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  1. Fulltext Training and Assessing Model for the Ability of Community Health Volunteers in Anthropometric Measurement Using the Rasch Stacking and Racking Analyses
    Sunjaya DK, Herawati DMD, Indraswari N, Megawati G, Sumintono B
    J Environ Public Health, 2021;2021:5515712.
    PMID: 34603456 DOI: 10.1155/2021/5515712
    Background: Inappropriate anthropometric measurements of infants and toddlers lead to a misclassification in nutritional status and loss of important interventions. Considering the practice conducted in this program within a country, its impact on millions of children must be considered. This study assesses the ability of community health volunteers (CHVs) before and after anthropometric training on infants and toddlers. Methods. This study used a quantitative approach with a quasiexperimental and pretest-posttest design. The pre- and posttraining assessments of CHVs were conducted by standardized trainers using instruments developed according to WHO standards. There were 11 and 13 statement items for infants' and toddlers' indicators of assessment in anthropometric measurements, respectively. The result of the assessment was then analyzed using Rasch modeling with stacking and racking data analysis techniques.

    Results: The CHVs' skills before training were far from adequate. Although widely varied, all trainees improved their abilities. Stacking analysis showed that the skills of all CHVs in measuring infants and toddlers increased by 2.68 and 3.34 logits (p < 0.01), respectively. Racking analysis showed a decrease in the perceived difficulty of all items by 2.61 and 3.07 logits for infant and toddler measurements, respectively (p < 0.01). The results of the racking analysis showed that the difficulty in measuring the anthropometrics of infants decreased more than that of toddlers.

    Conclusions: CHVs' capacity to monitor child growth must be refreshed regularly. Standardized and proper training and assessment were developed to make CHVs reliable in taking anthropometric measurements of infants and toddlers.

  2. Isolation of phenolic compound from Lawsonia inermis and its prediction as anti-diabetic agent using molecular docking and molecular dynamic simulation
    Musfiroh I, Ifaya M, Sahidin I, Herawati DMD, Tjitraresmi A, Abdurrahman S, et al.
    J Biomol Struct Dyn, 2023 Sep 29.
    PMID: 37776010 DOI: 10.1080/07391102.2023.2262595
    High blood sugar is a defining feature of chronic disease, diabetes mellitus (DM). There are numerous commercially available medications for the treatment of DM. However, managing the patient's glucose levels remain a challenge because of the gradual reduction in beta-cell function and some side effects from the long-term use of various medications. Previous research has shown that the phenolic compound of henna plant (Lawsonia inermis L.) has the potential as anti-diabetic agent since it is able to suppress the digesting of α-amylase enzyme. In these studies, the plant' phenolic compounds have been isolated and characterized using UV, IR, NMR and LC-MS methods. Furthermore, the compound interaction into the active site of the α-amylase enzyme has been analyzed using molecular docking and molecular dynamics, as well as into α-glucosidase enzyme for predicting of the affinities. The results showed that isolated compound has the molecular formula of C15H10O6 with eleven degrees of unsaturation (DBE; double bond equivalence). The DBE value corresponds to the structure of the luteolin compound having an aromatic ring (8), a carbonyl group on the side chain (1) and a ketone ring with (2). The interaction study of the isolated compound with α-amylase and α-glucosidase enzyme using molecular docking compared to the positive control (acarbose) gave binding energy of -8.03 and -8.95 kcal/mol, respectively. The molecular dynamics simulation using the MM-PBSA method, complex stability based on solvent accessible surface area (SASA), root mean square deviation (RMSD), and root mean square fluctuation (RMSF) revealed that the compound has a high affinity for receptors. The characteristics of skin permeability, absorption, and distribution using ADME-Tox model were also well predicted. The results indicate that the phenolic compound isolated from L. inermis leaf was luteolin and it has the potential as an anti-diabetic agent.Communicated by Ramaswamy H. Sarma.
  3. Fulltext Online Mental Health Survey for Addressing Psychosocial Condition During the COVID-19 Pandemic in Indonesia: Instrument Evaluation
    Sunjaya DK, Sumintono B, Gunawan E, Herawati DMD, Hidayat T
    Psychol Res Behav Manag, 2022;15:161-170.
    PMID: 35082539 DOI: 10.2147/PRBM.S347386
    BACKGROUND: Regular monitoring of the pandemic's psychosocial impact could be conducted among the community but is limited through online media. This study aims to evaluate the self-rating questionnaire commonly used for online monitoring of the psychosocial implications of the coronavirus disease 2019 (COVID-19) pandemic.

    METHODS: The data were taken from the online assessment results of two groups, with a total of 765 participants. The instruments studied were Self-Rating Questionnaire (SRQ-20), post-traumatic stress disorder (PTSD), and Center for Epidemiological Studies Depression Scale-10 (CESD-10), used in the online assessment. Data analysis used Rasch modeling and Winsteps applications. Validity and reliability were tested, and data were fit with the model, rating scale, and item fit analysis.

    RESULTS: All the scales for outfit mean square (MnSq) were very close to the ideal value of 1.0, and the Chi-square test was significant. Item reliability was greater than 0.67, item separation was greater than 3, and Cronbach's alpha was greater than 0.60; all the instruments were considered very good. The raw variance explained by measures for the SRQ-20, PTSD, and CESD-10 was 30.7%, 41.6%, and 47.6%, respectively. The unexplained eigenvalue variances in the first contrast were 2.3, 1.6, and 2.0 for the SRQ-20, PTSD, and CESD-10, respectively. All items had positive point-measure correlations.

    CONCLUSION: The internal consistency of all the instruments was reliable. Data were fit to the model as the items were productive for measurement and had a reasonable prediction. All the scales are functionally one-dimensional.

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