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

Assessing the Usage and Usability of a Mental Health Advice Telephone Service in Uganda: Mixed Methods Study

BACKGROUND

Harnessing mobile health (mHealth) solutions could improve the delivery of mental health services and mitigate their impact in Uganda and similar low-resource settings. However, successful adoption requires that mHealth solutions have good usability. We have previously implemented a telephone service to provide mental health information and advice in English and Luganda, utilizing an automated interactive voice response (IVR) system linked to live agents, including mental health care workers and peer support workers.

OBJECTIVE

This study aims to assess the usage and usability of this mental health telephone service.

METHODS

We obtained usage data from the system's call logs over 18 months to study call volumes and trends. We then surveyed callers to gather their characteristics and assess usability using the Telehealth Usability Questionnaire. Additionally, call recordings were evaluated for conversation quality by 3 independent health care professionals, using the Telephone Nursing Dialogue Process, and correlations between quality and usability aspects were investigated.

RESULTS

Over 18 months, the system received 2863 meaningful calls (ie, calls that went past the welcome message) from 1125 unique telephone numbers. Of these, 1153 calls (40.27%) stopped at the prerecorded IVR information, while 1710 calls (59.73%) opted to speak to an agent. Among those who chose to speak with an agent, 1292 calls (75.56%) were answered, 393 calls (22.98%) went to voicemail and were returned in the following working days, and 25 calls (1.46%) were not answered. Usage was generally sustained over time, with spikes in call volume corresponding to marketing events. The survey (n=240) revealed that most callers were caregivers of patients with mental health issues (n=144, 60.0%) or members of the general public (n=46, 19.2%), while a few were patients with mental health issues (n=44, 18.3%). Additionally, the majority were male (n=143, 59.6%), spoke English (n=180, 75.0%), had postsecondary education (n=164, 68.3%), lived within 1 hour or less from Butabika Hospital (n=187, 77.9%), and were aged 25-44 years (n=160, 66.7%). The overall usability score for the system was 4.12 on a 5-point scale, significantly higher than the recommended target usability score of 4 (P=.006). The mean scores for usability components ranged from 3.66 for reliability to 4.41 for ease of use, with all components, except reliability, scoring higher than 4 or falling within its CI. Usability scores were higher for Luganda speakers compared with English speakers, but there was no association with other participant characteristics such as sex, distance from the hospital, age, marital status, duration of symptoms, or treatment status. The quality of call conversations (n=50) was rated at 4.35 out of 5 and showed a significant correlation with usability (Pearson r=0.34, P=.02).

CONCLUSIONS

We found sustained usage of the mental health telephone service, along with a positive user experience and high satisfaction across various user characteristics. mHealth solutions like this should be embraced and replicated to enhance the delivery of health services in Uganda and similar low-resource settings.

Advancing mHealth Research in Low-Resource Settings: Young Women's Insights and Implementation Challenges with Wearable Smartwatch Devices in Uganda

In many regions globally, including low-resource settings, there is a growing trend towards using mHealth technology, such as wearable sensors, to enhance health behaviors and outcomes. However, adoption of such devices in research conducted in low-resource settings lags behind use in high-resource areas. Moreover, there is a scarcity of research that specifically examines the user experience, readiness for and challenges of integrating wearable sensors into health research and community interventions in low-resource settings specifically. This study summarizes the reactions and experiences of young women (N = 57), ages 18 to 24 years, living in poverty in Kampala, Uganda, who wore Garmin vívoactive 3 smartwatches for five days for a research project. Data collected from the Garmins included participant location, sleep, and heart rate. Through six focus group discussions, we gathered insights about the participants' experiences and perceptions of the wearable devices. Overall, the wearable devices were met with great interest and enthusiasm by participants. The findings were organized across 10 domains to highlight reactions and experiences pertaining to device settings, challenges encountered with the device, reports of discomfort/comfort, satisfaction, changes in daily activities, changes to sleep, speculative device usage, community reactions, community dynamics and curiosity, and general device comfort. The study sheds light on the introduction of new technology in a low-resource setting and also on the complex interplay between technology and culture in Kampala's slums. We also learned some insights into how wearable devices and perceptions may influence behaviors and social dynamics. These practical insights are shared to benefit future research and applications by health practitioners and clinicians to advance and enhance the implementation and effectiveness of wearable devices in similar contexts and populations. These insights and user experiences, if incorporated, may enhance device acceptance and data quality for those conducting research in similar settings or seeking to address population-specific needs and health issues.

Protocol for a mixed-methods study to explore implementation outcomes of the Phone-based Interventions under Nurse Guidance after Stroke (PINGS-II) across 10 hospitals in Ghana

BACKGROUND

Stroke survivors are at a substantially higher risk for adverse vascular events driven partly by poorly controlled vascular risk factors. Mobile health interventions supported by task shifting strategies have been feasible to test in small pilot trials in low-income settings to promote vascular risk reduction after stroke. However, real-world success and timely implementation of such interventions remain challenging, necessitating research to bridge the know-do gap and expedite improvements in stroke management. The Phone-based Interventions under Nurse Guidance after Stroke (PINGS-II) is a nurse-led mHealth intervention for blood pressure control among stroke survivors, currently being assessed for efficacy in a hybrid clinical trial across 10 hospitals in Ghana compared with usual care. This protocol aims to assess implementation outcomes such as feasibility, appropriateness, acceptability, fidelity, cost and implementation facilitators and barriers of the PINGS-II intervention.

METHODS AND ANALYSIS

This study uses descriptive mixed methods. Qualitative data to be collected include in-depth interviews and FGDs with patients who had a stroke on the PINGS-II intervention, as well as key informant interviews with medical doctors and health policy actors (implementation context, barriers and facilitators). Data will be analysed by thematic analysis. Quantitative data sources include structured questionnaires for clinicians (feasibility, acceptability and appropriateness), and patients who had a stroke (fidelity and costs). Analysis will include summary statistics like means, medians, proportions and exploratory tests of association including χ2 analysis.

ETHICS AND DISSEMINATION

Ethics approval was obtained from the Committee for Human Research Publication and Ethics at the Kwame Nkrumah University of Science and Technology, Kumasi, Ghana. Voluntary written informed consent will be obtained from all participants. All the rights of the participants and ethical principles guiding scientific research shall be adhered to. Findings from the study will be presented in scientific conferences and published in a peer-reviewed scientific journal. A dissemination meeting will be held with relevant agencies of the Ghana Ministry of Health, clinicians, patient group representatives, and non-governmental organisations.

Review of Genetic and Artificial Intelligence approaches to improving Gestational Diabetes Mellitus Screening and Diagnosis in sub-Saharan Africa

Background: Adverse outcomes from gestational diabetes mellitus (GDM) in the mother and newborn are well established. Genetic variants may predict GDM and Artificial Intelligence (AI) can potentially assist with improved screening and early identification in lower resource settings. There is limited information on genetic variants associated with GDM in sub-Saharan Africa and the implementation of AI in GDM screening in sub-Saharan Africa is largely unknown. Methods: We reviewed the literature on what is known about genetic predictors of GDM in sub-Saharan African women. We searched PubMed and Google Scholar for single nucleotide polymorphisms (SNPs) involved in GDM predisposition in a sub-Saharan African population. We report on barriers that limit the implementation of AI that could assist with GDM screening and offer possible solutions. Results: In a Black South African cohort, the minor allele of the SNP rs4581569 existing in the PDX1 gene was significantly associated with GDM. We were not able to find any published literature on the implementation of AI to identify women at risk of GDM before second trimester of pregnancy in sub-Saharan Africa. Barriers to successful integration of AI into healthcare systems are broad but solutions exist. Conclusions: More research is needed to identify SNPs associated with GDM in sub-Saharan Africa. The implementation of AI and its applications in the field of healthcare in the sub-Saharan African region is a significant opportunity to positively impact early identification of GDM.

Rule-based expert system for the diagnosis of maternal complications during pregnancy: For low resource settings

Objectives

Maternal complications are health challenges linked to pregnancy, encompassing conditions like gestational diabetes, maternal sepsis, sexually transmitted diseases, obesity, anemia, urinary tract infections, hypertension, and heart disease. The diagnosis of common pregnancy complications is challenging due to the similarity in signs and symptoms with general pregnancy indicators, especially in settings with scarce resources where access to healthcare professionals, diagnostic tools, and patient record management is limited. This paper presents a rule-based expert system tailored for diagnosing three prevalent maternal complications: preeclampsia, gestational diabetes mellitus (GDM), and maternal sepsis.

Methods

The risk factors associated with each disease were identified from various sources, including local health facilities and literature reviews. Attributes and rules were then formulated for diagnosing the disease, with a Mamdani-style fuzzy inference system serving as the inference engine. To enhance usability and accessibility, a web-based user interface has been also developed for the expert system. This interface allows users to interact with the system seamlessly, making it easy for them to input relevant information and obtain accurate disease diagnose.

Results

The proposed expert system demonstrated a 94% accuracy rate in identifying the three maternal complications (preeclampsia, GDM, and maternal sepsis) using a set of risk factors. The system was deployed to a custom-designed web-based user interface to improve ease of use.

Conclusions

With the potential to support health services provided during antenatal care visits and improve pregnant women's health outcomes, this system can be a significant advancement in low-resource setting maternal healthcare.