Minimizing attention of deficit hyperactivity disorder in children ages 7–10 years through early detection in state 1st SD 1 Poasia Kendari 2017

Armayani,; Yati, Mimi; Yusnayanti, Cici; Firman, Firman

doi:10.1016/j.enfcli.2019.11.026

Cited by 3 publications

(1 citation statement)

References 2 publications

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“…Dalam hal ini, gangguan hiperaktif merupakan gangguan yang sering terjadi pada gangguan perilaku anak. Masalah hiperaktifitas terkait dengan ADHD (Attention Deficit Dyperactive Disorder), yang sebagian besar terjadi pada masa kanak-kanak (Armayani et al, 2020). Prevalensi global GPPH diperkirakan sekitar 5% dari anak usia sekolah (Sulemba et al, 2016).…”

Section: Hasil Dan Pembahasanunclassified

Deteksi Dini Tumbuh Kembang pada Anak Usia Pra Sekolah

Nesy,

Pujaningsih

2023

J. Obs. J. Pendidik. Anak Usia Dini

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Deteksi dini tumbuh kembang sangat penting, namun penelitian menunjukkan bahwa pelayanan deteksi dini pada suatu wilayah masih rendah. Berdasarkan data TK Pertiwi, deteksi dini oleh pihak Puskesmas yang dilakukan ke sekolah hanya sebatas deteksi dini pertumbuhan saja. Oleh karena itu, penulis mengamati deteksi dini di empat area meliputi; pertumbuhan dan perkembangan, penyimpangan perilaku emosional (KMPE) dan gangguan pemusatan perhatian dan hiperaktifitas (GPPH). Tujuan dari penelitian ini adalah untuk mendeteksi penyimpangan tumbuh kembang pada anak. Penelitian ini mengguanakan deskriptif kualitatif dengan informan penelitian guru. Pengumpulan data dilakukan melalui cara observasi, wawancara, dan dokumentasi dengan subjek penelitian adalah sebanyak sepuluh anak hasil rujukan dari guru. Hasil deteksi dini pertumbuhan menunjukkan status gizi dua anak kurus, tujuh anak normal, dan satu anak gemuk. Satu anak kemungkinan mengalami penyimpangan pada area perkembangan, KMPE enam anak, dan GPPH tiga anak. Penelitian ini menunjukkan bahwa rujukan guru terkait permasalahan perkembangan anak mempunyai akurasi yang tinggi dan memerlukan skrining lebih lanjut sebagai awal upaya intervensi.

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Section: Hasil Dan Pembahasanunclassified

Deteksi Dini Tumbuh Kembang pada Anak Usia Pra Sekolah

Nesy,

Pujaningsih

2023

J. Obs. J. Pendidik. Anak Usia Dini

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Information System for Symptom Diagnosis and Improvement of Attention Deficit Hyperactivity Disorder: Protocol for a Nonrandomized Controlled Pilot Study

Pandria¹,

Petronikolou²,

Lazaridis³

et al. 2022

JMIR Res Protoc

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Background Attention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders during childhood; however, the diagnosis procedure remains challenging, as it is nonstandardized, multiparametric, and highly dependent on subjective evaluation of the perceived behavior. Objective To address the challenges of existing procedures for ADHD diagnosis, the ADHD360 project aims to develop a platform for (1) early detection of ADHD by assessing the user’s likelihood of having ADHD characteristics and (2) providing complementary training for ADHD management. Methods A 2-phase nonrandomized controlled pilot study was designed to evaluate the ADHD360 platform, including ADHD and non-ADHD participants aged 7 to 16 years. At the first stage, an initial neuropsychological evaluation along with an interaction with the serious game developed (“Pizza on Time”) for approximately 30-45 minutes is performed. Subsequently, a 2-week behavior monitoring of the participants through the mADHD360 app is planned after a telephone conversation between the participants’ parents and the psychologist, where the existence of any behaviors characteristic of ADHD that affect daily functioning is assessed. Once behavior monitoring is complete, the research team invites the participants to the second stage, where they play the game for a mean duration of 10 weeks (2 times per week). Once the serious game is finished, a second round of behavior monitoring is performed following the same procedures as the initial one. During the study, gameplay data were collected and preprocessed. The protocol of the pilot trials was initially designed for in-person participation, but after the COVID-19 outbreak, it was adjusted for remote participation. State-of-the-art machine learning (ML) algorithms were used to analyze labeled gameplay data aiming to detect discriminative gameplay patterns among the 2 groups (ADHD and non-ADHD) and estimate a player’s likelihood of having ADHD characteristics. A schema including a train-test splitting with a 75:25 split ratio, k-fold cross-validation with k=3, an ML pipeline, and data evaluation were designed. Results A total of 43 participants were recruited for this study, where 18 were diagnosed with ADHD and the remaining 25 were controls. Initial neuropsychological assessment confirmed that the participants in the ADHD group showed a deviation from the participants without ADHD characteristics. A preliminary analysis of collected data consisting of 30 gameplay sessions showed that the trained ML models achieve high performance (ie, accuracy up to 0.85) in correctly predicting the users’ labels (ADHD or non-ADHD) from their gameplay session on the ADHD360 platform. Conclusions ADHD360 is characterized by its notable capacity to discriminate player gameplay behavior as either ADHD or non-ADHD. Therefore, the ADHD360 platform could be a valuable complementary tool for early ADHD detection. Trial Registration ClinicalTrials.gov NCT04362982; https://clinicaltrials.gov/ct2/show/NCT04362982 International Registered Report Identifier (IRRID) RR1-10.2196/40189

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An Information System for Symptom Diagnosis and Improvement of Attention Deficit Hyperactivity Disorder: The ADHD360 Project (Preprint)

Pandria¹,

Petronikolou²,

Lazaridis³

et al. 2022

Preprint

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BACKGROUND Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common neurodevelopmental disorders during childhood, however the diagnosis procedure remains challenging as it is non-standardized, multi-parametric and highly dependent on subjective evaluation of the perceived behavior. OBJECTIVE To address the challenges of existing procedures for ADHD diagnosis, the ADHD360 project aims to develop a platform for (a) early detection of ADHD by assessing the user’s likelihood of having ADHD characteristics and (b) providing complementary training for ADHD management. METHODS A two-phase pilot study was designed to evaluate the ADHD360 platform, including ADHD and non-ADHD participants aged 7-16 years. Machine Learning methods were used to detect discriminative gameplay patterns among the two groups (ADHD, non-ADHD) and estimate a player’s likelihood of having ADHD characteristics. RESULTS A preliminary analysis of collected data showed that the trained models achieve high performance in correctly predicting a user’s label (ADHD or non-ADHD) from his gameplay session in the ADHD360 platform. CONCLUSIONS ADHD360 is characterized by notable capacity to discriminate player gameplay behavior as either ADHD or non-ADHD. Therefore, the ADHD360 platform could be a valuable complementary tool for early ADHD detection. CLINICALTRIAL ClinicalTrials.gov NCT04362982

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Minimizing attention of deficit hyperactivity disorder in children ages 7–10 years through early detection in state 1st SD 1 Poasia Kendari 2017

Cited by 3 publications

References 2 publications

Deteksi Dini Tumbuh Kembang pada Anak Usia Pra Sekolah

Deteksi Dini Tumbuh Kembang pada Anak Usia Pra Sekolah

Information System for Symptom Diagnosis and Improvement of Attention Deficit Hyperactivity Disorder: Protocol for a Nonrandomized Controlled Pilot Study

An Information System for Symptom Diagnosis and Improvement of Attention Deficit Hyperactivity Disorder: The ADHD360 Project (Preprint)

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