A Mobile Phone HIV Medication Adherence Intervention: Acceptability and Feasibility Study C. Andrew Martin, DNP, MS, RN, ACRN, CHPNÒ* Michele J. Upvall, PhD, RN, CNE We present the findings of a qualitative pilot study designed to describe the experience of HIV medication adherence using a mobile phone application. Nine semi-structured focus group discussions were conducted over a 3-month period at an AIDS Services Organization in Central Texas. The data were analyzed following the principles of thematic analysis. During analysis, four themes were identified, and relations between these themes were delineated to reflect the experiences of the 23 participants. The mobile phone application, Care4TodayÔ Mobile Health Manager, was the intervention tool. Collection of focus group discussion outcomes over a 3-month period with baseline versus end-of-study data determined the feasibility and acceptability of this medication adherence intervention. The findings suggest that when individuals are offered the necessary resources, such as a mobile phone medication reminder application, they may have greater success in performing the behavior. (Journal of the Association of Nurses in AIDS Care, -, 1-13) Copyright Ó 2016 Association of Nurses in AIDS Care Key words: Care4TodayÔ Mobile Health Manager, focus group methodology, medicine adherence/ nonadherence, mobile phone, mobile phone application
mbracing new technologies may improve medication adherence, offering support to people living with HIV (PLWH). International organizations such as the
World Health Organization and the Joint United Nations Programme on HIV/AIDS support the use of new technologies to provide universal access to HIV medications and have even included technology as a directive in achieving the goal of universal access (Lester et al., 2009). The majority of studies evaluating technology and medication adherence focus on chronic disease management and short message service (SMS) text messaging, but few studies address the use of advanced technology such as mobile phone applications and medication adherence in HIV, as a chronically managed disease. The purpose of our study was to determine the likelihood of PLWH adhering to daily HIV medication with the technological support of a mobile phone medication reminder application.
Background Medication adherence appears in the literature of multiple disciplines, such as nursing, medicine, and C. Andrew Martin, DNP, MS, RN, ACRN, CHPNÒ, is the RN Medical Case Manager in the Positive Living through Understanding and Support (PLUS) Program, AIDS Services of Austin, Inc., Austin, Texas, USA, and Online Adjunct Faculty, Saint Joseph’s College of Maine, Standish, Maine, USA, and the University of Saint Mary, Leavenworth, Kansas, USA. (*Correspondence to: [email protected]
). Michele J. Upvall, PhD, RN, CNE, is a Professor, University of Central Florida College of Nursing, Orlando, Florida, USA.
JOURNAL OF THE ASSOCIATION OF NURSES IN AIDS CARE, Vol. -, No. -, -/- 2016, 1-13 http://dx.doi.org/10.1016/j.jana.2016.07.002 Copyright Ó 2016 Association of Nurses in AIDS Care
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psychology, and it is directly related to behavioral actions and socioeconomic conditions. Rolnick, Pawloski, Hedblom, Asche, and Bruzek (2013) reported that their assessment of medication adherence in eight diseases (asthma or chronic obstructive pulmonary disease, cancer, depression, diabetes, hyperlipidemia, hypertension, multiple sclerosis, osteoporosis) yielded variable adherence rates. Of these eight medical conditions, the lowest reported adherence rates were for asthma (33%) and diabetes (51%). That study found overall adherence rates higher for White individuals living with a higher socioeconomic status than individuals in the lowest quartile of the living area variables of income, poverty, and education. Increasing comorbidity also resulted in lower adherence, as those with fewer conditions and fewer drugs had higher adherence rates. Research related to chronic disease and technology takes a building block approach, bringing a new body of knowledge from study to study as research has advanced from voice messaging to SMS text messaging (TM) and beyond. Chronic disease management studies have demonstrated progression from voice messaging to basic SMS TM interventions to the more sophisticated phone and tablet application interventions. Mobile phone applications need to engage the user; ‘‘favored apps were described as fast-paced, useful, fun, easy to navigate, user-friendly, having less text, and interactive’’ (Muessig et al., 2013, p. 218). Engaging applications most often include components that meet a need or desire in the lives of the users, but ‘‘the availability of interesting apps should not imply their effectiveness or efficacy for use in treatment’’ (Mu~ noz, Hoffman, & Brimo, 2013, p. 142). Tao, Xie, Wang, and Want (2015) conducted a meta-analysis of randomized controlled trials through January 2014 evaluating patient adherence to medication in chronic disease care when electronic reminders were used. Data from 20 studies were synthesized. The electronic reminders ranged from pagers to alarm devices, and SMS reminders. The meta-analysis revealed that the use of electronic reminders was associated with a small improvement in patient adherence to medication. They concluded that electronic reminders appeared to be an effective method to improve chronic medication adherence.
Medication adherence in PLWH is crucial; it determines how effective the HIV medication regime will be in decreasing viral load. Nonadherence becomes a population health issue when the virus is not suppressed (,200 copies/mL; Health Resources and Services Administration, 2015). Medication adherence for HIV infection is measured against an ideal of taking medication more than 95% of the time (National AIDS Manual aidsmap, n.d.). With oncea-day dosing, missing as few as two doses per month translates to less than 95% adherence. With twice-aday dosing, missing as few as four doses per month equals less than 95% adherence. This minimal 5% variance from scheduled dosing is considered to be nonadherent to HIV medication treatment. Poor adherence to antiretroviral therapy (ART) may be one of the strongest predictors of progression from HIV to an AIDS diagnosis (CD41T cell count , 200 cells/mm3) and to AIDS-related death. Adherence is key to treatment success; nonadherence may contribute to HIV drug resistance, the need for more expensive second-line ART regimes, and therapeutic failure (Shet et al., 2010). Monitoring adherence percentages is an important clinical indicator. Hardy and colleagues (2011) used this formula for calculating percent adherence: (number of prescribed doses 2 number of missed doses)/number of prescribed doses 3 100. Although medication adherence self-report is an imperfect measure, it continues to be the most common method for medication adherence assessment and has been used in greater than 70% of medication adherence studies (Burda, Haack, Duarte, & Alemi, 2012). The use of technology to facilitate medication adherence has been explored with HIV populations around the globe. SMS TM was the most common intervention explored in these studies, but there was little uniformity across study designs (Burda et al., 2012; Coomes et al., 2012; Crankshaw et al., 2010; Hardy et al., 2011; Lester et al., 2009; Lewis et al., 2013; Mbuagbaw et al., 2011; Shet et al., 2010; Sidney et al., 2012; Smillie et al., 2014; Tran & Houston, 2012; van Velthoven, Brusamento, Majeed, & Car, 2013). Target populations of PLWH and settings for evaluating technology and medication adherence also varied in three studies conducted in the United States from different populations. The three studies in the United States were conducted with (a) men who
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have sex with men recruited from a health clinic in the Midwest United States (Lewis et al., 2013); (b) 10 homeless individuals in Baltimore, Maryland (Burda et al., 2012); and (c) 23 participants from an outpatient clinic at Boston Medical Center (Hardy et al., 2011). Some studies were conducted in lowor middle-income countries in Africa, such as those conducted in Cameroon and Kenya, in addition to a study conducted in Durban, South Africa (Crankshaw et al., 2010; Lester et al., 2009; Mbuagbaw et al., 2011; van Velthoven et al., 2013). One study reached both urban and rural outpatient clinics in South India (Shet et al., 2010), a Canadian study had 25 clinic participants (Smillie et al., 2014), and a study in Vietnam had 1,016 injection-drug-user participants who were interviewed in Hanoi, Hai Phong, and Ho Chi Minh City (Tran & Houston, 2012). All of these studies represented study participants from both lower and higher socioeconomic levels. Homeless individuals, injection drug users, and clinic patients were some of the varied demographics of participants across studies. Although the settings and target populations were different, a concentration on the theme of acceptability and feasibility of SMS TM appeared to have generated positive acceptability outcomes in these settings. Time frames for delivery of SMS TM varied across studies. Dynamically tailored TM was delivered via mobile phone in some studies (Lewis et al., 2013). Other studies delivered weekly TM (Hardy et al., 2011; Lester et al., 2009; Mbuagbaw et al., 2011; Sidney et al., 2012; Smillie et al., 2014), and one study delivered weekly voice messages via mobile phone, secondary to participant preference (Shet et al., 2010). A study of 10 homeless individuals received daily messages (Burda et al., 2012). One study in Vietnam was an acceptability and feasibility study with a one-time telephone interview that did not deliver the adherence reminder intervention (Tran & Houston, 2012). In general, positive outcomes were achieved and reported through SMS TM (van Velthoven et al., 2013). No evidencebased practice literature was found regarding the use of mobile phone applications to support HIV medication adherence. The existing literature investigated SMS TM interventions, and most of the reviewed studies were of small scale and primarily addressed the acceptability and feasibility of the
intervention, as opposed to actually conducting and reporting trial studies. Few studies in the literature described theoretical constructs to support the proposed interventions of mobile phones as a tool to support individual HIV medication adherence.
Theoretical Frameworks A strong behavioral component is required for medication adherence; a strong behavioral
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component is also related to adopting and using new technology, such as installing and actively using a daily mobile phone application. To address related behavioral components, two theoretical frameworks, the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB) were used in our research project.
behavior (Ajzen, 1991). The TPB was particularly suited for conducting research using a focus group method. Focus group methods readily elicit accessible beliefs from participants in a free response format. When asking about a behavior of interest, such as medication adherence, participants’ responses reflect control factors, normative referents, and likely outcomes.
Technology Acceptance Model Davis’ TAM (Davis, Bagozzi, & Warshaw, 1989), based on Ajzen’s Theory of Reasoned Action (TRA) (Ajzen, 1991), hypothesized that the behavioral attitude of the user toward the technology was a determinant of whether the user would ultimately reject or use the technology; the three determining factors were: (a) perceived usefulness, (b) perceived ease of use, and (c) attitude toward the technology. Although the user might have little-to-no knowledge about the ease of use of a newly introduced technology, the user may have a preformed sense of how to use the technology. Davis considered that the use of the technology was a behavioral attitude and the TRA was an appropriate model to adapt in order to predict and explain this behavior (Chutter, 2009). TAM is a theoretical framework that specifically addresses technology acceptance attitudes that may affect actual use of a mobile phone application. Theory of Planned Behavior Ajzen’s TPB (Ajzen, 1991) originated from the TRA. The TRA was developed in the late 1960s by social psychologists Ajzen and Fishbein (1980) and explained the relationship between beliefs, attitudes, intentions, and behavior. The TRA goal was to understand and predict behaviors that were under the control of the individual. The TPB was developed to predict an individual’s intention to engage in a particular behavior at a specific time and place. Intention is the most important determinant of behavior and is something over which individuals may have some self-control. The key component to this model is behavioral intent; as such, the likelihood that the behavior will have an expected outcome is dependent upon the perceived risks and benefits of the outcome. A central assumption of the TPB is that behavioral intentions are the most important determinants of
Methods A qualitative descriptive design using focus groups to collect data allowed the researchers to understand the dynamic, ever-changing, real-life social environments of PLWH. The qualitative descriptive design also expanded information and addressed gaps in the existing knowledge, exploring both facilitators and barriers to medication adherence (Badahdah & Pedersen, 2011; Musumari et al., 2013). An accessible, convenience sample of 30 eligible participants was recruited from a population of approximately 250 PLWH who regularly attended an AIDS Services Organization (ASO) food bank located in a Southwest region of the United States. Inclusion eligibility criteria were: registered clients of the food bank, English-speaking as primary language, HIV-infected, at least 18 years of age, currently on ART for at least 3 months, reporting less than 95% adherence to ART over the previous 7 days, and consistent access to a mobile phone with data service. No identifiable information was collected other than first name, last initial, and preferred contact information in order to remind participants of focus groups scheduled throughout the study. Data Collection Tools Demographic information form. Descriptive statistics of participants’ demographic information were compiled to promote understanding of the participants. Data collected included the following: age, gender identification, race/ethnicity, highest level of education, primary language, frequency of cell phone use, reasons for using a cell phone, (calls, texting, e-mail, photos, phone apps), likelihood of using a smart phone app for medication reminders,
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number and frequency of HIV medications taken daily, access to medication for the previous 3 months, number of HIV medications missed over the previous 7 days, and consistent access to a working mobile phone for the previous 3 months. Brief Estimate of Health Knowledge and Action— HIV version (BEHKA-HIV). The BEHKA-HIV was administered during the Start-of-Study focus group meeting to assess both HIV knowledge and action. The strengths of this tool are that it may represent health literacy for PLWH better than more general tools measuring reading ability in a health context. Scores on the BEHKA-HIV are significantly associated with self-reported medication adherence. Limitations of this tool are that it is not a direct test of functional health literacy in terms of reading comprehension, and that further validation is needed (HIV Guidelines, n.d.). Osborn, Davis, Bailey, and Wolf (2010) reported that this psychometric tool demonstrated high internal consistency and construct validity, items were written at a fifth-grade reading level, and it was a strong predictor of HIV medication adherence. Haun, Valerio, McCormack, Sørensen, and Paasche-Orlow (2014) reported validation of this health literacy measurement tool, saying, ‘‘item-total correlations were significant: knowledge (0.63) and action (0.94). Scores predicted medication adherence with a sensitivity of 0.76, and specificity of 0.82’’ (p. 323). They noted that the ‘‘assessment of knowledge of HIV can be integrated into health care as strength’’ (p. 323) of the tool, and the tool’s limitation was ‘‘limited psychometric testing’’ (p. 323). The BEHKA-HIV is an eight-item assessment of HIV knowledge and treatment action. The knowledge subscale measured the participant’s ability to understand HIV health information, and the action subscale measured the participant’s ability to make decisions to obtain health information. Three of the eight items measure knowledge, and five measure action. The scales are: 0-3 score 5 low literacy; 4-5 score 5 marginal literacy; and 6-8 score 5 adequate literacy. Data collected on the BEHKA-HIV included the following: (a) Do you know what a CD4 count is? Please write a definition in your best words below. Is the goal of treatment to make the CD4 count
go up or down? (b) Do you know what a viral load is? Please write definition in your best words below. Is the goal of treatment to make the CD4 count go up or down? (c) What medications are you currently taking to treat HIV? (d) I don’t take my medicines when they make me feel bad. (e) I don’t take my medicines when I am too tired. (f) I don’t take my medicines when I am feeling down or low. (g) I don’t take my medicine because it tastes bad. (h) I don’t take my medicines when I feel good. Focus groups. Qualitative data obtained from the start-of-study, midpoint-of-study, and end-of-study focus group discussions were analyzed using the constant comparative method framework as described by Krueger and Casey (2015). Each of the 30 original participants was assigned to one group of 10 individuals with the intention to attend the three monthly sessions with the same assigned cohort. Focus group meetings were held for 90 minutes. They were scheduled on the week that the food bank was in session at the ASO and lunch was provided to encourage ongoing participation throughout the study. Care4todayÔ Mobile Health Manager. Participants installed the Care4todayÔ Mobile Health Manager phone application onto their mobile phones as a personal medication adherence report instrument. The instrument allowed the participants to enter prescribed HIV medications into the application from the mobile phone or from a computer. The application allowed the participant to set up medication reminders, included a medication database of 40,000 US Food and Drug Administration-approved medications and 20,000 images inclusive of generic and brand name medications, and prompted the participant when it was time to take a specific medication using an easy-to-understand color-coded system. Participants were able to track their weekly percent medication adherence via the report feature of this application. A green box indicated medication
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taken, a yellow box indicated medication taken late, and a red box indicated medication not taken. Weekly percentage adherence is also listed (Janssen Research & Development, 2015).
each question’s thematic summary, as appropriate. The researchers reviewed for emphasis comments that shared frequency, specificity, and emotion (Krueger & Casey, 2015).
An accessible, convenience sample of 30 eligible participants was recruited during Phase I from a population of approximately 250 PLWH who regularly attended the ASO food bank. The use of a convenience sample addressed the constraints of the limited 3-month period for this research study and maximized the number of voluntary participants from the accessible population. Inclusion eligibility criteria were: registered clients of the food bank, English-speaking as primary language, HIV-infected, at least 18 years old, currently on ART for at least 3 months, reporting less than 95% adherence to ART over the previous 7 days, and consistent access to a mobile phone with data service. During the recruitment period for the study, eligible participants were determined through an intake and eligibility process. All participants eventually enrolled in the study were predicted to be at high risk for HIV medication nonadherence. The final participants were determined when all completed surveys had been returned by the deadline date and reviewed for study eligibility. A total of 16 participants of the 30 invited participants attended all three focus group meetings.
Carlow University Institutional Review Board approval was obtained prior to the start of the study. This Committee administered both the General Assurance of Compliance with the US Department of Health and Human Services Policy for the protection of Human Subjects and the University policy covering the protection of human subjects. In addition, written approval was obtained from the Executive Director and Chief Operating Officer of the ASO where the study was conducted. Participants were assured that the results of the demographic survey, the audio recordings, and the subsequent transcriptions of the three focus groups would not be linked to individual study participants. No identifying information was collected other than first name, last initial, and preferred contact information in order to remind participants of the three scheduled focus groups throughout the study. Participants were informed about the purpose of the study, the voluntary nature of their participation in the study, and the confidentiality of all collected data before providing informed consent for study participation. Completion of a signed Informed Consent Letter at the first focus group meeting served as the participant’s consent to participate in the study. Written informed consent met confidentiality and Health Insurance Portability and Accountability Act standards. Any potentially identifying information collected during the study was placed in a confidential, separate, locked file, with access only to the researchers. No collected information was shared with the staff and volunteers at the food bank or the ASO. Refusal to participate in the research did not in any way interfere with the services provided to the food bank clients.
Data Collection Methods Audio recording was the primary strategy used to capture focus group data, with concurrent notetaking as a secondary means of gathering data. The researchers conducted a 15- to 30-minute debriefing meeting following each focus group to share highlights. An abridged transcript was prepared by the research assistant following all nine focus group meetings and a coding process began, placing labels on similar comments to arrive at analytic themes to report findings. After the coding and analysis process was completed, the researchers prepared a descriptive summary highlighting the findings for each of the questions. Quotes per category were included in
Design This qualitative descriptive study was conducted over four discrete phases to promote maximum recruitment and to retain participation:
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1. Phase I: Marketing the Intervention/Invitation to Participate (June-July 2015) 2. Phase II. Start-of-Study, Three Individual Focus Groups of 10 participants/focus group (August 2015: Tuesday, Wednesday, Thursday, 12:001:30 pm during Food Bank week) 3. Phase III. Midpoint-of-Study: Ongoing Data Collection, Three Individual Focus Groups of 10 participants/focus group (September 2015: Tuesday, Wednesday, Thursday, 12:00-1:30 pm during Food Bank week) 4. Phase IV. End-of-Study: Evaluation, Three Individual Focus Groups of 10 participants/focus group (October 2015: Tuesday, Wednesday, Thursday, 12:00-1:30 pm during Food Bank week)
Phase I. Marketing the Intervention/Invitation to Participate Phase I included promotional flyers placed in outgoing food bank grocery bags, lobby signage posted, and researchers available in the lobby during selected hours at the ASO food bank. The researchers were onsite during food bank hours to offer a laptop demonstration of the mobile phone application to generate interest. We considered that food bank clients might not know the researchers, and consequently would not have built trusting relationships with them. Therefore, existing food bank staff, food bank volunteers, and ASO case managers encouraged potential participants to complete and return the Study Introduction/Intent to Participate form to the researchers. Completed forms were returned by potential participants to a secure collection box at the food bank by the deadline date of June 30, 2015. The potential participant’s first name, last initial, and preferred return contact information was provided on the form for study follow-up, for focus group meeting schedule notification, and for reminder follow-ups. Thirty participants who consented to be approached for the research study by returning the Introduction to Study/Intent to Participate form and who met study eligibility inclusion criteria as determined on the flyer, were contacted and invited to participate in the study’s three scheduled focus groups to be conducted over a 3-month period.
Phase II. Start-of-Study: Three Individual Focus Groups Phase II marked initiation of the study and the first of 3 months of data collection. At the first focus group meeting, participants were informed about the purpose of the study, the voluntary nature of participation in the study, and the confidentiality of all collected data before providing informed consent for study participation. The 30 invited participants were randomly divided into three groups of 10 and assigned to one of three focus group lunch meetings, so that no more than 8-10 participants were engaged in each of the three monthly focus groups, to be held once monthly, during Tuesday through Thursday lunch meetings, over a 3-month timeframe. The initial focus group meetings were held in a private conference room located at the ASO. The researchers facilitated participants’ completion of the following documents: Informed Consent Letter; Participant Demographics; and Brief Estimate of Health Knowledge and Action—HIV version (BEHKA-HIV). Participants received the following education materials: Taking Medications for HIV: Adherence, and Care4TodayÔ Mobile Health Manager: Application Instructions. Participants were also given the primary researcher’s e-mail address/phone number where they could connect with the researchers to troubleshoot issues related to the application. For participants who had not installed the phone application prior to the initial focus group meeting, the researchers offered Care4TodayÔ Mobile Health Manager mobile phone application installation support following the focus group discussion. The intent of the initial discussion was to identify potential challenges and concerns related to using the phone application and to gather initial perceptions from the groups about its usefulness in improving daily medication adherence. All focus group discussions followed the Krueger and Casey (2015) outline for facilitating focus groups: opening, introduction, transition, key questions, ending questions, and oral summary/troubleshoot. At the completion of the initial focus group meetings, the 23 participants who attended were given a $10 gift card for participating in the initial meeting. The 3-month medication adherence self-report study period began at the end of the first focus group meeting.
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Phase III. Midpoint-of-Study: Ongoing Data Collection The intent of Phase III was for the participants to share positive and negative experiences using the application for medication adherence and changing perceptions about the application after 1 month of use. Additional mobile phone application technical support was also provided, as needed. At the completion of the midpoint-of-study focus group meeting, participants were given a $15 gift card for their ongoing participation in the study. Incremental incentives during the three monthly meetings were offered to promote ongoing study engagement with this targeted group of participants rather than offering one study-end incentive; this decision was made secondary to the sociodemographic nature of the participants and past experiences with focus group meetings conducted at the ASO. Phase IV. End-of-Study: Evaluation Phase IV was the overall evaluation of the study and determination of whether or not participants’ perceptions about mobile phone application usage matched adherence outcomes. At the completion of the End-of-Study Focus Group meeting, participants were given a $25 gift card for completing the study. The 3-month medication adherence self-report study period concluded with these final focus group meetings.
Data Analysis All focus groups were conducted with predetermined group questions and were audio recorded. Following each focus group session, the research assistant transcribed the audio recording. The researcher then verified the transcript for accuracy by listening to the audio recording and reviewing the transcript. Initially, focus group response data were transferred onto MicrosoftÒ Word (Microsoft Corporation, Redmond, WA) worksheets for organization and identification of themes with codes. The constant comparative analysis method, described by Krueger and Casey (2015), was used to identify patterns in the focus group data and to then reveal asso-
ciations between the concepts. The researchers compared one segment of data to another to identity similarities and differences. HyperRESEARCHÔ (ResearchWare, Inc., Randolph, MA), a computerassisted qualitative data analysis software (CAQDAS) tool, was also used to verify manually coded themes.
Results Table 1 summarizes the demographic characteristics of the participants. A convenience sample of 23 participants started the study with 16 participants completing the study to its conclusion, although 30 participants were initially recruited. Focus group participation attrition, as reported by the 14 original participants who dropped out over three 3-month periods, was secondary to issues such as: (a) lack of dependable transportation, (b) being out of town at scheduled focus group meetings, (c) unexpected medical care/illness, (d) lost/stolen mobile phone at time of focus group meeting, and (e) conflicting priorities. One participant who was unable to attend Focus Group #2 was allowed to attend Focus Group #3 to continue to participate in the study for the purpose of contributing to the study. Krueger and Casey (2015) suggested analyzing participant responses for frequency, specificity, and emotion, identifying common themes. Questions were asked to assess changes over time in medication adherence behaviors and acceptability and usability of the mobile phone application over the 3-month study period. Three themes emerged substantiating the acceptability of the technology. Theme 1: Ease of Use In our study, the mobile phone application was perceived to be easy to use, barring potential barriers such as lost/stolen phones or expired service minutes. Working through learning curves was noted at the midpoint focus group as participants described the application as a ‘‘helpful reminder’’ and a ‘‘keeper.’’ Participants discussed being compelled to use technology to ‘‘keep up with the times,’’ but as one participant stated, ‘‘I think I’ve gotten comfortable. It’s something different I learned that I never thought I
Martin, Upvall / Mobile Phone HIV Medication Adherence Intervention 9 Table 1.
Characteristics of 23 Participants Completing Patient Demographics
Characteristic Gender Male Female Age Minimum Maximum Mean SD Race/ethnicity Black/African American Hispanic/Latino White Mixed race Language English Other Level of Education , High school Black/African American Hispanic/Latino White Mixed race High school diploma Black/African American Hispanic/Latino White Mixed race Some college Black/African American Hispanic/Latino White Mixed race Associate degree Black/African American Hispanic/Latino White Mixed race Baccalaureate degree Black/African American Hispanic/Latino White Mixed race
n (%) 16 (69.6) 7 (30.4) 37 72 52.87 10.065 11 (47.8) 5 (21.7) 5 (21.7) 2 (8.7) 23 (100.0) 0 (0.00) 4 (17.4) 2 (18.2) 0 (0.0) 1 (20.0) 1 (50.0) 10 (43.5) 6 (54.5) 3 (60.0) 1 (20.0) 0 (0.0) 6 (26.1) 3 (27.3) 1 (20.0) 1 (20.0) 1 (50.0) 1 (4.3) 0 (0.0) 1 (20.0) 0 (0.0) 0 (0.0) 2 (8.7) 0 (0.0) 0 (0.0) 2 (40.0) 0 (0.0)
Note. SD 5 standard deviation.
would learn because I’m illiterate when it comes to these cell phones.’’ Descriptions of the application being easy to use continued through the final focus group. Participants were given a card during the final group to rate their perceptions of use. All participants rated the application as easy to use, confirming their
declarations of ease and ability to work through any barriers. Prior research (Crankshaw et al., 2010; Hardy et al., 2011) regarding usability of reminder applications concluded that participants did not perceive messages as key to success in medication adherence, and stated that participants actually preferred voice messages over TMs (Sidney et al., 2012). Our mobile phone application study did not support either of these findings. In fact, participants reported the opposite, stating that they found the application to be ‘‘very helpful’’ and useful in establishing a medication routine, as revealed in Theme 2. Theme 2: From Chaos to Order Medication nonadherence behavior has been related to myriad factors, which may be addressed by ‘‘assessing and enhancing patients’ social support, identifying and treating their depression and helping patients overcome cost-related treatment barriers’’ (DiMatteo, Zolnierek, & Martin, 2012, p. 74). From the first focus group, participants described themselves as needing support to take their medications. They recognized that they did not always take medications as prescribed or they might have other problems, such as being in a rush, ‘‘getting up late and taking off.’’ Some discussed feelings of fatigue, ‘‘I just get tired of taking them in the first place.’’ A financial barrier, such as having difficulty meeting co-pays at the pharmacy, was also mentioned by a participant, but this was not a common experience in the group. Theme 3: Someone Cared Participant acceptability of, and improvement in, the use of technology increased over time, in conjunction with a feeling that someone cared about the participants’ health. Nearly all participants stated that they would continue using the application after the study, saying that they ‘‘wouldn’t think of giving it up’’ or ‘‘I’m keeping it forever.’’ Using the application on an ongoing basis was viewed as extending the concept of caring. Participants stated, ‘‘for me, it’s more sentimental . it’s nice to know there are people out there that still do care,’’ ‘‘I love that people are still caring, that’s my biggest thing,’’ and ‘‘I don’t like that I can’t call the doctor’s office and get someone to
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speak right then and there. However, . this technology is sort of like getting a response.’’ Our mobile phone application study contributes to current knowledge by introducing a new theme, a caring component, not previously discussed in the literature. Although validating that the mobile application was generally useful as well as easy to use, participants repeatedly discussed the idea that they felt there was a component of caring in using the mobile phone application. They said that having someone/something monitoring their medication adherence, as well as being reminded on a daily basis, made them feel as though someone was invested in their health. This emotional element in feeling cared for was not found in prior research and is worth further investigation. It appears that mobile phone applications that are engaging to the user are key; if there is no consumer engagement, it undermines the effectiveness of the application.
Discussion Our study was a first step to expand medication adherence science by evaluating the acceptability and feasibility of a mobile phone application’s effect on ART adherence. Data from focus groups evaluated the perceived usefulness, perceived ease of use, and attitudes toward the smart phone application technology to support increased HIV medication adherence. Study findings indicated positive feasibility and usability of a smart phone application to support medication adherence reminders with the added value of a self-reported increase in medication adherence behaviors by our study participants. Overall, participants favored incorporating a reminder application into their everyday lives with minimal barriers, and found multiple benefits, noting positive outcomes. A common theme revealed throughout each focus group discussion was the accountability aspect, where participants felt more inclined to improve medication adherence when someone/something offered active concern regarding their behaviors, whether it be family, friends, or a health care provider. Although our study indicated that using a mobile phone application as a tool to increase medication adherence was both feasible and acceptable, it might have been the combined efforts of the
mobile phone application tool in conjunction with the caring support of a health care professional that contributed to the overall success. Further studies to look at the influence of the intervention on medication adherence are needed, including the addition of a mobile phone application platform. ‘‘The next decade of research with mobile phones will likely evolve into applying more smartphone applications in place of text message interventions. The efficacy of mobile phone applications versus text messaging has yet to be explored in research’’ (Park, HowieEsquivel, & Dracup, 2014, p. 1950). The Care4TodayÔ Mobile Health Manager (Janssen Research & Development, LLC, 2015) mobile phone application was a mobile technology-based application identified specifically for HIV care. The revealed themes supported the TAM in (a) perceived usefulness of the application, (b) perceived ease of use of the application, and (c) a positive attitude toward using the application technology. Themes also supported the TPB with the participants’ intentions to use the application with an overall behavior-normative belief that the application had positive consequences for overall health, especially with the support of a caring person/thing. Focus group responses supported the idea that the medication application had an overall positive influence on medication adherence, and acceptability views about the technology were overwhelming. All participants stated that the experience made them more amenable to using technology. Descriptors related to improving one’s routine served to discuss the impact participants felt the application had on their daily activities, with most participants stating that it had become a part of their lives, was not a burden or an additional task, and suggested the supportive role the application played in the lives of participants who often felt distracted, chaotic, and faced with multiple barriers. Limitations Limitations related to sampling were noted during the study. The self-selected study group introduced possible participant bias, attracting responders who had an interest in either phone technology to support medication adherence or the study participation incentives (focus group lunches and incremental gift cards) of being a part of a research study. The
Martin, Upvall / Mobile Phone HIV Medication Adherence Intervention 11
completion rate of participants attending all three focus group sessions was disappointing, although predictable, secondary to the socioeconomic demographics of the participants, potentially affecting the validity of the results. The participating individuals’ demographics may not represent the overall demographics of the approximately 5,000 PLWH living in the study’s geographical region. One study requirement was for participants to have an HIV viral load of more than 1,000 copies/mL, allowing for a potential quantitative, measurable outcome of increased medication adherence. Current HIV viral load was selfreported and some participants may have arrived into the study with a viral load of less than 1,000 copies/mL or a suppressed viral load of less than 200 copies/mL. Because the researchers, secondary to not obtaining the required consents to request lab value information from various primary care providers, did not verify start of study viral load, all selected individuals were allowed to participate in the study based on self-report of current viral load. However, viral loads may have increased and/or decreased for each individual at study start and over the study’s time, responding to whether or not the participant was actively taking ART as prescribed during the 3-month study period and just prior to the start of the study. The study included participants who owned both smart phones and basic cell phones. The majority of owners of smart phones had the capability of enhanced Care4TodayÔ Mobile Health Manager medication graphics and additional features, whereas the few basic cell phone owners had medication reminder TM capability only. A current e-mail address was needed to enroll into the Care4TodayÔ Mobile Health Manager application and it was discovered at study start that not all participants had an e-mail address, validating the low technology literacy of the participants. The research assistant facilitated opening an e-mail address for those participants. The individuals in the study with basic phones also did not have access to a home computer or the use of e-mail. The three basic phone participants had never used the TM feature on their phones. The research assistant offered assistance to address technology knowledge deficits by educating participants on basic TM functions in order to be able to participate in the study. Two participants and the
researcher reported a 2-day interruption in Care4TodayÔ Mobile Health Manager service over the 3-month study period, during which time the application was either not accurately logging in daily adherence reports or not responding to participants’ text responses. Care4TodayÔ Mobile Health Manager was contacted by the researcher and they reported that there was a 2-day glitch that had been corrected. The two affected participants were updated with the reason for interruption of services and given assurance that the application was capturing medication adherence data during this timeframe.
Conclusions Our study provided an understanding of the process by which the participants engaged in better HIV medication adherence with the support of a mobile phone application. There is a strong behavioral component of medication adherence and there is also a strong behavioral component related to adopting and using new technology, such as installing and actively using a daily mobile phone application. We believe that it may be the combination of a medication adherence tool, such as the Care4TodayÔ Mobile Health Manager mobile phone application, in conjunction with the participants’ perceived caring effect of the researchers over a 3-month period that contributed to increased medication adherence by the study participants. Participants may have desired to please the researchers, who had taken an interest in their medication adherence behaviors. Participants also displayed ongoing challenges with technology use, which the researchers were able to address, potentially affecting perceived usefulness, perceived ease of use, and attitude toward the technology on a one-on-one basis, and the oneon-one support feature enhanced participants’ technology use though a caring relationship. The caring component theme was a positive finding. PLWH would benefit from further exploration of this theme with a combination of quantitative and qualitative methods, including a more diverse sample, and studying covariates found to be potentially significant. Ongoing research may reveal whether there is a potential social outcome of increasing medication adherence through increased provider/client social
12 JANAC Vol. -, No. -, -/- 2016
interactions in conjunction with technology, such as the Care4TodayÔ mobile phone application.
Disclosures The authors report no real or perceived vested interests that relate to this article that could be construed as a conflict of interest.
Key Considerations Improving adherence to antiretroviral therapy (ART) is key in reducing the morbidity and mortality of HIV disease. Adherence to ART may be complicated by secondary personal, behavioral, and treatment factors. When individuals are offered the necessary resources, such as a mobile phone medication reminder application, they may have greater success in performing the behavior. Having someone/something monitoring medication adherence, as well as being reminded on a daily basis, made individuals feel as though someone was invested in their health, which was perceived as a caring component.
Acknowledgments Dr. Martin would like to thank Marie Flavin, DNP, RN, Carlow University School of Nursing; Cynthia C. Brinson, MD, and Sabrina Q. Mikan, PhD, RN, ACNS-BC. Acknowledgements also go to Mary Pomeroy, RN, University of Texas-Austin School of Nursing, MPH student; the leadership team at AIDS Services of Austin, Inc. for providing the facility for this project; Janssen Research and Development for their commercially available phone application tool, Care4TodayÔ Mobile Health Manager; and the 16 participants, living with HIV and struggling with medication adherence, who committed 3 months of their lives to be followed for this study.
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