A mobile health-based alcohol reduction intervention and its usability among persons with Human Immunodeficiency Virus in Uganda

Introduction

Human Immunodeficiency Virus (HIV) and alcohol use are prevalent issues in sub-Saharan Africa (sSA). Alcohol use has deleterious effects on the HIV continuum of care; heavy alcohol use is associated with low testing, delayed enrolment into care, and, once in care, virologic non-suppression (1,2); additionally, people with HIV (PWH) who engage in alcohol use are more likely to engage in risky sexual behavior, increasing the onward transmission of HIV thereby fueling the HIV epidemic (3). Therefore, reducing alcohol consumption among PWH is a public health priority.

One commonly recommended strategy to reduce alcohol use among PWH is the implementation of alcohol screening followed by brief counselling sessions as appropriate (4,5). In this approach, PWH undergo alcohol screening in primary care, and individuals identified as needing to cut down on alcohol consumption receive brief counselling sessions. The screening and counselling sessions are offered during routine patient clinic care (6); this strategy requires in-person visits, sessions typically last about 10–60 minutes and efficacy improves with more sessions (7). Several in-person visits may not always be feasible, particularly amidst pandemics, or social and political unrest. Additionally, infrastructural challenges like poor road access and transportation time and costs might hinder the effectiveness of in-person interventions. To address these barriers, the delivery of brief counselling sessions can be facilitated or enhanced using mobile health (mHealth) interventions to ensure wider reach and accessibility.

mHealth, which involves the use of mobile wireless devices to deliver healthcare services, has experienced significant growth in sSA over the past decade. This growth can be attributed to the rise in mobile phone accessibility and cellular infrastructure development (8). mHealth offers a versatile platform that can extend healthcare services to a wider population, including those in low-income areas, as it has the potential to reach individuals virtually anywhere. Various aspects of mHealth interventions, such as acceptability, usability, feasibility, and effectiveness on behavior change, including reducing alcohol use, have been investigated in several studies in sSA, yielding mixed results (9-12).

The usability of an intervention relates to how easily and effectively a participant interacts with it (13). This can vary depending on the community in which the intervention is implemented, the participants’ literacy levels, and other factors specific to the population under study. The usability of an mHealth intervention may influence a participant’s capacity to engage with or successfully complete the intervention sessions. While there have been many mHealth interventions implemented in sSA, most research has focused on the intervention outcomes. Factors that affect the intervention uptake, such as usability, are seldom described.

This analysis aims to describe the usability of an mHealth-boosted counselling intervention to reduce alcohol use and to examine the association between intervention usability and the participants’ uptake of the intervention in a cohort of PWH who self-reported unhealthy alcohol use in southwestern Uganda.

Methods

This was a secondary analysis of data from PWH who participated in a randomized controlled trial (RCT) of a brief counselling intervention to reduce alcohol use. Participants were enrolled between September 2019 and December 2020 (4). Inclusion criteria for the main study included: being infected with HIV and a patient at the Mbarara Regional Referral Hospital Immune Suppression Syndrome Clinic; being 18 years and older; self-reporting unhealthy alcohol use in the prior 3 months by the Alcohol Use Disorders Identification Test-Consumption (AUDIT-C positive; ≥3 women, ≥4 men), having daily access to a cell phone; being prescribed antiretroviral therapy (ART) for at least 6 months; living within a 2-hour driving distance or 60 km from study site and being fluent in English or Runyankole (the local language). Before randomization and during follow-up, participants completed baseline and follow-up questionnaires to collect sociodemographic data. Participants in this RCT were randomized to receive: (I) in-person brief workbook-based alcohol counselling at two regularly scheduled quarterly clinic visits plus interim boosters delivered every 3 weeks by phone (live call arm); (II) in-person brief workbook-based alcohol counselling at two regularly scheduled quarterly clinic visits plus twice-weekly mHealth boosters, described below (technology arm); or (III) standard of care which included brief advice, with a wait-listed intervention (control arm). The main trial results have been previously reported (4).

For this analysis, we analyzed data from the technology booster study arm. In this arm, participants received an in-person counselling intervention that was augmented by technology-based booster counselling sessions. These boosters were delivered via interactive voice response (IVR) or short message service (SMS) text messages, based on participant preference. Following the first in-person counselling session, the counselors trained the participants in the technology activities. This training included setting up and entering a personal identification number (PIN) for security, and accessing and responding to the SMS texts and the IVR messages delivered in the technology booster sessions, to erasing correspondences to ensure confidentiality, with repeated training as needed. We also provided a toll-free phone line to contact study staff for help as needed.

Of note, on March 30, 2020, after the Ugandan government issued restrictions on movement in response to the coronavirus disease (COVID)-19 pandemic, we ceased in-person study visits until restrictions were lifted on May 19, 2020; at this point, 15 participants in the technology booster arm had completed the first, but not the second, in-person counselling session. In order to avoid in-person contact, the delivery of booster sessions for these 15 participants was extended to 3.9 months (95 % CI: 3.6–4.3) compared to 2.9 months (95 % CI: 2.8–2.9) for those who were enrolled in the technology booster arm after the restrictions were lifted.

Ethical approvals

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the institutional review boards of the University of California, San Francisco (#IS 139), Mbarara University of Science and Technology (#10/02-17), and received approval from the Mbarara Regional Referral Hospital Administration and The Uganda National Council for Science and Technology (#16-20396). The study was also registered at ClinicalTrials.gov (NCT #03928418). All participants provided informed consent.

Dependent variable

Booster intervention uptake was assessed as the percentage of booster sessions in which the participant successfully entered a PIN needed to start the session.

Independent variable

The intervention’s usability was assessed at 6 months among participants who received the mHealth technology booster intervention. We assessed usability using the modified Systems Usability Scale (SUS) (14). The SUS asks participants how much they agree (or disagree) with 10 different statements about the phone messages they received as part of the study. Example statements include: “I think that I would like to use this phone system frequently”, “I found the phone system unnecessarily complex”, and “I thought the phone system was easy to use”. The SUS scores range from 0 to 100, with scores above 68 considered above average in some studies.

Covariates

Literacy

Literacy was assessed by asking the participants a single-item literacy screener, which states: “some people find it difficult to read documents. How often do you need to have someone help you when you read instructions, pamphlets, or other written materials?” (15), with response options of Never, Rarely, Sometimes, Often, and Always. This was followed by asking participants to read a sentence printed in either English or Runyankole. The research assistant then used the above information to categorize the participant as literate, somewhat literate, or not literate. We further combined literate and somewhat literate participants for this analysis, compared to those not literate. Literacy was only assessed at baseline.

Social desirability

Social desirability was measured using the 28-item version of the Marlowe-Crowne Social Desirability Scale (SDS), which has previously been used in sSA and in Uganda and had good reliability (16,17). Baseline SDS was included in the analysis as a continuous variable.

Alcohol use

We assessed alcohol use using the AUDIT-C, modified to cover the prior 3 months, as well as phosphatidylethanol (PEth), a biomarker of recent (past month) alcohol use (18). High-risk alcohol use at baseline was defined as PEth ≥200 ng/mL or AUDIT-C ≥6 (19).

Statistical analysis

We calculated summary statistics [proportions, means, medians, and interquartile ranges (IQR)]. Linear regression models were conducted to examine associations with booster response. The following baseline variables were chosen a priori for inclusion in the multivariable model: sex, age, literacy, social desirability, and high-risk alcohol use.

Results

Of the 86 randomized to the technology arm, 35% were female, with a median age of 40 years (IQR: 32–47 years, Table 1). Fifty-six (65%) chose to receive IVR calls, while 30 (35%) chose to receive SMS texts for their booster sessions. The median booster intervention uptake percentage among participants was 73% (IQR: 53–83%). The median SUS score (of a possible 100) was 90 (IQR: 85–90). The median social desirability score (of a possible 28) was 19 (IQR: 16–21). Seventy-three (85%) of the participants were literate/somewhat literate. In adjusted analysis, SUS score was associated with booster intervention uptake [β =0.65, 95% confidence interval (CI): 0.02–1.29, P=0.04, Table 2]. Literacy was also associated with intervention uptake (β =21.33, 95% CI: 7.65–35.01, P<0.01).

Discussion

In our analysis of usability among participants who participated in an mHealth-boosted counselling intervention aimed at reducing alcohol consumption, usability was high. Additionally, there was a positive association between usability and the uptake of the mHealth booster intervention. In comparison to similar studies that utilized the same scale, participants in our study exhibited notably high usability scores. A distinctive aspect of our study was the assessment of usability following the COVID-19 pandemic, which may have influenced the observed high scores. The pandemic necessitated a transition worldwide towards digital health, potentially augmenting access to mobile devices and enhancing familiarity with mobile technology for health and social purposes (20). Moreover, participants assigned to receive the booster mHealth intervention underwent training on inputting the PIN sent to activate the SMS or IVR session. This additional guidance could have contributed to the observed high usability levels. The high usability scores noted in our study could potentially also be attributed to high levels of social desirability within this population. Social desirability refers to the tendency to respond to questions in a manner that is perceived favorably by society, and this phenomenon has been documented in previous research studies (17). However, adjustment for social desirability did not impact our findings.

We additionally found that higher booster uptake was observed among those who were fully/somewhat literate, compared to those with no literacy, corresponding to a greater than 20% higher booster response. This was despite conducting several steps to make the intervention accessible to those with low/no literacy, e.g., training on the intervention with the participants, instituting a toll-free phone line, and allowing participants to choose between SMS and IVR; the latter was considered to require lower technology skill and literacy. Therefore, more work is needed to design technology-based interventions that are equally usable for those who are not literate. Further, literacy is linked with increased ownership of mobile phones, access to smartphones, and internet use (21), and literate individuals are better equipped to engage with mHealth interventions than those who are illiterate. This implies that in Uganda, where only about 74% of the population is literate (22), and despite 77% cell phone coverage (23), over a quarter of the population may not be able to fully use or navigate mHealth interventions. Conversely, however, persons in the general population with low levels of literacy have been observed to have higher levels of alcohol consumption. They would therefore benefit most from interventions to reduce alcohol use. This highlights the necessity of taking participant characteristics and user-centered aspects into account during the design phase of mHealth interventions to enhance intervention uptake (19).

One of the study’s strengths was the assessment and adjustment for social desirability, which could affect subjective measures like a SUS. A limitation was the small number of participants assigned to receive the mHealth boosted intervention, resulting in a limited sample size for assessing other associations with usability. Further, our intervention trial excluded persons without daily access to cell phones, who may have found lower usability of the booster interventions. Thus, our estimates of usability may not apply to the general population of PWH with unhealthy alcohol use.

In settings like sSA where healthcare workers are scarce, mHealth interventions may serve to bridge this gap in care. mHealth may also be an option for patients who have stigmatizing conditions, like alcohol use, and may not want to interface with traditional health care systems (24). Therefore, there is a critical need for evidence-based implementation of these interventions, taking into account context and population-specific aspects of the intervention (25), such as acceptability, usability, and feasibility and user-centered characteristics like literacy.

Conclusions

In conclusion, in this study involving a small sample of participants who received a booster mHealth intervention to reduce alcohol use in southwestern Uganda, usability was found to be very high and correlated with increased booster intervention uptake. However, booster completion was significantly lower in the small portion of the sample, which was not literate, suggesting the need to improve the design of mHealth interventions to expand their reach to this important sub-population. With the growing adoption of mHealth interventions to enhance healthcare delivery, understanding the usability aspects specific to each unique population is crucial. mHealth intervention design should consider participant literacy level to be effective and ensure equitable access.

Acknowledgments

The authors would like to acknowledge the hard work of the EXTEND study Team, especially the counselors and research assistants. We also thank the study participants for their time and participation.

Footnote

Data Sharing Statement: Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-25-25/dss

Peer Review File: Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-25-25/prf

Funding: This work was funded by the US National Institutes of Health [Grant Nos. R01AA24990 (J.A.H.) and K24AA022586 (J.A.H.)]; Harvard University Center for AIDS Research National Institutes of Health/NIAID [P30AI060354-17 (J.A.), NIH/FIC D43TW010543, D43TW011632 and K43TW012640 (J.A.)].

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-25-25/coif). All authors report that this work was funded by the US National Institutes of Health. The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the institutional review boards of the University of California, San Francisco (#IS 139), Mbarara University of Science and Technology (#10/02-17), and received approval from the Mbarara Regional Referral Hospital Administration and The Uganda National Council for Science and Technology (#16-20396). All participants provided informed consent.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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