Developing and quantitatively optimising a Just-In-Time-Adaptive-Intervention (JITAI) to improve adherence to medication in women with breast cancer.

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Breast cancer is the most common cancer in the UK and second most common cause of cancer death (ONS, 2019). Adjuvant hormonal therapy is an established treatment which reduces recurrence in women with breast cancer (EBCTCG, 2005) but up to 75% of women do not adhere to these therapies (Murphy et al., 2012), increasing their mortality risk (Hershman et al., 2011). Interventions to encourage adherence have largely failed to support the health and wellbeing of breast cancer survivors (Hurtado-de-Mendoza et al., 2016). As digital platforms evolve, e-Health solutions for personalised care are increasingly attractive. For example, the delivery of timely SMS messages are effective in increasing adherence to long-term therapies in other conditions (Thakkar et al., 2016). Recently, Just-In-Time-Adaptive-Intervention's (JITAIs) have been developed. JITAIs are digital applications with inbuilt decision rules, delivering notifications to users based on the specific time or their situation. JITAIs have been used successfully to increase physical activity (Hardeman et al., 2016) but have not been applied to medication adherence. Further evidence is also needed on how, when, and for whom JITAIs can be effective for behaviour change (Thomas & Bond, 2015). A cutting-edge experimental design, the Micro-Randomised Trial (MRT), has been proposed to address these questions (Murphy et al., 2015).This PhD will: 1) synthesise the evidence on previously reported JITAIs and establish how they can be applied to medication adherence; 2) develop effective SMS messages to use in a JITAI; 3) examine the feasibility of and evaluate a JITAI with women using adjuvant hormonal therapy within an MRT to quantitatively optimise the intervention; and 4) investigate the most effective combination of messages and analyse how effects might differ according to context, timing or for different patient groups.