Revolutionizing elderly care: Building a healthier aging society through innovative long‐term care systems and assessing the long‐term care acceptance model

With a growing elderly population, the demand for caregivers is increasing in Khon Kaen, Thailand, with approximately 17 000 elderly residents. This growing number of older people and a shortage of caregivers could overload the healthcare system.


Introduction
Recruitment criteria of the healthcare volunteers and collection of health data Healthcare volunteers were selected based on background, qualifications and experience, prioritizing expertise, and a genuine interest in the well-being of older adults.Training covered the LTC application, data collection, ethics and patient interaction.Proficiency assessments ensured data quality, with additional support provided as needed.Health data collection of 290 older adult participants took place across five medical centers: Prachasamosorn, Nong Yai, Mittrapad, Wat Nong Waeng and Chata Padung.The volunteers assessed the LTC system satisfaction and acceptance.The older adults were selected based on specific health conditions for LTC technology, excluding those outside the defined age range, residents not in the study area and those without specified health conditions.

Sample size determination in LTC study
Healthcare volunteers received smart technology and LTC system training, gaining real-workplace experience.The sample size was determined through G*Power package's analysis, using Cohen's formula 28 by setting the parameters as follows: the level of significance or α equals 0.01 (99% confidence level); power of test or (1-β) equals 0.99 (probability of judgment correct); the effect of size or L 2 is a statistic used to tell the size of the difference when effect of size is equal to 0.15 from the suggestion of using the effect of size when the dependent variable is expected to moderately influence the dependent variable; the numbers of variables used is equal to four variables (Fig. S2).As a result of these calculations, the researchers obtained a sample group consisting of 129 people including caregivers and elderly participants, to assess the reliability of the questionnaire used in the study.

Data collections of the study
Various health data of 290 elderly adults were measured, collected and recorded through a LTC application, as shown in Fig. 2 and Table 1.Participants were selected based on age, residency and health status, with those outside the defined age range excluded.The study aimed to address chronic illnesses and specific health needs that could benefit from the LTC system.The LTC application linked caregivers to local doctors through QR code-enabled smart cards to access medical information, aiding diagnosis.Vitally, this study complied with ethical standards, and was authorized by Khon Kaen University (HE622163) and the IRB (IRB00008614).All participants in the present study were informed to provide consent, emphasizing transparency and moral accountability following the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use Guideline for Good Clinical Practice.

Evaluation of usefulness and validation of questionnaires
To assess VHVs' intention to use the LTC system, we employed modified technology acceptance questionnaires (Table S1) based on the technology acceptance model.The study used a widely C Chokphukhiao et al.
used and validated questionnaire from Davis et al., 25 which has a history of application and validation in various contexts, especially in healthcare services (Table 2), ensuring its reliability and validity in measuring perceived usefulness and ease of use.In addition, the project director, committee boards with medical professionals and researchers participated in validating and confirming the suitability of selected questionnaires for the study.Key factors include perceived usefulness, perceived ease of use, attitude toward usage and behavioral intention, as shown in Fig. 3. 25 For carrying out the questionnaires evaluation, 21 revised questionnaires were used to test questionnaire reliability and Cronbach's alpha coefficient with volunteers.Complete data collected from 129 samples also assessed questionnaire sentiment.The researcher administered 21 questionnaires to actual volunteers, assessing questionnaire reliability through Cronbach's alpha coefficient analysis (Table S2).

Statistical analysis
The tool's reliability and statistical hypotheses were assessed through stratified (hierarchical regression) and multiple regression analyses on 129 samples.Tests involved data review, distribution (dispersion) and correlation analysis.Pearson's correlation 29 was used to determine the relationship between all variables to avoid the occurrence multicollinearity, and the distribution of data was examined by considering the skewness and kurtosis (Figs S3 and S4). 30Hypothesis testing was carried out to study technology Figure 1 Schematic diagram illustrating the collecting system of health information data from older persons in Khon Kaen Province.Consultation meetings were held with key stakeholders, including Khon Kaen Municipality, Khon Kaen Hospital, the provincial public health office and the Office of Digital Economy Promotion, as shown in Figures 1 and 2. These interactions aimed to enhance the project, which also involved planning and collaboration.IoT, Internet of Things; LTC, long-term care.

Results
Comprehensive analysis of older adults' health data reports and general participant data in LTC system field visits This project tested the LTC system to collect health data from 290 older adults in a northeastern municipality, using the LTC application for proactive home screening.The health data of 290 older adult participants were collected, with a median age of 71 years (range 61-96 years; Fig. S5).The LTC system recorded diseases; hypertension and diabetes were prevalent.General analysis encompassed seven variables: sex, age, occupation, education, income, job title and medical center (Table 1).Descriptive statistics and percentages were analyzed, and it was found that among LTC participants, 89.1% were women.The majority of LTC participants (55%) were aged >60 years; only 3.1% were aged 21-30 years.The primary occupation was housewife/butler (44.2%), and education below bachelor's degree (84.5%).Most had an income <15,000 Baht per month (approximately <$420.74USD) (79.8%) and lived in villages under VHVs' care (79.8%).
Nong Yai medical center accounted for most health data (30.2%).
Analyzing the confidence of the questionnaire used for accepting the LTC system Coefficients ranged from 0 to 1; values closer to 1 showed high LTC system confidence among older adults (Table S2).By the level that was in the acceptable criteria for various studies, 'α' must be ≥0.7. 31 Table S2 shows the questionnaire confidence results from 40 volunteers, yielding an overall confidence of 0.967.Cronbach's coefficients for perceived usefulness, perceived ease of use, attitude toward usage and behavioral intention were 0.928, 0.891, 0.900 and 0.884 respectively.The questionnaire's coefficient (0.884-0.928) meets research criteria.Correlations were established between statements and questions through coefficients.Variables and questions used in technology acceptance assessment were adopted from Table S3.Correlation across all questions (corrected item-total correlation) ranged from 0.464 to 0.877, similar to Table S4, suggesting retention of all questions for LTC application evaluation.

Evaluation of questionnaires
The technology acceptance study on the impact of the health data system and instant health report among VHVs took place in northeastern Thailand, involving the statistical analysis of 129 complete questionnaire responses.This analysis included a normality test, where data distribution was examined using skewness and kurtosis, resulting in values within the normal range (between À1.96 and 1.96, as shown in Fig. S3).Furthermore, a Pearson correlation coefficient analysis assessed variable relationships to prevent multicollinearity, showing that all variables had correlation coefficients <0.8, thus minimizing the risk of multicollinearity (Fig. S4).

Technology acceptance data analysis
The researcher assessed the mean and standard deviation for each question, presenting them with interpretative criteria.Analysis of total scores showed participants' agreement on perceived efficacy, ease of use, attitude and intention, with mean scores of 4.51, 4.29, 4.44 and 4.41 respectively (Table S5).VHVs have undertaken assumption research for hypothesis testing and the findings supported all hypothesis described in section 2.5.Direct relationships were summarized in an influence diagram (Fig. S1); hypotheses were tested (Table S6).Data analysis investigated factors impacting technology acceptance and attitudes influencing VHVs' intention to use the 'Health Data Collection System'.Linear and multiple regression, using hierarchical regression, were employed.Supplementary details, including Tables S7-S9, offer comprehensive and detailed explanations.
Comparing studies on technology acceptance and theories among older adults, and analyzing the problems of users in each context Globally, several technologies support older adult care, including wearables, telehealth, smart homes and assistive technologies.In contrast to previous studies (Table 2), the present study emphasizes introducing a LTC application for Khon Kaen's older adults, evaluating its use.The researcher categorized user-specific issues and constraints in Table S10.The 23 challenges, grouped into seven major categories, regard health data management issues ubiquitous across health service providers.Addressing these might alleviate broader challenges, such as understaffing, timeconsuming data analysis, heavy workloads, service overload, trust concerns and so on, enhancing overall functioning.

Discussion
The LTC system was designed to prioritize independence, autonomy, and dignity in LTC for older adults.LTC integrates its application into the existing care system, using smart technology and IoT medical devices.The LTC application consists of four components: mobile app, backend management system, MongoDB data storage system, and a Rest API.This methodology guarantees the effective installation and operation of the LTC application within the existing care system, delivering streamlined data collection, improved healthcare quality, robust data security and scalability.As the key stakeholders, the LTC system relies on various stakeholders, including healthcare professionals, caregivers, elderly individuals, digital health experts, local authorities, data centers, community health volunteers, researchers, regulatory bodies, funders, policymakers and patient advocacy groups.Healthcare professionals collect health data, caregivers access health information and older adults are the primary beneficiaries.Digital health experts design and implement the system, whereas local authorities provide infrastructure and regulatory frameworks.Data centers manage data storage and security, whereas community health volunteers provide data collection and support.The present study evaluated the adoption of a health data system using questionnaires to assess the factors influencing its use.The LTC application, which allows caregivers and doctors to access medical information through QR code-enabled smart A sample of 129 individuals, including caregivers and older adult participants, was selected based on age, residency and health status.The data from 290 elderly participants showed the prevalence of diseases, such as hypertension and diabetes, supported by questionnaire confidence and reliability evaluations.The present study recognized several challenges and limitations to the generalization of the LTC application.These include technical integration challenges, variability in user adoption, data privacy concerns and the complexity of stakeholder engagement.Additionally, there is a recognized need for uniform adoption of the system among healthcare professionals and caregivers, and an understanding of the evolving nature of stakeholder involvement and its implications for long-term sustainability.These considerations are vital for the successful implementation and efficacy of the LTC system in addressing the needs of the aging population.
The Smart City aims to improve community quality and ecosystems in northeastern Thailand by implementing smart technology models.The LTC system aims to track older adults' health using intelligent technology and automation.Village volunteers, who are primary carers for older adults, test the system as part of a research project.The study uses TAM to examine the adoption of new technologies and gather information from a sample group of volunteers.The reliability of the tool and the statistical hypothesis were tested using stratified regression analysis and multiple regression analysis.The present study showed that perceived usefulness, ease of use, attitude toward usage and intention to use technology significantly influence participants' perceptions.The LTC system's ease positively impacts usage attitude and intention to use health data, aiming to improve health through fast earth reports and standardized elderly care.
Revolutionizing elderly care © 2024 The Authors.Geriatrics & Gerontology International published by John Wiley & Sons Australia, Ltd on behalf of Japan Geriatrics Society.| 479 acceptance and behavior affecting intention to use health data collection and reporting systems with the following hypotheses; • Hypothesis 1. Perceived ease of use positively influences perceived usefulness.• Hypothesis 2. Perceived usefulness positively influences behavioral intention.• Hypothesis 3. Perceived usefulness positively influences attitude toward usage.• Hypothesis 4. Perceived ease of use positively influences attitude toward usage.• Hypothesis 5. Attitude toward usage positively influences behavioral intention in health data collection and instant health reports.

Figure 2
Figure 2 The long-term care (LTC) digital innovation model system's overall structure, including key technologies and the integrated community ecosystem for a healthy aging society.IoT, Internet of Things.

Figure 3
Figure 3 Technology acceptance model used in the study to investigate multiple factors: (1) perceived usefulness of technology, (2) perceived ease of use of technology, (3) attitude to use, and (4) behavioral intention.

Table 1
Demographic and disease/disorder classification of older patients for the long-term care system Abbreviations: CG, caregivers; VHVs, village health volunteers.Revolutionizing elderly care © 2024 The Authors.Geriatrics & Gerontology International published by John Wiley & Sons Australia, Ltd on behalf of Japan Geriatrics Society.| 481

Table 2
Comparison of the previous studies on the technology acceptance model cards, was evaluated using technology acceptance questionnaires.

Table 2 Continued
Abbreviations: PCT, privacy calculus theory; PMT, protection motivation theory; SWAM, smart wearable acceptance model; TAM, technology acceptance model; TPB, theory of planned behavior; TRA, theory of released action; UTAUT, unified theory of technology acceptance and use of technology; VAB, value-attitude-behavior.