Psychometric Properties of the Parental Stress Scale in Swedish Parents of Children with and without Neurodevelopmental Conditions.

Background: Parents of children with neurodevelopmental conditions (NDC) are at risk of experiencing elevated levels of parental stress. Access to robust instruments to assess parental stress is important in both clinical and research contexts. Objective: We aimed to evaluate the psychometric properties of a Swedish version of the Parental Stress Scale (PSS), completed by parents of 3- to 17-year-old children, with and without NDCs. Method: Main analyses were conducted on data from three independent samples: a community sample (n=1018), a treatment-seeking sample of parents of children with various disabilities (n=653), and a sample of parents of children with Attention-Deficit/Hyperactivity Disorder (ADHD) who themselves reported varying ADHD symptom severities (n=562). Additional analyses were enabled by the use of data from a complementary test-retest sample (n=337). Results: The internal consistency of the PSS was good (Cronbach's alpha, α=.87) and its test-retest reliability moderate (ICC=.66). The scale correlated in the expected direction with related constructs (r=.50-.56 in the community sample). An exploratory factor analysis found its internal structure to reflect two aspects of parental stress: Lack of Parental Rewards and Role Satisfaction (factor 1, α=.90) and Parental Stressors and Distress (factor 2, α=.85). The treatment-seeking parents of children with disabilities reported higher parental stress than community reference parents (p<.001; Cohen's d=1.17). Moreover, we found that parents with high ADHD symptom severity reported higher parental stress than parents with low ADHD symptom severity (p<.001; d=0.39). Conclusion: In summary, we found evidence in support of the reliability and validity of the PSS, which overall was judged to be useful as a measure of parental stress in a Swedish context. In addition, our results underline the importance of considering parental stress and related needs in assessments and intervention planning involving families of children with NDCs.

Differentiating Patients at the Memory Clinic With Simple Reaction Time Variables: A Predictive Modeling Approach Using Support Vector Machines and Bayesian Optimization.

Background: Mild Cognitive Impairment (MCI) and dementia differ in important ways yet share a future of increased prevalence. Separating these conditions from each other, and from Subjective Cognitive Impairment (SCI), is important for clinical prognoses and treatment, socio-legal interventions, and family adjustments. With costly clinical investigations and an aging population comes a need for more cost-efficient differential diagnostics. Methods: Using supervised machine learning, we investigated nine variables extracted from simple reaction time (SRT) data with respect to their single and conjoined ability to discriminate both MCI/dementia, and SCI/MCI/dementia, compared to-and together with-established psychometric tests. One-hundred-twenty elderly patients (age range = 65-95 years) were recruited when referred to full neuropsychological assessment at a specialized memory clinic in urban Sweden. A freely available SRT task served as index test and was administered and scored objectively by a computer before diagnosis of SCI (n = 17), MCI (n = 53), or dementia (n = 50). As reference standard, diagnosis was decided through the multidisciplinary memory clinic investigation. Bonferroni-Holm corrected P-values for constructed models against the null model are provided. Results: Algorithmic feature selection for the two final multivariable models was performed through recursive feature elimination with 3 × 10-fold cross-validation resampling. For both models, this procedure selected seven predictors of which five were SRT variables. When used as input for a soft-margin, radial-basis support vector machine model tuned via Bayesian optimization, the leave-one-out cross-validated accuracy of the final model for MCI/dementia classification was good (Accuracy = 0.806 [0.716, INS [0].877], P < 0.001) and the final model for SCI/MCI/dementia classification held some merit (Accuracy = 0.650 [0.558, 0.735], P < 0.001). These two models are implemented in a freely available application for research and educatory use. Conclusions: Simple reaction time variables hold some potential in conjunction with established psychometric tests for differentiating MCI/dementia, and SCI/MCI/dementia in these difficult-to-differentiate memory clinic patients. While external validation is needed, their implementation within diagnostic support systems is promising.