The research team, led by Yee Tak Derek Cheung and Guowen Zhang of the School of Nursing at the University of Hong Kong, designed their investigation as a one-group pre-post feasibility study. Between July and September 2023, they recruited older adults aged 65 and above who had dysphagia and lived in two residential care homes in Hong Kong. The study was registered as a clinical trial under number NCT05818501, and it received ethical approval from the Institutional Review Board of the University of Hong Kong and the Hong Kong Hospital Authority Hong Kong West Cluster, with informed consent obtained from all participants in accordance with the Declaration of Helsinki.

The study protocol was deliberately structured to compare ordinary drinking methods with the assistive cup under realistic daily conditions. During a five-day pre-test period running from seven in the morning to three in the afternoon, participants drank water the way they normally would, either by being spoon-fed or by using conventional cups. They then received training in the use of the Sippa cup over three days, allowing time for familiarization with the device. Finally, a five-day post-test period followed, in which participants used the Sippa cup exclusively for drinking water during the same daily window. This design allowed the researchers to capture each resident serving as their own control, minimizing the influence of individual differences in swallowing ability, cognition, and habitual fluid intake.

The primary outcome measure was daily water intake, a critical parameter because dehydration is one of the most common and dangerous consequences of dysphagia in institutionalized older adults. Secondary outcomes included the frequency and severity of choking or coughing episodes while drinking, as well as the participants’ willingness to drink water. On the analytical side, the team applied linear mixed models to the quantitative data, a statistical approach well suited to repeated measurements nested within individuals, and used Wilcoxon signed-rank tests to compare paired pre- and post-intervention values. Qualitative feedback was examined through content analysis of individual interviews with care staff and residents.

Sixteen older adults completed the trial. When the researchers compared the pre- and post-intervention periods, they found no statistically significant differences in any of the measured outcomes. Daily water intake showed a p-value of 0.725, the frequency of choking or coughing a p-value of 0.657, the severity of those episodes a p-value of 0.693, and willingness to drink water a p-value of 0.788. In plain terms, residents drank roughly the same amount, choked no more or less often, and expressed similar willingness to drink whether they used conventional methods or the Sippa cup during the observation window.

Those null findings might appear disappointing at first glance, but the authors frame the results with appropriate nuance for a preliminary feasibility study. The central question was not whether the cup outperformed existing methods in a statistically powered trial, but whether it could be deployed safely and acceptably in a care home environment with a small group of cognitively and physically impaired users. The qualitative data proved especially informative here. The older adults who used the cup generally perceived it as a safe alternative tool for drinking water, an important signal of user acceptance in a population where trust in a new device can make or break adoption.

The care staff, however, painted a more complicated picture. In individual interviews, ten care workers and six older adults shared their experiences, and the staff identified several practical barriers to routine use of the Sippa cup. The pads associated with the device were described as expensive and fragile, raising concerns about recurring consumable costs in facilities that operate on tight budgets. The cleaning procedures were viewed as comprehensive and demanding, adding to the workload of staff who already juggle numerous responsibilities. Perhaps most tellingly, the care teams reported a shortage of manpower to properly monitor residents’ use of the cup, a constraint that reflects the chronic staffing pressures facing residential care homes in Hong Kong and far beyond.

These findings illuminate a challenge that extends well beyond a single drinking device. Assistive technology products for older adults are often evaluated primarily on their technical performance in laboratory or clinic settings, yet the success or failure of such products in the real world hinges on a web of implementation factors. The authors conclude that while the Sippa cup, as an assistive technology product, holds promise in supporting safe water intake among older adults, several implementation barriers remain. They specifically highlight the need to consider product and consumable costs, the time required for training and user adaptation, and caregiver acceptance when introducing dysphagia cups into care settings. In other words, a device that works is not necessarily a device that will be used.

The study also contributes to the growing field of gerontechnology, the interdisciplinary effort to design technology that serves the needs of aging populations. Most existing dysphagia interventions, such as thickened liquids, postural techniques, or supervised feeding, place considerable cognitive and behavioral demands on the person with swallowing difficulty. The researchers note a lack of research on adaptive dysphagia interventions that are cognitively non-demanding for the recipient, which is precisely the niche that a purpose-designed cup is meant to fill. For residents with dementia or other cognitive impairments, an intervention that requires no active learning or cooperation may be the only viable option, making device-based approaches an attractive direction for future development.

As a preliminary study with sixteen completers, a single-group design, and no control arm, the trial cannot establish effectiveness, and the authors are careful not to overstate their claims. Its real value lies in mapping the terrain for larger, controlled trials and in surfacing the practical friction points, fragile pads, cleaning burdens, and staffing limits, that engineers, care home managers, and funders must address before such devices can deliver on their promise. The research was financially supported by the Gerontechnology Platform and the Social Innovation and Entrepreneurship Development Fund of the Hong Kong Special Administrative Region Government, and the authors acknowledge the collaborative role of the Hong Kong Council of Social Service and the staff and residents of the Haven of Hope Woo Ping Care & Attention Home. For a rapidly aging world in which dysphagia affects a large share of care home residents, the message of this study is clear: the engineering of safer drinking aids is advancing, but the human systems around those aids will ultimately decide whether a promising cup becomes a standard of care or a shelf-bound gadget.

Subject of Research: Feasibility of an assistive dysphagia cup for safe water intake among older adults in residential care homes

Article Title: Feasibility of dysphagia cup for older adults living in residential care homes: a preliminary study

Article References: Cheung, Y. T. D., Chan, T. H. R., Zuo, Y., Yuen, L. W. E., Chow, C. S., Au, A. K. Y., Lau, W. Y., Lau, C. Y., & Zhang, G. (2026). Feasibility of dysphagia cup for older adults living in residential care homes: a preliminary study. BMC Geriatrics. https://doi.org/10.1186/s12877-026-08372-z

Image Credits: AI Generated

DOI: 10.1186/s12877-026-08372-z

Keywords: dysphagia, older adults, residential care homes, assistive technology, gerontechnology, dehydration, choking, caregivers, feasibility study, water intake, geriatrics, Hong Kong

The review, conducted by Keyi Li of The University of Hong Kong, Chengliang Wang of East China Normal University and Australian Catholic University, and Zhuopeng Yu of The Hong Kong Polytechnic University, set out to map a research landscape that has become fragmented across two communities that rarely talk to each other. Computer scientists publish on sensing algorithms and predictive models; gerontologists publish on the lived experience of aging. Because the two literatures barely intersect, it has been genuinely difficult to answer basic questions: which AI technologies exist for supporting older adults at home, how mature they are, and how well they match everyday needs. The team addressed this by conducting a general systematic review with descriptive mapping, searching six major databases, including MEDLINE, CINAHL, Web of Science, ProQuest Central, PubMed, and Scopus, for articles published between 2005 and June 2025.

Following the PRISMA guidelines, the standard protocol for ensuring systematic reviews are transparent and reproducible, the search yielded 101 articles. That number alone tells a story. Research on artificial intelligence for Aging in Place has grown rapidly over the two decades covered by the review, reflecting both the maturation of machine learning and the mounting demographic pressure that gives the field its urgency. But the authors found a persistent disparity at the heart of this literature: computational advancement has consistently outpaced studies on real-world efficacy. In other words, the field is excellent at building systems and comparatively weak at demonstrating that those systems work in the messy, unpredictable environment of an actual older adult’s home.

To bring order to this sprawling literature, the review introduces a four-category taxonomy that classifies AI systems by their algorithmic complexity. At the simpler end sit classical machine learning approaches, which rely on statistical techniques to detect patterns in sensor data, activity logs, or health records. These methods are interpretable and computationally cheap, making them attractive for resource-constrained home environments. Further along the spectrum lie deep learning techniques, including convolutional neural networks, which excel at extracting meaning from raw signals such as camera footage, audio, or wearable sensor streams. Convolutional neural networks, in particular, have become a workhorse for recognizing falls, monitoring gait, and identifying changes in daily routines that might signal declining health.

The taxonomy’s upper tiers capture the newest and most powerful technologies. Large language models, the class of systems behind modern conversational AI, represent a qualitative leap in what home-based support might look like, offering natural-language interaction, personalized reminders, and the potential to serve as always-available companions or assistants. The review also highlights explainable AI, an increasingly important requirement in care contexts where a system’s recommendations must be understood and trusted by older adults, family caregivers, and clinicians alike. When an algorithm flags a risk or suggests an intervention, opacity is not just an inconvenience; it can be a barrier to adoption and a source of harm. Ordering systems along this complexity spectrum allows researchers and practitioners to see, at a glance, where the field’s energy is concentrated and where mature, simpler solutions may already be sufficient.

Beyond classifying the technologies themselves, the authors used topic modeling, a computational technique that identifies recurring themes across large bodies of text, to distill the 101 articles into five application scenarios. These scenarios map the practical domains where AI is being deployed to support Aging in Place, spanning the core needs the review identifies: safety, health, daily activities, and social connection. The scenario structure matters because it shifts the framing from what algorithms can do to what older adults actually require. A fall-detection model, however sophisticated, is only valuable insofar as it addresses a genuine risk an older person faces while living alone. By anchoring the analysis in application scenarios, the review creates a common vocabulary that both engineers and gerontologists can use.

The synthesis culminates in a three-layer organizing framework designed to connect technical capability to gerontological need. This kind of architecture is the review’s most consequential contribution, because it treats AI for Aging in Place not as a collection of isolated gadgets but as a system with distinct levels: the underlying technologies, the application scenarios they enable, and the human outcomes they are meant to serve. The framework is paired with a future research agenda that explicitly targets the disparity the authors documented. The implication is pointed: the next wave of research should not simply chase higher accuracy benchmarks but should demonstrate efficacy in real homes, with real older adults, over meaningful periods of time.

Why does this matter now? The demographic arithmetic is unforgiving. As the review’s authors note, the needs created by Aging in Place already outstrip available care resources, and the imbalance will only intensify as populations age. Artificial intelligence is attractive precisely because of its scalability: software can monitor, detect risks, and personalize support at a marginal cost approaching zero, extending the reach of a care workforce that cannot expand fast enough. A single well-designed monitoring system can watch over a home around the clock; a human caregiver cannot. The economics are compelling, but the review’s evidence base suggests the field must earn that promise through rigorous validation rather than assume it.

There are also quieter lessons in the review’s methodology. By searching six databases across two decades and applying PRISMA standards, the authors assembled one of the most comprehensive maps of this domain to date, and their descriptive-mapping approach, which uses topic modeling alongside traditional synthesis, shows how computational methods can be turned on the literature of computing itself. The work received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors, and the authors declare no competing interests. The article is open access, published on 1 October 2026, meaning its taxonomy and framework are freely available to the researchers, designers, and policymakers who will need them most.

The broader takeaway is a familiar one in technology and aging, but stated here with unusual clarity: the bottleneck is no longer imagination or even raw algorithmic power. It is alignment. Systems must be matched to the rhythms of daily life, evaluated in the environments where they will actually operate, and designed so that the people they serve can understand and trust them. The review’s four-category taxonomy, five application scenarios, and three-layer framework give the field a shared structure for that work. If the coming decade of research follows the agenda these authors lay out, the gap between what AI can compute and what older adults need may finally begin to close, and the goal of growing old in one’s own home could move from aspiration to standard practice.

Subject of Research: A systematic review of artificial intelligence technologies for supporting aging in place

Article Title: Artificial intelligence technologies for aging in place: a systematic review

Article References: Li, K., Wang, C., & Yu, Z. (2026). Artificial intelligence technologies for aging in place: a systematic review. Artificial Intelligence Review. https://doi.org/10.1007/s10462-026-11698-0

Image Credits: AI Generated

DOI: 10.1007/s10462-026-11698-0

Keywords: artificial intelligence, aging in place, gerontechnology, systematic review, machine learning, deep learning, large language models, convolutional neural networks, explainable AI, gerontology, older adults, home care

Horiuchi’s argument rests on a theory-driven synthesis that pulls together strands of research usually kept in separate silos: gerontechnology, social and assistive robotics, smart-home monitoring, the emerging scholarship on digital ageism, mainstream AI ethics, human enhancement debates, and capability-oriented accounts of aging and disability drawn from the tradition of Amartya Sen and Martha Nussbaum. From this interdisciplinary weave he extracts a four-pillar evaluative framework, comprising substantive opportunity, practical control, relation, and responsibility. Each pillar is a lens through which any AI- or robot-mediated care arrangement can be interrogated, and together they are meant to shift the conversation from deficit management toward what the author calls aging with dignity, relation, and room for revision and refusal.

The first pillar, substantive opportunity, borrows directly from the capability approach. It asks not whether a technology functions, but whether it widens the set of meaningful things a person can actually do and be. A fall-detection system that silently renders an apartment legible to remote monitors may reduce mortality statistics while doing nothing to restore a person’s ability to walk to the market, host friends, or pursue a lifelong hobby. Research on smart homes and home health monitoring, including systematic reviews of sensor technologies designed to support aging in place, has documented impressive technical reliability, but Horiuchi’s framework insists that reliability is not the same as opportunity. The question becomes: after the algorithm has done its work, is the older person’s real freedom larger or smaller?

The second pillar, practical control, tackles the everyday texture of human-machine interaction. Older adults are frequently positioned as passive beneficiaries of ambient intelligence, their homes instrumented with passive monitoring systems that require no action on their part. Ethnographic work on passive monitoring in low-income independent living has shown that residents are not simply grateful recipients; they actively interpret, negotiate, and sometimes resist the sense of being watched. Practical control asks who can turn the system off, who can correct its inferences, who understands what it is reporting, and whether the person retains what one might call breathing room in monitored space. A robot or monitoring platform that cannot be questioned, adjusted, or refused by its user, however technically elegant, fails this pillar.

The third pillar, relation, may be the most culturally charged. Socially assistive robots are already being trialed in dementia care, and studies of robots in advanced dementia care units report engagement and calming effects. Companion machines designed with trauma-informed care principles and humanoid robots used as memory trainers for people with mild cognitive impairment illustrate how far the field has moved. But Horiuchi, drawing on scholarship that explores affect and relationality across sites of intelligence and care, warns that a machine’s ability to occupy a relational slot does not automatically mean it enriches the human web of relations around the older person. The ethical question is whether a robot companion supplements fragile social networks or quietly substitutes for them, giving families and institutions a technological alibi for withdrawal.

The fourth pillar, responsibility, confronts the sprawling supply chains of contemporary AI. Large language models and generative systems in healthcare have prompted calls for urgent regulatory oversight, and research on the AI supply chain has revealed how modularity dislocates accountability, leaving developers with fragmented notions of who answers when something goes wrong. In eldercare, where misclassification can mean an overlooked fall or a false alarm of decline, this fragmentation is not an abstraction. Horiuchi argues that the ethical quality of a technology depends on the wider sociotechnical arrangement in which it is designed, deployed, maintained, interpreted, and refused, and that responsibility must therefore be assessed across that whole arrangement rather than pinned on a single device or codebase.

Two of the paper’s broader theses give the framework its intellectual bite. The first is that in super-aged societies, AI and robotics do not merely serve pre-existing needs; they help constitute what agency, dependency, care, and responsibility mean. This is the co-constitution thesis familiar from science and technology studies, where scholars have argued that aging and technology are mutually shaped rather than sequentially matched. A society that builds its care infrastructure around risk-minimizing sensors will produce different kinds of old age, and different kinds of dependency, than one that builds around mobility, creativity, and connection. The second thesis redefines enhancement itself. Against transhumanist framings that equate enhancement with youthfulness or optimization, Horiuchi argues that enhancement in later life should mean widening meaningful possibilities under changing bodily and social conditions, an idea that resonates with psychological models of successful aging built on selective optimization with compensation and with disability scholarship on the extended body.

The framework arrives at a moment of genuine technological ferment. Systematic reviews document a rapid proliferation of AI approaches for predicting and detecting cognitive decline, brain-computer interfaces being explored for cognitive enhancement in older people, personalized cognitive training platforms, and conversational agents whose perceptions and needs among older adults are only beginning to be mapped. Japan, the archetypal super-aged society, has poured public resources into eldercare automation, yet ethnographies of Japanese care robots have punctured boosterism by showing how robots reshape care work in ways that neither replace nor relieve human caregivers straightforwardly. Horiuchi’s contribution is to give researchers, care institutions, and policymakers a shared vocabulary for asking whether any of this machinery, at any point in its life cycle, is enabling or constraining the people it claims to serve.

Why does this reframing matter now? Because the default evaluative logic of the field is deficit management: count the falls prevented, the hospitalizations avoided, the minutes of caregiver time saved. Those metrics are not worthless, but they systematically undervalue what older people themselves often rank highest, namely autonomy, connection, and the ability to revise the terms of one’s own life. The capability-oriented literature on healthy aging has pushed in the same direction, shifting the lens from successful aging, which risks exalting a narrow standard of vigor, toward wellbeing through capability and human development for all older people, including those living with disability or frailty. Horiuchi’s four pillars give that philosophical shift a concrete evaluative instrument: for any AI-mediated arrangement, ask what substantive opportunities it opens, what practical control it grants, what relations it sustains or erodes, and where responsibility for its operation genuinely resides.

The paper also converges with a growing critical literature on AI ageism, which documents how algorithms trained on younger populations can misread older bodies and voices, and how digitalized services can exclude those least able to navigate them. By naming relation and responsibility as co-equal pillars alongside opportunity and control, the framework implicitly demands that ageism audits become part of standard deployment practice. And by insisting that refusal, the right to decline a technology altogether, is built into the ethical arrangement, it counters the quiet coercion that can seep into institutional care, where residents may feel they cannot say no to a monitor or a robot without seeming to reject care itself. In an era when longevity science is inflating expectations about radical life extension, Horiuchi’s modest, grounded proposal is a corrective: the measure of a technology for aging is not whether it defeats time, but whether it lets people fill the time they have with more of what matters to them.

Subject of Research: A conceptual framework for evaluating AI, robotics, and human enhancement by their effects on agency in aging societies

Article Title: Enabling later life: a four-pillar framework for AI, robotics, and human enhancement in super-aged societies

Article References: Horiuchi, S. (2026). Enabling later life: a four-pillar framework for AI, robotics, and human enhancement in super-aged societies. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03381-3

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03381-3

Keywords: artificial intelligence, robotics, aging, gerontechnology, human enhancement, digital ageism, care ethics, capability approach, social robotics, smart homes, AI ethics, super-aged societies

The study drew on data from the Widely Hokkaido Individual Training for Elderly (WHITE) Study, a community-based health checkup program conducted across 2024 and 2025 in Hokkaido, Japan. Of 158 individuals initially assessed, 97 smartphone-using older adults met the inclusion criteria after the researchers excluded non-users, participants with cognitive impairment defined as a mini-mental state examination score below 23, those with neurological or psychiatric histories such as stroke, depression, Alzheimer’s disease, or Parkinson’s disease, people certified as requiring long-term care, and those with missing key data. The final sample had a median age of 77 years, an interquartile range of 75 to 82 years, and was 73 percent female, reflecting the demographic reality of aging rural Japanese communities where such checkup programs operate.

To capture the breadth of digital engagement, participants were asked whether they used each application on a daily basis from a predefined list of twelve common categories: phone calls, email, camera, quick response code use, web browsing, video streaming, healthcare-related applications, pedometer, text messaging, Facebook, X (formerly Twitter), and other applications not on the list. The number of applications used daily was summed into a diversity score, and participants were split into a low application diversity group using fewer than five apps and a high application diversity group using five or more. The five-application threshold was grounded in prior United States survey data showing that usage clusters around five to six applications among roughly 40 percent of older smartphone users, and it matched the median app count in the present sample, lending the cutoff empirical credibility.

Occupational participation, the study’s central outcome, was measured with the Self-Completed Occupational Performance Index, a validated self-administered questionnaire rooted in the Canadian Model of Occupational Performance and Engagement. This framework conceptualizes health as emerging from dynamic interactions between the person, the environment, and occupation, and it treats technology as an environmental factor that can either enable or constrain daily performance. The SOPI assesses three domains: leisure, productivity, and self-care. Each domain captures perceived occupational control, meaning how much individuals feel able to decide when and how to engage in an activity; occupational balance, reflecting whether they can allocate sufficient time and energy; and satisfaction with performance, a subjective appraisal of how well activities are carried out. Each domain contains three items rated on five-point scales, yielding raw domain scores from 3 to 15 and a total score standardizable from 0 to 100.

The results were striking in their domain specificity. Overall SOPI scores were substantially higher in the high-diversity group, with a median of 75.0 compared with 55.6 in the low-diversity group, a statistically significant difference. When the researchers examined domains separately, leisure scores favored the high-diversity group, and the productivity gap was even more pronounced, with medians of 12.0 versus 6.0. Self-care showed no significant difference. Pattern analyses of application combinations revealed that low-diversity users clustered almost entirely around basic communication tools, namely phone calls, email, and text messaging, while high-diversity users combined those basics with web browsing, cameras, QR codes, healthcare applications, and video streaming, painting a picture of fundamentally different digital lives.

Crucially, the association survived rigorous statistical adjustment. Using generalized linear models, the team controlled for a comprehensive set of potential confounders: age, sex, education, living situation, medication use, employment status, years of smartphone use, daily usage time, gait speed measured by the 10-meter walk test, cognitive function via the mini-mental state examination, frailty status through the Kihon Checklist, and depressive symptoms on the 15-item Geriatric Depression Scale. Even after this adjustment, using five or more applications was independently associated with higher productivity scores, yielding an adjusted regression coefficient of 2.17 with a 95 percent confidence interval of 0.49 to 3.86. Leisure, by contrast, lost its association after adjustment, with a coefficient of 0.66 and a confidence interval spanning zero. Notably, the productivity effect persisted even though only 12 percent of participants held remunerative jobs and 74 percent were unemployed, suggesting the association reflects broader productive occupations such as household management, coordination, and supporting others rather than formal work alone.

Because the sample was modest, the researchers turned to bootstrap resampling with 2,000 iterations to test whether the finding was a statistical fluke. The bootstrap analysis produced a median regression coefficient of 2.24 with a 95 percent confidence interval of 0.49 to 3.86, closely mirroring the original estimate and confirming the stability of the association. This robustness check matters in small-sample occupational therapy research, where fragile coefficients often dissolve under resampling. Here, the diversity-productivity link held firm across thousands of simulated replications of the dataset, strengthening confidence that the observed relationship is not an artifact of a few influential participants.

Why would app diversity connect specifically to productivity? The authors argue that productive occupations, which encompass planning, coordination, information management, and role fulfillment, are precisely the activities that multifunctional smartphone use supports. Email and messaging facilitate information exchange; cameras document tasks and events; web browsing opens access to online services and resources; healthcare applications help manage appointments and health information; and QR codes streamline transactions. Prior research supports this interpretation: studies show older adults increasingly use digital methods for managing finances, navigating communities, and organizing to-do lists, and qualitative work demonstrates that seniors can successfully adopt smartphone calendars and reminder systems to structure daily activities. Leisure and self-care, in contrast, may depend less on digital tools or may be sustained through long-established routines that technology has yet to penetrate, which is consistent with the model’s prediction that environmental resources matter differently across occupational domains.

The findings also illuminate the persistent digital divide within older populations. High-diversity users were significantly younger, used smartphones longer, and reported more daily screen time, while low-diversity participants showed higher frailty and depression scores. Previous longitudinal research has linked cognitive capacity and physical function to internet use and digital competence, suggesting that app diversity may partly reflect accumulated experience, skills, and functional resources rather than preference alone. The authors are careful about causality, however. Reverse causation is plausible, since people with greater productive demands may simply adopt more apps, and residual confounding by socioeconomic resources or informal support cannot be excluded. Limitations also include the small sample, the predominance of women, self-reported app use without a technical smartphone definition, and the cross-sectional design that precludes any causal claim.

Nevertheless, the study opens a practical frontier for occupational therapy in an aging, digitalizing world. Rather than counting device ownership or screen time, clinicians could assess which applications older clients actually use and how those patterns align with occupational goals, then design individualized interventions that expand purposeful app use for scheduling, information access, and role management. Prior work linking smartphone proficiency to higher-level everyday competence suggests skill-building belongs at the center of such programs. If confirmed by longitudinal studies with larger, more sex-balanced samples, the humble app grid on an older adult’s home screen may become a meaningful clinical signal, a small window into how deeply a person remains woven into the productive fabric of daily life, and a tangible target for interventions aimed at preserving independence, purpose, and participation in the digital age.

Subject of Research: The association between smartphone application diversity and occupational participation among community-dwelling older adults.

Article Title: Association Between Smartphone Application Diversity and Occupational Participation Among Community-Dwelling Older Adults: A Cross-Sectional Study

Article References: Shimokihara, S., Yokoyama, K., Ihira, H., Matsuzaki-Kihara, Y., Mizumoto, A., Tashiro, H., Saito, H., Makino, K., Katsuura, S., Kobayashi, M., Shimada, K., Yama, K., Miyajima, R., Sasaki, T., & Ikeda, N. (2026). Association Between Smartphone Application Diversity and Occupational Participation Among Community-Dwelling Older Adults: A Cross-Sectional Study. Scandinavian Journal of Occupational Therapy, 33(1), Article 5. https://doi.org/10.1007/s44474-026-00007-1

Image Credits: AI Generated

DOI: 10.1007/s44474-026-00007-1

Keywords: smartphone apps, older adults, occupational participation, digital engagement, productivity, occupational therapy, aging, digital inclusion, gerontechnology, community-dwelling, SOPI, cross-sectional study