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Find studies

Showing 401–425 of 1,094 studies in Digital health tools (page 17 of 44) · 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.

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.

Integrative machine learning approach to risk prediction for dementia and Alzheimer’s disease

✓ Peer-reviewed alzhub usefulness score 50 out of 100. GeroScience 2025-08-27

Dementia, particularly Alzheimer's disease (AD), presents a growing global health challenge characterized by cognitive decline, behavioral changes, and loss of independence. 1 key finding →

Participants: 2,878 Evidence: moderate

This study developed machine learning models to predict Aβ positivity in Alzheimer’s disease by integrating early-phase 18 F-Florbetaben PET and clinical data to improve diagnostic accuracy. 2 key findings →

Participants: 176 Evidence: low

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

Sensitivity and False Alarm Rate of a Fall Sensor in Long-Term Fall Detection in the Elderly

✓ Peer-reviewed alzhub usefulness score 50 out of 100. Gerontology 2014-08-13

The fall detection system detected 12 out of 15 real-life falls, having a sensitivity of 80.0%, with a false alarm rate of 0.049 alarms per usage hour with the implemented real-time system. 3 key findings →

Participants: 15,500 Evidence: low

Classification of CT brain images based on deep learning networks

✓ Peer-reviewed alzhub usefulness score 50 out of 100. Computer methods and programs in biomedicine 2016-10-20

While computerised tomography (CT) may have been the first imaging tool to study human brain, it has not yet been implemented into clinical decision making process for diagnosis of Alzheimer's disease (AD). 2 key findings →

Participants: 285 Evidence: low

Algorithms developed from health administrative data are sensitive and specific for identifying older adults with AD-RD. 1 key finding →

Participants: 3,404 Evidence: low

The use of EHRs for AD-related research is promising, and AD-BERT shows superior predictive performance in modeling MCI-to-AD progression prediction. 1 key finding →

Participants: 3,657 Evidence: low

Clinical features of early-onset Alzheimer disease in a large kindred with an E280A presenilin-1 mutation

✓ Peer-reviewed alzhub usefulness score 50 out of 100. JAMA 1997-03-01

Other than the early onset, this clinical phenotype is indistinguishable from sporadic AD except that affected individuals frequently complained of headache preceding and during the disease. 3 key findings →

Participants: 128 Evidence: low

Progressive non-fluent aphasia (PNFA) is a syndrome in which patients lose the ability to communicate fluently in the context of relative preservation of single word comprehension and non-linguistic cognitive abilities. 4 key findings →

Evidence: low

Reducing between scanner differences in multi-center PET studies

✓ Peer-reviewed alzhub usefulness score 49 out of 100. NeuroImage 2009-02-06

This work is part of the multi-center Alzheimer's Disease Neuroimaging Initiative (ADNI), a large multi-site study of dementia, including patients having mild cognitive impairment (MCI), probable Alzheimer's disease (AD), as well as healthy elderly controls. 5 key findings →

Evidence: low

We studied the clinical features, pathology, and molecular genetics of a family (Mo) with an autosomal dominant disinhibition, frontal lobe dementia, parkinsonism, and amyotrophy. 5 key findings →

Evidence: low

Two patients with nonfluent progressive aphasia, who have been studied longitudinally, are contrasted with a group of 5 patients with fluent progressive aphasia or semantic dementia. 5 key findings →

Evidence: low

Due to the COVID-19 pandemic, most memory clinics have had to suspend their activities. 5 key findings →

Evidence: low

IoT Wearable Sensors and Devices in Elderly Care: A Literature Review

✓ Peer-reviewed alzhub usefulness score 49 out of 100. Sensors (Basel, Switzerland) 2020-05-16

The increasing ageing global population is causing an upsurge in ailments related to old age, primarily dementia and Alzheimer's disease, frailty, Parkinson's, and cardiovascular disease, but also a general need for general eldercare as well as active and healthy ageing. 5 key findings →

Evidence: low

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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