Background: Adolescents transitioning from childhood to adulthood need to be equipped with sexual reproductive health (SRH) knowledge, skills, attitudes, and values that empower them. Accessible, reliable, appropriate, and friendly information can be provided through mobile phone–based health interventions. Objective: This study aims to investigate the effectiveness and impact of an Unstructured Supplementary Service Data (USSD)–based app in increasing adolescents’ knowledge about contraceptives, gender-based stereotypes, sexually transmitted infections (STIs), abstinence, and perceived vulnerability, and helping adolescents make informed decisions about their SRH. Methods: A randomized controlled trial (RCT) methodology was applied to investigate the potential of a USSD-based app for providing on-demand SRH information. To be eligible, adolescents aged 15 to 19 years residing in Kibra, Kenya, had to have access to a phone and be available for the 3-month follow-up visit. Participants were randomly assigned to the intervention (n=146) and control (n=154) groups using sequentially numbered, opaque, sealed envelopes. The primary outcome was improved SRH knowledge. The secondary outcome was improved decision-making on SRH. The outcomes were measured using validated tools on adolescent SRH and user perceptions during the follow-up visit. A paired sample t test was used to compare the changes in knowledge scores in both groups. The control group did not receive any SRH information. Results: During the RCT, 54.9% (62/109) of adolescents used the USSD-based app at least once. The mean age by randomization group was 17.3 (SD 1.23) years for the control group and 17.3 (SD 1.12) years for the intervention group. There was a statistically significant difference in the total knowledge scores in the intervention group (mean 10.770, SD 2.012) compared with the control group (mean 10.170, SD 2.412) conditions (t179=2.197; P=.03). There was a significant difference in abstinence (P=.01) and contraceptive use (P=.06). Of the individuals who used the app, all participants felt the information received could improve decision-making regarding SRH. Information on STIs was of particular interest, with 27% (20/62) of the adolescents seeking information in this area, of whom 55% (11/20) were female. In relation to improved decision-making, 21.6% (29/134) of responses showed the adolescents were able to identify STIs and were likely to seek treatment; 51.7% (15/29) of these were female. Ease of use was the most important feature of the app for 28.3% (54/191) of the responses. Conclusions: Adolescents require accurate and up-to-date SRH information to guide their decision-making and improve health outcomes. As adolescents already use mobile phones in their day-to-day lives, apps provide an ideal platform for this information. A USSD-based app could be an appropriate tool for increasing SRH knowledge among adolescents in low-resource settings. Adolescents in the study valued the information provided because it helped them identify SRH topics on which they needed more information. Trial Registration: Pan African Clinical Trial Registry PACTR202204774993198; https://pactr.samrc.ac.za/TrialDisplay.aspx?TrialID=22623

Background: Neglected tropical diseases (NTDs) affect over 1.5 billion people worldwide, primarily impoverished populations in low- and middle-income countries. Skin NTDs, a significant subgroup, manifest primarily as skin lesions and require extensive diagnosis and treatment resources, including trained personnel and financial backing. The World Health Organization has introduced the SkinNTDs app, a mobile health tool designed to train and be used as a decision support tool for frontline health care workers. As most digital health guidelines prioritize the thorough evaluation of mobile health interventions, it is essential to conduct a rigorous and validated assessment of this app. Objective: This study aims to assess the usability and user experience of World Health Organization SkinNTDs app (version 3) as a capacity-building tool and decision-support tool for frontline health care workers. Methods: A cross-sectional study was conducted in Ghana and Kenya. Frontline health care workers dealing with skin NTDs were recruited through snowball sampling. They used the SkinNTDs app for at least 5 days before completing a web-based survey containing demographic variables and the user version of the Mobile Application Rating Scale (uMARS), a validated scale for assessing health apps. A smaller group of participants took part in semistructured interviews and one focus group. Quantitative data were analyzed using SPSS with a 95% CI and P≤.05 for statistical significance and qualitative data using ATLAS.ti to identify attributes, cluster themes, and code various dimensions that were explored. Results: Overall, 60 participants participated in the quantitative phase and 17 in the qualitative phase. The SkinNTDs app scored highly on the uMARS questionnaire, with an app quality mean score of 4.02 (SD 0.47) of 5, a subjective quality score of 3.82 (SD 0.61) of 5, and a perceived impact of 4.47 (SD 0.56) of 5. There was no significant association between the app quality mean score and any of the categorical variables examined, according to Pearson correlation analysis; app quality mean score vs age (P=.37), sex (P=.70), type of health worker (P=.35), country (P=.94), work context (P=.17), frequency of dealing with skin NTDs (P=.09), and dermatology experience (P=.63). Qualitative results echoed the quantitative outcomes, highlighting the ease of use, the offline functionality, and the potential utility for frontline health care workers in remote and resource-constrained settings. Areas for improvement were identified, such as enhancing the signs and symptoms section. Conclusions: The SkinNTDs app demonstrates notable usability and user-friendliness. The results indicate that the app could play a crucial role in improving capacity building of frontline health care workers dealing with skin NTDs. It could be improved in the future by including new features such as epidemiological context and direct contact with experts. The possibility of using the app as a diagnostic tool should be considered. International Registered Report Identifier (IRRID): RR2-10.2196/39393

Background: Despite the effective scale-up of HIV testing and treatment programs, only 75% of people living with HIV (PLWH) globally know their status, and this rate is lower among men. This highlights the importance of implementing HIV testing and linkage interventions with a high uptake in this population. In a cluster randomized controlled trial conducted in Kenya between 2013 and 2015, we found that assisted partner services (APS) for HIV-exposed partners of newly diagnosed PLWH safely reached more HIV-exposed individuals with HIV testing compared with client referral alone. However, more data are needed to evaluate APS implementation in a real-world setting. Objective: This study aims to evaluate the effectiveness, acceptability, fidelity, and cost of APS when integrated into existing HIV testing services (HTS) in Western Kenya. Methods: Our study team from the University of Washington and PATH is integrating APS into 31 health facilities in Western Kenya. We are enrolling females newly diagnosed with HIV (index clients) who consent to receiving APS, their male sexual partners, and female sexual partners of male sexual partners who tested HIV positive. Female index clients and sexual partners testing HIV positive will be followed up at 6 weeks, 6 months, and 12 months postenrollment to assess linkage to care, antiretroviral therapy initiation, and HIV viral load suppression. We will evaluate the acceptability, fidelity, and cost of real-world implementation of APS via in-depth interviews conducted with national, county, and subcounty-level policy makers responsible for HTS. Facility health staff providing HTS and APS, in addition to staff working with the study project team, will also be interviewed. We will also conduct direct observations of facility infrastructure and clinical procedures and extract data from the facilities and county and national databases. Results: As of March 2020, we have recruited 1724 female index clients, 3201 male partners, and 1585 female partners. We have completed study recruitment as well as 6-week (2936/2973, 98.75%), 6-month (1596/1641, 97.25%), and 12-month (725/797, 90.9%) follow-up visits. Preliminary analyses show that facilities scaling up APS identify approximately 12-18 new HIV-positive males for every 100 men contacted and tested. We are currently completing the remaining follow-up interviews and incorporating an HIV self-testing component into the study in response to the COVID-19 pandemic. Conclusions: The results will help bridge the gap between clinical research findings and real-world practice and provide guidance regarding optimal strategies for APS integration into routine HIV service delivery. International Registered Report Identifier (IRRID): DERR1-10.2196/27262

Background: Neglected tropical diseases (NTDs) affect over 1.5 billion people worldwide, the majority of them belonging to impoverished populations in low- and middle-income countries (LMICs). Skin NTDs are a subgroup of NTDs that manifest primarily as skin lesions. The diagnosis and treatment of skin NTDs entail considerable resources, including trained personnel and financial backing. Many interventions are being launched and evaluated, particularly mobile health (mHealth) interventions, such as Skin NTDs App, a training and decision support tool offered by the World Health Organization (WHO) for frontline health workers (FHWs). As most digital health guidelines prioritize the thorough evaluation of mHealth interventions, it is essential to conduct a rigorous and validated assessment of Skin NTDs App. Objective: We aim to assess the quality of version 3 of Skin NTDs App, developed for the WHO by Universal Doctor and Netherlands Leprosy Relief as a training and decision support tool for FHWs. Methods: A cross-sectional study will be conducted in 2 LMICs: Ghana and Kenya. We will use snowball sampling recruitment to select 48 participants from the target population of all FHWs dealing with skin NTDs. The sample group of FHWs will be asked to download and use Skin NTDs App for at least 5 days before answering a web-based survey containing demographic variables and the user Mobile App Rating Scale (uMARS) questionnaire. A semistructured interview will then be conducted. Quantitative and qualitative data will be analyzed using SPSS (version 25; SPSS Inc), with statistical significance for all tests set at a 95% CI and P≤.05 considered significant. Data derived from the semistructured interviews will be clustered in themes and coded to enable analysis of various dimensions using ATLAS.ti. Results: The estimated completion date of the study is in the third quarter of 2022. The results are expected to show that Skin NTDs App version 3 has a good reported user experience, as assessed using the uMARS scale. No differences are expected to be found, except for those related to experience in dermatology and the use of mobile technology that could influence the final score. Semistructured interviews are expected to complete the results obtained on the uMARS scale. Moreover, they will be the previous step before assessing other aspects of the app, such as its efficiency and how it should be disseminated or implemented. Conclusions: This study is the first step in a qualitative and quantitative assessment of Skin NTDs App as a training and support tool for FHWs diagnosing and managing skin NTDs. Our results will serve to improve future versions of the App. International Registered Report Identifier (IRRID): PRR1-10.2196/39393

Background: Neonatal mortality remains a major public health challenge in low- and middle-income countries (LMICs), particularly in sub-Saharan Africa, where health systems often lack effective triage mechanisms to identify and prioritize high-risk neonates. Existing clinical tools frequently fail to support timely decision-making during the critical early postnatal period. A previous machine learning (ML)–based neonatal risk prediction model developed using multicountry LMIC datasets demonstrated high predictive accuracy for neonatal mortality in the Indian context, achieving an area under the curve above 0.80. The model incorporates 11 neonatal parameters assessed from delivery through day 2 of life, with birth weight identified as the strongest predictor. Objective: This study aims to evaluate the contextual feasibility, applicability, and usability of the neonatal risk predictor variables embedded in the ML model in Kenyan health care facilities to inform potential adoption aligned with national health priorities and global targets, including Sustainable Development Goal 3.2. Methods: A mixed methods feasibility study will be conducted through the real-world preimplementation of a neonatal risk assessment tool in 3 Kenyan health facilities that provide neonatal services. Qualitative and quantitative approaches will be combined to strengthen methodological rigor and enhance the credibility of the findings. The study will be implemented in 3 sequential phases. Phase 1 will involve key informant interviews to capture contextual insights on existing workflows, stakeholder perceptions, and implementation considerations. Phase 2 will comprise a 4-month intervention period during which a paper-based neonatal risk tool will be integrated into routine clinical workflows to assess feasibility, applicability, and usability. Phase 3 will involve postintervention surveys conducted longitudinally at 2 time points to evaluate outcomes, user experience, and implementation barriers. Results: The neonatal risk predictors are expected to demonstrate strong contextual feasibility, allowing for integration into existing triage systems with minimal workflow disruption. Their applicability is expected to enable early identification of high-risk neonates within the first 48 hours of life, thereby enabling timely clinical decision-making. Usability assessments are expected to indicate positive user experience, acceptability, and perceived usefulness among health care workers. Six participants (frontline neonatal health workers) had been recruited by the time of initial submission of the manuscript (August 2025). Data analysis is ongoing and is expected to be concluded in March 2026. Publication of study findings is expected by June 2026. Conclusions: The study is expected to generate actionable evidence to support the translation of ML-based neonatal risk prediction into routine clinical practice in LMIC settings, thereby reducing neonatal mortality and advancing progress toward Sustainable Development Goal target 3.2.