Extending Signaling Theory in Online Health Communities to Address Medical Information Asymmetry: Systematic Review...

In online health communities (OHCs), signaling theory has become a valuable framework for mitigating information asymmetry and shaping patient decisions. However, the literature remains fragmented, lacking an integrative understanding of how signals, signalers, receivers, and contexts interact to influence trust and engagement.

This study aimed to establish a comprehensive and integrative signaling framework tailored to OHCs. It sought to clarify the core constructs of signals, categorize different signal types, and examine how signaling dynamics contribute to managing medical information asymmetry. Furthermore, this study identified key research gaps and outlined future research directions to advance the theoretical and practical application of signaling theory in digital health contexts.

We conducted a systematic literature review using narrative synthesis techniques. A total of 80 peer-reviewed studies published between 2010 and 2024 were identified through 7 databases. These studies were analyzed and coded across 5 components of the signaling process: signalers, signals, receivers, signaling environments, and signaling mechanisms.

Five key findings emerged. First, OHC research is overwhelmingly signal centric-96% (77/80) of the studies focused on signal attributes, whereas only 3% (2/80) examined the characteristics of signalers and 14% (11/80) investigated receivers. This imbalance limits our understanding of how signals are produced and interpreted. Second, signaling mechanisms remain fragmented, with limited exploration of signal-signal or signal-context interactions. Only 31% (25/80) of the studies considered interactions between signals, and just 30% (24/80) examined contextual moderators such as uncertainty or competition. Third, environmental factors, especially environmental uncertainty and competition, play a central moderating role. Uncertain disease contexts or dense signal environments diminish signal effectiveness, particularly for affective signals. Fourth, signal classification in OHCs has become increasingly multidimensional. Signals can be systematically analyzed by their source (eg, internal vs third party), medium (eg, online vs offline), form (eg, taglike vs narrative), and affect (informative vs affective), enabling a more structured and theoretically consistent understanding. Fifth, signal interpretation is highly dependent on patient-level attributes. Patients with severe, chronic, or privacy-sensitive conditions prioritize competence or privacy signals, whereas those with limited health literacy rely more on simplified cues and affective heuristics.

This review advances signaling theory in digital health by providing a unified framework that connects structure and context. It highlights the underexplored roles of signalers and receivers, the importance of environmental moderation, and the cognitive-emotional duality of signals. These findings offer theoretical integration and practical value for improving platform trust, patient engagement, and decision-making in OHCs.