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Making a correct genetically based diagnosis in patients with diseases associated with mitochondrial dysfunction can be challenging both genetically and clinically, as can further management of such patients on the basis of molecular-genetic data assessing the state of their mitochondria. In this opinion article, we propose a novel approach (which may result in a clinical protocol) to the use of a precise molecular-genetic tool in order to monitor the state of mitochondria (which reflects their function) during treatment of certain conditions, by means of not only signs and symptoms but also the molecular-genetic basis of the current condition. This is an example of application of personalized genomic medicine at the intersection of a person's mitochondrial genome information and clinical care. Advantages of the proposed approach are its relatively low cost (compared to various types of sequencing), an ability to use samples with a low input amount of genetic material, and rapidness. When this approach receives positive outside reviews and gets an approval of experts in the field (in terms of the standards), it may then be picked up by other developers and introduced into clinical practice.

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Computer adaptive tests have become popular assessments to screen students for academic risk. Research is emerging regarding their use as progress monitoring tools to measure response to instruction. We evaluated the accuracy of the trend-line decision rule when applied to outcomes from a frequently used reading computer adaptive test (i.e., Star Reading [SR]) and frequently used math computer adaptive test (i.e., Star Math [SM]). Analyses of extant SR and SM data were conducted to inform conditions for simulations to determine the number of assessments required to yield sufficient sensitivity (i.e., probability of recommending an instructional change when a change was warranted) and specificity (i.e., probability of recommending maintaining an intervention when a change was not warranted) when comparing performance to goal lines based upon a future target score (i.e., benchmark) as well as normative comparisons (50th and 75th percentiles). The extant dataset of SR outcomes consisted of monthly progress monitoring data from 993 Grade 3, 804 Grade 4, and 709 Grade 5 students from multiple states in the United States northwest. Data for SM were also drawn from the northwest and contained outcomes from 518 Grade 3, 474 Grade 4, and 391 Grade 5 students. Grade level samples were predominately White (range = 59.89%-67.72%) followed by Latinx (range = 9.65%-15.94%). Results of simulations suggest that when data were collected once a month, seven, eight, and nine observations were required to support low-stakes decisions with SR for Grades 3, 4, and 5, respectively. For SM, nine, ten, and eight observations were required for Grades, 3, 4, and 5, respectively. Given the length of time required to support reasonably accurate decisions, recommendations to consider other types of assessments and decision-making frameworks for academic progress monitoring are provided.

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Burnout is a syndrome characterized by mental and emotional fatigue or exhaustion, depersonalization, and a lessened sense of personal accomplishment and efficacy. Burnout leads to negative consequences for mental health clinicians and for mental health care organizations. Measurement-based care (MBC) is a clinical process in which clinicians and clients use patient-generated data, also called treatment feedback, to collaboratively monitor mental health care and to inform goal-setting and treatment planning. We propose that MBC may improve the experience of care for both clients and clinicians, and ultimately protect against each of the three components of burnout. When combined with other organizational changes, adoption of MBC may support organizational level efforts to reduce burnout in mental health services.

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Schools are a critical setting to promote healthy youth development through the provision of evidence-based programs (EBPs), yet preventive EBPs in schools are underutilized. The Exploration, Preparation, Implementation, Sustainment (EPIS) framework highlights numerous factors that may influence program adoption during the Exploration phase and progress monitoring during the Implementation phase. However, no research has systematically and simultaneously identified the factors that influence school administrators' decision-making during these important processes. We conducted semi-structured interviews with 24 school administrators in the Midwestern region of the U.S. to understand how they weigh various considerations that inform their adoption and progress monitoring of prevention programs. Results indicated that school administrators consider five separate factors during the adoption decision, prioritized in the following order: need for the program, school community buy-in, contextual fit, resources, and program characteristics (including the evidence-base). Further, administrators consider five indicators to monitor program performance, prioritized as follows: intervention fidelity, quantitative and qualitative data that determine if the identified need was met, school community buy-in, resource consumption, and program characteristics. Implications for prevention scientists and suggestions for future research are discussed.

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Data-based instruction (DBI) is a process in which teachers use progress data to make ongoing instructional decisions for students with learning disabilities. Curriculum-based measurement (CBM) is a common form of progress monitoring, and CBM data are placed on a graph to guide decision-making. Despite the central role that graph interpretation plays in the successful implementation of DBI, relatively little attention has been devoted to investigating this skill among special education teachers. In the present study, we examined the data decisions of 32 pre-service special education teachers (29 females and 3 males). Participants viewed data presented sequentially on CBM progress graphs and used a think-aloud procedure to explain their reasoning each time they indicated they would make instructional changes. We also asked participants to make the same type of decisions in response to static CBM progress graphs depicting 10 weeks of data. Overall, there was inconsistency in pre-service teachers' responses related to when or why they would make an instructional change. Decisions were often influenced by graph-related features, such as variability in the data. Furthermore, responses suggested misunderstandings that led to premature instructional change decisions and reliance on individual data points.

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This study investigated coded data retrieved from clinical dashboards, which are decision-support tools that include a graphical display of clinical progress and clinical activities. Data were extracted from clinical dashboards representing 256 youth (M age = 11.9) from 128 practitioners who were trained in the Managing and Adapting Practice (MAP) system (Chorpita & Daleiden in BF Chorpita EL Daleiden 2014 Structuring the collaboration of science and service in pursuit of a shared vision. 43(2):323 338. 2014, Chorpita & Daleiden in BF Chorpita EL Daleiden 2018 Coordinated strategic action: Aspiring to wisdom in mental health service systems. 25(4):e12264. 2018) in 55 agencies across 5 regional mental health systems. Practitioners labeled up to 35 fields (i.e., descriptions of clinical activities), with the options of drawing from a controlled vocabulary or writing in a client-specific activity. Practitioners then noted when certain activities occurred during the episode of care. Fields from the extracted data were coded and reliability was assessed for Field Type, Practice Element Type, Target Area, and Audience (e.g., Caregiver Psychoeducation: Anxiety would be coded as Field Type = Practice Element; Practice Element Type = Psychoeducation; Target Area = Anxiety; Audience = Caregiver). Coders demonstrated moderate to almost perfect interrater reliability. On average, practitioners recorded two activities per session, and clients had 10 unique activities across all their sessions. Results from multilevel models showed that clinical activity characteristics and sessions accounted for the most variance in the occurrence, recurrence, and co-occurrence of clinical activities, with relatively less variance accounted for by practitioners, clients, and regional systems. Findings are consistent with patterns of practice reported in other studies and suggest that clinical dashboards may be a useful source of clinical information. More generally, the use of a controlled vocabulary for clinical activities appears to increase the retrievability and actionability of healthcare information and thus sets the stage for advancing the utility of clinical documentation.

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Background: Globally, outpatient programs for substance use disorder (SUD) treatment have gained prominence. To assess the broader clinical implications of this trend we investigated shifts in functioning experienced by outpatients undergoing treatment. Methods: We describe the clinical characteristics of a cohort of 93 SUD patients in a Norwegian outpatient treatment clinic. Using paired-samples t-tests, we examined changes in perceived functioning, mental distress, and other clinically relevant outcome variables in a 5-month time interval during the treatment course. Results: We obtained follow-up data for 67 (72%) of the included patients, with no significant difference in patient-related factors between those who completed the treatment course and those who were not assessed at follow-up. Perceived functioning increased significantly from study inclusion (Time 0) (mean 19.8, standard deviation ± 8.8) to its conclusion (Time 1) (24.3, ±9.3; t (66) = 4.5, (95% CI: 2.5-6.5, p < 0.001). We also identified significant improvement in most other measured variables, including mental distress, self-reported sleep quality, restlessness, and obsessive thinking. Substance use-related variables showed a modest, non-significant improvement at T1. Conclusion: During a 5-month course of outpatient treatment, patients' subjective experience of functioning improved significantly. Those with the lowest functioning levels at T0 improved the most. Structured monitoring may be a valuable clinical tool for personalizing intervention, enhancing treatment outcomes, and supporting the clinical decision-making process.

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Curriculum-based measurement (CBM) is an approach to measuring student academic growth and evaluating the effectiveness of instruction (Deno, Exceptional Children, 52, 219-232, 1985) that was developed, in part, based on characteristics of applied behavior analysis. Learning to administer and use CBM data is commonly part of teacher preparation programs, but less common in behavior analysis graduate programs (Schreck et al. Behavioral Interventions, 31, 355-376, 2016; Schreck & Mazur, Behavioral Interventions, 23, 201-212, 2008). This article describes a sequence of steps that educational teams can follow to use CBM within the multi-tiered system of support (MTSS) framework. These steps include (1) selecting a CBM publisher and gathering materials; (2) practicing administering and scoring CBM; (3) administering, scoring, and comparing student scores to grade-level benchmarks; (4) using CBM data to write ambitious and realistic IEP goals; and (5) using data-based individualization. Each step is described and includes a description of a case study that is based on our experiences working with pre-service teacher candidates, and special education and behavior analysis graduate students in K-12 and after-school instructional programs.

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BACKGROUND AND AIMS: Clear and effective indicators for early detection of severe coronavirus disease 2019 (COVID-19) are insufficient. We investigated the clinical value of the plasma SARS-CoV-2 N antigen (plasma N antigen) for severe COVID-19 early identification and disease progression monitoring. MATERIALS AND METHODS: A cross-sectional study compared the diagnostic value of plasma N antigen levels detected within two days after hospital admission in 957 patients with COVID-19 during the BA2.2 outbreak in Shanghai (April 6-June 15, 2022). A follow-up study analyzed the plasma N antigen prognostic value in 274 non-severe patients, and a longitudinal study evaluated its continuous monitoring value in 16 patients with COVID-19 grade changes. RESULTS: Plasma N antigen concentrations were significantly higher in severely ill than in non-severely ill patients. The plasma N antigen was superior to nasopharyngeal nucleic acid CT values and established COVID-19 blood biomarkers in identifying severe COVID-19. Patients with high plasma N-antigen concentrations at initial admission were more prone to developing severe COVID-19. The changes in plasma N antigen concentrations were consistent with disease progression. Two logistic regression models, including and excluding plasma N antigen, were established, with model 1 (including plasma N antigen) (AUC = 0.971, 0.958-0.980) yielding a better diagnostic value for severe COVID-19 than Model 2 (plasma N antigen excluded). CONCLUSION: The plasma N antigen is superior to nasopharyngeal nucleic acids and established COVID-19 blood biomarkers for severe COVID-19 early recognition and progression monitoring, enabling the most accurate patient triaging and efficient utilization of medical resources.

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BACKGROUND: As much as 80% of children on the autism spectrum exhibit challenging behaviors (ie, behaviors dangerous to the self or others, behaviors that interfere with learning and development, and behaviors that interfere with socialization) that can have a devastating impact on personal and family well-being, contribute to teacher burnout, and even require hospitalization. Evidence-based practices to reduce these behaviors emphasize identifying triggers (events or antecedents that lead to challenging behaviors); however, parents and teachers often report that challenging behaviors surface with little warning. Exciting recent advances in biometric sensing and mobile computing technology allow the measurement of momentary emotion dysregulation using physiological indexes. OBJECTIVE: We present the framework and protocol for a pilot trial that will test a mobile digital mental health app, the KeepCalm app. School-based approaches to managing challenging behaviors in children on the autism spectrum are limited by 3 key factors: children on the autism spectrum often have difficulties in communicating their emotions; it is challenging to implement evidence-based, personalized strategies for individual children in group settings; and it is difficult for teachers to track which strategies are successful for each child. KeepCalm aims to address those barriers by communicating children's stress to their teachers using physiological signaling (emotion dysregulation detection), supporting the implementation of emotion regulation strategies via smartphone pop-up notifications of top strategies for each child according to their behavior (emotion regulation strategy implementation), and easing the task of tracking outcomes by providing the child's educational team with a tool to track the most effective emotion regulation strategies for that child based on physiological stress reduction data (emotion regulation strategy evaluation). METHODS: We will test KeepCalm with 20 educational teams of students on the autism spectrum with challenging behaviors (no exclusion based on IQ or speaking ability) in a pilot randomized waitlist-controlled field trial over a 3-month period. We will examine the usability, acceptability, feasibility, and appropriateness of KeepCalm as primary outcomes. Secondary preliminary efficacy outcomes include clinical decision support success, false positives or false negatives of stress alerts, and the reduction of challenging behaviors and emotion dysregulation. We will also examine technical outcomes, including the number of artifacts and the proportion of time children are engaged in high physical movement based on accelerometry data; test the feasibility of our recruitment strategies; and test the response rate and sensitivity to change of our measures, in preparation for a future fully powered large-scale randomized controlled trial. RESULTS: The pilot trial will begin by September 2023. CONCLUSIONS: Results will provide key data about important aspects of implementing KeepCalm in preschools and elementary schools and will provide preliminary data about its efficacy to reduce challenging behaviors and support emotion regulation in children on the autism spectrum. TRIAL REGISTRATION: ClinicalTrials.gov NCT05277194; https://www.clinicaltrials.gov/ct2/show/NCT05277194. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/45852.

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Utilisation of intensive inpatient treatment for eating disorders (EDs) has climbed in the last decade, illuminating a need for better consensus on what constitutes effective treatment and context-appropriate progress/outcome monitoring during residential stays. The novel Progress Monitoring Tool for Eating Disorders (PMED) measure is specifically designed for inpatient settings. Previous research supports the factorial validity and internal consistency of the PMED; however, additional work is needed to determine its appropriateness for complex patient populations. This study used measurement invariance (MI) testing to determine if the PMED administered at programme admission measures the same items in similar ways across patients with anorexia nervosa restricting- and binge-purge subtypes (AN-R; AN-BP) and bulimia nervosa (BN, N = 1121; Mage  = 24.33 years, SD = 10.20; 100% female). Progressively constrained models were used to determine the level of invariance upheld between the three groups. Results indicated that, while the PMED meets configural and metric MI, it does not display scalar invariance. Said otherwise, the PMED similarly assesses constructs and items across AN-R, AN-BP, and BN, however the same score overall may reflect different levels of psychopathology for patients in one diagnostic category versus another. Comparisons of severity between different EDs should be made with caution, however the PMED appears to be a sound tool for understanding the baseline functioning of patients with EDs in an inpatient setting.

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BACKGROUND: To tackle noncommunicable disease (NCD) burden globally, two sets of NCD surveillance indicators were established by the World Health Organization: 25 Global Monitoring Framework (GMF) indicators and 10 Progress Monitoring Indicators (PMI). This study aims to assess the data availability of these two sets of indicators in six ASEAN countries: Cambodia, Lao PDR, Malaysia, Myanmar, Thailand, and Vietnam. METHODS: As data on policy indicators were straightforward and fully available, we focused on studying 25 non-policy indicators: 23 GMFs and 2 PMIs. Gathering data availability of the target indicators was conducted among NCD surveillance experts from the six selected countries during May-June 2020. Our research team found information regarding whether the country had no data at all, was using WHO estimates, was providing 'expert judgement' for the data, or had actual data available for each target indicator. We triangulated their answers with several WHO data sources, including the WHO Health Observatory Database and various WHO Global Reports on health behaviours (tobacco, alcohol, diet, and physical activity) and NCDs. We calculated the percentages of the indicators that need improvement by both indicator category and country. RESULTS: For all six studied countries, the health-service indicators, based on responses to the facility survey, are the most lacking in data availability (100% of this category's indicators), followed by the health-service indicators, based on the population survey responses (57%), the mortality and morbidity indicators (50%), the behavioural risk indicators (30%), and the biological risk indicators (7%). The countries that need to improve their NCD surveillance data availability the most are Cambodia (56% of all indicators) and Lao PDR (56%), followed by Malaysia (36%), Vietnam (36%), Myanmar (32%), and Thailand (28%). CONCLUSION: Some of the non-policy GMF and PMI indicators lacked data among the six studied countries. To achieve the global NCDs targets, in the long run, the six countries should collect their own data for all indicators and begin to invest in and implement the facility survey and the population survey to track NCDs-related health services improvements once they have implemented the behavioural and biological Health Risks Population Survey in their countries.

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The purpose of this study was to measure and describe students' learning development in mental computation of mixed addition and subtraction tasks up to 100. We used a learning progress monitoring (LPM) approach with multiple repeated measurements to examine the learning curves of second-and third-grade primary school students in mental computation over a period of 17 biweekly measurement intervals in the school year 2020/2021. Moreover, we investigated how homogeneous students' learning curves were and how sociodemographic variables (gender, grade level, the assignment of special educational needs) affected students' learning growth. Therefore, 348 German students from six schools and 20 classes (10.9% students with special educational needs) worked on systematically, but randomly mixed addition and subtraction tasks at regular intervals with an online LPM tool. We collected learning progress data for 12 measurement intervals during the survey period that was impacted by the COVID-19 pandemic. Technical results show that the employed LPM tool for mental computation met the criteria of LPM research stages 1 and 2. Focusing on the learning curves, results from latent growth curve modeling showed significant differences in the intercept and in the slope based on the background variables. The results illustrate that one-size-fits-all instruction is not appropriate, thus highlighting the value of LPM or other means that allow individualized, adaptive teaching. The study provides a first quantitative overview over the learning curves for mental computation in second and third grade. Furthermore, it offers a validated tool for the empirical analysis of learning curves regarding mental computation and strong reference data against which individual learning growth can be compared to identify students with unfavorable learning curves and provide targeted support as part of an adaptive, evidence-based teaching approach. Implications for further research and school practice are discussed.

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Language sample analysis (LSA) is an important practice for providing a culturally sensitive and accurate assessment of a child's language abilities. A child's usage of literate language devices in narrative samples has been shown to be a critical target for evaluation. While automated scoring systems have begun to appear in the field, no such system exists for conducting progress-monitoring on literate language usage within narratives. The current study aimed to develop a hard-coded scoring system called the Literate Language Use in Narrative Assessment (LLUNA), to automatically evaluate six aspects of literate language in non-coded narrative transcripts. LLUNA was designed to individually score six literate language elements (e.g., coordinating and subordinating conjunctions, meta-linguistic and meta-cognitive verbs, adverbs, and elaborated noun phrases). The interrater reliability of LLUNA with an expert scorer, as well as its' reliability compared to certified undergraduate scorers was calculated using a quadratic weighted kappa (K qw ). Results indicated that LLUNA met strong levels of interrater reliability with an expert scorer on all six elements. LLUNA also surpassed the reliability levels of certified, but non-expert scorers on four of the six elements and came close to matching reliability levels on the remaining two. LLUNA shows promise as means for automating the scoring of literate language in LSA and narrative samples for the purpose of assessment and progress-monitoring.

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The necessity for automatic monitoring tools led to using 3D sensing technologies to collect accurate and precise data onsite to create an as-built model. This as-built model can be integrated with a BIM-based planned model to check the project's status based on algorithms. This article investigates the construction progress monitoring (CPM) domain, including knowledge gaps and future research direction. Synthesis literature was conducted on 3D sensing technologies in CPM depending on crucial factors, including the scanning environment, assessment level, and object recognition indicators' performance. The scanning environment is important to determine the volume of data acquired and the applications conducted in the environment. The level of assessment between as-planned and as-built models is another crucial factor that could precisely help define the knowledge gaps in this domain. The performance of object recognition indicators is an essential factor in determining the quality of studies. Qualitative and statistical analyses for the latest studies are then conducted. The qualitative analysis showed a shortage of articles performed on 5D assessment. Then, statistical analysis is conducted using a meta-analytic regression model to determine the development of the performance of object recognition indicators. The meta-analytic model presented a good sign that the performance of those indicators is effective where [p-value is = 0.0003 < 0.05]. The study is also envisaged to evaluate the collected studies in prioritizing future works from the limitations within these studies. Finally, this is the first study to address ranking studies of 3D sensing technologies in the CPM domain integrated with BIM.

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One of the earliest indicators of autism spectrum disorder (ASD) is delay in language and social communication. Despite consensus on the benefits of earlier diagnosis and intervention, our understanding of the language growth of children with ASD during the first years of life remains limited. Therefore, this study compared communication growth patterns of infants and toddlers with ASD to growth benchmarks of a standardized language assessment. We conducted a retrospective analysis of growth on the Early Communication Indicator (ECI) of 23 infants and toddlers who received an ASD diagnosis in the future. At 42 months of age, children with ASD had significantly lower rates of gestures, single words, and multiple words, but significantly higher rates of nonword vocalizations. Children with ASD had significantly slower growth of single and multiple words, but their rate of vocalization growth was significantly greater than benchmark. Although more research is needed with larger samples, because the ECI was designed for practitioners to monitor children's response to intervention over time, these findings show promise for the ECI's use as a progress monitoring measure for young children with ASD. Limitations and the need for future research are discussed.

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Monitoring the progress of student learning is an important part of teachers' data-based decision making. One such tool that can equip teachers with information about students' learning progress throughout the school year and thus facilitate monitoring and instructional decision making is learning progress assessments. In practical contexts and research, estimating learning progress has relied on approaches that seek to estimate progress either for each student separately or within overarching model frameworks, such as latent growth modeling. Two recently emerging lines of research for separately estimating student growth have examined robust estimation (to account for outliers) and Bayesian approaches (as opposed to commonly used frequentist methods). The aim of this work was to combine these approaches (i.e., robust Bayesian estimation) and extend these lines of research to the framework of linear latent growth models. In a sample of N = 4970 second-grade students who worked on the quop-L2 test battery (to assess reading comprehension) at eight measurement points, we compared three Bayesian linear latent growth models: (a) a Gaussian model, (b) a model based on Student's t-distribution (i.e., a robust model), and (c) an asymmetric Laplace model (i.e., Bayesian quantile regression and an alternative robust model). Based on leave-one-out cross-validation and posterior predictive model checking, we found that both robust models outperformed the Gaussian model, and both robust models performed comparably well. While the Student's t model performed statistically slightly better (yet not substantially so), the asymmetric Laplace model yielded somewhat more realistic posterior predictive samples and a higher degree of measurement precision (i.e., for those estimates that were either associated with the lowest or highest degree of measurement precision). The findings are discussed for the context of learning progress assessment.

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Despite their importance to evidence-based assessment, standardized assessments remain underutilized by mental health practitioners in practice. The underutilization has been attributed to a lack of appreciation of the importance of standardized assessments, lack of knowledge of standardized assessments, and practical barriers to implementation. This study sought to gather the first descriptive data on alcohol and other drug (AOD) practitioners' attitudes toward, and knowledge and self-reported use of, standardized assessments. Practical barriers to implementation in initial assessment and progress monitoring were also assessed. Ninety-nine Australian AOD practitioners recruited via newsletters of national representative bodies and practitioner networks completed an online survey. While practitioners' attitudes towards using standardized assessments for initial assessment and progress monitoring were generally positive and consistent with other populations of health practitioners, assessments remained underutilized in practice. Most AOD practitioners did not consider standardized assessments to be feasible to implement. The current findings highlight the importance of practical barriers, particularly organization-level barriers, in the underutilization of standardized assessments in AOD practice. Findings support an extension of dialogue surrounding evidence-based practice beyond treatment selection to include assessment practices at a more general level. The present study offers a starting point from which efforts to improve practitioner compliance with evidence-based best practices can be conceived, designed, and implemented.

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One of the main goals of the teacher and the school system as a whole is to close learning gaps and support children with difficulties in learning. The identification of those children as well as the monitoring of their progress in learning is crucial for this task. The derivation of comparative standards that can be applied well in practice is a relevant quality criterion in this context. Continuous normalization is particularly useful for progress monitoring tests that can be conducted at different points in time. Areas that were not available in the normalization sample are extrapolated, closing gaps in applicability due to discontinuity. In Germany, teachers participated in a state-funded research project to formatively measure their children's spelling performance in primary school. Data (N = 3000) from grade two to four were scaled, linked and translated into comparative values that can be used in classrooms independently from specific times. The tests meet the requirements of item response models and can be transferred well to continuous norms. However, we recommend using the 10th or 20th percentile as cut-off points for educational measures, as the 5th percentile is not discriminating enough.

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Measurement based care (MBC) improves client outcomes by providing clinicians with routine mental health outcome data that can be used to inform treatment planning but is rarely used in practice. The Monitoring and Feedback Attitudes Scale (MFA) and Attitudes Towards Standardized Assessment Scales-Monitoring and Feedback (ASA-MF) (Jensen-Doss et al., 2016) may identify attitudinal barriers to MBC, which could help trainings and implementation strategies. This study examines the psychometric properties of the MFA and ASA-MF, including the factor structure, longitudinal invariance, and indicators of validity, in a sample of community mental health clinicians (N = 164). The measures demonstrate adequate fit to their factor structures across time and predict MBC use as captured in a client's electronic health record. Given that clinician attitudes are associated with MBC use, using instruments with psychometric support to assess attitudes fills a research to practice gap.

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