Time to get personal? The impact of researchers choices on the selection of treatment targets using the experience sampling methodology:The impact of researchers choices on the selection of treatment targets using the experience sampling methodology

OBJECTIVE: One of the promises of the experience sampling methodology (ESM) is that a statistical analysis of an individual’s emotions, cognitions and behaviors in everyday-life could be used to identify relevant treatment targets. A requisite for clinical implementation is that outcomes of such person-specific time-series analyses are not wholly contingent on the researcher performing them. METHODS: To evaluate this, we crowdsourced the analysis of one individual patient’s ESM data to 12 prominent research teams, asking them what symptom(s) they would advise the treating clinician to target in subsequent treatment. RESULTS: Variation was evident at different stages of the analysis, from preprocessing steps (e.g., variable selection, clustering, handling of missing data) to the type of statistics and rationale for selecting targets. Most teams did include a type of vector autoregressive model, examining relations between symptoms over time. Although most teams were confident their selected targets would provide useful information to the clinician, not one recommendation was similar: both the number (0–16) and nature of selected targets varied widely. CONCLUSION: This study makes transparent that the selection of treatment targets based on personalized models using ESM data is currently highly conditional on subjective analytical choices and highlights key conceptual and methodological issues that need to be addressed in moving towards clinical implementation

Implicit and explicit COVID-19 associations and mental health in the United States: a large-scale examination and replication

Background: Given the sensitive nature of COVID-19 beliefs, evaluating them explicitly and implicitly may provide a fuller picture of how these beliefs vary based on identities and how they relate to mental health. Objective: Three novel brief implicit association tests (BIATs) were created and evaluated: two that measured COVID-19-as-dangerous (vs. safe) and one that measured COVID-19 precautions-as-necessary (vs. unnecessary). Implicit and explicit COVID-19 associations were examined based on individuals’ demographic characteristics. Implicit associations were hypothesized to uniquely contribute to individuals’ self-reports of mental health. Methods: Participants (N = 13,413 US residents; April-November 2020) were volunteers for a COVID-19 study. Participants completed one BIAT and self-report measures. This was a preregistered study with a planned internal replication. Results: Results revealed older age was weakly associated with stronger implicit and explicit associations of COVID-as-dangerous and precautions-as-necessary. Black and Asian individuals reported greater necessity of taking precautions than White individuals (with small-to-medium effects); greater education was associated with greater explicit reports of COVID-19-as-dangerous and precautions-as-necessary with small effects. Replicated relationships between COVID-as-dangerous explicit associations and mental health had very small effects. Conclusions: Implicit associations did not predict mental health but there was evidence that stronger COVID-19-as-dangerous explicit associations are weakly associated with worse mental health

Time to get personal? The impact of researchers choices on the selection of treatment targets using the experience sampling methodology

Objective: One of the promises of the experience sampling methodology (ESM) is that a statistical analysis of an individual's emotions, cognitions and behaviors in everyday-life could be used to identify relevant treatment targets. A requisite for clinical implementation is that outcomes of such person-specific time-series analyses are not wholly contingent on the researcher performing them. Methods: To evaluate this, we crowdsourced the analysis of one individual patient's ESM data to 12 prominent research teams, asking them what symptom(s) they would advise the treating clinician to target in subsequent treatment. Results: Variation was evident at different stages of the analysis, from preprocessing steps (e.g., variable selection, clustering, handling of missing data) to the type of statistics and rationale for selecting targets. Most teams did include a type of vector autoregressive model, examining relations between symptoms over time. Although most teams were confident their selected targets would provide useful information to the clinician, not one recommendation was similar: both the number (0–16) and nature of selected targets varied widely. Conclusion: This study makes transparent that the selection of treatment targets based on personalized models using ESM data is currently highly conditional on subjective analytical choices and highlights key conceptual and methodological issues that need to be addressed in moving towards clinical implementation