The psychological weight of a cancer diagnosis creates conditions that researchers have long recognised as fertile ground for misinformation to take root. When patients and their families receive life-altering news, their cognitive resources are often stretched to breaking point. The study under discussion, conducted at King Fahad Specialist Hospital in Dammam, Saudi Arabia, captures this dynamic in striking detail through semi-structured interviews with nineteen cancer patients and six caregivers. By grounding their analysis in two well-established psychological frameworks, the theory of planned behaviour and the health belief model, the researchers moved beyond simply cataloguing what participants searched for online and instead examined the underlying beliefs that shaped how they searched, what they trusted, and why they sometimes fell prey to false or misleading claims.

The theory of planned behaviour posits that human actions are guided by three kinds of beliefs: behavioural beliefs about the consequences of an action, normative beliefs about the expectations of others, and control beliefs about one’s own capacity to perform the behaviour. Applied to information seeking, this framework helps explain why a patient might type symptoms into a search engine late at night despite knowing that clinicians discourage it. If the patient believes that searching will bring relief from uncertainty, senses that family members expect them to be proactive, and feels confident in their ability to navigate the web, the intention to search online becomes powerful, regardless of the quality of what they find.

The health belief model adds another layer by focusing on perceptions of threat and benefit. A patient who perceives their cancer as severe and themselves as highly susceptible to progression may be more motivated to seek information aggressively, but that same heightened perception of threat can make dramatic, unverified claims about miracle cures or hidden causes feel plausible. Perceived barriers and self-efficacy, two further constructs of the model, determine whether patients verify what they read or accept it at face value. The interview study suggests that many participants possessed low self-efficacy when it came to evaluating sources, and this deficit became a critical vulnerability.

One of the most consequential findings is that participants relied on superficial methods to search for and evaluate online cancer information, largely to reduce cognitive effort. This behaviour has a rational core. Information search is fundamentally a problem-solving task involving inference and decision-making, and these are precisely the cognitive processes that cancer and its treatments can impair. Chemotherapy-related cognitive changes, often described by patients as difficulty concentrating or remembering, compound the challenge of parsing dense, technical, and sometimes contradictory web content. Under such conditions, shortcuts become attractive: judging a website by its professional appearance, trusting the first result a search engine returns, or accepting information because it appears repeatedly across multiple pages.

These heuristics, while efficient, are unreliable proxies for accuracy. Search engine rankings reflect popularity and optimisation rather than medical validity, and polished design is no guarantee of sound science. The study’s participants were not careless; they were conserving limited mental energy during an exhausting period of their lives. Recognising this distinction matters for anyone designing interventions, because simply urging patients to be more careful ignores the cognitive realities that make carefulness difficult. Practical tools that lower the effort required for evaluation, such as checklists that prompt a few key questions about a source, may succeed where exhortations to vigilance fail.

Equally revealing is the finding that peer experiences were regarded as a trustworthy source of cancer information. This trust is not irrational. Fellow patients offer lived experience, emotional resonance, and practical advice that clinical materials often lack. Hearing that another person tolerated a treatment regimen or managed a side effect can provide hope and a sense of community that no pamphlet can replicate. The difficulty arises when anecdotal experience is generalised into medical truth. A single patient’s response to an unproven remedy says nothing about its efficacy or safety for others, yet within online communities such stories circulate with persuasive force, particularly when they align with what an anxious reader hopes to hear.

The consequences of misinformation documented in the study extended beyond confusion. Participants reported impacts on both psychological and physical health outcomes. Psychologically, exposure to false or alarming claims fuelled anxiety, eroded hope, and sometimes created conflict between what patients read and what their clinicians told them. Physically, misinformation could lead to delayed treatment decisions, abandonment of prescribed therapies, or adoption of unverified interventions with real risks. These outcomes echo broader findings in the literature: systematic reviews of social media health information quality have identified cancer as one of the topics generating the most quality concerns, and reviews of misinformation prevalence report moderate rates of false content for noncommunicable diseases including cancer.

Against these risks, participants described mitigation strategies centred on trustworthy healthcare communication. Some verified online findings by bringing them to their physicians or nurses; others defaulted to information sources recommended by their care teams. These strategies transformed the clinical encounter into a verification checkpoint, a role that many clinicians already perform informally. The study suggests that this function could be made explicit and systematic. Directing patients to well-known national and international cancer information repositories, and introducing simple evaluation checklists during consultations, would give patients reliable starting points and reduce their dependence on whatever a general search engine surfaces first.

Yet the study also uncovered significant barriers to this kind of open dialogue. Fear of confronting physicians and reluctance to interfere with the consultation process prevented many participants from raising what they had read online. This hesitancy is understandable in contexts where medical authority carries substantial cultural weight and consultation time is limited. Patients may worry that questioning a doctor’s guidance with internet-sourced material will appear disrespectful, or that doing so will consume time needed for pressing clinical matters. The result is a silent gap: misinformation circulates unchallenged in patients’ lives while clinicians remain unaware of it, losing opportunities to correct false beliefs before they influence decisions.

Closing this gap requires deliberate effort on both sides. Clinicians can normalise the conversation by asking patients what they have read online, framing the question as routine rather than accusatory. Health systems can train staff to respond to online findings with curiosity rather than dismissal, since a dismissive reaction may push patients toward secrecy and further reliance on unreliable sources. Patients and caregivers, for their part, benefit from explicit permission and encouragement to bring questions arising from internet searches into their appointments. The study’s participants themselves recommended guidance on searching and evaluating credible information, indicating appetite for exactly this kind of support.

The unmet information needs documented in the study sit within a larger pattern. Integrative reviews of supportive care needs have found that cancer patients and caregivers are frequently dissatisfied with existing options for acquiring information, a finding consistent with international research on unmet information needs across cancer populations. Digital health tools have been proposed as a complement to conventional care precisely to address gaps such as coping support and cancer education, and surveys have identified information and education as the most needed digital health function for this group. The internet’s appeal is amplified for stigmatised conditions, where barriers to traditional information sources make anonymous online searching especially attractive.

The scale of the underlying challenge is considerable. Global cancer incidence is projected to reach 27.5 million new cases by 2040, with deaths rising to 16.3 million, which means the population of information-seeking patients and caregivers will grow substantially in the coming decades. A recent systematic review of information-seeking behaviour among cancer patients identified twenty quantitative studies but only four qualitative ones since 2002, and none of the qualitative work had applied a behavioural theory. The present study fills that methodological gap, and its theoretical grounding offers a template for future research seeking to understand not just what patients do online but why.

For caregivers, the findings carry particular weight. Family members often conduct searches on behalf of patients, filter information before sharing it, and shoulder the emotional labour of translating frightening content into reassurance. Because caregivers were interviewed separately in this study, their perspectives emerge distinctly, underscoring that safeguarding against misinformation is a family-level concern rather than an individual one. Interventions that equip caregivers with evaluation skills may therefore yield benefits that ripple outward to the patients they support.

Ultimately, the study reframes susceptibility to misinformation not as a failure of intelligence or diligence but as a predictable consequence of cognitive strain, emotional urgency, and unmet informational needs converging on a digital environment where credible and false content appear side by side. Solutions that acknowledge this reality, by lowering the effort of verification, strengthening the clinical relationship as a trusted channel, and providing authoritative repositories as default destinations, offer a realistic path forward. As digital health continues to expand its role in cancer care, ensuring that patients and caregivers can navigate it safely becomes not an optional enhancement but a core component of supportive care itself.

Subject of Research: Susceptibility of cancer patients and caregivers to online cancer misinformation

Article Title: Susceptibility of cancer patients and caregivers to online cancer misinformation: a qualitative interview study

Article References: Mohamed, H., Salsberg, J., Alanzi, T., Mohamed, M., & Kelly, D. (2026). Susceptibility of cancer patients and caregivers to online cancer misinformation: a qualitative interview study. Supportive Care in Cancer, 34(10), Article 987. https://doi.org/10.1007/s00520-026-11236-2

Image Credits: AI Generated

DOI: 10.1007/s00520-026-11236-2

Keywords: cancer, misinformation, caregivers, digital health literacy, health belief model, theory of planned behaviour, information-seeking, health communication, oncology, qualitative research, social media, patient safety

The research team recruited 60 healthy adults and asked them to complete a computerized decision-making task consisting of 50 trials. On each trial, participants faced three options. One was a safe choice, offering a modest but reliable payoff. The second was a probabilistic high-reward choice, which could deliver a large payoff but only with uncertain probability that participants had to learn from experience. The third was a costly information-seeking option: paying a small price to glimpse how the risky option was likely to behave. This design captures the classic explore–exploit dilemma in a laboratory setting, because learning about the risky option requires sampling it, yet sampling carries the danger of losses. Crucially, participants never received full descriptions of the probabilities; they had to infer them from the outcomes of their own choices, which is exactly how most real-world decisions work.

To manipulate cognitive load, the researchers used a trivia-based procedure. In the load condition, participants answered trivia questions of varying difficulty, while a control group completed the task without any trivia demands. Within the load subgroup, the trial-level analysis distinguished lower-load and higher-load trivia questions, allowing the team to examine whether harder mental work produced different decision patterns than easier mental work. The second manipulation was physiological: half of the participants received anodal transcranial direct current stimulation, or tDCS, over the left dorsolateral prefrontal cortex, a region long implicated in executive control, working memory, and the regulation of exploratory behavior. The other half received sham stimulation, which mimics the sensations of tDCS without delivering meaningful current. This produced a 2 × 2 between-subjects design crossing stimulation with cognitive-load condition.

tDCS is a non-invasive technique that passes a weak electrical current through the scalp, subtly shifting the excitability of neurons in the targeted cortical region. Anodal stimulation is generally thought to increase cortical excitability, making the underlying neurons more responsive. The left dorsolateral prefrontal cortex is a frequent target in such studies because it supports cognitive control processes that govern when we persist with familiar strategies and when we switch to alternatives. By combining this neuromodulatory approach with a behavioral load manipulation, the researchers could ask whether the effects of brain stimulation depend on how hard the brain is already working—a question that additive models of cognition would answer with a simple yes or no, but that the data answered in a more nuanced way.

The primary findings split cleanly along outcome lines. Cognitive load slowed participants down and made them less willing to take risks. When working memory was occupied by trivia, people responded more slowly and gravitated toward the safe option, consistent with the idea that mental strain pushes decision-makers toward conservative, low-effort strategies. Anodal tDCS over the left DLPFC produced the opposite pattern on the choice side: stimulated participants made more risky choices, sought more information, and alternated more frequently between the different choice types. Notably, stimulation did not significantly change reaction times, suggesting that the current influenced what people chose rather than how quickly they decided. This dissociation between choice content and response speed is important, because it implies that prefrontal neuromodulation alters the underlying decision policy rather than simply speeding up or slowing down general processing.

The most intriguing results emerged from the interaction analyses. Stimulation and cognitive load interacted significantly for two of the outcome measures: information seeking and alternation between choice types. In other words, the effect of anodal tDCS on these behaviors depended on whether participants were under trivia load, and the pattern of load effects differed between the stimulated and sham groups. For reaction time and risky choice, by contrast, the interactions were not significant, meaning the effects of load and stimulation on those measures were essentially additive. This outcome-specific pattern suggests that different components of experience-based decision making recruit the prefrontal cortex to different degrees and respond to neuromodulation under different cognitive circumstances. Information seeking—a behavior that reflects curiosity and the willingness to pay for knowledge—appears especially sensitive to the joint state of cortical excitability and mental workload.

The secondary, trial-level analysis added further texture. When the researchers compared trials immediately following higher-load trivia questions with those following lower-load questions, they found that participants took longer to decide after the harder questions. However, this trial-level load difference did not extend to the choice outcomes themselves: harder trivia did not reliably change risky choice, information seeking, or alternation on a trial-by-trial basis. This suggests that the burden of a single difficult question manifests mainly as slower deliberation, while the broader load condition shapes strategy at a more global level. The distinction between moment-to-moment cognitive strain and sustained cognitive demand may therefore be critical for understanding how mental workload influences decisions in everyday settings, from a surgeon working through a long operation to a trader processing a stream of market data.

Why should these findings matter beyond the laboratory? The explore–exploit tradeoff is one of the most fundamental problems in behavioral science, economics, and machine learning, and this study shows that its components can be pulled apart experimentally. Risk-taking, information seeking, and behavioral flexibility each responded to load and stimulation in their own way, which cautions against treating exploration as a single psychological quantity. The results also carry practical implications for the growing field of cognitive enhancement. If anodal DLPFC stimulation boosts risk-taking and information seeking in one context but interacts with mental workload in another, then claims about stimulation as a universal performance booster need refinement. The same current that encourages healthy exploration in a rested mind might behave differently when attention and memory are already taxed.

The study also speaks to clinical questions. Altered exploration and risk sensitivity are hallmarks of several psychiatric and neurological conditions, including addiction, attention-deficit disorders, and damage to the frontal lobes. Understanding how prefrontal neuromodulation shifts these behaviors in healthy adults provides a baseline for future work asking whether tDCS could help restore adaptive decision-making in clinical populations, or whether cognitive load might be used diagnostically to probe the integrity of prefrontal control systems. Because the current study involved 60 healthy adults and a single session, the authors’ conclusions are appropriately modest: they demonstrate outcome-specific joint effects of load and stimulation, including both additive and context-dependent patterns, rather than establishing long-term or therapeutic effects.

What emerges from Singh, Dutt, and Rao’s work is a picture of the deciding brain as a system whose exploratory urges, appetite for risk, and hunger for information are governed by partly independent levers. Fill the mind with trivia, and people retreat to safety and slow down. Excite the left dorsolateral prefrontal cortex with a gentle current, and they reach for the risky option, pay for information, and shuffle more freely among strategies. But the two levers do not simply add together; for some behaviors, the state of one changes how the other operates. As non-invasive brain stimulation moves closer to everyday use, studies like this one serve as a reminder that the brain’s decision circuits cannot be understood—or tuned—one factor at a time.

Subject of Research: Effects of cognitive load and prefrontal tDCS on exploration, risk-taking, and information seeking in experience-based decision making

Article Title: Cognitive load and prefrontal neuromodulation shape exploration, risk-taking, and information seeking in experience-based decision making

Article References: Singh, A., Dutt, V., & Rao, A. K. (2026). Cognitive load and prefrontal neuromodulation shape exploration, risk-taking, and information seeking in experience-based decision making. PLOS One, 21(10), e0360059. https://doi.org/10.1371/journal.pone.0360059

Image Credits: AI Generated

DOI: 10.1371/journal.pone.0360059

Keywords: cognitive load, tDCS, dorsolateral prefrontal cortex, decision making, exploration, risk-taking, information seeking, neuromodulation, working memory, PLOS One, Cognitive, load

The study, led by Neta Shanwetter Levit and Mor Saban of Tel Aviv University’s Gray Faculty of Medical and Health Sciences, surveyed 205 adult cancer patients in Israel between February and December 2025. Participants had all received a cancer diagnosis within the previous two years, and each completed a structured questionnaire tracing their reliance on eleven information sources across two critical phases: from symptom onset to diagnosis, and from diagnosis to the initiation of treatment. The sources ranged from oncologists and family physicians to spouses, social media, medical databases, patient organizations, alternative medicine, religious guidance, and, notably, generative artificial intelligence tools.

The questionnaire itself was the product of an unusually rigorous development process. The researchers conducted three rounds of semi-structured interviews with oncology patients—seven, ten, and sixteen participants respectively—to identify the key decision points and influences along the care journey. They then consulted senior oncologists and family medicine physicians to refine the instrument’s clinical relevance, and pilot-tested the final version with 37 patients across breast cancer, melanoma, thyroid, ovarian, and lymphoma diagnoses. The resulting Patient Decision Agents Questionnaire achieved a Cronbach’s alpha of 0.87, indicating good internal consistency, before being deployed to the full study sample.

When the researchers applied K-means cluster analysis to the 22 information-source variables—eleven sources measured at two time points—four distinct patient profiles emerged, validated by convergence across silhouette coefficient, Calinski–Harabasz index, and gap statistic measures. The largest group, accounting for 38.8 percent of participants, was the Family-Centered archetype: patients who leaned heavily on spouses and relatives for information and decision support, with the strongest family reliance of any group at a mean of 4.1 on a five-point scale, while showing minimal engagement with digital channels. The second largest, at 27.6 percent, were the Digital Natives, who curated information aggressively through internet searches, medical databases, social media health communities, and patient organizations—and who, despite their digital fluency, used AI tools only modestly.

The remaining two archetypes rounded out a picture of profound diversity. The Balanced Traditional group, 25.0 percent of the sample, engaged moderately and steadily with every source type, from internet searches to family members to medical professionals, without extreme dependence on any single channel. The smallest group, the Medical Professional-Focused archetype at 8.6 percent, placed near-total trust in physician authority: their mean reliance on their primary oncologist, 4.5, was the highest score observed across all sources and all archetypes, while their use of internet sources, medical databases, and AI tools was the lowest of any cluster. Crucially, 84.6 percent of this group identified as traditional, religious, or ultra-Orthodox, in sharp contrast to the predominantly secular composition of the other three clusters.

That pattern made religiosity the single strongest differentiator between archetypes, statistically significant at p = 0.005 with a Cramér’s V of 0.25. Age, education, health literacy, and self-management confidence also differed significantly across groups. The researchers argue that this finding challenges deficit-based models of patient engagement, in which lower information seeking is interpreted as disengagement. For religious patients, they suggest, deferring to physician authority may represent an active alignment with deeply held cultural and spiritual values about medical knowledge and decision-making authority—an epistemological framework as coherent as the autonomy-driven model assumed by mainstream shared decision-making theory, even if it looks very different from it.

The temporal dimension of the data added another layer of insight. Across all archetypes, information seeking intensified after diagnosis: reliance on family members rose to a mean of 3.27, internet searching to 3.20, engagement with additional medical providers to 2.95, and social media use to 2.75, all statistically significant changes. Importantly, patients did not abandon their initial sources in favor of new ones; they accumulated them, a triangulation strategy the authors interpret as a way of building confidence and resolving uncertainty in the face of high-stakes decisions. The diagnosis, in effect, triggers an expansion of the informational repertoire rather than a replacement of it.

One source conspicuously failed to follow that trend: generative AI. AI-based tools remained the least utilized digital resource throughout the entire patient journey, with mean scores of 1.67 before diagnosis and no significant change afterward, and the pattern held even among younger, digitally sophisticated patients. The Digital Natives, the most AI-friendly group, still averaged only 2.9 on AI tool use. The authors suggest that technological innovation alone is insufficient—AI tools must earn the trust and meet the personalized guidance needs of patients making emotionally charged medical decisions. They also caution that their data were collected relatively early in the public diffusion of consumer-facing generative AI; recent United States surveys indicate that nearly three in ten adults now use AI chatbots for health information at least monthly, almost double the share from mid-2024, so the picture may shift rapidly.

The qualitative arm of the study revealed how heavy the decisional burden actually feels. Of the 115 participants who answered open-ended questions about their dilemmas, 86 percent reported significant decision-making struggles. Two themes dominated: treatment selection, cited by 57 respondents, who weighed chemotherapy against biological therapy, radiation, clinical trials, and surgical versus non-surgical approaches; and choice of healthcare provider or facility, cited by 40, who juggled expertise, accessibility, wait times, and institutional resources. Smaller clusters of patients wrestled with timing and urgency, conflicting medical opinions, side effects and quality of life, and clinical trial participation. The authors interpret this near-universal dilemma burden as a systemic rather than individual failure: health systems that emphasize informed consent and shared decision-making may inadvertently transfer the weight of deliberation onto patients without providing adequate scaffolding—time, guidance, or emotional support—for carrying it.

The study’s implications point away from one-size-fits-all protocols and toward differentiated decision support. The authors propose brief screening at diagnosis to identify a patient’s archetype and trigger tailored interventions: algorithm-supported decision aids for Digital Natives, facilitated family meetings for Family-Centered patients, and culturally sensitive, physician-guided deliberation for Medical Professional-Focused patients. They acknowledge the limits of generalizing from a single national context—Israel’s universal coverage, high digital penetration, and pronounced religious diversity shape the specific configuration of archetypes—and note that in multi-payer systems like the United States, provider-selection dilemmas may intensify under insurance constraints, while in collectivist cultures family-mediated decision-making may loom even larger. Still, the core conclusion travels well: cancer patients follow genuinely different pathways through medical uncertainty, and health systems that fail to recognize that heterogeneity risk leaving the most vulnerable patients to navigate their most consequential decisions alone.

Subject of Research: Information-seeking patterns and decision-making pathways of adult cancer patients from symptom onset to treatment initiation

Article Title: Beyond one-size-fits-all: mapping information-seeking and decision-making pathways in cancer care

Article References: Shanwetter Levit, N., & Saban, M. (2026). Beyond one-size-fits-all: mapping information-seeking and decision-making pathways in cancer care. Supportive Care in Cancer, 34(10), Article 1003. https://doi.org/10.1007/s00520-026-11250-4

Image Credits: AI Generated

DOI: 10.1007/s00520-026-11250-4

Keywords: cancer care, information seeking, decision making, patient archetypes, shared decision-making, generative AI, digital health, religiosity, oncology, patient-centered care, decision support, Supportive Care in Cancer