China faces severe challenges from population aging, with the population aged 65 years and older exceeding 220 million, accounting for 15.6% of the total population by 2025 [1]. This demographic transformation has generated substantial demands for innovative care solutions, particularly as cases of cognitive impairment (CI) are growing alarmingly from 55 million globally in 2021 to an estimated 139 million by 2050, with China bearing the largest share worldwide [2]. China currently has approximately 9.83 million patients with Alzheimer disease, one of the most prevalent forms of CI, which imposes substantial medical costs and caregiving burdens [3].

In response, digital assistive technologies (DATs) have been increasingly promoted to support the care for people with CI. DATs refer to technology-based tools and systems that are designed to support individuals in maintaining functional ability, independence, and quality of life, particularly in the context of aging and disability [4]. In CI care, these technologies include cognitive training applications, wearable monitoring devices, smart home systems, and other digital solutions that support daily living, safety, and health monitoring [5,6]. While some DATs require active user interaction, others operate passively through ambient sensing and background monitoring, highlighting the need to align technological solutions with users’ cognitive capacities and care environments.

The Chinese government has incorporated CI care into national strategic planning and recent policies promoting smart health and older care models, including wearable devices, service robots, smart antiwandering terminals, medication and care reminder systems, and products for CI assessment and training [7]. Despite growing needs and technological development, a persistent gap remains between the potential of DATs and their effective use in everyday CI care. The usability and effectiveness of DATs are shaped by both technological design and practical deployment [8]. Usability refers to how well a technology enables users to achieve their goals, while effectiveness concerns the accuracy and completeness of that achievement [9]. However, these objective characteristics may not fully explain why some technologies are accepted in practice, while others are not accepted. A device may perform well by objective standards yet still be perceived as difficult to use or of limited value in a specific care context. Focusing solely on objective outcomes, therefore, risks overlooking the subjective factors that determine whether a technology is accepted. Accordingly, this study focuses on caregivers’ perceived usefulness and perceived ease of use, rather than on objective measures of system performance.

CI encompasses a spectrum of conditions affecting memory, attention, language, and executive function, ranging from mild to severe CI, and is associated with progressive loss of independence and increased reliance on caregivers [2]. Caregivers play a central role in mediating the use of DATs for older adults with CI, particularly when individuals experience diminished capacity to evaluate or communicate their preferences. As many older adults with moderate to severe cognitive decline may have difficulty articulating their needs, caregivers serve as key decision-makers, facilitators, and evaluators of technology use in real-world settings. Accordingly, this study examined both formal (trained professionals providing paid care) and informal (nonprofessional individuals, such as family members, relatives, or friends) caregivers to reflect the varied contexts in which DATs are introduced, integrated, and evaluated in CI care.

The Technology Acceptance Model (TAM) has been widely used to explain technology acceptance in health care settings [10,11]. TAM proposes that perceived usefulness and perceived ease of use are the primary determinants of users’ behavioral intention to use a technology [12]. It has also been applied to studies of technology uptake in aged care, including mobile health services, wearables, and smart home systems [13].

However, TAM does not fully capture the complexity of CI care settings, where acceptance may also be shaped by personalized training, safety concerns, cultural relevance, disease progression, and digital inequality [14]. A further consideration is that technology use does not necessarily reflect acceptance. Caregivers may continue using a device out of obligation or lack of alternatives, even when they do not perceive it as useful or easy to use. Given that DATs in CI care have not yet been widely embedded in routine practice, understanding the factors shaping acceptance is especially important. This study adopts TAM as its primary theoretical framework, with the interview guide structured around its core constructs. The subsequent thematic analysis was conducted inductively, remaining open to themes beyond TAM’s original scope. As technology continues to evolve, the factors shaping acceptance may also shift. The findings are therefore discussed through the lens of TAM and its extensions to explore whether the specific context of care for people with CI raises considerations not fully addressed by existing models.

Existing research has identified multiple factors influencing technology acceptance among caregivers and older adults with CI. A systematic review mapping barriers to TAM domains found that most relate to behavioral intentions to use technology, including cost, privacy concerns, stigma, and fear of misuse [15]. Physical and cognitive factors also pose significant challenges, including cognitive decline, reduced physical functioning, and visual, auditory, and language impairments [13]. While prior research has examined caregivers’ acceptance of DATs in the care of people with CI, factors beyond perceived usefulness and perceived ease of use have not been fully examined. This study therefore aims to explore other factors that may shape caregivers’ acceptance of DATs.

To address these gaps, this study adopts a qualitative approach to explore caregivers’ experiences with DATs in the care of people with CI. Specifically, it examines how caregivers use DATs in everyday care scenarios and identifies the factors influencing their acceptance and sustained use. The study is guided by 2 research questions (RQs):

This qualitative study used semistructured interviews to explore caregivers’ experiences with DATs in CI care among older adults aged 60 years and older. Caregivers were selected as the primary participants because they serve as the key decision-makers in technology acceptance and use in this context. As cognitive capacity varies across stages and types of CI, caregivers play a central role in evaluating, implementing, and sustaining the use of DATs in real-world care settings.

Purposive sampling was used with the following inclusion criteria: (1) age ≥18 years, (2) >1 year of experience caring for older people with CI, (3) used at least one DAT, (4) informal or formal caregiver, and (5) communication ability to clearly express care experiences. It should be noted that inclusion criterion (3) restricted participation to caregivers who had already adopted at least one DAT; the experiences and views of caregivers who had not used any DAT were therefore not captured in this study.

Fifteen caregivers were recruited through online communities, offline activities, and institutional recommendations, including 11 informal and 4 formal caregivers. The mean age of the participants was 39.67 (SD 15.0; range 21-78) years (9 women and 6 men), and the mean duration of care was 4.3 (SD 4.0; range 1-16) years.

Data saturation was assessed after each interview by evaluating thematic redundancy, adequacy of data to address the RQs, and the overall coherence of the emerging thematic structure. Initial data saturation indicators emerged by the 10th interview, when core themes around technology acceptance factors began stabilizing. No new themes emerged after the 13th interview, and data collection concluded after the 15th interview. Participant characteristics, caregiving contexts, and types of DATs used are summarized in Table 1.

Semistructured interviews lasting 40 to 60 minutes were conducted online and offline. The interview guide covered participants’ background information, DAT use experience, perceived ease of use and usefulness, and factors influencing technology acceptance. TAM informed the interview guide by sensitizing the study to perceived usefulness and perceived ease of use, but it did not determine the full scope of questioning. The semistructured format allowed participants to introduce concerns, experiences, and interpretations beyond the initial TAM-informed prompts. All interviews were recorded and transcribed verbatim, forming approximately 100,000 Chinese words of textual data.

The subsequent thematic analysis was conducted inductively rather than deductively. Codes and themes were generated from participants’ accounts following a systematic 6-step approach: (1) familiarization with data through repeated reading of transcripts and immersion in the dataset; (2) initial code generation to systematically identify and extract key concepts and patterns across the entire dataset; (3) searching for themes by collating codes into potential overarching themes and gathering relevant coded data extracts; (4) reviewing themes through iterative refinement to ensure internal coherence and clear distinctions between themes; (5) naming and defining themes, with later contextualization in relation to the TAM constructs where appropriate; and (6) producing the final analysis report with compelling evidence and analytical narrative [16,17].

The transcribed interviews were read repeatedly by author (XS). Guided by the study aims, relevant data segments were identified and extracted from individual responses, and meaning units were developed with attention to their content and context. These meaning units were interpreted at a semantic level rather than at a latent level. Codes were produced using keywords that captured the essence of the meaning units. The coded meaning units were compared and grouped into categories. After reviewing and refining the categories, they were organized into themes named using short sentences. These themes were finally contextualized and classified according to TAM through a process involving all authors, with authors (HC and PAK) providing final verification and validation. Details of the coding table for data analysis are provided in Multimedia Appendix 2. Research quality was ensured through expert consultation, supervisor discussions, double-checking procedures, diverse participant selection, and regular review of the analysis.

This nonmedical qualitative study was conducted in accordance with the Finnish National Board on Research Integrity (TENK) guidelines [18]. After the discussion with the Ethical Committee of Research at the University of Eastern Finland, no formal review board number was issued for this study. According to Finnish research ethics principles, interview-based human sciences research should follow the guidelines of TENK, including voluntary participation, informed consent, confidentiality, and protection of personal data. As this study involved qualitative interviews with caregivers rather than medical interventions or direct experimentation with older adults with CI, it would normally be assessed as nonmedical human sciences research under Finnish ethical review principles. Particular attention was nevertheless paid to the sensitive caregiving context, participants’ emotional burden, and anonymization of all interview data. Participation was voluntary, informed consent was obtained prior to data collection, and confidentiality and anonymity were strictly maintained throughout the study. To acknowledge participants’ time investment and valuable contributions, appropriate compensation was provided as an expression of gratitude for their participation. Strict confidentiality protocols were maintained throughout the study, with all personal information and sensitive data anonymized during data processing. Interview recordings were securely stored and deleted on completion of analysis, and all participants are referred to anonymously in research outputs to ensure privacy protection.

This section presents the key findings in responding to the 2 RQs. The first part addresses how DATs are used in CI care settings (RQ1), highlighting 5 core functional domains. The second part examines factors shaping caregivers’ acceptance of these technologies (RQ2), outlining both barriers and facilitators. These themes emerged from the thematic analysis of caregiver interviews.

It is important to note that the functional capabilities of a technology do not automatically translate into perceived benefits for users. A device may be designed to perform a specific function, yet caregivers may not experience it as useful or manageable in their particular care context. This gap between objective functionality and subjective perception is central to why this study focuses on caregivers’ perceived usefulness and ease of use, rather than on what technologies are technically designed to do.

The following findings address RQ1 by examining how DATs are used in CI care settings. The analysis of caregiver interviews revealed that DATs serve 5 functional domains in CI care, with applications spanning safety support, emotional and social engagement, daily living assistance, health monitoring, and cognitive function support.

The following findings address RQ2 by examining the factors that shape caregivers’ acceptance of DATs in CI care. These factors are organized into 3 categories: technology design, user psychology, and external environment, each of which encompasses both facilitators and barriers to acceptance.

This study explored how DATs are used in care for people with CI and what factors shape caregivers’ acceptance. The discussion first relates 4 identified themes to TAM-related models, including family attitudes, social support, technology anxiety, and emotional dependence. It then considers stage-specific care needs, the essential role of human caregivers, and coordinated support for DAT integration.

This study acknowledges several limitations. First, the sample was relatively small, comprising 15 caregivers primarily from family care settings in China, which limits representativeness across diverse geographical and cultural contexts. More detailed sociodemographic characteristics, such as education level, socioeconomic background, and digital literacy, were not systematically examined, and these factors may influence technology acceptance. As inclusion criteria (3) required prior DAT use, the sample reflects the experiences of adopters only, and the views of caregivers who had not used any DAT are not captured. The diversity of the sample in terms of caregiver type, age, and technologies used also means that findings cannot be attributed to any specific caregiver group or device category.

The qualitative methodology, while providing rich insights into caregiver experiences, inherently carries subjective interpretation risks and lacks quantitative validation. Additionally, the study focuses on currently available technologies rather than emerging innovations such as AI-powered robots or VR/AR applications, which remain limited in practical implementation due to cost and accessibility barriers [24].

A further limitation concerns the source of the data, as this study captures caregiver perspectives rather than direct experiences of care recipients, who may face communication challenges that complicate assessment of their actual technology preferences. In addition, CI stages reported in Table 1 were based on caregivers’ descriptions informed by prior clinical diagnoses, and no standardized cognitive assessments were conducted by this research. Future research should include larger, more diverse samples; use mixed method approaches, longitudinal designs to assess long-term technology adaptation, and develop innovative methods to capture the authentic experiences of care recipients themselves.

This study examined how DATs are used in care for people with CI and what factors shape caregivers’ acceptance of these technologies. Caregivers described DATs as being used across 5 functional domains: safety support, emotional and social engagement, daily living assistance, health monitoring, and cognitive function support. Their accounts also showed that acceptance was shaped not only by perceived usefulness and perceived ease of use but also by family attitudes, social support, technology anxiety, and emotional dependence. Technologies were described as more acceptable when they were reliable, easy to manage, and responsive to stage-specific needs. They were less acceptable when they increased caregiver burden, failed to match everyday care demands, or conflicted with family expectations and care values. DATs may be used in care, but acceptance still depends on perceived usefulness, ease of use, relational care, family attitudes, and support conditions. Overall, the findings suggest that DAT integration depends on relational, cultural, and social contexts rather than technical functionality alone. Across interviews, DATs were generally viewed as supportive tools within care, while human caregivers remained essential for emotional support, individualized judgment, and response to complex situations.

These findings have 3 practical implications. First, DAT design should prioritize simplicity, personalization, and flexibility across different stages of CI. Second, implementation should be accomplished by accessible training and sustained support for caregivers. Third, wider uptake is likely to depend on enabling family, community, and policy conditions. In summary, improving DAT integration in care for people with CI requires attention not only to what technologies are designed to achieve but also to how caregivers and recipients experience, assess, and sustain their use in everyday care. Effective DAT integration must be grounded in caregivers’ perceptions and in the broader social and cultural contexts of technologies. Future research should further examine these dynamics in larger and more diverse samples.