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Adam Mudinillah
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Journal of World Future Medicine, Health and Nursing
Published by Yayasan Adra Karima Hubbi
ISSN : 29880459     EISSN : 29887550     DOI : 10.70177/health
Core Subject :
Journal of World Future Medicine, Health and Nursing is a leading international journal focused on the global exchange of knowledge in medicine, health, and nursing, as well as advancing research and practice across health disciplines. The journal provides a forum for articles reporting on original research, systematic and scholarly reviews focused on health science, clinical practice and education from around the world. Journal of World Future Medicine, Health and Nursing publishes national and international research in an attempt to present a reliable and respectable information source for the researchers. Journal of World Future Medicine, Health and Nursing has been published since 2023, published three times a year January, May and September,. The articles submitted for publication are subjected to double-blind reviewing process. The journal publishes original articles in English.
Arjuna Subject : -
Articles 143 Documents
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Analysis of Risk Factors for Stunting in Toddlers in South Tapanuli Regency in 2024 Evi Irianti; Melva Melva
Journal of World Future Medicine, Health and Nursing Vol. 3 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v3i3.2050

Abstract

Stunting is a major public health issue with long-term effects on the quality of human resources. The prevalence of stunting in South Tapanuli Regency increased from 30.8% in 2021 to 39.8% in 2022. This study aims to identify risk factors contributing to stunting in toddlers. The study employs a cross-sectional design with a correlational approach. The sample consists of 350 toddlers selected through purposive sampling, with data collected via structured interviews. The main contributing factors to stunting include a history of low birth weight (p=0.009), the introduction of complementary feeding before six months of age (p=0.000), and incomplete immunization status, which increases the risk of stunting with an odds ratio (OR) of 2.510. Virtual care is associated with a stable or improved HRQOL and patient and family satisfaction in pediatric T1DM. Decision makers need to consider expanding virtual access to pediatric diabetes care that can improve equitable access to quality care across healthcare systems globally.
AI-Driven Diagnostic Imaging: Enhancing Early Cancer Detection Through Deep Learning Models Danang Ariyanto; Napat Chai; Pong Krit
Journal of World Future Medicine, Health and Nursing Vol. 3 No. 6 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v3i6.2369

Abstract

Early detection is critical for improving cancer survival rates, yet the interpretation of diagnostic images is subject to human error and variability. Artificial intelligence (AI), specifically deep learning, presents a transformative opportunity to enhance diagnostic accuracy and speed. This study aimed to develop and validate a deep learning model to improve the accuracy and efficiency of early-stage cancer detection in radiological images compared to human expert interpretation. A convolutional neural network (CNN) was trained and validated on a curated dataset of over 20,000 mammography images. The model's diagnostic performance was rigorously evaluated using key metrics, including accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC), against a biopsy-verified ground truth. The AI model achieved an overall accuracy of 97.2%, with a sensitivity of 98.1% and a specificity of 96.5%. The model's performance, with an AUC of 0.98, was comparable to that of senior radiologists and significantly reduced false-negative rates. AI-driven deep learning models are highly effective and reliable tools for augmenting diagnostic imaging. They can significantly enhance early cancer detection, reduce diagnostic errors, and serve as a powerful assistive tool for radiologists in clinical practice.
Gamification in Mobile Health Apps: A Quasi-Experimental Study on Adolescent Obesity Prevention Cau Kim Jiu; Ming Kiri; Sokha Dara
Journal of World Future Medicine, Health and Nursing Vol. 3 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v3i4.2370

Abstract

Adolescent obesity has become a global public health crisis, demanding effective and engaging intervention strategies. While mobile health (mHealth) apps offer a scalable platform for promoting healthy lifestyles, maintaining long-term engagement among adolescents remains a significant challenge. This study aimed to evaluate the effectiveness of a gamified Health application on promoting positive changes in physical activity, dietary habits, and anthropometric measures among adolescents at risk for obesity, compared to a non-gamified version of the same application. A 12-week, quasi-experimental study was conducted with 150 adolescents (aged 13-16) with a BMI above the 85th percentile. Participants were assigned to either an intervention group (n=75) using a gamified mHealth app featuring points, badges, and leaderboards, or a control group (n=75) using a non-gamified version with identical health content. The intervention group demonstrated significantly greater increases in moderate-to-vigorous physical activity (p < .01) and higher consumption of fruits and vegetables (p < .05) compared to the control group. Furthermore, the gamified app users showed a modest but statistically significant reduction in their BMI z-score (p < .05), a change not observed in the control group. App engagement metrics were also 70% higher in the intervention group. The integration of gamification elements into mHealth applications is a highly effective strategy for preventing obesity in adolescents.
Social Isolation and Cognitive Decline in Aging Populations: AI-Powered Monitoring Systems for Early Detection Omar Khan; Clara Mendes; Marcus Tan; Ardi Azhar Nampira
Journal of World Future Medicine, Health and Nursing Vol. 3 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v3i4.2371

Abstract

Social isolation is a significant and modifiable risk factor for accelerated cognitive decline and dementia in aging populations. Traditional methods for detecting cognitive changes, such as clinical screenings, are often infrequent and fail to capture the subtle, early behavioral shifts that precede a formal diagnosis. This study aimed to develop and validate an artificial intelligence model designed for the early detection of cognitive decline by passively monitoring behavioral and vocal biomarkers of social isolation in older adults living independently. A 24-month, prospective longitudinal study was conducted with a cohort of 200 community-dwelling adults aged 70 and older. A suite of unobtrusive in-home sensors was used to passively collect data on movement patterns, social communication (frequency and duration of conversations), and computer/phone usage. The AI-powered system identified individuals who would later show clinically significant cognitive decline with an accuracy of 91% and a lead time of approximately 7 months before formal assessment. The model successfully distinguished between simple loneliness and the specific behavioral patterns of social withdrawal associated with cognitive impairment. AI-powered passive monitoring systems are a highly effective and ecologically valid tool for the pre-clinical detection of cognitive decline linked to social isolation.
Heatwaves and Urban Elderly Mortality: A Retrospective Cohort Study Using Remote Sensing Data Sri Suparni; Clara Mendes; Bruna Costa
Journal of World Future Medicine, Health and Nursing Vol. 3 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v3i4.2372

Abstract

Climate change is intensifying the frequency and severity of heatwaves, posing a significant threat to public health. Urban elderly populations are particularly vulnerable due to the urban heat island effect and age-related physiological sensitivities. Quantifying this risk with precision is essential for developing targeted public health interventions. This study aimed to quantify the association between exposure to extreme heat events, as measured by remote sensing data, and all-cause mortality among an elderly urban population. A retrospective cohort study was conducted using health data for 50,000 urban residents aged 65 and over from 2015-2022. Land Surface Temperature (LST) data derived from Landsat satellites were used to define heatwave exposure at a granular, neighborhood level. Cox proportional hazards models were used to analyze the association between heatwave exposure and mortality, adjusting for confounding variables. A significant association was found between heatwave exposure and increased mortality risk. For each 1°C increase in LST during a heatwave, there was a 5.2% (95% CI: 4.5%-6.0%) increase in all-cause mortality. The risk was most pronounced in neighborhoods with lower green space coverage. Satellite-derived remote sensing data provide a powerful tool for assessing heatwave-related mortality risk in urban elderly populations. These findings underscore the urgent need for urban planning and public health strategies focused on heat mitigation to protect vulnerable residents.
SOCIAL ISOLATION AND COGNITIVE DECLINE IN AGING POPULATIONS: AI-POWERED MONITORING SYSTEMS FOR EARLY DETECTION Ava Lee; Thiago Costa; Clara Mendes
Journal of World Future Medicine, Health and Nursing Vol. 4 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v4i1.2373

Abstract

Social isolation is a significant and modifiable risk factor for accelerated cognitive decline and dementia in aging populations. Traditional methods for detecting cognitive changes, such as clinical screenings, are often infrequent and fail to capture the subtle, early behavioral shifts that precede a formal diagnosis. This study aimed to develop and validate an artificial intelligence model designed for the early detection of cognitive decline by passively monitoring behavioral and vocal biomarkers of social isolation in older adults living independently. A 24-month, prospective longitudinal study was conducted with a cohort of 200 community-dwelling adults aged 70 and older. A suite of unobtrusive in-home sensors was used to passively collect data on movement patterns, social communication (frequency and duration of conversations), and computer/phone usage. The AI-powered system identified individuals who would later show clinically significant cognitive decline with an accuracy of 91% and a lead time of approximately 7 months before formal assessment. Decreased frequency of vocal interactions and increased irregularity in daily routines were the most powerful predictors. AI-powered passive monitoring systems are a highly effective and ecologically valid tool for the pre-clinical detection of cognitive decline linked to social isolation.
Tele-Nursing in Post-Operative Care: Expanding Accessibility and Reducing Readmission Ratesh Catur Budi Susilo; Siri Lek; Pong Krit
Journal of World Future Medicine, Health and Nursing Vol. 3 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v3i5.2518

Abstract

Post-operative care is critical for ensuring optimal recovery, preventing complications, and reducing hospital readmission rates. Traditional follow up methods often face limitations, including geographic barriers, limited access to healthcare providers, and resource constraints, which can compromise patient outcomes. Tele-nursing offers a promising solution by delivering remote monitoring, education, and guidance, thereby enhancing accessibility and supporting continuity of care. This study investigates the effectiveness of tele-nursing interventions in post-operative care, focusing on patient outcomes, adherence to care protocols, and readmission rates. A mixed-methods approach was employed, integrating quantitative analysis of clinical metrics and readmission data with qualitative assessments of patient and nurse experiences. Data were collected from 180 post-operative patients across multiple surgical departments who received tele-nursing support over a 90 day period. Results indicated that tele-nursing significantly reduced 30 day readmission rates, improved adherence to post-operative care instructions, and increased patient satisfaction. Nurses reported enhanced ability to monitor patient recovery, provide timely interventions, and offer personalized education remotely. The study concludes that tele-nursing is an effective strategy to expand access to post-operative care, improve patient outcomes, and reduce healthcare system burden.
Robotics in Surgery: Enhancing Precision, Safety, and Patient Outcomes Anggun Wida Prawira; Vindi Tyastutik
Journal of World Future Medicine, Health and Nursing Vol. 3 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v3i4.2519

Abstract

Robotic-assisted surgery has revolutionized modern surgical practice by enhancing precision, reducing invasiveness, and improving patient outcomes. Traditional surgical techniques are often limited by human dexterity, fatigue, and the complexity of intricate procedures, which can increase the risk of complications and prolong recovery. Robotics offers advanced visualization, tremor filtration, and enhanced instrument control, enabling surgeons to perform complex procedures with greater accuracy and consistency. This study investigates the impact of robotic-assisted surgery on surgical precision, safety, and patient outcomes across multiple specialties. A systematic review and meta-analysis were conducted, synthesizing data from clinical trials, observational studies, and surgical outcome reports. Metrics analyzed included intraoperative precision, complication rates, operative time, postoperative recovery, and patient satisfaction. Results indicate that robotic surgery significantly reduces intraoperative errors, minimizes blood loss, shortens hospital stays, and enhances functional recovery compared to conventional techniques. Surgeons reported improved ergonomics and operative control, contributing to procedural consistency and reduced fatigue. The study concludes that robotics in surgery represents a critical advancement in surgical care, offering tangible benefits for both patients and clinicians. Successful integration requires continued training, technological refinement, and assessment of cost-effectiveness to ensure sustainable adoption. These findings support the expansion of robotic-assisted interventions as a standard of care in complex surgical procedures.
Artificial Intelligence in Precision Medicine: Transforming Genetic-Based Diagnostics and Patient Care Fitriah Handayani; Nurul Huda; Benny Novico Zani; Muntasir Muntasir
Journal of World Future Medicine, Health and Nursing Vol. 3 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v3i4.2520

Abstract

Precision medicine aims to tailor healthcare strategies to individual genetic, environmental, and lifestyle factors, enhancing diagnostic accuracy and treatment efficacy. Traditional approaches to genetic-based diagnostics often face challenges such as high complexity, large-scale data interpretation, and time-intensive analyses. Artificial intelligence (AI) offers transformative potential by enabling rapid, data-driven analysis of genomic information, supporting personalized patient care, and improving clinical decision-making. This study investigates the role of AI in enhancing genetic-based diagnostics and patient care within precision medicine. A systematic review and critical analysis were conducted, integrating findings from peer-reviewed research, clinical reports, and AI-based diagnostic applications. The methodology focused on evaluating AI algorithms for genetic variant detection, risk prediction, and therapeutic recommendations, as well as assessing their clinical integration and outcomes. Results indicate that AI significantly improves the speed, accuracy, and interpretability of genomic analyses, facilitating early disease detection, individualized treatment planning, and predictive risk assessment. Challenges include data privacy, algorithmic transparency, and the need for robust validation in diverse populations. The study concludes that AI integration in precision medicine represents a pivotal advancement in genetic diagnostics and patient-centered care, offering scalable solutions for complex healthcare challenges. Ethical, regulatory, and technical considerations are essential to ensure safe, equitable, and effective implementation in clinical practice.
AI and Robotics in Elderly Care: Sustainable Solutions for Aging Populations Thika Marliana; Dilara Sert Kasim; Samsuni Samsuni; Ton Kiat
Journal of World Future Medicine, Health and Nursing Vol. 3 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v3i5.2565

Abstract

The global rise in aging populations poses urgent challenges to healthcare systems, social welfare, and labor sustainability. Traditional caregiving models are increasingly strained by workforce shortages and escalating medical costs. Artificial Intelligence (AI) and robotics have emerged as transformative technologies offering innovative and sustainable approaches to elderly care. This study aims to examine how AI-driven systems and assistive robots enhance healthcare delivery, autonomy, and quality of life among older adults. Using a mixed-method design, the research combines a systematic review of 120 peer-reviewed studies (2012–2024) with case analyses of robotic implementations in Japan, Sweden, and Singapore. Findings reveal that AI-enabled monitoring, predictive diagnostics, and social robots significantly improve health outcomes, emotional well-being, and caregiving efficiency. However, ethical concerns regarding privacy, human empathy, and digital inequality remain critical barriers to widespread adoption. The study concludes that sustainable elderly care requires integrating technological innovation with human-centered design and policy frameworks that ensure inclusivity, accountability, and data ethics.   

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