Evidence map›Paper›PMID 40850371›Full record

ArticleContemporary clinical trials2025

Introducing the Adherence Promotion with Person-centered Technology (APPT) trial: Rationale, methods, and baseline characteristics.

Shenghao Zhang, Michael Dieciuc, Andrew Dilanchian, Mia Liza A Lustria, Dawn C Carr, Antonio Terracciano, Zhe He, Shayok Chakraborty, Neil Charness, Walter R Boot

Abstract readClinical Trial Protocol
PubMed Full text at PMC Publisher
In one paragraph

Article in Contemporary clinical trials, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
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

10 authors.

Shenghao ZhangDivision of Geriatrics and Palliative Medicine, Weill Cornell Medicine, New York, NY, USA. Electronic address: shz4011@med.cornell.edu.
Michael DieciucDepartment of Psychology, Florida State University, Tallahassee, FL, USA.
Andrew DilanchianDepartment of Psychology, Florida State University, Tallahassee, FL, USA.
Mia Liza A LustriaSchool of Information, Florida State University, Tallahassee, FL, USA.
Dawn C CarrDepartment of Sociology, Florida State University, Tallahassee, FL, USA.
Antonio TerraccianoCollege of Medicine, Florida State University, Tallahassee, FL, USA.
Zhe HeSchool of Information, Florida State University, Tallahassee, FL, USA; College of Medicine, Florida State University, Tallahassee, FL, USA.
Shayok ChakrabortyDepartment of Computer Science, Florida State University, Tallahassee, FL, USA.
Neil CharnessDivision of Geriatrics and Palliative Medicine, Weill Cornell Medicine, New York, NY, USA.
Walter R BootDivision of Geriatrics and Palliative Medicine, Weill Cornell Medicine, New York, NY, USA.

Funding

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
NIA NIH HHS R01 AG064529
6 · The paper itself

Abstract

Home-based cognitive training programs delivered via computers and tablets hold promise as cost-effective, population-level interventions to prevent or mitigate age-related cognitive decline. However, adherence to such programs is often low. Using message-tailoring techniques and an adaptive algorithm, we developed a person-centered reminder smart system that delivers motivational messages at times when participants are predicted to be available for training activities. This paper presents the background, study design, methodology, and baseline data for a randomized controlled trial examining the system's efficacy in supporting adherence to cognitive training. A total of 199 cognitively normal, community-dwelling older adults aged 62 to 88 were randomly assigned (1:1) to either the smart reminder or the control condition. Participants were instructed to engage in training activities for 30 min per day, five days per week, for 18 consecutive weeks. Those in the smart reminder condition received personalized messages, delivered at optimized times, and with targeted content, while those in the control condition received generic messages at a fixed time. Adherence rate will be the primary outcome measure, calculated and compared across conditions. Findings from this study will have implications not only for adherence support in cognitive training but also for broader applications of technology-mediated smart reminder systems, including physical exercise, nutrition, medication management, telehealth, and social connectivity. By enhancing intervention engagement, these systems have the potential to improve the health and well-being of older adults on a large scale.

Indexed as

Cognitive DysfunctionPatient ComplianceReminder SystemsAgedAged, 80 and overAlgorithmsFemaleHumansIndependent LivingMaleMiddle AgedMobile ApplicationsMotivationRandomized Controlled Trials as TopicCognitive trainingJust-in-time adaptive interventionMessage tailoringOlder adultsTechnology

Identifiers

PMID40850371
PMCPMC12624871

What OpenQuestion holds

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Registered trials

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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.