Despite receiving standardized initial training, many rural older adults displayed systematic misunderstandings about the intended functions of digital health technologies. These stemmed from a mismatch between users’ existing cognitive frameworks and the operational logic of new technologies, and typically appeared in three recurring patterns.

Type 1: Functional simplification. In this pattern, multimodal monitoring capabilities were reduced to a single perceived function. During the observation period, one older adult (male, age 72) in Village B consistently treated an intelligent voice blood pressure monitor (which was equipped with a 4G Cat.1 voice module and supported one-sentence wake-up, full-process voice guidance, AI-powered interpretation, and cloud-based data synchronization) as a simple one-button measurement device. In interviews, he explained that he relied only on the numerical readout and deliberately avoided the voice interaction features. He explained: “I have to say so many things just to measure blood pressure. I am afraid the device won’t understand my dialect if I say something wrong. Just reading the number is enough. These fancy features are a burden for me.” This tendency toward simplification illustrates a cognitive defense mechanism in response to perceived technological complexity.

Type 2: Functional misunderstanding. This pattern involves an incorrect interpretation of technical functions that becomes reinforced over time. An older adult (female, age 68) in Village A misperceived the telemedicine terminal, which requires activating an online doctor transfer mechanism via a designated consultation button, as a conventional telephone that “connects directly to the doctor with one click”. Field records indicate that during an episode of acute gastric discomfort, she failed to obtain timely assistance. She mistakenly pressed the information inquiry button, having confused it with the consultation button due to their visual similarity. A technical support staff member (male, age 37) stated: “Even though we repeatedly emphasized key positioning in training, using color coding and shape differentiation as multisensory cues, older adults still have difficulty distinguishing between the buttons, especially in emergencies.”

Type 3: Functional generalization. This pattern occurs when users idealize equipment capabilities beyond their actual design limits. An older adult (female, age 75) in Village A expressed unrealistic expectations toward the fall detection device. The device had a detection range of 4 meters by 5 meters and consisted of a human fall monitoring radar, a Wi-Fi network, and a cloud-based management platform with real-time alert functionality. She asked: “If the system can detect falls, why can’t it automatically call for help and send a doctor to my home?” This expectation of integrated detection and rescue reflects a significant mismatch between perceived capabilities and the actual availability of emergency medical resources in rural areas. As a result, her overall satisfaction with the device was substantially reduced. A physician (male, age 42) at a local primary healthcare facility explained, “Although we repeatedly emphasize the functional limitations of these devices, older adults often expect an immediate medical response that exceeds what rural emergency services can realistically provide. This gap between expectations and reality has become a major barrier to technology acceptance.”

Even when older adults in rural areas have developed a basic functional understanding of digital health technologies, many still face considerable difficulties in carrying out the required steps for operation. Prior research shows that complex user interfaces, multi-step operational sequences, and the lack of built-in error tolerance mechanisms constitute major barriers for this population (14). These procedural barriers not only reflect inadequate age-friendly design but also highlight a broader gap in technology developers’ understanding of the unique needs and usage habits of older adults living in rural settings.

A 79-year-old male participant in Village C demonstrated typical procedural difficulties when attempting to configure a smart pillbox. The setup process required six sequential steps: powering the device through charging or battery insertion, registering and linking the device to a mobile application, setting reminder parameters, placing medications into designated compartments, receiving scheduled alerts, and confirming medication intake. Field observations revealed that his first attempt failed. He did not press and hold the settings button for 3 s, which was the required condition to activate the device. During his second attempt, he successfully powered on the pillbox. However, he accidentally triggered the “dose memory” function, which reset all previously configured parameters. In the third attempt, he entered the wrong shortcut key combination for configuring reminders, which required simultaneous pressing of the “mode” and “time period” buttons. The configuration was ultimately completed with the assistance of a family member via video call, and the entire process took 42 min. The participant expressed frustration and self-doubt, stating, “The pillbox I used before just opened and worked. This one is far too complicated. I’ve spent so much time learning, and I still can’t do it. Maybe I’m just too old and useless now.”

Further observation indicates that execution barriers were significantly exacerbated by specific ergonomic micro-design flaws. During fieldwork, we noted that the smart pillbox used functional buttons measuring approximately 6 mm in diameter. The buttons were positioned too close together and offered no salient tactile cues or color coding to support discrimination. Such a configuration is particularly demanding for older adults with hand tremor or reduced fine motor control. Consequently, the participant repeatedly mis-pressed the reset button when attempting to confirm an action, which frequently interrupted the medication-taking sequence. Similar problems were reported by other users. For example, another older adult in Village C (female, age 76) Stated: “These small buttons are all crowded together. If my hand shakes even a little, I press the wrong one. I can’t tell them apart.”

Age-related declines in hand-eye coordination and visual function further intensify the operational challenges faced by older adults. An 83-year-old female participant in Village A, who experienced mild cataracts and hand tremors, struggled to operate the micro-lancing needle while using a smart blood glucose monitor. Due to instability during the sampling process, she failed multiple blood collection attempts, which resulted in finger pain and emotional distress. Furthermore, although the device was equipped with a 3.5-inch display screen, the default front size was approximately 12 points, and the interface utilized a black-on-light-gray color scheme. The contrast and visual settings did not align with older adults’ visual needs. As a result, she was unable to clearly read her glucose readings. During fieldwork, she repeatedly brought the device close to her eyes and kept changing the viewing angle. Even so, she could not identify the readings on her own and ultimately relied on family members to interpret them. As she said with resignation: “I can see a string of tiny lights, the numbers flickering there, but I just can’t make them out. Every time, I have to ask my daughter-in-law to read it for me.” This mismatch between age-related physiological decline and inadequately adapted device design represents a fundamental biological basis of the usage divide.

Dialect recognition failure in voice-interaction systems emerged as another salient barrier. In village B, an older adult (male, age 81) was provided with a “small wristband and gateway” package featuring voice interaction. In principle, this system allowed users to inquire about blood pressure, blood glucose, and medication reminders. Field observations, however, revealed that the speech recognition supported only standard Mandarin and had an extremely low recognition rate for the local dialect. Although the participant followed the training instructions, he repeatedly issued commands in his local dialect, such as “check today’s blood pressure.” The device either did not respond or returned irrelevant prompts, for example, “Unable to recognize your command, please try again.” After several failed attempts, he became frustrated and turned off the voice function entirely, using the wristband only as a regular watch. As he stated: “It can’t understand what I’m saying, and I can not be bothered talking to it anymore.”

Enhancing older adults’ ability to troubleshoot and solve problems, along with providing tailored instructional support, is essential for improving their effective use of digital health tools (5). However, rural older adults often lack the necessary strategies to respond to technical difficulties. This absence of problem-solving capacity is not limited to the technical level, but also reflects deeper differences in learning orientation and adaptation to unfamiliar digital environments.

A representative case involved a 70-year-old male participant in Village D. When his smart bracelet displayed a “network not connected” error, his first response was to restart the device, drawing on prior experience with household appliances. After this attempt failed, he adopted a passive waiting approach. He placed the device aside, hoping the issue would resolve itself over time. The problem remained unaddressed for 2 weeks, until it was eventually discovered and resolved by a community health worker during a routine home visit. During the interview, the participant shared: “I didn’t know what to do in this situation. I couldn’t understand the manual, and I felt embarrassed to keep asking others for help.”

This lack of strategic response can be traced to a fundamental generational gap in learning orientation. Unlike younger cohorts who developed digital literacy through iterative trial-and-error in technology-rich environments, rural older adults have neither the prior exposure nor the confidence to engage in exploratory learning.

A 78-year-old female participant in Village A shared her concerns about using a non-wearable sleep monitoring mattress. She explained: “The manual says it should be placed under the mattress to monitor sleep quality, heart rate, breathing, and movement. But I’m afraid to press the crescent-shaped buttons used to switch modes. What if I press the wrong one and damage the device? Our generation was taught to take care of things and not to touch unfamiliar objects without knowing how they work. And when something goes wrong, we don’t even know who to ask for help.” The community health worker responsible for training (female, age 36) further observed: “Even when we provide illustrated instructions and real-time practice opportunities, older adults often lack the basic skills to troubleshoot technical issues. This highlights the need for long-term capacity-building efforts, which cannot be addressed through one-time training sessions alone.”

The value perception of digital health technologies among rural older adults is deeply influenced by traditional health beliefs and longstanding cultural values, leading to a pronounced misalignment with the value propositions assumed by technology designers (39). Many participants expressed a passive health orientation, characterized by the belief that medical help is only necessary when physical discomfort arises. This orientation reflects an entrenched belief system that limits their appreciation for the potential benefits of preventive monitoring and proactive health management.

An 86-year-old male resident in Village E exemplifies resistance toward the anti-wandering wristband. He stated, “My mind is perfectly clear and my memory is good; what purpose does this thing serve at home?” The device incorporates GPS, Beidou, Wi-Fi, and accelerometer technologies to enable hybrid indoor-outdoor positioning. It is equipped with an electronic fence feature that provides real-time location tracking and triggers automatic alerts if the user crosses preset boundaries. Although family members repeatedly explained the bracelet’s preventive function, the older adult wore it only occasionally under his or her supervision. He never used it voluntarily, and no self-initiated activations were recorded during the month. He also demanded that the positioning function be disabled, expressing his frustration: “Do I really have to be monitored inside my own home? Am I a child or a criminal? I have lived this long, so why would I need a machine to tell me how to live?”

This divergence in value perception further extends to fundamentally different understandings of the nature of family-based care. Within the framework of traditional Chinese filial piety, emotional fulfillment is strongly associated with direct, in-person caregiving by children. Consequently, technology-mediated forms of remote assistance are often regarded not as a meaningful enhancement but as a substitute.

A 79-year-old female participant from Village B described her experience with an emergency call system installed in her bedroom: “The government installed a one-button call device and said it could be used for emergencies.” Although she understood its intended function, she questioned its emotional adequacy. She explained, “How can this compare to a real person? No matter how intelligent a machine is, it cannot sense how uncomfortable I feel. It is more reassuring to go to the village committee, where at least I can talk to someone and ease my loneliness.” A 34-year-old female nurse at a primary healthcare facility further commented, “Even though we demonstrated the system’s linkage features and emergency response speed during health education sessions, older adults still tend to trust the emotional support of face-to-face interactions more. Their acceptance of technology-based services remains generally low.”

Psychological factors constitute a major barrier to both the adoption and continued use of digital health technologies among older adults (40). Based on intensive field observations and in-depth interviews, this study identified four interrelated types of psychological barriers. These barriers capture the complex process of psychological adjustment that older adults experience when confronted with technological change.

Among them, the fear of causing damage emerged as the most common and consequential form of resistance. For instance, an 87-year-old woman in Village E persistently refused to operate a smart wheelchair on her own, despite repeated demonstrations by her family. The device, valued at more than 5,000 Chinese yuan (CNY), was equipped with advanced features such as electric drive, automatic obstacle avoidance, and remote control. She explained, “My eyesight is poor, and my hands are not steady. What if I press the wrong button and break it? Such an expensive machine is beyond what I can afford to replace. I would rather rely on my old wheelchair and feel at ease.” This fear was not only rooted in financial concerns; it also revealed her deeper anxiety over her declining physical and cognitive abilities.

Privacy and security concerns are particularly salient for devices that record health data. They highlight the instinctive vigilance older adults display toward personal information protection in the digital era. A 73-year-old male resident in Village C refused to use a terminal with an electronic health record function. He explained: “If my medication history and blood pressure records are uploaded online, my privacy will be completely exposed. What if someone exploits this information?” Despite the device’s use of end-to-end encryption and its compliance with the Personal Information Protection Law of the People’s Republic of China, and despite repeated explanations by technical staff, he remained cautious and ultimately decided not to use the feature.

Technology dependence anxiety reflects a deeper concern about losing autonomy. The ambivalence of a 78-year-old woman in Village B is telling. She stated: “If I become used to these devices, and one day they break down or the power goes out, will I be left helpless? As people grow old, they are already in decline. If we depend too much on machines, we may eventually lose even the basic ability to live.” This anxiety underscores the trade-off older adults perceive between technological convenience and the preservation of personal capability.

Concerns about technical accuracy arose when frequent false alarms undermined trust. During the second observation period, fall monitors often misinterpreted bending motions, such as picking up objects, as falls. Several families chose to deactivate the device. A 69-year-old man in Village D voiced his frustration: “Every time the alarm sounded, the community office called, which made the whole household nervous. Now I hesitate even to bend down while sweeping. Is this help or disturbance?” A 29-year-old technical support staff admitted: “Adjusting the sensitivity threshold can reduce false alarms, but in rural areas the limited coverage of professional technical support makes it difficult to address such issues promptly.”

Repeated setbacks in using digital health technologies can significantly reduce older adults’ technological self-efficacy, creating a vicious cycle described as failure-withdrawal-further failure (41). Field observations revealed that accumulated micro setbacks, such as device pairing failures, unstable synchronization, complex interfaces, and false or missed alarms, often led to learned helplessness and technological fatigue.

The experience of a 74-year-old male from Village C demonstrates this trajectory. At first, he showed strong motivation to adopt the smart bracelet, attending training sessions and exploring its functions. Yet a series of operational difficulties gradually eroded his confidence. The touch screen lacked sensitivity, causing frequent mistouches. Unstable connections repeatedly interrupted data synchronization. The emergency button was accidentally pressed multiple times, producing false alarms. After one incident in which community staff made a late-night visit to address a false alert, his attitude changed completely. He stated: “I’m too clumsy to learn these high-tech devices. I won’t use them anymore, or I’ll only create more trouble for others.”

Social comparison further intensified this loss of self-efficacy. A 66-year-old man in Village B contrasted his experience with that of his neighbor using an intelligent voice assistant. The device supported three preset emergency contacts, voice commands, and health reminders. He reflected: “My neighbor can set up three emergency numbers, make calls by pressing the emergency button, and even ask about the weather. I can barely use any of these functions except for a few accidental activations. People say I am like rotten wood that cannot be carved, and perhaps they are right.” Such self-deprecating judgments, rooted in social comparison, not only undermine individual adoption but may also generate negative collective perceptions within the community.

Although China’s policy framework for age-friendly home modifications includes a training component, the actual outcomes fall short of the intended objectives. Effective use of digital health technologies requires repeated reinforcement, individualized guidance, and sustained follow-up (5). However, current delivery models are systematically deficient along these dimensions.

Field observations point to a threefold formalization dilemma. First, there is a mismatch between training duration and content depth. Instruction is typically provided orally by installation personnel at the time of setup and lasts about two to 3 h. Within this window, trainers must explain and demonstrate the functions of three to five devices, leaving very limited time per function (often 15–20 min). Consequently, demonstrations are cursory, with little opportunity for hands-on practice or feedback.

Second, the language of instruction exceeds the cognitive threshold of many older adults. Although most participants possess only basic literacy, the training materials are saturated with technical terms such as “Bluetooth pairing,” “cloud synchronization,” and “threshold setting,” which are difficult to interpret without scaffolding or plain-language glosses. This disconnect raises the entry barrier and undermines confidence.

Third, post-training follow-up is largely absent. Sessions are commonly treated as a one-off service, with no structured refresher training, outcome monitoring, or mechanisms to identify and address persistent use problems over time. In combination, these shortcomings weaken skill acquisition and impede the sustained use of installed technologies.

An account from a primary-level community worker (male, age 41) illustrates how training is actually implemented: “To be honest, training is mostly about completing tasks assigned from above and taking photos for the record. We have little idea how much older adults remember or use afterward. Since the higher authorities require training records, we organize sessions, take attendance, and take pictures, but no one really evaluates the outcomes.” Feedback from older participants corroborates this tendency toward formalization. Many report that they “forget soon after the training”, and only a few describe it as “somewhat helpful, but insufficient”.

Moreover, the mismatch between training content and real-world needs further undermines effectiveness. Standardized sessions ignore individual differences in vision, hearing, cognitive ability, and learning pace, and they rely on a uniform instructional model. As one older adult (male, age 71) from Village C put it, “The training is like teaching in English to people who only understand the local dialect; it sounds like gibberish to us. What seems simple to younger people feels almost impossible for us.”

Clear attributional divergences emerged across implementation actors when explaining why training was often ineffective. Frontline community workers tended to frame the problem as a consequence of policy compliance pressures and resource constrains. As one community staff (female, age 38) noted: “There are deadlines from above, and we have to hit the coverage targets by a certain date. Training matters, sure, but what they check is the numbers. We do not really have a choice.” In contrast, technical support personnel responsible for device installation and troubleshooting more often attributed the problem to design deficiencies in the device themselves: “These devices are not made for older adults. You can train them all you want; it still will not work. If the interface stays the same, they will not remember all these steps. This is on the tech companies, not on us.” This divergence between institutional attribution and design-based attribution created a shift in perceived accountability. In everyday practice, responsibility for training was repeatedly pushed outward and reassigned to other parties, rather than being treated as a shared obligation within the implementation chain.

Families are ideally positioned to support older adults in adopting digital health technologies, yet widespread empty-nest patterns in rural China have created a support vacuum (43). In our sample of 24 households, 19 (79.2%) had adult children working away from home year-round and returning, on average, for fewer than 30 days each year. Even when children are present, intergenerational reverse mentoring in the digital domain encounters multiple, layered obstacles.

Insufficient time investment is the primary constraint. Family caregivers frequently report that technology instruction takes far longer than expected. One explained (male, age 42), “My parents learn very slowly; the same operation has to be repeated dozens of times before it barely sticks.” Another added (male, age 34), “Work pressure is high, time at home is limited, and our patience is in short supply.” Field observations indicate that a typical teaching interaction by adult children lasts about 10 min on average, which is far less than the time older adults generally need to consolidate new skills. This substantial gap in instructional time directly undermines learning outcomes.

Ineffective teaching methods further erode the value of family support. There is a marked mismatch between the rapid demonstration style favored by younger adults and the cognitive processing speed of older adults. For instance, one older adult (female, age 79) in Village B described her son completing the entire setup of a smart pillbox in 3 min: power on, select mode, set time periods, confirm dose. She recalled, “His fingers moved quickly over the small buttons. Before I could even locate the confirmation key, all three medication periods had been set. When I try to do it on my own, I cannot remember the first step.” This mismatch between fast input and slow processing constitutes a key barrier to intergenerational technology transfer.

A clear positional divide emerged between family caregivers and frontline community workers over who should take primary responsibility for household support. Family caregivers tended to push this responsibility toward community services and technology providers. As one adult child who worked away from-home (male, age 34) said: “I only come back for a few days a year. I can’t be there every day to keep teaching them. If the government is promoting these devices, there should be ongoing technical support. You can’t just install them and then walk away.” In contrast, frontline community staff argued that families should be the first line of support. A village committee member (female, age 45) responded: “We don’t have enough staff. We can’t be a ‘tech babysitter’ for every older adult. Their children know them best, and they should make time to teach their parents.” This disagreement, shaped by competing claims about time constraints and where responsibility should lie, left household support in a prolonged state of limbo in everyday practice.

Emotional factors create a deeper, often overlooked barrier. Many older adults refrain from seeking help out of a traditional reluctance to impose on their children, which produces a dilemma of wanting to learn but not daring to ask. One older adult (female, age 77) in Village A stated, “It is not easy for children who work away from home, and their job pressure is high. I do not want to trouble them with such a small matter. I will try to figure it out slowly, and if I still cannot use it, so be it.” Her family caregiver (female, age 52) offered the other side of this dynamic: “Each time I teach her a new function, she says she can manage because she does not want to take up my time. Yet when she actually needs it, she still cannot use it, and the cycle repeats.”

Community-level technical support is constrained by scarce resources and limited capacity. Within China’s primary healthcare system, staffing for aging-in-place services is critically inadequate. The shortfall spans both community staff and specialized technical personnel, and prevailing ratios remain well below those reported in high-income countries (44). This resource deficit translates into coverage gaps in support services, leaving many households without timely assistance.

Geographic accessibility markedly affects the effectiveness of support service. Remote villages located more than five kilometers from town centers face average response times of five to 7 days for technical support, whereas suburban villages typically wait only one to 3 days. These spatial disparities widen the usage divide within rural areas. A technical support worker (male, age 37) acknowledged the staffing shortage: “There are only a few technicians for the entire district, and we are responsible for maintaining age-friendly equipment across hundreds of villages. We must prioritize hardware failures, and most issues related to user guidance cannot be addressed promptly because we simply lack the personnel.”

Professional capacity limitations also undermine the quality of community support. Only a small share of frontline staff in primary-level community organizations have received systematic training in digital health technologies, and most instead rely on self-study or brief orientations. A village committee cadre (male, age 53) acknowledged the limits of his competence, “Some advanced devices, such as the AI health butler that integrates natural language processing and machine learning, are only partially understood even by us. We end up learning while teaching. When older adults ask more detailed questions, we cannot provide satisfactory answers.” This mode of support is unlikely to produce substantive results.

Resource-constrained work settings also shaped how community-based support actors assigned responsibility for using digital health technologies. A technical support personnel (male, age 29) member stressed design accountability, stating: “Tech companies should think about what older adults need when they design the product. They should not dump the age-friendly part on people like us at the very end.” In contrast, primary healthcare providers were more likely to attribute the difficulties to older adults’ limited digital literacy and entrenched health practices. As one village doctor (male, age 56) put it: “It’s not that older adults can’t learn. A lot of them just don’t want to, or they can’t be bothered. They think, ‘I’ve lived most of my life without this stuff and I was fine,’ so they reject it from the start. And some can barely read. What are we supposed to do then?” Although these attributions seem to point in opposite directions, they reflect a shared pattern of defensive boundary setting. Under chronic resource shortages and limited support capacity, different actors drew clear lines around what they saw as their own responsibilities and what they refused to carry.

The formation of stable technology habits depends on repeated reinforcement and a supportive environment. Evidence suggests that turning a new behavior into an automatic habit typically requires about 66 days of consistent practice (46). By contrast, our field observations reveal a cliff-like decline in the use of digital health technologies among older adults in rural areas.

The absence of environmental triggers is a primary obstacle to habit formation. Unlike smartphones and other technologies with clear usage contexts, digital health devices rarely offer natural cues that prompt action. A 74-year-old man in Village C illustrated the point: “When the phone rings, you answer. When a television program starts, you watch. These actions are woven into daily life. By contrast, health devices, though important, are easily forgotten without reminders. They sit unused like decorations and gradually gather dust.”

Competition with established routines further limits the uptake of new technologies. Field observations show that although virtual community terminals capable of supporting real-time video interaction for up to 20 participants were available, most older adults still preferred in-person contact when seeking social engagement. In the morning exercise area in Village B, many chose to walk 10 min to join in square dancing rather than use the devices for online health exercises. One older adult (female, age 68) from Village B explained: “Online communication always feels like there is a barrier. You cannot see a real person or feel warmth. At our age, what we seek is human presence, and no machine, however advanced, can replace it.”

A fundamental tension exists between the rapid iteration of digital technologies and the slower learning pace of older adults. During the observation period, several digital health devices received one or two system updates. Each update introduced interface changes, reorganized functions, or altered operating steps. For older users, this moving target creates an ongoing challenge, as procedures they had already learned become outdated and must be relearned.

The experience of a woman from Village D, age 67, illustrates the learning burden created by system updates: “I had finally learned how to check blood pressure and heart rate and could identify what each button did. After the update, the entire interface changed, the icons moved, and even the colors were different. Is this not making things difficult for older people like us?” Such skill resetting increases the cost of learning and, more importantly, erodes users’ confidence.

System updates often produce a pronounced skill depreciation effect among older adults. One resident (male, age 70) from Village B had mastered the original three-step video consultation on the telemedicine device. The sequence consisted of power on, select a doctor and initiate the call. After a subsequent optimization, the procedure expanded to five steps with the addition of identity verification and symptom prefilling. He expressed his frustration: “Everything I learned feels wasted, and I have to start over. At my age, how can I keep relearning again and again? Younger people may call it an optimization, but for us it often turns the system into something we can no longer use.”

Repeated setbacks during use have a cumulative effect that gradually erodes willingness to engage with the technology and eventually leads to abandonment. Field observations reveal a clear pattern. A single major setback, such as a failure to obtain emergency assistance, prompts most older adults to suspend use for at least 1 week. A series of minor setbacks, including operational errors or connection failures, then drives down the frequency of use. Once accumulated frustration exceeds an individual’s tolerance threshold, discontinuation becomes the most common outcome.

The emotional impact of frustration often generalizes beyond a single device, shaping perceptions of digital technologies as a whole. In Village B, a 64-year-old woman, after repeatedly failing to set up a smart pillbox, voiced deep self-doubt: “Am I too stupid to learn new things at this age?” Such negative self-attribution can prompt social withdrawal, as some older adults become reluctant to expose perceived incompetence. In a minority of cases, these reactions persist as low mood and feelings of worthlessness, further discouraging engagement with technology.

The absence of positive feedback mechanisms significantly accelerates abandonment. When usage fails to produce immediate, perceptible benefits, motivation declines rapidly. A family caregiver (female, age 49) described her father’s experience with a smart blood pressure monitor: “The device showed only the raw reading and read it aloud, without indicating whether the value was normal or offering personalized advice. After a month, my father said, ‘I cannot tell if this is good or bad, so the device is useless,” and he stopped using it.” This perceived imbalance between effort invested and benefits received directly undermines the willingness to continue using the technology.