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Research

27,380 Alzheimer's and dementia studies in plain English, each scored out of 100.

Find studies

Showing 1–25 of 75 studies in Digital health tools published 2026 (page 1 of 3) · 27,380 in the whole library · How the 0–100 score works

Browse by subject, author or institution

Each number is how many studies that link shows, with your other filters kept.

Other subjects

Authors

Names as each record prints them; the same person can appear under two spellings.

Institutions

From OpenAlex; 12,859 studies have no institution on record and are not counted here.

What “Digital health tools” covers

Digital health tools — Apps, wearables, online tests and other technology used to detect, monitor or support people with dementia.

A note on reading research. One study is rarely the final word.

We try to note the size of a study, whether it was in humans or animals, and how early-stage the finding is. If a summary here doesn't make that clear, treat it with extra caution and check the original source.

Digital biomarkers for early agitation detection in dementia: a scoping review of emerging wearable and smart technologies for personalized care

✓ Peer-reviewed alzhub usefulness score 56 out of 100. Frontiers in Neurology 2026-02-10

The reviewed studies demonstrated that wearable sensors, particularly those employing multimodal data and personalized machine learning models, enable reliable detection of agitation symptoms and support timely, tailored interventions. 5 key findings →

Design: systematic review Evidence: high

Full summary, score breakdown and related studies → Original source Free full text

An Ensemble Learning Artificial Intelligence Model for Alzheimer's Disease Detection Using OCT

✓ Peer-reviewed alzhub usefulness score 54 out of 100. Ophthalmology Science 2026-01-10

For detecting AD-dementia, the ensemble model achieved the area under the receiver operating characteristic curve (AUROC) of 0.943 (95% confidence interval, 0.906-0.980), 0.786 (95% confidence interval, 0.673-0.899), and 0.795 (95% confidence interval, 0.716-0.874) in the internal validation, External-1, and External-2, respectively. 4 key findings →

Design: case-control study Participants: 190 Evidence: moderate

Full summary, score breakdown and related studies → Original source Free full text

Developing machine learning-enhanced WHODAS 2.0 short forms for persons with dementia

✓ Peer-reviewed alzhub usefulness score 53 out of 100. Journal of the Formosan Medical Association 2026-05-01

Both ML short forms showed high internal consistency (α = 0.96 for ML-WHODAS-16 and 0.93 for ML-WHODAS-10) and excellent concurrent validity with the full WHODAS-32 (r = 0.98 for both). 5 key findings →

Design: longitudinal study Participants: 31,952 Evidence: moderate

Full summary, score breakdown and related studies → Original source Free full text

Advancing dementia identification using machine learning and real‐world sequential health data

✓ Peer-reviewed alzhub usefulness score 53 out of 100. Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring 2026-04-01

The best-performing model achieved an area under the curve of 0.86 (95% confidence interval [CI]: 0.84-0.87), with sensitivity of 73.3% (95% CI: 72.3-74.2) and specificity of 87.5% (95% CI: 86.7-88.2). 5 key findings →

Design: case-control study Participants: 8,195 Evidence: moderate

Full summary, score breakdown and related studies → Original source Free full text

Molecular features of human pathological tau distinguish tauopathy-associated dementias

✓ Peer-reviewed alzhub usefulness score 52 out of 100. Cell 2026-01-29

In Alzheimer's disease (AD), pathological tau protein shows a progressive accumulation of post-translational modifications (PTMs), reflecting disease severity, progression, and prion-like activity. 5 key findings →

Evidence: low

Full summary, score breakdown and related studies → Original source Free full text

Generative artificial intelligence in dementia care: a scoping review protocol on nursing roles, experiences and ethical perspectives

✓ Peer-reviewed alzhub usefulness score 51 out of 100. BMJ Open 2026-02-01

Critical appraisal will not be undertaken, as it is optional in scoping reviews and is not aligned with the primary mapping objectives of this review. 5 key findings →

Design: meta-analysis Evidence: high

Full summary, score breakdown and related studies → Original source Free full text

ActiTect: a generalizable machine learning pipeline for REM sleep behavior disorder screening through standardized actigraphy

✓ Peer-reviewed alzhub usefulness score 50 out of 100. npj Digital Medicine 2026-06-12

Isolated rapid eye movement sleep behavior disorder (iRBD) is a major prodromal marker of α-synucleinopathies, often preceding the clinical onset of Parkinson's disease, dementia with Lewy bodies, or multiple system atrophy. 3 key findings →

Participants: 31 Evidence: moderate

Full summary, score breakdown and related studies → Original source Free full text

Digital biomarkers in early Alzheimer's disease from wearable or portable technology: A scoping review

✓ Peer-reviewed alzhub usefulness score 49 out of 100. Journal of the Neurological Sciences 2026-01-06

After deduplication and screening of 8893 records, we included 109 studies describing a wide array of wearable or portable DHTs that obtained digital biomarkers. 5 key findings →

Participants: 109 Evidence: low

Full summary, score breakdown and related studies → Original source Free full text

Vascular cognitive impairment and dementia: Prevention, treatments, mechanisms and management options for the future

✓ Peer-reviewed alzhub usefulness score 48 out of 100. Neuropsychopharmacology 2026-01-19

Vascular Cognitive Impairment and Dementia (VCID), the second most common form of dementia, is becoming increasingly prevalent worldwide. 5 key findings →

Evidence: low

Full summary, score breakdown and related studies → Original source Free full text

Resource-stratified machine learning framework for cognitive status classification and mild cognitive impairment to dementia progression prediction

✓ Peer-reviewed alzhub usefulness score 46 out of 100. Alzheimer s Research & Therapy 2026-03-07

The Level 3 model achieved high classification performance (AUC: 93.98%), and the Level 1 model achieved comparable performance (AUC: 91.53%). 5 key findings →

Participants: 31,081 Evidence: low

Full summary, score breakdown and related studies → Original source Free full text

Discriminative Plasma Lipidomic Signatures of Dementia with Lewy Bodies and Alzheimer’s Disease: A Targeted Mass Spectrometry and Machine Learning Approach

✓ Peer-reviewed alzhub usefulness score 46 out of 100. Neuropsychiatric Disease and Treatment 2026-03-01

Significant alterations were observed in 7 lipid classes and 65 specific lipid species in DLB compared to AD. 5 key findings →

Design: cross-sectional Evidence: moderate

Full summary, score breakdown and related studies → Original source Free full text

The Application of Artificial Intelligence to Older Adults With Disabilities: A Scoping Review

✓ Peer-reviewed alzhub usefulness score 43 out of 100. Journal of Applied Gerontology 2026-01-20

ObjectiveTo review the application of artificial intelligence (AI) in the care of older adults with disabilities, identifying intervention type, target populations, and effects. 5 key findings →

Participants: 15 Evidence: high

Full summary, score breakdown and related studies → Original source Full text may need a subscription

Gait and movement analysis for discrimination between people with dementia and healthy control persons based on pose estimation and machine learning

✓ Peer-reviewed alzhub usefulness score 43 out of 100. Journal of Alzheimer s Disease 2026-03-15

BackgroundDementia disorders are affecting millions of people globally, characterized by memory loss, communication difficulties, and motor function decline. 1 key finding →

Participants: 64 Evidence: moderate

Full summary, score breakdown and related studies → Original source Free full text

Investigating the Utility of Explainable Artificial Intelligence for Neuroimaging‐Based Dementia Diagnosis and Prognosis

✓ Peer-reviewed alzhub usefulness score 41 out of 100. Human Brain Mapping 2026-02-01

Artificial intelligence and neuroimaging enable accurate dementia prediction but often involve 'black box' models that can be difficult to trust. 5 key findings →

Evidence: low

Full summary, score breakdown and related studies → Original source Free full text

Hearing and vision support in people with dementia living at home: Outcomes from the intervention development programme for the European SENSE-Cog Trial

✓ Peer-reviewed alzhub usefulness score 41 out of 100. HRB Open Res 2026-07-28

This intervention represents the output of the iterative development of a complex intervention to fulfil an unmet need for PwD and sensory loss. 2 key findings →

Evidence: low

Full summary, score breakdown and related studies → Original source

Human-Centered Ambient and Wearable Sensing for Automated Monitoring in Dementia Care: A Scoping Review

✓ Peer-reviewed alzhub usefulness score 39 out of 100. IEEE Internet of Things Journal 2026-01-22

We conducted a scoping review to map the rapidly evolving landscape of wearable and ambient sensing technologies for monitoring people with dementia across home and institutional settings. 4 key findings →

Evidence: low

Full summary, score breakdown and related studies → Original source Free full text

An Artificial Intelligence-Driven Multimorbidity Framework Reveals a Shared Metabolic and Immune Core Across Alzheimer’s Disease, Amyotrophic Lateral Sclerosis, and Frontotemporal Dementia

✓ Peer-reviewed alzhub usefulness score 38 out of 100. Biomedicines 2026-02-16

Across AD, ALS, and FTD, a convergent multimorbidity architecture centered on a shared metabolic and immune core was identified, accompanied by prominent neurobehavioral processes and intermediate systems including gastrointestinal, endocrine, hematological, hepatic, and sensory pathways. 5 key findings →

Evidence: low

Full summary, score breakdown and related studies → Original source Free full text

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Where “Free full text” comes from

Free full text links and the Full text may need a subscription label come from Unpaywall, Europe PMC and OpenAlex records of free copies, checked 10 Oct 2026. A study with neither is unknown, not paywalled. Show only studies that are free to read

How the 0–100 score works — and why some entries are capped

This is alzhub's own score. It is not an official metric, not an impact factor, and no journal, publisher or ranking body endorses it. We built it so that a reader who is not a scientist can tell a landmark trial from a conference abstract without having to already know the difference. Every point comes from one of 20 components, all of them printed on the study itself, so you can disagree with our arithmetic rather than having to trust the total.

  • Journal tier up to 10 pts
  • Journal's citation record up to 4 pts
  • Study design up to 12 pts
  • Strength of evidence up to 5 pts
  • Peer-review validation up to 10 pts
  • How recent up to 5 pts
  • Sample size up to 4 pts
  • Effect reported up to 3 pts
  • Studied in people up to 3 pts
  • Randomised / blinded up to 2 pts
  • Times cited up to 7 pts
  • Citations per year up to 5 pts
  • Impact for its field and year up to 6 pts
  • Cited by studies here up to 6 pts
  • Still being cited up to 4 pts
  • Senior author's track record up to 4 pts
  • Institution's track record up to 3 pts
  • Multi-centre collaboration up to 3 pts
  • Indexed in PubMed up to 2 pts
  • Free to read up to 2 pts

Journals are placed in ordered tiers, never given a numeric impact factor — we could not verify current impact factors from a source we control, and an invented one would be worse than none. Tier 1 is the top-tier general journals (NEJM, The Lancet, JAMA, Nature, Science, Nature Medicine); tier 2 is the leading specialty journals; tier 3 is every other peer-reviewed journal. A separate part scores how much the journal's own papers are cited, which tells the tier-3 journals apart.

Two rules we don't bend. First, we never fill in a missing value: if a record does not state a sample size or a design, that component scores zero, and if we simply have no data for a part, such as a citation count, that part is left out and the study is scored on the parts we could check. The study card says which. Second, anything that is not a peer-reviewed article is capped at 16/100 and carries a warning chip — so a conference abstract or a company press release can never outrank a peer-reviewed paper, no matter how good it looks.

We rank a rigorous null result — a good trial showing that something does not work — above an early positive finding. Someone deciding whether to buy a supplement is served by that, and the rigour is already counted by the design and journal components.

The score is computed from the published data alone and is reproducible: the same file always yields the same numbers.

See the whole rubric drawn out → — four charts covering all 27,380 studies: how the scores are spread and where the cap bites, which of the 20 components the library is strong and weak on, which subjects rest on stronger evidence, and what kinds of study each subject is actually built from.

What this library is — and isn't

This is a large sample of the Alzheimer's literature, not an exhaustive index of it. It holds 27,380 records, built mostly from automated pulls of Europe PMC (60.6% of the library) and OpenAlex (18.6%), plus smaller pulls from PubMed, Crossref and medRxiv. If a study, a drug, or a finding isn't here, that is not evidence it doesn't exist in the literature — it may simply not have been pulled in yet.

1,032 records (3.8% of the library) are not peer-reviewed — 962 of those are preprints posted before any journal review, and the rest are conference abstracts, regulatory notices and reports. Every study card on this page states its status plainly, and the score above is capped for anything that isn't a peer-reviewed article.

See the full, machine-readable breakdown of this library's coverage →

Newest studies, as a dated timeline →    Atom feed