Recent research advances in the field of HIV prevention suggest that strategic use of antiretroviral therapy (ART)—either as prophylaxis in populations at risk for HIV infection or as treatment of those who are HIV infected to reduce further transmission—can significantly curb the spread of HIV.1,2 These research results have generated optimism in the field of HIV prevention and provided the basis for Secretary of State Hillary Clinton's challenge to work toward the goal of an "AIDS-free generation" in an address to the National Institutes of Health (NIH) in November, 2011. Although there is enthusiasm about new tools that we can apply in meeting that goal, there is also the realization that some of our prevention tools are not potent enough and we have much to learn about how they can be best applied. Future challenges and opportunities in controlling the epidemic include 3 major goals: (1) identifying a maximum number of HIV-infected persons through voluntary HIV testing; (2) initiating ART to achieve long-term viral suppression; and (3) preventing new HIV infections. Approaches that integrate biomedical and behavioral sciences will be necessary to test efficacy and ultimately implement any combination intervention. In this article, we will describe some of the key challenges encountered in recent prevention trials using ART-based prevention methods. We will also discuss some of the behavioral science issues that have emerged as important in the design and implementation of these trials. Finally, we will present priorities for future research that will require biomedical and behavioral integration and some emerging models for implementing those priorities within combination prevention approaches.
STRATEGIC USE OF ANTIRETROVIRALS: CHALLENGES IN TESTING EFFICACY
The use of antiretroviral drugs as chemoprophylaxis—either applied topically or taken orally—to prevent HIV acquisition has been tested in large-scale efficacy trials with incongruent results (Table 1). Both CAPRISA 004 and the VOICE trial (MTN 003) tested the effect of vaginally applied tenofovir 1% gel in preventing HIV acquisition among HIV-negative women. In CAPRISA 004, women were randomized to use either tenofovir 1% gel or placebo in a coitally dependent manner, and women in the tenofovir gel arm experienced a 39% reduction in risk of HIV acquisition.3 These results contrasted with those of the VOICE trial (MTN 003), in which women were randomized to use daily tenofovir 1% gel or placebo, but comparably high rates of HIV were acquired in both these arms (6%), making continuation of this part of the study futile.8 The reasons that the same gel product provided protection against HIV acquisition in CAPRISA 004 but failed in VOICE are not clear. Adherence to gel use will clearly be a critical factor in understanding the results of these trials: women who reported higher levels of adherence to gel use in CAPRISA 004 had lower rates of HIV acquisition. We know less from reports of adherence levels of VOICE participants as follow-up in this trial is still ongoing. However, this apparent conflict in results of efficacy testing for a similar biomedical intervention illustrates the need to apply the best behavioral science methods to measure and support participant adherence in trials to meaningfully determine the efficacy of biomedical interventions.
Oral tenofovir (TDF) or tenofovir/emtricitabine (TDF/FTC) has also been tested as preexposure prophylaxis (PrEP) in randomized, placebo-controlled trials. Again, findings have not been consistent in different populations, and factors that determine adherence to the study drug may provide one explanation. In the iniciativa profilaxis preexposicion trial (iPrEx), HIV-negative men or transgendered women who reported having sex with other men were randomized to take either a placebo or an oral TDF/FTC daily.4 Those in the active drug arm had a statistically significant 44% reduced risk of HIV acquisition overall compared with those receiving placebo. Here again, there was much greater protection against HIV with higher levels of adherence (92% among participants with detectable drug levels in blood samples). The FEMPrEP trial was designed to address a research question similar to iPrEx, testing the efficacy of oral TDF/FTC but focused on women. The results were disappointing with early trial discontinuation for futility (5% HIV incidence in both active drug and placebo arms).5 The Microbicide Trials Network announced in 2011 that women enrolled in the VOICE (MTN 003) study who were randomized to receive oral tenofovir would exit the study as the drug would prove no better than placebo in preventing HIV acquisition.8 The VOICE trial arm that tested oral TDF/FTC against a placebo found no benefit.8 To make the situation even more complex, the Centers for Disease Control and Prevention announced that in their TDF2 trial, men and women who were randomized to receive oral tenofovir and who were retained in the study for follow-up had lower risk of HIV acquisition (62% reduction) than those receiving placebo.6 Furthermore, the Partners PrEP study, which focused on transmission between heterosexual serodiscordant couples, recently announced significant reduction of HIV transmission with both oral TDF (62%) and TDF/FTC (73%) compared with placebo.7 This level of protection was similar whether the HIV-uninfected partner was man or woman. Although there are complexities in each trial that contribute to the conflicting results, clearly the importance of robust behavioral science to support adherence is critical to complete a trial that will provide a measure of the "true" efficacy of an intervention. In addition, the fact that results vary across populations (eg, men who have sex with men, heterosexual women) raises the possibility that sociobehavioral factors such as risk perception and motivation for adherence complicated the results. For example, the fact that PrEP was efficacious in couples engaged in an established relationship suggests that complex motivational factors inherent in a supportive relationship may influence intervention adherence in a trial. Successful integration of behavioral and biomedical sciences would ideally lead to study designs that maximize adherence and minimize the impact of nonadherence on efficacy results and also improve our ability to interpret conflicting results.
A prevention study conducted by the NIH-supported HIV Prevention Trials Network (HPTN) using “treatment as prevention,” which refers to use of ART by infected individuals to suppress viral load and prevent transmission to others, was highly successful and is an example of the impact of well-designed strategies to ensure high adherence during a trial.9 In HPTN 052, HIV serodiscordant couples were enrolled and randomized to 1 of 2 strategies: early ART (index participant with 350–550 CD4 cells per cubic millimeter) or delayed ART (index was started on therapy when CD4 cell count had fallen to <250 cells per cubic millimeter). Results showed that the strategy of initiating ART at higher CD4 levels reduced risk of HIV transmission between couples by 96% and was associated with a significantly lower risk of developing clinical complications in the index partner. This dramatic success of treatment as prevention was heralded as the "Scientific Breakthrough of the Year" and put forward as one of the principle components of combination prevention approaches that—if brought to scale in a community with a generalized HIV epidemic—could reverse the spread of HIV.
One of the key factors in this study was the integration of counseling to the couples participating in the HPTN 052 protocol and was crucial to the trial's success. Structured counseling messages addressed sociobehavioral factors that are found to be associated with nonadherence, such as correcting misconceptions on the biology of HIV, anticipating common side effects of medication, improving self-efficacy and skills in disease self-management, engaging partners in reminders to take medication, and providing immediate feedback on viral load tests to identify and strategize on adherence problems early if they occurred.9 Had these behavioral counseling measures not been deployed in HPTN 052 or not been successful enough to support full virologic suppression in the vast majority of participants taking highly active antiretroviral therapy in the study, the key research question (ie, Does starting ART early reduce sexual transmission of HIV to uninfected partners?) would not have been testable in this protocol. Clearly, integration of the best behavioral counseling practices to support intervention adherence will be essential to understand the benefits of ART-based prevention in future clinical trials. In addition to behavioral counseling, reliable adherence assessment measures are critical, particularly when coupled with biomarkers of adherence, in this case plasma viral load, to ART-based interventions. These will also be crucial to interpreting future clinical trial results and could potentially serve as an intervention by providing a rapid feedback loop to participants regarding their adherence.
The clear, interpretable results of HPTN 052 are in contrast to the conflicting findings related to topical gel and oral TDF and TDF/FTC studies. No doubt, biomedical factors such as drug penetration and tissue levels play a role and must be characterized. But we must also reexamine sociobehavioral factors that are at play during the conduct of clinical trials for HIV prevention. To that end, it is critical to examine those theories of behavior change that underpin the behavioral science, as it is via strong theoretical frameworks that true integration of behavioral and biomedical sciences will be achieved.
DEVELOPING AND USING RELEVANT BEHAVIORAL MODELS IN HIV PREVENTION RESEARCH
Early in the epidemic, extensive research was conducted to understand the behaviors that put people at risk for HIV infection and how to change those behaviors. Although this has provided much insight and understanding of risk behaviors, managing risk continues to be difficult primarily because it includes sexual behaviors that typically occur in private contexts. It is therefore very difficult to motivate protection when and where transmission occurs.10 Behavioral science research continues to investigate the best ways to help individuals correctly and consistently use condoms. However, the science of HIV prevention has advanced beyond a major reliance on condom use to include combination prevention approaches of which condom use is one component.1,2,10 Concurrently, it has become evident that testing ART-based strategies in HIV prevention clinical trials also requires behavioral science expertise to determine product efficacy (via adherence) and enhance the understanding of participant behavior in the trial. Therefore, it is useful to review some of the theoretical models of behavior change that have been directed at HIV prevention efforts, examine their strengths and weaknesses when integrated in randomized trials of biomedical tools, and highlight the emerging science that could enhance the integration of behavioral and biomedical interventions.
From the beginning of the epidemic, behavioral interventions were developed based on theoretical frameworks that applied to reducing one's risk for acquiring HIV. In this context, behavioral models sought to address those factors associated with reduction of risky behaviors (eg, unprotected sex and/or injection drug use) and increase in condom use. Many of the models were intention based, emphasizing the behavioral intentions of individuals to predict acceptance and use of a given intervention, in this case, condoms. These models took into consideration certain determinants that would identify or explain intentions, such as attitudes and social influences. Though this article is not meant to be a comprehensive review of behavioral theories, some of the most widely used models attempt to identify those determinants of behavior change that could be modified by interventions (Table 2). Two examples of early theories are the theories of reasoned action13,14 and planned behaviors.21 Both include psychological variables that influence a behavior and have been used successfully as theoretical frameworks for many HIV prevention efforts directed at condom use.22 However, it is important to note that across multiple studies, the impact of interventions to increase condom use has been relatively moderate, with an average of 34%.13 Thus, behavioral models have produced interventions that are necessary for successful HIV prevention, however, may not be potent enough to reverse the HIV epidemic. In addition, many past behavioral intervention trials have not taken into account such complexities as change in attitudes and perceptions over time, factors beyond the level of the individual (eg, community, structural) and maintenance of behavior change.
To bolster the potency of behavioral interventions and ensure behavioral theories are relevant for the changing context, behavioral science research continues to build on and refine the early theoretical models. For example, Fisher and Fisher18 and Fisher et al19 comprehensively reviewed previous risk behavior change approaches and defined the information–motivation–behavioral skills model of AIDS-preventive behavior, which includes 3 fundamental determinants of AIDS risk reduction: information of necessary methods, motivation to use those methods, and the behavioral skills to perform the necessary preventive acts. This model has been successfully applied to numerous interventions targeting prevention behaviors, with particular success among adolescents, men who have sex with men, and substance users. In addition to preventive behaviors, this model also has been successfully used in the design of an effective and long-term adherence support intervention.23–25 Another example of theory advancement is the inclusion of theories of gender and power to develop appropriate HIV prevention interventions for African-American women.26–28
Theoretical models used in HIV prevention must be expanded as the science of combination interventions in HIV prevention advances. With the testing of biomedical tools, such as microbicides and ART for PrEP, the theoretical models underlying behavioral science must also advance to be able to address behaviors in the clinical trial context. In some cases, components of the current behavioral models may provide valuable information about use of a particular biomedical product. For example, the health belief model11,12 predicts that a person would modify their behavior if they felt that his or her health was in jeopardy, that the condition to be avoided was severe and would create negative outcomes, and that benefits stemming from the recommended behavior change would outweigh the negative consequences of the health impairment.29 Building this theory into PrEP research would suggest that for an uninfected person to use PrEP, he or she must first recognize that the risk of acquisition is great enough to accept the intervention and second determine that the benefits offered by PrEP outweigh the negative consequence of acquiring HIV. Thus, the health belief model may still provide insights into factors that could significantly influence the strategic use of ART as PrEP.
Although the use of known theoretical frameworks as a basis for intervention development and implementation has shown moderate success, it is generally agreed that integration of current behavioral theories into new multidisciplinary theoretical approaches must be developed to accommodate the changing epidemic and the expanded level of general public knowledge around HIV infection and prevention. A recent think tank sponsored by the NIH Office of AIDS Research made the strong recommendation that steps be taken to promote transdisciplinary theory development and testing with methodological diversity to explain how multilevel pathways, forces, and social factors influence the HIV epidemic.30 Multidisciplinary teams as part of small targeted studies and large multisite combination trials will be necessary to develop useful theories and appropriate trial designs to test those theories.
USING EMERGING TECHNOLOGIES TO ENHANCE BEHAVIORAL MODELS
Behavioral models may be further advanced by including emerging technologies and incorporating theories that have been developed to predict uptake and sustained use of novel products and tools. One area of technological advance in the context of HIV prevention clinical trials has been the use of technology to enhance assessment and capture data relevant to behavioral theories (eg, risk perception, sexual behavior). Face-to-face interviews for determining risky behaviors or to assess adherence may or may not be an effective approach depending on the population and context. The Audio Computer–Assisted Survey Instruments technology is an innovative assessment tool that was developed and validated to improve sexual behavior reporting (reviewed by Catania et al31). This technology was not based on a specific theoretical model but was designed to enhance the accuracy of data collected related to behavioral determinants such as intentions, beliefs, perceptions, and self-reported behaviors. Systematic reviews and meta-analyses of studies in low-, middle-, and high-income countries have compared traditional face-to-face interviews with innovative tools for reporting HIV risk behavior to determine the best approaches to assess behaviors around sexual activity. Results clearly vary depending on sensitivity of questions asked, population characteristics, study design, and outcome.32–34 Assessment methodology must also continue to evolve to build on our understanding of the context around high-risk behaviors and to develop assessment strategies that accurately capture outcomes. Expertise in behavioral science, when integrated as part of a multidisciplinary team approach, can inform the decision making around the use of technology like Audio Computer–Assisted Survey Instruments within any given clinical trial. Thus, true integration of behavioral and biomedical sciences would result in the design of studies that assess relevant determinants of behaviors and include pilot work to determine the most reliable combination of assessment tools for particular populations, in specific settings, and with attention to cultural complexities.
Likewise, the use of new technology holds promise not just for assessing but enhancing behavioral outcomes. With the use of mobile phone technology expanding in nearly all populations, we can communicate through short message service to deliver real-time, frequent, and long-term HIV prevention or adherence interventions to individuals or to specific population segments. New technologies for immediate, real-time adherence assessment have the advantage that any break in adherence can be identified and immediately acted upon, preventing accumulation of viral resistance mutations because of incomplete virologic suppression. Approaches that quickly address a lapse in adherence may fill a critical need in low resource settings where viral load monitoring may still be cost prohibitive and second-line regimens unavailable. Other exciting applications of mobile phone technology that allow for diagnostic testing are in development. These applications use microchips to be plugged into mobile phones that then test the user's body fluids (saliva, urine) for evidence of infection (HIV, other sexually transmitted infections) and provide an immediate test result.35,36 If these technologies prove accurate, they afford the opportunity to vastly expand access to laboratory testing and monitoring. Effectively combining mobile phone–based diagnostic testing modalities with adherence interventions or as part of combination prevention provides an opportunity and a challenge to biomedical and behavioral research integration.
There is increasing recognition that the approach to development of new technologies should be broadened to take into account the complexity of health care and the rituals and habits of potential stakeholders. The causal psychological and social processes and intermediate outcomes that may lead to success call for a theoretical framework that guides the selection of intervention components, the study design, and appropriate outcome measures.37,38 A number of integrated technology implementation theoretical frameworks grounded in behavioral science have been developed to improve the success of technology-based interventions, and these must be considered as new complicated, combination approaches to prevention are developed and implemented.39–41
IMPLEMENTATION AND FUTURE RESEARCH
A recent report by Cohen et al1 reviews the latest clinical trial results in ART-based prevention and discusses the potential for taking the use of ARTs for prevention to scale at a population level. Mathematical models have been generated using available data from different studies and have provided optimistic predictions about the potential for successful control of the epidemic. However, in these models, assumptions must be made around a number of important and uncertain issues, both biological and behavioral. Behaviors are complicated and can be uniquely influenced by gender, population, culture, beliefs, etc. Choices are also situational and depend on environment, current state of mind, needs and desires, and many other influences. In addition, needs and desires can change over time. Sustaining healthy behaviors to prevent or treat chronic diseases is one of the most challenging goals of health promotion today. These issues are similarly important in HIV prevention research and implementation, where the aggregate effect of radical and sustained behavior change in sufficient numbers of people will determine whether reduced transmission rates occur in a community.10
Success of combination prevention and treatment will require widespread uptake of HIV testing, perception of HIV risk, acceptance of recommended interventions, and adherence on a lifelong basis. Integration of behavioral and biomedical approaches will be required for the field of HIV prevention research to continue to advance and maximize prevention efforts. However, data compiled from public health surveillance and from clinic-based cohorts illustrate that formidable sociobehavioral, structural, and operational barriers exist that have impeded our ability to successfully and effectively treat HIV infection broadly in the United States and exert a major effect through treatment. The first step to engagement in care and treatment for an individual is to undergo HIV testing and learn their seropositive status. There have been many successful approaches to increase testing in communities across the country. Once a person learns that he or she is HIV infected, a series of successful steps must occur along a continuum of activities to access the benefits of therapy. This "cascade" requires linking to a system of care, initiating and maintaining therapy, and ultimately achieving full virologic suppression, which is critical to prevention of transmission.42 In the real world, drop-off occurs at each step of this treatment cascade. It is estimated that 28% of those living with HIV in the United States actually have achieved an undetectable HIV viral load.43 Social, behavioral, and structural factors create barriers at each step in the cascade, including accepting HIV testing, overcoming the stigma of an HIV diagnosis, and engaging and adhering to long-term HIV care. Behaviors at each point in the cascade act as impediments that contribute to drop-off. The behaviors that lead to full virologic suppression must also be maintained for a lifetime, and long-term maintenance of behavior change continues to be a challenge.
Similar behaviors affect the implementation of biomedical advances as part of combination prevention. Thus, to realize the benefits of ART-based HIV prevention, to integrate behavioral strategies in combination prevention, and to reach sufficient scale-up to achieve population effect over a sustained period of time, behavioral theories and science must remain current and integrated as part of a multidisciplinary approach to HIV prevention.
Future efficacy trials testing biomedical HIV interventions will require full integration of behavioral interventions based upon theoretically sound models to optimally test intervention effects. Meeting the implementation challenges of proven HIV prevention interventions used in combination at the level of the population will require integrated behavioral, social, and structural interventions to be successful. Effectively applying emerging technological tools in HIV treatment and prevention will require new behavioral models and new ways of integrating biomedical and behavioral research. HIV biomedical prevention strategies used in combination approaches now include the tools to make a major impact on HIV transmission and potentially end the epidemic. For any intervention to be effective, it must be used by the target population and it must be used effectively. The best drug in the world will only work if it is taken properly. Integration of effective behavioral components is critical to the successful prevention of new infections.
1. Cohen MS, Muessig KE, Smith MK, et al.. Antiviral agents and HIV prevention: controversies, conflicts and consensus. AIDS. 2012;26:1–14.
2. Dieffenbach CW, Fauci AS. Thirty years of HIV and AIDS: future challenges and opportunities. Ann Intern Med. 2011;154:766–772.
3. Abdool KQ, Abdool KSS, Frohlich JA, et al.. Effectiveness and safety of tenofovir gel, an antiretroviral microbicide, for the prevention of HIV infection in women. Science. 2010;329:1168–1174.
4. Grant RM, Lama JR, Anderson PL, et al.. Preexposure chemoprophylaxis for HIV prevention in men who have sex with men. N Engl J Med. 2010;363:2587–2599.
5. Van Damme L, Corneli A, Ahmed K, et al.. Preexposure prophylaxis for HIV infection among African women. N Engl J Med. 2012;367:411–422.
6. Thigpen MC, Kebaabetswe PM, Paxton LA, et al.. Antiretroviral preexposure prophylaxis for heterosexual HIV transmission in Botswana. N Engl J Med. 2012;367:423–434.
7. Baeten JM, Donnell D, Ndase P, et al.. Antiretroviral prophylaxis for HIV prevention in heterosexual men and women. N Engl J Med. 2012;367:399–410.
8. Marrazzo J, Ramjee G, Nair G, et al.. Pre-exposure prophylaxis for HIV in women: daily oral tenofovir, oral tenofovir/emtricitabine or vaginal tenofovir gel in the VOICE study (MTN 003) [abstract]. Presented at: 20th Conference on Retroviruses and Opportunistic Infections; March 3–6, 2013; Atlanta, GA.
9. Cohen MS, Chen YQ, McCauley M, et al.. Prevention of HIV-1 infection with early antiretroviral therapy. N Engl J Med. 2011;365:493–505.
10. Coates TJ, Richter L, Caceres C. Behavioural strategies to reduce HIV transmission: how to make them work better. Lancet. 2008;372:669–684.
11. Becker MH. The health belief model and personal health behavior. Health Educ Monogr. 1974;2:324–508.
12. Rosenstock IM. Historical origins of the health belief model. Health Educ Monogr. 1974;2:1–8.
13. Ajzen I, Fishbein M. Attitude-behavior relations: a theoretical analysis and review of empirical research. Psychol Bull. 1977;84:888–918.
14. Fishbein M, Ajzen I. Beliefs, Attitudes, Intentions, and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley; 1975.
15. Ajzen I. The theory of planned behavior. Organ Behav Hum Decis Process. 1991;50:179–211.
16. Ajzen I. Attitudes, Personality, and Behavior. Chicago, IL: Dorsey Press; 1988.
17. Bandura A. Self-efficacy: toward a unifying theory of behavioral change. Psychol Rev. 1977;84:191–215.
18. Fisher JD, Fisher WA. Changing AIDS-risk behavior. Psychol Bull. 1992;111:455–474.
19. Fisher JD, Fisher WA, Williams SS, et al.. Empirical tests of an information motivation behavioral skills model of AIDS-preventive behavior with gay men and heterosexual university-students. Health Psychol. 1994;13:238–250.
20. Ewart CK. Social action theory for a public health psychology. Am Psychol. 1991;46:931–946.
21. Schifter DE, Ajzen I. Intention, perceived behavioral control and weight loss: an application of the theory of planned behavior. J Pers Soc Psychol. 1985;49:843–851.
22. Albarracín D, Johnson BT, Fishbein M, et al.. Theories of reasoned action and planned behavior as models of condom use: a meta-analysis. Psychol Bull. 2001;127:142–161.
23. Bangsberg DR. Preventing HIV antiretroviral resistance through better monitoring of treatment adherence. J Infect Dis. 2008;197:S272–S278.
24. Haberer JE, Kahane J, Kigozi I, et al.. Real-time adherence monitoring for HIV antiretroviral therapy. AIDS Behav. 2010;14:1340–1346.
25. Ware NC, Wyatt MA, Examining theoretic models of adherence for validity in resource-limited settings—a heuristic approach. J Acquir Immune Defic Syndr. 2006;43:S18–S22.
26. Wingood GM, DiClemente RJ, Application of the theory of gender and power to examine HIV-related exposures, risk factors, and effective interventions for women. Health Educ Behav. 2000;27:539–565.
27. Wyatt GE, Williams JK, Gupta A, et al.. Are cultural values and beliefs included in U.S. based HIV interventions? Prev Med. 2011;55:362–370.
28. Connell RW. Gender and Power: Society, the Person and Sexual Politics. Stanford, CA: Stanford University Press; 1987.
29. Gerrard M, Gibbons FX, Bushman BJ, Relation between perceived vulnerability to HIV and precautionary sexual behavior. Psychol Bull. 1996;119:390–409.
30. National Institutes of Health. Advancing HIV Prevention Through Transformative Behavioral and Social Science Research (R01); Funding Opportunity Announcement. Bethesda, MD: National Institute of Mental Health, National Institutes of Health. Available at: http://grants.nih.gov/grants/guide/rfa-files/RFA-MH-12-080.html
. Accessed April 20, 2013.
31. Catania JA, Gibson DR, Chitwood DD, et al.. Methodological problems in AIDS behavioral-research—influences on measurement error and participation bias in studies of sexual behavior. Psychol Bull. 1990;108:339–362.
32. Dolezal C, Marhefka SL, Santamaria EK, et al.. A comparison of audio computer-assisted self-interviews to face-to-face interviews of sexual behavior among perinatally HIV-exposed youth. Arch Sex Behav. 2012;41:401–410.
33. Langhaug LF, Sherr L, Cowan FM, How to improve the validity of sexual behaviour reporting: systematic review of questionnaire delivery modes in developing countries. Trop Med Int Health. 2010;15:362–381.
34. Phillips AE, Gomez GB, Boily M, et al.. A systematic review and meta-analysis of quantitative interviewing tools to investigate self-reported HIV and STI associated behaviours in low- and middle-income countries. Int J Epidemiol. 2010;39:1541–1555.
35. Isikman SO, Greenbaum A, Lee M, et al.. Lensfree computational microscopy tools for cell and tissue imaging at the point-of-care and in low-resource settings. Anal Cell Pathol (Amst). 2012;35:229–247.
36. Tseng D, Mudanyali O, Oztoprak C, et al.. Lensfree microscopy on a cellphone. Lab Chip. 2010;10:1787–1792.
37. Pingree S, Hawkins R, Baker T, et al.. The value of theory for enhancing and understanding e-health interventions. Am J Prev Med. 2010;38:103–109.
38. Rimer B, Glanz K. Theory at a Glance: A Guide for Health Promotion and Practice. Bethesda, MD: National Cancer Institute; 2005.
39. Gagnon MP, Orruño E, Asua J, et al.. Using a modified technology acceptance model to evaluate healthcare professionals' adoption of a new telemonitoring system. Telemed J E Health. 2012;18:54–59.
40. Holden RJ, Karsh B. The technology acceptance model: its past and its future in health care. J Biomed Inform. 2010;43:159–172.
41. Kukafka R, Johnson SB, Linfante, et al.. Grounding a new information technology implementation framework in behavioral science: a systematic analysis of the literature on IT use. J Biomed Inform. 2003;36:218–227.
42. Gardner EM, McLees MP, Steiner JF, et al.. The spectrum of engagement in HIV care and its relevance to test-and-treat strategies for prevention of HIV infection. Clin Infect Dis. 2011;52:793–800.
43. Centers for Disease Control and Prevention. Vital signs: HIV prevention through care and treatment. MMWR Morb Mortal Wkly Rep. 2011;60:1618–1623.