# User Experience of Older People While Using Digital Health Technologies: A Systematic Review
## 4 Discussion
The FABS/M is designed to assess mobility-related assistive devices and examines the facilitators and barriers that users encounter when using these devices [61]. The PIADS measures the psychosocial impact of assistive devices, focusing on how these devices affect users' self-esteem, well-being, and participation in daily activities [63]. The TSQ-WT is tailored for assessing user satisfaction with wearable telehealth technologies, considering factors such as ease of use and satisfaction with remote health monitoring [66]. The USEQ is a general questionnaire for evaluating user satisfaction without focusing on specific domains [67]. These questionnaires are specialized for different purposes, such as system usability, assistive technology, mobility devices, psychosocial impact, telehealth,and overall user satisfaction. Researchers choose the more relevant ones based on study objectives and the technology being assessed. The findings of this review indicate that six customized questionnaires have been developed based on the TMT, Almere model, and TFI. The TAM, initially formulated by [PERSON] in 1989 [71], is a widely recognized theoretical framework in the field of technology adoption and user acceptance. It aims to clarify the factors that influence an individual's decision to accept and use a new technology or information system. Its core components are perceived usefulness and perceived ease of use, and these factors directly influence a user's intention to use technology, which in turn affects their actual usage behavior [71]. It has been used as a basis for developing questionnaires to assess user perceptions of new technologies [38; 40; 43; 44; 45]. These questionnaires, which aim to measure perceived usefulness and perceived ease of use, help predict whether users will adopt a technology. On the other hand, the Almere model functions as a conceptual framework in the field of UX design and research. It is designed to provide a structured approach to understanding and enhancing the UX of digital products and services, especially concerning users' acceptance of socially assistive robots [72]. It encompasses 12 constructs, as stated in the introduction part [72]. The present review reveals that the Almere model has been used to measure attitudes toward robot assistance in regard to activities of daily living among older people living independently [40]. The TFI is an instrument used to quantify the frailty of older adults from their own perspective across three domains: physical, psychological, and social [73]. [PERSON] et al. [36] employed the TFI to gain insights into health self-management requirements, frailty, age-related changes, and the health information support provided by consumer wearable devices, particularly among older adults living independently. Additionally, [PERSON] et al. [41] adapted the survey questionnaire items from relevant literature on healthcare, mobile, and information communication services to explain the intentions of consumers regarding the use of mHealth applications. This comprehensive assessment encompassed seven key constructs, each measured with multiple items. The scale items for health stress and the epistemic value as contextual personal values were adapted from [PERSON] et al. [74], while the measurement items for emotion (enjoymen) and intention to adopt were developed based on the work of [PERSON] et al. [71]. In addition, the items measuring reassurance were taken from [PERSON] and [PERSON] [75] and modified for the study. The convenience value was borrowed from [PERSON] et al. [76] and developed in the study. Finally, the measurement items for the usefulness value were developed with reference to [PERSON]'s items [71]. All of the measurement items were modified to emphasize health-related events and behaviors based on personal values with respect to mHealth [77; 78]. They also included the contents of mHealth services. Each item was measured on a five-point Likert scale ranging from \"Strongly disagree\" to \"Strongly agree\".