Evidence map›Paper›PMID 39621936›Full record

ArticleJMIR aging2024

mHealth Apps for Dementia Caregivers: Systematic Examination of Mobile Apps.

Ning Zou, Bo Xie, Daqing He, Robin Hilsabeck, Alyssa Aguirre

Abstract read
In one paragraph

Article in JMIR aging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing 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.

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

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

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Review
  6. Review
  7. Article
  8. Adoption of Telemedicine for Dementia Care in Nigeria: Scoping Review.Interactive journal of medical research · 2025
    Review
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

5 authors.

Ning ZouSchool of Computing and Information, University of Pittsburgh, Pittsburgh, PA, United States.ORCID 0000-0003-2287-0392
Bo XieSchool of Nursing, The University of Texas at Austin, Austin, TX, United States.ORCID 0000-0002-6016-6008
Daqing HeSchool of Computing and Information, University of Pittsburgh, Pittsburgh, PA, United States.ORCID 0000-0002-4645-8696
Robin HilsabeckGlenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, Department of Neurology, University of Texas Health Sciences Center at San Antonio, San Antonio, TX, United States.ORCID 0000-0001-8386-1508
Alyssa AguirreDepartment of Neurology, Dell Medical School, The University of Texas at Austin, Austin, TX, United States.ORCID 0000-0002-7522-6704

Funding

Tailoring Responses to ADRD Caregivers' InfOrmation wants (TRACO) through human-machine collaborationR56AG075770 · NIA · UNIVERSITY OF TEXAS AT AUSTIN · PI HE, DAQING, XIE, BO · 2022 to 2022
$561k
NIA NIH HHS R56 AG075770
6 · The paper itself

Abstract

Background: Informal caregivers of persons living with dementia are increasingly using mobile health (mHealth) apps to obtain care information. mHealth apps are seen as promising tools to better support caregivers' complex and evolving information needs. Yet, little is known about the types and quality of dementia care information that these apps provide. Is this information for caregivers individually tailored; if so, how? Objective: We aim to address the aforementioned gaps in the literature by systematically examining the types and quality of care-related information provided in publicly available apps for caregivers of persons living with dementia as well as app features used to tailor information to caregivers' information wants and situations. Methods: In September 2023, we used a multistage process to select mobile apps for caregivers of persons living with dementia. The final sample included 35 apps. We assessed (1) types of dementia care information provided in the apps, using our 3-item Alzheimer disease and related dementias daily care strategy framework, which encompasses educational information, tangible actions, and referral information; (2) quality of apps' care information, using the 11 indicators recommended by the National Library of Medicine; and (3) types of tailoring to provide personalization, feedback, and content matching, which are common tailoring strategies described in the literature. Results: Educational information was the most prevalent type of information provided (29/35 apps, 83%), followed by information about tangible actions (18/35, 51%) and referrals (14/35, 40%). All apps presented their objectives clearly and avoided unrealistic or emotional claims. However, few provided information to explain whether the app's content was generated or reviewed by experts (7/35, 20%) or how its content was selected (4/35, 11%). Further, 6 of the 35 (17%) apps implemented 1 type of tailoring; of them, 4 (11%) used content matching and the other 2 (6%) used personalization. No app used 2 types of tailoring; only 2 (6%) used all 3 types (the third is feedback). Conclusions: Existing dementia care apps do not provide sufficient high-quality, tailored information for informal caregivers. Caregivers should exercise caution when they use dementia care apps for informational support. Future research should focus on designing dementia care apps that incorporate quality-assured, transparency-enhanced, evidence-based artificial intelligence-enabled mHealth solutions for caregivers.

Indexed as

CaregiversDementiaMobile ApplicationsTelemedicineHumansdementiainformal careinformation qualitymobile health applicationsmobile phonetailoring

Identifiers

PMID39621936
PMCPMC11617330

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.