Effect Of Texting And Handwriting On Hand-Grip And Key-Pinch Strength Among Female-Collegiate Students: Randomized Controlled Trial.

OBJECTIVE: To evaluate immediate and long-term effect of texting or handwriting on hand-grip and key-pinch strength among healthy female students. Methods: The single-blind, randomised controlled trial was conducted between February and April 2021 after approval from the ethics review committee of the College of Medical Rehabilitation Sciences, Taibah University, Saudi Arabia, and comprised female Physio Therapy students aged 19-23 years who were right-hand dominant and had normal body mass index. The subjects used smartphones and electronic gadgets for >2hrs daily, writing more than 10min/day. They were randomised using sealed envelopes into five groups. Group A practised 10min texting, group B 15min texting, group C 10min writing, group D 15min writing, and group E used the phones only for talking or watching, with no texting or writing, and was taken as the control group. Hand-grip strength and key- pinch strength were assessed one minute before starting, and within one minute after having finished the assigned hand activity. All measurements were recorded in the sitting position using a single hand-grip dynamometer. Data was analysed using SPSS 23. RESULTS: Of the 65 individuals assessed, 60(92.3%) were initially enrolled, but the study was finished by 50(83.3%) subjects with a mean age of 20.88±0.98 years and mean body mass index 20.8±2.30kg/m2. There were 12(24%) subjects in group A, 7(14%) in group B, 12(24%) in group C, 10(20%) in group D and 9(18%) in group E. The association of the time-based groups with hand-grip and key-pinch strength was not significant (p>0.05). CONCLUSIONS: Texting and handwriting did not have any significant immediate effect on hand- grip or key-pinch strength. Clinical Trial Number: (NCT04810416).

Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination.

Handwritten Arabic character recognition has received increasing research interest in recent years. However, as of yet, the majority of the existing handwriting recognition systems have only focused on adult handwriting. In contrast, there have not been many studies conducted on child handwriting, nor has it been regarded as a major research issue yet. Compared to adults' handwriting, children's handwriting is more challenging since it often has lower quality, higher variation, and larger distortions. Furthermore, most of these designed and currently used systems for adult data have not been trained or tested for child data recognition purposes or applications. This paper presents a new convolution neural network (CNN) model for recognizing children's handwritten isolated Arabic letters. Several experiments are conducted here to investigate and analyze the influence when training the model with different datasets of children, adults, and both to measure and compare performance in recognizing children's handwritten characters and discriminating their handwriting from adult handwriting. In addition, a number of supplementary features are proposed based on empirical study and observations and are combined with CNN-extracted features to augment the child and adult writer-group classification. Lastly, the performance of the extracted deep and supplementary features is evaluated and compared using different classifiers, comprising Softmax, support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF), as well as different dataset combinations from Hijja for child data and AHCD for adult data. Our findings highlight that the training strategy is crucial, and the inclusion of adult data is influential in achieving an increased accuracy of up to around 93% in child handwritten character recognition. Moreover, the fusion of the proposed supplementary features with the deep features attains an improved performance in child handwriting discrimination by up to around 94%.

Handwriting legibility and fluency and their patterns of concurrent relations with spelling, graphomotor, and selective attention skills.

Recent research suggests that handwriting comprises two separate subskills: legibility and fluency. It remains unclear, however, how these subskills differ in their relationship to other abilities associated with handwriting, including spelling, graphomotor, and selective attention skills. In this study, we sought to examine the extent and nature of concurrent relationships that may exist among these skills. Children in Year 3 (n = 293), Year 4 (n = 291), and Year 5 (n = 283) completed a large, group-administered battery to assess each of the above skills. Using multigroup structural equation modeling, we found that spelling, graphomotor, and selective attention skills together explained a moderate amount of variance in handwriting legibility (R2 = .37-.42) and fluency (R2 = .41-.58) and that these subskills differed in their concurrent relations. Graphomotor skills accounted for a relatively greater proportion of variance in legibility than did spelling. Conversely, there were relatively stronger contributions from variations in spelling ability to variations in fluency than from graphomotor skills. Furthermore, selective attention predicted handwriting fluency only, and it partially mediated the influence of graphomotor skills. This study further demonstrates that handwriting legibility and fluency are separable and complex skills, each differentially related to spelling, graphomotor, and attentional abilities even during later primary school years.

Pashto Handwritten Invariant Character Trajectory Prediction Using a Customized Deep Learning Technique.

Before the 19th century, all communication and official records relied on handwritten documents, cherished as valuable artefacts by different ethnic groups. While significant efforts have been made to automate the transcription of major languages like English, French, Arabic, and Chinese, there has been less research on regional and minor languages, despite their importance from geographical and historical perspectives. This research focuses on detecting and recognizing Pashto handwritten characters and ligatures, which is essential for preserving this regional cursive language in Pakistan and its status as the national language of Afghanistan. Deep learning techniques were employed to detect and recognize Pashto characters and ligatures, utilizing a newly developed dataset specific to Pashto. A further enhancement was done on the dataset by implementing data augmentation, i.e., scaling and rotation on Pashto handwritten characters and ligatures, which gave us many variations of a single trajectory. Different morphological operations for minimizing gaps in the trajectories were also performed. The median filter was used for the removal of different noises. This dataset will be combined with the existing PHWD-V2 dataset. Various deep-learning techniques were evaluated, including VGG19, MobileNetV2, MobileNetV3, and a customized CNN. The customized CNN demonstrated the highest accuracy and minimal loss, achieving a training accuracy of 93.98%, validation accuracy of 92.08% and testing accuracy of 92.99%.

Is it possible to estimate sex from signatures and handwriting? A review of literature, observations, and future perspectives.

Estimation of sex holds great significance in the field of Forensic Science since it helps establish the identity of an individual during a crime scene investigation. Sex differences in human behaviour are the result of natural selection. Sexually dimorphic stimuli of cognitive and behavioural activities may influence the phenotypic expression of our motor skills. Human traits such as signatures and handwriting are phenotypic manifestation of these skills. These phenotypic biological and behavioural traits have inherent sexual dimorphism and may help to identify sex in different circumstances. For instance, to establish the sex of an individual or deceased, forensic samples of the human body such as voice samples, features of fingerprints and footprints, the skeleton, or its remains are helpful. Similarly, the sex of an individual may also be identified from their corresponding handwriting and signature. Handwriting experts can extract peculiar features from handwriting and signatures which could help establish whether the signatures belong to a male or a female. A female writer may have attractive, rounded, upright, tidy, skilled, well-formed strokes, artistic design, better penmanship, and greater length of the signature compared to the signature of a male. Here, we review the studies related to the identification of sex from signatures and handwriting and present inferences about vital features and methods of sex identification through handwriting. These mainly suggest that the accuracy of sex prediction from signature and handwriting ranges from 45 to 80%. We also present writing examples to show sex-based differences in the signature and handwriting of males and females. The female's handwriting is more decorative, arranged, aligned, neat, and clean as compared to that of the male. Based on the writing samples and the review of literature, we suggest that forensic handwriting experts may eliminate suspects based on the sex of the writer, which can simplify the identification process of disputed or questionable signatures and handwriting.

Machine-Learning Methods for Speech and Handwriting Detection Using Neural Signals: A Review.

Brain-Computer Interfaces (BCIs) have become increasingly popular in recent years due to their potential applications in diverse fields, ranging from the medical sector (people with motor and/or communication disabilities), cognitive training, gaming, and Augmented Reality/Virtual Reality (AR/VR), among other areas. BCI which can decode and recognize neural signals involved in speech and handwriting has the potential to greatly assist individuals with severe motor impairments in their communication and interaction needs. Innovative and cutting-edge advancements in this field have the potential to develop a highly accessible and interactive communication platform for these people. The purpose of this review paper is to analyze the existing research on handwriting and speech recognition from neural signals. So that the new researchers who are interested in this field can gain thorough knowledge in this research area. The current research on neural signal-based recognition of handwriting and speech has been categorized into two main types: invasive and non-invasive studies. We have examined the latest papers on converting speech-activity-based neural signals and handwriting-activity-based neural signals into text data. The methods of extracting data from the brain have also been discussed in this review. Additionally, this review includes a brief summary of the datasets, preprocessing techniques, and methods used in these studies, which were published between 2014 and 2022. This review aims to provide a comprehensive summary of the methodologies used in the current literature on neural signal-based recognition of handwriting and speech. In essence, this article is intended to serve as a valuable resource for future researchers who wish to investigate neural signal-based machine-learning methods in their work.

Motor Assessment With the STEGA iPad App to Measure Handwriting in Children.

IMPORTANCE: Handwriting and the fine motor control (hand and fingers) underlying it are key indicators of numerous motor disorders, especially among children. However, current assessment methods are expensive, slow, and subjective, leading to a lack of knowledge about the relationship between handwriting and motor control. OBJECTIVE: To develop and validate the iPad precision drawing app Standardized Tracing Evaluation and Grapheme Assessment (STEGA) to enable rapid quantitative assessment of fine motor control and handwriting. DESIGN: Cross-sectional, single-arm observational study. SETTING: Academic research institution. PARTICIPANTS: Fifty-seven typically developing right-handed children ages 9 to 12 yr with knowledge of cursive. OUTCOMES AND MEASURES: Predicted quality, measured as the correlation between handwriting letter legibility (Evaluation Tool of Children's Handwriting-Cursive [ETCH-C]) and predicted legibility (calculated from STEGA's 120 Hz, nine-variable data). RESULTS: STEGA successfully predicted handwriting (r2 = .437, p < .001) using a support vector regression method. Angular error was the most important aspect of STEGA performance. STEGA was much faster to administer than the ETCH-C (M = 6.7 min, SD = 1.3, versus M = 19.7 min, SD = 5.2). CONCLUSIONS AND RELEVANCE: Assessment of motor control (and especially pen direction control) may provide a meaningful, objective way to assess handwriting. Future studies are needed to validate STEGA with a wider age range, but the initial results indicate that STEGA can provide the first rapid, quantitative, high-resolution, telehealth-capable assessment of the motor control that underpins handwriting. What This Article Adds: The ability to control pen direction may be the most important motor skill for successful handwriting. STEGA may provide the first criterion standard for the fine motor control skills that underpin handwriting, suitable for rehabilitation research and practice.

Self-Adhesive, Anti-Freezing MXene-Based Hydrogel Strain Sensor for Motion Monitoring and Handwriting Recognition with Deep Learning.

Flexible strain sensors based on self-adhesive, high-tensile, super-sensitive conductive hydrogels have promising application in human-computer interaction and motion monitoring. Traditional strain sensors have difficulty in balancing mechanical strength, detection function, and sensitivity, which brings challenges to their practical applications. In this work, the double network hydrogel composed of polyacrylamide (PAM) and sodium alginate (SA) was prepared, and MXene and sucrose were used as conductive materials and network reinforcing materials, respectively. Sucrose can effectively enhance the mechanical performance of the hydrogels and improve the ability to withstand harsh conditions. The hydrogel strain sensor has excellent tensile properties (strain >2500%), high sensitivity with a gauge factor of 3.76 at 1400% strain, reliable repeatability, self-adhesion, and anti-freezing ability. Highly sensitive hydrogels can be assembled into motion detection sensors that can distinguish between various strong or subtle movements of the human body, such as joint flexion and throat vibration. In addition, the sensor can be applied in handwriting recognition of English letters by using the fully convolutional network (FCN) algorithm and achieved the high accuracy of 98.1% for handwriting recognition. The as-prepared hydrogel strain sensor has broad prospect in motion detection and human-machine interaction, which provides great potential application of flexible wearable devices.

Handwriting Evaluation Using Deep Learning with SensoGrip.

Handwriting learning disabilities, such as dysgraphia, have a serious negative impact on children's academic results, daily life and overall well-being. Early detection of dysgraphia facilitates an early start of targeted intervention. Several studies have investigated dysgraphia detection using machine learning algorithms with a digital tablet. However, these studies deployed classical machine learning algorithms with manual feature extraction and selection as well as binary classification: either dysgraphia or no dysgraphia. In this work, we investigated the fine grading of handwriting capabilities by predicting the SEMS score (between 0 and 12) with deep learning. Our approach provided a root-mean-square error of less than 1 with automatic instead of manual feature extraction and selection. Furthermore, the SensoGrip smart pen SensoGrip was used, i.e., a pen equipped with sensors to capture handwriting dynamics, instead of a tablet, enabling writing evaluation in more realistic scenarios.

Handwriting Quality: Psychometric Properties of Two Evaluation Scales With a Czech Sample.

IMPORTANCE: Approximately 7% to 30% of children contend with handwriting issues (HIs) in their school years. However, research studies to define and quantify HIs, as well as practical assessment tools, are lacking. OBJECTIVE: To confirm the validity and reliability of two screening scales for HIs: the Handwriting Legibility Scale (HLS) and the Concise Assessment Scale of Children's Handwriting (BHK). DESIGN: Structural equation modeling (SEM) and confirmatory factor analysis (CFA) of five different models were used to examine the construct and discriminant validity of both scales. Furthermore, internal consistency and interrater agreement were evaluated. The association among scales, grades, and children's self-evaluation was also explored. SETTING: Elementary schools and state counseling centers in the Czech Republic. PARTICIPANTS: On a voluntary basis, 161 children from elementary schools and state counseling centers in the Czech Republic were enrolled. The variable of children with typical handwriting development versus HIs was missing for 11 children. Thus, for discriminant validity analysis, 150 data records from children were used. OUTCOMES AND MEASURES: The HLS and BHK were used to evaluate the handwriting quality of the transcription task. The Handwriting Proficiency Screening Questionnaires for Children was used for children's self-evaluation. RESULTS: The study confirmed the validity and reliability of the shortened BHK and HLS. A strong relationship was found between the BHK and HLS, grades, and children's self-evaluation. CONCLUSIONS AND RELEVANCE: Both scales are recommended for occupational therapy practice worldwide. Further research should focus on developing standards and providing sensitivity studies. What This Article Adds: Both the HLS and the BHK are recommended for occupational therapy practice. Practitioners should also take the child's well-being into consideration in handwriting quality assessment.

The effect of fine motor skills, handwriting, and typing on reading development.

Discussions on the contribution of motor skills and processes to learning to read has a long history. Previous work is essentially divided into two separate strands, namely the contributions of fine motor skills (FMS) to reading and the influence of writing versus typing. In the current 2 × 2 × 3 mixed, single-blind, and randomly assigned experiment, we tested both strands together. A total of 87 children learned to decode pseudowords in either typing or writing conditions in which their FMS were either impaired or not. Decoding gains were measured at pretest, posttest, and follow-up, with FMS and working memory included as participant variable predictors. Findings indicated that FMS and working memory predicted decoding gains. Importantly, children performed best when typing if in the impaired FMS condition. Results have implications for motor representation theories of writing and for instruction of children with FMS impairments.

Offline text-independent writer identification using a codebook with structural features.

Understanding handwritten documents is a vital and challenging problem that attracts many researchers in the fields of forensic and authentication science. This paper presents an offline system for text-independent writer identification of handwritten documents. The system extracts a handwritten connected component contour, which in turn is divided into segments of specific length. The system utilizes the concept of a bag of features in the writer recognition domain and considers handwritten contour segments to extract two conceptually simple and effective structural features. These features are the contour point curve angle and the CONtour point CONcavity/CONvexity. The system uses the proposed features to train a k-means clustering algorithm to construct a codebook of size K. The method then uses occurrence histograms of the extracted features in the codebook to create a final feature vector for each handwritten document. The effectiveness of the proposed features is evaluated in the writer identification domain using two widely used classification methods: the nearest neighbor and the support vector machine techniques. The proposed writer identification is evaluated on two large and public datasets from different language domains, the Arabic KHATT and English IAM datasets. The experimental results show that the proposed system outperforms state-of-the-art methods on the IAM dataset and provides competitive results on the KHATT dataset with respect to the identification rate.

Writing letters in two graphic systems: Behavioral and neural correlates in Latin-Arabic biscripters.

Biscriptuality is the ability to read and write using two scripts. Despite the increasing number of biscripters, this phenomenon remains poorly understood. Here, we focused on investigating graphomotor processing in French-Arabic biscripters. We chose the French and Arabic alphabets because they have comparable visuospatial complexity and linguistic features, but differ dramatically in their graphomotor characteristics. In a first experiment we describe the graphomotor features of the two alphabets and showed that while Arabic and Latin letters are produced with the same velocity and fluency, Arabic letters require more pen lifts, contain more right-to-left strokes and clockwise curves, and take longer to write than Latin letters. These results suggest that Arabic and Latin letters are produced via different motor patterns. In a second experiment we used functional magnetic resonance imaging to ask whether writing the two scripts relies upon partially distinct or fully overlapping neural networks, and whether the elements of the previously described handwriting network are recruited to the same extent by the two scripts. We found that both scripts engaged the so-called "writing network", but that within the network, Arabic letters recruited the left superior parietal lobule (SPL) and the left primary motor cortex (M1) more strongly than Latin letters. Both regions have previously been identified as holding scale-invariant representations of letter trajectories. Arabic and Latin letters also activated distinct regions that do not belong to the writing network. Complementary analyses indicate that the differences observed between scripts at the neural level could be driven by the specific graphomotor features of each script. Overall, our results indicate that particular features of the practiced scripts can lead to different motor organization at both the behavioral and brain levels in biscripters.

An efficiently working brain characterizes higher mental flow that elicits pleasure in Chinese calligraphic handwriting.

The mental flow that commonly emerges during immersion in artistic activities is beneficial for maintaining mental health. However, there is not that much converging neurobiological evidence about how flow emerges and elicits pleasure in arts. Using an imitation task of Chinese calligraphic handwriting with self-rated subjective flow experience, we investigated the neural interactions supporting flow. Our results show that calligraphic handwriting requires cooperation between widespread multimodal regions that span the visual and sensorimotor areas along the dorsal stream, the top-down attentional control system, and the orbito-affective network. We demonstrate that higher flow is characterized by an efficiently working brain that manifests as less activation particularly in the brain regions within dorsal attention network and functional connectivity between visual and sensorimotor networks in calligraphy. Furthermore, we also propose that pleasure during calligraphy writing arises from efficient cortical activity in the emergence of flow, and the orbito-caudate circuit responsible for feelings of affection. These findings provide new insight into the neuropsychological representations of flow through art, and highlight the potential benefits of artistic activities to boost well-being and prosperity.

Rehabilitative interventions for impaired handwriting in people with Parkinson's disease: a scoping review.

BACKGROUND: People with Parkinson's disease (PD) often complain about handwriting difficulties. Currently, there is no consensus on the rehabilitative treatment and outcome measures for handwriting rehabilitation in PD. OBJECTIVES: This study aims to investigate evidence on handwriting rehabilitation in people with PD, examining characteristics of interventions and outcomes. METHODS: A scoping review was conducted according to Arksey and O'Malley's framework and PRISMA-ScR List. We searched electronic databases of PubMed, Physiotherapy Evidence Database, Cochrane Central Register of Controlled Trials, and Embase since inception to January 2023. We included interventional studies assessing the effects of structured rehabilitation programs for impaired handwriting in people with PD. Two reviewers independently selected studies, extracted data, and assessed the risk of bias using the Cochrane Collaboration's tool for assessing Risk of Bias version 2 or the Risk Of Bias In Non-randomized Studies. We performed a narrative analysis on training characteristics and assessed outcomes. RESULTS: We included eight studies. The risk of bias was generally high. Either handwriting-specific or handwriting-non-specific trainings were proposed, and most studies provided a home-based training. Handwriting-specific training improved writing amplitude while handwriting-non-specific trainings, such as resistance and stretching/relaxation programs, resulted in increased writing speed. CONCLUSIONS: The current knowledge is based on few and heterogeneous studies with high risk of bias. Handwriting-specific training might show potential benefits on handwriting in people with PD. Further high-quality randomized controlled trials are needed to reveal the effect of handwriting training in people with PD on standardized outcome measures. Handwriting-specific training could be combined to resistance training and stretching, which seemed to influence writing performance.

Application of optimized convolutional neural networks for early aided diagnosis of essential tremor: Automatic handwriting recognition and feature analysis.

Essential tremor (ET) is one of the most common neurological disorders, and its mainly clinical symptoms, including patient hand's kinetic tremor, dystonia, ataxia, etc., would influence the daily life of patients inordinately. Current ET diagnosis highly replies on the clinical evaluation and neurological examination, so the objective measurement indicators are particularly important in the auxiliary diagnosis of ET. In this research, the Archimedes spiral line freehand sketching samples without template assistance is collected and the Convolutional Neural Network (CNN) model of optimized structure is adopted to fully analyze the tremor, spacing of turns, shape, etc. shown in the handwriting samples of patients with ET, including the following main process: characteristics extraction, model visualization and subregional relevance evaluation. Dropout is used as a regularization technique in the network structure. The test group consisted of 50 patients with confirmed ET and the control group consisted of 40 healthy individuals. The main research objectives of this paper comprise two points: on the one hand, to achieve effective automatic classification of patients with ET and healthy controls using a scheme combining deep learning and simple hand mapping for the purpose of primary disease screening; on the other hand, to design sub-regional automatic classification experiments to demonstrate that Archimedean spiral hand drawings of patients with ET do have distinct local features, and to lay the experimental foundation for future hand drawing-based automatic aid for the identification of a variety of neurodegenerative diseases. Our model's average accuracy rate in test set reaches 89.3%, and average AUC is 0.972, with favorable stability and generalization performance. Besides, subregional characteristics recognition proofs that the spiral line samples of most of the patients with ET show more category-related characteristics in the local area of upper right, which provides evidences and theory update for predecessors' medical research.

Ready for handwriting? A reference data study on handwriting readiness assessments.

INTRODUCTION: Early evaluation of writing readiness is essential to predict and prevent handwriting difficulties and its negative influences on school occupations. An occupation-based measurement for kindergarten children has been previously developed: Writing Readiness Inventory Tool In Context (WRITIC). In addition, to assess fine motor coordination two tests are frequently used in children with handwriting difficulties: the modified Timed Test of In-Hand Manipulation (Timed TIHM) and the Nine-Hole Peg Test (9-HPT). However, no Dutch reference data are available. AIM: To provide reference data for (1) WRITIC, (2) Timed-TIHM and (3) 9-HPT for handwriting readiness assessment in kindergarten children. METHODS: Three hundred and seventy-four children from Dutch kindergartens in the age of 5 to 6.5 years (5.6±0.4 years, 190 boys/184 girls) participated in the study. Children were recruited at Dutch kindergartens. Full classes of the last year were tested, children were excluded if there was a medical diagnosis such as a visual, auditory, motor or intellectual impairment that hinder handwriting performance. Descriptive statistics and percentiles scores were calculated. The score of the WRITIC (possible score 0-48 points) and the performance time on the Timed-TIHM and 9-HPT are classified as percentile scores lower than the 15th percentile to distinguish low performance from adequate performance. The percentile scores can be used to identify children that are possibly at risk developing handwriting difficulties in first grade. RESULTS: WRITIC scores ranged from 23 to 48 (41±4.4), Timed-TIHM ranged from 17.9 to 64.5 seconds (31.4± 7.4 seconds) and 9-HPT ranged from 18.2 to 48.3 seconds (28.4± 5.4). A WRITIC score between 0-36, a performance time of more than 39.6 seconds on the Timed-TIHM and more than 33.8 seconds on the 9-HPT were classified as low performance. CONCLUSION: The reference data of the WRITIC allow to assess which children are possibly at risk developing handwriting difficulties.

Análise de estrutura interna do IBALEC / Analysis of the internal structure of IBALEC / Análisis de la estructura interna de IBALEC

O Instrumento para Breve Avaliação da Leitura, Escrita e Compreensão (IBALEC) se propõe a avaliar o desempenho de alunos de escola pública no domínio das habilidades básicas de alfabetização. Após a validação de conteúdo, por meio da análise de um grupo de especialistas, o IBALEC foi aplicado individualmente em 825 alunos (439 do sexo masculino e 386 do sexo feminino) do 1º ao 5º ano do ensino fundamental em duas escolas públicas, a fim de realizar a análise de sua estrutura interna. As análises fatoriais efetuadas demonstraram bom ajustamento do instrumento a um modelo de estrutura interna composto por cinco fatores correlacionados, correspondentes aos construtos teóricos que subsidiaram a sua construção: leitura de palavras, escrita de palavras, escrita de frases, leitura/compreensão de frases e, leitura/compreensão de texto. Esses resultados constituem forte evidência da validade de construto do IBALEC, para avaliar as habilidades a que se propõe. (AU)

The Instrument for Brief Assessment of Reading, Writing, and Comprehension (IBALEC) aims to assess the performance of public school students in the mastery of basic literacy skills. After content validation, based on the analysis of a group of specialists, the IBALEC was applied individually to 825 students (439 boys and 386 girls) from the 1st to the 5th year of Elementary Education in two public schools, to analyze its internal structure. The factorial analyses performed demonstrated a good fit of the instrument to an internal structure model composed of five correlated factors, corresponding to the theoretical constructs that subsidized its construction: word reading, word writing, sentence writing, sentence reading comprehension, and reading/understanding text. These results are strong evidence of the construct validity of IBALEC, to assess the proposed skills. (AU)

El Instrumento de Evaluación Breve de Lectura, Escritura y Comprensión (IBALEC) tiene como objetivo evaluar el desempeño de los estudiantes de escuelas públicas en las competencias básicas de alfabetización. Después de la validación del contenido, mediante el análisis de un grupo de especialistas, el IBALEC se aplicó de forma individual a 825 alumnos (439 niños y 386 niñas) de 1º a 5º año de primaria de dos centros públicos, con el fin de realizar el análisis de su estructura interna. Los análisis factoriales ejecutados demostraron un buen ajuste del instrumento a un modelo de estructura interna compuesto por cinco factores correlacionados, correspondientes a los constructos teóricos que subsidiaban su construcción: lectura de palabras, escrita de palabras, escrita de oraciones, lectura/comprensión de oraciones, y lectura/comprensión de texto. Estos resultados constituyen una fuerte evidencia de la validez de constructo del IBALEC para evaluar las habilidades propuestas. (AU)

Development and Psychometric Evaluation of the Computer-Aided Measure of Chinese Handwriting Legibility (CAM-CHL) for School-Age Children.

IMPORTANCE: Handwriting legibility is the main criterion for determining whether a child has handwriting difficulties. A comprehensive assessment of handwriting legibility with sound psychometrics is essential to timely identification of handwriting difficulties and outcome measurement after handwriting interventions. OBJECTIVE: To evaluate the psychometrics of the Computer-Aided Measure of Chinese Handwriting Legibility (CAM-CHL) and to investigate Chinese handwriting legibility in school-age children using the CAM-CHL. DESIGN: Cross-sectional, repeated observation, test-retest. SETTING: Elementary schools in Taiwan. PARTICIPANTS: We recruited 25 lower-grade children for the examination of test-retest reliability, 75 children from all grade levels, and 10 senior schoolteachers for the examination of the CAM-CHL's convergent validity and the investigation of handwriting legibility. OUTCOMES AND MEASURES: Children were asked to copy a set of Chinese characters as legibly as possible. We used the CAM-CHL to assess handwriting legibility in four domains: Size, Orientation, Position, and Deformation. The schoolteachers were asked to subjectively assess the handwriting legibility using a 3-point Likert-type scale. RESULTS: The CAM-CHL demonstrated good to excellent test-retest reliability and acceptable random measurement error in all legibility domains. The CAM-CHL had fair to moderate convergent validity with schoolteachers' perceptions. Additionally, upper-grade children had better handwriting legibility in the Size and Position domains than lower-grade children. CONCLUSIONS AND RELEVANCE: The CAM-CHL, a comprehensive and objective method of assessing Chinese handwriting legibility, has sound reliability and acceptable validity, suggesting its potential as an outcome measure for school-age children. What This Article Adds: The CAM-CHL can be used in comprehensive evaluations of Chinese handwriting legibility in school-age children. The CAM-CHL has acceptable psychometrics for use as an outcome measure.

Preliminary Rasch Construct and Reliability Validation of TeleWrite: A Telehealth-Based Handwriting Assessment.

BACKGROUND: Telehealth is rapidly expanding, and telehealth-based occupational therapy assessments must be developed and validated to keep pace with this transition. TeleWrite aims to bridge this gap. OBJECTIVES: To analyze the tool's initial psychometric properties using Rasch methods. METHOD: Internal construct validity and test reliability were analyzed using data from 148 children from first to third grade. RESULTS: Rasch analysis helped to identify that TeleWrite is composed of three separate constructs for rate, accuracy, and fluency. All Infit/Outfit mean square (MNSQ) values fell within acceptable ranges of 0.5 to 1.7 logits. Separation analysis indicated lower but acceptable person separation values for rate (0.68-0.76) and fluency (0.61-0.73), but accuracy scales were in the poor-fair range (0.20-0.60), given sample limitations. CONCLUSION: TeleWrite is comprised of three separate constructs, showed a good fit with the Rasch model, indicated strong construct and internal validity, and moderate ability to reliably separate abilities of students in terms of handwriting skills.