Comparison of the Utility of Two Assessments for Explaining and Predicting Productivity Change: Well-Being Versus an HRA

Predicting wellbeing over one year using sociodemographic factors, personality, health behaviours, cognition, and life events

Various sociodemographic, psychosocial, cognitive, and life event factors are associated with mental wellbeing; however, it remains unclear which measures best explain variance in wellbeing in the context of related variables. This study uses data from 1017 healthy adults from the TWIN-E study of wellbeing to evaluate the sociodemographic, psychosocial, cognitive, and life event predictors of wellbeing using cross-sectional and repeated measures multiple regression models over one year. Sociodemographic (age, sex, education), psychosocial (personality, health behaviours, and lifestyle), emotion and cognitive processing, and life event (recent positive and negative life events) variables were considered. The results showed that while neuroticism, extraversion, conscientiousness, and cognitive reappraisal were the strongest predictors of wellbeing in the cross-sectional model, while extraversion, conscientiousness, exercise, and specific life events (work related and traumatic life events) were the strongest predictors of wellbeing in the repeated measures model. These results were confirmed using tenfold cross-validation procedures. Together, the results indicate that the variables that best explain differences in wellbeing between individuals at baseline can vary from the variables that predict change in wellbeing over time. This suggests that different variables may need to be targeted to improve population-level compared to individual-level wellbeing.

Six-Week Online Multi-component Positive Psychology Intervention Improves Subjective Wellbeing in Young Adults

Improving mental wellbeing has a range of benefits for society, including increased productivity, longevity, and resiliency. However, interventions designed to improve mental wellbeing are often only compared to waitlist controls, leaving uncertainty regarding the mechanisms of their effectiveness. The current study in 326 participants assessed a six-week positive psychology intervention against an active control (n = 163) in an online randomized control trial. Outcome measures included life satisfaction, wellbeing (subjective and psychological wellbeing), stress, depression and anxiety symptoms, and self-compassion. The potential moderating effect of participating during the ongoing COVID-19 pandemic was also explored. The intervention group showed greater improvements in life satisfaction by week six (β = 0.18, p = .014) and were maintained through to 7 weeks post-baseline (β = 0.23, t = 3.07, p = .002) and remained significant when accounting for COVID-19 restrictions. An improvement in composite wellbeing from baseline to 7 weeks post-baseline was detected when accounting for COVID-19 restrictions. Composite wellbeing and total depression and anxiety symptoms improved significantly more in the intervention group for participants with low baseline resiliency resources. These findings support the efficacy of using online multi-component positive psychology interventions in boosting wellbeing and reducing distress symptoms particularly in individuals with fewer resiliency resources who may need added support.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10902-021-00449-3.

What's Mine is Yours: Evaluation of Shared Well-Being Among Married Couples and the Dyadic Influence on Individual Well-Being Change

OBJECTIVE

To evaluate the relationship between partner well-being and outcomes of chronically diseased individuals participating in an employer sponsored well-being improvement program.

METHODS

Using the Actor Partner Interdependence Model, we evaluated whether prior partner well-being was associated with well-being change among 2025 couples. Logistic regression models were then used to explore how spousal well-being risks relate to development and elimination of risks among program participants.

RESULTS

High well-being partners were associated with positive well-being change. Specifically, the partner effect for spouses' high well-being on disease management participants was a 1.5 point higher well-being in the following time period (P = 0.001) while the partner effect of participants' high well-being on spouses was nearly 1.1 points (P = 0.010).

CONCLUSIONS

Well-being within couples is interdependent, and partner well-being is an important predictor of individual well-being change.