Background: Antiretrovirals (ARVs) are key in the management of HIV. Although no cure exists, ARVs help patients live healthy lives and prevent transmission to others. Adherence to complex regimens is paramount to outcomes and in avoiding the emergence of drug-resistant viruses. The goal of therapy is to reach an undetectable viral load. However, adherence is a common problem, stemming from issues such as mental health, chaotic home situations, and busy work schedules. Mobile health (mHealth) represents a new approach in improving medication adherence, and multiple studies have been performed in this area.
Objective: This study aims to review the current implementation of mHealth in the management of HIV among different groups of patients.
Methods: We used PubMed, Academic Search Elite, and 1 journal database with various search terms to review the current implementation of mHealth in HIV care.
Results: Titles and abstracts were screened, and 61 papers were identified and fully reviewed. The literature was divided into lower- and higher-income nations, as defined by the United Nations. A total of 20 studies with quantitative results were identified, with 10 being text- and SMS-based interventions (the majority of these being in lower-income countries) and 8 being smartphone-based apps (primarily in higher-income countries). The majority of these studies determined whether there was an effect on adherence or biochemical parameters (viral load and CD4 count). Various qualitative studies have also been conducted, and many have focused on determining the specific design of interventions that were successful (frequency of messaging, types of messages, etc) as well as priorities for patients with regard to mHealth interventions.
Conclusions: There seems to be a role of mHealth in the management of HIV in lower-income nations; however, the optimal design of an intervention needs to be delineated. In higher-income countries, where the 2 significant risk factors were injection drugs and men who have sex with men, the benefit was less clear, and more research is needed.
HIV remains to be a significant public health concern, causing an estimated 770,000 deaths worldwide, with significant mortality from poorly managed disease, even in developed nations [, ]. Particularly in the developing world, there is significant morbidity with 79.1% of HIV prevalence centered in Africa (25,700,000 people) and Southeast Asia (3,500,000 people) [ , ].
The burden of this disease is not only health related but also impacts economics and development. A large proportion of deaths from HIV occurs in young and middle-aged adults; the same group of people who raise children, are caretakers, work, and teach future generations. Especially in lower-income countries, families who have had a death from HIV cope by pulling children out of school, reducing food intake, or using up agricultural resources prematurely . In addition, although first-line antiretrovirals (ARVs) cost US $80 per year, the cost of second- and third-line regiments (used with drug resistance) may be as high as US $2200 per year [ ].
Despite all these bleak consequences, studies have shown that with adherence to ARV therapy, a patient aged 20 years could expect an additional 43.3 years of life . In contrast, those who are not adherent and develop AIDS have a median survival of 12.1 months [ ]. Adherence to ARVs is key to prolonging life, improving immune function and quality of life, and preventing transmission [ - ].
Without proper adherence, there is a risk of disease progression or drug resistance. Despite increasing access to ARVs worldwide, adherence is far from perfect [, ]. One meta-analysis reported adherence at 62%, and studies on subgroups, such as illicit drug users, are as low as 27% [ , ].
Factors such as psychiatric disorders, cognitive impairment, social stigma, substance abuse, and volatile housing can prevent optimal adherence [, ]. Burch et al [ ] found clear associations between markers of the social determinants of health and HIV outcomes. Rachlis et al [ ] found that substance abuse and unemployment were associated with suboptimal care. The risk of transmission and resistance make it crucial to develop methods for improving adherence to medications. Indeed, international organizations have called for strategies to improve adherence to medication and its importance [ - ].
Consumer informatics, particularly mobile health (mHealth), has shown promise in improving ARV adherence. Indeed, research has shown that patients living with HIV are interested in apps to support HIV self-management, and much past research has been done to support disease management [, ].
Given the widespread adoption of cellphones (95% of the population) and smartphones (77%) in different parts of the world, mHealth is becoming an important tool to improve care . Furthermore, in North America, access to mobile data and the internet ranges close to 80%, and with expanding mobile computing capabilities, this represents further opportunities [ ].
We believe that the major benefits that mHealth could offer would be helping with medication and appointment adherence, improving HIV education, and increasing engagement in care. Some studies have shown promise in improving viral suppression and CD4 count. However, there are existing gaps in current reviews . First, as technology is a rapidly growing field, constantly updated work is needed to summarize developments. Second, and most importantly, people who live with HIV are a diverse group of patients with a variety of challenges, including living in developing countries with poor health infrastructure and having comorbid substance use issues [ - ]. Each of these patient populations has unique characteristics and concerns that may influence care. For instance, access to the internet and smartphones, health care, income, and psychiatric comorbidities could be vastly different in various groups. The majority of existing reviews homogenize and summarize the existing literature as a whole and do not consider the differences in patient populations [ - ]. An intervention may have varying levels of success in each group and likely needs to be tailored to fit unique needs. For instance, a smartphone app for disease education may be successful in college-educated, smartphone-owning patients; however, it may have a vastly different effect on a patient in the developing world without a smartphone or an advanced education. The underlying differences in patient groups need to be considered, and our review groups studies into subgroups and discusses the implications of such differences.
Two separate databases with peer-reviewed, reputable articles were used, PubMed and Academic Search Elite. All relevant articles reviewed were within the time period of 2010 to 2019.
For PubMed, we used the following search terms: “mHealth AND HIV AND medication adherence,” “app AND HIV AND adherence,” “smartphone AND app AND HIV AND anti-retroviral adherence,” and “mobile phone AND anti-retroviral adherence AND HIV.” On reviewing the literature using these search terms, we added the search terms “WelTel” and “CAMPS and retroviral” to capture relevant studies found in the references of the articles retrieved from the initial search.
We used the search terms “mHealth AND HIV AND medication adherence,” “mobile phone AND anti-retroviral adherence AND HIV,” and “app AND HIV AND adherence” for the Academic Search Elite database search.
Finally, we used the Journal of Medical Internet Research database, a group of journals that focuses on informatics implementations in health care, to identify articles listed under “mHealth for Treatment Adherence.”
Inclusion and Exclusion Criteria
A total of 386 search results were screened using the title or abstracts for relevance, and 61 articles were fully reviewed and are discussed in this study.
Articles were included if they were experimental in nature (encompassing qualitative or quantitative studies as well as randomized controlled trials [RCTs] or other studies). Study designs included RCTs, case studies, cohort studies, cross-sectional studies, and qualitative studies (focus groups, interviews, observations, and surveys). No age restrictions were set, although the majority of the literature focused on adults and studies were required to have all participants diagnosed with HIV. A significant number of articles were excluded (273/325, 84%). Reasons for exclusion were irrelevance to the topic discussed, inclusion of patients not diagnosed with HIV, articles written in a language other than English, reviews rather than original research on this topic, categorization as nonexperimental irrespective of whether qualitative or quantitative (for instance, a commentary of mHealth), or if they were protocols for experiments.
We defined mHealth in this study as any intervention that involved the use of mobile phones, smartphones, or other wireless devices (such as smart pill bottles). Although no formal definition of mHealth exists, this description is in agreement with the World Health Organization . Pilot trials found on literature review were included in our review.
A total of 61 papers were identified from the period of 2010 to 2019. In accordance with the mHealth categories previously described by the United Nations, we categorized apps according to type and found that these apps were focused on either treatment support (reminders) or education . In addition, we categorized apps as either push or pull and as one-way versus dual communication. Interventions that were primarily reminders tended to use push, whereas those that were education used pull ( ).
As factors such as technology accessibility, socioeconomic background, and internet or cellular connectivity vary based on geographical location, the authors chose to separate the discussion into studies performed in lower- and higher-income countries. Studies were divided into categories (upper middle and upper vs lower middle and low) as per the definition by the United Nations .
The risk factor for HIV acquisition in upper middle- and upper-income countries was primarily men who have sex with men (MSM) or intravenous drug users (IVDU). The majority of these studies were performed in North America (Canada and the United States).
For lower middle- and low-income countries, the majority of these studies were based on the African continent, with some conducted in Asia.
Interventions were text or SMS based, voice message based, or smartphone apps. All work in the lower-income countries consisted of simple text or SMS or voice interventions (7/7, 100% of quantitative studies) [- ]. In contrast, in higher-income countries, more of the work included advanced smartphone apps (8/13, 62%; ) [ - ]. Although we have summarized the quantitative studies on efficacy in , other studies discussed the optimal designs or considerations in intervention development and are reviewed in the following sections.
On an evaluation, using the Oxford Centre for Evidence-Based Medicine’s Levels of Evidence, there was a large amount of high-level evidence in developing countries, with many RCTs . On the basis of this, we believe that conclusions regarding efficacy are possible. There is also a high level of evidence (with a large number of RCTs) in higher-income countries; however, there remain some limitations to those studies ( ). The authors believe that further work may need to be performed in higher-income countries.
|Modes of intervention||Patient population||Designs||Effects||Push (central) vs pull (client) vs other||One-way vs dual communication||App type|
|Text message/SMS based (n=10)|
|Resource-limited settings (n=5)|
|Lester et al ; WelTel Kenya1||Low income, primarily urban population in Nairobi who were:||Individually randomized multisite to SMS or control; intervention was weekly SMS with response required within 48 hours; primarily zidovudine or stavudine+lamivudine+efavirenz or nevirapine||Improved rate of viral suppression (57% vs 48%, P=.04); improved self-reported adherence (62% vs 50%; P=.006)||Push||Dual||Diagnostic and treatment support (reminder)|
|Pop-Eleches et al ||Rural government clinic in Kenya.||720 patients randomized to short daily (70), long daily (72), short weekly (73), and long weekly (74) messages. Data gathered from MEMSb caps||Improved adherence of >90% (53% vs 40%; P=.03); decreased lapses of >48 hours (in the weekly message subgroup; 81% vs 90%; P=.03)||Push||One way||Diagnostic and treatment support (reminder)|
|Maduka et al ; SMS and counseling||Tertiary care Nigerian hospital, patients who had the following:||104 randomized to intervention of monthly adherence counseling and twice weekly ARV (52) or control with no SMS or counseling (52)||Improved self-reported adherence (76.9% vs 55.8%; P=.02); improved CD4 (193-575 cells/mL vs 131-361.5 cells/mL; P=.007)||Push||Dual||Diagnostic and treatment support (reminder)|
|Haberer et al ||Rural Southwestern Uganda.||63 patients who were randomized to scheduled SMS and RTAMc (21), triggered SMS from RTAM (20), or RTAM only (21); the scheduled SMS group was daily for 1 month and weekly for 2 months, followed by reminders triggered by late or missed doses. SMS was also sent to social supporters if there was no signal >48 hours; triggered SMS was sent only in response to late or missed doses||Improved adherence (91% vs 79% vs 79%; P=.04); decreased lapses (7 vs 11 vs 11 for 48 hours and 1 vs 3 vs 4 at 96 hours; P=.02); no significant differences in VLd suppression||Other (one arm was push with scheduled messages, another arm was triggered by smart pill bottle)||One way||Diagnostic and treatment support (reminder)|
|Mbuagbaw et al ||Hospital in Yaounde, Cameroon with patients who:||200 patients randomized to weekly SMS developed from focus groups that were varied, contemporary (eg, Season’s Greetings), with a call back number (101) vs usual care (99)||No significant difference in adherence||Push||Dual||Diagnostic and treatment support (reminder)|
|Higher-income countries (n=5)|
|Guo et al ||Large metropolitan South Chinese hospital.||62 primarily nonheterosexual males who were randomized to weekly SMS and reminders for ARVs and exercise with WeChat educational materials sent 3 times per month (31) or control (31)||No significant difference in CD4 counts or missed medications||Push||Dual||Diagnostic and treatment support (reminder)|
|Moore et al ; SMS for adherence and drug use, iTAB||Pilot at UCSDe whose patients:||75 randomized to iTAB, which were daily personalized texts built from focus groups and focused on responsibility to others, self-esteem, nonadherence risks, harm reduction, reminders, spirituality, celebration of health, and disease control (50) vs control (25). Also assessed methamphetamine use with daily texts||No significant difference in adherence (measured by MEMS caps); fewer methamphetamine use days (P=.05)||Push||Dual||Diagnostic and treatment support (reminder)|
|Ingersoll et al ; SMS including adherence, mood, and substance use||Pilot study from 2 outpatient clinics in Virginia serving mainly nonurban and rural, primarily male and white, patients who:||63 patients randomized to daily texts on medications, twice daily mood texts, and daily substance use texts eliciting responses from patients (33) or usual care (30)||No significant difference in proportion of missed visits (P=.12); no difference in substance use days; improved adherence (66%-85% vs 62%-71%; P=.04)||Push||Dual||Diagnostic and treatment support (reminder)|
|King et al ||Repeated measures study where patients recruited from a clinic for women and families living with HIV in Vancouver, British Columbia, who:||85 enrolled with 5 lost and administered intervention, which was modeled after WelTelKenya1 with SMS or texts every Monday asking “How are you?” requiring a response from patients within 1 day. If no response, this was followed-up by further texts and calls||Mean VL decreased from 1098 copies/mL to 439 copies/mL (P=.004); adherence to ARV (OR 1.14; P<.001); decreased appointment adherence (OR 0.81; P=.03)||Push||Dual||Diagnostic and treatment support (reminder)|
|Rana et al ||Patients recruited from Rhode Island clinic providing HIV services, primarily white, and MSMi as a risk factor with patients who:||32 patients enrolled, with 20 completing study (exclusions were due to death, incarceration, transferal of care, or no response) given a daily bidirectional texting intervention||Improved VL suppression (P=.002)||Push||Dual||Diagnostic and treatment support (reminder)|
|Smartphone or computer apps (n=8)|
|Higher-income countries (n=8)|
|Venter et al ; lab results, appointment reminders, disease information||Multisite RCTh in the inner city of Johannesburg, South Africa (1 community health center, 3 clinics, 1 tertiary care hospital) with patients who:||353 randomized to SmartLink, which provided appointment reminders, information about lab tests, ARV adherence and HIV info, and CD4 and VL results (181) as well as control (172)||No significant difference in adherence; improved linkage to care in the 18- to 30-year-old subgroup (20% increase, P=.02)||Push||One way||Diagnostic and treatment support (reminder) and education and awareness|
|Himelhoch t al ||Pilot study from a Baltimore urban outpatient HIV clinic whose patients:||30 patients randomized to the Heart2HAART app giving medication reminders; information regarding adherence; ecological momentary for side effects, depression, and cravings for drug use and tailored education; recommendation; and encouragement (20) vs control of smartphone only (10)||No significant difference in adherence||Push||One way||Diagnostic and treatment support (reminder) and education and awareness|
|Dillingham et al ||Study from a clinic in Virginia whose patients were:||77 enrolled in study primarily male, slightly less than half MSM given PositiveLinks iteratively designed user-centered app with tailored resources, queries of mood, stress, adherence; appointment reminders, and community forum||Improved retention in care at 12 months (P<.001); improved constant visits (P<.001); improved CD4 count (P<.001); improved VL suppression (P≤.001)||Pull||Dual||Diagnostic and treatment support (reminder) and education and awareness|
|Horvath et al ||Pilot study recruited from ads on Grindr from Miami, Orlando, Washington DC, Charlotte, Houston, and New Orleans as well as flyers or cards from organizations and clinics serving the targeted population. Patients were:||90 patients randomized to APP+ for MSM who use stimulants, giving informational material, a playable storyline of a fictional HIV+ character, and a tool to track personal adherence (45) vs control (45)||Temporary significant improvement in self-reported adherence (P=.04); no significant difference in substance use||Pull||One way||Education and awareness|
|Dworkin et al ||Patients in Chicago who were:||43 patients enrolled, with 11 lost to follow-up, given avatar conversational agent intervention on mobile phones||Improved adherence (via pill count adherence >80%; P=.05)||Pull||One way||Education and awareness|
|Whiteley et al ||Patients recruited from the greater Jackson, MS area who were:||66 patients who were primarily male, black, and nonheterosexual enrolled, with 5 patients lost receiving BattleViro mobile game designed from qualitative user feedback that helped ARV adherence, viral load, and other knowledge regarding HIV||Greater decrease in VL (0.96 log greater decrease in intervention; P=.04); improved adherence (71% vs 48%; P=.05)||Pull||One way||Education and awareness|
|Perera et al ||Patients who were primarily male and homosexual, recruited from the Auckland City Hospital Infectious Diseases Clinic and local HIV support organization who were:||28 randomized to augmented apps, including graphical representations of ARV concentrations and simulation of immune activity (17) vs normal apps with medication clock only (11)||Increased self-reported adherence via MARSi score (48.93 vs 47.09; P=.03); lower VL at 3 months (1.30 log10 vs 1.70 log10; P=.02)||Pull||One way||Education and awareness|
|Ownby et al ||Recruitment of patients in Broward County, FL with a large proportion of MSMg||124 participants recruited in educational intervention, with a final 109 patients analyzed for results||Improved adherence in subgroups with lower baseline adherence rates (in <85% adherence, P=.04; in <80% adherence P=.09)||Pull||One way||Education and awareness|
|Voice calls (n=2)|
|Resource-limited settings (n=2)|
|Shet et al ; HIVIND, interactive automated voice all and reminder pictorial messages||1 ambulatory and 1 private HIV clinics in India whose patients:||631 educated patients randomized to motivational call with inquiry of adherence requiring response and pictorial SMS (315) or control (316)||No significant difference in viral load; no significant difference in adherence||Push||Dual||Diagnostic and treatment support (reminder)|
|Rodrigues et al ||Bangalore, South Indian tertiary, nonprofit private facilities outpatients who:||Quasi experimental, where 150 primarily male patients were all given automated interactive voice response call and noninteractive neutral picture SMS every week for 6 months and studied at the end of the intervention||Improved adherence (85%-91% during intervention and 6 months after, P=.02)||Push||Dual||Diagnostic and treatment support (reminder)|
bMEMS: medication event monitoring system
cRTAM: real-time adherence monitoring.
dVL: viral load.
eUCSD: University of California at San Diego.
fDSM-IV-TR: Diagnostic and Statistical Manual of Mental Disorders – IV – Text Revision.
gMSM: men who have sex with men.
hRCT: randomized controlled trial.
iMARS: medication adherence report scale.
Lower-Income Countries Group
Given the rising rates of cellular access in the developing world, mHealth represents a significant opportunity to improve care .
A variety of studies have been conducted on mHealth interventions in the developing world, with a wide range of study methodologies. The quantitative experimental designs (including cohort studies and RCTs) are summarized infor ease of comparison. Seven studies with quantitative data were available, with the majority being text- or SMS-based modalities (5 studies) and a minority being voice call based (2 studies) [ - ].
Other studies investigated user acceptability and surveyed patient concerns regarding the use of mHealth. A pilot study in rural Uganda by Musiimenta et al  enrolled 63 patients and randomized them to a scheduled SMS, triggered SMS (by missed or delayed doses), or no SMS. The feedback received was positive, citing usefulness for motivation, reminders, and social support. However, confidentiality, shared phones, ability to use the phone, and availability of electricity were concerns [ ]. Similarly, an explorative study by Lepere et al [ ] demonstrated that mHealth was acceptable among people who live with HIV in Cote d’Ivoire, Togo, and Burkina Faso and advocated for its use. Mbuagbaw et al [ ] also demonstrated, in a qualitative arm of the CAMPS trial, that these interventions were acceptable, although content, timing, technical challenges, and privacy need to be considered.
Higher-Income Countries Group
Countries classified as high or high middle income were analyzed. The primary risk factors in these studies were either IVDU or MSM, in agreement with the previous literature [, ].
Quantitative experimental studies are described in[ - , - , ]. In addition, a number of nonquantitative or nonexperimental studies were conducted to investigate the nature of optimal interventions. These are summarized in [ - ].
Different studies have investigated aspects of mHealth apps other than efficacy. WelTel was studied in Vancouver, British Columbia, on patients involved with substance abuse using both quantitative and qualitative methodologies [, ]. One such study was done by Campbell et al [ ], who studied WelTelBC1 from an economic perspective on 85 viral load–positive vulnerable patients (defined as substance use and other psychosocial factors), finding that the cost of caring for a highly vulnerable patient was US $347.74, whereas the overall median cost was US $36.72.
Multiple quantitative studies investigated the acceptability of mHealth interventions. In Seattle, 224 patients with HIV were randomized to receive two-way pagers with personalized adherence messages. Of these patients, 55% identified as gay or lesbian, and the majority were white males. Participants were surveyed for the usefulness of the pager and asked whether they adhered to their medications; 73% reported liking the pager, 51% began finding messages annoying, and 48% believed that the pager did not improve their adherence. The overall response rate was only 42.8% but declined over 3 months. The self-reported pager adherence was 90.8% (SD 33%) compared with the medication event monitoring system showing 53.6% (SD 37%) adherence .
Similarly, a Peruvian study by Krishnan et al  with 359 transgender women or MSM patients asked about mHealth preferences using a 5-point Likert scale. They found an overall positive uptake, with a mean of 3.21 for technological medication reminders and 3.56 for anonymous internet chats to discuss HIV issues.
Other studies, using focus groups and other quantitative study methods, were used to determine how best to deliver mHealth interventions. For instance, Krishnan et al  also found that daily messages were preferred in terms of frequency within their study population. In Bangkok, Anand et al [ ] interviewed 16 MSM and 2 transgender women. They gathered information on preferences and how to address needs with electronic health. The group found that 39% (7/18) of patients preferred instant messaging and 11% (2/18) preferred phone calls. Of these, 39% (7/18) wanted a private website chat room and 11% (2/18) preferred Skype (Skype Communications) video chats. All patients desired personalized reminder messages; 50% (9/18) wanted reminders on an instant message app, 17% (3/18) preferred a stand-alone app, and 33% (6/18) wanted it as an SMS. The theme that emerged was web based, accessible, and reliable disease information [ ].
Work performed in higher-income countries also had diversity in intervention type. For instance, Skrajner et al  enrolled a patient with cognitive impairment in a video-conferenced, cognitive-psychosocial program that improved the pill count adherence rate from 75% to 97.9% in 1 month (80% in 2-3 months). Similarly, the weCare team, which has done non-HIV work on social media, is currently developing a tailored Facebook, text, and social and sexual networking app–based intervention to help engage care [ ]. Hwayoung et al [ ] combined an electronic pill bottle, fitness tracker, and phone alerts and found that the pill bottle encouraged adherence and self-management of medications. However, they mentioned that the smart pill bottle was too small to fit all the pills, although they liked how it was easy to open and discrete [ ].
Finally, Schnall et al  created the mVIP app, which attempted to provide self-care strategies for self-management of different HIV-related symptoms. They found improvements in a variety of symptoms, including anxiety (P=.001), depression (P=.001), neuropathy (P=.002), fevers or chills or sweats (P=.04), and weight loss (P=.02). Results for adherence were mixed, showing improvement only when measuring it via the center for adherence support evaluation adherence index [ ].
|Study||Designs||Themes reported by patients|
|Senn et al ||22 African American MSMa with HIV in Rochester completed surveys and qualitative interviews|
|LeGrand et al [, ]||EPIC Allies (University of North Carolina, Duke University) undergoing study now. Game-like app that uses game mechanics and social networking to improve HIV care. Development study looked at ARVb adherence needs, preferences, and usability testing in young MSM and transgendered women who have sex with men|
|Dworkin et al ||5 different focus groups composed of African American MSM with HIV in Chicago iteratively designed a talking avatar app providing information about disease, improved adherence, and helped with appointment attendance|
|Castel et al ||Focus groups and surveys on patients with HIV aged between 13 and 24 year (no delineation as to acquisition) for 3 different game prototypes that were linked with Wisepill dispensers|
|Morano et al [, ]||Primarily, 132 African American males enrolled to the Care4Today app that helped with medication management and appointments and tracked health, wellness, and goals|
|Cook et al ||37 patients in Colorado studied to determine if messages matched to psychological states were useful. Patients were primarily ethnic minorities and nonheterosexual|
|Olalla et al ||Spanish study on 30 patients (15 randomized to app group) who were >60 years old. The app offered medical news, reports about HIV, and an anonymous chat between patients|
|Westergaard et al ||Recruited patients with HIV who were absent from appointments, had substance use, and had an unsuppressed viral load. Investigated acceptability and uptake of a 2-part intervention that included a smartphone app (delivering tailored interventions and communication) and a peer navigator (psychosocial and logistical support)|
|Przybyla et al ||Used app for adult patients with HIV who had been on ARVs for >3 months with history of alcohol and a history of at least one recent day of nonadherence. Evaluated feasibility and acceptability of the app|
|Horvath et al ||Focus groups for stimulant using MSM with HIV (San Francisco, Minneapolis) and explored app components that were important for continued use and engagement|
|Rosen et al ||Focus groups of patients with HIV with a history of substance abuse using the iHAART app (which used visual representations of adherence, VLc, and CD4)|
|Smillie et al ||Pilot WelTel study in Vancouver, British Columbia, on patients with substance abuse using a qualitative methodology of semistructured interviews|
aMSM: men who have sex with men.
cVL: viral load.
Lower-Income Countries Group
Many studies have been performed using mHealth to improve adherence, with most in sub-Saharan Africa. Some of the studies were rigorous, large RCTs, such as Kenya WelTel . There were some limitations in other studies where mHealth was not studied independently. For instance, in the study by Maduka et al [ ], the effects of adherence counseling and SMS messages were not separated, and in the trial by Haberer et al [ ], the role of social supporters was unclear. However, most of the studies indicate an improvement in adherence and VL ( ) [ - , , ]. Furthermore, it seems that mHealth represents a cost-effective intervention compared with the alternatives, with 1 study showing that a nurse could manage 1000 patients per week [ ].
Although most studies found benefits, there is a gap in the literature regarding the design of a successful intervention. It was found that daily messages were not optimal, habituation likely being a significant factor [- ]. Pop-Eleches et al [ ] found that weekly interventions were more effective than daily interventions and long versus short messages made no difference, potentially reflecting that the messages were used more as reminders.
Most other successful interventions agreed with tapering schedules, twice weekly messaging in Nigeria, weekly WelTel Kenya1 messages, and the weekly South Indian study [, , , ]. Another variable was message interactivity and content. On the basis of the existing literature, it is unclear if the interactivity of messages is important. In terms of content, Pop-Eleches et al [ ] showed that a motivational message “This is your reminder. Be strong and courageous, we care about you” was not better than a generic impersonal message. These findings potentially indicate that an interactive or customized message does not negate the effects of habituation. Further work is necessary to determine the exact type of message that generates the maximal benefit.
Cultural contexts likely play a role in feasibility, and caution should be exercised when extrapolating results to different locations. Different views on health care, HIV stigma, baseline demographics, and cultural or religious views could be important when designing an intervention. The HIVIND study illustrates this, with high baseline adherence further boosted by the Hawthorne effect .
These studies collectively show a role for mHealth in improving HIV care in low-income or lower middle-income nations; however, we still need to delineate the best design. A less-than-daily message frequency seems to be optimal; however, interactivity and content need to be considered. Geographical and cultural differences may also affect efficacy.
Higher-Income Countries Group
The major risk factor for HIV in higher-income countries was either IVDU or MSM [- ]. Although in higher-income countries different factors were identified in HIV management compared with lower-income countries, optimizing adherence is still the key.
In those with IVDU as a risk factor, compliance is important as it has implications for disease transmission. Finding meaningful interventions to improve adherence is crucial as there are higher rates of drug use in patients with HIV+, and these patients often face barriers to adherence [, ].
However, there are a series of unique challenges for these patients that have implications in implementation and interpretation of studies. An inherent difficulty in studies performed on patients with a history of substance misuse is their heterogeneity, with a variety of substances used, including alcohol, marijuana, cocaine, amphetamines, and opioids. Prolonged use of drugs such as methamphetamines can cause psychotic episodes, resulting in more chaotic lives than other addictive substances [, ]. Opioids have substitution agents that represent an opportunity for frequent, recurrent health care interaction in dispensing [ ]. An intervention efficacious in one group may not be efficacious in another.
In addition, a variety of barriers exist that confound the ability of mHealth interventions to help with adherence. These include unstable financial and housing situations as well as psychiatric comorbidities . Some of these barriers may limit the ability for any intervention to be beneficial without first addressing them.
The majority of the interventions studied in patients with substance misuse were text or SMS based, whereas those focusing on populations with MSM as the major risk factor had more advanced smartphone apps () [ - , - ]. A potential explanation is the financial resources required for a smartphone and an accompanying plan, which supports the need for stable financial situations before a successful intervention.
Overall, across risk groups in higher-income countries, there seemed to be a theme of advanced mHealth interventions (using apps, social media, and game-like interventions). Examples in our review included EPIC Allies, the talking avatar, and the weCare interventions that contrasted mainly to call and text-related interventions in lower-income countries [- , ]. This could potentially be explained by the availability of smartphones and internet connectivity in higher-income countries [ , ].
Regardless of risk factors, a limitation of many of these individual studies was the small sample size. For instance, the iTAB study only showed an effect on methamphetamine use, but as this factor could contribute to nonadherence, a larger sample size may have had an effect on adherence .
In addition, studies outside North America should be interpreted with caution if implemented in North America, as many different considerations exist. For instance, different regions of the world preferentially use different messaging apps . These may have different functions and capabilities. Certainly, further work is required [ , ].
Design-related studies indicated the themes that interventions should be customizable, provide disease information, and maintain confidentiality [- ]. A reminder intervention seems to be acceptable in the patients studied [ , , , ]. The role of applications as a conduit for health care support was also emphasized [ , ].
A reasonable conclusion from the totality of the data is that there is a role in improving adherence in higher-income countries, but specific nuances need to be considered on a case-by-case basis. There are limitations affecting the ability to make conclusions, including small sample sizes, heterogeneous populations, and heterogeneous interventions [, , , , - , ].
This study attempted to review the existing literature on mHealth in HIV adherence, divided the literature to the largest subgroups for which evidence was found, and discussed existing studies with each of these groups as specific contexts.
On the basis of an evaluation of both the results and the quality of evidence available, we believe a clear role for mHealth interventions in developing countries exists; however, further work is needed before a final conclusion can be made in the higher-income countries, where the 2 main subgroup risk factors were MSM and IVDU. In addition, a gap in the literature in all 3 groups is the exact nature of interventions (optimal message frequency, content, and intervention themes).
In addition, as demonstrated by the variety and scope of interventions in our review, there is a difference between digital and mobile access, with some groups having the hardware and connectivity to access advanced apps such as smartphone apps, whereas other groups simply have mobile access and can only access text or SMS or voice call interventions.
Our review of the literature provides an optimistic outlook on the role of mHealth in improving HIV care; however, there are limitations to our work. Our narrative, compared with a systematic methodology, provides a broad and comprehensive overview of the subject area but does not have the stringent inclusion and exclusion criteria of a systematic review. However, our review provides a broad view of the subject and identifies specific focus areas for future systematic reviews. Specific areas of future work include better delineation of the efficacy of different types of interventions in higher-income countries and in specific risk groups within higher-income countries.
Conflicts of Interest
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|IVDU: intravenous drug user|
|mHealth: mobile health|
|MSM: men who have sex with men|
|RCT: randomized controlled trial|
Edited by G Eysenbach; submitted 16.05.19; peer-reviewed by J Mitchell, R Robbins, T Chavanduka, Y Guo; comments to author 31.10.19; revised version received 24.02.20; accepted 15.06.20; published 28.08.20Copyright
©Stephen B Lee, Joanne Valerius. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 28.08.2020.
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