164 Chapter 7 the wearable on and off, and 3) capture changes at very fast timescales, from second-to-second. This would complement the active diary data collection on slower timescales (e.g., day-to-day) very well, likely fine-graining real-life prediction of challenging behavioral incidents (de Looff et al., 2019; Olthof et al., 2020b). Conversely, using EMA as complementary to wearables may also help in addressing sensitivity and specificity issues that are inherent to physiological data (Peake et al., 2018). Third, the records that caretakers keep of clients – like the one we analyzed in Chapter 6 – are unexplored data goldmines that foster a wealth of contextualized information about change processes. Qualitative analysis like we did in Chapter 6 is time-consuming, making it unfeasible to repeat in many cases. Utilizing artificial intelligence (AI) through machine learning holds significant promise to analyze these data more efficiently. These models can process large amounts of text rapidly, extracting meaningful patterns and insights (Graham et al., 2019). Automating the analysis process with AI may not only reduce the workload for researchers and/or practitioners but also enhance understanding of client progress and contribute to treatment personalization. Fourth, qualitative methods are ideal to obtain insight into how people with a mild intellectual disability translate their experiences into a response on a Likert scale. For example, what makes someone on a specific day be “Not scared at all” and another day “Moderately scared”? Do they compare each day to their perceived average day, their ideal day, their previous EMA self-ratings, an extraordinary event, other people, or something else? Interviews could be conducted with participants and/ or their staff, during or directly after an EMA period, to unravel (individual differences in) response processes. It is important to note that clinical practice already inquires after this when EMA responses are discussed between the professional and the participant. However, it remains uncharted territory in the intellectual disability research field, possibly because the required level of self-insight is deemed too challenging for some people with a mild intellectual disability. I do expect that most of them are capable of sharing some insight on this matter. There is a story behind every answer on a survey and in my experience every participant is eager to share their story. Research should give them a voice to do so (cf. Truijens et al., 2019). The first exploratory studies show that the response processes of participants in EMA studies (without an intellectual disability) differ between people, with most people adopting combinations of strategies (e.g., comparing to the previous day and comparing to other people) in inconsistent ways over time (Schorrlepp et al., 2024). Now that EMA is becoming more common in the intellectual disability
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