Decision‐making styles and mental health—A person‐oriented approach through clustering

Machine learning identifies a COVID-19-specific phenotype in university students using a mental health app

Background

Advances in smartphone technology have allowed people to access mental healthcare via digital apps from wherever and whenever they choose. University students experience a high burden of mental health concerns. Although these apps improve mental health symptoms, user engagement has remained low. Studies have shown that users can be subgrouped based on unique characteristics that just-in-time adaptive interventions (JITAIs) can use to improve engagement. To date, however, no studies have examined the effect of the COVID-19 pandemic on these subgroups.

Objective

Here, we sought to examine user subgroup characteristics across three COVID-19-specific timepoints: during lockdown, immediately following lockdown, and three months after lockdown ended.

Methods

To do this, we used a two-step machine learning approach combining unsupervised and supervised machine learning.

Results

We demonstrate that there are three unique subgroups of university students who access mental health apps. Two of these, with either higher or lower mental well-being, were defined by characteristics that were stable across COVID-19 timepoints. The third, situational well-being, had characteristics that were timepoint-dependent, suggesting that they are highly influenced by traumatic stressors and stressful situations. This subgroup also showed feelings and behaviours consistent with burnout.

Conclusions

Overall, our findings clearly suggest that user subgroups are unique: they have different characteristics and therefore likely have different mental healthcare goals. Our findings also highlight the importance of including questions and additional interventions targeting traumatic stress(ors), reason(s) for use, and burnout in JITAI-style mental health apps to improve engagement.

Decision-Making Styles as a Moderator on the Efficacy of the StaySafe Tablet Intervention

Background: People with substance use disorders often differ in their decision-making styles. The present study addressed the impact of two decision-making styles (rational and dependent) on outcomes from a StaySafe tablet computer app intervention designed to improve decision-making around health risk behaviors and previously found to be effective for justice-involved people receiving treatment for a substance use disorder and under community supervision. Objectives: Participants were justice-involved residents in residential treatment. After completing a baseline survey, participants were randomly assigned to either complete the StaySafe app or to a standard procedure condition; and then asked to complete a post-intervention survey three months after baseline (this protocol has been registered with clinicaltrials.gov NCT02777086): 348 participants completed a baseline survey and 238 completed the post-test survey. Outcomes included measures of confidence and motivation around HIV knowledge and risks and getting tested. Multilevel analyses addressed the hypothesis that outcomes were related to decision-making style. Multiple imputation (MI) was used to address the effects of missing data. Results: StaySafe was more effective for those in the lower half of the decision-making dependent scale for HIV risks (HIV-Knowledge, Hepatitis testing, HIV Services testing, and Sex Risk, as well as motivation for treatment. The decision-making rational scale was less consistently related to HIV risk. Conclusions: The present study showed individuals with substance use disorders who differed in their decision-making styles reacted differently to the StaySafe intervention. Two scales, rational decision making, and dependent decision making are relevant to consider with respect to interventions targeting improving decision making among drug users.

Intolerance of uncertainty and reactions to the COVID-19 pandemic

The COVID-19 pandemic has presented a threat to mental health worldwide. The current study aims to investigate the role of intolerance of uncertainty in cognitive, emotional and behavioral reactions to this pandemic and propose a path model of these reactions. In the first two months of the COVID-19 pandemic in Slovakia, participants in a general sample (n = 1,011) as well as an older adult sample (n = 655) completed measures regarding intolerance of uncertainty, mental health (anxiety, well-being, perceived stress) and adherence to preventive measures. Two rounds of data collection were carried out in the first sample. Intolerance of uncertainty was found to be related to mental health indicators and the structural equation model showed a direct and indirect effect on them as well as on the adherence to preventive measures. However, the comparison of data from different time points has brought inconsistent results. The findings highlight the role of intolerance of uncertainty in reaction to threat and indicate the potential of uncertainty reduction e.g., getting clear messages from authorities, as a way of decreasing mental health problems.