaddens Rd., Glasgow, G4 0BA, United Kingdom: British Computer Society, 2008, pp.39–42.

39.

Hindricks

Taborsky

Glikson

, et al. Implant-based multiparameter telemonitoring of patients with heart failure (IN-TIME): a randomised controlled trial. Lancet (London, England) 2014 Aug; 384: 583–590.

40.

de Batlle

Massip

Vargiu

, et al. Implementing mobile health-enabled integrated care for complex chronic patients: patients and professionals’ acceptability study. JMIR mHealth uHealth 2020; 8.

41.

Schweitzer

Huber

Gorfer

, et al. An approach for dynamic vital parameter monitoring—prototype development. Stud Health Technol Inform 2018; 251: 47–50.

42.

Sarma

Nemser

Cole-Lewis

, et al. Effectiveness of SMS technology on timely community health worker follow-up for childhood malnutrition: a retrospective cohort study in sub-Saharan Africa. Glob Heal Sci Pract 2018; 6: 343–353.

43.

Miettinen

Tenhunen

. Digital follow-up application for cancer patients - value mechanisms regarding health professionals’ work. PapeHaugaard LB, Lovis C, Madsen IC, Weber P, Nielsen PH, Scott P (eds) Vol. 270, Digital personalized health and medicine. Aalto Univ, Inst Healthcare Engn Management & Architecture, HEMA Inst, Dept Ind Engn & Management, Espoo, Finland NR - 12 PU - IOS PRESS PI - Amsterdam PA - Nieuwe Hemweg 6B, 1013 BG Amsterdam, Netherlands, 2020, pp.572–576.

44.

Sendler

Pardo

. Pre-clinical and post-clinical process of committing to e-psychiatric treatment—an evaluation of patient perspective with emphasis on designing cost-effective longitudinal treatment. mHealth 2019; 5: 29.

45.

Angermann

Rosenkranz

. Telemonitoring and pulmonary artery pressure-guided treatment of heart failure. Internist (Berl) 2018; 59: 1041–1053.

46.

Scase

Kreiner

Ascolese

. Development and evaluation of cognitive games to promote health and wellbeing in elderly people with mild cognitive impairment [Internet]. G. S, D. H (eds) Vol. 248, 12th annual conference on health informatics meets eHealth, eHealth 2018. Division of Psychology, De Montfort University, Gateway, Leicester, LE1 9BH, United Kingdom: IOS Press BV, 2018, pp.255–262.

47.

Ejupi

Brodie

Lord

, et al. Wavelet-based sit-to-stand detection and assessment of fall risk in older people using a wearable pendant device. IEEE Trans Biomed Eng 2017 Jul; 64: 1602–1607.

48.

Villalba

Arredondo

Moreno

, et al. User interaction design and development of a heart failure management system based on wearable and information technologies. Conf Proc. Annu Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc Annu Conf. 2006; 2006: 400–403.

49.

van der Zander

QEW

Reumkens

van de Valk

, et al. Effects of a personalized smartphone app on bowel preparation quality: randomized controlled trial. JMIR mHealth uHealth 2021 Aug; 9: e26703.

50.

Serrano

Holthe

. Development and trial of ePoint.telemed - An open web-based platform for home monitoring of chronic heart failure patients. In Cornet R, StoicuTivadar L, Horbst A, Calderon CLP, Andersen SK, HercigonjaSzekeres M (eds) Vol. 210, Digital healthcare empowering Europeans. Univ Hosp North Norway, NST Norwegian Ctr Integrated Care & Telemed, Tromso, Norway, 2015, pp.311–315.

51.

Frederix

Sankaran

Coninx

, et al. MobileHeart, a mobile smartphone-based application that supports and monitors coronary artery disease patients during rehabilitation. In: 2016 38th annual international conference of the IEEE engineering in medicine and biology society (EMBC), 2016, pp.513–516.

52.

Boatin

Ngonzi

Wylie

, et al. Wireless versus routine physiologic monitoring after cesarean delivery to reduce maternal morbidity and mortality in a resource-limited setting: protocol of type 2 hybrid effectiveness-implementation study. BMC Pregnancy Childbirth 2021; 21.

53.

Pinna

Maestri

Roma

, et al. Home telemonitoring of chronic heart failure patients: novel system architecture of the home or hospital in heart failure study. In: Computers in Cardiology, 2003; 2003, pp.105–108.

54.

Gerdes

Smaradottir

Reichert

, et al. Telemedicine and cooperative remote healthcare services: COPD field trial. Stud Health Technol Inform 2015; 210: 455–457.

55.

Larkai

. Wireless heart rate monitor in personal emergency response system. In: Stamenkovic Z, Pleskacz W, Raik J, Verhaus HT (eds) 2015 IEEE 18th international symposium on design and diagnostics of electronic circuits & systems (DDECS 2015). Univ Greenwich, Sch Engn, Chatham, Kent, England NR - 8 PU - IEEE PI - New York PA - 345 E 47TH ST, New York, NY 10017, USA, 2015, pp.299–300.

56.

van Kalsbeek

van der Pal

HJH

Hjorth

, et al. The European multistakeholder PanCareFollowUp project: novel, person-centred survivorship care to improve care quality, effectiveness, cost-effectiveness and accessibility for cancer survivors and caregivers. Eur J Cancer [Internet] 2021; 153: 74–85.

57.

Garabedian

Ross-Degnan

LeCates

, et al. Uptake and use of a diabetes management program with a mobile glucometer. Prim Care Diabetes 2019; 13: 549–555.

58.

Lessard

Aslan

Zeggagh

, et al. Acceptability of a digital patient notification and linkage-to-care tool for French PrEPers (WeFLASH©): key stakeholders’ perspectives. Int J Std AIDS 2019; 30: 1397–1407.

59.

Panayides

Antoniou

Barberis

, et al. Abdominal Aortic Aneurysm medical video transmission. In: Proceedings of 2012 IEEE-EMBS international conference on biomedical and health informatics, 2012. pp.679–682.

60.

Ovbiagele

. Phone-based intervention under nurse guidance after stroke: concept for lowering blood pressure after stroke in sub-Saharan Africa. J Stroke Cerebrovasc Dis Off J Natl Stroke Assoc 2015 Jan; 24: 1–9.

61.

Leal-Costa

Lopez-Villegas

Catalan-Matamoros

, et al. Long-term socioeconomic impact of informal care provided to patients with pacemakers: remote vs. conventional monitoring. Healthcare 2020; 8.

62.

Torkamani

McDonald

Saez Aguayo

, et al. A randomized controlled pilot study to evaluate a technology platform for the assisted living of people with dementia and their carers. J Alzheimers Dis 2014; 41: 515–523.

63.

Moreno

Garcia-Pacheco

Charvill

, et al. iCarer: AAL for the informal carers of the elderly. Stud Health Technol Inform 2015; 210: 678–680.

64.

Luzio

Hatcher

Zahlmann

, et al. Feasibility of using the TOSCA telescreening procedures for diabetic retinopathy. Diabet Med 2004; 21: 1121–1128.

65.

Martin

Caon

Adorni

, et al. A mobile phone intervention to improve obesity-related health behaviors of adolescents across Europe: iterative co-design and feasibility study. JMIR mHealth uHealth 2020; 8: e14118.

66.

Odeny

Hughes

Bukusi

, et al. Text messaging for maternal and infant retention in prevention of mother-to-child HIV transmission services: a pragmatic stepped-wedge cluster-randomized trial in Kenya. PLOS Med 2019; 16.

67.

Essén

. The two facets of electronic care surveillance: an exploration of the views of older people who live with monitoring devices. Soc Sci Med 2008; 67: 128–136.

68.

Kouis

Papatheodorou

Kakkoura

, et al. The MEDEA childhood asthma study design for mitigation of desert dust health effects: implementation of novel methods for assessment of air pollution exposure and lessons learned. BMC Pediatr 2021 Jan; 21: 13.

69.

Griebel

Hinderer

Amr

, et al. The patient as genomic data manager - evaluation of the PROMISE App. In PapeHaugaard LB, Lovis C, Madsen IC, Weber P, Nielsen PH, Scott P (eds) Vol. 270, Digital personalized health and medicine. Erlangen, Germany: Friedrich Alexander Univ Erlangen Nurnberg, Chair Med Informat, 2020, pp. 1061–1065.

70.

Busnatu

Ion

Raileanu

, et al. The role of mHealth technology in the secondary prevention of cardiovascular disease. Rom Biotechnol Lett 2019; 24: 236–244.

71.

Hughes

CML

Louie

Sun

, et al. Development of a post-stroke upper limb rehabilitation wearable sensor for use in sub-Saharan Africa: a pilot validation study. Front Bioeng Biotechnol 2019; 7.

72.

Gortzis

Papadopoulos

Roelofs

, et al. Collaborative work during interventional radiological procedures based on a multicast satellite-terrestrial network. IEEE Trans Inf Technol Biomed 2007 Sep; 11: 597–599.

73.

Mupela

Mustarde

Jones

HLC

. Telemedicine in primary health: the virtual doctor project Zambia. Philos Ethics Humanit Med 2011 May; 6.

74.

Spanakis

Sfakianakis

Kallergis

, et al. PharmActa: personalized pharmaceutical care eHealth platform for patients and pharmacists. J Biomed Inform 2019 Dec; 100: 103336.

75.

Collado-Borrell

Escudero-Vilaplana

Ribed

, et al. Effect of a mobile app for the pharmacotherapeutic follow-up of patients with cancer on their health outcomes: Quasi-experimental study. JMIR mHealth uHealth 2020; 8.

76.

Garyga

Pochelu

Thivichon-Prince

, et al. GoPerio – impact of a personalized video and an automated two-way text-messaging system in oral hygiene motivation: Study protocol for a randomized controlled trial. Trials 2019 Dec; 20: 699.

77.

Vis

Kleiboer

Prior

, et al. Implementing and up-scaling evidence-based eMental health in Europe: The study protocol for the MasterMind project. Internet Interv 2015; 2: 399–409.

78.

Amavel

Cruz-Correia

Frias-Bulhosa

. Remote diagnosis of children dental problems based on non-invasive photographs - a valid proceeding? In: Adlassnig KP, Blobel B, Mantas J and Masic I (eds) Vol. 150, Medical informatics in a united and healthy Europe. Cintesis FMUP, Al Prof Hernani Monteiro, P-4200319 Oporto, Portugal NR - 7 PU - IOS Press PI - Amsterdam PA - NIEUWE HEMWEG 6B, 1013 BG Amsterdam, Netherlands, 2009, pp.458–462.

79.

Williamson

Chalmers

Beaver

. Patient experiences of nurse-led telephone follow-up following treatment for colorectal cancer. Eur J Oncol Nurs 2015 Jun; 19: 237–243.

80.

Gouse

Robbins

Mellins

, et al. Empowering Lay-counsellors with technology: Masivukeni, a standardized multimedia counselling support tool to deliver art counselling. AIDS Behav [Internet] 2018; 22: 3345–3356.

81.

Deng

Burkle

Holm

, et al. Last mile towards efficient healthcare delivery in Switzerland: eHealth enabled applications could speed up the care process. In Ugon A, Karlsson D, Klein GO and Moen A (eds) Vol. 247, Building continents of knowledge in oceans of data: the future of co-created eHealth. Bern, Switzerland: Bern Univ Appl Sci Switzerland, Inst Med Informat I4MI, 2018, pp.251–5.

82.

Saboia

Almeida

Pernencar

, et al. Gamification strategies in weight control applications, where “losing (weight) is winning.” In: 2018 IEEE 6th international conference on serious games and applications for health (SeGAH), 2018, pp.1–8.

83.

Ybarra

Agaba

Chen

, et al. Iterative development of in this together, the first mHealth HIV prevention program for older adolescents in Uganda. AIDS Behav 2020 Aug; 24: 2355–2368.

84.

Haywood

Brownsell

Hawley

, et al. Development of an integrated telehealth system to facilitate self management of long term conditions. In Gelderblom GJ, Soede M, Adriaens L and Miesenberger K (eds) Vol. 29, Everyday technology for independence and care. Univ Sheffield, ScHARR, Sheffield S10 2TN, S Yorkshire, England FU - EPSRCUK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC) [EP/F001835] FX - We are grateful to EPSRC Grant EP/F001835 for funding the work this paper, 2011, pp.1051–1057.

85.

Oberholzer

Banach

Haroske

, et al. Telemedicine worldwide - Two years of experience in telediagnostic and teleteaching with iPath. In Vizjak A, Ferluga D and Bussolati G (eds) Update in pathology, proceedings. Univ Basel, Dept Pathol, Basel, Switzerland NR - 7 PU - UNIV LJUBLJANA, PI - LJUBLJANA PA - Faculty Medicine, Inst Pathology, Ljubljana, 00000, Slovenia, 2003, pp.445–447.

86.

Bellazzi

Arcelloni

Bensa

, et al. Design, methods, and evaluation directions of a multi-access service for the management of diabetes mellitus patients. Diabetes Technol Ther 2003; 5: 621–629.

87.

Prescott

Meindersma

van der Velde

, et al. A European study on effectiveness and sustainability of current Cardiac Rehabilitation programmes in the elderly: design of the EU-CaRE randomised controlled trial. Eur J Prev Cardiol 2016; 23: 27–40.

88.

Buonaguro

Botti

Ascierto

, et al. The clinical and translational research activities at the INT – IRCCS “Fondazione Pascale” cancer center (Naples, Italy) during the COVID-19 pandemic. Infect Agent Cancer 2020; 15.

89.

Ramos

Rodriguez

Martinez-Beneyto

, et al. Use of telemedicine in the remote programming of cochlear implants. Acta Otolaryngol 2009; 129: 533–540.

90.

Hutchison

Breckon

. A review of telephone coaching services for people with long-term conditions. J Telemed Telecare 2011; 17: 451–458.

91.

Al-Hussain

. Principles and practice of thrombolysis via telestroke. Neurosciences (Riyadh) 2014 Jul; 19: 178–182.

92.

Lepage

Létourneau

Hamel

, et al. From teletraining to telehomecare—design of mobile and multi-stream telehealth systems. In: 2014 global information infrastructure and networking symposium (GIIS), 2014, pp.1–4.

93.

Griggs

Andronikou

Nell

, et al. World Federation of Pediatric Imaging (WFPI) volunteer outreach through tele-reading: the pilot project in South Africa. Pediatr Radiol 2014; 44: 648–54.

94.

Steward

Agnew

de Kadt

, et al. Impact of SMS and peer navigation on retention in HIV care among adults in South Africa: results of a three-arm cluster randomized controlled trial. J Int AIDS Soc 2021; 24.

95.

Ruton

Musabyimana

Gaju

, et al. The impact of an mHealth monitoring system on health care utilization by mothers and children: an evaluation using routine health information in Rwanda. Health Policy Plan 2018 Oct; 33: 920–927.

96.

Surtel

Maciejewski

Maciejewska

. IEEE. Processing circulatory of simultaneous biomedical signal data in system conditions diagnosis using mobile sensors during patient activity. 2013 signal processing: algorithms, architectures, arrangements, and applications (SPA). Lublin Univ Technol, Inst Elect, Nadbystrzycka 38a, PL-20618 Lublin, Poland, 2013, pp.163–167.

97.

Coetzer

. Application of HCI design principles in overcoming information illiteracy: Case of an M-health application for a rural community in South Africa. In: 2018 international conference on intelligent and innovative computing applications, ICONIC 2018 [Internet]. Central University of Technology, Bloemfontein, South Africa: Institute of Electrical and Electronics Engineers Inc., 2019.

98.

Doarn

Latifi

Poropatich

, et al. Development and validation of telemedicine for disaster response: the North Atlantic treaty organization multinational system. Telemed J E Health 2018 Sep; 24: 657–668.

99.

Kock

Kaya

Muller

, et al. A mobile application to manage and minimise the risk of late effects caused by childhood cancer. In: Cornet R, StoicuTivadar L, Horbst A, Calderon CLP, Andersen SK and HercigonjaSzekeres M (eds) Vol. 210, Digital healthcare empowering Europeans. Lubeck, Germany: Univ Lubeck, Inst Med Informat, 2015, pp.798–802.

100.

Fioravanti

Fico

Patón

, et al. mHealth-integrated system paradigm: diabetes management. In: Handbook of biomedical telemetry [Internet]. Life Supporting Technologies, Universidad Politécnica de Madrid, Madrid, Spain: Wiley-IEEE Press, 2014, pp.623–632.

101.

Fotso

Bellhouse

Vesel

, et al. Strengthening the home-to-facility continuum of newborn and child health care through mHealth: evidence from an intervention in rural Malawi. Etude la Popul Africaine 2015; 29: 1663–1682.

102.

Jones

Gay

Leijdekkers

. Body sensor networks for mobile health monitoring experience in Europe and Australia. In: Berntzen L, Bodendorf F, Lawrence E, Perry M and Smedberg A (eds) Fourth international conference on digital society: ICDS 2010, Proceedings. Univ Twente, EEMCS, POB 217, NL-7500 AE Enschede, Netherlands, 2010, pp.204–209.

103.

Riva

Banos

Botella

, et al. Personal health systems for mental health: the European projects. Stud Health Technol Inform 2011; 163: 496–502.

104.

Manyati

Mutsau

. Exploring the effectiveness of telehealth interventions for diagnosis, contact tracing and care of coronavirus disease of 2019 (COVID-19) patients in sub-Saharan Africa: a rapid review. Health Technol (Berl) 2021 Feb: 1–8.

105.

Vélez

Okyere

Kanter

, et al. A usability study of a mobile health application for rural Ghanaian midwives. J Midwifery Womens Health 2014; 59: 184–191.

106.

Aristomenopoulos

Fontana

Vatteroni

, et al. Remote management of left ventricular device assisted patients. In: 2014 4th international conference on wireless mobile communication and healthcare - transforming healthcare through innovations in mobile and wireless technologies (MOBIHEALTH), 2014, pp.59–62.

107.

Brodie

MAD

Coppens

MJM

Lord

, et al. Wearable pendant device monitoring using new wavelet-based methods shows daily life and laboratory gaits are different. Med Biol Eng Comput 2016; 54: 663–674.

108.

Shalan

Abdulrahman

Habli

, et al. YORwalK: designing a smartphone exercise application for people with intermittent claudication. In: Ugon A, Karlsson D, Klein GO and Moen A (eds) Vol. 247, Building continents of knowledge in Oceans of Data: the future of Co-created eHealth. York, N Yorkshire, England: York Teaching Hosp, 2018, pp.311–315.

109.

González Ramallo

. Outpatient intravenous antimicrobial therapy. Salud(I)Ciencia 2013; 20: 171–178.

110.

Kang

Park

. Development of hypertension management mobile application based on clinical practice guidelines. In: Cornet R, StoicuTivadar L, Horbst A, Calderon CLP, Andersen SK and HercigonjaSzekeres M (eds) Vol. 210, Digital healthcare empowering Europeans. Seoul Natl Univ, Coll Nursing & Syst Biomed Informat Res Ctr, Seoul, South Korea FU - National Research Foundation of Korea (NRF) - Korea government (MSIP) [2010-0028631] FX - This work was supported by the National Research Foundation of Korea (NRF) gran, 2015, pp.602–606.

111.

Mattila

PÄrkkÄ

Hermersdorf

, et al. Mobile diary for wellness management—results on usage and usability in two user studies. IEEE Trans Inf Technol Biomed 2008; 12: 501–512.

112.

Ameyaw

Ribera

Anim-Sampong

. Interregional newborn hearing screening via telehealth in Ghana. J Am Acad Audiol 2019; 30: 178–186.

113.

Eberl

Biskup

Reckwitz

, et al. The televisit system in patients care after discharge in clinical use–first experiences. Biomed Tech (Berl) 2005; 50: 132–136.

114.

Puigdomenech

Martin

Lang

, et al. Promoting healthy teenage behaviour across three European countries through the use of a novel smartphone technology platform, PEGASO fit for future: study protocol of a quasi-experimental, controlled, multi-Centre trial. BMC Med Inform Decis Mak 2019 Dec; 19: 278.

115.

Yamada

Shinkawa

Takase

, et al. Monitoring daily physical conditions of older adults using acoustic features: a preliminary result. In: Ugon A, Karlsson D, Klein GO, Moen A (eds) Vol. 247, Building Continents of Knowledge in Oceans of Data: the Future of Co-created eHealth. IBM Res Tokyo, Tokyo, Japan FU - Japan Science and Technology Agency (JST) under the Strategic Promotion of Innovative Research and Development Program FX - This research was supported by the Japan Science and Technology Agency (JST) under the Strategic; 2018. pp.301–305.

116.

Maglaveras

. Citizen health system: telehealth homecare. Stud Health Technol Inform 2003; 92: 117–125.

117.

Audebert

Boy

Jankovits

, et al.

Is mobile teleconsulting equivalent to hospital-based telestroke services?

Stroke 2008; 39: 3427–3430.

118.

Solano

Kim

Christiansen

, et al. Asynchronous telemedicine with ultrasound: improving maternal health in developing countries. In: 2009 IEEE international ultrasonics symposium, 2009, pp.2316–2319.

119.

Epstein

Petersiel

Klein

, et al. Pocket-size point-of-care ultrasound in rural Uganda – a unique opportunity “to see” where no imaging facilities are available. Travel Med Infect Dis 2018; 23: 87–93.

120.

Foerster

Anele

Adisa

, et al. Few losses to follow-up in a Sub-Saharan African cancer cohort via active mobile health follow-up. Am J Epidemiol 2020; 189: 1185–1196.

121.

Kotecha

Chua

WWL

Fabritz

, et al. European Society of Cardiology smartphone and tablet applications for patients with atrial fibrillation and their health care providers. Europace 2018 Feb; 20: 225–233.

122.

Goodman-Casanova

Guzmán-Parra

Guerrero

, et al. TV-based assistive integrated service to support European adults living with mild dementia or mild cognitive impairment (TV-AssistDem): study protocol for a multicentre randomized controlled trial. BMC Geriatr 2019 Sep; 19: 247.

123.

Ostermann

Vasudevan

Baumgartner

, et al. Do mobile phone-based reminders and conditional financial transfers improve the timeliness of childhood vaccinations in Tanzania? Study protocol for a quasi-randomized controlled trial. Trials 2019 Jul; 20: 397.

124.

Stoicu-Tivadar

Sicurello

, et al. A bridge between projects: interoperability with general practitioners and hospitals. In: Reichert A, Mihalas G, StoicusTivadar L, Schulz S and Engelbrecht R (eds) Integrating biomedical information: from E-cell to e-Patient. Fac Automat & Comp, V Parvan Bul 2, Timisoara 300223, Romania, 2006, p.64–+.

125.

Frydrysiak

Tesiorowski

. Health monitoring system for protecting elderly people. In: 2016 International multidisciplinary conference on computer and energy science (SpliTech), 2016, pp.1–6.

126.

Ball

Newbould

Corbett

, et al. Qualitative study of patient views on a “telephone-first” approach in general practice in England: speaking to the GP by telephone before making face-to-face appointments. BMJ Open 2018 Dec; 8: e026197.

127.

Stoicu-Tivadar

Sicurello

Stoicutivadar

, et al. Teleconsultations as a step towards hospital interoperability. In: Hasman A, Haux R, VanderLei J, DeClercq E and France FHR (eds) Vol. 124, Ubiquity: technologies for better health in aging societies. Timisoara, Romania: Univ Politehnica, 2006, p.455–+.

128.

Maguire

Fox

McCann

, et al. The eSMART study protocol: a randomised controlled trial to evaluate electronic symptom management using the advanced symptom management system (ASyMS) remote technology for patients with cancer. BMJ Open 2017 Jun; 7: e015016.

129.

Nichols

Singh

Sarfo

, et al. Post-intervention qualitative assessment of mobile health technology to manage hypertension among Ghanaian stroke survivors. J Neurol Sci 2019 Nov; 406: 116462.

130.

Scase

Marandure

Hancox

, et al. Development of and adherence to a computer-based gamified environment designed to promote health and wellbeing in older people with mild cognitive impairment [Internet]. In: D. H, G. S (eds) Vol. 236, 11th annual conference on health informatics meets eHealth, eHealth 2017. Division of Psychology, De Montfort University, Leicester, LE1 9BH, United Kingdom: IOS Press, 2017, pp.348–355.

131.

Molnar

Weerakkody

. Clinicians’ view of tele-glaucoma. Heal Policy Technol 2016; 5: 123–130.

132.

Manning

BRM

McCann

Benton

, et al. Active ageing: independence through technology assisted health optimisation. Stud Health Technol Inform 2008; 137: 257–262.

133.

Cain

Cornfeld

Waibel

, et al. Military medicine implements in-home virtual health in Europe. US Army Med Dep J 2018: 59–64.

134.

Newbould

Abel

Ball

, et al. Evaluation of telephone first approach to demand management in English general practice: observational study. Br Med J 2017 Sep; 358: j4197.

135.

Francis-Lyon

Attiga

Manjunath

, et al. Tablet app for child cognitive assessment in low and middle income countries. In: 2017 IEEE global humanitarian technology conference (GHTC). 2017. pp.1–5.

136.

Reichmuth

Schürle

Magno

. A non-invasive wearable bioimpedance system to wirelessly monitor bladder filling. In: 2020 design, automation & test in Europe conference & exhibition (DATE), 2020, pp.338–341.

137.

Wrzosek

Zimmermann

Balwicki

. A survey of patients’ opinions and preferences on the use of e-prescriptions in Poland. Int J Environ Res Public Health 2021; 18.

138.

Castro-Sanchez

Matarán-Peñarrocha

Gómez-García

, et al. Study protocol randomised controlled trial comparison of cost-utility and cost-effectiveness of a face-to-face rehabilitation programme versus a telemedicine programme in the treatment of patients with chronic low back pain. BMJ Open 2020 Dec; 10: e040633.

139.

Bedra

Wick

Brotman

, et al. Avatar-based interactive ileostomy education in hospitalized patients. In: International conference on informatics, management, and technology in healthcare, ICIMTH 2013 [Internet]. Johns Hopkins University, School of Medicine, Baltimore, MD, United States: IOS Press, 2013, pp.83–85.

140.

Paratore

. Designing an assisted self-help mobile app to cope with panic disorder: Preliminary results and open issues [Internet]. In: M. K (eds) Vol. 12183 LNCS, thematic area on human computer interaction, HCI 2020, held as part of the 22nd international conference on human–computer interaction, HCII 2020. Istituto di Informatica e Telematica – CNR, Via Moruzzi 1, Pisa, 56100, Italy: Springer, 2020, pp.103–116.

141.

Buchem

Merceron

Kreutel

, et al. Wearable enhanced learning for healthy ageing: conceptual framework and architecture of the “Fitness MOOC”. Interact Des Archit 2015; 24: 111–124.

142.

Suchy

Massen

Rognmo

, et al. Optimising exercise training in prevention and treatment of diastolic heart failure (OptimEx-CLIN): rationale and design of a prospective, randomised, controlled trial. Eur J Prev Cardiol 2014 Nov; 21: 18–25.

143.

Scali

de Azevedo Bellagamba

Ciampi

, et al. Stress echocardiography with smartphone: real-time remote reading for regional wall motion. Int J Cardiovasc Imaging 2017 Nov; 33: 1731–1736.

144.

Shenoi

Hayward

Curran

, et al. Telemedicine in pediatric rheumatology: this is the time for the community to embrace a new way of clinical practice. Pediatr Rheumatol Online J 2020; 18: 85.

145.

Wolfel

Schupfer

Konrad

, et al. Telemedicine in the management of diving accidents: correlation of phone-assessed symptom severity with clinical findings. Diving Hyperb Med 2011; 41: 189–194.

146.

Buzzacchino

Lanzola

Bossi

, et al. A mobile application supporting outpatient treatment and follow-up. In: Cornet R, StoicuTivadar L, Horbst A, Calderon CLP, Andersen SK and HercigonjaSzekeres M (eds) Vol. 210, Digital healthcare empowering Europeans. Univ Pavia, Dept Elect Comp & Biomed Engn, Pavia, Italy, 2015, pp.788–792.

147.

Bradway

Pfuhl

Joakimsen

, et al. Analysing mHealth usage logs in RCTs: explaining participants’ interactions with type 2 diabetes self-management tools. PLoS One 2018; 13.

148.

Cleland

JGF

Louis

Rigby

, et al. Noninvasive home telemonitoring for patients with heart failure at high risk of recurrent admission and death: the trans-European network-home-care management system (TEN-HMS) study. J Am Coll Cardiol 2005; 45: 1654–1664.

149.

Arrossi

Paolino

Orellana

, et al. Mixed-methods approach to evaluate an mHealth intervention to increase adherence to triage of human papillomavirus-positive women who have performed self-collection (the ATICA study): study protocol for a hybrid type I cluster randomized effectiveness-imp. Trials 2019; 20.

150.

Casacci

Pistoia

Borrelli

. The diabesity care project: diabetes integrated monitoring system for self-care empowering [Internet]. Vol. 544, Lecture Notes in Electrical Engineering. Liferesult Srl, Via Stefano de Stefano 23, Foggia, FG 71121, Italy: Springer Verlag, 2018, pp.207–219.

151.

Rowe

Duta

Demeyere

, et al. Validation of Oxford Cognitive Screen: Executive Function (OCS-EF), a tablet-based executive function assessment tool amongst adolescent females in rural South Africa. Int J Psychol 2021.

152.

Davey

Hares

Ponce

, et al. Evaluating SMS reminders in improving art and PMTCT adherence in Mozambique: challenges in achieving scale. In: 7th International conference on appropriate healthcare technologies for developing countries, 2012, pp.1–4.

153.

Rathonyi

Takacs

Szilagyi

, et al. Your physical activity is in your hand-objective activity tracking among university students in Hungary, one of the most obese countries in Europe. Front Public Heal 2021; 9.

154.

Pascual

Aminifar

Atienza

. A self-learning methodology for epileptic seizure detection with minimally-supervised edge labeling. In: 2019 design, automation & test in Europe conference & exhibition (DATE), 2019, pp.764–769.

155.

Sousa

Viola

Ferreira

, et al. An ecosystem of products and systems for ambient intelligence – the AAL4ALL users perspective. Stud Health Technol Inform 2012; 177: 263–271.

156.

Ghasemzadeh

. Head-mounted sensors and wearable computing for automatic tunnel vision assessment. In: Design, automation & test in Europe conference & exhibition (DATE), 2017; 2017, pp.634–637.

157.

Lind

. Evaluation of the use of digital pens for pain assessment in palliative home healthcare. Stud Health Technol Inform 2008; 136: 101–106.

158.

Barelle

Courtial

Vellidou

, et al. Tele-monitoring and diagnostic for fall prevention: the KINOPTIM concept. In: IEEE-EMBS international conference on biomedical and health informatics (BHI), 2014, pp.342–345.

159.

Farmer

Bobrow

Leon

, et al. Digital messaging to support control for type 2 diabetes (StAR2D): a multicentre randomised controlled trial. BMC Public Health 2021; 21. https://www-scopus-com-s.web.bisu.edu.cn/inward/record.uri?eid=2-s2.0-85117573197&doi=10.1186%2Fs12889-021-11874-7&partnerID=40&md5=43a2587dff070f7f9f0cf559524e0642 .

160.

Pavlopoulos

Anagnostaki

Koutsouris

, et al. Vital signs monitoring from home with open systems. In: Hasman A, Blobel B, Dudeck J, Engelbrecht R, Gell G and Prokosch HU (eds) Vol. 77, Medical Infobahn for Europe, Proceedings. Natl Tech Univ Athens, GR-10682 Athens, Greece NR - 10 PU - I O S Press PI - Amsterdam PA - NIEUWE HEMWEG 6B, 1013 BG Amsterdam, Netherlands, 2000, pp.1141–1145.

161.

Furlong

Darley

Fox

, et al. Adaptation and implementation of a mobile phone-based remote symptom monitoring system for people with cancer in Europe. JMIR Cancer 2019; 5(1): e10813.

162.

Frydrysiak

Tesiorowski

. Wearable textronic system for protecting elderly people. In: 2016 IEEE international symposium on medical measurements and applications (MeMeA), 2016, pp.1–6.

163.

Alonso-Solís

Ochoa

Grasa

, et al. A method to compare the delivery of psychiatric care for people with treatment-resistant schizophrenia. Int J Environ Res Public Health 2020; 17.

164.

Quintana

Anderberg

Berglund

, et al. Feasibility-usability study of a tablet app adapted specifically for persons with cognitive impairment—smart4md (support monitoring and reminder technology for mild dementia). Int J Environ Res Public Health 2020; 17: 1–21.

165.

Pers

Valsecchi

Mura

, et al. A randomized prospective open-label controlled trial comparing the performance of a connected monitoring interface versus physical routine monitoring in patients with rheumatoid arthritis. Rheumatology 2021; 60: 1659–1668.

166.

Cattivelli

Castelnuovo

Musetti

, et al. ACTonHEALTH study protocol: promoting psychological flexibility with activity tracker and mHealth tools to foster healthful lifestyle for obesity and other chronic health conditions. Trials 2018 Nov; 19: 659.

167.

Näverlo

Carson

Edin-Liljegren

, et al. Patient perceptions of a virtual health room installation in rural Sweden. Rural Remote Health 2016; 16: 3823.

168.

Kassem

Yıldırım

Turğut

, et al. Effectiveness of mobile electrocardiogram in healthcare: from mobile application and development to community reaction. In: 2017 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM), 2017, pp.896–903.

169.

Kauta

Groenewald

Arnolds

, et al. WhatsApp mobile health platform to support fracture management by non-specialists in South Africa. J Am Coll Surg 2020 Jan; 230: 37–42.

170.

Hughes

Pauling

Moore

, et al. Impact of COVID-19 on clinical care and lived experience of systemic sclerosis: an international survey from EURORDIS-rare diseases Europe. J Scleroderma Relat Disord 2021; 6: 133–138.

171.

Mattila

Korhonen

Salminen

, et al. Empowering citizens for well-being and chronic disease management with wellness diary. IEEE Trans Inf Technol Biomed 2010; 14: 456–463.

172.

Cain

Moore

Sturm

, et al. Clinical assessment and management of general surgery patients via synchronous telehealth. J Telemed Telecare 2017 Feb; 23: 371–375.

173.

Lin

Welstead

Morey

, et al. Return on investment analysis of health experts online at Portsmouth: A 2-year review of the Navy’s newest teleconsultation system. Mil Med 2017; 182: e1696–e1701.

174.

Villalba

Ottaviano

Arredondo

, et al. Wearable monitoring system for heart failure assessment in a mobile environment. In: 2006 computers in cardiology, 2006, pp.237–240.

175.

Mwendwa

. Assessing the fit of RapidSMS for maternal and new-born health: perspectives of community health workers in rural Rwanda. Dev Pract 2016; 26: 38–51.

176.

Odeny

Bukusi

Cohen

, et al. Texting improves testing: a randomized trial of two-way SMS to increase postpartum prevention of mother-to-child transmission retention and infant HIV testing. AIDS 2014; 28: 2307–2312.

177.

Janson

Moen

Fuglerud

. Design of the CAPABLE health empowerment tool: citizens’ needs and expectations. In: PapeHaugaard LB, Lovis C, Madsen IC, Weber P, Nielsen PH and Scott P (eds) Vol. 270, Digital personalized health and medicine. Akershus Univ Hosp, Div Diagnost & Technol, Nordbyhagen, Norway, 2020, pp.926–930.

178.

Giacomini

Peragallo

Bertone

, et al. Validation of the European Prototype for Integrated Care at municipal level in Savona: updating and maintenance. In: 2001 conference proceedings of the 23rd annual international conference of the IEEE engineering in medicine and biology society, 2001, pp.3670–3673 vol.4.

179.

Coleman

. Message-based interaction (MBI) mobile phone platform to expedite counselling and education to HIV patients in developing countries. Indian J Public Heal Res Dev 2018; 9: 249–254.

180.

Kurzynski

Puchala

Wozniak

. TelFam: a telemedicine system for the family doctor practices. In: Hasman A, Blobel B, Dudeck J, Engelbrecht R, Gell G and Prokosch HU (eds) Vol. 77, Medical Infobahn for Europe, Proceedings. Wroclaw Univ Technol, Fac Elect, Div Syst & Comp Networks, PL-50372 Wroclaw, Poland NR - 6 PU - I O S Press PI - Amsterdam PA - NIEUWE HEMWEG 6B, 1013 BG Amsterdam, Netherlands, 2000, pp.1131–1135.

181.

Mastellos

Andreasson

Huckvale

, et al. A cluster randomised controlled trial evaluating the effectiveness of eHealth-supported patient recruitment in primary care research: the TRANSFoRm study protocol. Implement Sci 2015 Feb; 10: 15.

182.

Beres

Mbabali

Anok

, et al. Mobile ecological momentary assessment and intervention and health behavior change among adults in Rakai, Uganda: pilot randomized controlled trial. JMIR Form Res 2021 Jul; 5: e22693–e22693.

183.

Leon

Namadingo

Cooper

, et al. Process evaluation of a brief messaging intervention to improve diabetes treatment adherence in sub-Saharan Africa. BMC Public Health 2021; 21.

184.

Househ

. Mobile Social Networking Health (MSNet-Health): Beyond the mHealth Frontier. Mantas J, Andersen SK, Mazzoleni MC, Blobel B, Quaglini S, Moen A (eds) Vol. 180, Quality of Life Through Quality Of Information. King Saud Bin Abdul Aziz Univ Hlth Sci, Coll Publ Hlth & Hlth Informat, Riyadh, Saudi Arabia NR - 13 PU - IOS Press PI - Amsterdam PA - NIEUWE HEMWEG 6B, 1013 BG Amsterdam, Netherlands; 2012. pp.808–812.

185.

Narvaez

Tobar

Lopez

, et al. Human-Centered design of an mHealth app for the prevention of burnout syndrome. In: Hoerbst A, Hackl WO, DeKeizer N, Prokosch HU, HercigonjaSzekeres M and DeLusignan S (eds) Vol. 228, Exploring Complexity IN Health: an Interdisciplinary Systems Approach. Univ Cauca, Telemat Engn Res Grp, Popayan, Colombia, 2016, pp.215–219.

186.

Hangaard

Schaarup

Hejlesen

. Participatory heuristic evaluation of the second iteration of the eWALL interface application. In: Hoerbst A, Hackl WO, DeKeizer N, Prokosch HU, HercigonjaSzekeres M and DeLusignan S (eds) Vol. 228, Exploring complexity in health: an interdisciplinary systems approach. Aalborg Univ, Dept Hlth Sci & Technol, Fredrik Bajers Vej 7C,C1-221, DK-9220 Aalborg O, Denmark NR - 10 PU - IOS PRESS PI - Amsterdam PA - NIEUWE HEMWEG 6B, 1013 BG Amsterdam, Netherlands, 2016, pp.599–603.

187.

Van Reijen

Vriend

Zuidema

, et al. The implementation effectiveness of the “Strengthen your ankle” smartphone application for the prevention of ankle sprains: design of a randomized controlled trial. BMC Musculoskelet Disord 2014; 15.

188.

Cullington

Kitterick

DeBold

, et al. Have cochlear implant, won’t have to travel: introducing telemedicine to people using cochlear implants. Am J Audiol 2016; 25: 299–302.

189.

Parham

Mwanahamuntu

Pfaendler

, et al. eC3—a modern telecommunications matrix for cervical cancer prevention in Zambia. J Low Genit Tract Dis 2010 Jul; 14: 167–173.

190.

Lipoff

Cobos

Kaddu

, et al. The Africa teledermatology project: a retrospective case review of 1229 consultations from sub-Saharan Africa. J Am Acad Dermatol 2015; 72: 1084–1085.

191.

Gárate

Chausa

Whetham

, et al. EmERGE mHealth platform: implementation and technical evaluation of a digital supported pathway of care for medically stable HIV. Int J Environ Res Public Health 2021; 18.

192.

Mamun

Sharma

Hoque

ASM

, et al. Remote patient physical condition monitoring service module for iWARD hospital robots. In: Asia-Pacific World Congress on Computer Science and Engineering. 2014. pp.1–8.

193.

Park

Visbal-Onufrak

Haque

, et al. mHealth spectroscopy for noninvasive blood hemoglobin assessments. In: D. L, A. O (eds) Optics and biophotonics in low-resource settings VII 2021 [Internet]. Weldon School of Biomedical Engineering, Purdue University IN 47907, United States: SPIE, 2021.

194.

Biester

Tauschmann

Chobot

, et al. The automated pancreas: a review of technologies and clinical practice. Diabetes Obes Metab 2021.

195.

David

Kah

Tyndall

, et al. Contextual contradictions to the scaling -up of mobile health in armed-conflict settings of north-eastern Nigeria: a case study of Almanach. In: 13th IADIS international conference information systems 2020, IS 2020 [Internet]. American University of Nigeria, Nigeria: IADIS, 2020, pp.99–106.

196.

Galán-Jiménez

Gazo-Cervero

González-Sánchez

. Exploiting the use of mobile technologies for chronic patients home distance monitoring. In: 6th Iberian conference on information systems and technologies (CISTI 2011), 2011, pp.1–6.

197.

Aikens

Rosland

Piette

. Improvements in illness self-management and psychological distress associated with telemonitoring support for adults with diabetes. Prim Care Diabetes 2015; 9: 127–134.

198.

Gómez

Caballero

Malpica

, et al. Optimisation and evaluation of an asynchronous transfer mode teleradiology co-operative system: the experience of the EMERALD and the BONAPARTE projects. Comput Methods Programs Biomed 2001; 64: 201–214.

199.

Lesecq

Foucault

Birot

, et al. INSPEX: design and integration of a portable/wearable smart spatial exploration system. In: Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017; 2017: pp.746–751.

200.

Burton

Tatar

McKinstry

, et al. Pilot randomised controlled trial of Help4Mood, an embodied virtual agent-based system to support treatment of depression. J Telemed Telecare 2016; 22: 348–355.

201.

Nasralla

Rehman

Sobnath

, et al. Computer vision and deep learning-enabled UAVs: proposed use cases for visually impaired people in a smart city. In: Vento M and Percannella G (eds) Vol. 1089, Computer analysis of images and patterns (CAIP 2019). Riyadh, Saudi Arabia: Prince Sultan Univ, Dept Commun & Networks Engn, 2019, pp.91–99.

202.

Barbera

Mangialasche

Jongstra

, et al. Designing an internet-based multidomain intervention for the prevention of cardiovascular disease and cognitive impairment in older adults: the HATICE trial. J Alzheimers Dis 2018; 62: 649–663.

203.

Istrate

Binet

Cheng

. Real time sound analysis for medical remote monitoring. Annu Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc Annu Int Conf 2008; 2008: 4640–4643.

204.

Dubner

Auricchio

Steinberg

, et al. ISHNE/EHRA expert consensus on remote monitoring of cardiovascular implantable electronic devices (CIEDs). Europace 2012 Feb; 14: 278–293.

205.

Gerdes

Gallefoss

Fensli

. The EU project “United4Health”: results and experiences from automatic health status assessment in a Norwegian telemedicine trial system. J Telemed Telecare 2019 Jan; 25: 46–53.

206.

Azakpa

Priuli

Ndayake

, et al. Telepathology practice in cancer diagnosis in Saint Jean de Dieu Hospital - Tanguieta, Benin. Arch Pathol Lab Med 2021 Jul; 145: 871–876.

207.

Barrett

Boyne

Brandts

, et al. Artificial intelligence supported patient self-care in chronic heart failure: a paradigm shift from reactive to predictive, preventive and personalised care. EPMA J 2019; 10: 445–464.

208.

Hilbel

Helms

Mikus

, et al. Telemetry in the clinical setting. Herzschrittmacherther Elektrophysiol 2008 Sep; 19: 146–154.

209.

Desteghe

Kluts

Vijgen

, et al. The health buddies app as a novel tool to improve adherence and knowledge in atrial fibrillation patients: a pilot study. JMIR mHealth uHealth 2017 Jul; 5: e98.

210.

Barton

O’Flynn

Tedesco

. A review of physical activity monitoring and activity trackers for older adults. In: Cudd P and DeWitte L (eds) Vol. 242, Harnessing the power of technology to improve lives. Univ Coll Cork, Tyndall Natl Inst, Cork T12 R5CP, Ireland FU - European Union’s Horizon 2020 research and innovation programme [689996] FX - This research is funded by the European Union’s Horizon 2020 research and innovation programme under grant agree, 2017, pp.748–754.

211.

O’Donovan

Buckley

Benson

, et al. Telehealth for delivery of haemophilia comprehensive care during the COVID-19 pandemic. Haemophilia 2020; 26: 984–990.

212.

Issom

Rochat

Hartvigsen

, et al. Preliminary evaluation of a mHealth coaching conversational artificial intelligence for the self-care management of people with sickle-cell disease. In: PapeHaugaard LB, Lovis C, Madsen IC, Weber P, Nielsen PH and Scott P (eds) Vol. 270, Digital personalized health and medicine. Geneva Univ Hosp, Div Med Informat Sci, Geneva, Switzerland, 2020, pp.1361–1362.

213.

Gatsios

Rigas

Konitsiotis

. Design of a clinical decision support system powered by mHealth for the management of Parkinson’s disease. In: 2021 6th South-East Europe design automation, computer engineering, computer networks and social media conference (SEEDA-CECNSM), 2021, pp.1–4.

214.

Falcó

Vaquerizo

Lain

, et al. AmI and deployment considerations in AAL services provision for elderly independent living: the MonAMI project. Sensors (Basel) 2013; 13: 8950–8976.

215.

Cho

Jung

Kim

. Establishing a teledermatological service system among Korea-China-Europe using TEIN. In: Proceedings of 7th international workshop on enterprise networking and computing in healthcare industry, 2005 HEALTHCOM 2005, 2005, pp.166–171.

216.

Ackrivo

Elman

Hansen-Flaschen

. Telemonitoring for home-assisted ventilation: a narrative review. Ann Am Thorac Soc 2021 Nov; 18: 1761–1772.

217.

Skrovseth

Arsand

Godtliebsen

, et al. Model driven mobile care for patients with type 1 diabetes. In: Mantas J, Andersen SK, Mazzoleni MC, Blobel B, Quaglini S and Moen A (eds) Vol. 180, Quality of life through quality of information. Tromso, Norway: Univ Hosp North Norway, Norwegian Ctr Integrated Care & Telemed, 2012, pp.1045–1049.

218.

Kakalou

Cosmaro

Dimitriadis

, et al. ICT toolkit for integrated prevention, testing and linkage to care across HIV, hepatitis, STIs and tuberculosis in Europe. Stud Health Technol Inform 2020 Jun; 270: 848–852.

219.

Klapper

Kühne

. Patient self-management by telehealth using the Bosch model of care. J Telemed Telecare 2010; 16: 193–195.

220.

Seiwerth

. The challenge of telepathology. In: Buzug TM, Handels H and Holz D (eds) Telemedicine: medicine and communication. Univ Zagreb, Fac Med, Inst Pathol, Zagreb 41000, Croatia NR - 7 PU - Kluwer Academic/Plenum Publ PI - New York PA - 233 Spring ST, New York, NY 10013, USA, 2001, pp.43–50.

221.

Debard

De Witte

Sels

, et al. Making wearable technology available for mental healthcare through an online platform with stress detection algorithms: The Carewear project. J Sensors 2020; 2020.

222.

Gourlay

Lun

Liya

. Telemedicinal virtual reality for cognitive rehabilitation. In: Hasman A, Blobel B, Dudeck J, Engelbrecht R, Gell G and Prokosch HU (eds) Vol. 77, Medical Infobahn for Europe, Proceedings. Natl Univ Singapore, Dept Community Occupat & Family Med, Med Informat Programme, Singapore 119260, Singapore NR - 14 PU - IOS PRESS PI - Amsterdam PA - NIEUWE HEMWEG 6B, 1013 BG Amsterdam, Netherlands, 2000, pp.1181–1186.

223.

Reiter

. HeartCycle: beyond building demonstrators. A structured approach to develop, implement and validate healthcare innovations in telemonitoring. Annu Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc Annu Int Conf 2010; 2010: 6847–9.

224.

Thompson

Sowunmi

Misra

, et al. An expert system for the diagnosis of sexually transmitted diseases ESSTD. J Intell Fuzzy Syst 2017; 33: 2007–2017.

225.

Conti

Rossi

Marceglia

, et al. An integrated diet monitoring solution for nutrigenomic research. In: Cornet R, StoicuTivadar L, Horbst A, Calderon CLP, Andersen SK and HercigonjaSzekeres M (eds) Vol. 210, Digital healthcare empowering Europeans. Kos Genet Srl, Milan, Italy, 2015, pp.632–636.

226.

Zhu

Meng

, et al. Learning from large-scale wearable device data for predicting epidemics trend of COVID-19. Discret Dyn Nat Soc 2020; 2020.

227.

Tandon

Raheja

Mishra

, et al. Trends in neurosurgical teleconsultation services across the globe during COVID-19 pandemic. World Neurosurg 2021 Jun; 150: e645–e656.

228.

Rassouli

Boutellier

Duss

, et al. Digitalizing multidisciplinary pulmonary rehabilitation in COPD with a smartphone application: an international observational pilot study. Int J Chron Obstruct Pulmon Dis 2018; 13: 3831–3836.

229.

Hermans

Elbireer

Tibakabikoba

, et al. Text messaging to decrease tuberculosis treatment attrition in TB-HIV coinfection in Uganda. Patient Prefer Adherence 2017; 11: 1479–1487.

230.

Velu A

van Beukering

Schaafsma

, et al. Barriers and facilitators for the use of a medical mobile app to prevent work-related risks in pregnancy: a qualitative analysis. JMIR Res Protoc 2017 Aug; 6: e163.

231.

Bourret

Bousquet

. An integrated approach to telemonitoring noncommunicable diseases: best practice from the European innovation partnership on active and healthy ageing. World Hosp Health Serv 2013; 49: 25–28.

232.

Jabrayilov

van Asselt

ADI

Vermeulen

, et al. A descriptive system for the Infant health-related quality of life instrument (IQI): measuring health with a mobile app. PLoS One 2018; 13: e0203276.

233.

Masiano

Machine

Mphande

, et al. Video-based intervention for improving maternal retention and adherence to HIV treatment: patient perspectives and experiences. Int J Environ Res Public Health 2021; 18: 1–16.

234.

English

Dunsmuir

Kumbakumba

, et al. The PAediatric Risk Assessment (PARA) mobile app to reduce postdischarge child mortality: design, usability, and feasibility for health care workers in Uganda. JMIR mHealth uHealth 2016 Feb; 4: e16–e16.

235.

Pagan

Risco-Martin

Moya

, et al. Grammatical evolutionary techniques for prompt migraine prediction. GECCO’16: proceedings of the 2016 genetic and evolutionary computation conference. Univ Complutense Madrid, DACYA, CCS, E-28040 Madrid, Spain, 2016, pp.973–980.

236.

Andruseac

Rotariu

Ciobotariu

, et al. E-health system for medical telesurveillance of patients with neurological dysfunctions. In: 2011 E-Health and Bioengineering Conference (EHB), 2011, pp.1–4.

237.

Macis

Loi

Ulgheri

, et al. Design and usability assessment of a multi-device SOA-based telecare framework for the elderly. IEEE J Biomed Heal Inform 2020; 24: 268–279.

238.

Paddick

S-M

Yoseph

Gray

, et al. Effectiveness of app-based cognitive screening for dementia by lay health workers in low resource settings. A validation and feasibility study in Rural Tanzania. J Geriatr Psychiatry Neurol 2020.

239.

Lanza

. Connecting hospitals through a distance learning center and permanent remote support network – a sicilian project to improve patient care. In: Sixth IEEE international symposium on cluster computing and the grid (CCGRID’06), 2006, p.38.

240.

Castellano-Tejedor

Moreno

Knittle

, et al. Assessing the user experience and usability of the PRECIOUS system: a randomized controlled trial in obese patients. Inform Health Soc Care 2020; 45: 410–427.

241.

Bellazzi

Montani

Riva

, et al. Web-based telemedicine systems for home-care: technical issues and experiences. Comput Methods Programs Biomed 2001; 64: 175–187.

242.

Omboni

Riva

Giglio

, et al. Validation of the Omron M5-I, R5-I and HEM-907 automated blood pressure monitors in elderly individuals according to the International Protocol of the European Society of Hypertension. Blood Press Monit 2007 Aug; 12: 233–242.

243.

Wojcicki

Ladyzynski

. Telematic support in intensive insulin treatment. Frequency of the data transfer. Diabetes Res Clin Pract 2006; 74: S225–S228.

244.

van den Elshout

MAM

Hoornenborg

Achterbergh

RCA

, et al. Improving adherence to daily preexposure prophylaxis among MSM in Amsterdam by providing feedback via a mobile application. AIDS 2021; 35: 1823–1834.

245.

Hughes

Mariscal

Baye

, et al. Development of an upper extremity stroke rehabilitation mHealth application for sub-Saharan Africa: a usability study. In: 2019 IST-Africa week conference (IST-Africa), 2019, pp.1–8.

246.

Sarfo

Adusei

Ampofo

, et al. Pilot trial of a tele-rehab intervention to improve outcomes after stroke in Ghana: a feasibility and user satisfaction study. J Neurol Sci 2018 Apr; 387: 94–97.

247.

Stut

Deighan

Cleland

, et al. Adherence to self-care in patients with heart failure in the HeartCycle study. Patient Prefer Adherence 2015; 9: 1195–1206.

248.

Sarfo

Treiber

Jenkins

, et al. Phone-based intervention under nurse guidance after stroke (PINGS): study protocol for a randomized controlled trial. Trials 2016 Sep; 17: 436.

249.

Duruturk

Ozkoslu

. Effect of tele-rehabilitation on glucose control, exercise capacity, physical fitness, muscle strength and psychosocial status in patients with type 2 diabetes: a double blind randomized controlled trial. Prim Care Diabetes 2019; 13: 542–548.

250.

Lilholt

Haesum

LKE

Hejlesen

. Exploring user experience of a telehealth system for the danish telecare north trial. In: Cornet R, StoicuTivadar L, Horbst A, Calderon CLP, Andersen SK and HercigonjaSzekeres M (eds) Vol. 210, Digital healthcare empowering Europeans. Aalborg, Denmark: Aalborg Univ, Dept Hlth Sci & Technol, 2015, pp.301–305.

251.

Winskell

Sabben

Akelo

, et al. A Smartphone game-based intervention (Tumaini) to prevent HIV among young Africans: pilot randomized controlled trial. JMIR mHealth uHealth 2018; 6.

252.

McCormack

McKenzie

Foerster

, et al. Breast cancer survival and survival gap apportionment in sub-Saharan Africa (ABC-DO): a prospective cohort study. LANCET Glob Heal 2020; 8: E1203–E1212.

253.

Monsalve

Puertas

Fernández

, et al. Using wearable devices in naturalistic driving to analyze brain activity in roundabout maneuvers. In: 2018 IEEE international conference on vehicular electronics and safety (ICVES), 2018, pp.1–6.

254.

Frühauf

Hofman-Wellenhof

Kovarik

, et al. Mobile teledermatology in sub-Saharan Africa: a useful tool in supporting health workers in low-resource centres. Acta Derm-Venereol 2013; 93: 122–3.

255.

Apóstolo

Bobrowicz-Campos

Moreno-Casbas

, et al. A study protocol for development and validation of a clinical prediction model for frailty (ModulEn): a new European commitment to tackling frailty. Transl Med @ UniSa 2019; 19: 36–41.

256.

Namisango

Ntege

Luyirika

EBK

, et al. Strengthening pharmaceutical systems for palliative care services in resource limited settings: piloting a mHealth application across a rural and urban setting in Uganda. BMC Palliat Care 2016 Feb; 15: 20.

257.

Istrate

Castelli

Vacher

, et al. Information extraction from sound for medical telemonitoring. IEEE Trans Inf Technol Biomed 2006 Apr; 10: 264–274.

258.

Krasnik

Stefanickova

Popov

, et al. Prevalence of age-related macular degeneration in Slovakia and associated risk factors: a mobile clinic-based cross-sectional epidemiological survey. Semin Ophthalmol 2018; 33: 506–511.

259.

Farmer

Bobrow

Leon

, et al. Mobile messaging support versus usual care for people with type 2 diabetes on glycemic control: protocol for a multicenter randomized controlled trial. JMIR Res Protoc 2019 May; 8: e12377–e12377.

260.

Salabelle

Sall

Eroukhmanoff

, et al. COVID-19 pandemic lockdown in young people with type 1 diabetes: positive results of an unprecedented challenge for patients through telemedicine and change in use of continuous glucose monitoring. Prim Care Diabetes 2021; 15: 884–886.

261.

Smaradottir

Gerdes

Fensli

, et al. Usability evaluation of a COPD remote monitoring application. In: Cornet R, StoicuTivadar L, Horbst A, Calderon CLP, Andersen SK and HercigonjaSzekeres M (eds) Vol. 210, digital healthcare empowering Europeans. Univ Agder, Ctr eHlth & Healthcare Technol, Kristiansand, Norway NR - 9 PU - IOS Press PI - Amsterdam PA - NIEUWE HEMWEG 6B, 1013 BG Amsterdam, Netherlands, 2015, pp.845–849.

262.

Maglaveras

Reiter

. Towards closed-loop personal health systems in cardiology: the HeartCycle approach. Annu Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc Annu Int Conf 2011; 2011: 892–895.

263.

Scott-Sheldon

LAJ

Lantini

Jennings

, et al. Text messaging-based interventions for smoking cessation: a systematic review and meta-analysis. JMIR mHealth uHealth 2016; 4: e49.

264.

Mano

Faiçal

Nakamura LH

, et al. Exploiting IoT technologies for enhancing health smart homes through patient identification and emotion recognition. Comput Commun 2016; 89–90: 178–190.

265.

Olajubu

Odukoya

Akinboro

. LWAs computational platform for e-consultation using mobile devices: cases from developing nations. Technol Health Care 2014; 22: 561–571.

266.

Sabben

Akelo

Mudhune

, et al. A smartphone game to prevent HIV among young Africans: protocol for a randomized pilot study of a mobile intervention. JMIR Res Protoc 2019; 8.

267.

Wagner

Franke

Otto

. Distance caregiving : empirical findings from the survey of health, ageing and retirement in Europe. Z Gerontol Geriatr 2019; 52: 529–536.

268.

Prentza

Maglavera

Leondaridis

. Delivery of healthcare services over mobile phones: e-vital and CHS paradigms. Conf Proc Annu Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc Annu Conf 2006; 2006: 3250–3253.

269.

Pliakas

Dermeitzoglou

Papachristos

, et al. A tele-medicine service over satellite network. Stud Health Technol Inform 2008; 137: 83–92.

270.

Calvo-Lerma

Martinez-Jimenez

Lázaro-Ramos

J-P

, et al. Innovative approach for self-management and social welfare of children with cystic fibrosis in Europe: development, validation and implementation of an mHealth tool (MyCyFAPP). BMJ Open 2017; 7: e014931.

271.

Battmann

Knitza

Janzen

, et al. Telemedicine: application of telepathology-remote microscopy for intraoperative diagnoses on frozen sections. In: Hasman A, Blobel B, Dudeck J, Engelbrecht R, Gell G and Prokosch HU (eds) Vol. 77, Medical Infobahn for Europe, Proceedings. Univ Giessen, Inst Pathol, D-35385 Giessen, Germany NR - 8 PU - IOS PRESS PI - Amsterdam PA - NIEUWE HEMWEG 6B, 1013 BG Amsterdam, Netherlands, 2000, pp.1127–1130.

272.

Willmann

Lanfermann

Saini

, et al. Home stroke rehabilitation for the upper limbs. Annu Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc Annu Int Conf 2007; 2007: 4015–4018.

273.

Koutkias

Chouvarda

Maglaveras

. Agent-based monitoring and alert generation for a home care telemedicine system. Proceedings AMIA Symp 2002: 395–9.

274.

Vilaplana

Petignat

Benski

A-C

, et al. Description of maternal morbidities amongst 1000 women during pregnancy in Ambanja, Madagascar – opportunities and challenges of using an mHealth system. Int J Womens Health 2020; 12: 823–833.

275.

Bourbeau

Casan

Tognella

, et al. An international randomized study of a home-based self-management program for severe COPD: the COMET. Int J Chron Obstruct Pulmon Dis 2016; 11: 1447–1451.

276.

Spruijt

Dijk

Visscher

, et al. Teleconsulting, teleguiding and teleteaching: gigabit network applications in cardiology. In: Computers in cardiology 2001 Vol28 (Cat No01CH37287), 2001, pp.581–584.

277.

Olalla

de Lomas

JMG

Marquez

, et al. Experience of using an app in HIV patients older than 60 years: pilot program. JMIR Mhealth Uhealth 2019; 7.

278.

Bebla

Diwan

Smith

, et al. HedhiHelp – a health education app for girls in rural Kenya. In: 2018 IEEE global humanitarian technology conference (GHTC), 2018, pp.1–6.

279.

McKinstry

Sheikh

. The use of global positioning systems in promoting safer walking for people with dementia. J Telemed Telecare 2013; 19: 288–292.

280.

Sousa-Pinto

Eklund

Pfaar

, et al. Validity, reliability, and responsiveness of daily monitoring visual analog scales in MASK-air®. Clin Transl Allergy 2021 Aug; 11: e12062.

281.

Tsoutsouras

Xydis

Soudris

. A HW/SW framework emulating wearable devices for remote wound monitoring and management. In: 2014 4th international conference on wireless mobile communication and healthcare – transforming healthcare through innovations in mobile and wireless technologies (MOBIHEALTH), 2014, pp.369–372.

282.

Stavropoulos

Lazarou

Diaz

, et al. Wearable devices for assessing function in Alzheimer’s disease: A European public involvement activity about the features and preferences of patients and caregivers. Front Aging Neurosci 2021; 13: 643135.

283.

Unterhinninghofen

Giesel

Dillmann

. MEDIFRAME–remote volume rendering visualization framework. Annu Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc Annu Int Conf 2011; 2011: 2368–2371.

284.

Plazzotta

Sommer

Fosser

SNM

, et al. Asynchronous dermatology teleconsultations using a personal health record. In: Ugon A, Karlsson D, Klein GO and Moen A (eds) Vol. 247, Building continents of knowledge in oceans of data: the future of co-created eHealth. Hosp Italiano Buenos Aires, Hlth Informat Dept, Buenos Aires, DF, Argentina, 2018, pp.690–694.

285.

Diby

Adoubi

Gnaba

, et al. Tele-expertise in the interpretation of the electrocardiogram of a black African population in the Ivory Coast (sub-Saharan Africa). Eur Res Telemed 2015; 4: 109–117.

286.

Chloros

Ringas

. Fluspot: seasonal flu tracking app exploiting wearable IoT device for symptoms monitoring. In: 2020 5th South-East Europe design automation, computer engineering, computer networks and social media conference (SEEDA-CECNSM), 2020, pp.1–7.

287.

Vilarinho

Floch

Stav

. Co-designing a mHealth application for self-management of cystic fibrosis [Internet]. R. B, M. W, G. D, A. J, D.K. B, J. O (eds) Vol. 10515 LNCS, 16th IFIP TC13 international conference on human–computer interaction, interact 2017. SINTEF Digital, Trondheim, Norway: Springer, Verlag, 2017, pp.3–22.

288.

Siedner

Santorino

Haberer

, et al. Know your audience: predictors of success for a patient-centered texting app to augment linkage to HIV care in rural Uganda. J Med Internet Res 2015 Mar; 17: e78–e78.

289.

Värri

Koivuluoma

Morvan

. Chapter 3.9—a computer-assisted visual sleep scoring program [Internet]. Vol. 78, Studies in Health Technology and Informatics. Digital Media Institute, Tampere University of Technology, P.O. Box 553, 33101 Tampere, Finland: IOS Press, 2000, pp.285–297.

290.

Bennebroek

Barroso

Atallah

, et al. Deployment of wireless sensors for remote elderly monitoring. In: The 12th IEEE international conference on e-Health networking, applications and services, 2010, pp.1–5.

291.

Katra

Chakravarthy

Libbus

. Remote at-home detection and monitoring of functional chronotropic incompetence in heart failure patients. J Cardiovasc Transl Res 2011 Feb; 4: 14–20.

292.

Hayn

Koller

Hofmann-Wellenhof

, et al. Mobile phone-based teledermatologic compliance management - preliminary results of the TELECOMP study. In: Adlassnig KP, Blobel B, Mantas J and Masic I (eds) Vol. 150, medical informatics in a united and healthy Europe. AIT Austrian Inst Technol GmbH, Dept Safety & Secur, Graz, Austria, 2009, pp.468–472.

293.

Zini

Tagliabue

Trentani

, et al. An mHealth application for educating and monitoring patients treated with a ketogenic diet regimen. In: Ugon A, Karlsson D, Klein GO and Moen A (eds) Vol. 247, Building continents of knowledge in oceans of data: the future of co-created eHealth. Univ Pavia, Dept Elect Comp & Biomed Engn, Via Ferrata 5, I-27100 Pavia, Italy, 2018, pp.481–485.

294.

Alasheev

Hubert

Santo

, et al. Recommendations on telestroke in Europe. Zhurnal Nevrol i psikhiatrii Im SS Korsakova 2020; 120: 33–41.

295.

Prescher

Bourke

Koehler

, et al. Ubiquitous ambient assisted living solution to promote safer independent living in older adults suffering from co-morbidity. Annu Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc Annu Int Conf 2012; 2012: 5118–5121.

296.

Kanoun

Mamaghanian

Khaled

, et al. A real-time compressed sensing-based personal electrocardiogram monitoring system. In: 2011 design, automation & test in Europe. 2011. pp.1–6.

297.

Wildenbos

Peute

Jaspers

MWM

. A framework for evaluating mHealth tools for older patients on usability. In: Cornet R, StoicuTivadar L, Horbst A, Calderon CLP, Andersen SK and HercigonjaSzekeres M (eds) Vol. 210, Digital healthcare empowering Europeans. Univ Amsterdam, Dept Med Informat, Acad Med Ctr, Amsterdam, Netherlands, 2015, pp.783–787.

298.

Vera-Muñoz

Arredondo

Ottaviano

, et al. HeartCycle: user interaction and patient education. In: 2013 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC), 2013, pp.6988–6991.

299.

Gutierrez

MMC

Duran-Vila

Ruiz-Labarta

, et al. A new multiplatform model for outpatient prenatal and postpartum care in a cohort of COVID-19-affected obstetric patients. Int J Environ Res Public Health 2021; 18.

300.

Musoke

Gakumo

Abuogi

, et al. A text messaging intervention to support option B + in Kenya: a qualitative study. J Assoc Nurses AIDS Care 2018; 29: 287–299.

301.

Eyitayo

Badejo

Atayero

. FirstSteps: a mHealth tool for children registration and monitoring. In: F.G. T, Q.-N. T, L. D, M.Q. Y, M.Q. Y, H.R. A (eds) 2017 international conference on computational science and computational intelligence (CSCI) [Internet]. Department of Electrical and Information Engineering, Covenant University, Canaan Land-Ota, Nigeria: Institute of Electrical and Electronics Engineers Inc., 2017, p.1782–6.

302.

Carbonaro

Tognetti

Anania

, et al. A mobile biosensor to detect cardiorespiratory activity for stress tracking. In: 2013 7th international conference on pervasive computing technologies for healthcare and workshops, 2013, pp.440–445.

303.

Clarke

de Folter

Palmer

, et al. Building point of care health technologies on the IEEE 11073 health device standards. In: 2013 IEEE point-of-care healthcare technologies (PHT), 2013, pp.117–119.

304.

Kingue

Angandji

Menanga

, et al. Efficiency of an intervention package for arterial hypertension comprising telemanagement in a Cameroonian rural setting: The TELEMED-CAM study. Pan Afr Med J 2013; 15.

305.

Crabbe

Barber

Bayford

, et al. The use of a European telemedicine system to examine the effects of pollutants and allergens on asthmatic respiratory health. Sci Total Environ 2004 Dec; 334–335: 417–426.

306.

Stoepel

Boland

Busca

, et al. Usefulness of remote monitoring in cardiac implantable device follow-up. Telemed J E Health 2009 Dec; 15: 1026–1030.

307.

Dai

Larnyo

Tetteh

, et al. Factors affecting caregivers’ acceptance of the use of wearable devices by patients with dementia: an extension of the unified theory of acceptance and use of technology model. Am J Alzheimers Dis Other Demen 2019; 35: 1533317519883493–1533317519883493.

308.

Mattila

Koskelo

Lappalainen

, et al. A concept for ICT assisted health promotion in the occupational healthcare. In: 2007 29th annual international conference of the IEEE engineering in medicine and biology society, 2007, pp.1786–1789.

309.

Austin

Frøsig

Buus

, et al. Service user experiences of integrating a mobile solution (IMPACHS) into clinical treatment for psychosis. Qual Health Res 2021 Apr; 31: 942–954.

310.

Brunetti

Amodio

De Gennaro

, et al. Telecardiology applied to a region-wide public emergency health-care service. J Thromb Thrombolysis 2009; 28: 23–30.

311.

Fioravanti

Fico

Arredondo

, et al. A mobile feedback system for integrated E-health platforms to improve self-care and compliance of diabetes mellitus patients. In: 2011 annual international conference of the IEEE engineering in medicine and biology society, 2011, pp.3550–3553.

312.

Wagner

Toftegaard

Bertelsen

. Context assessment during blood pressure self-measurement utilizing the sensor chair. In: Keyson DV, Maher ML, Streitz N, Cheok A, Augusto JC, Wichert R, et al. (eds) Vol. 7040, Ambient Intelligence. Aarhus Univ, Dept Engn, Helsingforsgade 14, DK-8200 Aarhus N, Denmark, 2011, pp.295–+.

313.

Gmez-Portes

Carneros-Prado

Albusac

, et al. PhyRe Up! A system based on mixed reality and gamification to provide home rehabilitation for stroke patients. IEEE Access 2021; 9: 139122–139137.

314.

Karvounis

Vavva

Giannakeas

, et al. A hospital healthcare monitoring system using internet of things technologies. In: 2021 6th South-East Europe design automation, computer engineering, computer networks and social media conference (SEEDA-CECNSM), 2021, pp.1–6.

315.

Van Halteren

Bults

Wac

, et al. Mobile patient monitoring: the MobiHealth system. J Inf Technol Healthc 2004; 2: 365–373.

316.

Giorgio

. A new FPGA-based medical device for the real time prevention of the risk of arrythmias. Int J Appl Eng Res 2016; 11: 6013–6018.

317.

Kropf

Modre-Osprian

Hayn

, et al. Telemonitoring in heart failure patients with clinical decision support to optimize medication doses based on guidelines. Annu Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc Annu Int Conf 2014; 2014: 3168–3171.

318.

Fayn

Rubel

. Toward a personal health society in cardiology. IEEE Trans Inf Technol Biomed 2010; 14: 401–409.

319.

Mastellos

Bliźniuk

Czopnik

, et al. Feasibility and acceptability of transform to improve clinical trial recruitment in primary care. Fam Pract 2016 Apr; 33: 186–191.

320.

Burri

Da Costa

Quesada

, et al. Risk stratification of cardiovascular and heart failure hospitalizations using integrated device diagnostics in patients with a cardiac resynchronization therapy defibrillator. Europace 2018; 20: e69–e77.

321.

Loscrí

Vegni

. An acoustic communication technique of nanorobot swarms for nanomedicine applications. IEEE Trans Nanobioscience 2015; 14: 598–607.

322.

Antonini

Gentile

Giglio

, et al. Acceptability to patients, carers and clinicians of an mHealth platform for the management of Parkinson’s disease (PD_Manager): study protocol for a pilot randomised controlled trial. Trials 2018 Sep; 19: 492.

323.

Muhindo

Mujugira

Castelnuovo

, et al. Text message reminders and peer education increase HIV and Syphilis testing among female sex workers: a pilot quasi-experimental study in Uganda. BMC Health Serv Res 2021; 21.

324.

Kamel Boulos

Lou

Anastasiou

, et al. Connectivity for healthcare and well-being management: examples from six European projects. Int J Environ Res Public Health 2009 Jul; 6: 1947–1971.

325.

Tsang

Kovarik

. The role of dermatopathology in conjunction with teledermatology in resource-limited settings: lessons from the African teledermatology project. Int J Dermatol 2011; 50: 150–156.

326.

Văleanu

Vasiliu

Vizitiu

, et al. Portable telemedicine workstation full prototype for technological transfer in critical interventions services. In: 2015 E-Health and Bioengineering Conference (EHB), 2015. p.1–4.

327.

Dimauro

Caivano

Girardi

, et al. The patient centered electronic multimedia health fascicle - EMHF. In: 2014 IEEE workshop on biometric measurements and systems for security and medical applications (BIOMS) proceedings, 2014, pp.61–66.

328.

Fallahzadeh

Ortiz

Ghasemzadeh

. Adaptive compressed sensing at the fingertip of Internet-of-Things sensors: an ultra-low power activity recognition. In: Design, automation & test in Europe conference & exhibition (DATE), 2017. pp.996–1001.

329.

Bryant

Spencer

King

, et al. IoT and smart city services to support independence and wellbeing of older people. In: 2017 25th international conference on software, telecommunications and computer networks (SoftCOM), 2017, pp.1–6.

330.

Bott

Hoffmann

Bergmann

, et al. Simulation based cost–benefit analysis of a telemedical system for closed-loop insulin pump therapy of diabetes. Stud Health Technol Inform 2006; 124: 435–440.

331.

Varma

Cygankiewicz

Turakhia

, et al. 2021 ISHNE/HRS/EHRA/APHRS Expert Collaborative Statement on mHealth in arrhythmia management: digital medical tools for heart rhythm professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European. Circ Arrhythm Electrophysiol 2021; 14: e009204. https://doi.org/10.1111/anec.12795

332.

Vogiatzaki

Gravezas

Dalezios

, et al. Telemedicine system for game-based rehabilitation of stroke patients in the FP7-“StrokeBack” project. In: 2014 European conference on networks and communications (EuCNC), 2014, pp.1–5.

333.

Salvi

Sacchi

Made

, et al. AID-GM: an advanced system supporting continuous monitoring of T1DM patients. In: Ugon A, Karlsson D, Klein GO and Moen A (eds) Vol. 247, building continents of knowledge in oceans of data: the future of co-created eHealth. Pavia, Italy: Univ Pavia, Dept Elect Comp & Biomed Engn, 2018, pp.616–620.

334.

Rubagumya

Nyagabona

Longombe

, et al. Feasibility study of a smartphone application for detecting skin cancers in people with albinism. JCO Glob Oncol 2020 Sep; 6: 1370–1375.

335.

Keller

Göbel

Ten Cate

, et al. Telemedicine-based specialized care improves the outcome of anticoagulated individuals with venous thromboembolism-results from the thrombEVAL Study. J Clin Med 2020; 9.

336.

Drabble

O’Cathain

Scott

, et al. Mechanisms of action of a web-based intervention with health professional support to increase adherence to nebulizer treatments in adults with cystic fibrosis: qualitative interview study. J Med Internet Res 2020; 22: e16782.

337.

Owens

Hinds

Manyakov

, et al. Selecting remote measurement technologies to optimize assessment of function in early Alzheimer’s disease: a case study. Front Psychiatry 2020; 11: 582207.

338.

Hart

D’Hooghe

Dancet

EAF

, et al. Self-monitoring of urinary hormones in combination with telemedicine – a timely review and opinion piece in medically assisted reproduction. Reprod Sci 2021 Nov: 1–14.

339.

Antali

Kulin

Lucz

, et al. Multimodal assessment of the pulse rate variability analysis module of a photoplethysmography-based telemedicine system. Sensors 2021; 21.

340.

Giannopoulou

Vlamos

. Analysis and design of an information system for cognitive training of patients with mild cognitive impairment using mobile devices. In: 2020 5th South-East Europe design automation, computer engineering, computer networks and social media conference (SEEDA-CECNSM), 2020, pp.1–6.

341.

Sutcliffe

Thuma

van Dijk

, et al. Use of mobile phones and text messaging to decrease the turnaround time for early infant HIV diagnosis and notification in rural Zambia: an observational study. BMC Pediatr 2017 Mar; 17: 66.

342.

Reiter

Tesanovic

Martinez-Romero

. HeartCycle: from insights to clinically evaluated ICT solutions for Telehealth. In: 2013 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC), 2013, pp.6992–6995.

343.

Glascock

Kutzik

. An evidentiary study of the uses of automated behavioral monitoring. In: 21st international conference on advanced information networking and applications workshops (AINAW’07), 2007, pp.858–862.

344.

Delisle Nyström

Chirita-Emandi

, et al. A randomized controlled trial for overweight and obesity in preschoolers: the more and less Europe study – an intervention within the STOP project. BMC Public Health 2019 Jul; 19: 945.

345.

Ilczak

Mikulska

. Telematic systems in emergency medical services, applied in treatment of acute coronary syndrome of STEMI Type. In: Mikulski J (eds) Vol. 329, Telematics in the Transport Environment. Univ Bielsko Biala, Fac Hlth Sci, PL-43309 Bielsko Biala, Poland NR - 9 PU - Springer-Verlag Berlin PI - Berlin PA - Heidelberger PLATZ 3, D-14197 Berlin, Germany, 2012, pp.87–93.

346.

Luprano

de Carvalho

Eilebrecht

, et al. Heart cycle: advanced sensors for telehealth applications. 2013 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC). CSEM, Jacquet Droz 1, CH-2002 Neuchatel, Switzerland, 2013, pp.6984–6987.

347.

Borsari

Stancanelli

Guarenti

, et al. An innovative mobile health system to improve and standardize antenatal care among underserved communities: a feasibility study in an Italian hosting center for asylum seekers. J Immigr Minor Heal 2018; 20: 1128–1136.

348.

Mgbemena

Sears

Levine

. Augmenting traditional cardiac and medical care in Africa via telemedicine: a pilot study. Cureus 2021 Aug; 13: e17483–e17483.

349.

Segura

Torres

. PhySeEar moving yourself to shine and sound in geriatric physiotherapy interventions. In: 2012 6th international conference on pervasive computing technologies for healthcare (PervasiveHealth) and workshops, 2012, pp.179–182.

350.

De Felice

D’Angelo

Ingargiola

, et al. A snapshot on radiotherapy for head and neck cancer patients during the COVID-19 pandemic: a survey of the Italian Association of Radiotherapy and Clinical Oncology (AIRO) head and neck working group. Radiol Med 2021 Feb; 126: 343–347.

351.

Hughes

CML

Baye

Gordon-Murer

, et al. Quantitative assessment of upper limb motor function in Ethiopian acquired brain injured patients using a low-cost wearable sensor. Front Neurol 2019; 10.

352.

Gahlaut

Kalyani

Sharan

, et al. Mobile based in situ detection of live/dead and antibiotic resistant bacteria by silver nanorods array sensor fabricated by glancing angle deposition. In: 2019 Conference on lasers and electro-optics Europe & European quantum electronics conference (CLEO/Europe-EQEC), 2019, p.1.

353.

Schmid-Grendelmeier

Masenga

Haeffner

, et al. Teledermatology as a new tool in sub-Saharan Africa: an experience from Tanzania. J Am Acad Dermatol 2000 May; 42: 833–835.

354.

Mbarika

VWA

. Is telemedicine the Panacea for sub-Saharan Africa’s medical nightmare? Commun ACM 2004; 47: 21–24.

355.

Jacob

Sanchez-Vazquez

Ivory

. Clinicians’ role in the adoption of an oncology decision support app in Europe and its implications for organizational practices: qualitative case study. JMIR mHealth uHealth 2019; 7: e13555.

356.

Cerello

Bagnasco

Bottigli

, et al. GPCALMA: a grid-based tool for mammographic screening. Methods Inf Med 2005; 44: 244–248.

357.

Weinberg

Kaddu

Gabler

, et al. The African teledermatology project: providing access to dermatologic care and education in sub-Saharan Africa. Pan Afr Med J 2009 Nov; 3: 16.

358.

Smillie

Van Borek

van der Kop

, et al. Mobile health for early retention in HIV care: a qualitative study in Kenya (WelTel Retain). Afr J AIDS Res 2014; 13: 331–338.

359.

Aznar-Gimeno

Labata-Lezaun

Adell-Lamora

, et al. Deep learning for walking behaviour detection in elderly people using smart footwear. Entropy 2021; 23.

360.

Njenga

Ongeri

Nguithi

, et al. Integrating telepsychiatry services in a care setting in Kenya: a case report. BJPsych Int 2021.

361.

Zenonos

Khan

Kalogridis

, et al. HealthyOffice: mood recognition at work using smartphones and wearable sensors. In: 2016 IEEE international conference on pervasive computing and communication workshops (PerCom Workshops), 2016, pp.1–6.

362.

Schmid-Grendelmeier

Doe

Pakenham-Walsh

. Teledermatology in sub-Saharan Africa. Curr Probl Dermatol 2003; 32: 233–246.

363.

Giokas

Anastasiou

Tsirmpas

, et al. Smart adaptable system for older adults’ daily life activities management – The ABLE platform. In: 2014 36th annual international conference of the IEEE engineering in medicine and biology society, 2014, pp.5816–9.

364.

Stockdale

Sinclair

Wiseman

, et al. The challenge of designing and integrating three motivating, sequential apps for optimising birth after C Section. In: eChallenges e-2014 conference proceedings, 2014. pp.1–8.

365.

Ferreira

Pires

Ponciano

, et al. Experimental study on wound area measurement with mobile devices. Sensors 2021; 21.

366.

Min

Ince

Tewfik

. Generalization capability of a wearable early morning activity detection system. In: 2007 15th European signal processing conference, 2007. pp.1556–1560.

367.

Cullington

Kitterick

Weal

, et al. Feasibility of personalised remote long-term follow-up of people with cochlear implants: a randomised controlled trial. BMJ Open 2018 Apr; 8: e019640.

368.

Brinkel

May

Krumkamp

, et al. Mobile phone-based interactive voice response as a tool for improving access to healthcare in remote areas in Ghana – an evaluation of user experiences. Trop Med Int Health 2017 May; 22: 622–630.

369.

Kanstrup

Bertelsen

Nunez

, et al. MOVE: a mobile app designed for social health relations in residential areas. In: Ugon A, Karlsson D, Klein GO and Moen A (eds) Vol. 247, building continents of knowledge in oceans of data: the future of co-created eHealth. Aalborg Univ, Dept Planning, Rendsburggade 14, DK-9000 Aalborg, Denmark, 2018, pp.496–500.

370.

Cuadrado

Cobo

Mateo

, et al. Telemedicine efficiently improves access to hepatitis C management to achieve HCV elimination in the penitentiary setting. Int J Drug Policy 2021; 88.

371.

Frandes

Timar

Tole

, et al. Mobile technology support for clinical decision in diabetic keto-acidosis emergency. In: Cornet R, StoicuTivadar L, Horbst A, Calderon CLP, Andersen SK and HercigonjaSzekeres M (eds) Vol. 210, Digital healthcare empowering Europeans. Univ Med & Farm Timisoara, Dept Funct Sci Med Informat & Biostat, Timisoara, Romania, 2015, pp.316–320.

372.

Lum

MJH

Rosen

King

, et al. Teleoperation in surgical robotics–network latency effects on surgical performance. Annu Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc Annu Int Conf 2009; 2009: 6860–6863.

373.

Berrocal

Garcia-Alonso

Murillo

, et al. Context-aware mobile app for the multidimensional assessment of the elderly. In: 2018 13th Iberian conference on information systems and technologies (CISTI), 2018, pp.1–6.

374.

Leon

Namadingo

Bobrow

, et al. Intervention development of a brief messaging intervention for a randomised controlled trial to improve diabetes treatment adherence in sub-Saharan Africa. BMC Public Health 2021; 21.

375.

Chiuchisan

Geman

Chiuchisan

, et al. NeuroParkinScreen – a health care system for Neurological Disorders Screening and Rehabilitation. In: 2014 international conference and exposition on electrical and power engineering (EPE), 2014, pp.536–540.

376.

Odnoletkova

Buysse

Nobels

, et al. Patient and provider acceptance of telecoaching in type 2 diabetes: a mixed-method study embedded in a randomised clinical trial. BMC Med Inform Decis Mak 2016 Nov; 16: 142.

377.

Saetchinikov

Tcherniavskaia

Zaitsev

, et al. Mobile e-health sensor for non-invasive multi parameter diagnostics of blood biochemistry. In: CLEO/Europe – EQEC 2009 – European conference on lasers and electro-optics and the European quantum electronics conference, 2009, p.1.

378.

Nelissen

Cremers

Okwor

, et al. Pharmacy-based hypertension care employing mHealth in Lagos, Nigeria – a mixed methods feasibility study. BMC Health Serv Res 2018 Dec; 18: 934.

379.

Giorgi Rossi

Ferrari

Amarri

, et al. Describing the process and tools adopted to cocreate a smartphone app for obesity prevention in childhood: mixed method study. JMIR mHealth uHealth 2020 Jun; 8: e16165.

380.

Dosbaba

Hartman

Hnatiak

, et al. Effect of home-based high-intensity interval training using telerehabilitation among coronary heart disease patients. Medicine (Baltimore) 2020 Nov; 99: e23126.

381.

Aiello

Aloise

Baldoni

, et al. Smart homes to improve the quality of life for all. In: 2011 annual international conference of the IEEE engineering in medicine and biology society, 2011, pp.1777–1780.

382.

Smith

Alexander

Marcinkute

, et al. Telemedicine strategy of the European Reference Network ITHACA for the diagnosis and management of patients with rare developmental disorders. Orphanet J Rare Dis 2020 Apr; 15: 103.

383.

Van der Veer

Beukenhorst

Ali

, et al. Development of a mobile digital manikin to measure pain location and intensity. In: PapeHaugaard LB, Lovis C, Madsen IC, Weber P, Nielsen PH and Scott P (eds) Vol. 270, Digital personalized health and medicine. Univ Manchester, Ctr Hlth Informat, Manchester, Lancs, England, 2020, pp.946–950.

384.

Scarfò

Karamanidou

Doubek

, et al. MyPal ADULT study protocol: a randomised clinical trial of the MyPal ePRO-based early palliative care system in adult patients with haematological malignancies. BMJ Open 2021 Nov; 11: e050256.

385.

Morse

Lambden

Quinn

, et al. A Mobile app to improve symptom control and information exchange among specialists and local health workers treating Tanzanian cancer patients: human-centered design approach. JMIR Cancer 2021 Mar; 7: e24062–e24062.

386.

Jensen

McKerrow

Wills

. Acceptability and uptake of an electronic decision-making tool to support the implementation of IMCI in primary healthcare facilities in KwaZulu-Natal, South Africa. Paediatr Int Child Health 2020 Nov; 40: 215–226.

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