Parallel session 2A: Methods in public health research
Robin Groen - For whom do intensive longitudinal studies work? Participation and compliance in a six-month daily diary study among individuals at risk for mental health problems
Intensive longitudinal (IL) measurement, which involves prolonged self- monitoring, is increasingly used as a method to collect data across health disciplines. The repeated measurement of individuals enables obtaining fine-grained data on within-individual processes, but also places higher burden on participants. This raises the question who will take part in and successfully complete such measurements. This study investigated which demographic, personality, economic, social, psychological, or physical participant characteristics are associated with (i) participation and (ii) compliance in an IL study conducted in individuals at-risk for developing psychopathology.
Young adults enrolled in the clinical cohort of the tracking adolescents’ individual Lives Survey (TRAILS) were invited to a six-month daily diary study. Participant characteristics came from five earlier TRAILS assessment waves collected from age 11 onwards. To evaluate participation, we compared diary study participants (N=134) to (i) non-participants (N=309) and (ii) a sex-matched subsample (N=1926) of individuals from the general population cohort of TRAILS. To evaluate compliance, we analyzed which characteristics were related to the proportion of completed diary entries.
Participants (mean age 23.6 years; SD=0.67) were largely similar to non- participants. However, compared to the general population, participants reported less advantageous scores on nearly all characteristics. Internalizing problems predicted higher compliance. Externalizing problems, antisocial behavior and daily smoking predicted lower compliance.
In youth at-risk for psychopathology, who were disadvantaged relative to the general population on nearly every measure, participation in a diary study is unbiased. Small biases in compliance occur, of which researchers should be aware. In conclusion, IL measurement is broadly applicable, which is a requirement for its usefulness as method to collect data for research questions that concern within-individual processes and have relevance for the wider population.
Keywords: Intensive longitudinal designs; selection bias and compliance; psychopathology
Nanda Kleinenberg-Talsma - Prevalence of frailty in The Netherlands: developing a Frailty Index based on the Public Health Monitor
The proportion of older adults in the Dutch population is increasing, and will continue to increase in the coming years (Van den Broek et al., 2016; Doekhie et al., 2014). This is caused by both an increase in life expectancy, as well as the aging of the so-called ‘babyboom generation’ that was born just after World War 2 (Van den Broek et al., 2016). With these developments, the proportion of frail older adults in the population also increases (Hoogendijk et al., 2019; Bock et al., 2016; RIVM, 2018), which is posing a great challenge on public health care (Cesari et al., 2016). To focus on prevention of frailty, it is necessary to have a good understanding of the prevalence of frailty in older adults (Liotta et. al., 2018; O ‘Caoimh et. al., 2018). In this study we intend to create a Frailty Index (FI), using data from the Public Health Monitor 2016, and to investigate the psychometric properties of the index using Item Response Theory (IRT).
to develop a Frailty Index with good psychometric properties based on the Public Health Monitor, to measure and monitor frailty levels among older adults in The Netherlands.
Items are selected and recoded according to the procedure as described previously (Rockwood et al., 2007; Searle et al., 2008). Psychometric properties will be investigated by using IRT for polytomous response categories: the Generalized Partial Credit Model (GPCM) and the Graded Response Model (GRM) will be fitted to the data. Model fit will be checked with, amongst others, Cronbach’s Alpha, Factor Analysis, and Point Polyserial Correlations.
The GRM seems to have a good fit with the data. Some items, however, do not seem to fit the data as well as others.
Although a few items fit the data slightly less, there are substantive reasons to include them in the FI.
Key words: Frailty Index, psychometric properties, older adults
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