3.2.1. Establishing Guideline Level Indicators

The technology acceptance model (TAM), proposed by Davis et al. in 1989, is one of the most influential theories in the field of information systems research. In the preliminary TAM, PU, and PEOU are the elements that directly impact the usage attitude and user behavior through attitude intention [27]. Davis et al. reported that PEOU refers to the effort customers perceive as being required to operate a new technology; PU refers to how many customers accept as true that the technological device will enhance their overall work performance [27]. Karah anna et al. demonstrated that PEOU and PU affect the users' use behavior, and PEOU additionally impacts PU [28]. Bildad et al. found that the ease of using Internet technology plays a key role in improving user faith in software builders [29]. Therefore, in the specific construction of the corresponding indicators, we used PEOU (B1) and PU (B2) [30,31].

Despite the broad applicability of the TAM (Figure 1), the model can be modified by adding external premises and theoretically sound elements, which can expand the predictive power of the model [32]. The self-determination principle has been widely used to help encourage physical activity in individuals, and intrinsic motivation represents an archetype of independent activity, where people are motivated by intrinsic motivation and are free to engage in activities independent of external factors [33–35]. According to self-determination theory, consumer motivation (the reason why a person engages in an activity) and consumer-aim (the purpose for this activity) is intently associated [36]. In the field of advertising and customer behavior studies, researchers typically agree that customers perceive the cost as a necessary factor influencing purchase decisions: and the greater the perceived cost-utility of a product, the greater the motivation to buy it [37]. Regarding the elements affecting the perceived value, most researchers have considered the antecedent variables of the perceived cost for empirical analysis. Perceived immediate use advantages (i.e., perceived gains) and perceived sacrifices (i.e., perceived losses) are the antecedent variables of perceived cost [38]. Some scholars have also used factors such as perceived risk, cost of purchase, quality of service, and the quality of the product as antecedent variables affecting the consumers' perceived value (Wood and Scheer, 1996; Zhong, K., 2013) [39]. Regarding the elements impacting the customer's perceived value, we introduced two achievable variables to the technology acceptance model: perceived cost (B3) and personal motivation (B4) [40].

**Figure 1.** Technology acceptance model. 3.2.2. Determination of Program-Level Indicators

3.2.2. Determination of Program-Level Indicators We analyzed and inductively screened 11 evaluation indicators from H1 to H11 according to the detailed division of the elements used for evaluating the first-level indicators (Table 1). To measure indicator B1 (PU), we used the scale developed by Yang et al. to set three measurement indicators: content adaptability (H1), content relevance (H2), and content quality (H3) [41]. To measure indicator B2 (PEOU), we used the scale developed by Gong et al.: the technology level (H4), interaction effectiveness (H5), and system compatibility (H6) [42–45]. For B3, the perceived value indicator, we measured the financial cost (H7) and privacy cost (H8) based totally on the evaluation by San et al., who focused on the effects of the perceived advantages and perceived dangers of people's transactional conduct [46–48]. To measure B4 (personal motivation indicator), we applied We analyzed and inductively screened 11 evaluation indicators from H1 to H11 according to the detailed division of the elements used for evaluating the first-level indicators (Table 1). To measure indicator B1 (PU), we used the scale developed by Yang et al. to set three measurement indicators: content adaptability (H1), content relevance (H2), and content quality (H3) [41]. To measure indicator B2 (PEOU), we used the scale developed by Gong et al.: the technology level (H4), interaction effectiveness (H5), and system compatibility (H6) [42–45]. For B3, the perceived value indicator, we measured the financial cost (H7) and privacy cost (H8) based totally on the evaluation by San et al., who focused on the effects of the perceived advantages and perceived dangers of people's transactional conduct [46–48]. To measure B4 (personal motivation indicator), we applied the scale developed by Park et al. and set three measures: health concerns (H9), outcome expectations (H10), and social influence (H11) [49–51].


H10: Outcome Expectations

expectations (H10), and social influence (H11) [49–51]. **Table 1.** Index system used for analyzing factors influencing use of fitness apps by adults aged 18–65 years.

the scale developed by Park et al. and set three measures: health concerns (H9), outcome

### H11: Social Impact *3.3. Questionnaire Design*

tion

*3.3. Questionnaire Design*  Based on the literature review of the effect of the pandemic and health apps, we chose eleven attributes to examine the factors influencing the use of health apps amongst adults aged 18–65 years to determine the impact of COVID-19. We assessed these 11 attributes Based on the literature review of the effect of the pandemic and health apps, we chose eleven attributes to examine the factors influencing the use of health apps amongst adults aged 18–65 years to determine the impact of COVID-19. We assessed these 11 attributes with a questionnaire (Table 2). We built the questionnaire with Questionnaire Star, and the first question required respondents to have used health apps or to have some knowledge of health apps. The questions could be answered on a scale, and every question consisted of a

Park et al. (2018) [49]

set of statements. Each statement had nine responses, ranging from 1 to 9 according to the evaluation of the degree of the effect, ranging from very unimportant to very important. The questionnaire included basic information (sex, age, education level, and whether they had used or known about fitness apps) and the evaluation of the importance of relevant factors influencing their use.

**Table 2.** Description of index conversion questionnaire.

