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Self-Health Monitoring by Smart Devices and Ontology Technology for Older Adults With Uncontrolled Hypertension: Quasi-Experimental Study

Self-Health Monitoring by Smart Devices and Ontology Technology for Older Adults With Uncontrolled Hypertension: Quasi-Experimental Study

In Thailand, the age-standardized prevalence of hypertension among older adults was estimated at 60% in 2019‐2020. Alarmingly, 77.3% of hypertensive patients nationwide had uncontrolled blood pressure (>140/90 mm Hg), reflecting a critical gap in disease management [1]. Bangkok, which officially became an aging society between 2020 and 2022, saw its older adult population increase from 20.40% to 21.6%. In 2021 alone, 1.23 million older adults were living in the city, 20.60% of whom had hypertension [2].

Sutteeporn Moolsart, Khajitpan M Kritpolviman

JMIR Nursing 2025;8:e73386


Effectiveness of a Participatory and Culturally Tailored Learning Program for Coronavirus Prevention in Muslim Older Adults With Hypertension and Diabetes: Quasi-Experimental Study

Effectiveness of a Participatory and Culturally Tailored Learning Program for Coronavirus Prevention in Muslim Older Adults With Hypertension and Diabetes: Quasi-Experimental Study

In Thailand, although the Sheikhul Islam issued preventive guidelines [5], tensions arose between religious practices and public health recommendations [6]. These included dilemmas regarding mosque attendance, social distancing, and following imams via online platforms. Understanding the cultural and spiritual beliefs of Muslims is essential for delivering effective and respectful health care.

Sutteeporn Moolsart, Choochat Phuangsomjit

Asian Pac Isl Nurs J 2025;9:e71671


Online Illicit Drug Distribution in the Thai Language on X: Exploratory Qualitative Content Analysis

Online Illicit Drug Distribution in the Thai Language on X: Exploratory Qualitative Content Analysis

The recreational misuse of medications is not a new phenomenon in Thailand. Chittrakarn and Assanangkornchai [58] investigated the misuse of therapeutic drugs, indicating the important role played by online shops in facilitating the purchase of such substances. However, the impact of social media, and more specifically, how social media use influences the online purchase of drugs, remains unknown in Thailand.

Francois Rene Lamy, Seung Chun Paek, Natthani Meemon

JMIR Infodemiology 2025;5:e71703


Nursing and Continuing Care Management Work Plan for People Living With COVID-19: Case Study of the Nakhon Pathom Province

Nursing and Continuing Care Management Work Plan for People Living With COVID-19: Case Study of the Nakhon Pathom Province

In Thailand, from April 2021 to October 2022, there have been 4,660,878 cumulative cases, 2616 new cases, and 32,828 cumulative deaths. Specifically, in Nakhon Pathom Province, from April 2021 to October 2022, there have been 83,004 cumulative cases, 30 new cases, and 802 cumulative deaths [1,2]. These statistics show a declining trend in infections; however, there is still no clear report on the long-term effects on those who have recovered from COVID-19.

Hathaichanok Buajaroen, Darin Photangtham, Wariya Chankham, Paisan Simalaotao, Ubonrat Sirisukpoca, Monchai Kongkamsook, Pantip Timtab, Tapanee Suasangei

JMIR Nursing 2025;8:e65310


Correction: Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study

Correction: Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study

In “Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study” (JMIR Aging 2025;8:e62942) one error was noted. Reference 44 was a duplicate of reference 36, which reads as follows: Thinuan P, Siviroj P, Lerttrakarnnon P, Lorga T. Prevalence and potential predictors of frailty among community-dwelling older persons in northern Thailand: a cross-sectional study. Int J Environ Res Public Health. Jun 8, 2020;17(11):4077.

Natthanaphop Isaradech, Wachiranun Sirikul, Nida Buawangpong, Penprapa Siviroj, Amornphat Kitro

JMIR Aging 2025;8:e75690


Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study

Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study

Researchers predicted that the proportion of people in Thailand aged ≥60 years would be more than 20% of the population in 2025 and more than 30% in 2031 [3,4]. The prevalence of frailty is high among older adults aged ≥60 years [5]. Global frailty prevalence ranges from approximately 10% to 12% [6-11]. The percentage varies by age, gender, and frailty classification tool.

Natthanaphop Isaradech, Wachiranun Sirikul, Nida Buawangpong, Penprapa Siviroj, Amornphat Kitro

JMIR Aging 2025;8:e62942


Updated Surveillance Metrics and History of the COVID-19 Pandemic (2020-2023) in East Asia and the Pacific Region: Longitudinal Trend Analysis

Updated Surveillance Metrics and History of the COVID-19 Pandemic (2020-2023) in East Asia and the Pacific Region: Longitudinal Trend Analysis

on economic development and geographical proximity, encompassing American Samoa, Australia, Brunei Darussalam, Cambodia, China, Fiji, French Polynesia, Guam, Hong Kong, Indonesia, Japan, Kiribati, People’s Democratic Republic of Korea, Republic of Korea, Lao People’s Democratic Republic, Macao, Malaysia, Marshall Islands, Federated States of Micronesia, Mongolia, Myanmar, Nauru, New Caledonia, New Zealand, Northern Mariana Islands, Palau, Papua New Guinea, the Philippines, Samoa, Singapore, Solomon Islands, Thailand

Alexander L Lundberg, Alan G Soetikno, Scott A Wu, Egon Ozer, Sarah B Welch, Yingxuan Liu, Claudia Hawkins, Maryann Mason, Robert Murphy, Robert J Havey, Charles B Moss, Chad J Achenbach, Lori Ann Post

JMIR Public Health Surveill 2025;11:e53214


Challenges and Opportunities for Data Sharing Related to Artificial Intelligence Tools in Health Care in Low- and Middle-Income Countries: Systematic Review and Case Study From Thailand

Challenges and Opportunities for Data Sharing Related to Artificial Intelligence Tools in Health Care in Low- and Middle-Income Countries: Systematic Review and Case Study From Thailand

Thailand is developing its AI capabilities and promoting AI adoption, but significant obstacles remain, particularly regarding data sharing. The fragmented nature of its health care service landscape and unclear data-sharing guidelines restrict the effective use of AI in health care. The Thailand National AI Strategy and Action Plan (2022-2027) attempts to close this gap by establishing a data-sharing guideline to enable AI deployment [16].

Aprajita Kaushik, Capucine Barcellona, Nikita Kanumoory Mandyam, Si Ying Tan, Jasper Tromp

J Med Internet Res 2025;27:e58338


Establishment, Implementation, Initial Outcomes, and Lessons Learned from Recent HIV Infection Surveillance Using a Rapid Test for Recent Infection Among Persons Newly Diagnosed With HIV in Thailand: Implementation Study

Establishment, Implementation, Initial Outcomes, and Lessons Learned from Recent HIV Infection Surveillance Using a Rapid Test for Recent Infection Among Persons Newly Diagnosed With HIV in Thailand: Implementation Study

Thailand joined the TRACE initiative in 2020. The project started as a demonstration project in Bangkok and expanded to include two additional provinces in 2021. During the first 2 years, RITA comprised RTRI and viral load (VL) testing for those diagnosed with RTRI-recent HIV; in combination with CS, this method was used to characterize HIV infections as recent or long-term [15]. In 2022, Thailand established the surveillance of recent HIV infections using RITA-CS.

Kriengkrai Srithanaviboonchai, Thitipong Yingyong, Theerawit Tasaneeyapan, Supaporn Suparak, Supiya Jantaramanee, Benjawan Roudreo, Suvimon Tanpradech, Jarun Chuayen, Apiratee Kanphukiew, Thananda Naiwatanakul, Suchunya Aungkulanon, Michael Martin, Chunfu Yang, Bharat Parekh, Sanny Chen Northbrook

JMIR Public Health Surveill 2024;10:e65124


Evaluating Online Cannabis Health Information for Thai Breast Cancer Survivors Using the Quality Evaluation Scoring Tool (QUEST): Mixed Method Study

Evaluating Online Cannabis Health Information for Thai Breast Cancer Survivors Using the Quality Evaluation Scoring Tool (QUEST): Mixed Method Study

In Thailand, cannabis has been used as a part of traditional medicine for centuries. The most commonly used forms of cannabis in nontraditional medicine involve the oral intake of crude oil extracts, raw plants (flowers, leaves, or whole plants with roots and stems), and topical skin products. There are 3 categories of cannabis-based products legalized for medicinal purposes in Thailand.

Thanarpan Peerawong, Tharin Phenwan, Meiko Makita, Sojirat Supanichwatana, Panupong Puttarak, Naowanit Siammai, Prakaidao Sunthorn

JMIR Cancer 2024;10:e55300