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Record W4412067250 · doi:10.2196/73050

Utilizing Smart Televisions as Assistive Technology to Enhance Communication and Social Lives of Older Adults: Systematic Review

2025· review· en· W4412067250 on OpenAlexaffvenue
Jayde Langdon, Cristina Tugahan Cabansag, Alexis Grigoris, Way Kiat Bong

Bibliographic record

VenueJMIR Rehabilitation and Assistive Technologies · 2025
Typereview
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsWestern University
Fundersnot available
KeywordsPreprintInternet privacyAssistive technologyGerontologyPsychologyComputer scienceTelecommunicationsHuman–computer interactionMedicineWorld Wide Web

Abstract

fetched live from OpenAlex

BACKGROUND: Over the past decade, the proportion of the world's population aged ≥65 years has grown exponentially, presenting significant challenges, such as social isolation and loneliness among this population. Assistive technologies have shown potential in enhancing the quality of life for older adults by improving their physical, cognitive, and communication abilities. Research has shown that smart televisions are user-friendly and commonly used among older adults. However, smart televisions have been underutilized as assistive technologies. OBJECTIVE: This study aimed to explore the state of the art in using smart televisions as assistive technologies to enhance communication and social interactions among older adults. METHODS: The search was conducted following the guidelines for performing a systematic literature review, which included 6 databases, that are, the IEEE, ACM, Google Scholar, ScienceDirect, Engineering Village, and Springer. A range of keywords were used in different combinations, including "smart TV," "older adults," "elderly," "communication," "messaging," "video call," and "application." A set of inclusion and exclusion criteria was defined before the search, and the screening was performed by 3 researchers. We analyzed the selected articles in accordance with the review's aim and the established inclusion and exclusion criteria. None of the articles were subjected to quantitative synthesis because of the significant variations in the data measured. RESULTS: After screening 2671 records from the abstract level to full text, 30 articles were identified as relevant studies, demonstrating both direct and indirect impacts on the social lives of older adults through the use of smart televisions as assistive technology. Some articles were part of the same or larger studies, which makes the number of actual projects even smaller. This indicates that smart televisions have been underutilized as assistive technologies for enhancing older adults' communication and social lives. More than half of the articles proposed their own prototype, and these prototypes were primarily targeted for use at home, while some were targeted for use at geriatric care units or nursing homes. User involvement among older adults was high among the included articles, and some also included other users, such as health care personnel, administrative staff, and engineers. The included studies were mostly from Europe. CONCLUSIONS: This review highlights the potential of smart televisions as assistive technologies to enhance social connectivity among older adults, and identifies several research gaps. Most studies focus on short-term usability and are geographically limited to Europe. Future research should include longitudinal studies, explore diverse cultural attitudes, and focus on adaptive solutions for various health conditions. We hope this review will inspire research on smart televisions as assistive technologies, enhancing social interactions and quality of life for older adults.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.029
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0100.011
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.388
Teacher spread0.370 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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