Distinguishing Cyberchondria from Adaptive Online Health Research: Clinical and Rehabilitative Advancements

The use of new technologies within healthcare practices is constantly growing. While these innovations represent clear progress, facilitating and improving care, in some cases they are a concern. The autonomous use by patients of technologies that can answer clinical questions is a source of apprehension. In this sense, cyberchondria represents a problematic pattern of behavior through which subjects seek information related to existing or plausible medical conditions. This is done in the absence of scientific training that can discriminate against the information received. Such behavior consistently has a negative impact on the clinical and rehabilitation pathways. This phenomenon is attracting increasing attention, yet clear definitions and distinctions are needed. Apart from a clear definition of cyberchondria, it should be distinguished from adaptive online health research. This paper aims to produce clear definitions, to relate cyberchondria with health anxiety and other related phenomena, as well as to produce a representation of current etiological theories and future clinical and rehabilitative implications. A better understanding and analysis of the state of the art would in fact produce improvement on clinical activities and on the classification of phenomena.

Authors

Publication Details

Journal
Messanae Universitas Studiorum (University of Messina)
Published
2025-12-30
DOI
https://doi.org/10.13129/3035-062x/prnr-5328
Primary Topic
Psychosomatic Disorders and Their Treatments
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Distinguishing Cyberchondria from Adaptive Online Health Research: Clinical and Rehabilitative Advancements

Liam Alexander MacKenzie Myles, Emanuele Maria Merlo, Sean Hill
Messanae Universitas Studiorum (University of Messina)
Psychosomatic Disorders and Their Treatments
article

Distinguishing Cyberchondria from Adaptive Online Health Research: Clinical and Rehabilitative Advancements

Liam Alexander MacKenzie Myles, Emanuele Maria Merlo, Sean Hill
article en

Abstract

The use of new technologies within healthcare practices is constantly growing. While these innovations represent clear progress, facilitating and improving care, in some cases they are a concern. The autonomous use by patients of technologies that can answer clinical questions is a source of apprehension. In this sense, cyberchondria represents a problematic pattern of behavior through which subjects seek information related to existing or plausible medical conditions. This is done in the absence of scientific training that can discriminate against the information received. Such behavior consistently has a negative impact on the clinical and rehabilitation pathways. This phenomenon is attracting increasing attention, yet clear definitions and distinctions are needed. Apart from a clear definition of cyberchondria, it should be distinguished from adaptive online health research. This paper aims to produce clear definitions, to relate cyberchondria with health anxiety and other related phenomena, as well as to produce a representation of current etiological theories and future clinical and rehabilitative implications. A better understanding and analysis of the state of the art would in fact produce improvement on clinical activities and on the classification of phenomena.

Messanae Universitas Studiorum (University of Messina)
Reduced inequalities
Openalex Percentile: Top 28%
Psychosomatic Disorders and Their Treatments
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.