Revolutionizing Alzheimer's Care with Wearable Technology to Enhance Quality of Life: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease is marked by progressive cognitive decline, including impairments in spatial awareness and short-term memory. With the global prevalence of dementia, including Alzheimer's disease, expected to rise from 57 million cases in 2019 to nearly 153 million by 2050, innovative solutions are urgently needed. This systematic review aims to explore the role of wearable technology in improving the daily lives of Alzheimer's patients by evaluating its effectiveness. METHOD: Utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a systematic review on the efficacy of wearable technology in enhancing the quality of life for Alzheimer's patients. A comprehensive search of PubMed, Google Scholar, Cochrane, and Scopus was performed using keywords such as "assistive technology," "wearable device," and "Alzheimer's." We included peer-reviewed studies, published in English between 2015 and 2025, involving human participants diagnosed with Alzheimer's that assessed the impact of wearable devices on quality of life. Data extraction was independently conducted by three reviewers, and quality assessment was completed using the Critical Appraisal Skills Programme (CASP). RESULT: Out of 13,077 studies, five met the inclusion criteria, with the majority excluded due to lack of relevant outcomes. Qualitative analysis revealed that three studies using SenseCam, a wearable camera employed as a memory aid, demonstrated consistently significant improvements in autobiographical, episodic, and semantic memory over both short- and long-term follow-ups, along with short-term reductions in depressive symptoms and enhancements in activities of daily living. A study with a Bluetooth earpiece that provided task reminders, instructions, and encouragement showed task initiation and completion rates increasing from below 35% to over 90%, indicating improved independence. On the other hand, the Microsoft HoloLens, an augmented reality headset, showed no significant improvements in task performance. CONCLUSION: This review identified wearable devices as promising tools for enhancing cognitive function, emotional well-being, and daily activities in Alzheimer's care. While some devices significantly improved patients' quality of life, others require further refinement. These findings highlight the transformative potential of wearable technology and emphasize the need for continued research and user-centered design to overcome implementation challenges and optimize integration in both clinical and home settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".