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Crowley, T.; Petinger, C.; Nchendia, A.I.; Wyk, B.V. Technology-Enabled Health Interventions for Adolescents with HIV. Encyclopedia. Available online: https://encyclopedia.pub/entry/43058 (accessed on 11 July 2025).
Crowley T, Petinger C, Nchendia AI, Wyk BV. Technology-Enabled Health Interventions for Adolescents with HIV. Encyclopedia. Available at: https://encyclopedia.pub/entry/43058. Accessed July 11, 2025.
Crowley, Talitha, Charné Petinger, Azia Ivo Nchendia, Brian Van Wyk. "Technology-Enabled Health Interventions for Adolescents with HIV" Encyclopedia, https://encyclopedia.pub/entry/43058 (accessed July 11, 2025).
Crowley, T., Petinger, C., Nchendia, A.I., & Wyk, B.V. (2023, April 14). Technology-Enabled Health Interventions for Adolescents with HIV. In Encyclopedia. https://encyclopedia.pub/entry/43058
Crowley, Talitha, et al. "Technology-Enabled Health Interventions for Adolescents with HIV." Encyclopedia. Web. 14 April, 2023.
Technology-Enabled Health Interventions for Adolescents with HIV
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This entry is adapted from the peer-reviewed paper 10.3390/ijerph20032464

Adolescents living with HIV (ALHIV) are challenged to remain adherent and engaged in HIV care. Technology-enabled interventions can be used to optimize healthcare delivery to adolescents. The largest proportion of ALHIV resides in sub-Saharan Africa. There is weak evidence that technology-enabled health interventions for ALHIV in low and middle-income countries (LMIC) improve treatment outcomes. However, most interventions appear to be acceptable and feasible. 

acceptability adolescents HIV technology

1. Introduction

With the advent of and increased access globally to antiretroviral treatment (ART), human immune deficiency virus (HIV) can be well controlled in individuals who maintain high levels of treatment adherence and viral suppression. However, long-term adherence and engagement in care remain a concern, especially for adolescents living with HIV (ALHIV) [1][2]. Adherence is challenging due to the physiological, emotional/psychological, and social changes that take place within and around adolescence [3]. As ALHIV transition from pediatric to adult care, they are required to take on increased responsibility for medication adherence, clinic visits, and managing their general physical and mental wellbeing [4][5][6].
ALHIV is recognized as a key population group that requires unique interventions. The majority (80%) of the 1.75 million ALHIV (80%) globally reside in sub-Saharan Africa [7]. Living in low and middle-income countries (LMIC) presents a myriad of sociopolitical and contextual challenges such as poverty, violence, and fewer resources to provide differentiated healthcare to adolescents [8][9]. There is a paucity of evidence-based interventions for improving the outcomes of ALHIV in LMICs. Technology-enabled interventions have shown potential to improve outcomes in high-income countries such as the USA [10], but the evidence regarding their effectiveness, acceptability, and feasibility in LMIC is lacking.
Adolescents regularly use the internet or other social media platforms to search for health information [11][12]. As a result, there has been an increase in technology-enabled health interventions for adolescents. Technology-enabled health interventions are defined as interventions that use electronic devices, such as mobile phones or computers, for accessing information and communicating via the internet or a mobile network [13][14][15]. These interventions can be used to support ALHIV with self-management, promoting autonomy, adherence, goal-setting, and problem-solving [3][16], by providing privacy, support, and feedback [12].
Systematic reviews have been conducted on the effectiveness of technology-enabled interventions for health promotion [15][17][18][19][20], prevention [21][22], mental health [23][24] and chronic conditions [10][14] amongst adolescents. However, the applicability of these interventions across varied contexts, marginalized groups and different health conditions is limited [25]. Reviews on the effect of technology-enabled interventions amongst adults living with HIV have found that they can improve treatment access, symptom- and self-management, adherence, retention in care, risk reduction, quality of life and mobilize health care and social support [5][26][27][28]. In sub-Saharan Africa, e-health interventions for HIV prevention and management were found to be a low-cost way to improve adherence and retention in care [29].
There has been an increase in the number of technology-enabled interventions for ALHIV. A 2015 systematic review on technology-enabled interventions for ALHIV found only one study outside the USA [2]. A review of interventions to improve ART adherence (2016–2018) found one technology-enabled intervention (mHealth/SMS) [30] and a systematic review of self-management interventions for ALHIV (2000–2019) found four technology-enabled interventions (none in LMIC) [31]. More recently, a systematic review on m-health interventions for adolescents and young adults across the HIV prevention and continuum of care in LMIC (2000–2021) identified nine interventions for the delivery of care and treatment for ALHIV [32]. However, the review focused mainly on quantitative HIV treatment outcomes and not on feasibility, acceptability, and fidelity, which are key to understanding intervention implementation in particular contexts and for guiding the development of future interventions. It is necessary to identify design features as well as adolescent preferences for technology-enabled health interventions to guide future intervention development and scale-up.

2. Study Characteristics

A majority of the included studies took place in Africa. Three studies were conducted in Nigeria [33][34][35], two in Uganda [36][37], two in South Africa [38][39], and two in Kenya [40][41][42]. The remaining studies were conducted in South America, with a randomized control trial in Guatemala [43], and a non-RCT in Argentina [44].
Five of the studies [33][35][36][37][43] used an RCT design, while four other studies [34][40][41][42][44] used a non-RCT pre-post design. One study [38] used a non-RCT matched controls design and one study [39] used a qualitative design.
A total of 1544 participants were included in all the selected studies. Out of this number, 846 participants were females while 387 were males. Nonetheless, two studies [37][43] did not report on the number of female and male participants that were used in their studies. The age range of the participants in the studies was 6–25 years. In addition to age and ART eligibility criteria, four studies [34][35][36][42] required participants to demonstrate basic internet, SMS, or web-based literacy. Five studies [33][36][37][43][44] required participants to have a personal mobile phone or have access to mobile phones as an inclusion criterion. In one study [40][41], the participants were provided with a smartphone, the WhatsApp® application preinstalled, a SIM card, and phone credit.

3. Quality Assessment

Three RCTs [35][36][43] were each graded as moderately strong, and two as strong [33][37]. Concerning the grading of the non-RCT quantitative studies, three pre-post studies [40][41][42][44] were respectively graded as moderately strong and one [34] as weak. The matched-controlled study [35] was graded as strong. Although the study by Henwood et al. [39] was graded as strong, it was a qualitative study.

4. Characteristics of Technology-Enabled Interventions

In terms of the technological design of the interventions, the majority (five) involved interactive groups [34][35][39][40][41][42]. This means that these studies involved interactions between group members. Three studies used interactive individual designs [33][38][44] and two non-interactive individual designs [37][43]. Interactive and non-interactive individual designs involved SMS messages that either required the participant to respond or engage, or not. One study [36] compared interactive (message and response) vs. non-interactive (message only) vs. control.
The delivery platforms were primarily social media and SMS-based. Four studies used social media (WhatsApp/Facebook/Mxit) to deliver the intervention [34][35][39][40][41], and four utilized SMSs as delivery [33][36][37][43]. For the remaining studies, one intervention was web-based [42], one mixed- using SMS, WhatsApp, and phone calls for delivery [38], and another provided the participants with the option to receive and respond to messages via phone or Facebook [44].
All interventions of the included studies involved end-users, and none specified whether a theoretical framework was used.
In terms of the duration, the interventions were conducted between six and 18 months. Most of the studies took place for 12 months [33][36][42][44]. Two studies had a duration of six months [34][40][41] and one for nine months [43]. Five studies had a duration of longer than 12 months, namely two for 13 months [34][39], one for 14 months [38], and one for 18 months [37].

5. Assessment of the Effectiveness of Technology-Enabled Interventions: Primary Outcomes

Primary outcomes are summarized in Table 1. Of the eight studies that measured adherence as one of the primary outcomes [33][35][36][37][40][41][42][43][44] two studies [43][44] found a significant intervention-related improvement in ART adherence. In the study by Steinkievich et al. [44], viral load (VL) was measured as an indication of adherence. After 32 weeks of consecutive implementation of the intervention (generic text messages), 20 of 22 patients had VL measured in the context of a routine clinical visit. The limit of detection of the VL test was 40 copies/mL. Thirteen of 20 (65%) patients had an undetectable VL and 14 of 20 (70%) had VL < 1000 copies/mL while six out of 20 (30%) of the patients had no changes in the VL. Similarly, the study by Sánchez et al. [43] found that, from the study initiation to the final adherence measure, the text message intervention group demonstrated improved adherence (measured by a four-day recall questionnaire) by 4% (p < 0.01) while the control group experienced a non-significant adherence improvement of 0.85 percentage points (p = 0.64).
Table 1. Summary of primary outcomes.
Author, Year Linkage to Care Adherence Viral Load Retention in Care HIV Knowledge Social Support Self-Efficacy for Adherence Mental Health Stigma Behavioural Health
Abiodun et al., 2021 [33] - No No - - - - - - -
Chory et al., 2022 [40][41] - No - - - - - No No No
Dulli 2020 [35] - No - No Yes No - No No -
Hacking 2019 [38] Yes - No No - - - - - -
Ivanova et al., 2019 [42] - No - - No - No - - -
Linnemayr 2017 [36] - No - - - - - - - -
McCarthy 2020 [37] - No - - - - - - - -
Sanchez 2021 [43] - Yes - - - - - - - -
Steinkievich et al., 2018 [44] - Yes Yes - - - - - - -
No significant improvement or differences across groups were found in the other studies that assessed adherence. Within the studies, adherence was measured in different ways ranging from subjective measures, such as a visual analogue scale (VAS) and the AIDS Clinical Trials Group (ACTG) adherence questionnaire [33], to the Comprehensive ART measure for Pediatrics (CAMP) questionnaire [40][41], and other self-report/recall measures [42][43]. Some studies also used objective measures such as pill count, viral suppression [33][44], Medical Event Monitoring System (MEMS) capsules, or the Wisepill device [36][37][40][41].
Three studies [33][38][44] measured VL and only Steinkievich et al. [44] found a significant improvement in VL as discussed above using a cut-off of VL < 1000 copies/mL. Hacking et al. [38] used a cut-off of VL < 400 copies/mL for viral suppression and Abiodun et al. [33] used a value of <20 copies/mL.
One study, the Virtual Mentor’s Programme [38] assessed linkage to care and reported improvement in linkage to care measured by increased ART initiation in 28 of 35 (80%) individuals in the mentee group vs. 30 out of 70 (42%) in the matched controls.
None of the studies that reported on retention in care [35][38] reported significant effects. Retention in care was measured as either not missing any appointments during a period, e.g., 28 days [35], or the number of participants retained in care after a period of six or 12 months [38].
One study showed a significant improvement in HIV knowledge [38], while another study did not know improvement [42]. Dulli et al. [38] used closed Facebook groups for online sessions and communication on a range of topics over six months. They found significantly better HIV-related knowledge (14 questions) in the intervention group at the end of the study (p = 0.003). Ivanova et al. [42] used a web-based digital peer support platform (12 months duration) and measured knowledge using 17 true/false items. They found an improvement in knowledge by 0.3 points, but it was not significant.
Studies that measured social support, self-efficacy, mental health, stigma, or behavioral outcomes [35][40][41][42] did not show any significant effect of the intervention on these outcomes.

6. Assessment of Secondary Outcomes

Concerning feasibility, only one study [43] did not report on the feasibility aspect of the intervention, but all the other studies showed high feasibility of the respective information-communication technology used in the respective interventions.
Regarding fidelity, three studies [34][35][37] showed high fidelity, and one study [39] low fidelity. Henwood et al. [39] used a virtual support group using Mxit and some participants did not participate due to forgetting the chat room password. Other fidelity challenges related to the device, network connectivity, and data challenges. One of the studies that used Facebook groups found that quizzes and polls did not appear correctly formatted and some struggled to upload photos of their adherence plans [34], indicating that phone capabilities should be considered. Fidelity appeared to be better in the online support groups if the facilitator was trained, reliable, and engaging and the participants felt comfortable with the facilitator [34][38].
Some participants of online groups were concerned about anonymity and confidentiality as they had no control over whether other participants shared content publicly [34][39][40][41]. Another challenge of the online groups was encouraging the participation of all members [34]. In the study that used virtual mentoring [39], participants commented that they preferred a more formal structure with topics [39]. There appeared to be a preference for the use of existing applications, e.g., WhatsApp or telephone calls in interventions that used Facebook or Mxit [34][35][39], because it also uses minimal data and chat histories are available should participants not be able to access the live chats [39].
Appropriate scheduling of online support groups or interactive SMS messages was identified as important as household or school responsibilities can be barriers to active participation. Some youth wanted to access groups and content at their leisure [39][40][41]. Caregiver engagement is important since some adolescents reported that caregivers did not approve of their phone use [40][41].
Qualitative data indicated the potential benefit of technology-enabled health interventions for ALHIV. Participants reported that the groups created a sense of hope, boosted morale, and provided a feeling of community and peer support among ALHIV that for many had not been previously available [37][40][41]. Further, some participants enjoyed the competition created through sharing adherence information. However, there were instances where the wrong information was shared due to technical difficulties which discouraged participants [37].

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Subjects: Infectious Diseases
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Talitha Crowley
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Charné Petinger
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Azia Ivo Nchendia
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Brian van Wyk
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Update Date: 17 Apr 2023
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