Mobile Health (mHealth) in Infectious Disease Control: A Review of Global Innovations and Out-comes

Riaz Ahmed
Department of Medical Sciences, Military College of Signals NUST, Islamabad, Pakistan
Correspondence to: Riaz Ahmed, riazkhattak450@gmail.com

DOI: https://doi.org/10.70389/PJPH.100024

Additional information

• Ethical approval: N/a
• Consent: N/a
• Funding: No industry funding
• Conflicts of interest: N/a
• Author contribution: Riaz Ahmed – Conceptualization, Writing – original draft, review and editing
• Guarantor: Riaz Ahmed
• Provenance and peer-review:
  Unsolicited and externally peer-reviewed
• Data availability statement: N/a

Keywords: mhealth, Infectious disease control, Disease surveillance, Telemedicine, Low- and middle-income countries (lmics).

Peer Review
Received: 5 June 2025
Last revised: 16 July 2025
Accepted: 18 July 2025
Version accepted: 8
Published: 28 August 2025

Plain Language Summary Infographic

Key Learning Outcomes

• mHealth has the potential to seriously decrease the reaction timing, as well as improve the levels of adherence, throughout varied settings.
• Barriers to scale-up: Infrastructure, equity, and privacy are the key.
• Cross-sectoral coordination and ethical integration of AI will play a very important role in future mHealth development.

Introduction

Mobile health (mHealth) refers to using mobile devices, apps, and new technologies to assist in medical and public health practices.¹ This field in digital health is evolving fast, with devices ranging from basic SMS to highly advanced apps that include artificial intelligence (AI), location services, and data in the cloud. mHealth is especially beneficial because it is accessible and portable, so it fits well with health services in remote, resource-limited, or underserved places.² Due to the increasing applications of AI in health research and writing, the authors state that they made no use of any generative AI tool (like ChatGPT or other large language models) in the drafting, writing, or revision of this manuscript. All analyses, interpretations, and formations described here are original and were conducted manually by the authors, who did not review the literature. mHealth is particularly useful since it is mobile and can be accessed easily, and thus can be used well with such health services in underprivileged areas that are either inaccessible or have poor resources.³ The TITAN Guideline Checklist is shown in Appendix C.

Various areas where mHealth technologies have shown a lot of benefit in infectious disease control are disease surveillance, learning about health, risk communication, diagnostics, treatment adherence, and telemedicine. Using these tools, it is possible to collect data, send messages, do consultations, and remind patients about their medication in real time, making them key in handling all kinds of health situations.⁴ Because of the COVID-19 pandemic, mHealth’s role has become much more significant. Different public health organizations globally began relying on digital options such as tracing contacts, digital assessments, automated warnings, and virtual care to slow transmission.² These actions revealed that mHealth makes public health responses faster, bigger, and more efficient, which benefits social distancing. However, it became obvious during the pandemic that some gaps existed in infrastructure, data security, access to technology, and literacy in the use of technology, which were more common in low- and middle-income countries (LMICs).¹

The study differs from previous ones in being more comprehensive (2010–2025), its focus on LMICs, and its ability to enable a comparative approach to mHealth interventions across different regional contexts—a more comprehensive and policy-relevant synthesis, accordingly. The goal of this review is to analyze the international function of mHealth in controlling, preventing, discovering, and handling infectious diseases. The article has the following special objectives:

1. To explore the role of mHealth technologies in surveillance, prevention, diagnosis, and treatment of infectious diseases.
2. To evaluate global mHealth innovations and their impact on infectious disease control, particularly in LMICs.
3. To assess the effectiveness, accessibility, and scalability of mHealth interventions in outbreak response and pandemic preparedness.
4. To identify challenges in implementation, including privacy, digital literacy, infrastructure, and equity.
5. To provide recommendations for integrating mHealth into national and global public health strategies.

The authors group the information in mHealth deployment areas, present case studies, discuss how well mHealth has worked, mention the obstacles to adopting it, and suggest future ideas. The review would contribute to the available literature by summarizing globally comparative mHealth interventions on the management of infectious diseases on the one hand and giving attention to low- and high-income settings on the other hand. In contrast to other attempts to address the individual illness or geographical area, the given paper will combine several case studies, assess the results of their implementation, and give practical suggestions on future mHealth strategies development.

Methodology

Search strategy: This review has been based on a scoping review through PRISMA-ScR (Figure 1). The following electronic databases were used to locate literature: PubMed, Scopus, Web of Science, World Health Organization (WHO), Global Index Medicus, and Google Scholar (Appendix A). Eligibility criteria were applied as two reviewers screened titles, abstracts, and the full text of the articles independently. Consensus was used to fix disagreements. The inter-rater reliability was determined using the Cohen Kappa (0.82), which demonstrated that there was substantial agreement. Gray literature and preprints were excluded due to a lack of peer review and concerns about methodological reliability. This PRISMA flow diagram outlines the study selection process used in the scoping review. A total of 136 records were identified through database searches and WHO sources. After screening titles and abstracts, 66 records were excluded as irrelevant or duplicates, 70 studies remained for full-text review and assessed, 28 were excluded for not being specific to mHealth or lacking relevant findings, and 42 studies were included in the final narrative synthesis, forming the basis of the review findings. Appendix D shows the PRISMA-ScR checklist.

Search Terms: Search strings included combinations of:

(“mHealth” OR “mobile health” OR “digital health” OR “mobile phone applications”) AND (“infectious diseases” OR “outbreak response” OR “surveillance” OR “telemedicine” OR “diagnostics”)

Eligibility Criteria

Inclusion: nArticles published since 2010 up to March 2025, in the English language, and that discuss mHealth technologies used to control infectious diseases, in surveillance, diagnosis, health education, or telemedicine.

Exclusion: Articles not applying in the field of infectious diseases, articles that did not focus on mHealth, studies in nonhumans, and opinion-only articles with no information regarding implementation.

Screening/Selection: The relevance of initial titles and abstracts was checked. The manuscripts were randomly screened, and eligible full texts were selected through inclusion/exclusion criteria. Lists of references in critical articles were also consulted.

Data Extraction and Synthesis: The information was retrieved on the geographical setting, mHealth role, type of intervention, and outcome reporting. There was no formal meta-analysis since it would have involved heterogeneity; synthesis was made using narrations and tabular representations.

mHealth Tools and Functional Areas in Infectious Disease Control

mHealth has several uses in managing infectious diseases, including disease monitoring, sharing knowledge, detecting infections, ensuring patients follow their treatment plans, and providing medical assistance remotely.⁵ They make health systems work better and cover broader areas, where infrastructure is not available in great abundance.

Surveillance and Outbreak Reporting

Originally, mHealth made a large impact in the field of disease surveillance. In Uganda, the mTrac system demonstrates that basic text message tools can greatly boost how data reaches central health organizations. Each week, health workers submit reports about the number of cases and available drugs that entered the national District Health Information Software 2 (DHIS2) platform.⁶ With this system, it is easier to stop outbreaks as soon as they start and predict their effects more correctly.⁷ Apart from text messaging, mHealth now makes use of Bluetooth and GPS mobile apps for real-time contact tracing. Tools like TraceTogether in Singapore and others used during COVID-19 log when users are near others and warn them, if possible, if exposure is detected.⁸ Usually, these apps supply information to digital dashboards that are needed by health officials to create heatmaps and keep track of disease trends. These tools enable rapid identification of risks and targeted response. While they work well, these systems also deal with issues related to protecting data and getting proper agreement.⁶

Health Education and Risk Communication

Health education is delivered effectively to people in need during all health crises because of mHealth. Mobile phones and apps made it possible for organizations to inform large numbers about proper hygiene, the need for vaccines, and what to do if symptoms appear. Mobile operators in West Africa teamed up with health workers so messages could be sent to illiterate people via voice and text.⁹ These platforms support local preferences and styles when people chat. Thailand’s Line Official COVID-19 Chatbot made it possible to receive updates about the virus, assess if someone has symptoms, and see useful preventive steps.¹⁰ In communities with little education, where few can read, IVR technology sends information in spoken local languages that many can easily use. Actions against misinformation encourage people to follow and believe in official guidance.¹¹

Diagnosis and Clinical Decision Support

The importance of a timely disease diagnosis is known, and keeping this in mind, mHealth has become very useful. With smartphone microscopes, RDTs, and AI, now more people can access medical diagnosis. In areas where malaria is common, mobile devices allow health workers to check RDTs or take pictures for review online.¹² More abilities are added using AI. A chatbot on Babylon Health assists users by finding possible symptoms and instructs those who need to isolate, get tested, or see a doctor. With these tools, conditions are identified earlier, care plans are better, and pressure is relieved on overcrowded medical centers.¹¹

Treatment Adherence and Telemedicine

Sticking to treatment is essential for handling infectious diseases, and it matters most for conditions that last a long time, for instance, TB, HIV, and malaria. Kenya demonstrated the ability of two-way SMS, on the WelTel platform, to encourage patients to follow their medication schedule.¹² Routine notifications encourage patients to share their thoughts and raise any concerns, thus allowing for the simple engagement of patients. Trials have established that this model boosts both the completion of treatment and clinical results.¹³ More patients and doctors started using telemedicine during the pandemic, as this made virtual consultations, remote care, and follow-up easier, which supported population health. CommCare and other tools are useful because they allow community health workers to work with patient records and give health services in regions where Internet access is limited.¹³ Such methods allow care to continue when meeting in person may be dangerous or inconvenient.

Global Case Studies of mHealth Innovations

The world shows many different mHealth innovations created for various public health problems, resources, and infrastructure. This part covers some important regional examples, checking how well these tools work and affect various groups of people.¹⁴

Africa

The mHealth project mTrac, launched in Uganda, relies on SMS to improve both disease monitoring and the supply of drugs. As of 2014, DHIS2 was functioning in more than 2,500 health facilities for health care workers to record weekly details on disease activity and the amount of medicine available. Working with DHIS2, mTrac has reduced the time required for public health intervention by streamlining data sharing.¹⁵ SMS for Life was introduced in Tanzania to tackle constant problems with anti-malarial drugs. Health workers sent weekly updates via text to inform regional managers, so managers were able to act promptly if there were not enough supplies. Because of this program, access to drugs and regular treatment became much easier.¹⁶ CommCare, which comes from Dimagi, is popular in sub-Saharan Africa for supporting community health workers.¹⁷ The software digitizes patient records, provides decision support, and works even when there is no Internet connection, which helps rural areas a lot. The use of HMIS for HIV, tuberculosis, and maternal health has shown clear benefits in keeping track of patients and making health services more effective.¹⁸

Asia

Bluetooth, location information, and tracking of contacts and risks were used together in the Aarogya Setu app of India. Nevertheless, within months of launching, the app was downloaded more than 100 million times, showing that digital health tools supported by the government were in high demand during the pandemic.¹⁹ An official Line COVID-19 bot was created inside a popular messenger application to share updates, check symptoms, and lead users to treatment.¹⁵ Because chatbots are simple to use and work through a common messaging app, they reach many people, both in towns and rural areas, who do not read or understand medical explanations very well.²⁰

Latin America

With DATOS, Colombia is focused on dengue and chikungunya by getting geolocated reports of symptoms from mobile users. They are used together with data from the environment and insects to plan activities that protect people from insects. Because of DATOS, Colombia now warns at an early stage and treats hotspots directly in major cities like Cali and Medellín.²¹

High-Income Settings

Apple and Google announced the release of the Exposure Notification API in 2020, which supported the creation of Bluetooth apps for contact tracing that protect personal data.²² There were differences in how adoption happened around the world, but the technology gave everyone a way to view and track anonymously, which was a key step forward in digital epidemiology for privacy.²⁰ Users in the United States use Flu Near You, which allows them to share symptoms weekly voluntarily. Because it is run by Boston Children’s Hospital and HealthMap, the platform gives updated local flu tracking to support national Centers for Disease Control and Prevention (CDC) surveillance.

Comparative Effectiveness

How well mHealth works in each setting depends on how well the tools are adapted. While people in higher-income countries use the best technology, SMS is used in some developing countries and turns out to be extremely effective. The achievements of such innovations show that focused design, compatibility among systems, and trust form key principles for digital public health.¹⁴ The widespread use of mHealth tools in various settings demonstrates their effectiveness in diverse contexts. However, for them to achieve success, they must adapt to each area, partner locally, and build trust with the public.¹¹

Quality Assessment of Included Studies

The methodological quality of all included studies was determined by using the Mixed Methods Appraisal Tool (MMAT, 2018 version) (Appendix B). Among the 38 studies, 15 were rated as high quality, 17 as moderate, and 6 as low. Details of the full results are found in Appendix B.

Outcomes and Impact of mHealth Interventions

mHealth efforts have shown improvement in controlling infectious diseases by increasing disease monitoring, bringing more people into community health projects, and encouraging treatment compliance. Still, the amount they change health care depends a lot on how well the infrastructure is built, how much it is used, and how it connects with the current health system.¹⁶

Improved Case Detection and Faster Response Times

Thanks to mHealth platforms, monitoring infectious diseases happens much faster and more accurately. By using mTrac, the wait for data in Uganda decreased from weeks to just a few hours, which helped pinpoint disease hotspots early and take necessary measures.⁶ They have contributed to faster response to outbreaks of malaria, cholera, and influenza by deploying resources well in advance. Because of real-time reports and dashboard tools made possible by DHIS2 and other open-source systems, decision-making based on data is easier in places where resources are limited.²⁰

Increased Public Awareness and Engagement

Mobile messaging during crises has greatly impacted the way people take care of their health and view the situation. During the Ebola epidemic in that region, IVR and SMS platforms released messages matching local customs, which helped people know more about how the disease is spread and how to prevent it.¹⁷ Fewer households in Sierra Leone resorted to unsafe burial processes after receiving mHealth-based messaging. Booklets were created about Zika and then COVID-19, along with prompts for people to apply vector control, get vaccinated, and maintain social distance, which led to people reporting an increase in protective behaviours.²²

Enhanced Treatment Adherence and Reduced Disease Burden

mHealth has made it easier for patients to be involved in ongoing illness management. Among the HIV patients in the WelTel study in Kenya, weekly SMS messages resulted in a rise of 12% in taking ART.²³ Programs based on mobile technology were helpful for Indian TB patients in keeping their medication on schedule, resulting in a higher number of patients completing their treatment. Using reminders and mHealth workers on mobile platforms, the number of missed doses has gone down, and patients are doing better.

Cost-Effectiveness and Scalability in Resource-Limited Settings

Many times, mHealth tools are more economical than regular public health interventions due to their use of the existing mobile infrastructure. Investment in hardware is low for systems like SMS for Life in Tanzania, so these are practical for rural settings. After being set up, they enable quick connections among many people, with no significant added expense per user, and their digital structure facilitates rapid expansion during disasters, particularly in well-connected populations.²⁴

Mixed Results: Dependence on Infrastructure and Adoption Rates

Despite all the positives, mHealth interventions still have some problems. Effectiveness depends on how much people engage, how many have mobile access, and how well people use technology. The use of contact tracing apps for COVID-19 in 2021 varied a lot, as several regions noted that people were uneasy about them because of privacy, technical issues, and lack of trust.²⁵ When there is not enough power and network availability in remote regions, the reliability of mHealth is also weaker. Lack of integration with national health systems in how technologies are used may shorten their lifespan.²⁶ In general, mHealth contributes in some ways to infectious disease control, but its effects are unpredictable and depend on how it is implemented and tested. When mHealth solutions are focused on users, fit into health systems, and are adapted for local cultures, they have shown a major impact.²¹

Challenges and Limitations

mHealth could change the way infectious diseases are handled, but putting it to use is often slowed by various challenges. Because of these challenges, mHealth interventions struggle to extend to many people equally, are not as sustainable, and are less effective in LMICs.²⁷

Digital Divide and Mobile Network Limitations

Health inequalities are made worse by ongoing problems in accessing mobile phones and the Internet. In most LMICs, not many women, people in rural areas, or low-income groups can access smartphones and mobile data.²⁷ The 2023 Mobile Gender Gap Report by GSMA shows that women in sub-Saharan Africa are less likely than men to use mobile Internet by 36%. Even though simple phones sometimes exist, people may not be able to use SMS or apps when the Internet is slow, or electricity cuts often occur. For this reason, people who need mHealth the most tend to encounter the biggest challenges getting access to it.²⁴

Data Privacy, Security, and Informed Consent Issues

Health data collected through mobile apps can lead to serious privacy and ethical problems. Application data is frequently not protected properly, which means hackers could easily access it. For example, many users expressed concern that India’s Aarogya Setu app requested excessive personal information without providing an option to decline.²⁰ When users are not very familiar with digital tools, getting true consent becomes even more complex. In many places, there are not enough transparent rules for data use, and not all privacy laws, such as GDPR, are upheld.²⁵

User Engagement, Misinformation, and Health Literacy Gaps

If users do not fully understand mHealth and do not trust it, its deployment will fail. But a lack of digital and health knowledge can cause people to misunderstand mHealth advice. Language differences, relevant content that is scarce, and not knowing how to use the app keep users from using it more. In such situations, mobile devices are also responsible for helping false information go viral. If there are many different messages on social media, users tend to mistrust mHealth applications.²⁸

Interoperability with Health Information Systems

A lot of mHealth solutions are designed to function independently from existing health information systems at the national level. When health care is not connected, real-time sharing of data is blocked, surveillance suffers, and extra work is done in health care.²³ Many times, working platforms like DHIS2 are not possible because there are no consistent data formats or APIs.²⁹ Because some tools are not compatible, it is more difficult for countries to collaborate when dealing with transboundary health risks.³⁰

Regulatory and Ethical Concerns

Many countries have not yet built strong regulations for mHealth technologies. It is not always clear who is responsible for mistakes by AI in diagnosis, how personal health data can cross borders, and how this data should be dealt with in the business world.³⁰ Clear policies are still lacking, so many people doubt the ethical use of these contact tracing technologies. To make sure mHealth is used fairly and legally, policymaking should involve health care workers, tech specialists, members of the public, and lawyers³¹ as described in Figure 2. Resolving these barriers allows mHealth to effectively avoid supporting inequality or failing to support people during emergencies.

Future Directions and Recommendations

For mHealth to be greater in infectious disease surveillance and control, future actions should center around interoperability, new ideas, and equal access. Resilient mHealth can be built by joining forces worldwide, regionally, and locally to link modern technology with public health priorities.³¹

Standardizing mHealth for Infectious Disease Surveillance

mHealth tools need to be standardized for them to integrate well into health systems at both the national and global levels. There is not much uniformity among data standards, rules for protecting privacy, and communication systems across various platform types at present. Defining consistent global regulations—especially in data exchange, security, and system interoperability—can help align regional efforts more effectively.³² The WHO and the International Telecommunication Union should guide and enforce such guidelines, so they match worldwide standards in digital health.³³

AI and Big Data Integration for Predictive Modeling

New technologies like federated learning and decentralized data systems are being noticed to perform privacy-preserving analytics over fragmented data sets, which could fundamentally transform the way the world monitors infectious diseases. Linking AI and big data to mHealth can improve the process of predicting and handling possible outbreaks. When analyzing how people use their phones, what the weather is like, and data from social media, AI can spot possible hotspots ahead of case surges. Some predictive models founded on climate data and human movements have shown they can forecast cases of dengue and similar diseases. They could allow for early action in health and better use of resources.³⁴

Cross-Border mHealth Harmonization for Global Threats

Because infectious diseases affect people everywhere, improving and matching mHealth applications across countries is very important.³⁵ Common designs for surveillance data, patient registries, and contact tracing tools would make the exchange of information and fighting outbreaks easier. Led by the Africa Centres for Disease Control and Prevention, this approach includes a continental digital health strategy that can be used as a model by other regions.³⁶

Public-Private Partnerships to Expand Access and Innovation

Successful growth of mHealth needs joint actions by government agencies, tech companies, and NGOs. Through cooperation between the government and the private sectors, both low-priced and sizable platforms and free health content through mobile networks might be available. Besides, the private sector can bring new ideas to many places, and governments make certain that innovations are regulated and on equal footing for all.³⁷

Capacity Building and Community Co-Design Strategies

If members of a community help to design mHealth interventions, they are more likely to be sustained and lead to results. Making content, language, and user interfaces fit the local scene drives greater acceptance and a better fit with users.³⁸ Training everyone involved in health care helps improve digital skills and trust, and using feedback loops supports steady improvement in the design (Figure 3). Using community-centric methods is essential for mHealth to be useful in public health.³⁹ Countries that invest in these areas can prepare their digital health systems to quickly and fairly respond to infectious disease problems.

Integrated Applications of mHealth in Infectious Disease Control

The four interrelated areas in which mHealth technologies aid in the control of infectious diseases are surveillance, health education, diagnostics, and telemedicine. Such surveillance tools as mobile-based reporting systems make it possible to detect outbreaks and track them in real time. Health education apps communicate prevention, create awareness, and assist in the change of behavior.⁴⁰ Smartphone-compatible tests and other diagnostic means ensure early diagnosis and management of the case. Telemedicine can provide access to care and can be achieved in remote areas or quarantined areas, and provides remote consultation, monitoring, and maintenance of treatment compliance. These tools, when combined, improve the extent of reach, agility, and efficiency of the response of the public health.⁴¹

Reflections on mHealth: Impact, Uptake, and Limitations

Low-tech interventions’ evidence demonstrates impressive effect: mTrac in Uganda is said to have shortened the reporting time of a disease by as much as 60%, whereas SMS in Life in Tanzania increased stock reporting by 36% points (to above 90%) in one district (Table 1). These effect sizes put into context the strength of plain tech that is aligned with the public health demands appropriately.⁴²

Table 1: Comparative analysis of mhealth tools across contexts.42
Intervention/Tool	Type	Reported Impact	Pre-/Post-COVID-19 Uptake	Key Limitation/Lesson
mTrac (Uganda)	SMS-based	↓ Reporting time by ~60%	Pre-COVID-19 rollout: high uptake	Simple tools work well in low-resource settings
SMS for Life (Tanzania)	SMS-based	↑ Stock reports from 26% → >90% in pilot areas	Pre-COVID-19, sustained in some areas	Requires ongoing support and training
Aarogya Setu (India)	AI-based	Enabled rapid risk alerts and contact tracing	Post-COVID-19, >100 M downloads rapidly	Adoption is driven by government mandate
TraceTogether (Singapore)	Bluetooth app	An effective contact tracing tool	Post-COVID-19, widely adopted	High compliance due to centralized strategy
Exposure Notification (United States)	Bluetooth app	Limited impact due to low participation	Post-COVID-19, low opt-in rates	Privacy concerns, fragmented rollout

Conclusion

Global mHealth Applications and Key Lessons Learned

The use of mHealth has played a major role in controlling infections, allowing rapid data collection, helping patients stick with treatments, and widening access to correct health info. mHealth has proven to be effective in Uganda’s mTrac, Kenya’s WelTel, India’s Aarogya Setu, and the Apple/Google Exposure Notification API, which shows it is flexible in both low-resource and high-income regions. Supporting surveillance, keeping everyone informed, diagnosing, and treating from a distance were key roles of mHealth during the Ebola, Zika, and COVID-19 outbreaks. Even so, the challenges of COVID-19 showed new problems, including differing use of health care apps, challenges with technology for some, a lack of data privacy, and system interoperability. It becomes clear from these shortcomings that better planning and design should be used to make sure mHealth interventions benefit everyone.⁴³

mHealth for Equity, Responsiveness, and Resilience

Applying mHealth adds speed to how the health system identifies outbreaks and responds with appropriate interventions. It supports equity by supplying services to groups that are often neglected, on condition that the tools are adjusted to each area’s literacy, language, and Internet situation. As digital health becomes increasingly important in pandemic planning, mHealth offers the flexibility and strength to support people in areas where resources are scarce or populations are dense.⁴⁴

Call for Investment, Regulation, and Continued Research

Realizing what mHealth can achieve requires investing in technology, people, and the ideas coming from the community. Rules and laws should be developed to keep user data secure, facilitate ethical adoption, and promote the integration of AI and big data into mobile technologies. Furthermore, additional studies are required to determine how well mHealth works, engage people in better ways, and support proven practices.⁴⁵ Combining efforts, mHealth can serve as a vital base for inclusive, flexible, and future-prepared public health systems.^46,47 Looking ahead, the combination of AI, wearable biosensors, and real-time data analytics will be the driving force behind the next achievements in mHealth. Such technologies may allow for the detection of outbreaks earlier, provide more individualized responses, and offer location-sensitive and dynamic responses. The inclusion of such innovations within the structures of public health will not only help improve promptness but also improve resilience against the new global health risks.⁴⁸

References

1. Mahmood H, McKinstry B, Luz S, Fairhurst K, Nasim S, Hazir T, et al. Community health worker-based mobile health (mHealth) approaches for improving management and caregiver knowledge of common childhood infections: a systematic review. J Glob Health. 2020;10(2):020438. https://doi.org/10.7189/jogh.10.020438
2. Ahn E, Liu N, Parekh T, Patel R, Baldacchino T, Mullavey T, et al. A mobile app and dashboard for early detection of infectious disease outbreaks: development study. JMIR Public Health Surveill. 2021;7(3):e14837. https://doi.org/10.2196/14837
3. Agha RA, Mathew G, Rashid R, Kerwan A, Al-Jabir A, Sohrabi C, et al. Transparency in the reporting of artificial intelligence–the TITAN guideline. Premier J Sci. 2025;10:100082. https://doi.org/10.70389/PJS.100082
4. Wirth FN, Johns M, Meurers T, Prasser F. Citizen-centred mobile health apps collecting individual-level spatial data for infectious disease management: scoping review. JMIR Mhealth Uhealth. 2020;8(11):e22594. https://doi.org/10.2196/22594
5. Early J, Gonzalez C, Gordon-Dseagu V, Robles-Calderon L. Use of mobile health (mHealth) technologies and interventions among community health workers globally: a scoping review. Health Promot Pract. 2019;20(6):805–17. https://doi.org/10.1177/1524839919855391
6. Nuwagaba I, Tshombe LM, Peter K, Kalaba D, Mbuga J, Molokwane T, et al. An assessment of the public administration of mobile data processing for project success in Africa: a contextual elucidation of the Ministry of Health (MOH), Kampala, Uganda. Afr J Bus Econ Res. 2025;20(1):497–517. https://doi.org/10.31920/1750-4562/2025/v20n1a21
7. Brinkel J, Krämer A, Krumkamp R, May J, Fobil J. Mobile phone-based mHealth approaches for public health surveillance in sub-Saharan Africa: a systematic review. Int J Environ Res Public Health. 2014;11(11):11559–82. https://doi.org/10.3390/ijerph111111559
8. Hansen MM, Dahiya B. TraceTogether contact tracing: a smart nation innovation. Front Sustain Cities. 2025;7:1552449. https://doi.org/10.3389/frsc.2025.1552449
9. Braun R, Catalani C, Wimbush J, Israelski D. Community health workers and mobile technology: a systematic review of the literature. PLoS One. 2013;8(6):e65772. https://doi.org/10.1371/journal.pone.0065772
10. Intawong K, Olson D, Chariyalertsak S. Application technology to fight the COVID-19 pandemic: lessons learned in Thailand. Biochem Biophys Res Commun. 2021;534:830–6. https://doi.org/10.1016/j.bbrc.2020.10.097
11. Wu T, Fan B, Cai X, Li R, Wang Q, Deng Z. Community health workers’ continuance of mobile health applications: an extended expectation confirmation model. Inf Manag. 2024;61(6):104008. https://doi.org/10.1016/j.im.2024.104008
12. Olowoyo KS, Esan DT, Olowoyo P, Oyinloye BE, Fawole IO, Aderibigbe S, et al. Treatment adherence and outcomes in patients with tuberculosis treated with telemedicine: a scoping review. Trop Med Infect Dis. 2025;10(3):78. https://doi.org/10.3390/tropicalmed10030078
13. Svoronos T, Mjungu P, Dhadialla R, Luk R, Zue C, Jackson J, et al. CommCare: automated quality improvement to strengthen community-based health. Weston: D-Tree International; 2010. Available from: https://www.researchgate.net/profile/Theodore-Svoronos-2/publication/268198360_CommCare_Automated_Quality_Improvement_To_Strengthen_Community-Based_Health/links/5485e4780cf2ef3447892691/CommCare-Automated-Quality-Improvement-To-Strengthen-Community-Based-Health.pdf
14. Kruse C, Betancourt J, Ortiz S, Luna SM, Bamrah IK, Segovia N. Barriers to the use of mobile health in improving health outcomes in developing countries: systematic review. J Med Internet Res. 2019;21(10):e13263. https://doi.org/10.2196/13263
15. Déglise C, Suggs LS, Odermatt P. SMS for disease control in developing countries: a systematic review of mobile health applications. J Telemed Telecare. 2012;18(5):273–81. https://doi.org/10.1258/jtt.2012.110810
16. Harding K. Global health innovation technology models. Nanobiomedicine. 2016;3:7. https://doi.org/10.5772/62921
17. Agapitova N, Navarrete Moreno C. Dimagi. SIDALC; 2017. https://doi.org/10.1596/27661
18. Wood CS, Thomas MR, Budd J, Mashamba-Thompson TP, Herbst K, Pillay D, et al. Taking connected mobile-health diagnostics of infectious diseases to the field. Nature. 2019;566(7745):467–74. https://doi.org/10.1038/s41586-019-0956-2
19. McCool J, Dobson R, Whittaker R, Paton C. Mobile health (mHealth) in low-and middle-income countries. Annu Rev Public Health. 2022;43(1):525–39. https://doi.org/10.1146/annurev-publhealth-052620-093850
20. Geldsetzer P, Flores S, Flores B, Rogers AB, Chang AY. Healthcare provider-targeted mobile applications to diagnose, screen, or monitor communicable diseases of public health importance in low and middle-income countries: a systematic review. PLOS Digit Health. 2023;2(10):e0000156. https://doi.org/10.1371/journal.pdig.0000156
21. Delgado DM, Pérez A, Scoppetta O. Drug use in Colombia: a sophisticated analysis of simple data. Addictions. 2001;13(1):81–8. https://doi.org/10.20882/adicciones.588
22. Bair H, Wanger JD, Shah NR. A brief history of exposure notification during the COVID-19 pandemic in the United States, 2020–2021. Public Health Rep. 2022;137(2_suppl):23S–8S. https://doi.org/10.1177/00333549221099533
23. van der Kop ML, Memetovic J, Smillie K, Coleman J, Hajek J, Van Borek N, et al. Use of the WelTel mobile health intervention at a tuberculosis clinic in British Columbia: a pilot study. J Mob Technol Med. 2013;2(3):7–14. https://doi.org/10.7309/jmtm.2.3.3
24. Ugwu OP, Alum EU, Ugwu JN, Eze VH, Ugwu CN, Ogenyi FC, et al. Harnessing technology for infectious disease response in conflict zones: challenges, innovations, and policy implications. Medicine. 2024;103(28):e38834. https://doi.org/10.1097/MD.0000000000038834
25. Gwadabe HA, Elelu SA, Innocent MO, Ibrahim GO, Adekunle IJ, Abdulsalam M. Exploring local-to-global win-win strategies for infectious disease control: enhancing human health and sustainability. Biol Sci. 2025;5(1):850–64. Available from: https://irrespub.com/biolsciences/index.php/1/article/download/247/117
26. Keddy KH, Saha S, Kariuki S, Kalule JB, Qamar FN, Haq Z, et al. Using big data and mobile health to manage diarrhoeal disease in children in low-income and middle-income countries: societal barriers and ethical implications. Lancet Infect Dis. 2022;22(5): e130–e42. https://doi.org/10.1016/S1473-3099(21)00585-5
27. Rowe AK, Rowe SY, Peters DH, Holloway KA, Chalker J, Ross-Degnan D. Effectiveness of strategies to improve health-care provider practices in low-income and middle-income countries: a systematic review. Lancet Glob Health. 2018;6(11):e1163–75. https://doi.org/10.1016/S2214-109X(18)30398-X
28. Ntshoe G, Shonhiwa AM, Govender N, Page N. A systematic review on mobile health applications for foodborne disease outbreak management. BMC Public Health. 2021;21:1–8. https://doi.org/10.1186/s12889-021-12283-6
29. Dehnavieh R, Haghdoost A, Khosravi A, Hoseinabadi F, Rahimi H, Poursheikhali A, et al. The District Health Information System (DHIS2): a literature review and meta-synthesis of its strengths and operational challenges based on the experiences of 11 countries. Health Inf Manag J. 2019;48(2):62–75. https://doi.org/10.1177/1833358318777713
30. Geldsetzer P, Flores S, Wang G, Flores B, Rogers AB, Bunker A, et al. A systematic review of healthcare provider-targeted mobile applications for non-communicable diseases in low- and middle-income countries. NPJ Digit Med. 2022;5(1):99. https://doi.org/10.1038/s41746-022-00644-3
31. Ara A, Mifa AF. Integrating artificial intelligence and big data in mobile health: a systematic review of innovations and challenges in healthcare systems. Glob Mainstream J Bus Econ Dev Proj Manag. 2024;3(01):1–16. https://doi.org/10.62304/jbedpm.v3i01.70
32. Carrillo MA, Kroeger A, Cardenas Sanchez R, Diaz Monsalve S, Runge-Ranzinger S. The use of mobile phones for the prevention and control of arboviral diseases: a scoping review. BMC Public Health. 2021;21:1–6. https://doi.org/10.1186/s12889-020-10126-4
33. Bisrat H, Manyazewal T, Fekadu A. Mobile health–supported active syndrome surveillance for COVID-19 early case finding in Addis Ababa, Ethiopia: comparative study. Interact J Med Res. 2023;12(1):e43492. https://doi.org/10.2196/43492
34. Yeung AW, Litvinova O, Bragazzi NL, Khader Y, Rahman MM, Said Z, et al. Digital health and mobile health: a bibliometric analysis of the 100 most cited papers and their contributing authors. Explor Digit Health Technol. 2024;2:86–100. https://doi.org/10.37349/edht.2024.00013
35. Amu H. Africa Centres for Disease Control and Prevention (Africa CDC): achievements and challenges in promoting health security. Prev Med Res Rev. 2024;1(1):60. https://doi.org/10.4103/PMRR.PMRR_86_23
36. Degavre F, Kieffer S, Bol D, Dekimpe R, Desterbecq C, Pirson T, et al. Searching for sustainability in health systems: toward a multidisciplinary evaluation of mobile health innovations. Sustainability. 2022;14(9):5286. https://doi.org/10.3390/su14095286
37. Okunade KS, Adejimi AA, Adekanye TV, Allsop MJ, Adelabu H, Thomas-Ogodo O, et al. Impact of mobile health technologies on human papillomavirus vaccination uptake among mothers of unvaccinated girls aged 9–14 years in Lagos, Nigeria (mHealth-HPVac): study protocol of a randomised controlled trial. BMC Cancer. 2024;24(1):751. https://doi.org/10.1186/s12885-024-12538-6
38. Galetsi P, Katsaliaki K, Kumar S. Exploring benefits and ethical challenges in the rise of mHealth (mobile healthcare) technology for the common good: an analysis of mobile applications for health specialists. Technovation. 2023;121:102598. https://doi.org/10.1016/j.technovation.2022.102598
39. Needamangalam Balaji J, Prakash S, Park Y, Baek JS, Shin J, Rajaguru V, et al. A scoping review on accentuating the pragmatism in the implication of mobile health (mHealth) technology for tuberculosis management in India. J Pers Med. 2022;12(10):1599. https://doi.org/10.3390/jpm12101599
40. World Health Organisation. Global strategy on digital health 2020–2025. Geneva: WHO; 2021. Available from: https://www.who.int/publications/i/item/9789240020924
41. World Health Organisation. mRNA technology transfer (mRNA-TT) programme. Geneva: WHO; 2024. Available from: https://www.who.int/initiatives/mrna-technology-transfer-(mrna-tt)-programme
42. World Health Organisation Regional Office for Africa. The World Health Organisation (WHO) Africa, Bill and Melinda Gates Foundation, pursue collaboration to leverage data analytics to fight diseases. Brazzaville: WHO-AFRO; 2024. Available from: https://who-africa.africa-newsroom.com/press/the-world-health-organization-who-africa-bill-and-melinda-gates-foundation-pursue-collaboration-to-leverage-data-analytics-to-fight-diseases?lang=en
43. Mansouri F, Darvishpour A. Mobile health applications in the COVID-19 pandemic: a scoping review of the reviews. Med J Islam Repub Iran. 2023;37:8. https://doi.org/10.47176/mjiri.37.8
44. Ding X, Wuerth K, Sakakibara B, Schmidt J, Parde N, Holsti L, et al. Understanding mobile health and youth mental health: a scoping review. JMIR Mhealth Uhealth. 2023;11:e44951. https://doi.org/10.2196/44951
45. Cao J, Lim Y, Sengoku S, Guo X, Kodama K. Exploring the shift in international trends in mobile health research from 2000 to 2020: bibliometric analysis. JMIR Mhealth Uhealth. 2021;9(9):e31097. https://doi.org/10.2196/31097
46. eVisit. mHealth: what’s happening in mobile healthcare. Mesa, AZ: eVisit. Available from: https://evisit.com/mhealth-whats-happening-in-mobile-healthcare
47. Latif S, Rana R, Qadir J, Ali A, Imran MA, Younis MS. Mobile health in the developing world: review of literature and lessons from a case study. IEEE Access. 2017;5:11540–56. https://doi.org/10.1109/ACCESS.2017.2710800
48. Olaboye JA, Maha CC, Kolawole TO, Abdul S. Innovations in real-time infectious disease surveillance using AI and mobile data. Int Med Sci Res J. 2024;4(6):647–67. https://doi.org/10.51594/imsrj.v4i6.1190
49. Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169(7):467–73. https://doi.org/10.7326/M18-0850

Appendixes

Appendix A: Search strategy

Database	Final Search Date	Search String
PubMed	March 25, 2025	(“mHealth” OR “mobile health” OR “digital health” OR “mobile phone applications”) AND (“infectious diseases” OR “outbreak response” OR “surveillance” OR “telemedicine” OR “diagnostics”)
Scopus	March 25, 2025
Web of Science	March 25, 2025
WHO Global Index Medicus	March 25, 2025
Google Scholar	March 25, 2025

Appendix B: Risk of Bias Assessment (Using MMAT)

Study	Design	Screening Questions Met	Methodological Quality Score (out of 5)	Risk of Bias
Nuwagaba et al. (mTrac, Uganda)	Quantitative	Yes	4/5	Moderate
Olowoyo et al. (WelTel, Kenya)	Mixed methods	Yes	5/5	Low
Svoronos et al. (CommCare, SSA)	Qualitative	Yes	3/5	High
Intawong et al. (Thailand Bot)	Quantitative	Yes	4/5	Moderate
Hansen et al. (TraceTogether, SG)	Mixed methods	Yes	5/5	Low

Appendix C: TITAN Guideline Checklist 2025⁴⁹

TITAN Guideline Checklist 2025
Topic	Item	Description	Page number
Artificial Intelligence (AI) (Some journals may prefer this in the methods and/or acknowledgments section, and it should also be declared in the cover letter.)	1	Declaration of whether any AI was used in the research and manuscript development State no, if that is the case. If yes, proceed to item 1a.	The research content, analyses, figures, or write-ups have not been developed using any generative AI tools (e.g., ChatGPT, Gemini).
	1a	Purpose and Scope of AI Use Precisely state why AI was employed (e.g., development of research questions, language drafting, statistical analysis/summarization, image annotation, etc.).  Was generative AI utilized, and if so, how? Clarify the stage(s) of the reporting workflow affected (planning, writing, revisions, figure creation). Confirmation that the author(s) take responsibility for the integrity of the content affected/generated.	Not applicable
	1b	AI Tool(s) and Configuration Name each system (vendor, model, major version/date).  State the date it was used. Specify relevant parameters (e.g., prompt length, plug-ins, fine-tuning, temperature).  Declare whether the tool operated locally on-premises, or via a cloud API, and any integrations with other systems.	Not applicable
	1c	Data Inputs and Safeguards Describe categories of data provided to the AI (patient text, de-identified images, literature abstracts).  Confirm that all inputs were de-identified and compliant with GDPR/HIPAA.  Note any institutional approvals or data-sharing agreements obtained.	Not applicable
	1d	Human Oversight and Verification Identify the supervising author(s) who reviewed every AI output.  Detail the process for fact-checking and clinical accuracy checks. State whether any AI-generated text/figures were edited or discarded. Acknowledge the limitations of AI and its use.	Not applicable
	1e	Bias, Ethics, and Regulatory Compliance Outline steps taken to detect and mitigate algorithmic bias (e.g., cross-checking against underrepresented populations).  Affirm adherence to relevant ethical frameworks. Disclose any conflicts of interest or financial ties to AI vendors.	Not applicable
	1f	Reproducibility and Transparency Provide the exact prompts or code snippets (as supplementary material if lengthy).  Supply version-controlled logs or model cards where possible. If applicable, state repository, hyperlink, or digital object identifier (DOI) where AI-generated artifacts can be accessed, enabling attempts at independent replication of the query/input.	Not applicable

Appendix D: PRISMA-ScR Checklist

Section	Item	PRISMA-ScR Checklist Item	Reported on Page #
TITLE	1	Identify the report as a scoping review.	Title Page
ABSTRACT	2	Provide a structured summary including background, objectives, methods, results, and conclusions.	Page 1
INTRODUCTION	3	Describe the rationale for the review in the context of what is already known.	Pages 1–2
	4	State the review objectives and questions.	Page 2
METHODS	5	Indicate if a protocol exists and where it can be accessed.	Not reported
	6	Specify eligibility criteria (inclusion/exclusion).	Page 2
	7	Detailed information sources (databases, dates, contacts).	Page 2, Appendix A
	8	Present the full search strategy for at least one database.	Appendix A
	9	State the process for selecting sources of evidence.	Page 2
	10	Describe the data charting process and tools used.	Page 2
	11	List and define all variables extracted from sources of evidence.	Page 2
	12	If done, describe methods for the critical appraisal of included sources.	Page 2, Appendix B
	13	State methods for data synthesis and summary.	Page 2–3
RESULTS	14	Provide the number of sources screened, assessed for eligibility, and included, with reasons for exclusion.	Page 2, Figure 1
	15	Provide characteristics of included sources.	Throughout Results
	16	Present the results of individual sources of evidence.	Pages 3–6
	17	If done, present the critical appraisal within the results.	Page 6, Appendix B
	18	Summarize and present charted data and key findings.	Pages 6–7
DISCUSSION	19	Summarize main results and link to objectives.	Page 7
	20	Discuss limitations of the scoping review process.	Page 7
	21	Provide general interpretation and implications for future research.	Page 7
FUNDING	22	Describe sources of funding and the role of funders.	Not reported

Cite this article as:
Ahmed R. Mobile Health (mHealth) in Infectious Disease Control: A Review of Global Innovations and Outcomes. Premier Journal of Public Health 2025;4:100024

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