Enhancing Adherence to Antiretroviral Therapy Among Adolescents Living With HIV Through Group-Based Therapeutic Approaches in Uganda: Findings From a Pilot Cluster-Randomized Controlled Trial

Abstract

Objective

We examine the preliminary impact of group-cognitive behavioral therapy (G-CBT) versus a family-strengthening intervention delivered via multiple family group (MFG) in improving ART adherence among adolescents living with HIV (ALHIV) in Uganda.

Methods

We analyzed data from a pilot cluster-randomized trial (2020–2022) conducted in 9 clinics in Uganda among 89 participants, who were eligible out of the 147 ALHIV screened. Participants were eligible if they were aged 10–14 years, HIV positive, taking ART, and living with a family. Adolescents were randomized, at the clinic level, to receive the usual care (n = 29), MFG (n = 34), or G-CBT (n = 26). The interventions were delivered over 3 months. Overall, the mean percentage attendance for the 10 G-CBT and MFG sessions was 87.7% and 90.2%, respectively. Three ALHIV were lost to follow-up, while 1 child died. Adherence was assessed using pharmacy records collected at baseline and 4 additional pharmacy visits. We used mixed-effects logistic regression analysis to examine the effect of the interventions on ART adherence.

Results

We found statistically significant main effects for the intervention, χ²(2) = 7.76, p = .021, time, χ²(2) = 39.67, p < .001, and intervention–time interaction effect χ²(6)= 27.65, p < .001. Pairwise comparisons showed increasing adherence in the MFG group compared to usual care at visit 3 (odds ratio [OR] = 4.52 [1.01–20.11], p = .047) and visit 5 (OR = 3.56 [1.42–8.91], p = .007). Also, compared to usual care, participants who received G-CBT showed higher adherence at visit 4 (OR = 2.69 [1.32–5.50], p = .007).

Conclusions

Our study showed preliminary evidence that G-CBT and MFG might have contributed to improved ART adherence among ALHIV. Moreover, G-CBT is a low-cost alternative to expensive individual therapy, especially in low-resource settings. The results warrant the need for more extensive studies to better understand the role of these interventions in the routine care of ALHIV. The trial is registered at ClinicalTrials.Gov (#NCT04528732).

Introduction

Globally, over 1.7 million children under 15 are living with HIV. Of these, the majority (85%) are in sub-Saharan Africa (SSA) (UNAIDS, 2022). The Joint United Nations Program on HIV/AIDS (UNAIDS) set a target of 95-95-95 to control the HIV epidemic by the year 2030., wherein 95% of individuals living with HIV are aware of their condition, 95% of those diagnosed are undergoing treatment, and 95% of those on treatment have achieved viral suppression (Frescura et al., 2022; UNAIDS, 2014).

Despite ART adherence being critical in HIV management and improving outcomes in ALHIV (Tarantino et al., 2020), factors such as stigma and discrimination often lead to suboptimal adherence, particularly in low-resource settings like SSA (Ahmed et al., 2017; Madiba & Josiah, 2019). In this context, ALHIV must navigate many psychosocial issues and maintain adherence to medication amid rapid physical and psychological development. Consequently, these adolescents have suboptimal ART adherence (Firdu et al., 2017; Nasuuna et al., 2018).

Family and social support have been identified as crucial factors influencing adherence to treatment protocols (Damulira et al., 2019; Nabunya et al., 2020). This support may come in various forms, such as emotional backing, facilitation of access to HIV care, financial and instrumental aid, provision of motivation to adhere to treatment, and direct reminders to take medication. However, when stigma permeates families, it results in rejection and social isolation (Algarin et al., 2020). The stigmatization may exacerbate feelings of guilt and shame within families, undermining their caregiving roles and ability to foster a supportive environment for ART adherence (Camacho et al., 2020; Madiba & Josiah, 2019). Consequently, the stigma hinders attachment bonds and impedes the development of self-esteem and emotional regulation in adolescents, further exacerbating poor ART adherence.

Given this interconnected landscape of stigma, family dynamics, and adherence, there is an urgent need for interventions targeting families to improve overall health outcomes for ALHIV. However, group-based interventions that strengthen family and social support to boost ART adherence are rarely investigated. Yet, in low-resource settings, comprehensive psychosocial services are often lacking, leaving families primarily responsible for providing care and support (Chem et al., 2022; Kimera et al., 2019; Nestadt et al., 2013). Group-based treatments like G-CBT and MFG, characterized by shared experiences, social support, and social networking, provide a unique opportunity for normalization, positive peer modeling, reinforcements, and exposure to social situations and feedback sources (Fordham et al., 2021; McKay et al., 1995). Thus, these interventions may be essential, especially among adolescents whose adherence needs depend primarily on their caregivers.

This study explores the role of group-based cognitive behavioral therapy (G-CBT), delivered to adolescents only, and a family-strengthening intervention delivered to adolescents and their families via multiple family groups (MFG) in improving ART adherence among ALHIV aged 10–14 years in Uganda. We focused on adolescents 10–14 years old because this critical early adolescent phase sees significant cognitive and emotional shifts, which could potentially impact ART adherence (Patton et al., 2016). Also, it is a period when children are preparing to transition from pediatric to adolescent/adult care (Meloni et al., 2020). Hence, this age group faces unique challenges in ART adherence, especially in low-income settings. Moreover, there are few interventions that target this age group, and our study aimed to address this research gap with age-appropriate interventions.

Theories Informing the Study

This study is guided by the HIV Stigma Framework and the Family Systems Theory (Earnshaw & Chaudoir, 2009; Bowen 1977). The HIV Stigma Framework posits that HIV stigma manifests through distinct mechanisms of internalized, anticipated, and enacted stigma (Earnshaw & Chaudoir, 2009). Notably, this stigma extends to family members of people living with HIV (PLWH), who experience stigma by association via analogous mechanisms. The Family Systems Theory illuminates the intricate dynamics within family units, accentuating how family members influence each other’s emotional functioning (Bowen 1977). When applied in the context of this study, it enhances understanding of how family dynamics, such as family caregiving roles and overall family functioning, influence ART adherence.

MFG facilitates an open communication space, cultivates mutual support, normalizes common experiences, and bolsters optimism, morale, and interpersonal and coping skills. It effectively tackles internalized and family-level stigma, critical elements recognized in both theoretical frameworks. Meanwhile, G-CBT, rooted in the HIV Stigma Framework, addresses internalized stigma through fundamental components such as educational psychotherapy, cognitive modification, and skills development to boost adaptive coping mechanisms (Ruffolo et al., 2005). These interventions are anticipated to influence psychological, behavioral, and health outcomes for ALHIV and their families, ultimately promoting better ART adherence. As such, we hypothesize that ALHIV receiving these two interventions will exhibit improved ART adherence compared to the control group.

Methods

Study Design, Setting, and Participants

The three-arm cluster-randomized study utilized data from the “Suubi4Stigma” study, a two-year pilot study (2020–2022) that aimed to address HIV-related stigma among ALHIV and their caregivers in Uganda. We enrolled 89 ALHIV (and their caregivers) from nine health clinics across the Masaka, Kyotera, Kalungu, and Lwengo districts in Southern Uganda. The districts were strategically chosen due to their relatively high HIV prevalence rate of 11.7%, compared to the national average of 5.4% (UAC, Uganda AIDS Commission, 2021). Adolescents were eligible for the study if they were living with HIV and aware of their status, aged between 10 and 14 years, receiving antiretroviral therapy from participating clinics and residing within a family setup, including the extended family.

Sample Size and Participant Recruitment

Between November 2020 and May 2021, potential participants were identified from nine government HIV health clinics in the study region offering HIV-related services. Clinic staff prepared a list of eligible ALHIV from their medical records and discussed the project with their adult caregivers during appointment visits. Interested caregivers gave verbal consent to be contacted by the research staff present on adolescent clinic days. Subsequently, the research staff held individual meetings with caregivers, taking them through an informed consent process, and obtained their written consent for their participation and that of their child. Out of 147 adolescents and caregivers who attended the screening from the nine healthcare clinics, 89 dyads met the inclusion criteria and were incorporated into the study. Details of the recruitment process are elaborated in the study protocol (Nabunya et al., 2022).

Randomization

Cluster randomization was utilized, whereby the health clinics were the randomization units. Prior to randomization, stratified random sampling was used to divide the clinics into two, based on the clinic level of care services provided (Health Center III or IV), based on the Uganda Ministry of Health classification (Uganda Ministry of Health, 2020), and number of ALHIV served at the clinic. Within each stratum, the clinics were randomly assigned to one of the three study arms, as follows: usual care (n = 29), G-CBT (n = 26), and MFG (n = 34). To minimize contamination, participants in each clinic received the same intervention corresponding to the group to which the clinic was assigned. The randomization was done using Stata software and was conducted by an independent research associate based at Washington University in St. Louis.

Interventions

Usual Care

Participants in this group received standard clinic-based care, including medical treatment and psychosocial support for HIV, which involves counseling the ALHIV and their families, and providing adherence support. The support is provided by clinic counselors and expert clients, including PLHIV, who are stationed at the clinic. We bolstered the standard care by providing the ALHIV with literature on living positively with HIV, inspired by real-life stories of ALHIV in Uganda (Kabajaasi et al., 2015).

G-CBT Intervention

In addition to the bolstered usual care, participants in this group underwent ten sessions of G-CBT, with each group having a maximum of 10 ALHIV. The sessions were administered in person once every 2 weeks by two trained health para-counselors who were already working with ALHIV in the study region and had experience in mental health support. The sessions employed core CBT elements such as psychoeducation, cognitive restructuring, and skill-building to enhance adaptive coping (Beck, 1993). The intervention was only administered to the ALHIV. The sessions were delivered biweekly, with each session lasting approximately 1 hour.

MFG Intervention

Participants in this group received the MFG intervention on top of the bolstered usual care. Specifically, they underwent 10 sessions of MFG, delivered in person by two trained peer parents. Families, including the ALHIV and their caregivers, were combined into groups, each with a maximum of 10 dyads. During the sessions, core components of MFG, also known as the 4Rs and 2S (rules, responsibility, relationships, respectful communication, stress, and social support), were addressed (Sensoy Bahar et al., 2020). The sessions took place once every two weeks, each lasting approximately 1 hour.

Overall, for the 10 G-CBT sessions, the average attendance was 87.7%, ranging from a minimum of 84.6% (22 out of 26 participants) in four sessions to a maximum of 92.3% (24 out of 26). On the other hand, the MFG group maintained a mean attendance of 90.2% across the 10 sessions, with attendance figures fluctuating between 88.2% (30 out of 34 participants) and 91.2% (31 out of 34 participants). The primary reason for missing sessions was related to COVID-19 lockdown and the associated lack of transportation that made it hard for them to move from their villages to go to the session venues.

Ethical Considerations

The study received approval from the Washington University in St. Louis Institutional Review Board (IRB # 202009185). Additional approval was sought from the in-country ethics bodies, including the Uganda Virus Research Institute (GC/127/20/10/792) and the Uganda National Council for Science and Technology (SS632ES). All ALHIV provided written assent in addition to the consent obtained from their caregivers prior to study participation. This manuscript was drafted following the CONSORT checklist, and the final checklist is available as Supplementary File 1.

Data Collection

Study data were collected at baseline, 3 and 6 months post-intervention initiation by trained Uganda interviewers (Nabunya et al., 2022). All materials related to the study underwent translation from English into Luganda, the predominant language within the study region. Subsequently, the documents were back-translated into English to maintain uniformity and coherence in the content. Certification of translation was acquired from Makerere University to authenticate the translation process. Additionally, to uphold ethical standards and ensure competent engagement with study participants, all interviewers completed training in the protection of human subjects and received Good Clinical Practice certification before their involvement in the study.

Measurements

ART Adherence

The primary outcome variable in this analysis was ART adherence, determined by examining pharmacy records. The utilization of clinical records as an adherence measure bypasses the limitations such as recall bias and social desirability bias, often associated with self-reported adherence, especially prevalent in low-income settings(Orrell et al., 2017). The clinic dispensaries, where the adolescents collect their ART refills, supplied these records.

Each patient visit prompted the dispenser to record specific details, including the patient’s current drug regimen, number of daily pill intake, and remaining pills in the bottle brought to the clinic (all patients were required to bring any leftover pills to their refill appointments). Additionally, the dispenser noted the total pills dispensed and the projected return date, corresponding to the quantity of dispensed pills.

ART adherence at each of the five participant clinic visits—over a period of 9 months, was calculated by dividing the number of pills taken by the number of pills the patient was expected to take. The number of pills consumed within a specific period was determined by subtracting the pill balance from the total pills dispensed at the previous visit while accounting for the pill balance from the prior appointment. The resultant adherence was then translated into a percentage and further dichotomized into ‘good adherence’ for patients achieving at least 90% and ‘poor adherence’ for all other scenarios. The 90% threshold aligns with the consolidated guidelines for HIV prevention and treatment in Uganda (Ministry of Health, 2020). In calculating the baseline adherence, clinic pill counts at the last visit before the start of the study were considered.

The independent variable was participating in the interventions, coded as 0 = control group, 1 = MFG, and 2 = G-CBT groups.

Data Analysis

The data analysis used an intent-to-treat approach in Stata version 17.0. We summarized the baseline characteristics through means, standard deviations for continuous variables, counts, and percentages for categorical variables. We provided a stratified representation of summary statistics for baseline characteristics per study group. To examine the effect of the interventions on ART adherence, we utilized mixed-effects logistic regression analysis, an ideal method considering the clustered and correlated structure of the data (McNeish & Kelley, 2019). Our model consisted of three levels: repeated adherence measures (primary outcome of our analysis) under each study participant at level 1, participants clustered under the study clinics at level 2, and the clinics themselves at level 3. We included the random effect for the hospitals and participants to address the variability between hospitals and the participants, respectively, utilizing a restricted maximum likelihood estimation method. During model construction, we opted for an unstructured covariance structure for the random effects, which is the most flexible covariance structure. In the unstructured covariance, each variance and covariance is estimated separately without imposing any particular pattern or structure.

The model incorporated categorical effects for the group (G-CBT vs. MFG vs. control), time (visits), and the interaction between group and time as fixed effects. We evaluated pairwise group comparisons in ART adherence at different visits, applying Sidak’s adjustment statistics—a method for adjusting p-values to account for multiple comparisons. In applying this adjustment, the family of interest consisted of all pairwise comparisons within a given time point, controlling the family-wise error rate for each time point. This correction method adjusted the significance level for each individual test to ensure that the overall type I error rate was maintained. The intra-class correlation coefficients were determined using the calculated variances at each level. We had some missing data on adherence ranging between 5% at baseline to 18% at visit 5, with no significant differences in missingness across the groups. Despite these missing data, our models relied on have inherent ability to utilize all the available data. For this reason, no participant was excluded for having partially missing data, and no multiple imputations were done. After all, while some studies have discouraged imputations involving the outcomes, literature has also shown that often, there are minimal differences between models based on imputed data versus those based on complete-case analysis (Kontopantelis et al., 2017). We reported odds ratios (ORs) along with their Huber–White cluster-adjusted confidence intervals. The threshold for statistical significance was established at a p-value of 0.05.

Results

Baseline sample characteristics are outlined in Table I. The participants had an average age of 12.2 years, with a majority being female (62.9%, n = 56). Slightly over half of the participants, constituting 55.1% (n = 49), were non-orphans, and each household accommodated an average of 7 members. In terms of ART adherence, 68.7% (n = 57) of the participants demonstrated good adherence (≥90%). All baseline characteristics were comparable across the three study arms.

Table I.

Baseline Sample Characteristics of 89 Adolescents Living With HIV in Uganda

Variables	Total Sample N = 89 (%) n (%)/Mean (SD)	Usual Care n = 29 (%) n (%)/Mean (SD)	MFG n = 34 (%) n (%)/mean (SD)	G-CBT n = 26 (%) n (%)/Mean (SD)
Age (years) (min/max: 10–14)	12.21 (1.41)	12.45 (1.35)	11.65 (1.52)	12.69 (1.09)
Gender
Male	33 (37.08)	12 (41.38)	12 (35.29)	9 (34.62)
Female	56 (62.92)	17 (58.62)	22 (64.71)	17 (65.38)
Household size (min/max: 2–14)	6.42 (2.66)	6.14 (2.60)	6.79 (3.00)	6.23 (2.25)
Orphanhood status
Non-orphan	49 (55.06)	14 (48.28)	20 (58.82)	15 (57.69)
Single orphan	33 (37.08)	13 (44.83)	11 (32.35)	9 (34.62)
Double orphan	7 (7.87)	2 (6.90)	3 (8.82)	2 (7.69)
ART adherence
Adherence <90%	26 (31.33)	6 (20.69)	10 (35.71)	10 (38.46)
Adherence ≥90%	57 (68.67)	23 (79.31)	18 (64.29)	16 (61.54)

Note. ART = Antiretroviral therapy; G-CBT = group-based cognitive behavioral therapy; MFG = multiple family group; SD = standard deviation.

Impact of G-CBT and MFG Interventions on ART Adherence

Table II highlights the intervention effects and shows that the main effect of the intervention was statistically significant (χ²(2) = 7.76, p = .021), underscoring the efficacy of the interventions in improving ART adherence. Concurrently, the main effect for time was also significant (χ²(6) = 39.67, p < .001), suggesting an improvement in ART adherence over the observation period, irrespective of the study intervention. Furthermore, contrasts showed increasing adherence in the G-CBT group χ²(2) = 39.25, p < .001 and MFG group χ²(2) = 20.66, p < .001, but not control group, χ2(2) = 1.53, p = .465. The intervention–time interaction effects were also statistically significant (χ²(6) = 27.65, p < .001). To further examine the significant intervention–time interaction effects, we performed pairwise group comparisons at the different visits which showed increasing adherence in the MFG group compared to usual care at visit 3 (OR = 4.52 [1.01–20.11], p = .047) and visit 5 (OR = 3.56 [1.42–8.91], p = .007). Also, compared to usual care, participants who received G-CBT showed higher adherence at visit 4 (OR = 2.69 [1.32–5.50], p = .007) (Table III).

Table II.

Mixed-Effects Regression Model on the Effect of the Intervention on ART Adherence Among 89 Adolescents Living With HIV

Parameter	OR (95% CI)	p-Value
Group: χ2(df)	7.76 (2)	0.021
Usual care	1
Multiple family group	0.47 (0.08–2.79)	0.406
Group-CBT	0.42 (0.06–2.77)	0.365
Time (visits): χ2(df)	39.67 (4)	<0.001
First visit	1
Second visit	0.43 (0.11–1.65)	0.217
Third visit	0.24 (0.01–3.92)	0.319
Fourth visit	0.34 (0.09–1.25)	0.104
Fifth visit	0.59 (0.06–6.19)	0.657
Group-by-time interaction: χ2 (df)	27.65 (6)	<0.001
First visit × usual care	1
Second visit × multiple family group	1.77 (0.27–11.82)	0.553
Third visit × multiple family group	9.63 (0.47–198.0)	0.142
Fourth visit × multiple family group	2.46 (0.60–10.01)	0.210
Fifth visit × multiple family group	7.57 (0.53–109.2)	0.137
Second visit × G-CBT	2.00 (0.50–7.95)	0.327
Third visit × G-CBT	2.58 (0.11–60.9)	0.557
Fourth visit × G-CBT	6.45 (1.09–38.2)	0.040
Fifth visit × G-CBT	5.86 (0.23–146.0)	0.281
Constant	3.83 (0.78–18.7)	0.097
Random effects
Clinic variance	<0.001 (<0.001 to <0.001)
Participant variance	<0.001 (<0.001 to <0.001)
Clinic level ICC	<0.001 (<0.001 to <0.001)
Participant ICC	<0.001 (<0.001 to <0.001)
Number of participants	89
Number of observations	351

Note. CI = confidence interval; df = degrees of freedom; G-CBT = group-based cognitive behavioral therapy; OR = odds ratio.

Bold text signifies statistically significant findings.

Table III.

Pairwise Comparisons for Adherence Between Participants in the Different Intervention Groups at Each Time Point

Outcomes	Adherence to Antiretroviral Therapy
Group Comparisons Over Time	Odds Ratios (95% CI)	p-Value
Visit 1
Multiple family group vs. usual care	0.47 (0.08–2.79)	0.406
Group-CBT vs. usual care	0.42 (0.06–2.77)	0.365
Visit 2
Multiple family group vs. usual care	0.83 (0.28–2.46)	0.741
Group-CBT vs. usual care	0.83 (0.31–2.27)	0.722
Visit 3
Multiple family group vs usual care	4.52 (1.01–20.11)	0.047
Group-CBT vs. usual care	1.08 (0.26–4.39)	0.918
Visit 4
Multiple family group vs. usual care	1.15 (0.44–3.04)	0.772
Group-CBT vs. usual care	2.69 (1.32–5.50)	0.007
Visit 5
Multiple family group vs. usual care	3.56 (1.42–8.91)	0.007
Group-CBT vs. usual care	2.44 (0.57–10.44)	0.227
Number of participants	89
Number of observations	351

Note. CI = confidence interval; G-CBT = group-based cognitive behavioral therapy; MFG = multiple family groups; SD = standard deviation.

Bold text signifies statistically significant findings.

Discussion

This study examined the preliminary impacts of G-CBT and MFG interventions on ART adherence among ALHIV in Uganda. Our results indicate that these interventions may enhance treatment adherence in this group with low ART adherence. Following the interventions, we observed a significant increase in adherence among ALHIV participating in the G-CBT and MFG intervention groups, compared to those receiving usual care, supporting our hypothesis. Hence, these interventions may be critically important in managing HIV and improving overall health outcomes in ALHIV. We also found only 69% of ALHIV attained a good ART adherence at baseline. This proportion is low if the 95-95-95 targets that call for 95% of all people on ART to achieve viral suppression are to be realized (Frescura et al., 2022; UNAIDS, 2023). This is because achieving viral suppression requires ART adherence of at least 90%. These results further underline the need for interventions to improve adolescent adherence.

The considerable effectiveness of G-CBT and MFG on treatment adherence may be due to the several unique attributes of these interventions, specifically, fostering shared experiences and peer support (McKay et al., 1995; Beck, 1993). A profound sense of solidarity is cultivated by creating a space where ALHIV can exchange their experiences, draw lessons from each other’s journeys, and extend mutual support(McKay et al., 1995). This exchange mitigates feelings of isolation and stigma and embodies a sense of normalization, catalyzing motivation to adhere to treatment regimens. Furthermore, these group interventions introduce behavioral change techniques that play a pivotal role in reshaping participant attitudes and actions. G-CBT aids explicitly in identifying and rectifying unhelpful thought patterns and behaviors, equipping ALHIV to manage stress better and develop effective coping mechanisms (Yalom & Leszcz, 2020). MFG builds on the role of the family in HIV management, particularly for adolescents who may depend significantly on their caregivers (McKay et al., 1995). This approach can create a conducive ART adherence environment by fostering family understanding and empathy. The interventions further help the adolescents develop skills in self-management areas, such as problem-solving and emotional regulation, empowering adolescents to take more active ownership of their health journey. Finally, these interventions aim to diminish the impact of the stigma associated with HIV (Dow et al., 2020). They do so by facilitating open discussions and promoting knowledge sharing, which can rectify misconceptions, enhance mental well-being, and indirectly encourage medication adherence. Coupled with the guidance provided by trained professionals and regular follow-ups integral to these interventions, they equip adolescents with the tools and support necessary for improved compliance with ART.

Our findings unearthed the importance of incorporating group-based strategies into HIV care in low-resource settings. Future research should consider conducting large-scale trials to evaluate the effectiveness of these interventions further and explore potential strategies for their effective implementation and scalability. Despite promising preliminary findings, the complete realization of the potential of these interventions necessitates their long-term impact assessment. Such evaluations ensure that the positive effects we observed are not fleeting but rather enduring improvements in ART adherence.

This study has several considerable strengths. It notably represents one of the few studies undertaken in low-income settings that rigorously assess the impact of group-based interventions—specifically, G-CBT and MFG, on ART adherence amongst ALHIV. These interventions have previously been proven effective in addressing a range of mental health disorders (Fordham et al., 2021). Thus, our findings hold substantial potential to guide and shape future research in this critical area of public health. Further enhancing the integrity of our findings is the methodological robustness underpinning our research. The study was designed as a cluster-randomized controlled trial, thereby minimizing potential sources of bias and enhancing the credibility of our results. A key aspect of our methodological approach was using clinic records to measure ART adherence. This approach offers a significant advantage over subjective adherence measures, as it is not affected by the recall or social desirability biases, which often pose significant challenges to adherence research in low-resource settings. In our analysis, we opted for mixed-effects modeling, specifically designed to account for correlated and hierarchical data. This enabled us to efficiently evaluate the effects of the interventions while accounting for the multi-level nature of our data, thus further reinforcing the rigor of our findings.

Our study, despite its strengths, acknowledges several limitations. The reliance on clinic records, a necessary feature of our research design, introduced potential challenges due to the paper-based nature of record keeping in the participating clinics. This could result in incomplete or missing records, affecting the adherence data’s reliability. Also, the pilot nature of our study, with its inherent small sample size, constrains the power for hypothesis testing. Consequently, this could lead to some potentially significant associations remaining undetected. Similarly, as discussed by Freedland et al., pilot studies with small sample sizes can show false positive effects (Freedland, 2020). Our study’s relatively short follow-up period precludes an assessment of the interventions’ long-term impact. Given that our interventions are fundamentally behavioral, their effects may require a more extended period to manifest. In addition, the execution of our study coincided with the height of the COVID-19 pandemic. The associated restrictions and challenges could have influenced the intervention effects on ALHIV, potentially confounding the outcomes. These factors are critical considerations for future research in this field, necessitating further, more comprehensive investigations to corroborate our findings. Also, our study lacked an attention control, potentially attributing observed effects to the additional attention in the two active intervention arms rather than the interventions themselves.

Conclusions

This study presents preliminary evidence suggesting that G-CBT and MFG interventions might have improved ART adherence among ALHIV in low-income settings. Our findings also underscore the critical role of family and social support fostered in group settings, in facilitating ART adherence among ALHIV. With comprehensive psychosocial services often lacking in low-resource settings—especially those affected by HIV—enhancing family support systems is imperative. There is a need for further investigation of these interventions within existing HIV care frameworks to better understand their potential role in strengthening family caregiving and improving ART adherence in ALHIV.

Supplementary Data

Supplementary data can be found at: https://academic.oup.com/jpepsy.

Author Contributions

Samuel Kizito (Data curation [equal], Formal analysis [equal], Software [equal], Writing—original draft [equal], Writing—review & editing [equal]), Proscovia Nabunya (Conceptualization [equal], Data curation [equal], Funding acquisition [equal], Investigation [equal], Methodology [equal], Project administration [equal], Resources [equal], Supervision [equal], Validation [equal], Writing—review & editing [equal]) and Fred M. Ssewamala (Conceptualization [equal], Funding acquisition [equal], Investigation [equal], Methodology [equal], Project administration [equal], Resources [equal], Supervision [equal], Validation [equal], Writing—review & editing [equal])

Funding

This work was supported by the National Institute of Mental Health (NIMH; grant no R21MH121141, 2020-2022; MPIs: Proscovia Nabunya, PhD, and Fred M. Ssewamala, PhD). NIMH had no role in the study design, data collection, analysis, interpretation of findings, and preparing this manuscript. The content of this paper is solely the responsibility of the authors and does not necessarily represent the official views of the NIMH.

Conflicts of interest

None declared.

Data Availability

The data used for the analysis in this article can be availed upon submitting a reasonable request to the corresponding author.