Cryptocurrency Trading and Associated Mental Health Factors: A Scoping Review

Abstract

Background:

Cryptocurrency trading seemingly mirrors the high-risk, high-reward nature of gambling, and may cause significant psychological challenges to traders. As cryptocurrency trading becomes mainstream, this scoping review aims to synthesize evidence from empirical studies to understand the emotional, cognitive, and social influences on cryptocurrency traders, and identify associated mental health traits/attributes influencing their behaviors.

Methods:

This review adhered to PRISMA-ScR guidelines, pooling in 13 studies involving 11,177 participants across multiple countries. A detailed literature search was conducted up to August 4, 2024, and was rerun on October 9, 2024 using databases including PubMed/Medline, Web of Science, Embase, and Scopus. Keywords used included psychiatry, psychology, mental health, cryptocurrency, trading behavior, mental health, substance use, gambling, investment, and/or emotional impact. These terms were refined through iterative searches to retrieve the most relevant studies.

Results:

The scoping review found several key psychological factors affecting cryptocurrency trading behaviors. Many traders exhibited addiction-like behaviors, compulsively trading even when it leads to financial losses. Social media was found to have a strong influence, encouraging herd behavior and impulsive decision-making to follow trends. High levels of psychological distress, including anxiety and depression, were found to be linked to the market’s volatility and risks. Overconfidence bias was observed to make traders underestimate risks and overestimate their ability to predict the market. Cognitive biases like confirmation bias and the disposition effect caused traders to hold onto losing investments and sell winning ones too early.

Conclusion:

Due to the shared psychological traits between cryptocurrency trading and gambling, it is imperative to implement targeted early interventions to mitigate the risk of its progression into a pathological condition. Tools like the Problematic Cryptocurrency Trading Scale may help identify and manage risky behaviors. Ongoing research is crucial to identify both positive and negative impact of cryptocurrency trading to develop effective support systems and regulatory policies to address traders’ mental well-being.

Introduction

Cryptocurrency, first conceptualized in 2009, was proposed as a potential solution to the 2008 financial crisis and represents a significant departure from traditional financial systems. ¹ The creation of a new digital currency that can be securely used through online marketplaces like the silk road marked a radical shift, often described as a financial revolution. ² It is a decentralized digital currency not backed by any central bank or federal institution (unlike fiat currency), primarily generated and secured through cryptographic means. ³

Cryptocurrency trading differs from traditional stock trading in that cryptocurrency has no inherent value whereas buying a stock gives ownership of a fraction of a company with publicly released records. Stock trading requires a brokerage account (with identity verification) and participation in a stock exchange which is regulated by government agencies like the U.S. Securities and Exchange Commission; while cryptocurrency trading has no such restrictions. ⁴ In stock and foreign exchange trading one can research what they are buying (ie, company stock/foreign currency), make informed decisions about changes in their value, and have a network of information sources to stay informed; whereas there is limited independent reporting on individual cryptocurrencies, with trading occurring round the clock. ⁴ These factors lead to Cryptocurrency being inherently volatile with sudden highs and lows for no premeditated rationale to the user. Adding to this volatility is the fact that Cryptocurrencies are vulnerable to cyberattacks, and there are major concerns about their long-term stability, with up to 1600 cryptocurrencies that have completely vanished, causing total loss for the owner.

The high volatility leads to high risk, but also high reward. Furthermore, the lack of state control and anonymity provided by cryptocurrencies has spurred the launch of numerous other digital currencies, some of which have been scams targeting inexperienced users.^3,5 The desire for high returns has led to several market bubbles and since 2009,^3,5 cryptocurrency has seen fluctuating fortunes, with Bitcoin (BTC), the original cryptocurrency, maintaining its position as the most dominant among thousands now available for trading. ⁶ Despite reaching a record value of over $73,000 in 2024, the volatile nature of these digital assets presents substantial financial risks, evidenced by a dramatic drop in BTC’s value by 64% in 2022 before hitting new highs. ⁷ Nevertheless, the allure of high rewards has led to the global user base for cryptocurrencies expanding significantly, growing by approximately 190% from 2018 to 2020, demonstrating a growing interest despite the risks. ⁸

Over the past several years, researchers have increased their focus on behavioral addictions, with gambling addiction being added to DSM 5 as the first behavioral addiction. Stock exchange trading has been identified as a type of gambling behavior by some researchers, and the COVID-19 pandemic appears to have played a role in exacerbating it. ⁹ Looking at the criteria of Gambling disorder, Cryptocurrency trading appears more likely to have gambling-like features, with Johnson et al. ¹⁰ observing in their 2023 scoping review that continuous nature of cryptocurrency markets (cryptocurrency trading happens 24 hours a day, 7 days a week, 365 days a year) and the availability of mobile trading applications increases that likelihood and individuals spend a significant portion of their time awake doing trading, increasing the risk of them ignoring their “regular” life and engaging in risky trading patterns. Johnson et al. ¹⁰ also cited a study of UK residents, which found that 31% of participants cited gambling as the reason why they purchased cryptocurrencies. ¹¹

Johnson et al. ¹⁰ also identified that the sudden, significant financial losses often seen due to the volatility of cryptocurrency trading can also lead to negative mental health outcomes such as depression and anxiety. Furthermore, a systematic literature review by Almeida and colleagues in 2023 highlighted key traits influencing cryptocurrency investors. ¹² Notably, these included a propensity for risk-taking and a tendency toward herding behavior influenced by social media and public sentiment, which contributed to market volatility and irrational trading patterns. ¹² The review also highlighted the disposition effect, where investors held on to losing assets while hastily divesting from profitable ones, indicating a strong emotional component in trading decisions. ¹² Other researchers have observed that several elements of cryptocurrency trading are also seen in gambling and betting, including the willingness to take risks, the potential for quick returns, and the possibility of substantial gains or losses. Senturk et al. ¹³ observed that individuals who dedicate a significant amount of time to cryptocurrency trading often display key elements of behavioral addiction.

It is also important to note that cryptocurrency may also offer potential benefits for users’ mental health. One primary advantage is the sense of control and empowerment individuals may experience when they are in charge of their financial investments; this can reduce anxiety and improve self-esteem. ¹⁴ Another possible benefit is the strategic and analytical nature of trading that is inherently stimulatory to cognition, keeping the mind engaged. ¹⁰ Importantly, financial gains from successful trading can improve financial security, thereby reducing the associated stress that people face with economic instability. ¹⁰ Cryptocurrency traders are also in a position to find supportive communities in online forums that create a sense of belonging while reducing feelings of isolation, which is imperative in a post-COVID world. ¹⁵

In this scoping review we aim to identify the mental health factors that may influence cryptocurrency trading behaviors. As the popularity and volatility of this trading platform increase, understanding the specific psychological drivers (net positive or negative) that influence trader behavior is vital.

Methods

Theoretical Framework and Search Strategy

This scoping review was conducted in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. ¹⁶ The final search was conducted on August 4, 2024, and was rerun on October 9, 2024, to ensure the inclusion of the most recent literature.

To identify relevant studies on mental health factors influencing cryptocurrency use, we developed a concept-centric approach, similar to the methodology described by Webster and Watson ¹⁷ . We aimed to capture a comprehensive range of studies across the following databases: PubMed/Medline, Web of Science, Embase, Cochrane, and Scopus. Additionally, we manually screened the reference lists of selected studies to enhance the breadth of coverage. The final search query utilized a combination of keywords with Boolean logic as follows:

(“psychiatry” OR “psychology” OR “mental health” OR “substance use” OR “gambling” OR “investment” OR “emotional impact”) AND (“cryptocurrency” OR “crypto trading” OR “trading behavior” OR “digital currency”).

This combination was refined iteratively to improve relevance, based on preliminary findings. Boolean (AND/OR) logic was systematically applied to connect key concepts and expand the search results effectively. This approach helped ensure that we obtained studies capturing a wide range of mental health factors associated with cryptocurrency trading behaviors.

Eligibility Criteria

Inclusion Criteria:

• Empirical articles examining psychiatric factors influencing cryptocurrency use.

• Research articles, including trials, observational studies, reports, and qualitative studies.

• Articles published in peer-reviewed journals.

Exclusion Criteria:

• Studies not specifically addressing mental health factors related to cryptocurrency use.

• Reviews, commentaries, and conference papers.

• Studies without full-text availability, even after contacting authors or using library resources.

This review includes studies regardless of language (both English and non-English) and covers all demographic groups and geographical locations. This broad approach ensures a comprehensive examination of global perspectives on psychiatric factors in cryptocurrency use.

Data Extraction and Synthesis

All identified studies were imported into Endnote X9 (Clarivate Analytics, London, UK) for management, where duplicates were removed using Endnote’s deduplication tool. The remaining studies were screened in 2 stages: first by title and abstract, and then by full-text review. Any disagreements during the screening process were resolved through consensus among the reviewers.

Data were extracted using a standardized form capturing details such as primary author, year, study design, sample size, population studied, gender distribution, data collection methods, data analysis techniques, main psychiatric factors identified, and the relationship between psychiatric factors and trading behavior.

To identify key themes, we employed a thematic synthesis approach, where the reviewers independently reviewed extracted data to identify recurring psychiatric and behavioral factors. Identified themes were refined through multiple rounds of discussion to ensure consensus, with any conflicting data points resolved through comparison with original study findings and, if necessary, consultation with a third reviewer. This iterative process ensured the themes were robust and reflective of the study findings.

To visually summarize the key psychiatric factors identified across studies, we used R for data visualization. A summary chart was generated to highlight the psychiatric factors identified, study types, and publication years, providing a comprehensive visual overview of the main findings.

Results

A total of 3581 studies were identified from database searching. Of those 424 duplicates records were removed; 3157 studies were screened (L.J., Z.S., and L.V.). During the title and abstract review stage, 3128 studies were omitted as they did not meet the inclusion criteria at the onset. In the full-text screening phase, 31 studies were retrieved. Of those, 13 were included in the scoping review (Figure 1).

Figure 1.

PRISMA flowchart depicting the study selection process.

Distribution of Countries and Journal Names Across the Identified Literature

The studies were published in a variety of journals, indicating the interdisciplinary nature of research on cryptocurrency use and its psychiatric implications. The journals range from those focusing on psychology and psychiatry to those addressing technology and organizational studies (Figure 2).

Figure 2.

Distribution of journal names across the identified literature.

The studies originated from countries worldwide, indicating a global interest in the topic of cryptocurrency use and its associated psychiatric factors (Figure 3).

Figure 3.

Distribution of countries across the identified literature.

Study and Methodological Characteristics

The study and methodological characteristics are enlisted in Table 1.

Table 1.

Study and Methodological Characteristics.

Primary author, year	Study design	Sample size or sample source	Population studied/information source	Gender (male) (%)	Data collection methods	Data analysis techniques
Littrell et al. (2024)	Cross-sectional survey	2001	American adults (18+ years old)	900 (45)	Online survey by Qualtrics, partnered with Cint and Dynata	Logistic regression, multivariate analysis, bivariate correlations
Yadav et al. (2024)	Mixed methods, quantitative and qualitative	59	Young investors primarily from the 18-24 age group, which accounted for 79.7% of the sample	45 (76.3)	Online surveys and semi-structured interviews	Descriptive statistics, correlation analysis, thematic analysis
Grubbs et al. (2023)	Quantitative survey	4363	U.S. adults matched to 2019 American Community Survey demographics	2243 (51.4)	Online survey conducted via YouGov platform	Multinomial logistic regression, hierarchical regression
Johnson et al. (2023)	Qualitative, semi-structured interviews	17	Adults (median age 25 years) with self-reported problems due to cryptocurrency trading from the US (n=12) and Australia (n=5)	13 (76.5)	Online Zoom interviews (median duration 42 minutes)	Thematic analysis using NVivo 12, reflexive thematic analysis framework
Rahardja et al. (2023)	Cross-sectional survey	337	Cryptocurrency owners or those having financial investment experience	183 (54.3)	Online questionnaire	PLS-SEM, bootstrapping
Khan et al. (2022)	Cross-sectional survey	207	Cryptocurrency investors	NA	Self-report questionnaire	Statistical analysis, SMART PLS
Oksanen et al. (2022)	Cross-sectional survey	1530	Finnish adults (18-75 years old)	50.33	Web-based survey via Norstat	Multinomial logistic regression, negative binomial regression, descriptive statistics
Shahani et al. (2022)	Quantitative explanatory and exploratory	313	Cryptocurrency investors across Pakistan primarily aged 26-35 (83.4%)	261 (83.4)	Online surveys and interviews through social media groups	Partial Least Square Structural Equation Modeling (PLS-SEM), Descriptive Analysis
Delfabbro et al. (2021)	Quantitative survey	84	Adults (18-30 years being the most common among crypto only groups [67.9%, n = 57]) who traded cryptocurrency at least monthly	65 (77.4)	Online survey on Prolific	Regression analysis, group comparisons
Menteş et al. (2021)	Quantitative; reliability and validity study of the Problematic Cryptocurrency Trading Scale	1314	Cryptocurrency traders using the TrueFeedBack BlackStar survey platform, utilizing blockchain technology for reward distribution	1081 (82.3)	Online surveys conducted through Google Forms; modified DSM-5 criteria for gambling disorder and internet addiction scales used for item development	Exploratory and confirmatory factor analysis, item discrimination tests, reliability analysis using Cronbach’s alpha
Kim et al. (2020)	Quantitative, survey-based study with psychological profiling	307	Registered members of Embrain®, Seoul, South Korea, over the age of 20, classified into Bitcoin investors, share investors, and non-investors	162 (52.8)	Online surveys distributed and collected via email	One-way ANOVA, Chi-square tests, hierarchical logistic regression
Gagarina et al. (2019)	Cross-sectional survey	263	Bachelor and Master students from various Moscow universities	118 (45)	Self-administered “Attitudes Toward Cryptocurrencies Questionnaire”	Frequency analysis, confirmatory factor analysis, regression analysis
Mills et al. (2019)	Cross-sectional survey	876	Frequent gamblers from Amazon’s Mechanical Turk (MTurk)	511 (58.33)	Online survey	Student’s t-tests, ANOVAs, bivariate correlations, stepwise regression

Overview of Study Designs

Of the 13 studies reviewed, 10 were quantitative^{18 -27} 1 was qualitative, ¹⁴ and 2 employed a mixed-methods approach.^28,29 The total sample size across these studies was 11 177 participants. Quantitative studies primarily utilized online surveys and statistical analyses, including regression analysis and structural equation modeling. Qualitative studies relied on semi-structured interviews, while mixed-methods studies integrated online surveys with qualitative interviews to provide a comprehensive understanding.

Quantitative Studies

The 10 quantitative studies are described as follows. Littrell et al. ¹⁹ conducted a cross-sectional survey with 2001 American adults (aged 18+) via an online survey on Qualtrics, in collaboration with Cint and Dynata, and used logistic regression, multivariate analysis, and bivariate correlations. Grubbs et al. ²⁰ surveyed 4363 U.S. adults, matched to 2019 American Community Survey demographics, using the YouGov platform, analyzed with multinomial logistic regression and hierarchical regression. Rahardja et al. ²¹ conducted a cross-sectional study with 337 cryptocurrency owners or those with financial investment experience, using an online questionnaire analyzed through PLS-SEM and bootstrapping. Khan ²² performed a cross-sectional survey with 207 cryptocurrency investors, utilizing SMART PLS for statistical analysis. Oksanen et al. ²³ conducted a web-based survey with 1530 Finnish adults (aged 18-75) via Norstat, employing multinomial logistic regression, negative binomial regression, and descriptive statistics. Shahani and Fayaz Ahmed ²⁹ surveyed 313 cryptocurrency investors across Pakistan, primarily aged 26 to 35, using Partial Least Square Structural Equation Modeling (PLS-SEM) and descriptive analysis. Delfabbro et al. ²⁴ surveyed 84 adults who traded cryptocurrency at least monthly, analyzing data with regression analysis and group comparisons. Menteş et al. ²⁵ developed and validated the Problematic Cryptocurrency Trading Scale with 1314 cryptocurrency traders using the TrueFeedBack BlackStar platform, conducting exploratory and confirmatory factor analysis, item discrimination tests, and reliability analysis using Cronbach’s alpha. Kim et al. ²⁶ conducted a survey with 307 registered members of Embrain^® in Seoul, South Korea, over the age of 20, profiling Bitcoin investors, share investors, and non-investors, and analyzed with one-way ANOVA, chi-square tests, and hierarchical logistic regression. Gagarina et al. ²⁷ conducted a cross-sectional survey with 263 Bachelor’s and Master’s students from Moscow universities, using frequency analysis, confirmatory factor analysis, and regression analysis. Mills et al. ¹⁸ surveyed 876 frequent gamblers from Amazon’s Mechanical Turk (MTurk) platform, with data analyzed using t-tests, ANOVAs, bivariate correlations, and stepwise regression.

Qualitative and Mixed-Methods Studies

Johnson et al. ¹⁴ conducted semi-structured interviews with 17 adults who reported problems due to cryptocurrency trading. Yadav et al. ²⁸ employed mixed methods with 59 young investors, primarily aged 18 to 24.

Data Analysis Techniques

Eight studies used inferential statistics,^{18 -21,23,24,26,29} 2 studies used descriptive statistics,^22,28 3 applied exploratory and confirmatory factor analysis,^22,25,27 and 2 utilized thematic analysis.^14,28

In the inferential statistics group, Littrell et al. ¹⁹ applied logistic regression, multivariate analysis, and bivariate correlations, while Grubbs and Kraus ²⁰ applied multinomial logistic regression and hierarchical regression. Both Shahani ²⁹ and Rahardja et al. ²¹ applied PLS-SEM for inferential statistics in their study. Oksanen et al. ²³ employed multinomial logistic regression and negative binomial regression, while Delfabbro et al. ²⁴ did regression analysis and group comparisons. Kim et al. ²⁶ led one-way ANOVA, Chi-square tests, and hierarchical logistic regression. Mills and Nower ¹⁸ conducted Student’s t-tests, ANOVAs, bivariate correlations, and stepwise regression.

Descriptive statistics were applied by Yadav and Rai ²⁸ to summarize survey responses. Khan ²² applied descriptive statistics to summarize and describe the characteristics of the data collected from survey respondents. Additionally, they utilized exploratory and confirmatory factor analysis to identify and confirm the underlying structures within their dataset, helping to validate the constructs and relationships proposed in their research model. Exploratory and confirmatory factor analysis was applied by Menteş et al., ²⁵ where the authors conducted exploratory and confirmatory factor analysis, item discrimination tests, reliability analysis using Cronbach’s alpha ²⁵ ; and by Gagarina et al., ²⁷ who did confirmatory factor analysis. ²⁷

Thematic analysis was conducted by Johnson et al. ¹⁴ using NVivo 12,11 and Yadav and Rai ²⁸ using interview data.

Measurement Tools Used

The measurement tools for each study are depicted in Table 2. PGSI was used in 3 studies by Grubbs et al., ²⁰ Mills et al., ¹⁸ and Delfabbro et al. ²⁴ In addition to PGSI, Grubbs et al. ²⁰ used NIDA-ASSIST for substance use and adapted questionnaires for other behaviors. Delfabbro et al. ²⁴ used trading frequency scales. Khan et al. ²² applied the Temperament and Character Inventory-Revised-Short Version (TCI-RS) for personality assessment. Rahardja et al. ²¹ utilized an online questionnaire using a Likert seven-point scale, SmartPLS 3.3.6 for SEM analysis. Mills et al ¹⁸ used the PHQ-2 and GAD-2 scales. The Problematic Cryptocurrency Trading Scale—a newly developed 16-item scale—was applied by Menteş et al. ²⁵ Gagarina et al. ²⁷ used the Value Scale of Fantalova, Moral Foundations Questionnaire (MFQ) by Haidt, Money Beliefs and Behaviors Scale (MBBS) by Furnham, and The Trust Scale by Haff and Kelly. Validated psychological assessment scales and structured interview protocols were used by Yadav and Raj. ²⁸ Johnson et al. ¹⁴ did not use specific measurement tools; however, they led an analysis based on thematic interpretation of interview transcripts. Five-point Likert scales for assessing predictor variables such as intergroup bias, subjective norms, overborrowing, and spending control were utilized by Shahani et al., ²⁹ Kim et al. ²⁶ used Cloninger’s TCI-RS, FoMO Scale, MDQ, and STAI-T measurement tools. Littrell et al. ¹⁹ employed a range of scales and measures including Patternicity, Subjective Numeracy, Desire for Simple Solutions, Intolerance of Uncertainty, Conspiracy Thinking, Scientific Literacy, Anti-Intellectualism, Confidence in Scientific Community, Political Engagement, Personality Traits (eg, Narcissism, Machiavellianism), Dark Personality Traits (eg, Sadism, Psychopathy), Media Usage Patterns, Political Behaviors, and Emotional Experiences. Oksanen et al. ²³ used an extensive set of tools, including the Problem Gambling Severity Index (PGSI), Internet Gaming Disorder Test (IGDT), Compulsive Internet Use Scale (CIUS), Alcohol Use Disorders Identification Test (AUDIT-C), Mental Health Inventory (MHI-5), Perceived Stress Scale (PSS), COVID-19 Anxiety Scale (adapted from STAI-6), and the Revised UCLA Loneliness Scale.

Table 2.

Measurement Tools Used Across the Studies.

Primary author, year	Measurement tools used
Littrell et al. (2024)	Patternicity, subjective numeracy, desire for simple solutions, intolerance of uncertainty, conspiracy thinking, scientific literacy, anti-intellectualism, confidence in scientific community, political engagement, personality traits (e.g., Narcissism, Machiavellianism), dark personality traits (e.g., Sadism, Psychopathy), media usage patterns, political behaviors, emotional experiences
Yadav et al. (2024)	Validated psychological assessment scales, structured interview protocols
Grubbs et al. (2023)	PGSI for gambling, NIDA-ASSIST for substance use, adapted questionnaires for other behaviors
Johnson et al. (2023)	No specific measurement tools reported; analysis based on thematic interpretation of interview transcripts
Rahardja et al. (2023)	Online questionnaire using a Likert seven-point scale, SmartPLS 3.3.6 for SEM analysis
Khan et al. (2022)	Temperament and Character Inventory-Revised-Short Version (TCI-RS)
Oksanen et al. (2022)	Problem Gambling Severity Index (PGSI), Internet Gaming Disorder Test (IGDT), Compulsive Internet Use Scale (CIUS), Alcohol Use Disorders Identification Test (AUDIT-C), Mental Health Inventory (MHI-5), Perceived Stress Scale (PSS), COVID-19 Anxiety Scale (adapted from STAI-6), Revised UCLA Loneliness Scale
Shahani et al. (2022)	Structured questionnaire with five-point Likert scale adapted from various studies
Delfabbro et al. (2021)	PGSI and gambling frequency scales
Menteş et al. (2021)	Problematic Cryptocurrency Trading Scale: a newly developed 16-item scale
Kim et al. (2020)	loninger’s TCI-RS, FoMO Scale, MDQ, STAI-T
Gagarina et al. (2019)	“Value Scale” of E.B. Fantalova, “Moral Foundations Questionnaire (MFQ)” by J. Haidt, “Money Beliefs and Behaviors Scale (MBBS)” by A. Furnham, “The Trust Scale” by L. Haff and L. Kelly
Mills et al. (2019)	PGSI, PHQ-2, GAD-2 Scale

Key Mental Health Factors and Relationship with Trading Behavior

While the mental health factors overlapped in different studies, the subsequent text presents key findings in a group-based summary of findings (Table 3).

Table 3.

Key Mental Health Factors and Relationship with Trading Behavior.

Primary author, year	Main psychiatric factors identified in cryptocurrency traders	Relationship between psychiatric factors and cryptocurrency trading behavior
Littrell et al. (2024)	Conspiracism, patternicity, intolerance of uncertainty, narcissism, machiavellianism, paranoia, need for chaos, sadism, negative emotional affect	Crypto owners exhibited higher levels of conspiratorial thinking, intolerance of uncertainty, dark personality traits, and greater engagement in alternative media sources
Yadav et al. (2024)	Attitude effect, influence by others, reliance on trends, overconfidence, emotional influences, fear of loss, confirmation bias, disposition effect	Loss aversion leads to holding losing investments, herd mentality sways decisions, reliance on current market info, emotional reactions (fear, greed) strongly affect decisions, overconfidence impacts risk assessment, disposition effect a observed in selling behavior
Grubbs et al. (2023)	Addictive behaviors correlated with trading: gambling, alcohol, nicotine, THC/marijuana, prescription drug misuse, illicit drug use	Strong positive correlations between trading frequency and addictive behaviors, suggesting that engagement in one may predict the other
Johnson et al. (2023)	Anxiety from market volatility, depression from financial loss, elation from profits, stress alleviation from gains, addiction-like trading behaviors	Anxiety and depression linked to negative outcomes, elation associated with gains, and addictive behaviors perpetuate trading despite losses
Rahardja et al. (2023)	Technology mindfulness b and negative technology readiness	Technology mindfulness boosts trust and reduces risk perceptions in cryptocurrency trading, whereas negative technology readiness heightens risk and lowers trust among traders
Khan et al. (2022)	Novelty seeking, higher gambling tendencies	Higher novelty seeking and gambling tendencies were associated with unique investment patterns and substantial investments in crypto trading
Oksanen et al. (2022)	Distress, stress, anxiety (COVID-19), perceived loneliness	Cryptocurrency traders showed higher levels of psychological distress, stress, COVID-19 anxiety, and perceived loneliness compared to non-investors
Shahani et al. (2022)	Money anxiety, stress, social interaction, internal locus of control, herding bias, representativeness bias, overconfidence bias, self-serving bias	Cognitive and socio-psychological factors significantly affect investment decisions, with notable influences from herding and overconfidence due to stress
Delfabbro et al. (2021)	Problem Gambling Severity Index scores, frequency of trading	Higher gambling severity associated with cryptocurrency trading
Menteş et al. (2021)	Withdrawal and tolerance, money-seeking behavior c , and denial	Problematic cryptocurrency trading strongly associated with withdrawal and tolerance, as well as money-seeking behaviors
Kim et al. (2020)	Psychological traits: novelty seeking, harm avoidance, reward dependence, persistence, self-directedness, cooperativeness, self-transcendence	Traits like novelty seeking and low cooperativeness linked to increased likelihood of cryptocurrency investment
Gagarina et al. (2019)	Beliefs in cryptocurrency as a payment instrument, emotional experiences about its use, willingness to engage with cryptocurrencies	Beliefs, emotions, and willingness to use cryptocurrencies significantly influence attitudes and intended behaviors
Mills et al. (2019)	Problem gambling severity, depression, anxiety, engagement in high-risk stock trading, betting on sports	Problem gambling, depression, and anxiety correlate with higher frequency of cryptocurrency trading; high-risk behaviors significantly predict trading frequency

^aThe disposition effect in crypto selling behavior refers to the tendency of investors to sell assets that have increased in value while holding onto assets that have decreased in value. This behavioral finance phenomenon is driven by the desire to realize gains quickly to secure profits while avoiding the realization of losses, hoping that the losing investments will eventually rebound. This effect is prevalent in the cryptocurrency market, where volatility and emotional decision-making are common.

^bTechnology mindfulness refers to the active awareness and engagement with technological environments, influencing users’ trust and risk perceptions in cryptocurrency trading.

^cMoney-seeking behavior in the context of cryptocurrency refers to the actions and decisions made by investors and traders driven primarily by the desire to make quick and significant financial gains. This behavior often prioritizes short-term profits over long-term stability and can manifest in various ways within the cryptocurrency market.

The studies highlight a notable impact of social media on cryptocurrency trading behaviors, a factor less emphasized in traditional financial markets such as stocks or bonds. Social media platforms like Twitter, Reddit, and YouTube rapidly generate hype and encourage “herd mentality,” which significantly impacts decision-making among cryptocurrency traders. This behavior contrasts with traditional investment strategies, where decisions are generally informed by fundamental analyses, long-term projections, and expert guidance rather than short-term social trends.

In cryptocurrency markets, FoMO, often driven by viral content and peer influence, contributes to impulsive trading patterns. This contrasts with traditional trading, where FoMO is moderated by established financial indicators and industry norms, resulting in less volatility from social media influence. Additionally, the studies suggest that social media may drive addiction-like behaviors in cryptocurrency trading, with users frequently returning to platforms for real-time updates and validation, potentially leading to riskier financial decisions. This reliance on social media differentiates cryptocurrency trading behaviors from other financial markets, underscoring a unique, socially driven dynamic within this sector.

Cognitive and Emotional Influences

This review produced findings in several cognitive and emotional influences on trading behavior. The factors observed were: attitude effects, influence by others, reliance on trends, overconfidence, emotional influences, fears of loss, confirmation bias, and the disposition effect were significant.

Yadav and Raj ²⁸ demonstrated that traders often exhibit loss aversion, leading them to hold onto losing investments longer than is rational, hoping they will recover, while selling winning investments too early to secure gains. Rahardja et al. ²¹ explored the concept of technological mindfulness, defined as an individual’s intentional and reflective approach to technology use, fostering awareness of both its potential benefits and risks. This mindfulness was found to boost trust and mitigate risk perceptions, as users become more conscious of how they interact with technology. This indicated a significant cognitive and emotional influence on trading behavior. This same study also broached the concept of negative technological readiness which was observed to increase risk perceptions and lower trust, highlighting the impact of technology-related attitudes on investment decisions. ²¹

Other concepts expressed that phenomenon such as herd mentality, which refers to individual actions driven by the actions of other traders or trends on social media, can sway decisions and make traders follow the crowd rather than analysis. ²⁷ The disposition effect, where traders sell assets that have increased in value while holding onto assets that have decreased, is another common cognitive bias affecting decision-making.^27,28 Gagarina et al. ²⁷ found that despite the idea of fluctuation in cryptocurrency, users were willing to buy cryptocurrency if they had the funds.

Addictive and Behavioral Factors

Several studies in our analysis revealed a strong correlation between trading frequency and compulsive behaviors. Grubbs and Kraus ²⁰ identified that traders often engage in cryptocurrency trading with compulsive traits similar to those observed in gambling, as well as in substance use behaviors like alcohol, nicotine, THC/marijuana, prescription drug misuse, and illicit drug use. These compulsive behaviors were positively associated with higher trading frequencies in cryptocurrency.

In cases of high-frequency and high-volume trading, some traders may employ legitimate financial strategies, such as adjusting trade sizes based on market analysis or previous outcomes. However, these strategies often overlap with gambling-like behaviors, where impulsive adjustments in trade sizes and risk probabilities mirror betting adjustments seen in traditional casinos. ²⁵ This overlap highlights the blurred boundary between structured financial tactics and gambling behaviors in cryptocurrency trading.

Furthermore, data indicated that psychiatric factors such as withdrawal, tolerance, money-seeking behavior, and denial were strongly associated with problematic cryptocurrency trading, explaining a substantial portion of the variance in these behaviors. ²⁵ This suggests that, while some trading practices may begin as calculated strategies, they can evolve into patterns that closely resemble compulsive gambling.

Anxiety and Depression

Anxiety and depression were commonly reported among cryptocurrency traders. Johnson et al. ¹⁰ demonstrated the impact of anxiety from market volatility, depression after significant financial loss, and addiction-like behaviors on trading outcomes. Anxiety and depression were linked to negative trading outcomes and distress, while elation and excitement were associated with successful trades. ¹⁰ This anxiety is exacerbated during market downturns, where the fear of financial loss can lead to depressive symptoms. For example, significant financial losses have been linked to increased rates of depression among traders. Additionally, the addiction-like behaviors associated with trading contribute to a cycle of stress and depression, where the temporary elation from successful trades is quickly overshadowed by the stress of potential losses. ¹⁰ Problem gambling severity is another factor that correlates with higher trading frequencies; individuals who engage in high-risk stock trading and betting on sports are also more likely to trade cryptocurrencies frequently, further contributing to anxiety and depression. ¹⁸

Social and Normative Pressures

Several psychosocial stressors such as financial anxiety, herding bias, representativeness bias, overconfidence bias, and self-serving bias are significant cognitive and socio-psychological factors that affect investment decisions. ²⁹ Shahani et al ²⁹ demonstrated that behaviors such as herding and overconfidence are influenced due to stress.

Gambling Habits

Studies also showed that there was a positive correlation between gambling severity and more intense cryptocurrency trading behavior.^22,24 Delfabbro et al. ²⁴ confirmed this by analyzing factors such as betting frequency, bet sizes, response patterns to wins and losses, total amount wagered, and duration of betting to investigate how specific psychiatric traits could influence gambling behaviors. Khan ²² corroborated the findings described above and also added a positive correlation between higher novelty seeking and more substantial investments which suggested a stronger correlation between gambling habits and investment behaviors in the cryptocurrency market.

Integrating Psychological Theories With Findings

The cognitive biases and addiction-like behaviors observed in cryptocurrency trading align with established psychological theories. Prospect Theory helps explain the disposition effect observed in traders, where losses are held longer and gains are sold quickly. ³⁰ This bias, driven by an aversion to realizing losses and a preference for securing gains, reflects a fundamental tendency in high-risk trading environments. Similarly, FoMO, often fueled by social media, can be understood through Social Learning Theory, where peer influence reinforces herd behaviors. ³¹ This theory provides insight into why traders frequently mimic others’ decisions, especially when influenced by viral success stories on social media.

Dual Process Theory is relevant in explaining the compulsive trading behaviors noted in the studies. ³² Here, system-1 (impulsive, automatic responses) often drives trading actions, overriding system-2 (deliberate, reflective processes), leading to addiction-like patterns in decision-making. This understanding situates the high impulsivity observed among traders within a broader framework of cognitive processing biases, linking these behaviors to established psychological principles.

Summary of Key Mental Health Factors Synthesized in the Literature

This scoping review identifies a range of mental health factors that influence cryptocurrency trading behaviors, as shown in Figure 4. The analysis highlights that anxiety, addiction-like behaviors, and depression are the most frequently cited factors, with anxiety being the most predominant concern across studies. Other notable factors include gambling tendencies, alcohol use, and fear of loss. These mental health factors are associated with increased risk-taking and impulsive decision-making within the cryptocurrency trading environment, suggesting substantial impacts on trader behavior and financial outcomes.

Figure 4.

Snapshot summary of key mental health factors synthesized in the literature.

Discussion

This review identifies a significant issue in the fragmented nature of the research landscape surrounding cryptocurrency trading, with studies dispersed across disciplines like psychiatry, finance, and computer science (Figure 2). This fragmentation complicates efforts to build a cohesive understanding of the psychological effects of cryptocurrency trading, highlighting the need for greater interdisciplinary collaboration in future research efforts.^33,34 Alongside this, our review identified a range of psychological factors, including cognitive biases, anxiety, and addiction, that shape trading behaviors. Different methodologies, study designs, and focal points have made building a cohesive body of knowledge challenging. There is a significant need for better integration across these domains to address the gaps in understanding the mental health outcomes associated with cryptocurrency trading.

Several cognitive biases significantly impact cryptocurrency trading behaviors.^35,36 Overconfidence bias is prevalent, where traders overestimate their market knowledge and engage in high-risk trading. ³⁷ Confirmation bias further reinforces this by causing traders to seek information that validates their beliefs, leading to overconfident decisions. These biases align with theories such as Prospect Theory, which describes how individuals are prone to holding onto losing investments while hastily selling winning ones, hoping to secure profits. ³⁸

This review found strong correlations between addictive behaviors and trading frequency. The adverse factors, such as the addictive potential of trading, was noted in the literature.^10,13,25,39 Such behaviors can lead to noteworthy financial and psychological consequences. Cognitive biases, such as overconfidence and confirmation bias, significantly impact trading behaviors. ⁴⁰ One of the primary psychological drivers in cryptocurrency trading is the FOMO, which can exacerbate these biases. ⁴¹ This fear may compel traders to make quick decisions to not miss out on potential profits, especially when they see others achieving financial success; it may also inspire users who make careful decisions to engage in impulsive decisions. The trading platforms usually operate 24 hours a day, and the rapidity of information sharing on social media increases fear, pushing traders to engage in compulsive actions. ²⁴ However, with responsible use of social media and resources, traders may use the inspiration positively and proactively. While the idea of a close-knit community is noteworthy for individuals in the post-COVID world, the influence of peers can be both positive or negative in shaping trading behaviors. ⁴² Therefore, social media and groups can enable individuals to make well-informed decisions or allow group-based actions associated with impulsive trading and market volatility. ⁴³ The strong presence of social media in traders’ lives can be a source of stress and depression, so one must be wary of the potentially increased psychological stress as traders continually compare their success to others.^44,45

Several studies noted that cryptocurrency trading mirrors gambling behaviors, where traders exhibit compulsive tendencies, continuing to trade even in the face of financial losses. ¹⁰ These behaviors are driven by the excitement of potential gains and the compulsive need to trade in response to market volatility. High-frequency traders, in particular, showed increased risk-taking behaviors similar to those observed in problem gamblers.

The Problematic Cryptocurrency Trading Scale identifies withdrawal, tolerance, and money-seeking behavior as core traits of problematic trading. ²⁵ This mirrors the behavioral characteristics observed in substance abuse, suggesting that cryptocurrency trading can evolve into a form of behavioral addiction. While subjective, traders may either overestimate or underestimate their knowledge and abilities, which leads to over- or under-confidence in making investment decisions. ³⁸ The overestimation of skills is termed cognitive bias, where users may attribute random success to self-abilities; this can instead lead to riskier and more frequent trades, potentially leading to financial losses. ²⁴

Market volatility is strongly linked to increased anxiety and depressive symptoms among cryptocurrency traders. Traders experience heightened anxiety during market downturns, driven by fears of financial loss. ¹⁴ Prolonged exposure to the stresses of trading in a volatile market environment can lead to chronic anxiety and depressive symptoms, particularly when financial losses are substantial.

Additionally, while traders may experience emotional highs following successful trades, these moments of elation are typically short-lived. The fear of future losses quickly overshadows these gains, leading to a cycle of stress and depression. This emotional rollercoaster mirrors the experiences of high-stakes gamblers, where wins provide brief relief but are followed by increased stress and anxiety from potential losses. ¹⁸

In our analysis, we observed novelty-seeking and gambling tendencies as traits among cryptocurrency investors. These factors were associated with distinct investment patterns, suggesting that certain personality traits may lead to higher and riskier engagement in cryptocurrency markers. ²² Current literature contributes to our understanding of the speculative bubbles in cryptocurrency markets, urging regulatory bodies to target investor education and regulatory frameworks that consider these psychological traits. ⁴⁶ This scoping review also introduced the concepts of technology mindfulness and damaging technology readiness in cryptocurrency trading. ²¹ Technology mindfulness increases trust and reduces perceived risks, which could be beneficial for investors navigating the often volatile cryptocurrency markets. Whereas negative technology readiness increases perceived risks and lowers trust, could deter engagement or lead to overly cautious investment behaviors. Rahardja et al. ²¹ suggest that personal dispositions toward technology can significantly influence financial decision-making processes. In the realm of research and application, this can potentially help stakeholders focus on digital platform designs, their user interfaces, and risk communication strategies to better suit different investor psychologies. ⁴⁷

The significant psychological and behavioral implications associated with cryptocurrency trading underscore the need to include psychiatric services in discussions of regulatory frameworks. Governments and regulatory bodies looking to manage the risks associated with cryptocurrency markets should consider including these experts. Their insights into compulsive and risky behaviors could be crucial in developing guidelines and protections that address financial and technological aspects and the mental health concerns arising from trading activities.

While much of the literature focuses on the negative psychological outcomes associated with cryptocurrency trading, such as anxiety, depression, and compulsive behaviors, there are potential positive effects that deserve further exploration. This highlights the lack of research attention given to the potential mental health benefits of cryptocurrency trading, which presents a critical gap in the literature. For some traders, cryptocurrency trading can foster a sense of empowerment and autonomy, as individuals take control of their financial decisions and potentially achieve financial independence. ⁴⁸ Additionally, online trading communities offer a sense of belonging and shared purpose, which can promote positive social engagement and reduce feelings of isolation. ²³ However, it is important to note that empirical research into these positive effects remains limited, and future studies should investigate these aspects more thoroughly to provide a balanced understanding of the psychological impact of cryptocurrency trading.

Implications and Future Directions

Understanding that published literature has presented a myriad of adverse mental health drivers is important to make sure that we have targeted measures in healthcare. It must also be considered that publication bias is a key driver of inherent limitation in this review. ⁴⁹ Despite the many regulatory policies and interventions, we must ensure that the negative impacts of trading are addressed and that positive impacts must be reported and made accessible to all stakeholders.^50,51

One important step has been the validation of the Problematic Cryptocurrency Trading Scale. If users are deemed mentally ‘unproblematic’ on this quantitative scale, they should be cleared from the ‘risky’ behavior user group. While this scale is one of the first in cryptocurrency trading, researchers must make sure to use and adapt the scale in positive and negative hypothesis testing; this may improve the scale’s validity and reliability. ²⁵

Educational and awareness programs are recognized as essential in the context of cryptocurrency trading. Proactive measures and educational campaigns have been designed for individuals and communities to develop a heightened sense of self-awareness. This enables these communities to identify psychological biases effectively. These measures are critical for mitigating the prevalence of herd behavior and impulsive decision-making, which are often exacerbated by disseminating misinformation.

Several strategies can be considered for users identified as potentially problematic traders at this juncture. These may include cognitive-behavioral therapy, which is widely used for depression and anxiety and can also address co-occurring substance misuse, gambling and other traits in user groups. With tested interventions such as CBT, individuals may help understand individual specific triggers and mitigate risk of unchecked trading and possibly leading to healthier coping tools. Support groups can also provide information to individuals who can share personalized experiences and individualized coping strategies. Trained therapists and psychiatrists may promote practical actions to manage trading behaviors while also offering emotional support. This is key in reducing isolation feelings and can lead to net positive effects. As ascertained by the healthcare provider, medications can help treat underlying psychiatric comorbidities that lead to potential problematic training behaviors. Medications such as selective serotonin reuptake inhibitors can help mitigate depression or anxiety that may be a source of compulsion in cryptocurrency trading. Self-exclusion and monitoring tools can additionally lead to close monitoring of trading habits where users set limits on trading activities. Self-exclusion features, similar to those used in online gambling platforms, can help individuals take a break from trading if they recognize that their behavior is becoming problematic. These tools could help identify traders at risk for anxiety, depression, or addiction-like behaviors, enabling early intervention. Additionally, financial education programs could help traders better understand the market’s risks and avoid impulsive decisions driven by cognitive biases or social pressures

Additionally, there is a pressing need for more research on the positive psychological effects of cryptocurrency trading, as current literature focuses predominantly on its risks. Understanding the positive and negative aspects will help create a balanced perspective and inform the development of interventions and regulatory policies to protect traders’ mental well-being.

Limitations

The scoping review has several limitations. Despite a thorough search, relevant studies, mainly unpublished or non-peer-reviewed literature, might have yet to be noticed. The focus on peer-reviewed journals could overlook significant gray literature, introducing a publication bias where studies with positive findings are more likely to be published. Another critical limitation that must be stated is the overreporting of adverse mental health outcomes with a lack of data on potential benefits associated with trading. Geographical representation may only partially capture global perspectives, affecting the generalizability of the findings. The variety in study designs and reliance on self-reported data can introduce biases and affect data quality and comparability. Future research focusing on neutral reporting without preconceived notions about cryptocurrency trading could benefit from a broader search, including more gray literature and standardized methodologies.

A key limitation of this review is the potential for publication bias, where studies reporting negative mental health outcomes may be favored over those with positive findings. This could exaggerate the risks of cryptocurrency trading while downplaying potential benefits. Pre-registering trials and publishing study protocols could help address this by ensuring research plans are transparent from the start. Including gray literature, such as theses and non-peer-reviewed studies, may also provide a more balanced view.

Another limitation of this review is the geographical concentration of studies, primarily from regions with high cryptocurrency adoption rates, such as North America and East Asia. This limits the generalizability of the findings to the other areas where cryptocurrency trading may be less prevalent or where different cultural and regulatory contexts influence trading behavior. Future research should include more diverse samples to ensure a broader understanding of how cryptocurrency trading impacts mental health across different socio-cultural environments.

Conclusion

This review examined how cryptocurrency trading can impact mental health, highlighting how emotional, cognitive, and social factors influence trader behavior. Key findings of this paper show that addiction-like behaviors, social influences, and anxiety from market volatility are potential contributors influencing trading decisions. These behaviors, similar to gambling, may pose some serious risks, potentially leading to substantial personal and financial costs. Our analysis also emphasizes the powerful role of social media and peer influence in promoting herd behavior. The fear of missing out can drive traders to make hasty decisions, often influenced by online success stories. Additionally, traders may overestimate their market knowledge, leading to overconfidence and risky bets that can result in significant losses. The review suggests the need for targeted strategies to address associated psychological distress. While the review underscores the need for strategies to address the psychological distress related to cryptocurrency trading, it also acknowledges potential, though under-reported, benefits to mental health. These include a sense of control and empowerment over one’s financial future, which can improve self-esteem and reduce anxiety. The strategic and analytical aspects of trading can augment satisfaction and intellectual stimulation.

Traders are often likely to find support in online communities that can reduce isolation and provide a sense of belonging. Successful trading can lead to financial security, mitigating stress-related financial instability. These influences can create a dichotomy, and we want to emphasize how crucial ongoing research is in developing effective support systems and regulatory policies. Such measures help address traders’ mental well-being while allowing them to engage in cryptocurrency trading responsibly. Future research should continue to explore both the risks and benefits of cryptocurrency trading. Understanding the associations between psychological factors and trading behaviors will help create comprehensive interventions supporting traders’ mental health and financial stability.