Performance Measures Research Articles

Functional Capacity, Functional Performance, and Symptoms in People With Heart Failure: An Integrative Review.

Heart failure (HF) is a clinical syndrome that has a negative effect on functional status, a multidimensional concept characterizing one's ability to provide for the necessities of life. Symptoms might be associated with different aspects of functional status in HF. However, the terms functional capacity and performance have been misused interchangeably, and no previous review has focused on the extent to which symptoms contribute to functional capacity and performance among people with HF. The aim of this study was to comprehensively review and synthesize the existing literature on the relationship between symptoms, functional capacity, and functional performance in people with HF. We conducted an integrated review of observational studies in which authors examined the relationship between at least 1 symptom and at least 1 aspect of functional capacity or performance in people with HF 18 years or older. The Joanna Briggs Institute's critical appraisal tools were used to analyze the quality of studies. We included 23 studies with 7219 participants and an age range of 40 to 86 years. Fifteen symptom measures were used to measure dyspnea, fatigue, pain, insomnia symptoms, depressive symptoms, and anxiety symptoms. Three functional capacity measures were assessed in 9 studies, and 7 functional performance measures were assessed in 17 studies. As often measured with the Six-Minute Walk Test, functional capacity was inversely associated with pain/discomfort, insomnia symptoms, and psychological symptoms. Functional performance, often measured by the New York Heart Association functional class and Medical Outcome Scale Short Form 36, was associated with sleep difficulties, fatigue, depressive symptoms, and anxiety. Four studies combined functional capacity and performance measures to elicit functional status. Depressive symptoms, fatigue, pain, and insomnia symptoms are associated with decreased functional performance and capacity. Commonly, symptoms experienced in HF are associated with declining functional status. However, this finding depends on the measures or metrics used to assess symptoms, functional capacity, and performance. The observed dissimilarities in the relationship between symptoms and functional capacity and performance highlight the conceptual distinctions, suggesting that authors of future studies should judiciously select appropriate dimensions and measures of functional status based on the study's purposes, design, and available resource.

Anthropometric equation has sufficient diagnostic capacity to identify sarcopenia in women with breast cancer

BackgroundSarcopenia is a common condition in women with breast cancer, however still presents limitations for an effective diagnosis. This study aimed to evaluate the agreement and diagnostic accuracy of an anthropometric equation in diagnosing sarcopenia in women with breast cancer based on different constructs.MethodsCross-sectional study carried out with women with breast cancer aged ≥ 20 years. Sarcopenia was identified according to the European Working Group on Sarcopenia in Older People 2 (EWGSOP2) criteria. Muscle strength was obtained by the handgrip strength test (HGS) and muscle mass (MM) by dual-energy x-ray absorptiometry (DXA) and by the anthropometric predictive equation. For the diagnosis of sarcopenia, eight constructs were proposed based on the MM assessment method (equation or DXA) and different cutoff points. Agreement analyses using Cohen’s Kappa test and diagnostic performance measures were performed. The significance level for all tests was 5%.ResultsA total of 122 women, with a mean age of 55.3 ± 11.4 years, were evaluated. There was a predominance of brown participants (50.8%), insufficiently active (57.4%), with diagnosis time ≤ 3 months (54.1%), and with invasive breast carcinoma (69.7%). The prevalence of sarcopenia ranged from 3.3 to 8.2%, depending on the construct used. The constructs determined from the cutoff points < 16.0 kg/< 7.58 m² and < 23.0 kg/< 7.58 m² were the ones that showed the best ability to detect sarcopenia.ConclusionThe anthropometric equation showed sufficient diagnostic capacity to be used as an alternative in identifying sarcopenia in women with breast cancer.

Relationship between different muscle mass indices and physical performance measures in Turkish older adults

BackgroundSarcopenia, characterized by the loss of skeletal muscle, is assessed using appendicular skeletal muscle mass indices (ASMI). Various international groups propose different ASMI thresholds for assessing sarcopenia. However, the optimal ASMI that correlates best with physical performance measures in older Turkish adults remains unexplored. This study aims to determine which ASMI is most closely associated with physical performance measures, particularly low handgrip strength (dynapenia), in Turkish older adults.MethodsThe study included 326 individuals aged 60 and above. Comprehensive geriatric assessments were conducted on all participants, along with anthropometric evaluations and body composition analyses. ASMI was calculated by adjusting height squared, weight, and body mass index (BMI). Physical performance was assessed through handgrip strength, gait speed, and the chair stand test.ResultsThe mean age of the participants was 74 ± 5.77 years, with 59.8% being women and 37.5% having dynapenia. Height-squared adjusted ASMI was not significantly associated with gait speed or the chair stand test. Weight-adjusted ASMI correlated with handgrip strength and gait speed but not with the chair stand test. Both height and weight-adjusted ASMI did not differ significantly between participants with and without dynapenia (p > 0.05). BMI-adjusted ASMI significantly correlated with all physical performance parameters (p < 0.05). Furthermore, in multivariate regression analysis, BMI-adjusted ASMI (OR = 0.028, 95% CI = 0.01–0.31, p = 0.006) was independently associated with dynapenia.ConclusionThe study indicates that ASMI adjusted for BMI shows stronger correlations with all physical performance parameters and is independently associated with dynapenia. Utilizing ASMI adjusted for BMI may improve sarcopenia diagnosis in Turkish older adults.

A review of economic, relational, social and environmental measures of agricultural cooperatives performance: trends, sectoral, and geographical association

Abstract There is increasing interest in the performance of agricultural cooperatives. However, there is little consensus on what measures are most appropriate to use or how best to capture the social and environmental impact of these organisations. Despite this, to date, there are no studies that review the literature on agricultural cooperative performance to establish how the most used performance measures have evolved over time and establish any relationship between the sectors and locations of agricultural cooperatives. Thus, this paper seeks to address this gap by (i) identifying the foremost measures that have been used to evaluate the performance of agricultural cooperatives; and (ii) exploring the trends, sectoral and geographical association with the use of these performance measures. A multistage analytical framework, comprising a journal article network analysis and a qualitative meta-analysis, was used to extract relevant information from 124 journal articles and perform content analysis. Subsequently, a non-parametric test was used to examine the association between the year of publication, sector and geographical location of agricultural cooperatives and the performance measures. The results highlight a diverse list of indicators utilised to assess the performance of agricultural cooperatives. However, there is a narrow focus and dominant use of short-term economic metrics, and limited use of environmental and sustainability measures. Also, the results show a significant increase in the use of liquidity indicators in more recent publications. There exists a significant association between the sector of the agricultural cooperative and the most used performance measures but no association with the geographical location. The findings highlight the need to develop performance measures that evaluate the positive spill-over effects of agricultural cooperative activities on non-members, communities, and the natural environment. Also, the findings provide a rubric for benchmarking the performance and identifying best practices that can be shared across different cooperatives.

G4 & the balanced metric family – a novel approach to solving binary classification problems in medical device validation & verification studies

BackgroundIn medical device validation and verification studies, the area under the receiver operating characteristic curve (AUROC) is often used as a primary endpoint despite multiple reports showing its limitations. Hence, researchers are encouraged to consider alternative metrics as primary endpoints. A new metric called G4 is presented, which is the geometric mean of sensitivity, specificity, the positive predictive value, and the negative predictive value. G4 is part of a balanced metric family which includes the Unified Performance Measure (also known as P4) and the Matthews’ Correlation Coefficient (MCC). The purpose of this manuscript is to unveil the benefits of using G4 together with the balanced metric family when analyzing the overall performance of binary classifiers.ResultsSimulated datasets encompassing different prevalence rates of the minority class were analyzed under a multi-reader-multi-case study design. In addition, data from an independently published study that tested the performance of a unique ultrasound artificial intelligence algorithm in the context of breast cancer detection was also considered. Within each dataset, AUROC was reported alongside the balanced metric family for comparison. When the dataset prevalence and bias of the minority class approached 50%, all three balanced metrics provided equivalent interpretations of an AI’s performance. As the prevalence rate increased / decreased and the data became more imbalanced, AUROC tended to overvalue / undervalue the true classifier performance, while the balanced metric family was resistant to such imbalance. Under certain circumstances where data imbalance was strong (minority-class prevalence < 10%), MCC was preferred for standalone assessments while P4 provided a stronger effect size when evaluating between-groups analyses. G4 acted as a middle ground for maximizing both standalone assessments and between-groups analyses.ConclusionsUse of AUROC as the primary endpoint in binary classification problems provides misleading results as the dataset becomes more imbalanced. This is explicitly noticed when incorporating AUROC in medical device validation and verification studies. G4, P4, and MCC do not share this limitation and paint a more complete picture of a medical device’s performance in a clinical setting. Therefore, researchers are encouraged to explore the balanced metric family when evaluating binary classification problems.

ML-Based Models as a Strategy to Discover Novel Antiepileptic Drugs Targeting Sodium Receptor Channel.

Epilepsy remains the most common and chronic disorder demanding longterm management. The impact of epilepsy disease is a cause of great concern and has resulted in efforts to develop treatment for epilepsy. It occurs due to an increase in neuronal excitability produced by changes affecting the voltage-dependent properties of Voltage-gated Sodium Channels (VGSCs). Weka, a popular suite for machine learning techniques, was used on a dataset comprising 1781 chemical compounds, showing inhibition activity for sodium channel protein IX alpha subunit. After the analysis of the dataset obtained from ChEMBL, molecular fingerprints were computed for the molecules by the ChemDes server. Different classifiers available in the Weka software were explored to find out the algorithm that could be more suitable for the dataset or produce the highest accuracy for the classification of molecules as active or inactive. In this work, a comprehensive comparison of different classifiers in the Weka suite for the prediction of active, inactive, and intermediate classes of molecules showing inhibition against human NaV1.7 protein was made. The prediction accuracy of these classifiers was assessed based on performance measures, including accuracy, Root Mean Squared Error (RMSE), Receiver Operating Characteristic (ROC), precision, Mathews Correlation Coefficient (MCC), recall, and Fmeasure. The comparison of results for model performance demonstrated that the OneR classifier performed best over others when validated using percentage split, cross-validation, and supplied test methods. J48 and Bagging also performed equally well in the prediction of different classes with an MCC value of 1, ROC area equal to 1, and RMSE close to 0. Machine Learning (ML) tools provide a fast, reliable, and cost-effective approach required to identify or predict inhibitory molecules for the treatment of a disease. This study shows that the ML methods, particularly OneR, J48, and Bagging have the ability to identify active and inactive classes of compounds for the human NaV1.7 protein target. Such predictive models may provide a reliable and time-saving approach that can aid in the design of potential inhibitors for the treatment of epilepsy disease.

Initial Validation of AUGS-PERFORM: Construct Validity and Test-Retest Reliability.

The American Urogynecologic Society's Prolapse pERFORmance Measure (AUGS-PERFORM) patient-reported outcome measure contains 11 items designed to assess symptoms relevant for assessing the quality of treatment for pelvic organ prolapse. The aim of the study was to test AUGS-PERFORM's construct validity and test-retest reliability. For this prospective validation study, we recruited English-speaking adult participants, at a single academic institution, seeking care for pelvic organ prolapse. Participants completed AUGS-PERFORM, the Pelvic Floor Distress Inventory, and several Patient-Reported Outcomes Measurement Information System short forms at baseline. We compared the 11 AUGS-PERFORM items against items testing the same concepts on other questionnaires using percent agreement, kappa statistics, and linear regression to determine construct validity. Two weeks later and before any pelvic organ prolapse therapy, participants completed AUGS-PERFORM a second time. Test-retest reliability was assessed using intraclass correlation coefficients. We enrolled 148 participants between 27 and 86 years of age: 81% self-identified as White, 56% were sexually active, and 84% elected surgery for treatment. The AUGS-PERFORM items assessing bulge presence and bother had a high percent agreement with the Pelvic Floor Distress Inventory item #3 (83.5 and 70%, respectively). The percent agreement ranged from 69% to 75% for items assessing urinary and defecatory symptoms and from 49% to 56% for pain-related questions. Sexual function items had a strong negative correlation (expected direction based on scoring) with validated sexual function questionnaires. The intraclass correlation coefficient was estimated to be 0.86, indicating excellent test-retest reliability. The AUGS-PERFORM demonstrated good construct validity for prolapse, urinary incontinence, defecatory dysfunction, and sexual function questions, and moderate construct validity for pain-related questions. There was excellent test-retest reliability.

Twenty Years of Sustained Improvement in Quality of Care and Outcomes for Patients Hospitalized With Stroke or Transient Ischemic Attack: Data From The Get With The Guidelines-Stroke Program.

The Get With The Guidelines-Stroke program is a quality improvement initiative designed to enhance adherence to evidence-based stroke care. Since its inception in 2003, over 2800 hospitals in the United States have participated in the program. We examined patient characteristics, adherence to performance measures, and in-hospital outcomes in patients hospitalized for acute ischemic stroke, subarachnoid hemorrhage, intracerebral hemorrhage, and transient ischemic attack in The Get With The Guidelines-Stroke hospitals from 2003 through 2022. We quantified temporal changes in performance measure adherence and clinical outcomes over time. Performance measure denominators consisted of patients who were eligible, excluding those with contraindications. Over the 20 years of the program, a total of 7837 849 stroke cases (median age 71 years, 51.0% female; 69.2% ischemic strokes, 3.9% SAHs, 11.5% ICHs, and 15.3% TIAs) were entered into the registry. Except for antithrombotics at discharge, in which the baseline performance was >92%, there was sustained improvement in all performance metrics regardless of type of cerebrovascular event (P<0.01 for all). In patients with acute ischemic stroke, large improvements were observed for anticoagulation for atrial fibrillation (55.7% in 2003 to 97.2% in 2022), smoking cessation counseling (44.7%-97.8%), dysphagia screening (53.8%-83.5%), thrombolytic treatment for patients arriving by 3.5 hours, treat by 4.5 hours (15.2%-92.9%), door-to-needle time within 60 minutes (19.0%-75.3%), and endovascular door-to-puncture time within 90 minutes (54.7%-62.8%). Similar improvements were also observed for measures relevant to patients with subarachnoid hemorrhage, intracerebral hemorrhage, and transient ischemic attack. Multivariable analysis showed that there was a sustained increase in odds of receiving each performance measure over time, independent of patient and hospital characteristics for each type of cerebrovascular event. After risk adjustment, there were temporal trends that patients were less likely to be discharged to a skilled nursing facility, and, for ischemic stroke only, more likely to be discharged directly home. During the first 20 years, Get With The Guidelines-Stroke participation was associated with sustained improvement in evidence-based care and outcomes for patients with stroke and transient ischemic attack in the United States.

Developing Second Language Mandarin Fluency Through Pedagogic Intervention and Study Abroad: Planning Time, Speech Rate, and Response Duration

AbstractThis longitudinal study examines the effects of a pre‐study abroad (SA) pedagogic intervention and subsequent SA experience on second language (L2) Mandarin fluency. It explores two temporal aspects of oral fluency—planning time and speech rate—along with one performance measure, duration of response. Additionally, L2 contact data were included as a supplementary variable in the analysis. The experimental group was assessed at three points: before instruction (T1), after 2 weeks of instruction (T2), and post‐SA (T3). A non‐instructed control group that participated in the SA period provided baseline data. Both groups demonstrated improved fluency after the SA period, with the experimental group showing superior performance in planning time, speech rate, and duration of response. The greatest reduction in between‐group differences occurred at T2 and persisted over time. These findings highlight that combining targeted instruction with exposure is highly effective, with L2 contact strongly correlating with overall fluency gains.

Machine learning predictive models to guide prevention and intervention allocation for anxiety and depressive disorders among college students

AbstractCollege student mental health has been a critical concern for professional counselors. Anxiety and depressive disorders have become increasingly prevalent over the past decade. Utilizing machine learning, a subset of artificial intelligence (AI), we developed predictive models (i.e., eXtreme Gradient Boosting [XGBoost], Random Forest, Decision Tree, and Logistic Regression) to identify US college students at heightened risk of diagnosable anxiety and depressive disorders. The dataset included 61,619 students from 133 US higher education institutions and was partitioned into a 90:10 ratio for training and testing the models. We employed hyperparameter tuning and cross‐validation to optimize model performance and examined multiple measures of predictive performance (e.g., area under the receiver operating characteristic curve [AUC], accuracy, sensitivity). Results revealed strong discriminative power in our machine learning predictive models with AUC of 0.74 and 0.77, indicating current financial situation, sense of belonging on campus, disability status, and age as the top predictors of anxiety and depressive disorders. This study provides a practical tool for professional counselors to proactively identify students for anxiety and depressive disorders before these conditions escalate. Application of machine learning in counseling research provides data‐driven insights that help enhance the understanding of mental health determinants, guide prevention and intervention strategies, and promote the well‐being of diverse student populations through counseling.

Re-evaluating critical current density in solid-state batteries: Challenges and proposing an alternative

The critical current density (CCD) test protocols are widely adopted to examine the quality of solid electrolyte (SE), the stability of lithium (Li)/SE interfaces, and solid-solid interfacial kinetics in all solid-state lithium batteries (SSLBs). This work systematically investigated the impact of major experimental parameters on CCD values of Li symmetric cells using argyrodite Li6PS5Cl SE. Four categories of experimental parameters were investigated: (i) SE parameters, (ii) Li metal parameters, (iii) cell die parameters, and (iv) CCD testing protocols. Each parameter uniquely influences the density of the SE or the SE/Li interface, thereby affecting the resulting CCD values. Notably, CCD values are highly dependent on the cell operating histories, particularly the operating period (h) and the applied current density (mA/cm2). Consequently, without standardizing and unifying CCD testing protocols, obtaining reliable and repeatable CCD values remain challenging. To overcome this limitation, we propose the cumulative areal capacity (CAC) test protocol – the total capacity (mAh/cm2) delivered by a cell before its failure – as a more accurate measure of cell performance. CAC analysis of over 30 Li symmetric cells demonstrated statistical convergence in CAC values, highlighting its potential as a supplementary or even alternative testing protocol to conventional CCD methods.

Methodology to Generate Strategic Manufacturing Indicators to Support Decision-Making – Case Study of a Food Company

This study proposes a methodology that selects key performance indicators and measures their influence on the strategic manufacturing objectives of a food company. The proposed hybrid methodology used the focus model centered on a value function to select criteria and classify key performance indicators and the multi-criteria model DEMATEL to identify the level of influence of key performance indicators on strategic manufacturing objectives. This new method constructs a value function with input from all production line members. This allows the degree of direct and indirect influence of selected key performance indicators to be measured to achieve the organization’s strategic objectives. Finally, seven key performance indicators were selected, the most influential being the level of automation, performance, set-up times, and resource use.

Racial disparities and potential channels in the NBA digital collectibles market

ABSTRACT We analyse National Basketball Association (NBA) digital collectibles’ prices to test whether there exist any racial disparities based on NBA players’ demographics. Using a novel and rich dataset, controlling for performance measures, we do not find evidence of price disparities between Black versus White players, Hispanic versus non-Hispanic players and foreign versus U.S. players. However, we find evidence suggesting that non-Black minority players’ product prices are statistically and economically lower than those of comparable players. We also identify two possible channels which might contribute to such racial disparities – a product design channel and a media coverage channel.

The ambivalent relationship of e-scooters and public transport: Evidence from France

This paper aims at assessing competitiveness of shared e-scooters relative to public transport proposing a methodology that uses empirical data at the individual trip level and comparing e-scooter trips with the alternative public transit route available to the traveler at the time of the trip, as estimated by a navigation and public transit application. This allows us to obtain information at the individual transit trip level, such as in-vehicle time, walking, waiting, and other. We then explore the competitiveness between the two alternatives using two performance measures – travel time (TT) and an estimation of the generalized cost (GC) of a trip – for different levels of aggregation. Results show that in general, e-scooters are more competitive in terms of travel time. However, due to the higher pricing, generalized cost of transit is generally lower than that of e-scooter. In 60% of the cases users would lower their generalized cost by using public transport instead of e-scooter. On average, e-scooter is a better option for trips up to 15min, while public transit is better for longer trips. They are also more competitive during hours when transit frequency is reduced, as well as in areas with reduced service. Finally, we also find that one in five trips complement public transport service offering transportation outside of transit service hours or between areas with no connectivity. Further research should include surveys in order to obtain more details on the psychological constructs of the users.

Exploring the relationship between strategic attention breadth and performance: The role of corporate social responsibility

Abstract The attention-based view contends that executives possess limited attentional capabilities that must be carefully allocated across different strategic issues. Although many scholars contend that narrow strategic attention breadth leads to better performance, others argue that broad strategic attention breadth may be more beneficial due to better opportunity scanning. We posit that the relationship between strategic attention breadth and performance will be inverted U-shaped, where strategic attention breadth is positively related to firm performance up to an optimal point, after which firms will see declining benefits due to executive cognitive overload. Furthermore, we propose that executives’ assessment of strategic opportunities will be influenced by the firm’s corporate social responsibility perspective, as the firm’s environmental and ethical commitment may mitigate executive blind spots and enhance opportunity selection. We support our hypotheses with multiple measures of firm performance and a content analysis of annual reports corresponding to a 5-year longitudinal sample of 2,245 S&amp;P 500 firms.

Identifying successful football teams in the European player transfer network

This paper considers the European transfer market for professional football players as a network to study the relation between a team’s position in this network and performance in its domestic league. Our analysis is centered on eight top European leagues. The market in each season is represented as a weighted directed network capturing the transfers of players to or from the teams in these leagues, and we also consider the cumulative network over the past 28 years. We find that the overall structure of this transfer market network has properties commonly observed in real-world networks, such as a skewed degree distribution, high clustering, and small-world characteristics. To assess football teams we first construct a measure of within-league performance that is comparable across leagues. Regression analysis is used to relate league performance with both the network position and level of engagement of the team in the transfer market, under two complimentary setups. Network position variables include, e.g., betweenness centrality, closeness centrality and node clustering coefficient, whereas market engagement variables capture a team’s activity in the transfer market, e.g., total number of player transfers and total paid for players. For the season snapshots, the number of transfers correspond to weighted in- and out-degree. Our analysis first corroborates several recent findings relating aspects of market engagement with teams’ league performance. A higher number of incoming transfers indicates worse performance and better resourced teams perform better. Then, and across specifications, we find that network position variables remain salient even when engagement variables are already considered. This substantiates the notion in the existing literature that a high degree corresponds to better team performance and suggests that network aspects of trading strategy may affect a team’s success in their respective domestic league (or vice versa). In this sense, the approach and findings presented in this paper may in the future guide team’s player acquisition policies.

Pulmonary Emphysema Disease Classification Model Using Deep Structured Techniques

Emphysema is normally employed in the sense of morphology, and thus, imaging the modalities has a significant part in detecting the disorder. Most importantly, the High-Resolution Computed Tomography (HRCT) is a trustworthy component for illustrating the emphysema pathology. Hence, the pulmonary emphysema diagnosis at the starting stage is very important and can secure the life of a human. When there is a failure in the technique, there is a possibility of the death that displays the importance of effective diagnosis. The endotracheal intubation’s delay is prevented by implementing distinct mechanisms that result in various requirements. The presented work develops a deep learning-aided pulmonary emphysema disease classification model that is implemented to defeat the above difficulties. At first, the images are accumulated from a standard dataset. Then, the collected images are provided to the phase of segmentation. Here, the newly developed Adaptive Trans-Residual Dense UNet (ATRDUNet) is employed to perform the segmentation process. Also, the recommended ATRDUNet model helps to locate the disease in the appropriate location. Moreover, the performance of the segmentation process is enriched to fine-tune the parameter using the Fitness Revised Position of Wild Horse Optimizer (FRP-WHO). While optimizing the parameters effectively, it tends to handle the complex issues to strengthen the model accurately. The segmented results from the ATRDUNet are passed to Vision Transformer-based Residual DenseNet with LSTM layer (ViTrans-RDLSTM) for getting the final classified outcome. To this end, the analysis of the suggested model is correlated to find the effectiveness of the recommended model. However, these results are examined with the help of standard performance measures. Also, the experimentation is carried out using the standard “Automatic emphysema detection using weakly labeled HRCT lung images” dataset to attain better outcomes in the developed model. The findings of the recommended approach show 95% in terms of accuracy. Also, the statistical analysis of the developed model shows 23.9%, 12.3%, 15.2%, and 13.0% better performance than BWO, CO, HBA, and WHO in terms of mean analysis to show enriched performance.

The good, the bad and the ugly: What do we really do when we identify the best and the worst organisations?

Identifying high and poorly performing organisations is common practice in healthcare. Often this is done within a frequentist inferential framework where statistical techniques are used that acknowledge that observed performance...

Applying CO-OP Approach in Children With Specific Learning Disorder: Single-Subject Outcome Study.

This study aimed to evaluate the impact of the Cognitive Orientation to daily Occupational Performance (CO-OP) in enhancing motor-based occupational performance challenges in five children with specific learning disorders (SLDs). Five children with SLD aged 9 to 12 years, participated in a multiple-baseline single-subject experimental design. Outcome measures included the Canadian Occupational Performance Measure (COPM) and the Bruininks-Oseretsky Test of Motor Proficiency-Edition 2 (BOT-2). The CO-OP Approach comprised 12 sessions, conducted twice weekly. Based on the BOT-2 results, the intervention resulted in better motor performance scores in all five participants. Clinically significant improvement was found in both performance and satisfaction rating using the COPM after intervention as assessed by both parents and children. This study demonstrates the potential of the CO-OP Approach to enhance motor skills and achieve selected goals in five children with SLD.

The Relationship Between the Burpee Movement Program and Strength and Endurance Performance Measures in Active Young Adults: A Cross-Sectional Analysis.

This paper aimed to assess the motor performance in the Burpee Movement Program through the acceleration recorded by the Phyphox mobile app and define its relationship to strength and endurance parameters. Altogether, 15 students in physical education teaching completed the 3 × 3 min Burpee Movement Program, consisting of the repeated execution of a single burpee with maximum effort at regular intervals triggered by a sound signal. During the load phase, the intensity of the burpee and the fatigue index expressed in percentages were evaluated by means of the acceleration recorded through a mobile phone. In the second part of testing, we evaluated the performance parameters during a bench press and squat where the intensity was measured using a linear displacement transducer (Tendo Power Analyzer) and aerobic endurance was assessed with a 20 m shuttle run test (20 mSR). The average intensity of the burpee ranged from 3.12 to 11.12 ms-2. The fatigue index ranged from -21.95% (which represented an increase in performance) to 33.63% (which represented a decrease in performance). The performances in the bench presses ranged from 58 to 480 W and from 175 to 696 W during the squats. The distance in the 20 m shuttle run test (20 mSR) ranged from 540 to 2000 m. The intensity of the burpee showed a significant correlation to the performances achieved in the bench presses and squats r = 0.82 and 0.79. The fatigue index showed a significant correlation to the 20 m shuttle run test (20 mSR) r = -0.67. These findings indicate that in, our case, the results from the Burpee Movement Program are significantly associated with the participants' strength and endurance abilities. We recommend using BMP for the development of strength-endurance abilities, but further exploration is needed regarding the potential use of BMP as a diagnostic test.