Data Collection Applications Research Articles

The California collaborative network to promote data driven care and improve outcomes in early psychosis (EPI-CAL) project: rationale, background, design and methodology

BackgroundA prolonged first episode of psychosis (FEP) without adequate treatment is a predictor of poor clinical, functional, and health outcomes and significant economic burden. Team-based “coordinated specialty care” (CSC) for early psychosis (EP) has established effectiveness in promoting clinical and functional recovery. However, California’s CSC program implementation has been unsystematic and could benefit from standardizing its processes and data collection infrastructure. To address this, we established a consortium of EP clinics across the state via a Learning Health Care Network (LHCN) framework to develop the Early Psychosis Intervention Network of California (EPI-CAL). EPI-CAL’s LHCN developed a core battery of evidence-based measures for service users and family members and linked them together using a unique data collection and visualization application, Beehive.Methods and objectivesEPI-CAL’s LHCN collects, visualizes, and aggregates data at the individual and clinic level for EP programs across California via Beehive. Beehive was designed to: (1) collect outcomes data from service users receiving care at EP programs and their support persons, (2) provide the data to providers on a secure web-based dashboard to support measurement-based care, and (3) allow data to be used for program or research analysis. We will (1) determine the feasibility of implementing an LHCN across a diverse, decentralized network of early psychosis programs, (2) determine if the implementation of an LHCN increases the delivery of measurement-based care, and (3) determine if the implementation of measurement-based care is associated with significant improvements in key service user outcomes. EPI-CAL’s network will contribute data to the Early Psychosis Intervention Network (EPINET) program.DiscussionThe current study aims to establish an LHCN of EP clinics in California that implements harmonized data collection using Beehive and assesses the feasibility of establishing such a network. Our goal is for this harmonized data collection approach to be used to inform decisions and develop learning opportunities for service users, staff, and administrators, and to improve outcomes for service users and their supporters in CSC care. Further, the data will enable programs and research teams to examine what elements of care lead to program success and improved treatment outcomes for service users.Clinical trials registrationwww.ClinicalTrials.gov, identifier NCT04007510; registered 07/05/2019.

Leaf disease detection and classification with a CNN-LSTM hybrid model

Abstract. Smart agriculture refers to the use of modern information technology, Internet of Things technology, and artificial intelligence to achieve accurate management, efficient operation and sustainable development of the entire process of agricultural production. Smart agriculture mainly includes the application of data collection and analysis, intelligent agricultural machinery, precise fertilization, disease and pest detection and agricultural products traceability. Leaf disease detection and classification are considered as challenging yet important tasks in smart agriculture. Deep leaning methods have been proven effective for these image-based recognition tasks. In this study, two advanced deep learning methods, namely, Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) are combined together to achieve a further improvement. Numerical results demonstrate that the proposed method outperforms both CNN and LSTM variants.

GeriNOT in the Surgical Inpatient Setting.

The guideline of the Federal Joint Committee (G-BA) on quality assessment measures for the care of patients with hip fracture makes it mandatory for hospitals to use an appropriate geriatric screening instrument in the context of acute inpatient care. After systematic application of GeriNOT and data collection in the admission process with integration into the Hospital Information System (HIS), it is possible to identify potential risks in geriatric patients with other diagnoses as well.With the integration of GeriNOT into the acute inpatient admission process, it was examined whether vulnerable geriatric patients with other diagnoses could benefit from the early initiation of risk identification.The data base for the present study was a retrospective bicentric collection of electronic case records (May 2014 to April 2015, n=3,443). From this primary data set, the subgroup of inpatient acute admissions (n=821) in the orthopaedic/trauma surgery of a study centre was analysed and evaluated with respect to the endpoints "utilisation of needs-based post-inpatient care services" and "new admission to inpatient permanent/short-term care". The predictive power and classification accuracy of GeriNOT of these patients who were 70 years and older to the endpoints were assessed for several groups: total acute admissions, total fractures, hip fracture, and spinal disorders including spinal fractures.A total of 821 patients were admitted as acute inpatients during the study period. The mean age of the patients was 81.4±6.8 years (n=821; 68.1% women, 31.9% men). The following subgroups were formed and analysed: total fractures (n=490), spinal disorders (n=265) including spinal fractures (n=174), and hip fracture (n=108). Both in the overall group (n=821; M=4.279; SD=2.180) and in the subgroups, the mean GeriNOT score was above the threshold ≥4. The highest score was found in the hip fracture group (M=4.852; SD=2.022), and the lowest in the spine fracture group (M=4.177; SD=2.171). At admission, if the terms of variables for requiring treatment were "polypharmacy" and "nursing services already used as needed", there were only slight differences in the diagnostic groups. Admissions from short-term and long-term care occurred in the total group in 16.44% of cases, most frequently with 31.48% in the group of hip fractures, compared to spinal diseases with 6.79%. For this group, GeriNOT detected an elevated risk with respect to the defined endpoints. However, only 4.26% of all patients with identified geriatric risk potential received further geriatric care.The results showed increased geriatric risk in all analysed groups, but most pronouncedly within the group of spinal diseases. The HIS-supported use of GeriNOT offers the possibility of systematic risk identification in acute inpatient admission management. The continuous visualisation of results at HIS workstations throughout the workflow could be used as a starting point for the subsequent application of standardized assessment tools and risk-adjusted treatment pathways. These findings could potentially improve outcomes.

The Shape(s) of Information Practice: Using Radial Mapping Qualitatively

ABSTRACTInformation practices comprise both seeking and avoidance. Although information practices scholars use qualitative and visual methods to understand seeking practices, they rarely do so to understand avoidance. This paper proposes a new visual method that supports revealing and explaining the complex interplay of seeking and avoidance in rich qualitative data. We introduce seven dimensions of information seeking and avoidance practice (intensity, granularity, engagement, control, relevance, quality, and timeliness). We conceptualize these visually as axes radiating outward from a central origin. We use Excel radar charts to depict these dimensions, allowing us to identify and characterize the shapes of information seeking and avoidance in everyday information practices. We apply our approach to two cases from published literature to show how mapping reveals the interplay between seeking and avoidance at one point in time and over time. We propose potential data collection and analysis applications of this method for information practices research.

Information System for Data Collection on Building Tools and Materials Using the Rational Unified Process (RUP) Method

UD. FLORA records the re-inventory of building equipment that has been sold by reading the transactions one by one in the ledger. A proper way is needed to overcome the problems that occur. This research aims to create an information system software. In making information system software, a method is needed that can carry out the stages from initial design to implementation of the software. This research uses the Rational Unified Process (RUP) method, namely a software development approach that is carried out repeatedly (iterative), focuses on architecture (architecture-centered), is more directed based on use cases (use case driven). This research produces a tool data collection application at UD. FLORA using the RUP method. The existence of an information system for data collection on building equipment using the Rational Unified Process method means that UD. FLORA can be helped in managing building equipment data collection. The system enables real-time transaction recording, providing better insight into inventory status, and supporting more accurate decision-making.

Criteria determination for selecting a projects management information system

The object of research is the process of selecting a project management information system. When selecting a PMIS for the customer organization, there is a need for a specific methodology to formalize the selection stages. The main existing selection methods and their criteria do not provide sufficient and objective information for selecting a PMIS. This research aims to develop a model and method based on it to systematize this process and ensure an objective and rational choice of PMIS, which in turn enhances the productivity and quality of project management. The method of analytic hierarchy process and a developed model are chosen as the basis for developing the method for selecting PMIS. The developed method consists of five stages: determining the basic assessments of PMIS criteria and their weights using experts; determining the assessment of PMIS sub-criteria; determining the global weight of the criteria; evaluating alternatives for each sub-criterion; and selecting the best alternative. An experimental verification of the developed model and method was conducted by creating a web application for data collection and processing within the considered stages. Python and specific libraries that enable the integration of the developed model and method into the company's website were used to develop the information technology. The obtained results demonstrate the feasibility of the chosen criteria in evaluating alternative PMIS by the customer using the formed model and method.

Outcomes of a Short-Duration, Large-Scale Canine Rabies Vaccination Campaign in Central Cambodia.

WHO and WOAH advocate for annual high-coverage canine rabies vaccination campaigns as the most sustainable approach to eliminate the risk of dog rabies transmission to humans. It is estimated that Cambodia has one of the highest human rabies deaths per capita of any country (5.8 human deaths per 100,000 people), highlighting the urgent need to implement an effective canine rabies vaccination program. To this end, a coalition of government and non-government organizations conducted a pioneering short-duration dog rabies vaccination campaign over 10 days across Phnom Penh and Kandal Provinces in May 2023. Over 10 working days, 120 vaccination teams, each consisting of two vaccinators and one tuk-tuk driver, traveled door-to-door to deliver parenteral rabies vaccines to all dogs that could be held by the teams or members of the community. Spatial team management and data collection were conducted through the WVS Data Collection Application. A total of 74,983 dogs were vaccinated, giving a mean vaccination rate of 62.5 dogs per team per day. An additional 2145 cats were vaccinated. Of all dogs encountered by the teams, 84.0% could be vaccinated, with 99.1% of those being identified as owned. Post-vaccination surveys of 4356 households estimated a mean vaccination coverage of 70.7% in owned dogs across the districts of Phnom Penh Province. Short-duration, large-scale canine rabies vaccination campaigns can achieve high vaccination coverage using a door-to-door approach in urban centers of Cambodia. Data gathered through the campaign yielded insights to support the refinement and planning of a wider rabies control strategy and is anticipated to drive further support for subsequent campaigns in Cambodia.

The Effectiveness of Updating Data on Small and Medium Industrial Business Actors in the Industrial Sector at the Cirebon City Cooperatives, Small and Medium Enterprises, Trade and Industry Office

The development of Science and Technology (IPTEK) affects various aspects of human life, including in the context of organizations and government agencies. In Cirebon City, the management of Small and Medium Industries (SMEs) by the Trade and Industry Small and Medium Business Cooperative Office faces challenges in updating data that has not been fully computerized. This study uses a descriptive qualitative approach to evaluate the effectiveness of the IKM data update process. The study results show that the number of SMEs is increasing every year, although there are still obstacles, such as incomplete information and manual processes that slow down efficiency. The application of digital technologies, such as applications for data collection and update, is proposed as a solution to improve the efficiency and accuracy of processes. It is hoped that better data updates, facilitated by digital technologies, can significantly support the development of the creative economy of Cirebon City through the identification of more appropriate needs and opportunities, as well as the optimization of government programs in the SME sector.

Application of Wireless Network Data Collection Based on Optical Topology Sensors in Sports Technology Evaluation

Application of Wireless Network Data Collection Based on Optical Topology Sensors in Sports Technology Evaluation

Complex system for analysis and monitoring of technological processes based on tomography

The main objective was to build a mobile impedance/capacitance tomograph for flow analysis and process optimization. The product responds to the identified needs of target customers who operate in process monitoring, control, and optimization of manufacturing processes. They need information on data processing and analysis and spatial analysis of technological processes. The designed solution's target market is the food, chemical, pharmaceutical, petrochemical, and mining industries. The paper presents the device's design and an application for data collection and visualization using tomography. Tomography is a technique for imaging a cross-section of an object based on measurements taken from the outside. Tikhonov regularisation reconstructions were used to reconstruct tomographic images. Test measurements were taken to confirm the correct operation of the completed system. Keywords: tomography, production analysis, optimization, process management, process modeling, process control.

Analisis Kerentanan Keamanan Website Menggunakan Metode PTES (Penetration Testing Execution And Standart)

With rapid advances in Information Technology (IT), the need for an IT-based learning framework and mechanism has become an unavoidable necessity. This requires solid security in a system, using the Penetration Testing Execution and Standard (PTES) method. This research method is quantitative and descriptive, used to illuminate website security and provide input for improvement. The research involved Tenable Nessus Professional tools that emit vulnerabilities, such as Browseable Web Directory and Potentially Clickjacking Web. The PTES stages covered include Planning, Data Collection, Scanning, Access Rights, Security, and Web Application Firewall (WAF) configuration. The hope is that this method will provide an in-depth understanding of potential threats and vulnerabilities in government information systems, as well as present solutions. The research results are expected to provide education and insight into website security. This allows related parties to take preventive and corrective steps, improve security, and protect the integrity and confidentiality of data entities. As a result, information systems are expected to be more secure and resistant to potential cyber threats.

Developing Android-based Disabled Data Information System to List Students with Disabilities (SIDABEL)

Manually registering the disabled population in riverbank areas is time-consuming and error-prone. This research aimed to develop an Android-based application for efficient and effective data collection of the disabled population. Using the ADDIE design within the Research and Development (RD) framework, participants included six teachers from Paramita Graha B/C Special School, ten teachers from Public Special School 3 Banjarmasin, twelve teachers from Harapan Bunda Special School, and ten Banua Anyar sub-district officers. Data collection involved interviews, questionnaires, and documentation, analyzed through both quantitative and qualitative methods. The study identified the needs of teachers and sub-district officers, guiding the development of the Disabled Data Information System (SIDABEL) application. The application’s feasibility was validated with average scores of 3.71 from material experts and 3.63 from media experts, indicating it was appropriate with revisions. Teacher assessments rated the application 3.81, 3.94, and 3.63 from Paramita Graha, Public Special School 3 Banjarmasin, and Harapan Bunda, respectively, categorizing it as good with some suggestions for improvement. After revisions, sub-district officers tested the application, resulting in a final score of 4.55 in the very good category. The SIDABEL application addresses inefficiencies and errors in manual data collection, leading to high user satisfaction and effectiveness in its final iteration. Further research should explore broader implementations and continuous improvements. The Android-based SIDABEL application significantly enhances the efficiency and accuracy of data collection for the disabled population in riverbank areas.

Subjective brain fog: a four-dimensional characterization in 25,796 participants.

Brain fog is associated with significant morbidity and reduced productivity and gained increasing attention after COVID-19. However, this subjective state has not been systematically characterised. To characterise self-reported brain fog. We systematically studied the cross-sectional associations between 29 a priori variables with the presence of "brain fog." The variables were grouped into four categories: demographics, symptoms and functional impairments, comorbidities and potential risk factors (including lifestyle factors), and cognitive score. Univariate methods determined the correlates of brain fog, with long-COVID and non-long-COVID subgroups. XGBoost machine learning model retrospectively characterised subjective brain fog. Bonferroni-corrected statistical significance was set at 5%. Digital application for remote data collection. 25,796 individuals over the age of 18 who downloaded and completed the application. 7,280 of 25,796 individuals (28.2%) reported experiencing brain fog, who were generally older (mean brain fog 35.7 ± 11.9 years vs. 32.8 ± 11.6 years, p < 0.0001) and more likely to be female (OR = 1.2, p < 0.001). Associated symptoms and functional impairments included difficulty focusing or concentrating (OR = 3.3), feeling irritable (OR = 1.6), difficulty relaxing (OR = 1.2, all p < 0.0001), difficulty following conversations (OR = 2.2), remembering appointments (OR = 1.9), completing paperwork and performing mental arithmetic (ORs = 1.8, all p < 0.0001). Comorbidities included long-COVID-19 (OR = 3.8, p < 0.0001), concussions (OR = 2.4, p < 0.0001), and higher migraine disability assessment scores (MIDAS) (+34.1%, all p < 0.0001). Cognitive scores were marginally lower with brain fog (-0.1 std., p < 0.001). XGBoost achieved a training accuracy of 85% with cross-validated accuracy of 74%, and the features most predictive of brain fog in the model were difficulty focusing and following conversations, long-COVID, and severity of migraines. This is the largest study characterising subjective brain fog as an impairment of concentration associated with functional impairments in activities of daily living. Brain fog was particularly associated with a history of long-COVID-19, migraines, concussion, and with 0.1 standard deviations lower cognitive scores, especially on modified Stroop testing, suggesting impairments in the ability to inhibit cognitive interference. Further prospective studies in unselected brain fog sufferers should explore the full spectrum of brain fog symptoms to differentiate it from its associated conditions.

Does electronic data collection perform better than paper-based data collection in health research fieldwork? A participatory action research in Zanzibar

IntroductionTechnological advancement in low-resource settings is opening the gateway to implementation of electronic data collection methods that improve data quality. We examined the concerns to use electronic data collection tool...

Factors Affecting Academic Resilience During Crises: Cases of Secondary School Students in Phuket, Thailand

Academic resilience is crucial in today&amp;rsquo;s crisis-prone society. This qualitative study explored the factors that shaped academic resilience during the COVID-19 pandemic to strengthen the global literature on resilience and postpandemic policy and practice in education. This study adopted a multiple-case study design, with the application of replication logic and data collection via semistructured interviews. The case studies featured interviews with three academically resilient students in Phuket and nine relevant informants, including parents, homeroom teachers, and local stakeholders. These interviews covered various factors surrounding personal qualities, families, peer groups, schools and teachers, communities and cultures, and the pandemic. Through thematic analysis, seven overarching themes emerged from the data: (1) achievement-oriented characteristics, (2) high aspirations, (3) COVID-19-driven adaptability, (4) self-directed learning in the use of online resources, (5) healthy family functioning, (6) role models, and (7) social support in the context of a giving culture.

Harnessing AI, IoT and Quantum Resonance Magnetic Analysis for Digital Diagnosis and Treatment

This research presents the development and implementation of a novel healthcare ecosystem leveraging Internet of Things (IoT), Artificial Intelligence (AI), Deep Learning and Quantum Magnetic Resonance Analysis (QMRA) technology. The aim is to streamline medical diagnostics and treatment processes by integrating a hardware device capable of conducting comprehensive body scans in minutes, coupled with an Android application for data collection and presentation. The hardware device utilizes Quantum Magnetic Resonance Analyzer for rapid and accurate body scanning, enabling users to record symptoms through voice clips and capture images of wounds or other concerns. The collected data is securely transmitted to a backend database hosted on Firebase for storage and retrieval. The Android app facilitates seamless access to patient profiles by registered doctors, empowering them to analyze scan reports, review recorded symptoms, and view images for informed diagnosis and treatment planning. By combining IoT, AI, and Quantum Magnetic Resonance Analysis, this healthcare ecosystem offers a transformative approach to medical care, improving efficiency, accessibility, and accuracy in diagnosis and treatment.

Perancangan Sistem Pendataan Santri Dan Pendataan Hafalan Berbasis Android Di Ma’had Tahfizhul Qur’an Ahlus Shuffah

This research aims to further analyze how to design and implement a student data collection application at Ma'had Tahfizhul Qur'an "Ahlus Shuffah" Balikpapan which can run on two different platforms, namely Detskop and Android. This research aims to produce an information system for managing student data and mobile-based student memorization data that runs on the Android platform and controlling student development and student memorization will become easier and more computable. The research method uses the waterfall method. The novelty of this research uses the design of a data collection application built using Android Studio and storing the data in an online database, which previously still used a manual system which was very troublesome, especially the invalidity and mismatch of data. The software used to build this application is Android Studio 3.1.2 using a database that can be operated online, namely Firebase. The results of this research make it easier to control the development of students' memorization and make it easier to check the data that has been input.

Application of Grey Prediction Models for Efficient Traffic Flow Prediction

Traffic flow prediction is crucial in transportation management and optimizing control strategies and infrastructure planning. However, traditional prediction methods often need help handling traffic data's inherent uncertainty and complexity. This paper explores the application of Grey prediction models as a promising approach for efficient traffic flow prediction. Grey prediction models, rooted in Grey systems theory, offer a robust framework for modelling systems with incomplete and uncertain information. The paper provides a comprehensive overview of Grey prediction models, including the Grey Verhulst, Grey-Markov, and Grey differential equation models. It discusses the process of data collection, pre-processing, and model application in traffic flow prediction. Case studies and experimental results demonstrate the effectiveness of Grey prediction models in accurately forecasting traffic flow patterns. Despite challenges such as data sparsity and model complexity, Grey prediction models offer significant advantages over traditional methods, including improved prediction accuracy and adaptability to dynamic traffic conditions. The paper concludes with recommendations for practitioners and researchers interested in leveraging Grey prediction models for efficient traffic management.

Pathophysiology characterization of Alzheimer's disease in South China's aging population: for the Greater-Bay-Area Healthy Aging Brain Study (GHABS).

The Guangdong-Hong Kong-Macao Greater-Bay-Area of South China has an 86million population and faces a significant challenge of Alzheimer's disease (AD). However, the characteristics and prevalence of AD in this area are still unclear due to the rarely available community-based neuroimaging AD cohort. Following the standard protocols of the Alzheimer's Disease Neuroimaging Initiative, the Greater-Bay-Area Healthy Aging Brain Study (GHABS) was initiated in 2021. GHABS participants completed clinical assessments, plasma biomarkers, genotyping, magnetic resonance imaging (MRI), β-amyloid (Aβ) positron emission tomography (PET) imaging, and tau PET imaging. The GHABS cohort focuses on pathophysiology characterization and early AD detection in the Guangdong-Hong Kong-Macao Greater Bay Area. In this study, we analyzed plasma Aβ42/Aβ40 (A), p-Tau181 (T), neurofilament light, and GFAP by Simoa in 470 Chinese older adults, and 301, 195, and 70 had MRI, Aβ PET, and tau PET, respectively. Plasma biomarkers, Aβ PET, tau PET, hippocampal volume, and temporal-metaROI cortical thickness were compared between normal control (NC), subjective cognitive decline (SCD), mild cognitive impairment (MCI), and dementia groups, controlling for age, sex, and APOE-ε4. The prevalence of plasma A/T profiles and Aβ PET positivity were also determined in different diagnostic groups. The aims, study design, data collection, and potential applications of GHABS are summarized. SCD individuals had significantly higher plasma p-Tau181 and plasma GFAP than the NC individuals. MCI and dementia patients showed more abnormal changes in all the plasma and neuroimaging biomarkers than NC and SCD individuals. The frequencies of plasma A+/T+ (NC; 5.9%, SCD: 8.2%, MCI: 25.3%, dementia: 64.9%) and Aβ PET positivity (NC: 25.6%, SCD: 22.5%, MCI: 47.7%, dementia: 89.3%) were reported. The GHABS cohort may provide helpful guidance toward designing standard AD community cohorts in South China. This study, for the first time, reported the pathophysiology characterization of plasma biomarkers, Aβ PET, tau PET, hippocampal atrophy, and AD-signature cortical thinning, as well as the prevalence of Aβ PET positivity in the Guangdong-Hong Kong-Macao Greater Bay Area of China. These findings provide novel insights into understanding the characteristics of abnormal AD pathological changes in South China's older population.

An Internal Consistency Reliability Study of the Catalyst Datafinch Applied Behavior Analysis Data Collection Application With Autistic Individuals.

Introduction Many psychometric studies have scrutinized the dependability of different instruments for evaluating and treating autism using applied behavior analysis (ABA). However, there has been no exploration into the psychometric attributes of the Catalyst Datafinch Applied Behavior Analysis Data Collection Application, namely, internal consistency reliability measures. Materials and methods Four datasets were extracted (n=100, 98, 103, and 62) from published studies at The Oxford Center, Brighton, MI, ranging from March 19, 2023, through January 8, 2024, using Catalyst Datafinch as the data collection tool. All data were gathered by Board Certified Behavior Analysts (BCBAs) and behavioral technicians and designed to replicate how practitioners collect traditional paper and pencil data. SPSS Statistics (v. 29.0) computed internal consistency reliability measures, including Cronbach's alpha, inter-item, split-half, and interclass correlation coefficients. Results Dataset #1: Cronbach's alpha was 0.916 with seven items, indicating excellent reliability. Cronbach's split-half reliability for Part 1 was 0.777, indicating good reliability, and for Part 2 was 0.972, indicating excellent reliability. Guttman split-half coefficient was 0.817, indicating good reliability. Inter-item correlation coefficients ranged from0.474 to0.970. The average measures interclass correlation (ICC) was 0.916, indicating excellent reliability. Single measures (ICC) reliability was 0.609, indicating acceptable reliability. Dataset #2: Cronbach's alpha was 0.954 with three items, indicating excellent reliability. Cronbach's split-half reliability for Part 1 was 0.912, indicating excellent reliability, and for Part 2 was 0.975, indicating excellent reliability. Guttman split-half coefficient was 0.917, indicating excellent reliability. Inter-item correlation coefficients ranged from0.827to0.977. Average measures (ICC) was 0.954, indicating excellent reliability. Single measures (ICC) reliability was 0.875, indicating good reliability. Dataset #3: Cronbach's alpha was 0.974 with three items, indicating excellent reliability. Cronbach's split-half reliability for Part 1 was 0.978, indicating excellent reliability. Split-half reliability for Part 2 was 0.970, indicating excellent reliability. Guttman split-half coefficient was 0.935, indicating excellent reliability. Inter-item correlation coefficients ranged from0.931to0.972. The average measures (ICC) was 0.974, indicating excellent reliability. Single measures (ICC) reliability was 0.926, indicating excellent reliability. Dataset #4: Cronbach's alpha was 0.980 with 12 items, indicating excellent reliability. Cronbach's split-half reliability for Part 1 was 0.973, indicating excellent reliability. Split-half reliability for Part 2 was 0.996, indicating excellent reliability. Guttman split-half coefficient was 0.838, indicating good reliability. Inter-item correlation coefficients ranged from0.692to0.999. The average measures (ICC) was 0.980, indicating excellent reliability. Single measures (ICC) reliability was 0.804, indicating good reliability. Conclusions These results suggest that Catalyst Datafinch demonstrates high internal consistency reliability when used with individuals with autism. This indicates that the application is reliable for collecting and analyzing behavioral data in this population. The ratings ranged from good to excellent, indicating a high consistency in the measurements.