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.