Evidence map›Paper›PMID 40093660›Full record

ArticleBMC digital health2023

New Opportunities for the Early Detection and Treatment of Cognitive Decline: Adherence Challenges and the Promise of Smart and Person-Centered Technologies.

Zhe He, Michael Dieciuc, Dawn Carr, Shayok Chakraborty, Ankita Singh, Ibukun E Fowe, Shenghao Zhang, Mia Liza A Lustria, Antonio Terracciano, Neil Charness and 1 more

Registry-linked trialAbstract read
In one paragraph

Article in BMC digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07180147 (Arlington Longitudinal Optimal Healthy Aging Study), which is not on this map. Cited by 28 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT07180147 active not recruitingnot on this mapstarted 2025, after this paper: background citation

Arlington Longitudinal Optimal Healthy Aging Study (ALOHA)

TypeobservationalSponsorMarymount UniversityRan2025 to 2031Enrolled500ConditionsAlzheimer Disease (AD), Cardio Vascular Disease, Mild Cognitive Impairment (MCI), Frailty Syndrome
3 · Its place in the literature

Who cites it

28 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  5. Adaptive cascading artificial intelligence for Alzheimer's disease assessment: a clinically oriented narrative review and implementation framework.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Zhe HeSchool of Information, Florida State University, Tallahassee, Florida USA.
Michael DieciucDepartment of Psychology, Florida State University, Tallahassee, Florida USA.
Dawn CarrDepartment of Sociology, Florida State University, Tallahassee, Florida USA.
Shayok ChakrabortyDepartment of Computer Science, Florida State University, Tallahassee, Florida USA.
Ankita SinghDepartment of Computer Science, Florida State University, Tallahassee, Florida USA.
Ibukun E FoweDepartment of Psychology, Florida State University, Tallahassee, Florida USA.
Shenghao ZhangDepartment of Psychology, Florida State University, Tallahassee, Florida USA.
Mia Liza A LustriaSchool of Information, Florida State University, Tallahassee, Florida USA.
Antonio TerraccianoDepartment of Geriatrics, Florida State University, Tallahassee, Florida USA.
Neil CharnessDepartment of Psychology, Florida State University, Tallahassee, Florida USA.
Walter R BootDepartment of Psychology, Florida State University, Tallahassee, Florida USA.

Funding

Using social networks to map and evaluate team science across CTSA hubsUL1TR001427 · NCATS · UNIVERSITY OF FLORIDA · PI MITCHELL, DUANE A. · 2015 to 2024
$37.2M
The Adherence Promotion with Person-centered Technology (APPT) Project: Promoting Adherence to Enhance the Early Detection and Treatment of Cognitive DeclineR01AG064529 · NIA · FLORIDA STATE UNIVERSITY · PI BOOT, WALTER RICHARD, CHAKRABORTY, SHAYOK · 2019 to 2023
$3.2M
NCATS NIH HHS UL1 TR001427NIA NIH HHS R01 AG064529
6 · The paper itself

Abstract

Early detection of age-related cognitive decline has transformative potential to advance the scientific understanding of cognitive impairments and possible treatments by identifying relevant participants for clinical trials. Furthermore, early detection is also key to early intervention once effective treatments have been developed. Novel approaches to the early detection of cognitive decline, for example through assessments administered via mobile apps, may require frequent home testing which can present adherence challenges. And, once decline has been detected, treatment might require frequent engagement with behavioral and/or lifestyle interventions (e.g., cognitive training), which present their own challenges with respect to adherence. We discuss state-of-the-art approaches to the early detection and treatment of cognitive decline, adherence challenges associated with these approaches, and the promise of smart and person-centered technologies to tackle adherence challenges. Specifically, we highlight prior and ongoing work conducted as part of the

Indexed as

AdherenceAlzheimer’s Disease and Related DementiasCognitive Training

Identifiers

PMID40093660
PMCPMC11908691

What OpenQuestion holds

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Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.