A Difference a Literature Review Makes

Using Behavioral Science to Explore Telehealth Barriers in HIV

Jeff Brodscholl, Ph.D
Greymatter Behavioral Sciences

A Tale of Two Clinics

In the spring of 2020, clinics serving the HIV community were confronted with a remarkable challenge: They needed to comply with forced shutdowns and social distancing measures in response to the COVID-19 pandemic while, at the same time, doing everything possible to maintain care continuity for a population that depends heavily on a regular cadence of office visits, tests, medication refills, and other wraparound services to realize the benefits of treatment. Healthcare delivery disruptions would prove to be a major problem for most patient populations throughout the pandemic, but they posed a particular threat to people living with HIV for whom retention in regular care is one of the most significant factors in determining whether the right oral antiretroviral treatment regimens, or "ARTs", are prescribed and adhered to, and HIV viral loads are kept suppressed to clinically safe levels.

To tackle this issue, two clinics – one in San Francisco, the other associated with the University of Nebraska Medical Center in Omaha – joined a broader movement to transition HIV care delivery from in-person to telehealth in the hope that the majority of healthcare services could be handled remotely without losing their quality or cadence. This transition was made possible by the relaxation of regulations which had previously made telehealth a highly constrained, spurious option for many HIV patients in the US [1]. The expectation was that, if the transition to telehealth succeeded in yielding a genuinely “good enough” substitute for in-person visits, then visit scheduling and attendance, clinical decision-making, and ART adherence would continue roughly as they were prior to April, 2020, and this would show up in a corresponding maintenance of pre-pandemic viral suppression rates.

  • The Nebraska clinic offered telephone-based telehealth to a subset of patients based on an algorithm that filtered out those who might be at high risk of not achieving or maintaining viral suppression. For this clinic, the switch to telehealth appeared to be successful; in the first 6 months of the pandemic, adequate viral suppression was observed in 97% of the patients who received telehealth versus 91% who stayed in-person – statistics comparable to the 90%+ rate the clinic had managed to achieve through in-person visits in the three years prior [2].
  • The story of the San Francisco clinic, however, was more mixed. They also transitioned to telephone-based telehealth with a filtering mechanism that allowed the patient to opt out if they preferred to stay in-person or if their provider felt that in-person care would be the better option for the patient. Within the first month of the pandemic, the telehealth option succeeded at keeping appointment no-show rates at pre-pandemic levels – and yet, despite this, overall viral suppression failure rates still managed to increase by nearly a third [3].

These were obviously first stabs at telehealth that were carried out under extraordinary circumstances, but they were part of a broader wave of first stabs that, in the aggregate, amounted to the first population-level test of telehealth as an option for HIV. They emerged in a context in which arguments had already been advanced for making telehealth available to the HIV community given the barriers many HIV patients face when trying to access and utilize in-person care [4,5]. Given the desire to make telehealth a fixture of HIV care for the foreseeable future, one might think that the mix of pandemic experiences could hold valuable lessons for how these services need to be designed for the HIV community if the goal is to address access barriers while achieving the rates of care retention, ART adherence, and viral suppression that are at least equal to those observed when people with HIV are retained in face-to-face care. The insights could then be parlayed into ideas for design features to include or avoid, or additional processes and resources to consider developing that could address telehealth’s weak points or enhance its benefits.

Suppose, then, that we were handed the task of developing the next generation of telehealth services for HIV, and we needed to obtain exactly the types of insights we just described, for the reasons just discussed. What steps could we take to ensure that those insight generation efforts are put on the most productive footing?

In this post, I use this hypothetical case study to discuss what can be gained by including a literature review as one of the very first steps in generating insights for a challenge such as this. This is a step that is practically de rigeur for people who work in the behavioral and life sciences, yet it’s one that is sometimes overlooked in many industry contexts, where the inclination may be to shoot ahead to commissioning original primary research without a deep consideration for what might already be published in peer-reviewed journals, working papers, or other academic sources.

Yet, this misses what, in some cases, can be a treasure trove of information lying within the academic literature that may be directly relevant to a team’s learning objectives. This information may exist in the literature precisely because it has been contributed by professionals who work in areas that are directly relevant to the domain within which the team’s core challenge resides. Some of this work may contain learnings that speak directly to the team’s challenge (e.g., “what is the increase in no-show rates when care delivery switches to telehealth?”). Some of it may contain links to other pockets of knowledge, such as basic behavioral science knowledge, that can transform the learnings into deeper insights (e.g., “what are the psychosocial characteristics of HIV patients and their dynamics that are relevant to understanding appointment no-shows?”). And even when those latter links haven’t yet been made, they can be forged by a subject-matter expert who, during review, can see connections to bodies of scientific knowledge that allow otherwise-overlooked ideas to emerge – all of which can be a boon to the design of original research or other insight generation activities by sending them in critical but easy-to-miss directions.

For this post, I’ll use the telehealth example to walk through the steps I’d take to dig into the literature for a case such as that one, and then describe some of what can emerge when those steps are subsequently implemented. To be fair, HIV is an area rich with published research that is relevant to understanding the behavior and experiences of people living with the condition. That level of richness may not apply when working problems in other categories – yet that doesn’t mean that what’s in academic literature may have nothing to offer those challenges. Key here is to keep in mind how much crosstalk there is between academia, government, the nonprofit sector, and industry, and how that crosstalk has often given rise to bodies of work, such as in consumer behavior, that may contain anything from a few disparate papers to entire programs of research that are pitched toward the needs of very specific applied categories or industry sectors. I also like the HIV example as the very richness of it can stimulate thinking about what’s possible with a literature review, which can then at least inspire the idea of attempting one irrespective of what might ultimately come out of a first-pass effort with it.

Lastly, I should note that, because I use the telehealth example to show how a literature review can act as an early step in insight generation, I focus here on how such a review can be leveraged to acquire knowledge, spark hypotheses, and surface new questions that can then be addressed by follow-on activities such as original research – but not necessarily to carry the entire burden of generating definitive answers on its own. This is an important point, as it gives us latitude to use the literature in a very expansive, exploratory way, but with the tradeoff that the answers we get may, at times, be quite provisional and in need of further work before they can be fully banked. By taking this approach, though, we’re able to begin developing a picture of the world that’s anchored in what’s already known while keeping ourselves open to new information and discovery. And, by positioning the review as an early step, we’re able to use it to orient later activities toward insights that we might otherwise have missed simply because, absent the review, we would never have thought to consider them to begin with.

What Is a Literature Review?

Before going further, it might be worth it to pause and provide a quick tutorial on what we mean when we talk about a literature review. Specifically, what is this thing we keep calling “the literature”, and what exactly is involved in reviewing it?

Put simply, a literature review is a way of extracting learnings from a body of written work that has been built up in a scientific or technical field. The items in this body of work usually span the gamut from articles in peer-reviewed journals to books and book chapters, conference presentations, and even working papers, dissertations, and manuscripts that have not been published anywhere. The content in these items may take the form of:

  • Case studies which describe a particular experience and its takeaways
  • Research reports which describe the methods and findings of a particular scientific study
  • Systematic reviews which summarize the learnings across studies to identify what is known and what is not, and where there are controversies to resolve
  • Theoretical papers which go beyond summarizing findings to extracting general statements about how some specific corner of the world works

These items are often represented in databases containing summary records that provide key bibliographic information along with a gist description of each item’s content. These records can be searched using tools such as PubMed, EconLit, and PsycINFO (among others), and the corresponding items retrieved from institutions such as academic libraries, from the internet, or directly from publishers themselves. Moreover, because these items are meant to contribute to a field’s collective knowledge, they almost always contain citations to other relevant publications that have been produced by the people working within that field. These citations can then be looked up, and the citations within those items reviewed to pull up yet more items – a method that is known as “snowballing”.

What goes into any one review from there, then, comes down to the types of items that are sought and the methods that are chosen for retrieving and reviewing them – all of which depend heavily on the goals of the review and the amount of rigor that needs to be put to it.

On the one end of the spectrum are reviews that are highly rigorous and systematic. These reviews are particularly useful when the goal is to try to understand what all the available evidence to date might tell us about a specific phenomenon or way of intervening in the world. For instance:

  • What are all the factors that are related to whether breast cancer patients adhere to aromatase inhibitors following cancer treatment?
  • How robust is the tendency for people to choose riskier options when outcomes are framed in terms of losses versus gains?
  • How well do graph-based representations of risk work as a way to overcome base rate neglect?

These questions may be answered in a fairly descriptive way by placing retrieved findings in tables and then drawing conclusions from a careful perusal of the tabulated findings. In some cases, the questions may be addressed by what's called a "meta-analysis" in which statistical techniques are applied to retrieved findings to measure the overall size of an effect of interest and determine whether it’s statistically meaningful, whether it tends to vary from study to study, and, if it does, whether the variability can be explained by study characteristics that shed light on the causes of the effect or when it’s likely to hold.

In all these cases, steps are taken to ensure that the review is as thorough and unbiased as possible, and to integrate published findings in a way that allows better-quality evidence to upstage poorer evidence as drivers of the review’s conclusions. This usually means:

  • Devising a search strategy that will ensure full coverage of what’s available
  • Setting and applying very clear inclusion and exclusion criteria so that only relevant items get included
  • Implementing procedures to give greater weight to studies that are considered particularly credible based on their design

Naturally, these reviews are time and labor-intensive, and, as such, are usually most appropriate when the questions to answer are quite specific, the costs of drawing incorrect conclusions are high, and the yield is going to be worth the time, money, and effort that’s required to pull the review off.

At the other end of the spectrum, though, are reviews that are more exploratory and expansive, but with a certain tradeoff that’s made in the amount of rigor that’s put to them. These reviews are similar to ones that are sometimes conducted by behavioral scientists before they embark on a research project, where the goal is simply to scope out what’s known in a given area, identify knowledge gaps, and, if possible, derive testable hypotheses that can be examined in a new study. These reviews may be more informal, but that doesn’t mean they are completely divorced from the standards that apply to more rigorous reviews: They still require care to avoid inferences that are already contradicted by other published findings, or that are too bold to pronounce relative to the current state of knowledge and the quality of the evidence that’s used to arrive at them.

We’ll focus on this latter form of review for the telehealth example, as it aligns with how we’d use a literature review as a practical, front-end component to a broader base of applied insights work. In what follows, we’ll use the post to answer two questions:

  • Method: How would we conduct such a review for our HIV telehealth challenge?
  • Payoff: What types of insights, hypotheses, and questions would the review produce?

The HIV Telehealth Example: How Would We Conduct the Review?

A good way to think about how we’d conduct a literature review for our HIV telehealth challenge is with the five steps shown below.

Post6_Graphic1_Workflow

Step 1: Identify the Learning Objectives

The first step would be for us to articulate the learning objectives for the review: What are the key questions we’d want to answer, or things we’d want to learn, that will bring us closer to the insights we need to achieve?

In this case, we’d want to understand what would make a telehealth option for HIV succeed or fail as a proper substitute for in-person care – and we’d want to do so in a way that can inform future telehealth design changes or identify where they may be needed. To achieve this aim, we'd likely want the definition of “success” to include patient willingness to adopt telehealth as the standard mode of care delivery – but we’d also likely want it to include the ability of telehealth to perform at parity or better on key behavioral and clinical outcomes, such as care retention, ART adherence, and viral suppression. We think the experiences of HIV clinics during the pandemic may contain information that’s relevant to understanding the impact of telehealth on these outcomes, so one of our key learning objectives would likely be to understand:

  • "How did the transition to telehealth during the pandemic impact care retention, ART adherence, and viral suppression, as well as patient interest in staying with telehealth as the mode of care delivery?"

Of course, if we're going to use insights from the review to inform the design of future HIV telehealth services, then we’d need to go beyond the relationships unearthed in pursuit of this objective to understanding why those relationships exist. As with any outcome that arises from the interaction between a product or service and its users, we’d need to look at this issue both from the side of the telehealth services themselves and from the side of the patients who are exposed to those services. We’d therefore likely want to embellish the above learning objective to include an attempt to find out:

  • "What aspects of the way telehealth is designed and deployed create, or might create, positive or negative downstream consequences for the clinical and behavioral outcomes that are of interest to us?"

And, to achieve that aim, we’d likely want to make sure the review is designed to uncover two things:

  • "How, and in what way, do HIV patients’ interactions with healthcare providers, or “HCPs”, translate, or appear to translate, into ART adherence, care retention, and viral suppression?"
  • "How do, or how might, the mediation of those interactions by various aspects of telehealth services conspire with the characteristics of HIV patients to influence these outcomes?"

Note the use of “do or might” in these objectives, which has the effect of permitting a picture of the world to emerge that could be based as much on a circumstantial case as on direct evidence – all important to striking the balance we seek between rigor and making the most of what the literature has to offer us. Note too, though, that, in a real-world engagement, these objectives wouldn’t be decided in a vacuum; they’d be carefully vetted or co-created to ensure alignment with needs of stakeholders and their appetite for expansiveness and risk. We’ll freeze on these items as the review’s learning objectives for now, as they appear reasonable for the purpose to which the review would be put; that'll at least give us a concrete foundation off of which we can take specific actions at the next set of steps.

Step 2: Define Content Territories

Having specified the learning objectives, the next step would be for us to think through the types of content we’d want to extract from the literature to give us the information we need. For this example, we could conceivably outline content territories with a map that looks something like this:

Post6_Graphic2_ContentTerritories

We already have publications pulled for the two San Francisco and Omaha experiences, but we’d likely want to continue digging to see whether there are other case reports, as well as review papers and more rigorous studies, that could shed light on HIV clinics’ experiences with telehealth, focusing on the way these services were designed and deployed, the patients to whom they were offered, and what the consequences appeared to be for changes in care retention, ART adherence, and viral suppression rates. We’d be particularly interested in anything that would allow us to see direct connections between these outcomes and specific telehealth features, as well as anything that could tell us about the desire to continue using of telehealth under circumstances in which in-person care is an available option – a good clue regarding telehealth's potential staying power among patients.

Alas, we’d need to be prepared for the likelihood that any data we uncover would be ambiguous or inconclusive, having been obtained under real-world conditions that would normally be hard to tease apart as-is let alone with a pandemic added to the mix. We’d therefore want our content territories to include information that could permit reasonable inferences about how precisely telehealth might impact the outcomes we care about, which could then help us disambiguate the real-world experience data, and even permit us to anticipate challenges or benefits that are only currently hinted at elsewhere. And to make that possible, we’d want to make sure to do the following:

(1) First, we’d want to unearth anything that could tell us more about the behavioral implications of telehealth generally – and, for that, we’d likely want to dig not only into the literature on telehealth itself, but also into anything on technology-mediated human interactions that could shed light on the ways telehealth might impact patients cognitively, behaviorally, and emotionally. As noted at Step 1, we’d also want to understand the role that care delivery in general plays in the outcomes that are of interest to us, which would mean digging into literatures on the clinical and health behavioral aspects of HIV treatment to try to tease apart the determinants of these outcomes that an experience with telehealth might influence. That would be content that would correspond to what’s on the right side of the map.

(2) Then, for the left side, we’d certainly want to search for any HIV patient self-report studies that could tell us about their attitudes toward, and experiences with, telehealth – but we’d also want to gather any information about the patients that would allow us to make inferences beyond self-report. To that end, we’d want to develop a picture of people living with HIV that can tell us who they are as people, focusing on what it could mean for how they might respond to telehealth given its typical features. More specifically:

  • We’d want to understand where, relative to the overall population, people with HIV land with respect to clinical and psychosocial characteristics that would be relevant to how they experience, react to, and cope with their condition and its management, and how their social and environmental worlds work to support or burden them
  • We’d also want to learn where they land demographically, as that information would point to additional social, cultural, and economic forces that could help us further understand patient’s telehealth-related drivers and barriers

Finally, because we’d be interested in couching all of this in terms of what drives patients’ management of their viral loads and how they react to the features of their healthcare, we’d want to make sure to look at literature that can tell us how aspects of HIV patient engagement, such as their adherence to ART, correlate with the types of demographic and psychosocial characteristics we’d be looking to unearth. That would give us a further way to illuminate the interconnections between telehealth, patient characteristics, and the outcomes that are of concern to us.

Step 3: Identify Literature Sources

Once we’ve identified what content we want to hunt for, we’d then make some decisions about which corners of the literature to target in our quest to go hunting for that content.

In this case, literatures in epidemiology, public health, health psychology, social psychology, and medical anthropology look like they’d be good targets for much of the content on the left side of our map. For the content on the right, we’d also likely want to look at literatures in medicine, public health, health informatics, and human-computer interaction to try to surface anything more about telehealth for HIV specifically, and about the behavioral implications of telehealth generally.

Note that we wouldn’t want to be dogmatic here about the literatures we’d target for reviewing; we’d simply want to have this list available to help us make some key decisions at the next step. That would give us room as we’re reviewing to turn to yet other corners of the literature that we might not have considered initially, but that could speak quite well to some of the objectives we’ve set for ourselves.

Step 4: Devise and Execute a Search Strategy

At this point, we’d now make decisions about the means by which we’d seek out and retrieve the material we’re interested in reviewing and get the review under way.

In this instance, because of the breadth of content we’d want to cover, we’d need to deploy an execution strategy that could balance time and resource constraints with the ability to profit from the review’s ambitions. In a case like this, I would almost certainly rely heavily on a snowballing strategy to conduct the review, likely relying on an internet search to get the process started and then turning to more specific tools such as PubMed, Web of Science, or PsycINFO to refine the effort. (The latter is where the identification of sources at the prior step is helpful: If you know that you’re going to want to poke around in the medical literature, then a tool for searching life sciences publications such as PubMed becomes obvious to put on the table.) As part of a snowballing strategy, I would certainly look for review articles and book chapters that have already covered quite a bit of any corner of the terrain in which I’m interested, and I’d use that plus the references sections of research reports to find other items. As I’m pulling up these items, I would be reading abstracts and doing a cursory content examination to ensure that I’m on the right track and am maintaining some degree of quality control. For instance:

  • I would be checking publication dates, and assessing the degree of fit between item content and the domain in which I’m working (e.g., HIV in the US), to ensure that I’m not pulling in items that are too out of date or too far afield to be of any value to the review
  • I would also be looking to maintain balance and coverage by not allowing myself to feel too satisfied with any single item being the one on which to rely for any particular learning, and by seeing whether I’m getting a diversity of authors so as not to get too sucked into any one author’s preferred narrative
  • Lastly, I’d be keeping an eye on markers of quality, such as publication source, methodology, etc., giving priority to higher-quality items, but not necessarily skipping over lesser ones unless I thought they’d clearly send me down the wrong path

All of this would require activating a decent amount of background knowledge to support rapid sniff tests and keep the review on the rails. That, of course, could run the risk of introducing biases and inefficiencies owing to the limits of the reviewer’s own domain expertise – but, again, that wouldn’t necessarily be fatal so long as the risks are acknowledged and the original purpose of the review is respected. As I note below, team check-ins can provide an opportunity to hear other perspectives and ensure the direction in which the review is heading is still withing everyone’s comfort zone, all of which can inform adjustments to the decisions that are made at this step.

Step 5: Read, Note-Take, Integrate, and Iterate

The almost-final step would then be a deep dive into the items that have been dredged up in the literature search, accompanied by careful note-taking and summarizing to put the key takeaways of each item in a place where patterns can be seen, and interpretations made, that can ladder up to the learnings toward which the review was designed to strive. I say “almost final” as what’s discovered at this step could potentially result in new questions being raised, or gaps in retrieved material becoming evident, that then prompt the adoption of new learning objectives and the search for additional material, which then cause new insights to be developed and, potentially, old directions to be dropped.

How do we know when the review is complete? We know when we’ve arrived at a body of insights, hypotheses, and questions that we can feel comfortable enough to do something with, when we’ve run out of time, or both. This is a step where, like Step 1, we would make sure to have team touchpoints to discuss what the review is revealing and to make sure everyone is comfortable with where the review is likely to land. Ultimately, we should be able to come out of this step with material that can answer a number of key questions or empower a team to begin answering; I list some of these in the box below.

Post6_Graphic3_QuestionsBoard

The HIV Telehealth Example: What Might We Learn?

The steps I’ve outlined may seem like a lot, but the rewards can be worth it – and we can begin to see that when we look at the results we get by applying those steps to our telehealth example.

To begin with, we have published papers on the effects of the pandemic on viral suppression rates and care continuity, which, except for the previously described San Francisco experience [3], paint a rough picture of disaster averted owing, in part, to the transition to telehealth. These papers show viral suppression rates either to have decreased only modestly within the first few months of the pandemic or to have stayed similar to pre-pandemic levels, all in a context in which telehealth came to constitute anywhere from one-third to more than three-quarters of HIV clinic visits in the pandemic’s first phase [2, 6-8]. We also learn that the record was more mixed with respect to indicators of care retention, with some clinics showing higher completed attendance rates with telehealth, and others the opposite, but with none of the findings tracking clearly with other data that could shed light on how, if at all, telehealth may have affected patient willingness or ability to maintain a regular cadence of HCP visits [9-10] (see also [2,3, 6, 8, 11] for aggregate pre-pandemic to pandemic changes).

Yet, as we continue to review, we also come across some other interesting bits of data, such as captured in this chart showing the monthly rates of telehealth usage experienced by a Ryan White clinic in Seattle, WA from the beginning of the pandemic to the end of 2020 [12]:

Post6_Graphic4_TelehealthChart1_Alt

Fig. 1: Estimated monthly percentage of in-person, telephone, and video-based telehealth visits at a Ryan White-clinic in Seattle, WA from March to December, 2020 (based on data presented in [12]).

What stands out in this graph is not only the way telehealth usage drops following full clinic reopening in the late spring, but how much of the dropoff is specifically related to telephone-based telehealth – a modality a number of clinics defaulted to out of concern that some of their patients wouldn’t have access to the technology required for a video visit. Some of the dropoff could have been due to fact that, in HIV, patients need to go for routine blood draws for viral load and immune system monitoring [13], and those in-person activities would have been possible again once lockdown orders were dropped. But some of the drop could also have been driven by patient preferences for in-person visits – preferences that might have existed across all modalities but been particularly acute when telehealth was delivered via telephone. Note that we encounter a similar dropoff in telehealth usage experienced by another Ryan White-funded clinic in Atlanta, GA for the 2020 calendar year [14]:

Post6_Graphic5_TelehealthChart2_Alt2

Fig. 2: Estimated monthly percentage of in-person vs. telephone-based telehealth visits at a Ryan White-funded clinic in Atlanta, GA over the course of 2020 (approximated from visit count data presented in [14]).

Then there are self-report studies we’re able to pull from the literature which begin to shed light on HIV patients’ telehealth-related attitudes and experiences. These studies contain a mix of surveys and in-depth interviews with pre-pandemic and pandemic samples, with the latter often dominated by patients who have had firsthand experience with telehealth. What we see in these studies is that:

  • Self-stated attitudes toward telehealth tend to be generally quite favorable – though hardly unanimously so [9, 14-17]
  • Patients like telehealth both for practical reasons (e.g., reduced disruptions and travel burdens) and for the ability to keep their visits with HIV clinics discrete [9, 15-19]
  • But – patients can also face security concerns, and run into technical and logistical issues (e.g., buggy apps, slow connections, cramped living quarters with little personal space) that make telehealth visits degraded or impossible to carry out [9, 14-18]
  • And, except when the patient already has an established relationship with the HCP, or their goals for a session are highly transactional (e.g., learning about test results), they can find the telehealth experience awkward, rushed (despite objective evidence suggesting they aren’t [4]), and lacking the ingredients for establishing trust and intimacy [9, 15-19]

So, what these studies begin to point to are some clear bright spots for telehealth, but also potential pockets of weakness that could very well drive some patients away from the technology or, worse, cause them to become disengaged when using it. Some of the latter issues surfaced in the self-report data appear to be primarily technological, owing to a combination of access issues (e.g., to good-quality internet services and devices) and the deployment of digital apps and tools that are glitchy or complicated for patients to use. Some of them also appear to be logistical or related to data security concerns. But some of them appear to be more psychological, owing to the impact that technology-mediated interactions, such as those executed through phone or video, might have on the patient’s experience of their encounters with HCPs.

How much weight should we give to the various issues patients raise about telehealth, and how can we disentangle them so as to put us on the pathway to solutions to them? It's here, in the attempt to understand the significance of the self-report data and to rank the deeper challenges they point to, that we can start to see the value that a robust exploration of the literature can generate for us.

Start with what the literature begins to tell us about the profile of people living with HIV:

  • From epidemiology and public health research, we begin to see how the HIV population, though hardly monolithic, has evolved to one that indexes highly on social and economic marginalization and disadvantage. It continues to lean heavily toward males, particularly men who have sex with men – but it is also one in which new diagnoses are much more likely among people of color and those at the lower end of the socioeconomic spectrum [20,21] (see Fig. 3). It is also one that is growing fastest in the South [20], where the cultural climate is more conservative, and the health and economic safety net tends to be thin. And it is one in which rates of poverty (36%), homelessness (8.1%), disability (40%), and incarceration (3.5%) are anywhere from 2.5 to 40 times the national average [22].
Post6_Graphic6_EpiCharts_V3

Fig. 3: Approximate % of new HIV diagnoses by gender, male sexual orientation, and race/ethnicity, and by census tract-level household income and education within combinations of gender and race/ethnicity. MSM = men who have sex with men. Sources: *AIDSVu. Emory University, Rollins School of Public Health. https://aidsvu.org/local-data/united-states/. Accessed January 19, 2024. **Centers for Disease Control and Prevention. HIV Surveillance Report, 2021; vol. 34. http://www.cdc.gov/hiv/library/reports/hiv-surveillance.html. Published May 2023. Accessed January 19, 2024. ***Based on data presented in [21].

  • These data add to what the literature highlights is an already fraught dynamic in HIV in which disease processes conspire with pre-existing psychosocial vulnerabilities to create the conditions for disrupted personal relationships, heightened rejection expectancies, internalized stigma, social isolation, and depression [23-41], with consequences for health behaviors, such as regular clinic visits [41-48] and ART adherence [33,34,37,47-52], that can be substantial – moreso, in the case of adherence, than those that follow even from low health literacy or medication side-effects [50]. Through the literatures on depression, stigma, and rejection, we’re able to see how the features of this dynamic hint at the presence of other vulnerabilities, such as decrements in attention and executive function [57,58,60], challenges in coping and emotion regulation [61], and potentially, among those scarred by stigma, rapid attention capture by subtle rejection cues and a readiness to detect rejection [62-67], that would be expected to disrupt key health behaviors and turn interpersonal interactions with HCPs and others into emotional hot spots if they proved to be operative. Finally, by turning back to the published work on health disparities, we’re able to discover not only how ART nonadherence rates increase across minorities and those with low income and education [68-74], but also how the features of their backgrounds (e.g., cultural conservatism within their communities, legacies of race-based discrimination, financial precarity) can aggravate the challenges of living with HIV by increasing the risks of stigmatization [75], stress [76,77], and depression [24,78], while being associated with social and material barriers to health behavior that would be hard to overcome even under the best psychosocial circumstances.
Post6_Graphic7_PsychosocialDynamics

Fig. 4: Schematic diagram of HIV psychosocial dynamics uncovered in the review and their relationships to behavioral and health outcomes. Lines represent bivariate relationships between key constructs identified in the review; double arrows are used throughout to reflect the correlational nature of much of the published data. Red lines = barriers to care retention and ART adherence, green lines = facilitators of care retention and ART adherence. Numbers in brackets are references. * = observed in general population, † = observed outside of HIV but within a relevant population (e.g., LGBT individuals). Highlighted boxes represent hot spots for potential telehealth tripwires uncovered later in the review.

These learnings elevate the self-report data by casting them into a new, important light. At a minimum, they work to reinforce concerns that we would likely have had with certain aspects of telehealth as a matter of course:

  • Access barriers, which we can now expect to be even more prevalent among people with HIV given the implications of their demographics for the quality of the technologies they may have available to them
  • Challenges overcoming UX friction points, which we can expect to be exacerbated by the background effects of hopelessness and depression on motivation, cognition, and coping

But they also work to unearth a potentially significant sleeper issue by helping us to understand why, and how deeply, some HIV patients may quietly come to find telehealth off-putting beyond these matters, and despite the benefits they derive from the technology in other key respects (e.g., reduced visit burden, increased privacy, etc.). And the issue particularly starts to come into focus once we compare what we've unearthed about the burdens of living with HIV to what the literature has to say about the psychology of technology-mediated communication:

  • In the case of telephone-based telehealth, the limited nature of the medium likely robs patients of many of the cues they would normally rely upon to rapidly size up their HCPs' thoughts and feelings and establish a sense of shared presence with them [79]. This aspect of telephone-mediated interaction might actually be welcomed by some HIV patients owing to what the literature tells us are the ways that stigma concerns and experiences of social isolation can lead people to develop a heightened desire to maintain physical and psychological distance with others [19,80-82]. Yet, by turning back to what we've already unearthed, we're able to see how what might be taken to be an emotional benefit in this case could also act as a hidden liability by quietly feeding the chronic sense of loneliness and disconnection that may put some HIV patients at risk for disengagement to begin with. Moreover, for those who worry about stigma-based rejection, the lack of visual cues could also run headlong into the desire to monitor the HCP for signs of discrimination, and to have available the full range of cues for disambiguating the HCP's behavior whenever impending prejudice is suspected – all of which could provoke frustration and a failure to establish trust that, itself, sets the stage for disruption and disengagement.
  • From this, we might think that video-based telehealth would be the better option to default to assuming the technological challenges with it could be effectively resolved – yet, from the review, we're able to uncover an emerging body of research that suggests that this might not necessarily be the case. The reason has to do with what some research suggests is a tendency for video to activate the brain to expect a face-to-face encounter but to then deny it many of the cues that are needed to make such an encounter seamless [79]. Thus, it may lead certain behaviors that are seen on camera, such as the drifting of eyes toward the side, to remain hopelessly ambiguous, or to be perceived as meaning one thing (e.g., loss of interest) when they actually mean something else (e.g., distraction by a text message) [83]. The medium may even create conditions that actively undermine the interaction – for instance, by encouraging the more dominant speaker such as the HCP to hold the floor for undesirably long periods [4,84], or by triggering self-presentation concerns among those who are already predisposed to worry about being judged from their behavior or appearance [85,86]. All of this may occur quite rapidly, causing video interactions to quickly become mentally taxing [85,87,88] and to create the very conditions for conversational disruption and miscommunication that video is supposed to avoid. Once we add in the interpersonal sensitivities that are prevalent in HIV, we can begin to see how even video-based telehealth could trigger concerns that may make the experience feel disengaging, and perhaps even alienating, to certain sets of HIV patients (e.g., those with high levels of internalized stigma, rejection sensitivity, and depression, or who have not yet already developed a solid, trusting relationship with the HCP).

Finally, we might want to ask how concerned we should be about all of this given the broader picture of telehealth pros and cons that the review helps us appreciate. To answer this, we need to understand how interactions with HCPs translate into clinical decisions and patient behaviors are vital to sustained viral suppression – and for that, we can turn back to the medical, public health, and applied behavioral science literatures to create a picture that looks something like this:

Post6_Graphic8_HCPPtDynamics

Fig. 5: Flow diagram for HCP (blue) and patient (orange) interaction behaviors and their downstream consequences for the patient's attitudes, engagement, and behaviors in the encounter, the information that's made available to the HCP during the encounter, and the antecedents to the patient's later health behaviors (treatment adherence and care retention) as suggested by the review. Red boxes and lines represent barriers to specific patient behaviors in the HCP-patient interaction; white arrows and boxes capture verbal behaviors of one party that influence specific verbal behaviors of the other party. 

In other words, what the literature brings into focus are the ways in which medical appointments are not just for diagnosis, treatment selection, and monitoring, but are also linchpins for keeping patients engaged and on track with their care – specifically, by providing opportunities to boost patient motivation through feedback and encouragement, to discuss the patient’s functional and emotional treatment barriers, to make the patient a partner in decision-making, and to help the patient feel cared for and understood. These features of the HCP-patient dialog turn out to have clear positive effects on medication adherence [92,96], and they get reflected in patients’ perceptions and feelings of trust and satisfaction that predict higher rates of adherence and care retention in their own right [89-91,93-99].

Yet, they also turn out to be fragile, being open to disruption by quirks in the HCP and patient’s own interpersonal interaction styles (e.g., the HCP's use of eye gaze) [100] and by such subtle, transient events as the failure of one party to say or do something that would trigger the other to take a key action (e.g., the patient failing to proactively vocalize their concerns and opinions, which would otherwise nudge the HCP to increase their information-sharing, encouragement, and partnership-building) [98,101,102]. The same literature helps us to understand how these tripwires could be particularly problematic for people with HIV who, depending on their demographic and psychosocial profile, may be predisposed to avoiding a direct conversational style with physicians [98,101,103] or be subject to differential treatment simply because of the way the HCP thinks and feels about patients from minority or low socioeconomic backgrounds [98,101,103,104]. When the exchange falters, it can leave the patient feeling rushed, confused, and excluded from treatment decision-making [103], which can become a recipe for patient dissatisfaction and disengagement. And when it triggers a perception of being discriminated against – a not-uncommon experience in HIV [43,105] – it can put adherence and care retention at risk by elevating the odds of depression and internalized stigma [27,34,106], and by interfering with the patient's ability to establish a rapport with future HCPs that involves the requisite positivity and warmth [107].

These findings may not speak directly to telehealth on their own, but they do create the context for understanding why some of the technology’s potential impacts on the quality of the doctor-patient encounter – disruptions from cognitive overload, heightened interpersonal concerns, and opportunities for misperception owing to the medium itself – may need to be taken quite seriously in HIV, if not as an immediate action item, then at least as something to verify and size with additional research before being tabled for further consideration. That's no small thing when we consider how the patient self-report studies can often encourage treating this particular issue as if it were something that's merely "mentioned in passing", making issues of access, usability, and “tech literacy” seem more important simply because they are the ones that are more likely for respondents and researchers alike to talk about in depth. Together, the review gives us a more sophisticated way to unpack this issue and weigh it against the broader pros and cons that the review works to surface, allowing us to make decisions from a foundation that would have been lacking had the review not been conducted.

The HIV Telehealth Example: Summary

To summarize, then, we can see how a literature review can be a vehicle for creating a very rich set of insights that can form a foundation for a broad range of further work. In the case of our telehealth example, we get:

  • A list of telehealth’s upsides and downsides, some based more on deeper inference than on direct observation, but all capable of being accompanied by information capturing the logic and nature of the evidence behind each
  • An emerging picture of telehealth in HIV, including the outcomes associated with its early-pandemic rollout, inklings into its subsequent staying power, and hints about how, and how well, it has been implemented – all complimented with broader findings regarding the psychological consequences of technology-mediated interaction
  • A portrait of people with HIV, their opinions and attitudes toward telehealth, who they are demographically and psychosocially, and the implications for health behavior and reactions to telehealth specifically – all of which could readily be summarized in personas or other collateral for a wider team of strategists, designers, and other professionals tasked with optimizing telehealth for HIV care delivery

Note that none of this presumes that what we get from a literature review is meant to be a set of certitudes that are based strictly on smoking guns. Rather, what we get are hypotheses that vary in their credibility and actionability, but which, having at least some foot in published, peer-reviewed evidence, we can feel sufficiently good about putting on the table for further consideration. Some of these hypotheses may feel trustworthy and important enough to begin taking action with them without further ado; others we may feel require further pressure-testing or gap-filling with additional research before they can be pulled into strategy development or solutions ideation.

Yet, as we see from the example, what we get from published work can help us entertain hypotheses that we might not have considered at all, or that we might have paid short shrift to, had it not been for the review itself. That pays dividends in terms of the insights we obtain – but it also provides benefits beyond the initial insights themselves. In particular:

  • It can make subsequent insight generation activities more efficient by giving them a tight focus around hypothesis-testing rather than bottom-up inference – key to swapping out labor-intensive projects for faster, cheaper alternatives such as targeted survey, experimentation, or interview work;
  • When the review is expansive, we can begin assembling a picture of the world that has enough texture for us to not only see the potential challenges we need to address, but also begin envisioning the solutions to them, which can at least help us determine whether our work is heading in a fruitful direction. (In our HIV example, we might begin to envision potential design maneuvers for video-based telehealth, such as removal of cues that can amplify self-awareness and prompt conversation-irrelevant behavior, or implementation of some combination of HCP training and triggers for desired on-camera HCP actions, that could avoid the medium's pitfalls and help recreate, as best as possible, the qualities of the ideal face-to-face encounter that the review helps to surface.)

How much weight to give to any of the emerging insights would need to be a judgment call made in a team context, but at least they would have an evidence base to which they could turn to inform those judgments.

In Conclusion

We’ve used this post to describe what we stand to gain from digging deep into academic and technical literature as a key step in solutions-focused insight generation. In closing, I note that, while we used a relatively extensive example to demonstrate what a literature review can yield (about 3 ½ weeks’ worth of retrieving and reviewing in this case – further out from the usual 2-3 week initiatives I’ve conducted for clients in the past), not all reviews have to be this intense; they can run from multi-week initiatives to something you do in a few days, or even a few hours, depending on what needs to be learned and the domain in which learning needs to occur. They can also be included in an initiative in any number of ways, being tacked onto a broader stream of insights work with other insights activities run in parallel or sequentially, or even called upon later during solutions design to review what’s known about interventions that would be relevant to idea generation.

Either way, literature reviews can be a potent part of the journey from insight to action and are worth testing the waters with to see where they could take you, whether they're taken in small quantities or large. In fact, we would argue that they should be considered a standard part of the armamentarium, and treated as an indispensable tool to pull from the toolkit, whenever the challenge looks like it's going to be complex and the goal is to activate social and behavioral science knowledge as extra firepower to support insights and solutions development.

References (Were We Got All of This)

I: Experiences With Telehealth in HIV

  1. Budak, J.Z., Scott, J.D., Dhaniereddy, S., & Wood, B.R. (2021). The impact of COVID-19 on HIV care provided via telemedicine – past, present, and future. Current HIV/AIDS Reports, 18, 98-104. https://doi.org/10.1007/s11904-021-00543-4
  2. Fadul, N., Regan, N., Kaddoura, L., Swindells, S. (2021). A midwestern academic HIV clinic operation during the COVID-19 pandemic: Implementation strategy and outcomes. Journal of the International Association of Providers of AIDS Care, 20, 1-5. https://doi.org/10.1177/23259582211041423
  3. Spinelli, M.A., Hickey, M.D., Glidden, D.V., Nguyen, J.Q., Oskarsson, J.J., Havlir, D., & Gandhi, M. (2020). Viral suppression rates in a safety-net HIV clinic in San Francsico destabilized during COVID-19. AIDS, 35, 2828-2331. https://doi.org/10.1097/QAD.0000000000002677
  4. Dandachi, D., Freytag, J., Giordano, T.P., & Dang, B.N. (2020). It is time to include telehealth in our measure of patient retention in HIV care. AIDS and Behavior, 24, 2463-2465. https://doi.org/10.1007/s10461-020-02880-8
  5. Ohl, M.E., Richardson, K., Rodriguez-Barradas, M.C., Bedimo, R., Marconi, V., Morano, J.P., Jones, M.P., Vaughan-Sarrazin, M. (2019). Impact of availability of telehealth programs on documented HIV viral suppression: A cluster randomized program evaluation in the Veterans Health Administration. Open Forum Infectious Diseases, 6(6), ofz206. https://doi.org/10.1093/ofid/ofz206
  6. Mayer, K.H., Levine, K., Grasso, C., Multani, A., Gonzalez, A., & Biello, K. (2020). Rapid migration to telemedicine in a Boston community health center is associated with maintenance of effective engagement in HIV care. Open Forum Infectious Diseases, 7(Suppl 1), S337-S338. https://doi.org/10.1093/ofid/ofaa439.735
  7. Mohr, K.B., Lee-Rodriguez, C., Semiezade-Yazd, Z., Lam, J.O., Imp, B.M., & Luu, M.N. (2019). Impact of the coronavirus disease 2019 pandemic on antiretroviral therapy initiation and care delivery for people with newly diagnosed HIV in an integrated healthcare system. Open Forum Infectious Diseases, 9(12), ofac639. https://doi.org/10.1093/ofid/ofac639
  8. Norwood, J., Khesthi, A., Shepherd, B.E., Rebeiro, P.F., Ahonkhai, A., Kelly, S., & Wanjalla, C. (2022). The impact of COVID-19 on the HIV care continuum in a large urban southern clinic. AIDS and Behavior, 26, 2825-2829. https://doi.org/10.1007/s10461-022-03615-7
  9. Auchus, I.C., Jaradeh, K., Tang, A., Marzan, J., & Boslett, B. (2021). Transitioning to telehealth during the COVID-19 pandemic: Patient perspectives and attendance at an HIV clinic in San Francisco. AIDS Patient Care and STDs, 35, 249-254. https://doi.org/10.1089/apc.2021.0075
  10. Boshara, A.I., Patton, M.E., Hunt, B.R., Glick, N., & Johnson, A.K. (2022). Supporting retention in HIV care: Comparing in-person and telehealth visits in a Chicago-based infectious disease clinic. AIDS and Behavior, 26, 2581-2587. https://doi.org/10.1007/s10461-022-03604-w
  11. Friedman, E.E., Devlin, S.A., Gilson, S.F., & Ridgway, J.P. (2022). Age and racial disparities in telehealth use among people with HIV during the COVID-19 pandemic. AIDS and Behavior, 26, 2686-2691. https://doi.org/10.1007/s10461-022-03607-7
  12. Wood, B.R., Lan, K.F., Tao, Y., Mose, E.Y., Aas, E., Budak, J.Z., Dhanireddy, S., & Kim, H.N. (2021). Visit trends and factors associated with telemedicine uptake among persons with HIV during the COVID-19 pandemic. Open Forum Infectious Diseases, 8(11), ofab480. https://doi.org/10.1093/ofid/ofab480
  13. Labisi, T., Regan, N., Davis, P., & Fadul, N. (2022). HIV care meets telehealth: A review of successes, disparities, and unresolved challenges. Current HIV/AIDS Reports, 19, 446-453. https://doi.org/10.1007/s11904-022-00623-z
  14. Galaviz, K., Shah, N.S., Guiterrez, M., Collins, L.F., Lagiri, C.D., Moran, C.A., Szabo, B., Sumitani, J., Rhodes, J., Marconi, V.C., Nguyen, M.L., Cantos, V.D., Armstrong, W.S., & Colasanti, J.A. (2022). Patient experiences with telemedicine for HIV care during the first COVID-19 wave in Atlanta, Georgia. AIDS Research and Human Retroviruses, 38, 415-420. https://doi.org/10.1089/AID.2021.0109

II: HIV Patient Perspectives on Telehealth

  1. Baim-Lance, A., Angulo, M., Chiasson, M.A., Lekas, H.M., Schenkel, R., Villarreal, J., Cantos, A., Kerr, C., Nagaraja, A., Yin, M.T., & Gordon, P. (2022). Challenges and opportunities of telehealth digital equity to manage HIV and comorbidities for older persons living with HIV in New York state. BMC Health Services Research, 22(1), 609. https://doi.org/10.1186/s12913-022-08010-5
  2. Dandachi, D., Dang, B., & Giordano, T. (2020). The attitude of patients with HIV about telehealth for HIV care. Open Forum Infectious Diseases, 7(Suppl 1): S550-S551. https://doi.org/10.1093/ofid/ofaa439.1228
  3. Harsono, D., Deng, Y., Chung, S., Barakat, L.A., Friedland, G., Meyer, J.P., Porter, E., Villanueva, M., Wolf, M.S., Yager, J.E., & Edelman, E.J. (2022). Experiences with telemedicine for HIV care during the COVID-19 pandemic: A mixed-methods study. AIDS and Behavior, 26, 2099-2111. https://doi.org/10.1007/s10461-021-03556-7
  4. Bleasdale, J., Leone, L.A., Morse, G.D., Liu, Y., Taylor, S., & Pryzbyla, S.M. (2022). Socio-structural factors and HIV care engagement among people living with HIV during the COVID-19 pandemic: A qualitative study in the United States. Tropical Medicine and Infectious Diseases, 7(10), 259. https://doi.org/10.3390/tropicalmed7100259
  5. Walker, D., Moucheraud, C., Butler, D., de Vente, J., Tangonan, K., Shoptaw, S., Currier, J.S., Gladstein, J., & Hoffman, R. (2023). Experiences with telemedicine for HIV care in two federally qualified health centers in Los Angeles: A qualitative study. BMC Health Services Research, 23(1), 156. https://doi.org/10.1186/s12913-023-09107-1

III: HIV Epidemiology in the United States

  1. Centers for Disease Control and Prevention. HIV Surveillance Report, 2021; vol. 34. http://www.cdc.gov/hiv/library/reports/hiv-surveillance.html. Published May 2023. Accessed January 19, 2024.
  2. Lyons, S.J., Gant, Z., Jin, C., Dailey, A., Nwangwu-Ike, N., & Johnson, A.S. (2022). A census tract-level examination of differences in social determinants of health among people with HIV, by race/ethnicity and geography, United States and Puerto Rico, 2017. Public Health Reports, 137, 278-290. https://doi.org/10.1177/0033354921990373
  3. Dasgupta, S., McManus, T., Tie, Y., Lin, C.Y., Yuan, X., Sharpe, D., Fletcher, K.M., & Beer, L. (2023). Comparison of demographic characteristics and social determinants of health between US adults with diagnosed HIV and all adults in the US. AJPM Focus, 2(3), 100115. https://doi.org/10.1016/j.focus.2023.100115

IV: Psychosocial Dynamics in HIV

  1. Brody, D.J., Pratt, L.A., & Hughes, J. (2018). Prevalence of depression among adults aged 20 and over: United States, 2013-2016. NCHS Data Brief, 303. Hyattsville, MD: National Center for Health Statistics.
  2. Gokhale, R.H., Weiser, J., Sullivan, P.S., Luo, Q., Shu, F., & Bradley, H. (2019). Depression prevalence, antidepressant treatment status, and association with sustained HIV viral suppression among adults living with HIV in care in the United States, 2009-2014. AIDS and Behavior, 23, 3452-3459. https://doi.org/10.1007/s10461-019-02613-6
  3. Simoni, J.M., Safren, S.A., Manhart, L.E., Lyda, K., Grossman, C.I., Rao, D., Mimiaga, M.J., Wong, F.Y., Catz, S.L., Blank, M.B., DiClemente, R., & Wilson, I.B. (2011). Challenges in addressing depression in HIV research: Assessment, cultural context, and methods. AIDS and Behavior, 15, 376-388. https://doi.org/10.1007/s10461-010-9836-3
  4. Archiopoli, A., Ginossar, T., Wilcox, B., Avila, M., Hill, R., & Oetzel, J. (2016). Factors of interpersonal communication and behavioral health on medication self-efficacy and medication adherence. AIDS Care, 28, 1607-1614. https://doi.org/10.1080/09540121.2016.1192577
  5. Crockett, K.B., Edmonds, A., Johnson, M.O., Neilands, T.B., Kempf, M., Konkle-Parker, D., Wingwood, G., Tien, P.C., Cohen, M., Wilson, T.E., Logie, C.H., Sosanya, O., Plankey, M., Golub, E., Adimora, A.A., Parish, C., Weisler, S.D., Turan, J.M., & Turan, B. (2019). Neighborhood racial diversity, socioeconomic status, and perceptions of HIV-related discrimination and internalized HIV stigma among women living with HIV in the United States. AIDS Patient Care and STDs, 33, 270-281. https://doi.org/10.1089/apc.2019.0004
  6. De Jesus, M., Ware, D., Brown, A.L., Egan, J.E., Haberlen, S.A., Palella, F.J., Detels, R., Friedman, M.R., & Plankey, M.W. (2021). Social-environmental resiliencies protect against loneliness among HIV-positive and HIV-negative older men who have sex with men: Results from the Multicenter AIDS Cohort Study (MACS). Social Science & Medicine, 272, 113711. https://doi.org/10.1016/j.socscimed.2021.113711
  7. Feinstein, B.A., Wadsworth, L.P., Davila, J., & Goldfried, M.R. (2014). Do parental acceptance and family support moderate associations between dimensions of minority stress and depressive symptoms among lesbians and gay men? Professional Psychology: Research and Practice, 45, 2239-246. http://dx.doi.org/10.1037/a0035393
  8. Feinstein, B.A., Davila, J., & Dyar, C. (2017). A weekly diary study of minority stress, coping, and internalizing symptoms among gay men. Journal of Consulting and Clinical Psychology, 85, 1144-1157. https://doi.org/10.1037/ccp0000236
  9. Fekete, E.M., Williams, S.L., & Skinta, M.D. (2018). Internalised HIV-stigma, loneliness, depressive symptoms and sleep quality in people living with HIV. Psychology & Health, 33, 398-415. https://doi.org/10.1080/08870446.2017.1357816
  10. Galvan, F.H., Davis, E.M., Banks, D., & Bing, E.G. (2008). HIV stigma and social support among African-Americans. AIDS Patient Care and STDs, 22, 423-436. https://doi.org/10.1089/apc.2007.0169
  11. Johnson, M.O., Neilands, T.B., Dilworth, S., Morin, S.F., Remien, R.H., & Chesney, M.A. (2007). The role of self-efficacy in HIV treatment adherence: Validation of the HIV treatment adherence self-efficacy scale (HIV-ASES). Journal of Behavioral Medicine, 30, 259-270. https://doi.org/10.1007/s10865-007-9118-3
  12. Kay, E.S., Rice, W.S., Crockett, K.B., Ghislaine, C.A., Batey, D.S., & Turan, B. (2018). Experienced HIV-related stigma in healthcare and community settings: Mediated associations with psychosocial and health outcomes. Journal of Acquired Immune Deficiency Syndromes, 77, 257-263. https://doi.org/10.1097/QAI.0000000000001590
  13. Malika, N., Bogart, L.M., Mutchler, M.G., Goggin, K., Klein, D.J., Lawrence, S.J., & Wagner, G.J. (2023). Loneliness among black/African-American adults living with HIV: Sociodemographic and psychosocial correlates and implications for adherence. Journal of Racial and Ethnic Health Disparities. https://doi.org/10.1007/s40615-023-01712-4
  14. Pachankis, J.E., Hatzenbuehler, M.L., & Starks, T.J. (2014). The influence of structural stigma and rejection sensitivity on minority men’s daily tobacco and alcohol use. Social Science & Medicine, 103, 67-75. https://doi.org/10.1016/j.socscimed.2013.10.005
  15. Rueda, S., Mitra, S., Chen, S., Gogolishvili, D., Globerman, J., Chambers, L., Wilson, M., Logie, C.H., Shi, Q., Morassaei, S., & Rourke, S.B. (2016). Examining the associations between HIV-related stigma and health outcomes in people living with HIV/AIDS: A series of meta-analyses. BMJ Open, 6(7), e011453. https://doi.org/10.1136/bmjopen-2016-011453
  16. Slimowicz, J., Siev, J., & Brochu, P.M. (2020). Impact of status-based rejection sensitivity on depression and anxiety symptoms in gay men. International Journal of Environmental Research and Public Health, 17, 1546. https://doi.org/10.3390/ijerph17051546
  17. Travaglini, L.E., Himelhoch, S.S., & Fang, L.J. (2018). HIV stigma and its relation to mental, physical, and social health among black women living with HIV/AIDS. AIDS and Behavior, 22, 3783-3794. https://doi.org/10.1007/s10461-018-2037-1
  18. Vyavaharkar, M., Moneyham, L., Corwin, S., Saunders, R., Annang, L., & Tavakoli, A. (2010). Relationships between stigma, social support, and depression in HIV-infected African American women living in the rural Southeastern United States. Journal of the Association of Nurses in AIDS Care, 21, 144-152. https://doi.org/10.1016/j.jana.2009.07.008

V: Retention in Care and ART Adherence in HIV

  1. Valverde, E., Rodriguez, A., White, B., Guo, Y., & Waldrop-Valverde, D. (2018). Understanding the association of internalized HIV stigma with retention in HIV care. Journal of HIV and AIDS, 4, https://doi.org/10.16966/2380-5536.159
  2. Forney, D.J., Sheehan, D.M., Dale, S.K., Li, T., de la Rosa, M., Spencer, E.C., & Sanchez, M. (2023). The impact of HIV-related stigma on racial/ethnic disparities in retention in HIV care among adults living with HIV in Florida. Journal of Racial and Ethnic Health Disparities. https://doi.org/10.1007/s40615-023-01715-1
  3. Kalichman, S.C., Katner, H., Banas, E., Hill, M., & Kalichman, M.O. (2020). Cumulative effects of stigma on retention in HIV care among men and women in the rural southeastern United States. AIDS Patient Care and STDs, 34, 484-490. https://doi.org/10.1089/apc.2020.0144
  4. Pearson, C.A., Johnson, M.O., Neilands, T.B., Dilworth, S.E., Sauceda, J.A., Mugavero, M.J., Crane, H.M., Frederickson, R.J., Mathews, W.C., Moore, R.D., Napravnik, S., Mayer, K.H., & Christopoulos, K.A. (2021). Internalized HIV stigma predicts suboptimal retention in care among people living with HIV in the United States. AIDS Patient Care and STDs, 35, 188-193. https://doi.org/10.1089/apc.2020.0244
  5. Pence, B.W., Mills, J.C., Bengston, A.M., Gaynes, B.N., Breger, T.L., Cook, R.L., Moore, R.D., Grelotti, D.J., O’Cleirigh, C., & Mugavero, M.J. (2018). Association of increased chronicity of depression with HIV appointment attendance, treatment failure, and mortality among HIV-infected adults in the United States. JAMA Psychiatry, 75, 379-385. https://doi.org/10.1001/jamapsychiatry.2017.4726
  6. Tarantino, N., Brown, L.K., Whiteley, L., Fernandez, M.I., Nichols, S.L., Harper, G., & The ATN 086 Protocol Team for the Adolescent Medicine Trials Network for HIV/AIDS Intervention (2018). Correlates of missed clinic visits among youth living with HIV. AIDS Care, 30, 982-989. https://doi.org/10.1080/09540121.2018.1437252
  7. Vanable, P.A., Carey, M.P., Blair, D.C., & Littlewood, R.A. (2006). Impact of HIV-related stigma on health behaviors and psychological adjustment among HIV-positive men and women. AIDS and Behavior, 10, 473-482. https://doi.org/10.1007/s10461-006-9099-1
  8. Yigit, I., Bayramoglu, Y., Weiser, S.D., Johnson, M.O., Mugavero, M.J., Turan, J.M., & Turan, B. (2020). Changes in internalized stigma and HIV health outcomes in individuals new to HIV care: The mediating roles of depression and treatment self-efficacy. AIDS Patient Care and STDs, 34, 491-497. https://doi.org/10.1089/apc.2020.0114
  9. Gonzalez, J.S., Batchelder, A.W., Psaros, C., & Safren, S.A. (2011). Depression and HIV/AIDS treatment nonadherence: A review and meta-analysis. Journal of Acquired Immune Deficiency Syndromes, 58, 181-187. https://doi.org/10.1097/QAI.0b013e31822d490a
  10. Langbeek, N., Gisolf, E.H., Reiss, P., Vervoort, S.C., Hafsteindottir, T.B., Richter, C., Sprangers, M.A.G., & Nieuwkerk, P. (2014). Predictors and correlates of adherence to combination antiretroviral therapy (ART) for chronic HIV infection: A meta-analysis. BMC Medicine, 12, 142. https://doi.org/10.1186%2Fs12916-014-0142-1
  11. Sayles, J.N., Wong, M.D., Kinsler, J.J., Martins, D., & Cunningham, W.E. (2009). The association of stigma with self-reported access to medical care and antiretroviral therapy adherence in persons living with HIV/AIDS. Journal of General Internal Medicine, 24, 1101-1108. https://doi.org/10.1007/s11606-009-1068-8
  12. Waite, K.R., Paasche-Orlow, M., Rintamaki, L.S., Davis, T.C., & Wolf, M.S. (2008). Literacy, social stigma, and HIV medication adherence. Journal of General Internal Medicine, 23, 1367-1372. https://doi.org/10.1007/s11606-008-0662-5

VI: Relations of Care Retention and ART Adherence to HIV Viral Suppression

  1. Mungavero, M.J., Westfall, A.O., Zinski, A., Davila, J., Drainoni, M., Gardner, L.I., Keruly, J.C., Malitz, F., Marks, G., Metsch, L., Wilson, T.E., & Giordano, T.P. (2012). Measuring retention in HIV care: The elusive gold standard. Journal of Acquired Immune Deficiency Syndromes, 61, 574-580. https://doi.org/10.1097/QAI.0b013e318273762f
  2. Reveles, K.R., Juday, T.R., Labreche, M.J., Mortensen, E.M., Koeller, J.M., Seekins, D., Oramasionwu, C.U., Bollinger, M., Copeland, L.A., Jones, X., & Frei, C.R. (2015). Comparative value of four measures of retention in expert care in predicting clinical outcomes and health care utilization in HIV patients. PLoS One, 10(3), e0120953. https://doi.org/10.1371/journal.pone.0120953
  3. Altice, F., Evuarherhe, O., Shina, S., Carter, G., & Beaubrun, A.C. (2019). Adherence to HIV treatment regimens: Systematic review and meta-analysis. Patient Preference and Adherence, 13, 475-490. https://doi.org/10.2147/PPA.S192735
  4. Bezabhe, W.M., Chalmers, L., Bereznicki, L.R., Peterson, G.M. (2016). Adherence to antiretroviral therapy and virologic failure: A meta-analysis. Medicine, 95(15), e3361. https://doi.org/10.1097/MD.0000000000003361

VII: Effects of Depression on Cognitive, Emotional, and Self-Regulatory Functioning

  1. Hammar, A., & Ardal, G. (2009). Cognitive functioning in major depression – a summary. Frontiers in Human Neuroscience, 3, 26. https://doi.org/10.3389/neuro.09.026.2009
  2. Nuno, L., Gomez-Benito, J., Carmona, V.R., & Pino, O. (2021). A systematic review of executive function and information processing speed in major depressive disorder. Brain Science, 11, 147. https://doi.org/10.3390/brainsci11020147
  3. Pagoni, I., Gobbi, E., Alaimo, C., Campana, E., Rossi, R., Manenti, R., Balconi, M., & Cotelli, M. (2022). The relationship between theory of mind and executive function in major depressive disorders: A review. Frontiers in Psychiatry, 13, 980392. https://doi.org/10.3389/fpsyt.2022.980392
  4. Rock, P.L., Roiser, J.P., Riedel, W.J., & Blackwell, A.D. (2014). Cognitive impairment in depression: A systematic review and meta-analysis. Psychological Medicine, 44, 2029-2040. https://doi.org/10.1017/S0033291713002535
  5. Visted, E., Vellestad, J., Nielsen, M.B., & Schanche, E. (2018). Emotion regulation in current and remitted depression: A systematic review and meta-analysis. Frontiers in Psychology, 9, 746. https://doi.org/10.3389/fpsyg.2018.00756

VIII: Social Cognitive Effects of Rejection Experiences and Chronic Rejection Concerns

  1. Berenson, K.R., Gyurak, A., Ayduk, O., Downey, G., Garner, M.J., Mogg, K., Bradley, B.P., & Pine, D.S. (2009). Rejection sensitivity and disruption of attention by social threat cues. Journal of Research in Personality, 43, 1064-1072. https://doi.org/10.1016/j.jrp.2009.07.007
  2. Downey, G., Mougios, V., Ayduk, O., London, B.E., & Shoda, Y. (2004). Rejection sensitivity and the defensive motivational system: Insights from the startle response to rejection cues. Psychological Science, 15, 668-673. https://doi.org/10.1111/j.0956-7976.2004.00738.x
  3. Ehrlich, K.B., Gerson, S.A., Vanderwert, R.E., Cannon, E.N., & Fox, N.A. (2015). Hypervigilance to rejecting stimuli in rejection sensitive individuals: Behavioral and neurocognitive evidence. Personality and Individual Differences, 85, 7-12. https://doi.org/10.1016/j.paid.2015.04.023
  4. Richman, L.S., Martin, J., & Guadagno, J. (2015). Stigma-based rejection and the detection of signs of acceptance. Social Psychological and Personality Science, 7, 53-60. https://doi.org/10.1177/1948550615598376
  5. Syrjamaki, A.H., Lyyra, P., & Hietanen, J.K. (2020). I don’t need your attention: Ostracism can narrow the cone of gaze. Psychological Research, 84, 99-110. https://doi.org/10.1007/s00426-018-0993-8
  6. Tanaka, H., & Ikegami, T. (2015). Fear of negative evaluation moderates effects of social exclusion on selective attention to social signs. Cognition & Emotion, 29, 1306-1313. https://doi.org/10.1080/02699931.2014.977848

IX: Psychosocial and Behavioral Implications of HIV Health Disparities

  1. Crim, S.M., Tie, Y., Beer, L., Weiser, J., & Dasgupta, S. (2020). Barriers to antiretroviral therapy adherence among HIV-positive Hispanic and Latino men who have sex with men – United States, 2015-2019. MMWR Morbidity and Mortality Weekly Report, 69, 1438-1442.
  2. Johnson, M.O., Chesney, M.A., Goldstein, R.B., Remien, R.H., Catz, S., Gore-Felton, C., Charlebois, E., Morin, S.F., and The NIMH Healthy Living Project Team (2006). Positive provider interactions, adherence self-efficacy, and adherence to antiretroviral medications among HIV infected adults: A mediation model. AIDS Patient Care and STDs, 20, 258-268. https://doi.org/10.1089/apc.2006.20.258
  3. Kong, M.C., Nahata, M.C., Lacombe, V.A., Seiber, E.E., & Balkrishnan, R. (2012). Association between race, depression, and antiretroviral therapy adherence in a low-income population with HIV infection. Journal of General Internal Medicine, 27, 1159-1164. https://doi.org/10.1007/s11606-012-2043-3
  4. Menza, T.W., Hixson, L.K., Lipira, L., & Drach, L. (2021). Social determinants of health and care outcomes among people with HIV in the United States. Open Forum Infectious Diseases, 8(7), ofab330. https://doi.org/10.1093/ofid/ofab330
  5. Phillips, J.C., Webel, A., Rose, C.D., Corless, I.B., Sullivan, K.M., Voss, J., Wantland, D., Nokes, K., Brion, J., Chen, W., Iipinge, S., Eller, L.S., Tyler-Viola, L., Rivero-Mendez, M., Nicholas, P.K., Johnson, M.O., Maryland, M., Kemppainen, J., Portillo, C.J., Chaiphibalsarisdi, P., Kirksey, K.M., Sefcik, E., Reid, P., Cuca, Y., Huang, E., & Holzemer, W.L. (2013). Associations between the legacy context of HIV, perceived social capital, and HIV antiretroviral adherence in North America. BMC Public Health, 13, 736. https://doi.org/10.1186/1471-2458-13-736
  6. Simoni, J.M., Huh, D., Wilson, I.B., Shen, J., Goggin, K., Reynolds, N.R., Remien, R.H., Rosen, M.I., Bangsberg, D.R., & Liu, H. (2012). Racial/ethnic disparities in ART adherence in the United States: Findings from the MACH14 study. Journal of Acquired Immune Deficiency Syndrome, 60, 466-472. https://doi.org/10.1097/QAI.0b013e31825db0bd
  7. Tchakoute, C.T., Rhee, S., Hare, C.B., Shafer, R.W., & Sainani, K. (2022). Adherence to contemporary antiretroviral treatment regimens and impact on immunological and virologic outcomes in a US healthcare system. PLoS One, 17, e0263742. https://doi.org/10.1371/journal.pone.0263742
  8. Sutton, M.Y., & Parks, C.P. (2011). HIV/AIDS prevention, faith, and spirituality among Black/African-American and Latino Communities in the United States: Strengthening scientific faith-based efforts to shift the course of the epidemic and reduce HIV-related health disparities. Journal of Religion and Health, 52, 514-530. https://doi.org/10.1007/s10943-011-9499-z
  9. Paradies, Y., Ben, J., Denson, N., Elias, A., Priest, N., Pieterse, A., Gupta, A., Kelaher, M., & Gee, G. (2015). Racism as a determinant of health: A systematic review and meta-analysis. PLoS One, 10(9), e0138511. https://doi.org/10.1371/journal.pone.0138511
  10. Pascoe, E.A., & Richman, L.S. (2009). Perceived discrimination and health: A meta-analytic review. Psychological Bulletin, 135, 531-554. https://doi.org/10.1037/a0016059
  11. Do, A.N., Rosenberg, E.S., Sullivan, P.S., Beer, L., Strine, T.W., Schulden, J.D., Fagan, J.L., Freedman, M.S., & Skarbinski, J. (2014). Excess burden of depression among HIV-infected persons receiving medical care in the United States: Data from the Medical Monitoring Project and the Behavioral Risk Factor Surveillance System. PLoS One, 9(3), e92842. https://doi.org/10.1371/journal.pone.0092842

X: Psychology of Technology-Mediated Human Interaction Relevant to Telehealth

  1. Reidl, R. (2022). On the stress potential of videoconferencing: Definition and root causes of Zoom fatigue. Electronic Markets, 32, 153-177. https://doi.org/10.1007/s12525-021-00501-3
  2. Gifford, R., & Sacliotto, P.A. (1993). Social isolation and personal space: A field study. Canadian Journal of Behavioural Science, 25, 165-174. https://doi.org/10.1037/h0078784
  3. Layden, E.A., Cacioppo, J.T., & Cacioppo, S. (2018). Loneliness predicts a preference for larger interpersonal distance within intimate space. PLoS ONE, 13(9), e0203491. https://doi.org/10.1371/journal.pone.0203491
  4. Worchel, S. (1986). The influence of contextual variables on interpersonal spacing. Journal of Nonverbal Behavior, 10, 230-254. https://doi.org/10.1007/BF00987482
  5. Bailenson, J.N. (2021). Nonverbal overload: A theoretical argument for the causes of Zoom fatigue. Technology, Mind, and Behavior, 2(1). https://doi.org/10.1037/tmb0000030
  6. Tomprou, M., Kim, Y.J.,Chikersai, P., Wooley, A.W., & Dabbish, L.A. (2021). Speaking out of turn: How video conferencing reduces vocal synchrony and collective intelligence. PLoS One, 16(3), e02476555. https://doi.org/10.1371/journal.pone.0247655
  7. Fauville, G., Luo, M., Queiroz, A.C.M., Lee, A., Bailenson, J.N., & Hancock, J. (2023). Video-conferencing usage dynamics and nonverbal mechanisms exacerbate Zoom Fatigue, particularly for women. Computers in Human Behavior Reports, 10(1), 100271. https://doi.org/10.1016/j.chbr.2023.100271
  8. Shockley, K.M., Gabriel, A.S., Robertson, D, Rosen, C.C., Chawla, N., Ganster, M.L., & Ezerins, M.E (2021). The fatiguing effects of camera use in virtual meetings: A within-person field experiment. Journal of Applied Psychology, 106, 1137-1155. https://doi.org/10.1037/apl0000948
  9. Hinds, P.J. (1999). The cognitive and interpersonal costs of video. Media Psychology, 1, 283-311. https://doi.org/10.1207/s1532785xmep0104_1
  10. Shoshan, H.N., & Whert, W. (2022). Understanding “Zoom fatigue”: A mixed-method approach. Applied Psychology, 71, 827-852. https://doi.org/10.1111/apps.12360

XI: HCP-Patient Interaction Dynamics

  1. Blackstock, O.J., Addison, D.N., Brennan, J.S., & Alao, O. (2012). Trust in primary care providers and antiretroviral adherence in an urban HIV clinic. Journal of Health Care for the Poor and Underserved, 23, 88-98. https://doi.org/10.1353/hpu.2012.0006
  2. Schneider, J., Kaplan, S.H., Greenfield, S., Li, W., & Wilson, I.B. (2004). Better physician-patient relationships are associated with higher reported adherence to antiretroviral therapy in patients with HIV infection. Journal of General Internal Medicine, 19, 1096-1103. https://doi.org/10.1111/j.1525-1497.2004.30418.x
  3. Whetten, K., Leserman, J., Whetten, R., Ostermann, J., Thielman, N., Swartz, M., & Stangl, D. (2006). Exploring lack of trust in care providers and the government as a barrier to health service use. American Journal of Public Health, 96, 716-721. https://doi.org/10.2105/AJPH.2005.063255
  4. Haskard Zolnierek, K.B., & DiMatteo, M.R. (2009). Physician communication and patient adherence to treatment: A meta-analysis. Medical Care, 47, 826-834. https://doi.org/10.1097/MLR.0b013e31819a5acc
  5. Chen, W., Wantland, D., Reid, P., Corless, I.B., Eller, L.S., Iipinge, S., Holzemer, W.L., Nokes, K.L., Sefcik, E., Rivero-Mendez, M., Voss, J., Nicholas, P., Phillips, J.C., Brion, J.M., Dawson Rose, C., Portillo, C.J., Kirksey, K., Sullivan, K.M., Johnson, M.O., Tyler-Viola, L., & Webel, A.R. (2013). Engagement with health care providers affects self-efficacy, self-esteem, medication adherence and quality of life in people living with HIV. Journal of AIDS & Clinical Research, 4, 256. https://doi.org/10.4172/2155-6113.1000256
  6. Corless, I.B., Guarino, A.J., Nicholas, P.K., Tyler-Viola, L., Kirksey, K., Brion, J., Dawson Rose, C., Eller, L.S., Rivero-Mendez, M., Kemppainen, J., Nokes, K., Seficik, E., Voss, J., Wantland, D., Johnson, M.O., Phillips, C.J., Webel, A., Iipinge, S., Portillo, C., Chen, W., Maryland, M., Hamilton, M.J., Reid, P., Hickey, D., & Holzemer, W.L. (2013). Mediators of antiretroviral adherence: A multisite international study. AIDS Care, 25, 364-377. https://doi.org/10.1080/09540121.2012.701723
  7. Oetzel, J., Wilcox, B., Avila, M., Hill, R., Archiopoli, A., & Ginossar, T. (2015). Patient-provider interaction, patient satisfaction, and health outcomes: Testing explanatory models for people living with HIV/AIDS. AIDS Care, 27, 972-978. https://doi.org/10.1080/09540121.2015.1015478
  8. Hall, J.A., Roter, D.L. & Katz, N.R. (1988). Meta-analysis of correlates of provider behavior in medical encounters. Medical Care, 26, 657-675. https://doi.org/10.1097/00005650-198807000-00002
  9. Minde, K., Tidmarsh, L., & Hughes, S. (2001). Nurses’ and physicians’ assessment of mother-infant mental health at the first postnatal visits. Journal of the American Academy of Child and Adolescent Psychiatry, 40, 803-810. https://doi.org/10.1097/00004583-200107000-00015
  10. Nobile, C., & Drotar, D. (2003). Research on the quality of parent-provider communication in pediatric care: Implications and recommendations. Journal of Developmental and Behavioral Pediatrics, 24, 279-290. https://doi.org/10.1097/00004703-200308000-00010
  11. Street, R.L., & Buller, D.B. (1987). Nonverbal response patterns in physician-patient interactions: A functional analysis. Journal of Nonverbal Behavior, 11, 234-253. https://doi.org/10.1007/BF00987255
  12. Harrigan, J.A., Oxman, T.F., & Rosenthal, R. (1985). Rapport expressed through nonverbal behavior. Journal of Nonverbal Behavior, 9, 95-110. https://doi.org/10.1007/BF00987141
  13. Street, R.L. (1991). Information-giving in medical consultations: The influence of patients’ communicative styles and personal characteristics. Social Science & Medicine, 32, 541-548. https://doi.org/10.1016/0277-9536(91)90288-N
  14. Wissow, L.S., Roter, D.L., & Wilson, M.E. (1995). Pediatrician interview style and mothers’ disclosure of psychosocial issues. Pediatrics, 93, 289-295. https://doi.org/10.1542/peds.93.2.289
  15. Verlinde, E., De Laender, N., De Maesschalck, S., Deveugele, M., & Willems, S. (2012). The social gradient in doctor-patient communication. International Journal for Equity in Health, 11, 12. https://doi.org/10.1186/1475-9276-11-12
  16. Elliott, A.M., Alexander, S.C., Mescher, C.A., Mohan, D., & Barnato, A.E. (2016). Differences in physicians’ verbal and nonverbal communication with Black and White patients at the end of life. Journal of Pain Symptom Management, 51, 1-8. https://doi.org/10.1016/j.jpainsymman.2015.07.008
  17. Padilla, M., Patel, D., Beer, L., Tie, Y., Nair, P., Salabarria-Pena, Y., Henny, K.D., Thomas, D., & Dasgupta, S. (2022). HIV stigma and health care discrimination experienced by Hispanic or Latino persons with HIV – United States, 2018-2020. MMWR Morbidity and Mortality Weekly Report, 71, 1293-1300.
  18. Turan, B., Rogers, A.J., Rice, W.S., Atkins, G.C., Cohen, M.H., Wilson, T.E., Adimora, A.A., Merenstein, D., Adedimeji, A., Wentz, E.L., Ofotokun, I., Metsch, L., Tien, P.C., Johnson, M.O., Turan, J.M., & Weiser, S.D. (2017). Association between perceived discrimination in healthcare setting and HIV medication adherence: Mediating psychosocial mechanisms. AIDS and Behavior, 21, 3431-3439. https://doi.org/10.1007/s10461-017-1957-5
  19. Hausmann, L.R.M., Hannon, M.J., Kresevic, D.M., Hanusa, B.H., Kwoh, K., & Ibrahim, S.A. (2011). Impact of perceived discrimination in health care on patient-provider communication. Medical Care, 49, 626-633. https://doi.org/10.1097/MLR.0b013e318215d93c

© 2024 Grey Matter Behavioral Sciences, LLC. All rights reserved.