JMIR Publications

ABSTRACT

Background:

The rapid advancement of technology has made mobile health (mHealth) a promising tool to mitigate health problems, particularly among older adults. Despite the numerous benefits of mHealth, assessing individual acceptance is required to address the specific needs of older people and promote their intention to use mHealth.

Objective:

This study aims to adapt and validate the Senior Technology Acceptance Model (STAM) questionnaire for accessing mHealth acceptance in Thai context.

Methods:

In this cross-sectional study, we adapted the original 38-item English version of the STAM using a ten-point Likert scale for mHealth acceptability among the Thai population. We translated the mHealth STAM into Thai using the standard procedure, and subsequently, the expert panel assessed and improved it to ensure the instrument's comprehensibility and cross-cultural compatibility. The construct validity of the Thai mHealth STAM was evaluated by a multidimensional approach, including exploratory and confirmatory factor analysis and nonparametric item response theory analysis. Discriminative indices consisting of sensitivity, specificity, and area under the receiver operating characteristic (AuROC) were utilized to determine appropriate banding and discriminant validity for the intention to use mHealth. Internal consistency was assessed using Cronbach’s α and McDonald’s ω coefficients.

Results:

Of 1,100 participants with a mean age of 62.3 (SD ±8.8) years, 360 were adults aged 45–59 years, and 740 were older adults aged 60 and over. Exploratory factor analysis identified 22 candidate items with factor loadings > 0.4 across seven principal components, explaining 91.45% of the variance. Confirmatory factor analysis confirmed that eight-dimensional sets of items had satisfactory fit indices. The score banding (low ≤151, moderate 152–180, high ≥181) was preferred as the optimal 22-item Thai mHealth STAM cut-off scores based on the highest sensitivity of 89.0% (95% CI 86.1–91.5%) and AuROC of 72.4% (95% CI 70.0–74.8%) for predicting the intention to use mHealth. The final Thai mHealth STAM, consisting of 22 items, exhibited remarkable internal consistency, as evidenced by Cronbach's α coefficient of 0.88 (95% CI 0.87–0.89) and McDonald's Ή coefficient of 0.85 (95% CI 0.83–0.87). For all 22 items, the corrected item-total correlations ranged between 0.26 and 0.71.

Conclusions:

The 22-item Thai mHealth STAM demonstrated satisfactory psychometric properties in both validity and reliability. The instrument has the potential to serve as a practical tool in assessing the acceptance and intention to use mHealth among pre-older and older adults.