Evidence map›Paper›PMID 42711082›Full record

ArticleBMJ open2026

Passive Digital Marker-enabled identification and engagement for Alzheimer's disease and related dementias care in primary care: the INCLUDE study protocol.

Diana Summanwar, Leonard Lee, Deanna R Willis, Zina Ben Miled, Christina Baucco, Emily C Webber, Anthony J Perkins, April Y Powell, Nicole R Fowler

Registry-linked trialAbstract readClinical Trial Protocol
In one paragraph

Article in BMJ open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07016178 (Innovative Cognitive Impairment Case Finding and Engagement for Diverse Participation in Alzheimer's Disease Care, Treatment, And Research), which is not on this map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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.

NCT07016178 nacompletednot on this map

Innovative Cognitive Impairment Case Finding and Engagement for Diverse Participation in Alzheimer's Disease Care, Treatment, And Research

TypeinterventionalSponsorIndiana UniversityRan2025 to 2026Enrolled3,454ConditionsMild Cognitive Impairment (MCI), Alzheimer Disease, Alzheimer's Disease (Incl Subtypes), DementiaArmsAcceptability and Feasibility of Twistle messaging for PDM+ patients
3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Diana SummanwarDepartment of Family Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA dsummanw@iu.edu.ORCID http://orcid.org/0000-0002-7529-3632
Leonard LeeDepartment of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA.ORCID http://orcid.org/0009-0004-4844-9904
Deanna R WillisDepartment of Family Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA.ORCID http://orcid.org/0000-0001-6639-9729
Zina Ben MiledPhillip M. Drayer Department of Electrical and Computer Engineering, Lamar University, Beaumont, Texas, USA.
Christina BauccoIndiana University Center for Aging Research, Indianapolis, Indiana, USA.
Emily C WebberDepartment of Pediatrics, Indiana University School of Medicine, Indianapolis, Indiana, USA.ORCID http://orcid.org/0000-0001-5877-6707
Anthony J PerkinsDepartment of Biostatistics and Health Data Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA.ORCID http://orcid.org/0000-0003-1278-5397
April Y PowellIndiana University Health Inc, Indianapolis, Indiana, USA.
Nicole R FowlerDepartment of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionMore than half of older adults living with Alzheimer's disease and related dementias (ADRD) never receive a formal diagnosis, and when a diagnosis occurs, it is often years after symptom onset. Primary care clinicians are ideally positioned to detect ADRD early; however, current workflows lack scalable tools that support systematic identification and follow-up. The Passive Digital Marker (PDM), a machine learning model that uses structured electronic health record (EHR) data, can identify patients at elevated risk for ADRD without adding burden to clinicians. This protocol outlines a feasibility study to develop and evaluate a patient-informed secure messaging intervention paired with PDM-based risk stratification to enhance patient engagement in cognitive assessment in primary care settings. METHODS AND ANALYSIS: This will be a non-randomised pilot study conducted across 12 single health system primary care clinics. The PDM will be applied to EHR data to identify patients aged ≥65 years who are at high risk for ADRD. High-risk patients will receive a co-designed secure message prior to and after upcoming primary care visits encouraging follow-up evaluation with a trained nurse, the Brain Health Navigator (BHN). The primary objectives are to: (1) determine the feasibility of applying the PDM to EHR data across 12 primary care clinics; (2) assess the feasibility of engaging patients identified as positive on the PDM through secure text messaging prior to a primary care encounter and (3) evaluate engagement with the BHN following secure text messaging. Study outcomes will assess the feasibility of implementing the PDM and secure messaging workflow, including identification of high-risk patients using the PDM, message delivery and patient engagement measured through message open rates, completion of cognitive concern questions and appointments scheduled with the BHN. Quantitative data will be analysed using descriptive statistics. ETHICS AND DISSEMINATION: This study was deemed exempt as part of enhanced patient care. The findings will be disseminated through peer-reviewed publications, professional conferences, health system reports and public-facing communications. TRIAL REGISTRATION NUMBER: NCT07016178.

Indexed as

Alzheimer DiseaseDementiaElectronic Health RecordsPatient ParticipationAgedDigital HealthFeasibility StudiesHumansMachine LearningPilot ProjectsPrimary Health CareResearch DesignRisk AssessmentDementiaElectronic Health RecordsImplementation ScienceMachine LearningPrimary Care

Identifiers

PMID42711082
PMCPMC13561024

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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.