Evidence map›Paper›PMID 40371286›Full record

ArticleFrontiers in public health2025

Examining older adults' vulnerability to online health scams: insights from routine activity theory.

Hongliang Chen, Miao He, Xiaowen Xu, David Atkin

Abstract read
In one paragraph

Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

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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

7 citing papers in PubMed.

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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

4 authors.

Hongliang ChenCollege of Media and International Culture, Zhejiang University, Hangzhou, China.
Miao HeCollege of Media and International Culture, Zhejiang University, Hangzhou, China.
Xiaowen XuCollege of Communication, Butler University, Indianapolis, IN, United States.
David AtkinDepartment of Communication, University of Connecticut, Storrs, CT, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Online health fraud has emerged as a significant digital risk for older adults in China, leading to considerable financial losses. This study aims to investigate the mechanisms underlying health fraud targeting and victimization among older adult populations, refining Routine Activity Theory (RAT) to account for the distinct stages of fraud exposure and victimization. Methods: A survey was conducted among retired individuals in July 2022. After removing incomplete responses, the final sample consisted of 471 participants. The survey assessed digital behavior patterns, exposure to promotional messages, health conditions, and fraud-related experiences. Results: The findings revealed that older adults who installed numerous mobile applications, frequently used social networking sites, and engaged in risky online behaviors were more likely to be targeted by health fraud. Additionally, exposure to promotional messages-both online and offline-and the presence of chronic diseases were significantly associated with increased fraud exposure. Regarding victimization, younger family intervention was found to mitigate financial losses, whereas older adults with limited digital protection skills experienced a higher likelihood of falling victim after exposure. Discussion: This study refines the Routine Activity Theory by conceptualizing online health fraud as a two-stage process: exposure and victimization. The results highlight both digital behavior and offline contextual factors in shaping fraud vulnerability among older adults. The findings contribute to the theoretical understanding of cybercrime targeting the older adult and offer practical insights for designing preventive interventions tailored to this vulnerable population.

Indexed as

Crime VictimsFraudInternetAgedAged, 80 and overChinaFemaleHumansMaleMiddle AgedSurveys and Questionnairesexposure to fraudfraud victimizationolder adultsonline health fraudroutine activity theory

Identifiers

PMID40371286
PMCPMC12074955

What OpenQuestion holds

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LicenceCC BY
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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.