Systematic adaptation of the adherence improving self‐management strategy to support breast cancer survivors' adherence to adjuvant endocrine therapy: An intervention mapping approach
2022; Wiley; Volume: 31; Issue: 6 Linguagem: Inglês
10.1111/ecc.13721
ISSN1365-2354
AutoresAnna M. Janssen, Joëlle Dam, Judith B. Prins, Laurien M. Buffart, Marijn de Bruin,
Tópico(s)Health Literacy and Information Accessibility
ResumoEuropean Journal of Cancer CareEarly View e13721 ORIGINAL ARTICLEOpen Access Systematic adaptation of the adherence improving self-management strategy to support breast cancer survivors' adherence to adjuvant endocrine therapy: An intervention mapping approach Anna M. Janssen, Corresponding Author Anna M. Janssen anna.janssen@radboudumc.nl orcid.org/0000-0002-2012-3118 Department of IQ Health, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands Correspondence Anna M. Janssen, Department of IQ Healthcare, Radboud Institute for Health Sciences, Radboud University Medical Center, Postbus 9101, Huispost 160, 6500 HB Nijmegen, The Netherlands. Email: anna.janssen@radboudumc.nlSearch for more papers by this authorJoëlle Dam, Joëlle Dam Department of IQ Health, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The NetherlandsSearch for more papers by this authorJudith Prins, Judith Prins Department of Medical Psychology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The NetherlandsSearch for more papers by this authorLaurien M. Buffart, Laurien M. Buffart Department of Physiology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The NetherlandsSearch for more papers by this authorMarijn de Bruin, Marijn de Bruin Department of IQ Health, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The NetherlandsSearch for more papers by this author Anna M. Janssen, Corresponding Author Anna M. Janssen anna.janssen@radboudumc.nl orcid.org/0000-0002-2012-3118 Department of IQ Health, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands Correspondence Anna M. Janssen, Department of IQ Healthcare, Radboud Institute for Health Sciences, Radboud University Medical Center, Postbus 9101, Huispost 160, 6500 HB Nijmegen, The Netherlands. Email: anna.janssen@radboudumc.nlSearch for more papers by this authorJoëlle Dam, Joëlle Dam Department of IQ Health, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The NetherlandsSearch for more papers by this authorJudith Prins, Judith Prins Department of Medical Psychology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The NetherlandsSearch for more papers by this authorLaurien M. Buffart, Laurien M. Buffart Department of Physiology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The NetherlandsSearch for more papers by this authorMarijn de Bruin, Marijn de Bruin Department of IQ Health, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The NetherlandsSearch for more papers by this author First published: 20 October 2022 https://doi.org/10.1111/ecc.13721AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract Objective Non-adherence to adjuvant endocrine therapy (AET) for breast cancer leads to increased recurrence and mortality risk and healthcare costs. Evidence on feasible, effective AET adherence interventions is scarce. This paper describes the systematic adaptation of the cost-effective adherence improving self-management strategy (AIMS) for patients with HIV to AET for women after breast cancer treatment. Methods We followed the intervention mapping protocol for adapting interventions by conducting a needs assessment, reviewing target behaviours and determinants, reassessing behaviour change methods and adapting programme content. Therefore, we performed a literature review, consulted behavioural theory and organised nine advisory board meetings with patients and healthcare professionals. Results Non-adherence occurs frequently among AET users. Compared to HIV treatment, AET is less effective, and AET side effects are more burdensome. This drives AET treatment discontinuation. However, the key determinants of non-adherence are largely similar to HIV treatment (e.g. motivation, self-regulation and patient–provider relationship); therefore, most strategies in AIMS-HIV also seem suitable for AIMS-AET. Modifications were required, however, regarding supporting patients with coping with side effects and sustaining treatment motivation. Conclusion AIMS seems to be a suitable framework for adherence self-management across conditions and treatments. Intervention mapping offered a transparent, systematic approach to adapting AIMS-HIV to AET. 1 INTRODUCTION Breast cancer is the most prevalent cancer in women, with an annual incidence of 14,935 and a 10-year prevalence of 139,029 in the Netherlands (Netherlands Cancer Registry, 2020). Most breast cancers (among 75%) are hormone sensitive (Li et al., 2003), and these women are recommended to use oral adjuvant endocrine therapy (AET) for at least 5 years (Netherlands Cancer Registry, 2017). This reduces mortality by 33% on average but varies largely for each individual (Early Breast Cancer Trialists' Collaborative Group, 2005). However, AET is associated with side effects, including hot flushes, mood swings, fatigue and musculoskeletal symptoms (Condorelli & Vaz-Luis, 2018). Consequently, medication adherence is suboptimal, which is shown in irregular intake (i.e. decreased implementation; Vrijens et al., 2012) and early discontinuation (i.e. non-persistence; Vrijens et al., 2012) in about 50% of patients (Moon et al., 2019; Murphy et al., 2012). This enhances the risk of cancer recurrence, mortality and increases healthcare costs in case of secondary cancer (Early Breast Cancer Trialists' Collaborative Group, 2005; Font et al., 2019; Hershman et al., 2011; McCowan et al., 2013). Hence, supporting AET adherence has important benefits for women using AET and for the (cost)effectiveness of healthcare. A recent systematic review of studies examining the effectiveness of interventions promoting AET adherence in women with breast cancer found no evidence of feasible, acceptable and (cost)effective interventions (Ekinci et al., 2018). However, interactive in-person delivered behavioural theory-based interventions were shown to be promising for supporting AET adherence (Ekinci et al., 2018). Across conditions, successful adherence interventions appeared to be based on theory and various behaviour change methods (BCMs), targeted to patients with adherence difficulties and focused on habits and prompts to perform the behaviour (Nieuwlaat et al., 2014; Oberjé et al., 2013). One meta-analysis studying potential effective components of medication adherence interventions across various treatments found that combining in-person support with electronic monitoring and feedback on adherence seemed particularly effective (Demonceau et al., 2013). Additionally, reducing side effects of AET may help to prevent non-persistence. Physical activity has shown to be an effective strategy to improve quality of life and reduce AET side effects (Allen et al., 2014; Campbell et al., 2019; Duijts et al., 2012). Hence, a behavioural intervention supporting adherence directly, and indirectly via promoting physical activity, may be particularly beneficial in the treatment of AET. An emerging body of evidence supports the benefits of electronic monitoring (e.g. a wearable activity tracker) to stimulate physical active behaviour in the general population (Laranjo et al., 2021), as well as in women with breast cancer (Hartman et al., 2018; Pudkasam et al., 2021; Singh et al., 2020). The Adherence Improving self-Management Strategy (AIMS) is an in-person delivered adherence intervention that utilises electronic monitoring and feedback as core BCMs (de Bruin et al., 2005). AIMS has been developed in collaboration with HIV patients and healthcare providers (HCPs), is based on empirical literature and behavioural theories, addresses multiple behavioural determinants (knowledge, motivation and self-regulation) and has been designed to be delivered by trained HCPs during routine clinical visits. Hence, AIMS incorporates the above-mentioned key ingredients of a successful adherence intervention. AIMS is delivered by nurses during regular clinical visits and consists of the following steps: (1) discussing patients knowledge on adherence and providing targeted information on adherence, (2) goal setting and talking about patients motivation for medication intake behaviour, (3) comparing the set goal with patients medication intake behaviour (patient has used an electronic monitoring device to track his medication intake. In the consultation, nurse presents a report of patients medication intake to the patient) and (4) definitive goal setting for medication intake behaviour, action and coping planning (de Bruin et al., 2005). The AIMS intervention has been applied to promote HIV treatment adherence and demonstrated to be feasible to deliver, acceptable to patients and HCPs, effective in improving adherence and clinical outcomes (e.g. a reduction of treatment failure of 61%) and cost-saving (de Bruin et al., 2005, 2010, 2017; Wijnen et al., 2018). Instead of developing a new intervention, we presumed AIMS to be a suitable intervention to promote AET adherence. Besides, adapting evidence-based interventions makes science potentially more efficient and cumulative (Copeland et al., 2021). Since the original AIMS intervention does not address side effect management, an adaptation of the AIMS intervention would be required. The adaptation of an existing intervention should be undertaken systematically and be reported in detail (Copeland et al., 2021). Intervention mapping (IM) is a commonly used framework for developing (Rammant et al., 2021; van Noort et al., 2020) and adapting (Boekhout et al., 2017; Jans et al., 2020) behavioural interventions. The process of adapting interventions to a new context, population and/or behaviour includes six steps (Bartholomew, 2011); our study centres upon Steps 1–4, presented in Table 1. TABLE 1. Steps of intervention mapping for adaptation Step Questions to be answered 1. Needs assessment and logic model of the problem What are the consequences of non-adherence to AET for health-related quality of life and survival? Which behaviours and determinants are related to (non)-adherence to AET? Which environmental (i.e. organisational, communal and societal) factors can influence the behaviour? What are the key differences compared to the HIV population? 2. Logic model of change and matrices Which behavioural outcomes and performance objectives need to be added, deleted or adapted for the new population? Which determinants and change objectives need to be deleted, added or revised for the new population and new performance objectives? 3. Methods and practical applications Are the methods and strategies of the original programme applicable for the new population? Are the methods feasible and practical in the new community context? Which methods need to be added or modified? 4. Programme components and delivery channels Given the modifications in objectives and/or methods, what content should remain the same, should be adapted or deleted and what content should be added? 2 METHODS This study was approved by the Medical ethical committee Arnhem-Nijmegen (reference number: NL 2019-5517). To answer the questions in each step of the protocol, a literature review was performed, multiple iterative advisory board sessions with patients and HCPs were held, and a rapid feasibility test for key elements that could pose implementation difficulties was performed (Figure 1). FIGURE 1Open in figure viewerPowerPoint The adaptation process of AIMS-HIV to AIMS-AET 2.1 The AIMS intervention The AIMS intervention was developed for patients with HIV. The intervention consists of five building blocks that are shown in Figure 2. In each building block, a set of different BCMs is applied. A detailed description of the original intervention rationale, components and protocol can be found elsewhere (de Bruin et al., 2005). In this paper, we describe the steps that were undertaken to adapt AIMS-HIV to AET users and present the rationale, components and protocol of the adapted AIMS intervention for AET users (AIMS-AET) in Section 3. FIGURE 2Open in figure viewerPowerPoint The building blocks of the AIMS intervention In the adaptation process to AIMS-AET, we added the discussion and electronic monitoring of physical activity behaviour as a core strategy for managing side effects. We expect the adapted intervention to increase medication adherence by using the building blocks and strategies of the original AIMS intervention. Additionally, we expect the intervention to decrease experienced side effects by increasing physical activity. A diminished side effect burden should contribute to medication adherence, too. The anticipated working mechanism of AIMS-AET is shown in Figure 3. FIGURE 3Open in figure viewerPowerPoint The anticipated working mechanism of AIMS-AET 2.2 Literature review A literature search was performed in July 2019 in the databases PsycINFO, Medline and Embase to identify literature about the determinants of (non)-adherence to AET with keywords for breast cancer, adjuvant endocrine therapy, adherence and persistence and determinants (see Appendix A for search details). Firstly, we searched for reviews. Secondly, we added the keywords qualitative, focus group* and interview* to identify qualitative literature on (modifiable) determinants of (non-)adherence. Finally, we identified studies presenting quantitative data on determinants of (non-)adherence published after the search date of the latest review (December 2017). Only articles in full text and in English language were included. Articles describing interventions, protocols and results of randomised controlled trials (RCT) and conference abstracts were excluded. All articles were screened on title/abstract. Full-text articles of potentially relevant articles were assessed for eligibility. Data on sociodemographic and behavioural determinants of (non-)adherence and barriers and facilitators were extracted from eligible articles. Extracted data were then compared with the AIMS-HIV intervention to identify similarities and discrepancies. 2.3 Advisory board meetings The researchers recruited eight HCPs from Radboudumc (n = 4) and two collaborating community hospitals, Rijnstate hospital (n = 2) and Bernhoven hospital (n = 2), to participate in advisory board meetings. Subsequently, the HCPs in two hospitals approached patients who currently received AET or who recently discontinued AET, were able to understand Dutch and were willing to come to the hospital for the advisory board meeting. We aimed to recruit a sample with a variety in social economic status, ethnicity, age, family situation and state of adherence. Nine patients (eight from Radboudumc and one from Bernhoven) agreed to participate and gave informed consent. The advisory board meetings were led by one researcher (AJ) and supported by at least one other researcher (MdB, JD) and lasted between 98 and 180 min. We asked participants to reflect on determinants of non-adherence found in the literature, to discuss their relevance for this context and to identify missing determinants. We described the intended AIMS modules and asked participants to reflect on the selected intervention strategies, materials and BCMs from their personal or professional experience. In particular, we were interested in intervention feasibility, compatibility with goals and values, perceived complexity and relative advantage (Rogers, 2003). All sessions were audiotaped, input from participants was discussed within the research group, findings were compared with literature, and modifications were incorporated in the intervention programme. In the next step, the adapted programme was presented in another advisory board meeting with different participants. In total, three iterative advisory board meetings with HCPs were performed between September 2019 and December 2019, and six iterative advisory board meetings with patients were performed between September 2019 and July 2020. 2.4 Patient representative A patient representative from the Dutch breast cancer organisation gave input on the design of the study, the interpretation of the results and how they were used to adapt AIMS. The Dutch breast cancer organisation was reimbursed for this work. 2.5 Feasibility testing of the electronic devices In the AIMS-AET intervention, electronic devices were proposed as self-monitoring tools to measure medication adherence (medication button: MEMS Adherence Hardware Button, Aardex) and physical activity (activity tracker). There is also substantive evidence that using such trackers as part of (counselling) interventions is effective in supporting behaviour change (Demonceau et al., 2013; Hartman et al., 2018; Pudkasam et al., 2021; Singh et al., 2020). In order to quickly pre-test the user-friendliness of the selected devices, five patients who had participated in advisory board meetings were asked to use the devices for 4 weeks. Three of them agreed to test the devices. Additionally, the patient representative was asked to participate in the feasibility testing. Two different types of activity trackers (Garmin vivofit 4 and Yamax EX210) were sent to four participants, each to be used for 2 weeks sequentially, after which participants were interviewed by phone about their user experiences by one researcher (AJ). Participants were asked to reflect upon the attractiveness, clarity of usage and efficiency of the devices. Further, they were interviewed about their perception of the data output of the devices, their motivation to use the device and any other remarks (see the interview guide in Appendix E). Interviews were audiotaped and lasted between 24 and 59 min. 3 RESULTS 3.1 Literature review The literature search (search date: 8 July 2019) yielded 1374 unique results, including 17 eligible reviews. Additionally, 129 qualitative studies were identified, of which 20 were eligible. Further, we identified 220 quantitative articles (published between December 2017 and July 2019), of which nine met the inclusion criteria. The flow charts of these searches and the article references are included in Appendix B1-B3. 3.2 IM Step 1: Needs assessment and logic model of the problem 3.2.1 Consequences of non-adherence to AET for health and quality of life Not taking AET consistently (28% to 72% use less than 80% of their medication) and the premature treatment discontinuation (non-persistence: 31%–73% before Year 5) were found to be associated with a 71% higher risk for cancer recurrence, 52% less time until cancer recurrence and a 26%–49% decreased survival (Chirgwin et al., 2016; Font et al., 2019; Hershman et al., 2011; McCowan et al., 2013; Murphy et al., 2012). Consistent intake of medication appeared to decline by treatment duration (Moon et al., 2019). Up to 84% of patients experience side effects that may reduce QoL (Boehm et al., 2009; Ferreira et al., 2019; Ganz et al., 2016; Kumar, 2018; Lambert, Balneaves, Howard, & Gotay, 2018; Moon, Moss-Morris, Hunter, Carlisle, & Hughes, 2017; Paranjpe et al., 2019). Examples of side effects are hot flushes, impaired sleep, fatigue and musculoskeletal pain (Couzi et al., 1995; Pan et al., 2018; Sousa et al., 2017). Women who reported (severe) side effects were four to five times more likely to discontinue AET early compared to women without side effects (Milata et al., 2018; Moon, Moss-Morris, Hunter, Carlisle, & Hughes, 2017). Side effects may also reduce physical activity levels, cause difficulties with (close and sexual) relationships, decrease self-confidence and cause social isolation (Brett et al., 2018). This may induce a downward spiral, where women conclude that the potential benefits of the treatment outweigh the costs, leading to non-persistence (Moon, Moss-Morris, Hunter, & Hughes, 2017). 3.2.2 Behaviours and determinants related to (non-)adherence to AET Our literature review revealed that both implementation and non-persistence are influenced by patients' demographics, knowledge, motivation, subjective norms, self-regulation, management of side effects and support. Table 2 provides an overview of the demographic and behavioural determinants associated with adherence identified in the literature. A narrative description of these results is provided in Appendix C. TABLE 2. Determinants related to AET adherence Determinants related to AET adherence Positively associated with adherence Negatively associated with adherence Demographics Being married or having a higher income (Paranjpe et al., 2019) Aged 65/75 (Moon, Moss-Morris, Hunter, Carlisle, & Hughes, 2017; Paranjpe et al., 2019), higher baseline body mass index (BMI) (Hagen et al., 2019) Knowledge Being able to explain working mechanism of AET (Farias et al., 2017), receiving timely and relevant information about working mechanism, efficacy and side effects of AET (Brett et al., 2018; Humphries et al., 2018; Iacorossi et al., 2018; Moon, Moss-Morris, Hunter, & Hughes, 2017; Pellegrini et al., 2010; van Londen et al., 2014; Verbrugghe et al., 2017) Thinking that one doses of AET does not affect efficacy (Harrow et al., 2014), not receiving information about side effects and how to manage them, inadequate timing of information about side effects or receiving too much info at one time (Bluethmann et al., 2017; Brett et al., 2018; Verbrugghe et al., 2017) Motivation, attitude and beliefs Beliefs: necessity beliefs: Tamoxifen keeps me alive, fear of death and repeated cancer treatment (Iacorossi et al., 2018; Moon, Moss-Morris, Hunter, & Hughes, 2017); strong necessity and control beliefs or belief in benefits outweigh concerns (Cahir, Dombrowski, et al., 2015; Harrow et al., 2014; Iacorossi et al., 2018; Lambert, Balneaves, Howard, Chia, & Gotay, 2018; Moon, Moss-Morris, Hunter, & Hughes, 2017) Outcome expectations: having positive outcome expectations for AET (Bluethmann et al., 2017; Cahir, Dombrowski, et al., 2015) Emotions: fear of cancer recurrence (Cahir, Dombrowski, et al., 2015; Iacorossi et al., 2018), positive emotions towards AET (happy to take medication, protection, control) (Harrow et al., 2014; Humphries et al., 2018; Iacorossi et al., 2018; Lambert, Balneaves, Howard, Chia, & Gotay, 2018; Moon, Moss-Morris, Hunter, & Hughes, 2017), anticipated regret (Cahir, Dombrowski, et al., 2015) Risk perception: belief to be at high risk for recurrence (Lambert, Balneaves, Howard, Chia, & Gotay, 2018) Intentions and goals: being intrinsically motivated for AET, having strong intention to take AET for 5 years to prevent cancer recurrence (Cahir, Dombrowski, et al., 2015) Beliefs: burden of side effects outweigh benefits (Moon, Moss-Morris, Hunter, & Hughes, 2017), prioritising quality of life over survival/recurrence risk (Brett et al., 2018; Lambert, Balneaves, Howard, Chia, & Gotay, 2018; van Londen et al., 2014), being sceptical about the benefits of AET, conflicting beliefs regarding disadvantages and advantages (Brett et al., 2018; Moon, Moss-Morris, Hunter, & Hughes, 2017), having concerns about long-term use of AET (toxicity, medication use in general, physical changes, wish for child-bearing) (Brett et al., 2018; Hackett et al., 2018; Iacorossi et al., 2018; Moon, Moss-Morris, Hunter, & Hughes, 2017; Pellegrini et al., 2010) Emotions: negative emotions towards AET (reminder of cancer, discomfort) (Hackett et al., 2018; Iacorossi et al., 2018; Moon, Moss-Morris, Hunter, Carlisle, & Hughes, 2017) Risk perception: risk perception decreases over time (Brett et al., 2018; Lambert, Balneaves, Howard, Chia, & Gotay, 2018), belief to be at low risk for recurrence (Brett et al., 2018; Moon, Moss-Morris, Hunter, & Hughes, 2017) Subjective norms Witness significant others as role model in taking medication (Hackett et al., 2018), extrinsic motivation by family (Cahir, Dombrowski, et al., 2015) Discomfort of taking AET in front of others (Iacorossi et al., 2018) Self-efficacy and self-regulation High levels of coping self-efficacy and self-determination (Cahir, Dombrowski, et al., 2015), organisation and planning around AET (Cahir, Dombrowski, et al., 2015), using reminders to prevent forgetting medication intake (Iacorossi et al., 2018), exhibit or talk about action and coping planning, self-monitoring and develop a routine (Cahir, Dombrowski, et al., 2015) Stopping for a shorter period for drug holiday/relief/unintentionally (Brett et al., 2018; Lambert, Balneaves, Howard, Chia, & Gotay, 2018) Managing side effects Using coping strategies adopting healthy lifestyles to tolerate medication or that help alleviate side effects (Ahlstedt Karlsson et al., 2019; Brett et al., 2018; Farias et al., 2017; Lambert, Balneaves, Howard, Chia, & Gotay, 2018; Moon, Moss-Morris, Hunter, & Hughes, 2017; van Londen et al., 2014) Advice of doctors on how to manage side effects do not help (Bluethmann et al., 2017), perceived side effects or higher impact of side effects on daily functioning (Bluethmann et al., 2017; Brett et al., 2018; Moon, Moss-Morris, Hunter, & Hughes, 2017), side effects occur frequently, severely and unpredictably, leading to a restriction of social activities (Brett et al., 2018) Social support Making use of various sources for social support (informational, emotional, instrumental, appraisal) (Toledo et al., 2019), social support from peers (Humphries et al., 2018; Toledo et al., 2019; Verbrugghe et al., 2017), social support from family and friends (Brett et al., 2018; Humphries et al., 2018; Iacorossi et al., 2018; Lambert, Balneaves, Howard, Chia, & Gotay, 2018) Experiencing a lack of validation of side effects and/or support (Lambert, Balneaves, Howard, Chia, & Gotay, 2018; Moon, Moss-Morris, Hunter, & Hughes, 2017; Pieters et al., 2019; van Londen et al., 2014; Verbrugghe et al., 2017; Yamamoto et al., 2015) Relationship and communication with the HCP Shared decision making (Farias et al., 2017), social support of HCP (Brett et al., 2018; Farias, 2015; Humphries et al., 2018; Iacorossi et al., 2018; van Londen et al., 2014), trust in HCP, relying on doctors' opinion, good patient–HCP relationship (Brett et al., 2018; Cahir, Dombrowski, et al., 2015; Farias, 2015; Harrow et al., 2014; Iacorossi et al., 2018; Lambert, Balneaves, Howard, Chia, & Gotay, 2018), receiving psychological support (Iacorossi et al., 2018), continuation of care: having appointments with the same HCP always (Iacorossi et al., 2018) Perceiving a lack of interest and support in side effects from HCPs (Brett et al., 2018; van Londen et al., 2014) Note: The results are organised in a similar way to the behavioural determinants also used in AIMS-HIV (de Bruin et al., 2005). 3.2.3 Key differences and similarities with AIMS-HIV There are many similarities in the determinants that drive adherence to AET and adherence to antiretroviral therapy for HIV: Adherence is influenced by demographic variables, with a lower SES or not having a partner being associated with non-adherence (García & Côté, 2003; Paranjpe et al., 2019). Further, personal beliefs and perceptions about the medication and the illness play a substantial role. If personal risk perception is low or medication beliefs are negative, adherence is found to be lower (García & Côté, 2003; Moon, Moss-Morris, Hunter, Carlisle, & Hughes, 2017). Next, subjective norms show some effects on adherence: If the patient perceives that important people in her personal environment believe in the medication or want the patient to take the medication, adherence is found to be higher (Cahir, Guinan, et al., 2015; García & Côté, 2003). Self-efficacy, skills and self-regulation play an important role in adherence: If the patient perceives herself to be capable to adhere to treatment and possesses about the skills to perform and regulate her behaviour, odds for adherence are much higher compared to having a low self-efficacy (Cahir, Guinan, et al., 2015; García & Côté, 2003). Last, social support and the relationship between HCP and patient seem to be of undeniable influence on adherence: Patients with sufficient social support and a good relationship with their HCP usually show better adherence (Brett et al., 2018a; García & Côté, 2003; Toledo et al., 2019). A key difference, however, is that while the efficacy of the lifelong HIV treatment is reflected in a non-detectable viral load and improved health, AET is an adjuvant treatment, typically prescribed for 5 years, with uncertainty about personal effectiveness and a possible side effect burden, contributing to higher rates of non-persistence (Cahir, Guinan, et al., 2015; Moon, Moss-Morris, Hunter, & Hughes, 2017; Paranjpe et al., 2019; Verbrugghe et al., 2017). 3.3 IM Step 2: Logic model of change Behavioural outcomes specify which behaviour patients should demonstrate after the intervention (Bartholomew, 2011). The main behavioural outcome in AIMS-AET is the regular and continued use of medication (i.e. patient takes the right doses of medication in the right interval), similar to AIMS-HIV. Our literature review and advisory board meetings showed that, also maintaining motivation for treatment persisten
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