Accepted for/Published in: JMIR Formative Research
Date Submitted: Jan 21, 2026
Open Peer Review Period: Jan 23, 2026 - Mar 20, 2026
Date Accepted: May 27, 2026
(closed for review but you can still tweet)
Gerontechnology Acceptance in Thai Elderly Care Facilities: A Mixed-Methods Study of Sensor Network and Machine Learning Adoption
ABSTRACT
Background:
Thailand's accelerated population aging transformation, with 28% of citizens projected to reach 60+ years by 2030, requires innovative digital health solutions addressing family-centered care systems. Interconnected sensor networks, machine learning systems, and cloud-based analytics infrastructure present opportunities for revolutionizing elderly care provision, yet adoption patterns and implementation viability in Thai contexts remain underexplored.
Objective:
The objective of our study was to assess the viability and adoption patterns of interconnected sensors and machine learning technologies in Thai elderly care facilities, examining therapeutic effectiveness, user acceptance factors, and geographic preference variations.
Methods:
An integrated quantitative-qualitative methodology combining the Gerontechnology Adoption Framework (GTAF) and Service Exchange Value Creation Logic (SEVCL) was employed. Technology specialist assessments (n=12) and consumer evaluations across Bangkok and Chiang Mai (n=120) were conducted. Technology assessment followed digital health evaluation protocols incorporating user experience testing, data protection impact analysis, and healthcare workflow integration assessment. Quantitative examination included descriptive analytics, predictive modeling, and multi-criteria evaluation techniques, while qualitative information underwent systematic thematic examination.
Results:
Sensor-based fall prevention systems achieved superior therapeutic effectiveness scores (M=4.5/5.0, SD=0.3) with 89% adoption success metrics and favorable deployment complexity (M=2.8/5.0), demonstrating potential 25-30% emergency response cost reductions. Machine learning-powered early alert systems showed greatest clinical impact capability (M=4.7/5.0) with 30-35% hospitalization reduction potential and 76% user adoption despite deployment complexity (M=4.2/5.0). Digital health acceptance varied significantly by digital literacy levels, with high digital confidence participants showing 2.3x higher acceptance rates (p<0.001). Therapeutic gardens emerged as optimal sustainable intervention (M=4.8/5.0 benefit rating) correlating with 17% psychotropic medication reduction (r=0.78, p<0.001). Geographic preferences revealed Bangkok's preference for medical IoT technologies opposed to Chiang Mai's environmental digital solutions emphasis.
Conclusions:
Integrated smart technology implementation demonstrates simultaneous clinical outcome improvement and operational efficiency enhancement when properly configured for older adult populations. Success factors including phased IoT deployment, comprehensive digital health training, and human-technology balance respecting cultural values provide a systematic implementation framework for digital health transformation in elderly care settings across developing nations. Clinical Trial: -none-
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