Pre-appointment online assessment of patient complexity: Towards a personalized model of neuropsychological assessment

Longitudinal Patterns of Engagement and Clinical Outcomes: Results From a Therapist-Supported Digital Mental Health Intervention

OBJECTIVE

Digital mental health interventions (DMHIs) are an effective treatment modality for common mental disorders like depression and anxiety; however, the role of intervention engagement as a longitudinal "dosing" factor is poorly understood in relation to clinical outcomes.

METHODS

We studied 4978 participants in a 12-week therapist-supported DMHI (June 2020-December 2021), applying a longitudinal agglomerative hierarchical cluster analysis to the number of days per week of intervention engagement. The proportion of people demonstrating remission in depression and anxiety symptoms during the intervention was calculated for each cluster. Multivariable logistic regression models were fit to examine associations between the engagement clusters and symptom remission, adjusting for demographic and clinical characteristics.

RESULTS

Based on clinical interpretability and stopping rules, four clusters were derived from the hierarchical cluster analysis (in descending order): a) sustained high engagers (45.0%), b) late disengagers (24.1%), c) early disengagers (22.5%), and d) immediate disengagers (8.4%). Bivariate and multivariate analyses supported a dose-response relationship between engagement and depression symptom remission, whereas the pattern was partially evident for anxiety symptom remission. In multivariable logistic regression models, older age groups, male participants, and Asians had increased odds of achieving depression and anxiety symptom remission, whereas higher odds of anxiety symptom remission were observed among gender-expansive individuals.

CONCLUSIONS

Segmentation based on the frequency of engagement performs well in discerning timing of intervention disengagement and a dose-response relationship with clinical outcomes. The findings among the demographic subpopulations indicate that therapist-supported DMHIs may be effective in addressing mental health problems among patients who disproportionately experience stigma and structural barriers to care. Machine learning models can enable precision care by delineating how heterogeneous patterns of engagement over time relate to clinical outcomes. This empirical identification may help clinicians personalize and optimize interventions to prevent premature disengagement.

Improved parent self-efficacy following pediatric evaluation: Evidence for value of a telemedicine approach in psychological and neuropsychological assessment

Objective: While considerable inquiry is currently underway into the comparability of psychological test results obtained in onsite/in-person settings versus telemedicine settings, there has been less attention given to the comparability of the impact/outcome of the assessment process across settings. The current quality improvement study conceptualized impact/outcome according to the model of Austin et al. and sought to determine whether the prior finding of increased parent self-efficacy following onsite neuropsychological assessment was also observed when psychological and neuropsychological assessment was conducted via a telemedicine modality. Method: In the course of standard care delivery, ratings from Austin et al.'s four parent self-efficacy items were obtained at time 1 prior to patients' assessment visits and then again at time 2 either (1) following their last assessment/feedback visit (the Complete Assessment group; n = 157) or (2) in the middle of the assessment process prior to the last planned visit (the Incomplete Assessment group; n = 117). Results: Analyses revealed significant findings for time and time × group. Parent self-efficacy ratings improved over time in both groups, with significantly higher ratings in the Complete Assessment group at time 2. When compared to reference means from the in-person/onsite Austin et al. study, ratings from the current study found comparable improvement in parent self-efficacy achieved via telemedicine assessment in the Complete Assessment group. Conclusions: These data support the use of telemedicine based psychological and neuropsychological evaluation and provide preliminary evidence that the impact/outcome is comparable with in-person/onsite assessment.

Sluggish cognitive tempo profiles in survivors of childhood cancer as compared to children with attention-deficit/hyperactivity disorder

PURPOSE

Neurocognitive late effects including problems with attention have been reported in pediatric oncology survivors. While some researchers have characterized these late effects as similar to symptoms of attention-deficit/hyperactivity disorder, inattentive presentation (ADHD-I), there remains some controversy as to whether these concerns in oncology patients are best conceptualized according to an ADHD-I or sluggish cognitive tempo (SCT) framework. The aim of this study was to describe SCT symptoms in children with ADHD-I or oncology diagnoses; identify groups of SCT symptoms among children with brain tumors (BT), acute lymphoblastic leukemia (ALL), or ADHD-I; and identify whether specific SCT profiles are associated with these diagnoses.

METHODS

The sample was comprised of 364 youth (146 BT, 149 ADHD-I, 69 ALL) referred for a neuropsychological evaluation at an academic medical center. Caregivers completed the SCT scale as part of the clinical evaluation.

RESULTS

Groups differed on mean scores for the SCT scales (Total, Sleepy/sluggish, Low initiation, and Daydreamy) by diagnosis (all p < 0.05), with the ADHD-I group having higher SCT symptoms on all scales. Latent profile analysis showed significant differences between latent SCT classes according to ADHD-I versus cancer diagnosis. The ADHD-I group was significantly more likely to be in the high SCT class compared to the oncology groups.

CONCLUSION

Findings add to the understanding of SCT symptoms in pediatric oncology survivors. There is utility in applying the SCT framework to the oncology population; however, pediatric survivors are likely to be rated differently than youth with ADHD-I. Implications and future directions are discussed.

University Students and Their Ability to Perform Self-Regulated Online Learning Under the COVID-19 Pandemic

The COVID-19 pandemic has affected all aspects of the educational system, including students' learning styles, which are heavily dependent on self-regulated studying strategies and motivation. The purpose of this study was to discover whether Central European students, in this case the Slovak and Czech students, were able to perform self-regulated learning during online learning under the COVID-19 pandemic to achieve their learning goals and improve academic performance, as well as to propose a few practical recommendations how to develop and maintain students' self-regulation learning in this new online environment. The methodology was based on a questionnaire survey conducted among 268 students at two Central European universities in February and March 2021. The findings indicate that Central European students seemed to be able to perform their online self-study, especially in regard to personal competencies, meaningfulness and motivation. They reported higher awareness of their strengths and weaknesses in learning, time management, and/or the usefulness of making an effort to study. However, the findings reveal an urgent need for more work to be done in the area of metacognitive strategies, such as reflective and critical thinking, analyzing and evaluating. In this respect, the teacher's role is replaceable since s/he serves as a facilitator and promotes these metacognitive strategies by providing students with constructive feedback, monitoring their learning, reviewing their progress, and/or providing opportunities to reflect on their learning. There were not any striking differences between the Czech and Slovak students. Nevertheless, Slovak students (females in particular) seemed to be more self-disciplined and goal-oriented in their learning.