About 1.3 million people in Kenya are living with HIV, and Homa Bay County has the highest rate in the country. Even though HIV treatment has improved, many men still face challenges staying in care, taking their medicine, and achieving good health. This is often because of stigma, male gender norms, and lack of support designed specifically for men.

Nishauri is a mobile health (mHealth) app created to help people living with HIV by sending reminders, health tips, and other support through their phones. It has already reached over 300,000 users in Kenya. However, it is not yet clear how well it works for men in improving care and treatment.

This study, led by Maseno University in Kenya and the University of California San Francisco in the U.S., will test how the Nishauri app affects men's HIV care. We will work with 347 men aged 18 to 55 who own a smartphone or tablet and are already receiving HIV treatment at four clinics in Homa Bay. We will collect information through surveys before and after using the app, and also conduct focus group discussions to better understand what helps or makes it hard for men to use the app.

We believe that using Nishauri will help improve men's treatment outcomes-like staying in care, taking medicine regularly, and having lower viral load.

Introduction Background Globally, men are 27% less likely than women to seek HIV testing and often present to care at later stages of illness. In sub-Saharan Africa, men's participation in HIV care is further constrained by cultural expectations that prioritize economic provision and emotional suppression. Kenya continues to face significant challenges in the fight against HIV/AIDS, with an estimated 1.3 million people living with HIV. Despite advances in antiretroviral therapy (ART) and HIV care services, men experience persistent barriers to accessing and engaging with care. Masculine norms around strength, stoicism, and self-reliance often discourage men from seeking health services, including HIV testing and treatment, contributing to lower engagement and adherence among men living with HIV. The lack of male-targeted interventions compounds these challenges, making it critical to develop and evaluate strategies that effectively engage men in care to achieve the UNAIDS 95-95-95 targets by 2030.

Mobile health (mHealth) interventions have emerged as promising tools for improving healthcare delivery and patient engagement, particularly in resource-constrained settings. By leveraging mobile devices to deliver health-related information and services, mHealth strategies help bridge critical gaps in access, communication, and continuity of care. Evidence from low- and middle-income countries (LMICs) suggests that mHealth interventions can enhance antiretroviral therapy (ART) adherence, retention in care, and health literacy among people living with HIV.

Globally, tools such as SMS reminders and mobile applications have demonstrated significant potential to improve clinical outcomes, including ART adherence, retention, and viral suppression. In sub-Saharan Africa, where healthcare access is often limited, mHealth approaches have been effective in reducing loss to follow-up and improving engagement in care. For instance, studies in Uganda and South Africa show that mobile reminders and SMS-based counseling significantly improve adherence and retention among HIV-positive individuals. These tools facilitate behavior change through personalized messaging, medication reminders, and educational content. Critically, gender-sensitive and culturally adapted mHealth interventions are more effective among men, as they address stigma and align health-seeking behaviors with socially accepted masculine norms. However, there remains limited evidence on the implementation outcomes, service outcomes, and long-term sustainability of mHealth interventions specifically tailored for men in HIV care in Kenya.

While mHealth technologies have shown promise in improving HIV care outcomes, their adoption and sustained use face significant challenges. These include digital literacy gaps, inconsistent device ownership, limited infrastructure, and concerns around data privacy and confidentiality. In Kenya, mobile phone ownership and digital access vary by gender, education level, and socioeconomic status, with individuals who own smartphones and have higher education levels being more likely to engage with mHealth services. Moreover, the integration of mHealth tools into routine HIV care remains limited, particularly in rural, high-prevalence settings like Homa Bay County. Although urban and educated populations may benefit more readily from these tools, long-term adoption, utilization, and sustainability, especially among men living with HIV, are still poorly understood. These structural and behavioral challenges underscore the need to examine how mHealth can be more equitably implemented across diverse populations.

Despite their potential, many mHealth interventions are not adequately aligned with the gendered and socio-cultural realities that shape care-seeking behaviors, particularly among men. Programs often lack meaningful involvement of healthcare providers and fail to tailor their content or delivery to address male-specific barriers or local norms. Discreet and convenient by design, mHealth tools may still fall short if they do not resonate with prevailing masculine identities or reframe care-seeking in ways that affirm strength and responsibility. In high-burden regions like Homa Bay County, where HIV prevalence stands at 16.2%, men often disengage from care due to stigma and perceived threats to masculinity. Addressing these critical gaps in implementation, cultural alignment, and gender responsiveness is essential for developing effective, sustainable mHealth solutions that can improve HIV treatment outcomes for men.

About Nishauri mHealth Intervention The Nishauri mHealth intervention is a client-centered digital platform developed by Palladium, in collaboration with Kenya's Ministry of Health and funded by PEPFAR through the CDC, to enhance HIV care and treatment outcomes. Designed for people living with HIV in Kenya, Nishauri supports key aspects of HIV care management, including appointment scheduling, ART referrals, and ongoing patient engagement. Its core functionalities include automated appointment and medication adherence reminders, tailored health education messages, two-way communication between patients and healthcare providers, and behavior-change communication strategies to support retention in care. HIV patients using Nishauri can access their treatment engagement information and schedule or reschedule appointments through the application.

Nishauri is interoperable with national health information systems such as the Kenya Electronic Medical Records (KenyaEMR) and the Ushauri platform, allowing for real-time data exchange and continuity of care during patient transfers. While technologically aligned with these systems, minor discrepancies in language semantics have led to some inconsistencies in data entry identifiers. Despite its national scale-up and integration into comprehensive care clinics, the uptake and utilization of Nishauri remain low and highly variable across facilities, providers, and patient populations. This variability hints at challenges with its implementation and effectiveness among men living with HIV - a population that faces unique sociocultural barriers to care. Existing research often overlooks the role of gender norms and contextual factors in influencing men's engagement with digital health interventions. Addressing this gap is essential for designing scalable, culturally relevant mHealth solutions that promote sustained engagement and improved clinical outcomes among men.

This Study We will use a mixed-methods- explanatory sequential, cluster randomized stepped wedge design to evaluate both implementation and preliminary effects of Nishauri mHealth intervention among men living with HIV in Homa Bay County. We will leverage a partnership with the Palladium Group to access backend usage data from the Nishauri app, providing detailed insights into how different app functions are used in real-world settings. We will also explore how masculine norms influence men's engagement with the intervention.

Methods Study Design This study will use an explanatory sequential mixed methods design, incorporating a cluster randomized stepped-wedge approach across four health facilities in Homa Bay County. Quantitative data will be collected through structured surveys, chart reviews, and app analytics to assess implementation outcomes (e.g., acceptability, uptake, retention, viral load suppression) at baseline and six months post-intervention. Each facility will serve as its own control prior to intervention rollout, which will occur sequentially in a randomized order over a six-month period. This mixed methods design will enable both within- and between-facility comparisons, offering a nuanced understanding of how implementation processes and gendered experiences shape the intervention's effectiveness.

Study Setting The study will be conducted in 4 sub-County HIV Comprehensive Care Clinics (CCC) in Homa Bay County, Kenya- Mbita, Ogongo, Ndhiwa and Pala. Homa Bay County was chosen for its highest HIV prevalence, 16.2% [4] in Kenya making it suitable to assess the implementation process and outcomes of an mHealth intervention for enhancing HIV care. The county covers an area of about 3,154 square kilometers [26,27]. As of the 2019 Kenya National Census, it had an estimated population of approximately 1.1 million people [27]. There are approximately 160 health facilities providing HIV comprehensive care services across the county. ART uptake is at 96% while VLS prevalence is at 83.8% among people living with HIV, with significant disparities between men and women [4,24,28].

Participants and Recruitment The primary study population will comprise men living with HIV who initiated care and treatment in 2016 or later, following the updated national HIV treatment guidelines. Eligible participants must be between 18 and 55 years of age, have initiated HIV care from 2016 onwards, own a smartphone or tablet, and be able and willing to provide written informed consent. Men with severe comorbidities or chronic co-infections (e.g., cancer or hypertension) that may affect adoption or use of mHealth interventions will be excluded.

A small sample of healthcare providers (e.g., nurses, adherence counselors, peer educators) and Nishauri app developers will also participate in focus group discussions (FGDs).

The CCCs will be randomized in sequence using a computer-generated order for phased implementation of the intervention, transitioning from standard care to the mHealth intervention arm. Participant recruitment in each CCC will proceed according to probability proportional to size sampling until the required sample size of 347 participants is reached (316 calculated, with an added 10% to account for attrition). For the qualitative component, 5-6 FGDs will be conducted: 2-3 with men living with HIV, 2 with healthcare providers, and 1 with app developers. FGD sampling will be stratified by factors such as age, ART adherence, missed clinic visits, and role within the health system, and data collection will continue until thematic saturation is achieved.

Recruitment will occur during routine clinic visits. Trained study staff or peer educators will introduce the study to clients, provide study information, and collect contact information from those expressing interest. Screening for eligibility will take place in private, either in-clinic or at a location convenient and confidential for the participant. Those eligible will then undergo informed consent and complete a baseline survey, which will be used as a point of comparison for future evaluation of the intervention's effects.

To preserve the integrity of the data, most FGD participants will be different from those in the quantitative survey arm to reduce contamination, social desirability bias, and respondent fatigue. However, a limited number of individuals who participated in the quantitative arm may be included in FGDs to help explain emerging patterns in the data, as this study employs an explanatory mixed methods approach. Healthcare providers and app developers will also be purposively sampled to ensure representation of different implementation perspectives across the system.

Retention strategy Participants will be reminded a week to their scheduled visit appointment by a text message. For each missed visit, study staff will attempt to reach the participant through a phone call or text message to reschedule the visit as soon as possible. In an event this doesn't work, the study staff will arrange to visit and meet the participant face-to-face and conduct the follow up survey. Study staff will also call those who relocate out of the study area to arrange for data collection as soon as possible in an appropriate, safe and private location.

Baseline and 6-month follow up surveys Quantitative data collection and storage Quantitative data will be collected at two main time points- baseline and six-month follow-up. After providing written informed consent, eligible participants will undergo screening and complete locator information before the baseline survey. The baseline survey will last approximately 40 minutes. It includes validated and pilot-tested modules on socio-demographics, technology acceptance, HIV-related stigma, ART adherence, retention in care (as proportion of missed visits), health literacy, masculinity norms, the System Usability Scale, and intervention acceptability. The survey will be administered electronically using tablets with the REDCap platform, hosted by the San Francisco Coordinating Center (SFCC). Research Assistants (RA) will perform daily quality checks on all forms to identify and resolve errors prior to secure upload to REDCap servers. These servers are firewall-protected, regularly backed up, and housed in a secure environment. Additional clinical data, including missed visits - ART adherence, and viral load suppression - will be obtained from chart reviews at the health facilities. These will be recorded in Excel spreadsheets and validated against app analytics from the Nishauri platform maintained by Palladium.

All data will be transferred securely to the SFCC network via a secure FTP site under a formal data transfer agreement between University of California San Francisco (UCSF) and the local research center. Data will be stored on encrypted, password-protected servers with limited access, using study ID numbers in place of participant names. Physical documents will be stored securely on-site, and all sensitive data will be separated from identifying information to ensure confidentiality.

Quantitative Data Analysis Data from REDCap will be exported to a Microsoft Access backend for relational modeling, then cleaned and analyzed in STATA version 18.0 BE. Descriptive statistics will be used to summarize demographic variables, intervention adoption, ART adherence, retention, and viral load suppression.

To assess the effects of the intervention, McNemar's test will be used for binary pre- and post-intervention comparisons. Generalized Estimating Equations (α = .05, β = .2, 95% CI) will be applied to analyze longitudinal changes and examine the influence of masculinity on intervention acceptability and uptake (Objectives II & III). The Mantel-Haenszel test will assess baseline effect modification and multiplicative interactions. Direct Acyclic Graphs (DAGs) will be used during analysis planning to identify and adjust for confounding and effect modification. Findings will be presented in both tables and graphical formats.

Focus Group Discussions Qualitative data collection To complement the quantitative findings, focus group discussions (FGDs) will be conducted with men living with HIV, healthcare providers, and App-Developers post-intervention. The FGD guides have been organized thematically and include open-ended questions and follow-up probes to facilitate in-depth exploration of participants' experiences with the intervention. We will explore contextual factors - including masculinity, stigma, and social norms - that influence adoption, utilization, and sustainment of the Nishauri mHealth intervention. The FGDs will be audio-recorded using digital recorders and conducted in private settings to ensure confidentiality and adherence to ethical standards.

Qualitative data analysis Audio recordings will be transcribed verbatim and translated into English and analyzed using Dedoose. We will use a thematic analysis approach combining deductive coding guided by Proctor's Implementation Outcomes Framework [29,30] with inductive coding to capture emergent themes not covered by the framework. Descriptive statistics will summarize FGD participant demographics. Narrative summaries, tables, diagrams, and cross-case comparisons will be used to deepen interpretation. Triangulation with app analytics (e.g., proportion of missed visits, usage metrics) and member checking will enhance credibility. This study employs an explanatory sequential mixed methods design, using quantitative data to assess intervention uptake and outcomes, followed by qualitative inquiry to explain how factors like masculine norms, stigma, and system-level challenges influence these patterns. Qualitative findings will be integrated with application data from the Nishauri dashboard to uncover underlying mechanisms and contextual factors shaping implementation outcomes. Standard operating procedures will guide FGD facilitation, qualitative data handling and analysis.

Dissemination plan Study findings will be shared with participants, healthcare providers, Homa Bay County Ministry of Health officials, and other key stakeholders. Results will be disseminated through presentations at local and international conferences focused on HIV prevention, treatment, and behavioral medicine, as well as through publication in peer-reviewed journals. The study team has established an authorship agreement outlining contributions to primary and secondary publications. Participants' identities will remain confidential and will not appear in any reports or publications. Direct feedback will also be provided to research participants and community partners to ensure inclusive and transparent communication of the findings.

Ethical Considerations This study will adhere to the ethical principles of the Belmont Report, respect for persons, beneficence, and justice. Ethical approval will be obtained from the Maseno University Scientific Ethical Review Committee. All study materials, including protocols, tools, and amendments, will be submitted to the IRB for review prior to implementation. Study staff will complete training in human subjects' protection and Good Clinical Practice.

Written informed consent will be obtained by trained research assistants before any study procedures are conducted. Consent forms will be available in English, Dholuo, and Swahili, and participants will choose their preferred language. Literate participants will read and sign the form; those with limited literacy will have the form read aloud in the presence of an impartial witness who will document the consent accordingly. All participants will receive a copy of the signed consent form, or if deemed unsafe to keep, may store it at the clinic and receive a contact slip for future reference.

Strict confidentiality measures will be observed given the sensitivity of HIV-related data. Personal identifiers will be anonymized or pseudonymized, and all data will be stored in secure, encrypted systems accessible only to authorized, trained personnel. Participants will be informed of confidentiality protections during the consent process. Hard copies of study documents will be stored securely for five years before being shredded; electronic and audio files will be permanently deleted at that time.

Anticipated Results and Discussion This study is guided by a conceptual framework which is an integration of the Proctor Implementation Framework and the Technology Acceptance Model (TAM). It is designed to generate evidence on the implementation and impact of the Nishauri mHealth intervention on HIV care and treatment among men living with HIV in Homa Bay County, Kenya. With this model guiding data collection and analysis, we anticipate findings that will address critical gaps in understanding how mHealth tools perform in resource-constrained, gendered contexts.

By identifying both individual- and system-level factors that influence the adoption, utilization, and sustainment of the Nishauri platform, this study will generate valuable insights into the dynamic interplay between client- and organizational-level implementation outcomes. Barriers such as limited digital literacy, stigma, poor network coverage, and workflow integration challenges, as well as facilitators like ease of use, perceived usefulness, provider support, and trust in the health system, will be explored in depth. These findings will inform the development of gender-responsive, culturally grounded digital health strategies aimed at improving long-term health outcomes for men living with HIV in Kenya and advancing national efforts to close gender gaps in HIV care.

We hypothesize that there will be measurable improvements in HIV service outcomes - particularly ART adherence and clinic retention - among men exposed to the intervention, compared to baseline and control periods within the stepped-wedge design. These outcomes will be interpreted in light of confounding variables such as age, education, and stigma, and supported by app analytics and chart review data. Furthermore, the client outcome of viral load suppression (VLS) will offer a critical downstream indicator of the intervention's potential clinical impact.

This study will contribute critical insights into how masculine norms shape the implementation and impact of digital health tools for HIV care. By unpacking how behavioral constructs such as autonomy, stoicism, and fear of vulnerability influence engagement with mHealth interventions, the findings will inform the development of more gender-responsive and contextually grounded digital strategies. These insights have the potential to guide national policies and programs aiming to close gender gaps in HIV care by tailoring interventions to the unique needs and experiences of men.

By integrating implementation science and gender theory within a mixed methods design, this study will generate a nuanced understanding of individual and system-level factors influencing the success of mHealth tools among men in HIV care. While the findings may be context-specific to Homa Bay County, they offer valuable insights to inform the design and scale-up of culturally relevant, gender-sensitive digital health strategies in Kenya and similar low-resource settings. Future research should build on these results to co-develop scalable interventions and policy frameworks that support sustainable integration into routine HIV care for men.

Strengths This study has several strengths, including its use of a stepped wedge design within real-world public health settings, enhancing both rigor and relevance. By focusing on men-an underserved population in HIV care-and applying Proctor's Implementation Outcomes Framework, the study offers a structured analysis of adoption, utilization, and sustainability. The explanatory sequential mixed methods approach, combined with mHealth application data, allows for deep insights into both outcomes and underlying mechanisms. Additionally, the study's gender-informed lens and alignment with national HIV priorities position it to generate policy-relevant evidence for more effective and equitable digital health interventions.

Limitations While this study employs a stepped-wedge cluster randomized design, several limitations should be noted. First, the number of clusters (health facilities) is small, which may limit statistical power and the ability to control for facility-level confounding. Second, reliance on self-reported survey measures for some implementation outcomes (e.g., acceptability, adherence) may introduce recall or social desirability bias. Third, inclusion criteria requiring smartphone or tablet ownership may exclude more socioeconomically disadvantaged men, potentially limiting the generalizability of findings. Lastly, variations in how the intervention is implemented across facilities-due to staffing, infrastructure, or engagement differences-may influence observed effects. Despite these limitations, the study's mixed-methods approach and triangulation of data sources will enhance the robustness and contextual relevance of the findings.

Conclusions The Nishauri mHealth intervention was developed to address persistent gaps in HIV care engagement and outcomes among people living with HIV in Kenya, a group historically underserved by conventional HIV programming. While mHealth solutions have shown promise in improving HIV-related outcomes, few have been systematically implemented or evaluated with a gender lens in high-burden settings like Homa Bay County. This study will evaluate the real-world implementation and effects of Nishauri using a mixed-methods, stepped wedge design-generating evidence on both process and clinical outcomes while integrating perspectives from users, providers, and developers. If successful, Nishauri could offer a scalable, gender-responsive mHealth model that enhances retention, adherence, and viral suppression among men living with HIV. The findings will inform future efforts to strengthen digital health strategies and promote more equitable HIV care for men in Kenya and similar resource-limited settings.

Inclusion Criteria:

• Male, own a smart phone or tablet, aged 18-55, willing and able to provide written informed consent.

Exclusion Criteria:

• Any mental or serious chronic illness that may affect particiattion and credibility of data being collected for the study.

De-identified participant-level data on socio-demographics, intervention uptake, HIV care outcomes (retention, adherence, viral load), and survey responses will be shared.

The IPD will eb available as from May 2026 until July 2026 (or further depending on approval of study period renewal).

De-identified individual participant data (IPD) and supporting documents will be accessible to qualified researchers upon reasonable request, following approval by the study team, via secure, password-protected data transfer.