Evidence map›Paper›PMID 40324770›Full record

ArticleJMIR aging2025

Online Communities as a Support System for Alzheimer Disease and Dementia Care: Large-Scale Exploratory Study.

Sidharth Kaliappan, Chunyu Liu, Yoshee Jain, Ravi Karkar, Koustuv Saha

Abstract read
In one paragraph

Article in JMIR aging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Trial
  2. 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

5 authors.

Sidharth KaliappanManning College of Information & Computer Sciences, University of Massachusetts Amherst, Amherst, MA, United States.ORCID https://orcid.org/0009-0003-0334-2196
Chunyu LiuSchool of Information Sciences, University of Illinois Urbana-Champaign, Champaign, United States.ORCID https://orcid.org/0009-0007-4026-3314
Yoshee JainSiebel School of Computing and Data Science, The Grainger College of Engineering, University of Illinois Urbana-Champaign, Urbana, IL, United States.ORCID https://orcid.org/0009-0005-6291-5438
Ravi KarkarManning College of Information & Computer Sciences, University of Massachusetts Amherst, Amherst, MA, United States.ORCID https://orcid.org/0000-0003-1467-4439
Koustuv SahaSiebel School of Computing and Data Science, The Grainger College of Engineering, University of Illinois Urbana-Champaign, Urbana, IL, United States.ORCID https://orcid.org/0000-0002-8872-2934

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlzheimer disease (AD) is the leading type of dementia, demanding comprehensive understanding and intervention strategies. In the United States, where over 6 million people are impacted, the prevalence of AD and related dementias (AD/ADRD) presents a growing public health challenge. However, individuals living with AD/ADRD and their caregivers frequently express feelings of marginalization, describing interactions characterized by perceptions of patient infantilization and a lack of respect.

objectiveThis study aimed to address 2 key research questions (RQs). For RQ1, we investigated the needs and concerns expressed by participants in online social communities focused on AD/ADRD, specifically on 2 platforms-Reddit's r/Alzheimers and ALZConnected. For RQ2, we examined the prevalence and distribution of social support corresponding to these needs and concerns, and the association between these needs and received support.

methodsWe collected 13,429 posts and comments from the r/Alzheimers subreddit spanning July 2014 to November 2023, and 90,113 posts and comments from ALZConnected between December 2020 (the community's earliest post) and November 2023. We conducted topic modeling using latent Dirichlet allocation (LDA), followed by labeling to identify the major topical themes of discussions. We used transfer learning classifiers to identify the occurrences of emotional support (ES) and informational support (IS) in the comments (or responses) in the discussions. We built regression models to examine how various topical themes are associated with the kinds of support received.

resultsOur analysis revealed a diverse range of topics reflecting community members' varying needs and concerns of individuals affected by AD/ADRD. These themes encapsulate the primary discussions within the online communities: memory care, nursing and caregiving, gratitude and acknowledgment, and legal and financial considerations. Our findings indicated a higher prevalence of IS compared to ES. Regression models revealed that ES primarily occurs in posts relating to nursing and caring, and IS primarily occurs in posts concerning medical conditions and diagnosis, legal and financial, and caregiving at home.

conclusionsThis study reveals that online communities dedicated to AD/ADRD support engage in discussions on a wide range of topics, such as memory care, nursing, caregiving, and legal and financial challenges. The findings shed light on the key pain points and concerns faced by individuals managing AD/ADRD in their households, revealing how they leverage online platforms for guidance and support. These insights underscore the need for targeted institutional and social interventions to address the specific needs of AD/ADRD patients, caregivers, and other family members.

Indexed as

Alzheimer DiseaseCaregiversDementiaSocial MediaSocial SupportAgedFemaleHumansMaleUnited StatesAlzheimer diseasemachine learningnatural languageonline communitiessocial mediasocial support

Identifiers

PMID40324770
PMCPMC12089880

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.