Purpose: This study aimed to identify factors that influence the intention to use smart monitor-based mobile health (SBM) technology among middle-aged inpatients, based on the technology acceptance model II (TAM II). Methods: A total of 222 participants were surveyed.Data were analyzed using SPSS Statistics 23.0 and IBM SPSS Amos 23. Seven exogenous variables–social influence (SI), personal self-efficacy, (PSE), environmental self-efficacy (ESE), health literacy, health concerns, resistance to innovative technology (RIT), accessibility (AC)– and three endogenous variables–perceived ease of use (PEOU), perceived usability (PU), and intention to use (ITU)–were investigated. Results: The hypothesized path model demonstrated a good fit for the data. SI (β = .13, p = .042), PU (β = .46, p < .001), and PEOU (β = .16,p = .008) had significant direct effects on the ITU, which explained 39.5% of the variance. Additionally, SI (β = .27, p < .001), ESE (β = .16, p= .010), RIT (β = - .12, p = .026), AC (β = .28, p < .001), and PEOU (β = .20, p = .001) indirectly affected ITU through PU, which explained 50.7% of the variance. Furthermore, PSE (β = .38, p < .001) indirectly influenced ITU via PEOU, which explained 38.4% of the variance. Conclusion This study demonstrates that the TAM II can be used to effectively predict ITU in SBMs among middle-aged inpatients. To expand the intention to use SBMs, it is necessary to develop SBMs that include content and programs that promote PU, SI, and PEOU.

Purpose: This study aimed to identify factors that influence the intention to use smart monitor-based mobile health (SBM) technology among middle-aged inpatients, based on the technology acceptance model II (TAM II). Methods: A total of 222 participants were surveyed.Data were analyzed using SPSS Statistics 23.0 and IBM SPSS Amos 23. Seven exogenous variables–social influence (SI), personal self-efficacy, (PSE), environmental self-efficacy (ESE), health literacy, health concerns, resistance to innovative technology (RIT), accessibility (AC)– and three endogenous variables–perceived ease of use (PEOU), perceived usability (PU), and intention to use (ITU)–were investigated. Results: The hypothesized path model demonstrated a good fit for the data. SI (β = .13, p = .042), PU (β = .46, p < .001), and PEOU (β = .16,p = .008) had significant direct effects on the ITU, which explained 39.5% of the variance. Additionally, SI (β = .27, p < .001), ESE (β = .16, p= .010), RIT (β = - .12, p = .026), AC (β = .28, p < .001), and PEOU (β = .20, p = .001) indirectly affected ITU through PU, which explained 50.7% of the variance. Furthermore, PSE (β = .38, p < .001) indirectly influenced ITU via PEOU, which explained 38.4% of the variance. Conclusion This study demonstrates that the TAM II can be used to effectively predict ITU in SBMs among middle-aged inpatients. To expand the intention to use SBMs, it is necessary to develop SBMs that include content and programs that promote PU, SI, and PEOU.

Purpose: This study aimed to identify factors that influence the intention to use smart monitor-based mobile health (SBM) technology among middle-aged inpatients, based on the technology acceptance model II (TAM II). Methods: A total of 222 participants were surveyed.Data were analyzed using SPSS Statistics 23.0 and IBM SPSS Amos 23. Seven exogenous variables–social influence (SI), personal self-efficacy, (PSE), environmental self-efficacy (ESE), health literacy, health concerns, resistance to innovative technology (RIT), accessibility (AC)– and three endogenous variables–perceived ease of use (PEOU), perceived usability (PU), and intention to use (ITU)–were investigated. Results: The hypothesized path model demonstrated a good fit for the data. SI (β = .13, p = .042), PU (β = .46, p < .001), and PEOU (β = .16,p = .008) had significant direct effects on the ITU, which explained 39.5% of the variance. Additionally, SI (β = .27, p < .001), ESE (β = .16, p= .010), RIT (β = - .12, p = .026), AC (β = .28, p < .001), and PEOU (β = .20, p = .001) indirectly affected ITU through PU, which explained 50.7% of the variance. Furthermore, PSE (β = .38, p < .001) indirectly influenced ITU via PEOU, which explained 38.4% of the variance. Conclusion This study demonstrates that the TAM II can be used to effectively predict ITU in SBMs among middle-aged inpatients. To expand the intention to use SBMs, it is necessary to develop SBMs that include content and programs that promote PU, SI, and PEOU.

Purpose: This study aimed to identify factors that influence the intention to use smart monitor-based mobile health (SBM) technology among middle-aged inpatients, based on the technology acceptance model II (TAM II). Methods: A total of 222 participants were surveyed.Data were analyzed using SPSS Statistics 23.0 and IBM SPSS Amos 23. Seven exogenous variables–social influence (SI), personal self-efficacy, (PSE), environmental self-efficacy (ESE), health literacy, health concerns, resistance to innovative technology (RIT), accessibility (AC)– and three endogenous variables–perceived ease of use (PEOU), perceived usability (PU), and intention to use (ITU)–were investigated. Results: The hypothesized path model demonstrated a good fit for the data. SI (β = .13, p = .042), PU (β = .46, p < .001), and PEOU (β = .16,p = .008) had significant direct effects on the ITU, which explained 39.5% of the variance. Additionally, SI (β = .27, p < .001), ESE (β = .16, p= .010), RIT (β = - .12, p = .026), AC (β = .28, p < .001), and PEOU (β = .20, p = .001) indirectly affected ITU through PU, which explained 50.7% of the variance. Furthermore, PSE (β = .38, p < .001) indirectly influenced ITU via PEOU, which explained 38.4% of the variance. Conclusion This study demonstrates that the TAM II can be used to effectively predict ITU in SBMs among middle-aged inpatients. To expand the intention to use SBMs, it is necessary to develop SBMs that include content and programs that promote PU, SI, and PEOU.