How Study Environments Foster Academic Procrastination: Overview and Recommendations.

Procrastination is common among students, with prevalence estimates double or even triple those of the working population. This inflated prevalence indicates that the academic environment may appear as "procrastination friendly" to students. In the present paper, we identify social, cultural, organizational, and contextual factors that may foster or facilitate procrastination (such as large degree of freedom in the study situation, long deadlines, and temptations and distractions), document their research basis, and provide recommendations for changes in these factors to reduce and prevent procrastination. We argue that increased attention to such procrastination-friendly factors in academic environments is important and that relatively minor measures to reduce their detrimental effects may have substantial benefits for students, institutions, and society.

Does the EQ-5D usual activities dimension measure what it intends to measure? The relative importance of work, study, housework, family or leisure activities.

BACKGROUND: The EQ-5D is the most widely used generic preference-based health-related quality of life measure. It comprises five dimensions: mobility, self-care, usual activities, pain/discomfort and anxiety/depression. The usual activities dimension asks respondents to evaluate the severity of problems in their usual activities, such as work, study, housework, family or leisure activities. The primary aim of this study is to investigate whether the EQ-5D (five-level) usual activities dimension captures those activities that it intends to capture. We further assess the relative importance of each of these activities for the usual activities dimension. METHODS: Data include 7933 respondents from six countries: Australia, Canada, Germany, Norway, the UK, and the US. Logistic regression and ordinary least square regression models investigate the relationship between the usual activities dimension and its main predictors (work/study, housework, family, and leisure activities). A Shapley value decomposition method was applied to measure the relative importance of each predictor. RESULTS: Work/study, housework, family, and leisure activities were all significant (p < 0.001) determinants of usual activities dimension. The respective marginal contribution (in %) of housework, leisure, work/study and family to UA dimension (as a share of goodness-of-fit) is 28.0, 26.2, 20.8 and 14.7 in the logistic regression model. This finding is consistent when linear regression is used as an alternative model. CONCLUSIONS: The usual activities dimension in EQ-5D reflects the specific activities described to respondents. Therefore, the usual activities dimension measures what it really intends to measure.

Procrastination and Personal Finances: Exploring the Roles of Planning and Financial Self-Efficacy.

Procrastination is related to unhealthy personal financial behaviors, such as postponing retirement savings, last minute shopping, and not paying bills on time. The present paper explores factors that could explain why procrastinators demonstrate more financial problems compared to non-procrastinators. Study 1 (N = 675) focused on planning, as both procrastination and poor financial habits are negatively related to planning. Results confirmed that procrastination was a significant predictor of personal finances, but the propensity to plan was not. Study 2 (N = 500) explored the roles of procrastination and financial self-efficacy in two facets of financial behavior, financial impulsivity and financial planning. Results indicated that the effect of procrastination on financial behavior was fully mediated by financial self-efficacy. Hence, these results suggest that procrastination operates primarily through its self-efficacy component to impact financial behavior negatively.

Exploring the causal and effect nature of EQ-5D dimensions: an application of confirmatory tetrad analysis and confirmatory factor analysis.

BACKGROUND: The relationship between the various items in an HRQoL instrument is a key aspect of interpreting and understanding preference weights. The aims of this paper were i) to use theoretical models of HRQoL to develop a conceptual framework for causal and effect relationships among the five dimensions of the EQ-5D instrument, and ii) to empirically test this framework. METHODS: A conceptual framework depicts the symptom dimensions [Pain/discomfort (PD) and Anxiety/depression (AD)] as causal indicators that drive a change in the effect indicators of activity/participation [Mobility (MO), Self-care (SC) and Usual activities (UA)], where MO has an intermediate position between PD and the other two effect dimensions (SC and UA). Confirmatory tetrad analysis (CTA) and confirmatory factor analysis (CFA) were used to test this framework using EQ-5D-5L data from 7933 respondents in six countries, classified as healthy (n = 1760) or in one of seven disease groups (n = 6173). RESULTS: CTA revealed the best fit for a model specifying SC and UA as effect indicators and PD, AD and MO as causal indicators. This was supported by CFA, revealing a satisfactory fit to the data: CFI = 0.992, TLI = 0.972, RMSEA = 0.075 (90% CI 0.062-0.088), and SRMR = 0.012. CONCLUSIONS: The EQ-5D appears to include both causal indicators (PD and AD) and effect indicators (SC and UA). Mobility played an intermediate role in our conceptual framework, being a cause of problems with Self-care and Usual activities, but also an effect of Pain/discomfort. However, the empirical analyses of our data suggest that Mobility is mostly a causal indicator.

Assessment of outcome measures for cost-utility analysis in depression: mapping depression scales onto the EQ-5D-5L.

BACKGROUND: Many clinical studies including mental health interventions do not use a health state utility instrument, which is essential for producing quality-adjusted life years. In the absence of such utility instrument, mapping algorithms can be applied to estimate utilities from a disease-specific instrument.AimsWe aim to develop mapping algorithms from two widely used depression scales; the Depression Anxiety Stress Scales (DASS-21) and the Kessler Psychological Distress Scale (K-10), onto the most widely used health state utility instrument, the EQ-5D-5L, using eight country-specific value sets. METHOD: A total of 917 respondents with self-reported depression were recruited to describe their health on the DASS-21 and the K-10 as well as the new five-level version of the EQ-5D, referred to as the EQ-5D-5L. Six regression models were used: ordinary least squares regression, generalised linear models, beta binomial regression, fractional logistic regression model, MM-estimation and censored least absolute deviation. Root mean square error, mean absolute error and r2 were used as model performance criteria to select the optimal mapping function for each country-specific value set. RESULTS: Fractional logistic regression model was generally preferred in predicting EQ-5D-5L utilities from both DASS-21 and K-10. The only exception was the Japanese value set, where the beta binomial regression performed best. CONCLUSIONS: Mapping algorithms can adequately predict EQ-5D-5L utilities from scores on DASS-21 and K-10. This enables disease-specific data from clinical trials to be applied for estimating outcomes in terms of quality-adjusted life years for use in economic evaluations.Declaration of interestNone.

Do country-specific preference weights matter in the choice of mapping algorithms? The case of mapping the Diabetes-39 onto eight country-specific EQ-5D-5L value sets.

PURPOSE: To develop mapping algorithms that transform Diabetes-39 (D-39) scores onto EQ-5D-5L utility values for each of eight recently published country-specific EQ-5D-5L value sets, and to compare mapping functions across the EQ-5D-5L value sets. METHODS: Data include 924 individuals with self-reported diabetes from six countries. The D-39 dimensions, age and gender were used as potential predictors for EQ-5D-5L utilities, which were scored using value sets from eight countries (England, Netherland, Spain, Canada, Uruguay, China, Japan and Korea). Ordinary least squares, generalised linear model, beta binomial regression, fractional regression, MM estimation and censored least absolute deviation were used to estimate the mapping algorithms. The optimal algorithm for each country-specific value set was primarily selected based on normalised root mean square error (NRMSE), normalised mean absolute error (NMAE) and adjusted-r2. Cross-validation with fivefold approach was conducted to test the generalizability of each model. RESULTS: The fractional regression model with loglog as a link function consistently performed best in all country-specific value sets. For instance, the NRMSE (0.1282) and NMAE (0.0914) were the lowest, while adjusted-r2 was the highest (52.5%) when the English value set was considered. Among D-39 dimensions, the energy and mobility was the only one that was consistently significant for all models. CONCLUSIONS: The D-39 can be mapped onto the EQ-5D-5L utilities with good predictive accuracy. The fractional regression model, which is appropriate for handling bounded outcomes, outperformed other candidate methods in all country-specific value sets. However, the regression coefficients differed reflecting preference heterogeneity across countries.

Preference Weighting of Health State Values: What Difference Does It Make, and Why?

BACKGROUND: Most patient-reported outcome measures apply a simple summary score to assess health-related quality of life, whereby equal weight is normally assigned to each item. In the generic preference-based instruments, utility weighting is essential whereby health state values are estimated through preference elicitation and complex algorithms. OBJECTIVES: To examine the extent to which preference-weighted value sets differ from unweighted values in the five-level EuroQol five-dimensional questionnaire and the 15D instrument, on the basis of a comprehensive data set from six member countries of the Organisation for Economic Co-operation and Development, each with a representative healthy sample and seven disease groups (N = 7933). METHODS: Construct validities were examined. The level of agreement between preference-weighted and unweighted values was also assessed using intraclass correlation coefficient (ICC), Bland-Altman plots, and reduced major axis regression. RESULTS: The performances of preference-weighted and unweighted measures were comparable with regard to convergent and known-group validities for each instrument. Although unweighted values in the five-level EuroQol five-dimensional questionnaire differ considerably from the preference-weighted values at the individual level, the discrepancy is minimal at the group level with a mean difference of 0.023. The ICC (0.96) and the Bland-Altman plot also suggest strong overall agreement. For the 15D, both the ICC (0.99) and the Bland-Altman plot revealed almost perfect agreement, with a negligible mean difference of -0.001. Results from the reduced major axis regression also showed small bias. CONCLUSIONS: Overall, preference weighting has minimal effect if the unweighted values are anchored on the same scale as the preference-weighted value sets.

Health state utility instruments compared: inquiring into nonlinearity across EQ-5D-5L, SF-6D, HUI-3 and 15D.

PURPOSE: Different health state utility (HSU) instruments produce different utilities for the same individuals, thereby compromising the intended comparability of economic evaluations of health care interventions. When developing crosswalks, previous studies have indicated nonlinear relationships. This paper inquires into the degree of nonlinearity across the four most widely used HSU-instruments and proposes exchange rates that differ depending on the severity levels of the health state utility scale. METHODS: Overall, 7933 respondents from six countries, 1760 in a non-diagnosed healthy group and 6173 in seven disease groups, reported their health states using four different instruments: EQ-5D-5L, SF-6D, HUI-3 and 15D. Quantile regressions investigate the degree of nonlinear relationships between these instruments. To compare the instruments across different disease severities, we split the health state utility scale into utility intervals with 0.2 successive decrements in utility starting from perfect health at 1.00. Exchange rates (ERs) are calculated as the mean utility difference between two utility intervals on one HSU-instrument divided by the difference in mean utility on another HSU-instrument. RESULTS: Quantile regressions reveal significant nonlinear relationships across all four HSU-instruments. The degrees of nonlinearities differ, with a maximum degree of difference in the coefficients along the health state utility scale of 3.34 when SF-6D is regressed on EQ-5D. At the lower end of the health state utility scale, the exchange rate from SF-6D to EQ-5D is 2.11, whilst at the upper end it is 0.38. CONCLUSION: Comparisons at different utility levels illustrate the fallacy of using linear functions as crosswalks between HSU-instruments. The existence of nonlinear relationships between different HSU-instruments suggests that level-specific exchange rates should be used when converting a change in utility on the instrument used, onto a corresponding utility change had another instrument been used. Accounting for nonlinearities will increase the validity of the comparison for decision makers when faced with a choice between interventions whose calculations of QALY gains have been based on different HSU-instruments.