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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

Rationale, Design, and Intervention Development of a Mobile Health–Led Primary Care Program for Management of Type 2 Diabetes in Rural Thailand: Protocol for a SMARThealth Diabetes Study

Rationale, Design, and Intervention Development of a Mobile Health–Led Primary Care Program for Management of Type 2 Diabetes in Rural Thailand: Protocol for a SMARThealth Diabetes Study

South-East Asian countries (including Thailand) have been experiencing a steady rise in the burden of noncommunicable diseases (NCDs). The Global Burden of Disease Study 2015 reported diabetes and chronic kidney diseases (CKD) as Thailand's third and fifth leading causes of death [1]. According to the 5th Thai National Health Examination survey conducted in 2014 [2], Thailand has 4.63 million adults with diabetes (9.9% of the adult population), which is expected to grow to 5.2 million by 2035.

Methee Chanpitakkul, Devarsetty Praveen, Renu John, Arpita Ghosh, Salyaveth Lekagul, Malulee Kaewhiran, Kriang Tungsanga, Vivekanand Jha

JMIR Res Protoc 2024;13:e59266

A Web-Based, Respondent-Driven Sampling Survey Among Men Who Have Sex With Men (Kai Noi): Description of Methods and Characteristics

A Web-Based, Respondent-Driven Sampling Survey Among Men Who Have Sex With Men (Kai Noi): Description of Methods and Characteristics

Our goal was to design and conduct a web RDS among MSM in Thailand. The objectives were 2-fold: to create a ready-to-use (coded) web RDS system and to pilot the feasibility of collecting HIV-related biomarkers through such a sampling design. There was no physical survey office.

Samart Karuchit, Panupit Thiengtham, Suvimon Tanpradech, Watcharapol Srinor, Thitipong Yingyong, Thananda Naiwatanakul, Sanny Northbrook, Wolfgang Hladik

JMIR Form Res 2024;8:e50812

Digital Group–Based Intervention for Physical Activity Promotion Among Thai Adults During the COVID-19 Lockdown: Randomized Controlled Trial

Digital Group–Based Intervention for Physical Activity Promotion Among Thai Adults During the COVID-19 Lockdown: Randomized Controlled Trial

Gen Y in Thailand (those born between 1981 and 2000) [22-26] who have access to the internet, comprising a total of 17,644,290 (26.6%) people [16], are the most active in using the social media platform compared to other working-age groups [16]. They are exceptionally quick to access new information and knowledge and share this at lightning speed among their social networks. They are creative and have high self-esteem, and they prefer working in teams or working together in groups [27].

Nanthawan Pomkai, Piyawat Katewongsa, Aphichat Chamratrithirong, Kanokwan Tharawan, Teeranong Sakulsri, Bhubate Samutachak, Dyah Anantalia Widyastari, Niramon Rasri, Boonyanuch Wijarn, Yodchanan Wongsawat

J Med Internet Res 2024;26:e43366

Adapting Effective mHealth Interventions to Improve Uptake and Adherence to HIV Pre-Exposure Prophylaxis Among Thai Young Men Who Have Sex With Men: Protocol for a Randomized Controlled Trial

Adapting Effective mHealth Interventions to Improve Uptake and Adherence to HIV Pre-Exposure Prophylaxis Among Thai Young Men Who Have Sex With Men: Protocol for a Randomized Controlled Trial

In Thailand, only 9.3% of MSM who were offered Pr EP agreed to take it [16]. Commonly cited barriers to initiating or staying on Pr EP have included low self-perceived risk, concerns about medication side effects, concerns about maintaining daily oral Pr EP regimen, drug use, and HIV stigma [17-19].

Bo Wang, Rena Janamnuaysook, Karen MacDonell, Chokechai Rongkavilit, Elizabeth Schieber, Sylvie Naar, Nittaya Phanuphak

JMIR Res Protoc 2023;12:e46435

Association of Generation and Group Size With the Usage of a Mobile Health App in Thailand: Secondary Analysis of the ThaiSook Cohort Study

Association of Generation and Group Size With the Usage of a Mobile Health App in Thailand: Secondary Analysis of the ThaiSook Cohort Study

In Thailand, 66.8% of the general population used health and wellness apps in 2020. “Sports and fitness activities” and “diet and nutrition” were the 2 most frequently used functions [15]. Factors influencing the frequent use of m Health tracking apps include sex [12,16], education [12,17], technological literacy [17], peer influence [18], performance expectancy, social influence, and facilitating conditions [19].

Tharoj Inchusri, Decho Surangsrirat, Papichaya Kwanmuang, Prapasiri Poomivanichakij, Ponnapat Apiwatgaroon, Surathep Ongprakobkul, Apissara Kongchu, Anda Klinpikul, Ammarin Taneeheng, Nannapat Pruphetkaew, Therdpong Thongseiratch, Pitchayanont Ngamchaliew, Polathep Vichitkunakorn

J Med Internet Res 2023;25:e45374