TY - JOUR AU - Aisyah, Nur Dewi AU - Utami, Astri AU - Rahman, Mauly Fauziah AU - Adriani, Humaira Nathasya AU - Fitransyah, Fiqi AU - Endryantoro, Aziz M. Thoriqul AU - Hutapea, Yosephine Prima AU - Tandy, Gertrudis AU - Manikam, Logan AU - Kozlakidis, Zisis PY - 2025/3/27 TI - Using an Electronic Immunization Registry (Aplikasi Sehat IndonesiaKu) in Indonesia: Cross-Sectional Study JO - Interact J Med Res SP - e53849 VL - 14 KW - immunization KW - registry KW - digital KW - puskesmas KW - public health center KW - mobile app N2 - Background: Electronic immunization registries (EIRs) are being increasingly used in low- and middle-income countries. In 2022, Indonesia?s Ministry of Health introduced its first EIR, named Aplikasi Sehat IndonesiaKu (ASIK), as part of a comprehensive nationwide immunization program. This marked a conversion from traditional paper-based immunization reports to digital routine records encompassing a network of 10,000 primary health centers (puskesmas). Objective: This paper provides an overview of the use of ASIK as the first EIR in Indonesia. It describes the coverage of the nationwide immunization program (Bulan Imunisasi Anak Nasional) using ASIK data and assesses the implementation challenges associated with the adoption of the EIR in the context of Indonesia. Methods: Data were collected from primary care health workers? submitted reports using ASIK. The data were reported in real time, analyzed, and presented using a structured dashboard. Data on ASIK use were collected from the ASIK website. A quantitative assessment was conducted through a cross-sectional survey between September 2022 and October 2022. A set of questionnaires was used to collect feedback from ASIK users. Results: A total of 93.5% (9708/10,382) of public health centers, 93.5% (6478/6928) of subdistricts, and 97.5% (501/514) of districts and cities in 34 provinces reported immunization data using ASIK. With >21 million data points recorded, the national coverage for immunization campaigns for measles-rubella; oral polio vaccine; inactivated polio vaccine; and diphtheria, pertussis, tetanus, hepatitis B, and Haemophilus influenzae type B vaccine were 50.1% (18,301,057/36,497,694), 36.2% (938,623/2,595,240), 30.7% (1,276,668/4,158,289), and 40.2% (1,371,104/3,407,900), respectively. The quantitative survey showed that, generally, users had a good understanding of ASIK as the EIR (650/809, 80.3%), 61.7% (489/793) of the users expressed that the user interface and user experience were overall good but could still be improved, 54% (422/781) of users expressed that the ASIK variable fit their needs yet could be improved further, and 59.1% (463/784) of users observed sporadic system interference. Challenges faced during the implementation of ASIK included a heavy workload burden for health workers, inadequate access to the internet at some places, system integration and readiness, and dual reporting using the paper-based format. Conclusions: The EIR is beneficial and helpful for monitoring vaccination coverage. Implementation and adoption of ASIK as Indonesia?s first EIR still faces challenges related to human resources and digital infrastructure as the country transitions from paper-based reports to electronic or digital immunization reports. Continuous improvement, collaboration, and monitoring efforts are crucial to encourage the use of the EIR in Indonesia. UR - https://www.i-jmr.org/2025/1/e53849 UR - http://dx.doi.org/10.2196/53849 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/53849 ER - TY - JOUR AU - Nagpal, S. Meghan AU - Jalali, Niloofar AU - Sherifali, Diana AU - Morita, P. Plinio AU - Cafazzo, A. Joseph PY - 2024/8/8 TI - Managing Type 2 Diabetes During the COVID-19 Pandemic: Scoping Review and Qualitative Study Using Systematic Literature Review and Reddit JO - Interact J Med Res SP - e49073 VL - 13 KW - type 2 diabetes KW - social media KW - patient-generated health data KW - big data KW - machine learning KW - natural language processing KW - COVID-19, COVID-19 stress syndrome, health behaviors KW - Reddit KW - qualitative KW - analysis KW - diabetes KW - scoping review N2 - Background: The COVID-19 pandemic impacted how people accessed health services and likely how they managed chronic conditions such as type 2 diabetes (T2D). Social media forums present a source of qualitative data to understand how adaptation might have occurred from the perspective of the patient. Objective: Our objective is to understand how the care-seeking behaviors and attitudes of people living with T2D were impacted during the early part of the pandemic by conducting a scoping literature review. A secondary objective is to compare the findings of the scoping review to those presented on a popular social media platform Reddit. Methods: A scoping review was conducted in 2021. Inclusion criteria were population with T2D, studies are patient-centered, and study objectives are centered around health behaviors, disease management, or mental health outcomes during the COVID-19 pandemic. Exclusion criteria were populations with other noncommunicable diseases, examining COVID-19 as a comorbidity to T2D, clinical treatments for COVID-19 among people living with T2D, genetic expressions of COVID-19 among people living with T2D, gray literature, or studies not published in English. Bias was mitigated by reviewing uncertainties with other authors. Data extracted from the studies were classified into thematic categories. These categories reflect the findings of this study as per our objective. Data from the Reddit forums related to T2D from March 2020 to early March 2021 were downloaded, and support vector machines were used to classify if a post was published in the context of the pandemic. Latent Dirichlet allocation topic modeling was performed to gather topics of discussion specific to the COVID-19 pandemic. Results: A total of 26 studies conducted between February and September 2020, consisting of 13,673 participants, were included in this scoping literature review. The studies were qualitative and relied mostly on qualitative data from surveys or questionnaires. Themes found from the literature review were ?poorer glycemic control,? ?increased consumption of unhealthy foods,? ?decreased physical activity,? ?inability to access medical appointments,? and ?increased stress and anxiety.? Findings from latent Dirichlet allocation topic modeling of Reddit forums were ?Coping With Poor Mental Health,? ?Accessing Doctor & Medications and Controlling Blood Glucose,? ?Changing Food Habits During Pandemic,? ?Impact of Stress on Blood Glucose Levels,? ?Changing Status of Employment & Insurance,? and ?Risk of COVID Complications.? Conclusions: Topics of discussion gauged from the Reddit forums provide a holistic perspective of the impact of the pandemic on people living with T2D, which were found to be comparable to the findings of the literature review. The study was limited by only having 1 reviewer for the literature review, but biases were mitigated by consulting authors when there were uncertainties. Qualitative analysis of Reddit forms can supplement traditional qualitative studies of the behaviors of people living with T2D. UR - https://www.i-jmr.org/2024/1/e49073 UR - http://dx.doi.org/10.2196/49073 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/49073 ER - TY - JOUR AU - Wang, Meng AU - Peng, Yun AU - Wang, Ya AU - Luo, Dehong PY - 2024/7/9 TI - Research Trends and Evolution in Radiogenomics (2005-2023): Bibliometric Analysis JO - Interact J Med Res SP - e51347 VL - 13 KW - bibliometric KW - radiogenomics KW - multiomics KW - genomics KW - radiomics N2 - Background: Radiogenomics is an emerging technology that integrates genomics and medical image?based radiomics, which is considered a promising approach toward achieving precision medicine. Objective: The aim of this study was to quantitatively analyze the research status, dynamic trends, and evolutionary trajectory in the radiogenomics field using bibliometric methods. Methods: The relevant literature published up to 2023 was retrieved from the Web of Science Core Collection. Excel was used to analyze the annual publication trend. VOSviewer was used for constructing the keywords co-occurrence network and the collaboration networks among countries and institutions. CiteSpace was used for citation keywords burst analysis and visualizing the references timeline. Results: A total of 3237 papers were included and exported in plain-text format. The annual number of publications showed an increasing annual trend. China and the United States have published the most papers in this field, with the highest number of citations in the United States and the highest average number per item in the Netherlands. Keywords burst analysis revealed that several keywords, including ?big data,? ?magnetic resonance spectroscopy,? ?renal cell carcinoma,? ?stage,? and ?temozolomide,? experienced a citation burst in recent years. The timeline views demonstrated that the references can be categorized into 8 clusters: lower-grade glioma, lung cancer histology, lung adenocarcinoma, breast cancer, radiation-induced lung injury, epidermal growth factor receptor mutation, late radiotherapy toxicity, and artificial intelligence. Conclusions: The field of radiogenomics is attracting increasing attention from researchers worldwide, with the United States and the Netherlands being the most influential countries. Exploration of artificial intelligence methods based on big data to predict the response of tumors to various treatment methods represents a hot spot research topic in this field at present. UR - https://www.i-jmr.org/2024/1/e51347 UR - http://dx.doi.org/10.2196/51347 UR - http://www.ncbi.nlm.nih.gov/pubmed/38980713 ID - info:doi/10.2196/51347 ER - TY - JOUR AU - El Morr, Christo AU - Tavangar, Farideh AU - Ahmad, Farah AU - Ritvo, Paul AU - PY - 2024/5/13 TI - Predicting the Effectiveness of a Mindfulness Virtual Community Intervention for University Students: Machine Learning Model JO - Interact J Med Res SP - e50982 VL - 13 KW - machine learning KW - virtual community KW - virtual care KW - mindfulness KW - depression KW - anxiety KW - stress KW - students KW - online KW - randomized controlled trial KW - Canada KW - virtual KW - artificial intelligence KW - symptoms KW - behavioral therapy KW - sociodemographic KW - mindfulness video KW - online video N2 - Background: Students? mental health crisis was recognized before the COVID-19 pandemic. Mindfulness virtual community (MVC), an 8-week web-based mindfulness and cognitive behavioral therapy program, has proven to be an effective web-based program to reduce symptoms of depression, anxiety, and stress. Predicting the success of MVC before a student enrolls in the program is essential to advise students accordingly. Objective: The objectives of this study were to investigate (1) whether we can predict MVC?s effectiveness using sociodemographic and self-reported features and (2) whether exposure to mindfulness videos is highly predictive of the intervention?s success. Methods: Machine learning models were developed to predict MVC?s effectiveness, defined as success in reducing symptoms of depression, anxiety, and stress as measured using the Patient Health Questionnaire-9 (PHQ-9), the Beck Anxiety Inventory (BAI), and the Perceived Stress Scale (PSS), to at least the minimal clinically important difference. A data set representing a sample of undergraduate students (N=209) who took the MVC intervention between fall 2017 and fall 2018 was used for this secondary analysis. Random forest was used to measure the features? importance. Results: Gradient boosting achieved the best performance both in terms of area under the curve (AUC) and accuracy for predicting PHQ-9 (AUC=0.85 and accuracy=0.83) and PSS (AUC=1 and accuracy=1), and random forest had the best performance for predicting BAI (AUC=0.93 and accuracy=0.93). Exposure to online mindfulness videos was the most important predictor for the intervention?s effectiveness for PHQ-9, BAI, and PSS, followed by the number of working hours per week. Conclusions: The performance of the models to predict MVC intervention effectiveness for depression, anxiety, and stress is high. These models might be helpful for professionals to advise students early enough on taking the intervention or choosing other alternatives. The students? exposure to online mindfulness videos is the most important predictor for the effectiveness of the MVC intervention. Trial Registration: ISRCTN Registry ISRCTN12249616; https://www.isrctn.com/ISRCTN12249616 UR - https://www.i-jmr.org/2024/1/e50982 UR - http://dx.doi.org/10.2196/50982 UR - http://www.ncbi.nlm.nih.gov/pubmed/38578872 ID - info:doi/10.2196/50982 ER -