Facilitators and barriers to medication adherence with adjuvant endocrine therapy in women with breast cancer: a structural equation modelling approach

Tan, Eng Hooi; Wong, Andrea Li Ann; Tan, Chuan Chien; Wong, Patrick; Tan, S. G.; Ang, Li En Yvonne; Lim, Siew Eng; Chong, Wan Qin; Ho, Jingshan; Lee, Soo‐Chin; Tai, Bee Choo

doi:10.1007/s10549-021-06204-9

Cited by 4 publications

(9 citation statements)

References 29 publications

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“…As hypothesized, greater barriers were associated with lower adherence as measured via both electronic adherence monitoring devices and self-report. These results are consistent with those from studies including AYAs with other medical conditions [19][20][21][22] and adults with cancer, [23][24][25] Confused about how to take it 3 (5%) 3 (6%) 1 (3%) 1 (4%) Throughout the study, AYAs endorsed an average of four barriers, with "tired of living with cancer" and "tired of taking it" being among the top three most common across time points and medication type.…”

Section: Discussionsupporting

confidence: 85%

“…The pediatric self-management model and larger adherence science literature suggest that barriers at the patient/family (e.g., forgetting), community (e.g., social stigma), and healthcare system levels (e.g., access) may be modifiable drivers of nonadherence. 3 Among AYAs with noncancer medical conditions [19][20][21][22] and adults with cancer, [23][24][25] greater barriers are associated with lower medication adherence. A recent cross-sectional study by Psihogios et al 26 found that barriers were also associated with general medication adherence (including adherence to medications not prescribed as part of cancer treatment, e.g., antidepressants, anti-anxiety medications) among AYAs with cancer.…”

Section: Introductionmentioning

confidence: 99%

“…[4][5][6][7] To enhance the rigor of this body of research, we assessed adherence via a multi-method strategy. 27 Specifically, because adherence data obtained from selfreport measures (i.e., those used in prior work [23][24][25][26] ) can be impacted by recall bias and social desirability, 28 we used electronic monitoring devices to obtain our primary assessment of adherence. We also included the self-report measure administered by Psihogios et al 26 as a secondary measure of adherence to facilitate comparisons across studies and allow for the inclusion of AYAs for whom electronic adherence monitoring devices may not be appropriate (e.g., AYAs who currently use a pillbox and would be adversely impacted by switching to an electronic pill bottle).…”

Section: Introductionmentioning

confidence: 99%

“…The pediatric self‐management model and larger adherence science literature suggest that barriers at the patient/family (e.g., forgetting), community (e.g., social stigma), and healthcare system levels (e.g., access) may be modifiable drivers of nonadherence 3 . Among AYAs with noncancer medical conditions 19–22 and adults with cancer, 23–25 greater barriers are associated with lower medication adherence. A recent cross‐sectional study by Psihogios et al 26 .…”

Section: Introductionmentioning

confidence: 99%

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Barriers to medication adherence among adolescents and young adults with cancer

McGrady

Ketterl

Norris

et al. 2023

Pediatric Blood & Cancer

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Background Adherence promotion is a critical component of adolescent and young adult (AYA) cancer care, but predictors of nonadherence that could be targeted in intervention efforts remain largely unknown. The purpose of this multi‐site longitudinal observational study was to examine the relationship between barriers and medication adherence among AYAs with cancer. Procedure Sixty‐five AYAs (ages 15–24 years; mean age = 18.97 years, SD = 2.51; Mmean time since diagnosis = 1.42 years, SD = 1.95) with newly diagnosed or relapsed cancer completed self‐report measures of barriers and adherence at quarterly study visits and used an electronic adherence monitoring device for 12 months. Longitudinal mixed effects models were used to examine our primary hypothesis that greater barriers are related to lower adherence over time. Descriptive statistics were used to explore our secondary aim of describing the frequency and patterns of barriers endorsed by AYAs with cancer. Results After controlling for covariates (time, medication type, race, ethnicity, diagnosis, time since diagnosis), a greater number of barriers was associated with lower electronically monitored (β = −5.99, p = .005) and self‐reported (β = −1.92, p < .001) adherence. The specific barriers endorsed by AYAs differed across participants, and the majority of AYAs endorsed an entirely different pattern of barriers than any other AYA in the study. Conclusion Barriers are associated with nonadherence and may be a promising target for intervention. Individual variability across barriers, however, suggests that tailoring may be necessary, and a promising next step is to explore personalized approaches to adherence promotion.

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Section: Discussionsupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Barriers to medication adherence among adolescents and young adults with cancer

McGrady

Ketterl

Norris

et al. 2023

Pediatric Blood & Cancer

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“…Forgetfulness Women with breast cancer commonly report memory problems following chemotherapy, which can increase forgetfulness and consequently unintentional nonadherence [18,37,41,61,[67][68][69].…”

Section: Factor Associated With Adherence Explanation Evidencementioning

confidence: 99%

Supporting adjuvant endocrine therapy adherence in women with breast cancer: the development of a complex behavioural intervention using Intervention Mapping guided by the Multiphase Optimisation Strategy

Green

French²,

Graham³

et al. 2022

BMC Health Serv Res

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Background Adjuvant endocrine therapy (AET) reduces the risk of breast cancer recurrence and mortality. However, up to three-quarters of women with breast cancer do not take AET as prescribed. Existing interventions to support adherence to AET have largely been unsuccessful, and have not focused on the most salient barriers to adherence. This paper describes the process of developing four theory-based intervention components to support adherence to AET. Our aim is to provide an exemplar of intervention development using Intervention Mapping (IM) with guidance from the Multiphase Optimisation Strategy (MOST). Methods Iterative development followed the six-stage IM framework with stakeholder involvement. Stage 1 involved a literature review of barriers to adherence and existing interventions, which informed the intervention objectives outlined in Stage 2. Stage 3 identified relevant theoretical considerations and practical strategies for supporting adherence. Stage 4 used information from Stages 1-3 to develop the intervention components. Stages 1-4 informed a conceptual model for the intervention package. Stages 5 and 6 detailed implementation considerations and evaluation plans for the intervention package, respectively. Results The final intervention package comprised four individual intervention components: Short Message Service to encourage habitual behaviours surrounding medication taking; an information leaflet to target unhelpful beliefs about AET; remotely delivered Acceptance and Commitment Therapy-based guided self-help to reduce psychological distress; and a website to support self-management of AET side-effects. Considerations for implementation within the NHS, including cost, timing and mode of delivery were outlined, with explanation as to how using MOST can aid this. We detail our plans for the final stage of IM which involve feasibility testing. This involved planning an external exploratory pilot trial using a 24-1 fractional factorial design, and a process evaluation to assess acceptability and fidelity of intervention components. Conclusions We have described a systematic and logical approach for developing a theoretically informed intervention package to support medication adherence in women with breast cancer using AET. Further research to optimise the intervention package, guided by MOST, has the potential to lead to more effective, efficient and scalable interventions.

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