Abstract
Background
There are well-established literatures documenting the associations between mental disorders and unhealthy behaviors such as poor diet quality, sedentary behavior, and cannabis and tobacco use. Few studies have attempted to understand the respective findings in light of each other, however.
Objective
The purpose of this review was to assemble comparable data for each behavior-disorder association and assess the associations in terms of their overall strength. The review aimed to include a representative, but not exhaustive, range of studies that would allow for explorative comparisons.
Methods
Eligible studies were identified via Pubmed searches and citation searching, restricted to publications no older than 2015 written in English. To obtain comparable data, only studies that reported findings as odds ratios were included, and risk of bias related to study samples, behavioral measurement disparities, and control variables was assessed via sensitivity analyses. Findings for each disorder were compared on the basis of different measures of central tendency.
Results
From 3,682 records, 294 studies were included. The review found evidence of associations between each of the four unhealthy behaviors and psychosis, depression, anxiety, bipolar disorder, attention-deficit/hyperactivity disorder (ADHD), and post-traumatic stress disorder (PTSD), while personality disorder was only investigated in relation to cannabis and tobacco use. In overall comparison, the associations were generally of similar strength, and only the association between cannabis use and personality disorder was exceptional in terms of being significantly stronger than its counterparts across disorders and across behaviors. Analyses of bias risk identified some influence from behavioral measurement disparities and lack of adequate statistical control, but findings were generally robust across a range of sensitivity analyses.
Conclusion
This explorative and comparative review found that poor diet quality, sedentary behavior, and cannabis and tobacco use are about equally strongly associated with a range of different mental disorders. Given the general nature of these associations, we should probably understand them to reflect a general and shared etiology. However, the findings in this review should be regarded as tentative until confirmed by more comprehensive investigations.
Keywords: cannabis, diet quality, sedentary behavior, tobacco, mental health, mental disorder
Highlights
In terms of mental disorder, moderate cannabis use appears to be about as harmful as moderate tobacco use.
In terms of mental disorder, moderate cannabis use appears to be about as harmful as watching TV for more than 2–4 h per day or eating fast food and drinking sugary beverages every day.
The association between cannabis use and psychosis is not stronger than the associations between cannabis use and other mental disorders.
The association between psychosis and cannabis use is not stronger than the associations between psychosis and poor diet quality, sedentary behavior, or tobacco use.
Introduction
Associations between unhealthy behaviors and mental disorders are well established in research literature. This review considers four different unhealthy behaviors—cannabis use, tobacco use, sedentary behavior, and poor diet quality—and a range of mental disorders across the range from common mental disorders (CMDs) to serious mental illnesses (SMIs) and including both internalizing and externalizing disorders. Aiming for a broad but not exhaustive range of disorders that can provide a convincing basis for comparison, the seven disorders psychosis, depression, anxiety, bipolar disorder, personality disorder, attention deficit/hyperactivity disorder (ADHD), and post-traumatic stress disorder (PTSD) were included in the review.
Psychosis
Several recent reviews have confirmed an association between psychosis and cannabis use (1–6), while a few have failed to identify a statistically significant relationship (7, 8). The strength of associations identified in meta-analyses ranged from strong for the most severe cannabis users [(5): OR 3.90] to moderate [(8): OR 1.786; (3): RR 1.71; (4): OR 1.75 for abuse/dependence; (5): OR 1.97 for any use] or weak [(7): RR 1.11; (4): OR 1.14 for lifetime use]. Several recent reviews have also confirmed an association between psychosis and tobacco use (8–11), with the strength of the association ranging from moderate to strong in meta-analyses (respectively, OR 2.22, 3.04, 3.22/2.18 for case-control/prospective studies, and RR 1.99). One review did not find evidence of any significant association (12). Those reviews that have compared the strength of the relationship between cannabis and psychosis to that between tobacco and psychosis have found that the latter appears to be somewhat stronger (8, 13). The associations between psychosis and poor diet quality or sedentary behavior have not been extensively studied, although reviews have suggested that people with psychosis tend to have poor diet quality (14, 15) and low levels of physical activity (16, 17).
Depression
A number of recent review articles support an association between cannabis use and depression, although with some mixed results (12, 18–28), while one found that the relationship is unclear (29). The meta-analysis by Onaemo et al. (26) identified a strong association between cannabis use disorder and major depression (OR 3.22), while those by Esmaeelzadeh et al. (18) and Gobbi et al. (19) as well as an earlier meta-analysis by Lev-Ran et al. (30) identified a weak to moderate association (respectively, OR 1.29, 1.37 and 1.62) for overall cannabis use.
For tobacco, recent reviews by Weinberger et al. (31) and Fluharty et al. (32) confirmed that people with depression are more likely to smoke and to meet criteria for nicotine dependence, with the latter also indicating that smoking may lead to depression. A meta-analysis by Esmaeelzadeh et al. (18) found that the association between tobacco use and depression was moderately strong with an OR of 1.65, which was stronger than that for the corresponding association between cannabis use and depression. Another meta-analysis by Groenman et al. (33) found that childhood depression was a risk factor for subsequent nicotine use disorder (OR 2.56). Reviews by Zeng and Li (34) and Han et al. (35) also confirmed that secondhand smoking is associated with depression, with their meta-analyses obtaining ORs of 1.60 and 1.32, respectively. Furthermore, recent reviews have also indicated that e-cigarette use is associated with depression (36, 37).
There are also a number of reviews supporting associations between depression and sedentary behavior (17, 38–47) and poor diet quality (48–57), although with some mixed results. In meta-analyses, the associations were weak to moderate in strength, with RR 1.42 (47) and RR 1.25 (46) for overall sedentary behavior, OR 1.28 (45) and OR 1.12 (42) for screen time, RR 1.18 for TV viewing (41), OR 1.44 for ultra-processed food consumption (49), RR 1.25 (58) and RR 1.31 (48) for sugar-sweetened beverage consumption, OR 1.18 for western-style unhealthy dietary pattern (59), and OR 1.62 (52) and OR 1.11 (54) for fast/junk food.
Anxiety
Some reviews have identified an association between cannabis use and anxiety (18, 20, 26, 27, 60–62), although others have maintained that the relationship is unclear (19, 22, 28, 29, 63, 64). In meta-analyses, moderate to strong associations were identified between cannabis use disorder and anxiety [(60): OR 1.68; (26): OR 2.99], while the associations for overall use were generally weak [(18): OR 1.36; (19): OR 1.18; (60): OR 1.24; (61): OR 1.15; (62): OR 1.25]. Reviews have also identified an association between tobacco use and anxiety (12, 18, 65–67), although with some mixed results. A meta-analysis by Esmaeelzadeh et al. (18) found a strong association between tobacco use and anxiety with an OR of 2.21, which was stronger than that for the corresponding association between cannabis use and anxiety.
Reviews have also found evidence of an association between anxiety and sedentary behavior (68–71) and poor diet quality (49, 52). Associations in meta-analyses were of weak to moderate strength, with regular physical activity reducing the odds for anxiety [(72): OR 0.54; (70): OR 0.74] and sedentary behavior [(68): OR 1.48], ultra-processed food [(49): OR 1.48] and junk food [(52): OR 1.24] increasing the odds for anxiety.
Bipolar disorder
The relationship between cannabis use and bipolar disorder has not been intensively studied, but a number of reviews have suggested a positive association (20, 23, 25, 73–78). Meta-analyses by Gibbs et al. [(73): OR 2.97] and Hunt et al. [(74): OR 2.35] identified a strong association between cannabis use disorder and bipolar disorder. Some reviews have also suggested an association between tobacco use and bipolar disorder (75, 79), although the evidence is mixed (12). Nevertheless, the meta-analysis by Jackson et al. (79) identified a strong association with an OR of 3.5. Reviews have also identified tentative associations between bipolar disorder and sedentary behavior (17, 80, 81) and diet quality (82, 83), but there are few available studies.
Personality disorders
While the association between personality disorders and general substance use is well established, the evidence specifically for cannabis (20, 28, 84–86) and tobacco (12, 87) is quite limited. Similarly, the research into the relationships between personality disorders and sedentary behavior and diet quality is very limited (87, 88).
Attention-deficit/hyperactivity disorder
ADHD is commonly diagnosed in childhood, and most studies investigating the relationship between the disorder and substance use focus on ADHD as a risk factor for subsequent substance use. For cannabis, meta-analyses have found strong associations between childhood ADHD and regular [(89): OR 2.45] and lifetime use [(90): OR 2.78] as well as a moderate association for abuse or dependence [(90): OR 1.58]. For tobacco, meta-analyses similarly identified childhood ADHD as a strong risk factor both for subsequent nicotine use [(89) OR 2.16; (90): OR 2.08] and nicotine dependence or use disorder [(33): OR 2.52; (90): OR 2.82]. The evidence for e-cigarette use is very limited (36).
Reviews have also supported associations between ADHD and sedentary behavior (91–95) and diet quality (96–99). Meta-analyses of sedentary behavior found moderate to strong associations between electronic media use and ADHD [(91): OR 1.94; (92): 2.597], while meta-analyses of diet quality found weak to strong associations for sugar and soft drink consumption [(97): pooled effect size 1.22], junk food [(99): OR 1.51], and general unhealthy diets [(96): OR 1.41; (98): OR 2.24].
Post-traumatic stress disorder
Reviews have identified an association between cannabis use and PTSD (100, 101) and investigated the potential for therapeutic use (102–104). Reviews have also identified an association between tobacco use and PTSD (105–109), with one meta-analysis by van den Berk-Clark et al. (109) finding a strong association (OR 2.13) for unadjusted studies and a weak association (OR 1.22) for a single adjusted study. The evidence for e-cigarette use is very limited (36).
Furthermore, reviews have suggested that both sedentary behavior and poor diet quality are associated with PTSD, although with some mixed results (70, 109–111). In meta-analyses, high physical activity was moderately protective against PTSD [(70): OR 0.57], while PTSD in turn was a weak risk factor for low physical activity [(109): OR 0.91 for adjusted studies and OR 0.69 for unadjusted studies]. The meta-analysis by van den Berk-Clark et al. (109) on diet quality was somewhat unclear, as four unadjusted studies predicted a healthier diet (OR 1.25) while a single adjusted study predicted a less healthy diet (OR 0.95).
While these reviews indicate a broad range of associations between unhealthy behaviors and mental disorders, they also demonstrate that such associations have not often been understood in relation to each other across various behaviors and disorders, which would allow for both a calibration of their relative importance and a widened perspective on their etiology. Instead, individual associations are usually analyzed and discussed in isolation, which encourages the perspective that the association under scrutiny is unique and reflects a particular and individual etiology. Should it be true that the range of disorders here under scrutiny are generally associated with a range of unhealthy behaviors related to diet, exercise, and licit and illicit substance use, however, it might seem unlikely that these parallel associations are best understood in isolation from one another. If the association between a given behavior and a given mental disorder has parallels across a range of disorders and behaviors, its lack of specificity indicates that the association probably does not reflect a causal effect from the behavior itself (112). Instead, such a field of associations most likely point to general and shared dynamics. One such dynamic may involve underlying issues of social misery and psychological trauma that perhaps give rise to both unhealthy behaviors and to mental disorders. To take one example, long-term unemployment may entail poverty, boredom, and social isolation, which in turn may push a person into unhealthy behaviors as means of escapism and diversion and also contribute to a mounting sense of hopelessness and depression.
Indeed, there is strong evidence supporting a relationship between mental disorder and factors relating to misery and trauma. To take a few noteworthy examples, childhood abuse has been associated with psychosis at ORs of 7.3 (113) and 5.9 (8), with depression at ORs of 19.1 (114) and 4.2 (115), and with ADHD at ORs of 7.3 (91) and 3.3 (116), as well as with cannabis abuse at ORs of 5.8 (117), 2.7 (118), and 2.2 (119). Violence victimization has similarly been associated with psychosis at ORs of 5.9 (120), 4.0 (121), 2.2 (122), and 1.9 (123), with PTSD and depression at ORs of 32.4 and 5.4 (124), and with nicotine dependence at ORs of 22.4 (124) and 5.5 (125). Afifi et al. (118) for their part found that living in an unsafe community was associated with cannabis use at an OR of 5.0 and with tobacco use at an OR of 3.8. Comparable findings exist for childhood bullying victimization (120, 126), while poverty, low education, and unemployment have been associated with psychosis at ORs of 8.2 (127) and 3.0 (122), with depression at ORs of 9.1 (128), 5.0 (129), 3.4 (130), 3.2 (131), 2.8 (132), and 2.6 (133), with nicotine dependence at ORs of 37.0 (134), 2.4 (135), and 2.3 (125), and with cannabis use disorder at an OR of 2.5 (136). It should also be noted that factors relating to trauma and misery might accumulate in an individual and thereby increase risk. In a study by McMahon et al. (120), cumulative adverse life events were strongly associated with psychotic experiences, with four or more adverse life events incurring an OR of 16.8.
Thus, it seems possible that both unhealthy behaviors and mental disorders might be caused by underlying problems related to social misery and psychological trauma, and that the observed associations between those behaviors and disorders are largely spurious. As a basis for the discussion of the specificity of individual associations, this article will use the probably most discussed associations among those here under purview, namely that between cannabis use and psychosis, which is commonly taken to reflect a causal effect from cannabis use. A recent consensus paper by D'Souza et al. (1) pointed to a study by Starzer et al. (137) as evidence supporting the specificity of this association, and on this basis drew the inference that “[c]annabis is more likely to be associated with psychosis outcomes than other psychiatric diagnoses” (p. 732). This consensus paper also pointed to a somewhat broader range of evidence to support the specificity of the cannabis-psychosis association as compared to other forms of substance use, drawing the inference that “[t]he risk for a psychosis outcome is highest for cannabis relative to other drugs” (ibid.).
However, there is also evidence contradicting the consensus view presented in D'Souza et al. (1). As Ksir and Hart (138) pointed out, research has associated cannabis use with depression, which may challenge the specificity of the cannabis-psychosis association. Furthermore, a recent review by Johnstad (13) found that tobacco use tended to be at least as strongly associated with psychosis as is cannabis use. The cannabis-psychosis association may therefore lack specificity both across disorders and across behaviors, which would open for the perspective that the association may be spurious and reflective of a general etiology perhaps related to underlying psychological trauma and social misery.
This interpretation is supported by a study by Shakoor et al. (139) which found that in a sample of adolescent twins, “[e]nvironmental influences explained all of the covariation between cannabis use and paranoia, cognitive disorganization and parent-rated negative symptoms,” with a bivariate common environment of 69%−100% (p. 144). Cannabis use explained 2%−5% of variance in positive, cognitive, and negative psychotic experiences. Sideli et al. (140) for their part found that less than daily cannabis use was not associated with psychosis (OR 0.97, p = 0.92) unless it co-occurred with having experienced child abuse, in which case the association was strong (OR 2.51, p < 0.05), while Copeland et al. (141) found that cannabis use before the age of 16 was strongly associated with depression (OR 2.3) and anxiety (OR 2.9) in an analysis adjusted only for sex and race/ethnicity, but that the association disappeared with adjustment for childhood disorders and adversities (OR 0.6 for both depression and anxiety). A meta-analysis by Fusar-Poli et al. (8) similarly found that risk for psychosis was related to environmental risk factors such as childhood trauma and further accumulated by low education and unemployment, while finding no significant impact from cannabis use.
In sum, the worry that unhealthy behaviors such as cannabis use cause psychosis may be a case of projecting the social characteristics of the drug-using population onto the drug itself and understand these characteristics as pharmacological effects inherent to the drug use. This comparative review aimed to assemble data on a broad range of associations between unhealthy behaviors and mental disorders in order to compare their respective strengths and to gain insight into the influence of factors relating to trauma and misery by recording the inclusion of relevant control variables and subsequently running sensitivity tests on the dataset. It reviews recent (2015 through January 2023) research on associations between a range of unhealthy behaviors and mental disorders, using the much-debated association between cannabis use and psychosis as a basis for comparison. In order to obtain comparable figures, the review included only records that presented their findings as odds ratios, and attempted to account for variations in behavior intensity, sample generalizability, and covariates via subsequent sensitivity analyses. By thus gaining a comparative overview of the relationship between unhealthy behaviors and mental disorders, the review hoped to achieve three specific aims:
Understand individual associations between a given unhealthy behavior and a given mental disorder in relation to other such disorders.
Understand individual associations between a given unhealthy behavior and a given mental disorder in relation to other such behaviors.
Understand the association between unhealthy behaviors and mental disorders in relation to statistical control for covariates.
Methods
The pre-planned criteria for inclusion were that studies must investigate associations between any of the relevant behaviors and disorders and report their findings as odds ratios in a manner that allows for cross-study comparisons. In order to keep the number of included studies manageable, only recent studies (no older than 2015) would be included. Furthermore, the studies should be published in English, be available in the Pubmed database, and their sample size should be at least 100.
Two Pubmed searches were performed (February 3, 2023). The search related to cannabis and tobacco use was [(tobacco OR nicotine OR cigarette OR cannabis OR marijuana) AND (psychosis OR depression OR anxiety OR bipolar OR “personality disorder” OR adhd OR ptsd) AND associat* AND (use OR “use disorder” OR dependence OR abuse) AND (comorbid OR dual OR psychiatric)] while the search related to diet quality and sedentary behavior was [(“poor diet” OR “sedentary behavior” OR “fast food” OR soda OR “sugar-sweetened”) AND (psychosis OR depression OR anxiety OR bipolar OR “personality disorder” OR adhd OR ptsd) AND associat*]. The author assessed all records. Reported associations in included studies were classified as cross-sectional or longitudinal, with retrospective analyses being classified as longitudinal. In order to investigate directionality as openly as possible, longitudinal studies of both unhealthy behaviors leading to mental disorders and mental disorders leading to unhealthy behaviors were included. Study quality was assessed on the level of sample representativeness, behavioral measurement disparities, and the inclusion of control variables related to social misery and psychological trauma. Results are reported according to PRISMA guidelines (142).
The selection procedure is shown in the flow chart of Figure 1. Criteria related to language and publication date were implemented in the search itself. Four reports were excluded for having samples lower than n = 100, while 309 reports were excluded because they did not report any relevant odds ratios that allowed for comparisons. This was related to the following reasons:
Figure 1.
Flow diagram for article inclusion.
The reports combined several types of drugs or disorders into a single construct (e.g., “substance use,” “psychological distress,” “internalizing disorders”).
The reports subdivided disorders into specific symptoms or symptom classes (e.g., auditory and visual hallucinations in psychosis).
Findings were reported in other terms than odds ratios (e.g., risk ratios, hazard ratios).
Some reports especially of sedentary behavior were also excluded because they reported ORs for continuous variables that were not readily comparable to the other studies included in the review.
A few reports utilizing Mendelian randomization methodology were excluded because they were not comparable to the cross-sectional or longitudinal studies included in the review and did not constitute a meaningful basis for comparison on their own.
Variables
The review focused on moderate cannabis and tobacco use as compared to high levels of sedentary behavior and poor diet quality. This approach was founded on the observation in Johnstad (13) that heavy substance use is very likely associated with underlying factors of social misery and psychological trauma, and that analyses of the heavy drug-using population are therefore at high risk of confusing the social characteristics of this population segment with the effects from the drug use itself. While heavy chronic drug use is commonly driven by coping motives related to underlying misery and trauma, moderate use is driven by a much broader range of motivations (143–145). In order to understand pharmacological effects from substance use in and of themselves, it therefore seems advisable to focus on moderate usage patterns.
In this review, moderate cannabis use was defined as one or two use occasions per week (146, 147). For tobacco, moderate use could be defined as daily use of about 10 cigarettes (148–150). When the studies included in this review reported effects related to several different usage patterns, the one most closely resembling a moderate pattern was selected. The review did not exclude any study on the basis of divergences in the usage pattern, however, and several included studies focused exclusively on use disorders or similarly problematic behavior. For sedentary behavior and diet quality, the review focused on high exposure represented by such measures as the worst quartile, daily consumption of soda or fast-food, not meeting physical activity guidelines, or more than 4–8 h of screen time per day. This approach was based on the observation that unlike cannabis and tobacco use, sedentary behavior and unhealthy food consumption are universal behaviors with which moderate levels of engagement are ubiquitous. When studies reported figures from several different multivariate models, the review included the figures from the final, most stringently adjusted model. Sensitivity analyses attempted to account for the inclusion of studies focusing on specialty samples and disordered behavior patterns.
Analyses
Across studies
The meta-analysis compared mean odds ratios for associations between behaviors and disorders across studies. To compare odds ratio means related to a given unhealthy behavior and a given mental disorder in relation to other such disorders, the data was restructured so as to combine odds ratios for different disorders into a single variable with different corresponding labels. T-tests were used for each pair of disorders for cross-sectional and longitudinal (behavior before disorder) studies. With 21 pairs of disorders, 4 behaviors, and 2 analytical approaches, a total of 168 different comparative analyses were performed. To compare odds ratio means between different unhealthy behaviors, findings for each disorder were compared for each pair of behaviors for cross-sectional and longitudinal (behavior before disorder) studies. With seven disorders, six pairs of behaviors, and two analytical approaches, a total of 84 different comparative analyses were performed between studies of different behaviors. It might be noted that none of these comparisons were Bonferroni corrected, which would probably have rendered them all non-significant.
Within studies
Some studies reported comparable odds ratios for several different disorders and/or behaviors. In studies of cannabis use that reported figures relating both to psychosis and to other disorders, the review compared the respective odds ratios and classified the figure for psychosis as stronger if it exceeded the other figure by 0.25, as weaker if the other figure exceeded the figure for psychosis by 0.25, and otherwise as being at the same level. Similarly, for studies that provided odds ratios relating to a given disorder for both cannabis/tobacco use and diet quality/sedentary behavior, the review classified either as stronger if it exceeded its counterpart by 0.25, and otherwise as being at the same level.
Sensitivity analyses
Sample groups
The studies included in the review analyzed participant samples ranging from representative samples of the nation's adult population to more narrowly defined groups such as children, older adults, male military conscripts, university students, and patient groups. In order to control for the possibility that narrow samples unduly impacted overall dataset analyses, dichotomous variables for “children” (samples below age 18), “older adults” (samples above age 60), “patient” (all patient or high-risk groups without controls), and “special” (samples consisting of university students or employees, all-male or all-female samples, military service members or veterans, and civil servants) were defined. Subsequently, these variables were combined into a single dichotomous indicator for specialty sample.
Behavioral measurement disparities
The studies included in the review spanned behaviors across the risk spectrum, from any (or ever) use of tobacco or weekly fast-food consumption to nicotine use disorder and binge eating disorder. In order to control for the possibility that the disordered behavior measured in some studies unduly impacted overall analyses, a dichotomous “problem behavior” indicator was defined to include behavioral variables including “binge eating disorder,” “emotional eating,” “maladaptive/pathological internet use,” “smartphone addiction,” “Facebook overuse,” “problematic gaming,” “internet addiction,” tobacco/cannabis problem use, abuse, dependence, use disorder, or high-risk use, and smoking during pregnancy. Especially for tobacco, however, it was not clear whether use measured in terms of daily smoking could be meaningfully differentiated from use measured in terms of abuse, dependence etc.
Control variables
The studies included in the review used a wide variety of control variables in their statistical analyses. In this review, systematic differences in association strengths related to statistical control for diet quality or sedentary behavior (for tobacco and cannabis analyses), tobacco use (for diet quality, sedentary behavior, or cannabis analyses), and cannabis use (for diet quality, sedentary behavior, or tobacco analyses) were controlled for by the application of dichotomous adjustment variables (0 = no control, 1 = control). Furthermore, dichotomous variables for adjustment for social misery (1 = inclusion of any control variable relating to poverty, education, or unemployment) and psychological trauma (1 = inclusion of any control variable relating to childhood abuse/neglect, bullying victimization, or violence victimization) were defined. Finally, a variable for studies that reported bivariate associations (1 = bivariate) was defined.
It should be noted, however, that some studies analyzed potential covariates on a bivariate basis and only included those that passed a significance threshold of p < 0.05 in the final model. This review accepted such variables as having been controlled for, although it should be clear that a covariate may exert meaningful influence on the outcome of a multivariate regression even if its bivariate correlation with the outcome variable is significant only at the 90% (p < 0.1) level, for instance.
Results
Overall findings
From 3,682 records, 294 were included. Some publications reported figures for several behaviors and methodological approaches (commonly, both cross-sectional and longitudinal), and counting these the review included 97 studies of cannabis use, 132 studies of tobacco use, 88 studies of sedentary behavior, and 47 studies of diet quality. The dataset included a total of 570 associations between behaviors (cannabis use, tobacco use, sedentary behavior, and diet quality) and disorders (psychosis, depression, anxiety, bipolar disorder, personality disorder, ADHD, and PTSD).
Sample groups
The review included 320 associations (out of 570) marked as relating to specialty samples. In the full dataset of cross-sectional and longitudinal associations, the specialty sample indicator was not significantly correlated with odds ratios for any disorder or behavior. Specialty samples were most commonly used in studies of diet quality and sedentary behavior (69%) and least commonly used in cannabis studies (44%). As a sensitivity test, the analyses reported below were repeated with studies using specialty samples removed from the dataset. This new set of analyses identified marginally significant differences (t = 2.30, df = 5.76, two-sided p = 0.06) for PTSD between cannabis use and tobacco use, with the former having higher ORs.
Behavioral measurement disparities
The review included 101 associations (out of 570) marked as relating to disordered or problematic behavior. In the full dataset of cross-sectional and longitudinal associations, the problem behavior indicator was significantly associated with odds ratios for depression (r = 0.15, p = 0.015, N = 251) and ADHD (r = 0.31, p = 0.021, N = 55). The inclusion of studies investigating problem behaviors may thus have served to increase association strengths for these disorders. Across disorders, the indicator for problem behavior was significantly correlated with the odds ratio strength in non-tobacco associations (r = 0.12, p = 0.021, N = 366) but not in associations with tobacco use (r = 0.08, p = 0.252, N = 204). This finding supports the notion that tobacco use measured in terms of daily smoking cannot necessarily be differentiated from use measured in terms of abuse or dependence. Problem behaviors were most commonly studied in relation to cannabis use (29%) and least commonly in relation to diet quality and sedentary behavior (10%). As a sensitivity test, the analyses reported below were repeated with studies investigating disordered or problematic behavior removed from the dataset. This new set of analyses identified significant differences (t = 2.31, df = 8.38, two-sided p < 0.05) for PTSD between cannabis use and tobacco use, as well as between cannabis use and sedentary behavior (t = 2.32, df = 8.13, two-sided p < 0.05), with ORs for cannabis in both cases being higher. Furthermore, these analyses identified marginally significant differences (t = 1.93, df = 32, two-sided p = 0.06) between cannabis-psychosis and cannabis-PTSD, with the latter having higher ORs. Finally, the review included a few studies that investigated the association between depression and secondhand smoking (N = 5) and e-cigarette use (N = 6). These studies did not diverge substantially from other tobacco studies in terms of their association strengths with mental disorders.
Control variables
Some studies reported bivariate associations (33 out of 570), generally because their main focus lay elsewhere and the relationships investigated in this review were only a secondary concern in their analysis. In the full dataset, the bivariate indicator was significantly associated with odds ratios for anxiety (r = 0.19, p = 0.043, N = 112) and ADHD (r = 0.33, p = 0.013, N = 55). The inclusion of studies reporting bivariate associations may thus have served to increase association strengths for these disorders. Across disorders and behaviors, the bivariate indicator was marginally correlated with odds ratios (r = 0.08, p = 0.064, N = 570). Bivariate findings were most commonly reported in tobacco studies (8%) and least commonly in studies of sedentary behavior and diet quality (4%). As a sensitivity test, the analyses reported below were repeated with studies reporting bivariate associations removed from the dataset. This new set of analyses identified marginally significant differences (t = 1.96, df = 18.57, two-sided p = 0.07) for PTSD between cannabis use and sedentary behavior, with the former having higher ORs.
Overall, 51 (out of 570) associations were adjusted for psychological trauma, and the indicator for trauma adjustment was marginally significant for psychosis (r = −0.25, p = 0.056, N = 61). Across disorders and behaviors, the indicator was marginally correlated with odds ratios (r = −0.07, p = 0.103, N = 570), and such adjustment was most commonly employed in cannabis studies (14%) and least commonly in studies of diet quality (2%). By contrast, 441 (out of 570) associations were adjusted for social misery, and such adjustment was most commonly employed in studies of diet quality (82%) and least in studies of cannabis and tobacco (76%). Across disorders and behaviors, the indicator for misery adjustment trended weakly in a negative direction (r = −0.06, p = 0.151, N = 570) but was marginally correlated with odds ratios in cannabis studies (r = −0.13, p = 0.093, N = 178).
Only 1% of cannabis studies and 14% of tobacco studies controlled for diet quality and/or sedentary behavior, while studies of the latter behaviors often controlled for tobacco (55%) but rarely for cannabis (8%). Furthermore, 35% of cannabis studies controlled for tobacco while 24% of tobacco studies controlled for cannabis. With regard to specific disorders, the indicator for cannabis adjustment was significant only in (non-cannabis) studies of psychosis (r = −0.55, p < 0.001, N = 32) while tobacco adjustment was significant in (non-tobacco) studies of anxiety (r = −0.24, p = 0.033, N = 80) and marginally of depression (r = −0.15, p = 0.059, N = 163). Across disorders, tobacco adjustment was significantly correlated with odds ratios in non-tobacco studies (r = −0.19, p < 0.001, N = 366) and cannabis adjustment was significantly correlated with odds ratios in non-cannabis studies (r = −0.13, p = 0.010, N = 392). The indicator for adjustment for diet quality and/or sedentary behavior was nowhere significant.
In exploratory linear regression analyses, the set of study quality variables (sample size, specialty sample, disordered behavior, and control variables) explained 2.7% of the variance in odds ratios in the overall dataset and 12.4% of the variance in longitudinal studies. For cross-sectional studies of individual behaviors, regression models explained between 9.0% (tobacco use) and 27.5% (poor diet quality) of the variance in odds ratios, and for individual disorders between 1.4% (personality disorder) and 37.5% (bipolar disorder). See Supplementary Appendix B for the specification of these models. In these models, disordered behavior commonly reached significance as a positive influence on odds ratios, indicating that studies investigating such behaviors tended to find higher risk for mental disorders. In the full dataset, higher sample size reduced odds ratios, indicating that large-N studies tended to obtain more moderate risks; this was an issue especially for longitudinal (behavior before disorder) studies. In studies of cannabis and tobacco use, adjustment for its counterpart was a highly significant negative influence on odds ratios.
Findings for individual disorders
Psychosis
This review included 45 cross-sectional and 16 longitudinal figures for the association between unhealthy behaviors and psychosis (Tables 1a, 1b). The cross-sectional studies provide convincing evidence for associations between psychosis and cannabis and tobacco use and some evidence for associations with sedentary behavior and diet quality. The longitudinal studies provide some evidence that cannabis and tobacco use may lead to subsequent psychosis and more tenuous evidence that psychosis may lead to subsequent cannabis and tobacco use.
Table 1a.
Cross-sectional psychosis studies (N = 45).
| N | Area | Sample | Exposure | OR | Covariate adjustments | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| Cannabis (N = 20) | SD | TO | MI | TR | BV | |||||
| Livne et al. (170) | 79,402 | USA | Adults 18+ | Weekly to daily | 3.95 | • | • | |||
| Davies et al. (171) | 2,475 | UK | Adults 18–20 | Problem | 3.64 | • | ||||
| McMahon et al. (120) | 928 | IRL | Schoolchildren | Past year | 3.52 | ø | ||||
| Bhavsar et al. (172) | 1,680 | UK | Adults 16–90 | Weekly to daily | 3.00 | ø | ||||
| Bassir Nia et al. (173) | 594 | USA | Adult patients | Any non-synth. | 1.87 | |||||
| Padwa et al. (151) | 8,940 | USA | Adult patients and contr. | Any | 1.69 | • | ||||
| Wainberg et al. (174) | 109,308 | UK | Adults 40–69 | Weekly | 1.69 | • | ||||
| Pardo et al. (175) | 152 | SPA | Child patients and contr. | Non-daily | 1.65 | |||||
| Campbell et al. (176) | 101,405 | USA | Patients 18+ | Disorder | 1.62 | • | • | |||
| Ferraro et al. (177) | 2,261 | Int. | Adult patients and contr. | Current | 1.61 | |||||
| Degenhardt et al. (121) | 514 | AUS | Adolescent offenders | Weekly | 1.50 | • | • | • | ||
| Corsi-Zuelli et al. (178) | 409 | BRA | Patients and controls | <daily | 1.48 | • | • | |||
| Carney et al. (179) | 731 | AUS | Help-seeking youth | Current | 1.40 | ø | ||||
| Di Forti et al. (180) | 2,138 | Int. | Adult patients and contr. | Weekly >1 | 1.40 | • | • | |||
| McGuckin et al. (181) | 81,809 | CAN | Adult patients | Past 30 days | 1.31 | • | • | • | ||
| Hines et al. (182) | 1,087 | UK | Adults | High potency | 1.29 | • | ||||
| Chan et al. (183) | 181,870 | Int. | Adults 18+ | Potent herb | 1.28 | • | ||||
| Di Forti et al. (184) | 780 | UK | Adult patients and contr. | Weekly | 1.04 | • | • | |||
| Bassir Nia et al. (185) | 983 | USA | Adult patients | Any non-synth. | 1.02 | |||||
| Sideli et al. (140) | 445 | UK | Patients and controls | Non-daily | 0.97 | • | • | |||
| Tobacco (N = 19) | SD | CA | MI | TR | BV | |||||
| Molla et al. (186) | 422 | ETH | Adult patients | Dependence | 5.26 | • | ||||
| McMahon et al. (120) | 928 | IRL | Schoolchildren | Past year | 3.61 | ø | ||||
| Dickerson et al. (187) | 1,938 | USA | Adult patients and contr. | Current | 3.58 | • | ||||
| Ferraro et al. (177) | 2,261 | Int. | Adult patients and contr. | Current | 3.47 | |||||
| Zolezzi et al. (188) | 196 | QAT | Patients 15+ | Current | 2.48 | ø | ||||
| Werneck et al. (189) | 60,202 | BRA | Adult patients and contr. | Current | 2.39 | • | ||||
| Wolfe et al. (190) | 930 | USA | University students | Current | 2.31 | • | ||||
| Clark et al. (191) | 421 | AUS | Patients 14–25 | Current | 2.17 | • | ||||
| Mustonen et al. (192) | 5,926 | FIN | Adolescents | Daily | 2.17 | ø | ||||
| Yilmaz Kafali et al. (193) | 684 | TUR | Adol. Patients and controls | Past 30 days | 2.11 | • | ||||
| Li et al. (51) | 1,102 | CHN | Adult patients | Daily | 2.00 | • | ||||
| Davies et al. (171) | 2,491 | UK | Adolescence->adulthood | Weekly + | 1.68 | • | ||||
| Chang et al. (122) | 5,719 | CHN | Adults 16–75 | Current | 1.65 | • | • | • | ||
| Carney et al. (179) | 731 | AUS | Help-seeking youth | Daily | 1.50 | ø | ||||
| Bhavsar et al. (172) | 1,680 | UK | Adults 16–90 | Daily | 1.47 | • | • | |||
| Mallet et al. (194) | 34,653 | USA | Adults | Current | 1.36 | • | • | |||
| Fang et al. (195) | 469 | CHN | Adult patients and contr. | Any | 1.32 | |||||
| Degenhardt et al. (121) | 514 | AUS | Adolescent offenders | Daily | 1.10 | • | • | • | ||
| Bourbon et al. (125) | 10,985 | FRA | University students | Daily | 1.01 | • | • | • | ||
| Sedentary behavior (N = 4) | TO | CA | MI | TR | BV | |||||
| Werneck et al. (189) | 60,202 | BRA | Adult patients and contr. | Tv >5 h/day | 3.84 | • | ||||
| McMahon et al. (120) | 749 | IRL | Schoolchildren | Pathological | 2.70 | |||||
| Kim et al. (196) | 6,510 | KOR | Adults 18–64 | Addiction | 2.32 | • | ||||
| Zhang et al. (197) | 7,121 | CHN | Adults 18–81 | Tv >3 h/day | 1.61 | • | • | |||
| Diet quality (N = 2) | ||||||||||
| Werneck et al. (189) | 60,202 | BRA | Adult patients and contr. | Daily sweets | 2.36 | • | ||||
| Mutiso et al. (198) | 9,742 | KEN | High school students | Binge eating | 1.41 | • | • | • | ||
| Median | Unweighted mean (SD) | Weighted mean | ||||||||
| Cannabis | 1.56 | 1.85 (0.91) | 1.82 | |||||||
| Tobacco | 2.11 | 2.24 (1.07) | 1.97 | |||||||
| Sedentary behavior | 2.51 | 2.62 (0.93) | 3.48 | |||||||
| Diet quality | 1.88 | 1.88 (0.67) | 2.23 | |||||||
OR, odds ratio; SD, sedentary behavior and diet quality; TO, tobacco; CA, cannabis; MI, social misery; TR, psychological trauma; BV, bivariate (unadjusted).
Table 1b.
Longitudinal psychosis studies (N = 16).
| Longitudinal: behavior before disorder (N = 9) | Covariate adjustments | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Cannabis (N = 5) | N | Area | Sample | Exposure | OR | SD | TO | MI | TR | BV |
| van Os et al. (199) | 6,615 | Int. | Adolescents and adults | >5 times | 7.03 | • | ||||
| Jones et al. (200) | 5,300 | UK | Adolescence->adulthood | Any | 3.12 | • | • | |||
| Bechtold et al. (201) | 908 | USA | Adolescent boys | Weekly | 1.52 | ø | ||||
| Degenhardt et al. (202) | 30,902 | Int. | Adults | Any | 1.00 | • | ||||
| Ryan et al. (203) | 11,755 | USA | Adults 18–34 | Before age 16 | 0.96 | • | • | • | ||
| Tobacco (N = 4) | SD | CA | MI | TR | BV | |||||
| Rognli et al. (204) | 2,602 | NOR | Adolescence->adulthood | Daily, low dep. | 3.79 | • | ||||
| Ryan et al. (203) | 11,755 | USA | Adults 18–34 | Before age 16 | 1.60 | • | • | • | ||
| Degenhardt et al. (202) | 30,902 | Int. | Adults | Any | 1.10 | • | ||||
| Jones et al. (200) | 5,300 | UK | Adolescence->adulthood | Any | 0.96 | • | • | • | ||
| Median | Unweighted mean (SD) | Weighted mean | ||||||||
| Cannabis | 1.52 | 2.73 (2.56) | 1.92 | |||||||
| Tobacco | 1.35 | 1.86 (1.31) | 1.34 | |||||||
| Longitudinal: disorder before behavior (N = 7) | Covariate adjustments | |||||||||
| Cannabis (N = 4) | N | Area | Sample | Outcome | OR | SD | TO | MI | TR | BV |
| Jones et al. (200) | 5,300 | UK | Adolescence->adulthood | Any use | 1.50 | • | • | |||
| Degenhardt et al. (202) | 30,902 | Int. | Adults | Any use | 1.30 | • | ||||
| van Os et al. (199) | 6,466 | Int. | Adolescents and adults | >5 times | 0.59 | • | ||||
| Davies et al. (171) | 2,602 | UK | Adolescence->adulthood | Problem use | 0.54 | • | ||||
| Tobacco (N = 3) | SD | CA | MI | TR | BV | |||||
| Jones et al. (200) | 5,300 | UK | Adolescence->adulthood | Any use | 1.43 | • | • | • | ||
| Davies et al. (171) | 2,627 | UK | Adolescence->adulthood | Weekly + | 1.11 | • | ||||
| Degenhardt et al. (202) | 30,902 | Int. | Adults | Any use | 1.10 | • | ||||
| Median | Unweighted mean (SD) | Weighted mean | ||||||||
| Cannabis | 0.95 | 0.98 (0.49) | 1.18 | |||||||
| Tobacco | 1.11 | 1.21 (0.19) | 1.15 | |||||||
OR, odds ratio; SD, sedentary behavior and diet quality; TO, tobacco; CA, cannabis; MI, social misery; TR, psychological trauma; BV, bivariate (unadjusted).
Depression
The present review included 208 cross-sectional and 43 longitudinal figures for the association between unhealthy behaviors and depression (Tables 2a, 2b). The cross-sectional studies provide convincing evidence for associations between depression and cannabis, tobacco, sedentary behavior, and diet quality. The longitudinal studies provide some evidence that sedentary behavior and use of cannabis and tobacco may lead to subsequent depression and that depression may lead to subsequent cannabis and tobacco use. There was very limited longitudinal evidence related to diet quality.
Table 2a.
Cross-sectional depression studies (N = 208).
| N | Area | Sample | Exposure | OR | Covariate adjustments | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| Cannabis (N = 43) | SD | TO | MI | TR | BV | |||||
| Metrik et al. (205) | 301 | USA | Male military veterans | Weekly 2+ | 4.88 | |||||
| Halladay et al. (206) | 43,466 | CAN | Persons 15+ | Weekly >1 | 3.91 | |||||
| Wahby et al. (207) | 302 | CAN | Patients 18+ | Current | 3.90 | • | ||||
| Davies et al. (171) | 2,475 | UK | Adults 18–20 | Problem | 3.55 | • | ||||
| Magklara et al. (153) | 2,427 | GRC | Adolescents | Any | 3.48 | |||||
| Risal et al. (208) | 2,100 | NPL | Adults 18–65 | Any | 3.10 | ø | ||||
| Porras-Segovia et al. (114) | 810 | ESP | Adults 18–80 | Any | 2.95 | • | ||||
| Hasin et al. (136) | 36,309 | USA | Adults 18+ | Disorder | 2.80 | • | ||||
| Hasin and Walsh (20) | 36,309 | USA | Adults 18+ | Disorder | 2.60 | • | ||||
| Padwa et al. (151) | 8,940 | USA | Adult patients and contr. | Any | 2.46 | • | ||||
| Gorfinkel et al. (209) | 16,216 | USA | Adults 20–59 | Daily or near | 2.29 | • | ||||
| Carrà et al. (210) | 527,446 | USA | Adults 18+ | Weekly to daily | 2.28 | |||||
| Cougle et al. (211) | 43,093 | USA | Adults | Weekly | 2.27 | • | ||||
| Livne et al. (169) | 36,309 | USA | Adults 18+ | Non-CUD | 2.25 | • | ||||
| Young-Wolff et al. (123) | 196,022 | USA | Pregnant women | During pregnancy | 2.25 | • | ||||
| Pacek et al. (212) | 728,691 | USA | Persons 12+ | Past 30 days | 2.17 | • | ||||
| Cougle et al. (213) | 43,093 | USA | Adults | Weekly | 2.03 | • | ||||
| Wu et al. (214) | 10,734 | USA | Patients 18+ | Disorder | 1.74 | • | ||||
| Hill et al. (215) | 3,157 | USA | Military veterans | Non-CUD | 1.65 | • | ||||
| Keith et al. (216) | 1,776 | USA | University students | Weekly 1–2 | 1.60 | |||||
| Mannes et al. (217) | 932 | USA | Adult patients | Past year | 1.50 | • | ||||
| Chadi et al. (218) | 26,821 | USA | Adolescents | Past 30 days | 1.49 | • | ||||
| Halladay et al. (219) | 43,466 | CAN | Persons 15–60 | Monthly + | 1.48 | |||||
| Butler et al. (220) | 6,550 | CAN | Adolescents | Any | 1.40 | • | • | |||
| Estévez et al. (221) | 5,677 | SWI | Male military conscripts | Weekly >1 | 1.35 | • | ||||
| Leventhal et al. (222) | 3,177 | USA | Adolescents | Past 30 days | 1.31 | • | • | |||
| Hines et al. (182) | 1,087 | UK | Adults | High potency | 1.28 | • | ||||
| Prestage et al. (223) | 3,017 | AUS | Gay and bisexual men | Past 6 months | 1.27 | ø | ||||
| Kerridge et al. (224) | 36,309 | USA | Adults 18+ | Disorder | 1.25 | • | • | |||
| Rubenstein et al. (225) | 262 | USA | African americans 18+ | Any | 1.22 | • | • | |||
| Chan et al. (183) | 181,870 | Int. | Adults 18+ | Potent herb | 1.18 | • | ||||
| Seaman et al. (226) | 2,555 | USA | Adults 21–30 | Past 30 days | 1.18 | • | • | |||
| Gukasyan and Strain (227) | 87,952 | USA | Adolescents 12–17 | Weekly + | 1.16 | • | ||||
| Wang and Peiper (228) | 13,526 | USA | High school students | Past 30 days | 1.15 | • | • | • | ||
| Hill et al. (229) | 4,069 | USA | Military veterans | Past 6 months | 1.12 | • | • | |||
| Campbell et al. (176) | 101,405 | USA | Patients 18+ | Disorder | 1.08 | • | • | |||
| Lekoubou et al. (230) | 400,391 | USA | Patients 18+ | Disorder | 1.07 | • | ||||
| Bonsaksen et al. (231) | 4,527 | NOR | Adults 18+ | Any | 1.06 | • | ||||
| Fink et al. (232) | 392 | USA | Adult patients | Mod. Disorder | 1.05 | • | ||||
| Thompson et al. (233) | 662 | CAN | Adolescence->adulthood | >1/week | 0.93 | • | • | |||
| Cannabis (N = 43) | SD | TO | MI | TR | BV | |||||
| Welsh et al. (234) | 483 | USA | Patients 11–24 | Disorder | 0.78 | |||||
| Tiburcio Sainz et al. (235) | 710 | MEX | University students | Mod-high risk | 0.73 | • | • | |||
| Gaete et al. (236) | 935 | CHL | Young offenders 14–23 | Any | 0.60 | • | • | • | ||
| Tobacco (N = 71) | SD | CA | MI | TR | BV | |||||
| Raffetti et al. (237) | 3,062 | SWE | Adolescents | Past 30 days | 3.90 | • | ||||
| Farrell et al. (238) | 4,961 | USA | Adults 18+ | E-cig. | 3.61 | • | ||||
| Klein et al. (239) | 11,785 | USA | Children 9–12 | Any | 3.37 | • | ||||
| Mossie et al. (128) | 590 | ETH | Adults 18+ | Past 30 days | 3.15 | • | ||||
| Shahwan et al. (134) | 6,126 | SGP | Adults 18+ | Dependence | 3.00 | |||||
| Albasara et al. (130) | 342 | SAU | Adult patients | Any | 2.99 | • | • | |||
| Mohammadi et al. (240) | 299 | AFG | Healthcare work. 18–64 | Current | 2.96 | • | ||||
| Conti et al. (241) | 220 | BRA | Men 18–65 | Any | 2.81 | • | ||||
| Kelishadi et al. (242) | 13,486 | IRN | Children 6–18 | Daily | 2.65 | • | ||||
| Bernard et al. (243) | 334 | Int. | Patients 50+ | Any | 2.60 | • | • | • | ||
| Wang et al. (244) | 10,349 | USA | Adults 20+ | >100 cig. | 2.56 | • | • | |||
| Rahe et al. (245) | 1,420 | GER | Patients and controls | Current | 2.39 | • | ||||
| Ma et al. (246) | 3,787 | SWE | Adolescents | Monthly | 2.34 | • | • | |||
| Islam et al. (129) | 600 | BGD | Older adults P and C | Current | 2.33 | • | • | |||
| Weinberger et al. (247) | 496,805 | USA | Persons 12+ | Non-daily | 2.22 | • | ||||
| Davies et al. (171) | 2,491 | UK | Adults 18–20 | Weekly + | 2.19 | • | ||||
| van Binnendijk et al. (248) | 22,471 | NLD | Adults 18–70 | Dependence | 2.18 | • | ||||
| Chido-Amajuoyi et al. (249) | 2,034 | USA | Adults 18+ | Past 30 days | 2.12 | • | ||||
| Magklara et al. (153) | 2,427 | GRC | Adolescents | Daily | 2.05 | |||||
| Ye et al. (250) | 1,280 | KOR | Women 40–60 | Secondhand | 2.04 | • | ||||
| Lee and Lee (251) | 62,276 | CHN | Adolescents | Past 30 days | 2.04 | • | ||||
| López-Sánchez et al. (133) | 4,157 | ESP | Adult patients | Current | 2.03 | • | • | |||
| Cougle et al. (211) | 43,093 | USA | Adults | Weekly | 2.01 | • | ||||
| Islam et al. (252) | 563 | BGD | Adolescents | Current | 2.00 | • | • | |||
| Nam et al. (131) | 4,145 | KOR | Adults 20+ | Current | 1.94 | • | • | |||
| Matcham et al. (253) | 7,878 | USA | Adult patients | Current | 1.93 | |||||
| Gorfinkel et al. (254) | 32,636 | UK | Adolescents | Past 30 days | 1.93 | • | • | |||
| Melin et al. (255) | 1,027 | SWE | Adult patients | Current | 1.90 | ø | ||||
| Chen et al. (256) | 17,837 | TWN | Persons 12–64 | Any | 1.75 | • | • | |||
| Wiernik et al. (257) | 35,337 | CHN | Adults 18–69 | Current e-cig | 1.73 | • | ||||
| Luo et al. (258) | 49,317 | FRA | Older adults | Current | 1.73 | ø | ||||
| Cougle et al. (213) | 43,093 | USA | Adults | Daily | 1.69 | • | ||||
| Formagini et al. (259) | 48,282 | BRA | Adults 18+ | Daily | 1.66 | • | ||||
| Patanavanich et al. (260) | 4,237 | THA | Adolescents | Any e-cig. | 1.66 | • | • | |||
| Masana et al. (261) | 2,718 | Int. | Older adults | Current | 1.60 | • | • | |||
| Bandiera et al. (262) | 5,438 | USA | University students | Past 30 days | 1.58 | |||||
| Prestage et al. (223) | 3,017 | AUS | Gay and bisexual men | Daily | 1.54 | • | • | |||
| Tobacco (N = 71) | SD | CA | MI | TR | BV | |||||
| Kim (263) | 3,700 | KOR | Older adults 65+ | Current | 1.53 | • | • | |||
| Kim et al. (264) | 366,405 | KOR | Adolescents | Any | 1.51 | • | • | |||
| Risal et al. (208) | 2,100 | NPL | Adults 18–65 | Any | 1.50 | ø | ||||
| Seaman et al. (226) | 2,555 | USA | Adults 21–30 | Past 30 days | 1.49 | • | • | |||
| Liu et al. (265) | 1,300 | USA | Adults 20–80 | Daily | 1.47 | • | • | |||
| Patten et al. (266) | 184,305 | CAN | Adults | Secondhand | 1.40 | • | ||||
| Porras-Segovia et al. (114) | 810 | ESP | Adults 18–80 | Dependence | 1.40 | ø | ||||
| Kelishadi et al. (242) | 13,486 | IRN | Children 6–18 | Secondhand | 1.37 | • | ||||
| Chadi et al. (218) | 26,821 | USA | Adolescents | E-cig. | 1.37 | |||||
| Werneck et al. (189) | 60,202 | BRA | Adult patients and contr. | Current | 1.37 | • | ||||
| Clyde et al. (267) | 1,614 | CAN | Adult patients | Daily | 1.34 | • | ||||
| Chen et al. (268) | 2,590 | TWN | Women 45–55 | Secondhand | 1.33 | • | ||||
| Liu et al. (269) | 5,965 | CHN | Men 40–79 | Current | 1.32 | |||||
| Okunna (135) | 277,034 | USA | Adults 18+ | >100 cig. | 1.30 | • | • | |||
| Salimi et al. (270) | 29,654 | USA | Pregnant women | Postpartum use | 1.28 | • | ||||
| Chou et al. (271) | 36,309 | USA | Adults 18+ | Disorder | 1.26 | • | ||||
| Peltzer and Pengpid (272) | 20,222 | Int. | University students | Current | 1.25 | • | • | • | ||
| Wang and Peiper (228) | 13,526 | USA | High school students | Past 30 days | 1.25 | • | • | • | ||
| Estévez et al. (221) | 5,677 | SWI | Male military conscripts | Daily | 1.24 | • | ||||
| Bourbon et al. (125) | 10,985 | FRA | University students | Daily | 1.24 | • | • | • | ||
| Sumbe et al. (273) | 2,439 | USA | Adolescents and adults | Past 30 days | 1.23 | • | • | |||
| Kastaun et al. (274) | 11,937 | GER | Adults 18+ | Current | 1.20 | • | ||||
| Ellis et al. (275) | 3,468 | USA | Adults | Any | 1.17 | • | ||||
| Tiburcio Sainz et al. (235) | 710 | MEX | University students | Mod-high risk | 1.17 | • | • | |||
| Pengpid and Peltzer (276) | 4,782 | ZAF | Adults 40+ | Current | 1.16 | • | • | |||
| Assari et al. (277) | 740 | USA | African Am. adults 55+ | Current | 1.13 | • | ||||
| Li et al. (278) | 1,504 | CHN | University students | Current | 1.06 | ø | ||||
| Sawchuk et al. (279) | 2,774 | USA | American Indians 15–54 | >100 cig. | 1.01 | • | ||||
| Chou et al. (280) | 36,309 | USA | Adults 18+ | Any e-cig. | 1.00 | • | • | |||
| Hruby et al. (281) | 12,708 | USA | Military service members | Past 30 days | 0.93 | • | • | • | ||
| Mannes et al. (217) | 932 | USA | Adult patients | Past 30 days | 0.86 | • | ||||
| Zhu et al. (282) | 4,043 | CHN | Adult patients | Current | 0.77 | • | • | |||
| Zolezzi et al. (188) | 196 | QAT | Patients 15+ | Current | 0.70 | ø | ||||
| Welsh et al. (234) | 483 | USA | Patients 11–24 | Disorder | 0.57 | |||||
| Sedentary behavior (N = 61) | TO | CA | MI | TR | BV | |||||
| Humer et al. (283) | 6,703 | AUT | Adolescents | Phone >8 h/day | 6.79 | |||||
| Xie et al. (284) | 2,134 | CHN | University students | Worst quartile | 5.76 | • | • | |||
| Zhu et al. (285) | 4,043 | CHN | Adult patients | Sedentary | 4.73 | • | • | |||
| Galán-Arroyo et al. (286) | 17,141 | ESP | Adults 18–69 | Sedentary | 4.32 | |||||
| Xu et al. (287) | 480 | CHN | University students | Worst half | 4.31 | • | ||||
| Sedentary behavior (N = 61) | TO | CA | MI | TR | BV | |||||
| Wang et al. (56) | 2,679 | CHN | Older adults 60+ | TV >3 h/day | 3.59 | • | • | |||
| Adamson et al. (288) | 3,045 | USA | Adults 18+ | Worst quartile | 3.47 | • | • | |||
| Luo et al. (258) | 49,317 | CHN | Older adults | >6 h/day | 3.16 | ø | ||||
| Pengpid and Peltzer (276) | 4,782 | ZAF | Adults 40+ | >11 h/day | 3.00 | • | • | |||
| Jiang et al. (289) | 28,298 | CHN | University students | Sedentary | 2.94 | • | • | |||
| Liu et al. (265) | 1,300 | USA | Adults 20–80 | Low PA | 2.76 | • | • | |||
| Albasara et al. (130) | 342 | SAU | Adult patients | Low PA | 2.65 | • | • | |||
| Kim et al. (196) | 6,510 | KOR | Adults 18–64 | Addiction | 2.65 | • | ||||
| Vadlin et al. (290) | 2,110 | SWE | Patients and controls | Problematic gaming | 2.47 | • | ||||
| Madhav et al. (132) | 3,201 | USA | Adults 20+ | scr. t. >6 h/day | 2.35 | • | ||||
| Alageel et al. (291) | 506 | Int. | University students | Addiction | 2.11 | • | ||||
| Werneck et al. (292) | 60,202 | BRA | Adults 18+ | TV >5 h/day | 2.10 | • | • | |||
| Vancampfort et al. (293) | 4,082 | Int. | Adult patients | >8 h/day | 1.99 | • | • | |||
| Yu et al. (294) | 18,994 | CHN | Adults | TV >10 h/day | 1.95 | • | • | |||
| Trinh et al. (295) | 2,660 | CAN | Adolescents | scr. t. >2 h/day | 1.92 | • | ||||
| Vancampfort et al. (296) | 6,903 | IRL | Adults 50+ | >8 h/day | 1.88 | • | • | |||
| Wu et al. (297) | 4,747 | CHN | University students | scr. t. >2 h/day | 1.86 | • | ||||
| da Costa et al. (298) | 293 | BRA | Older adults 60+ | >4.5 h/day | 1.81 | • | ||||
| Lazarevich et al. (299) | 615 | MEX | Female uni. Students | PA < 75 min/week | 1.80 | |||||
| Melin et al. (255) | 1,027 | SWE | Adult patients | PA < 1/week | 1.80 | ø | ||||
| Liu et al. (300) | 13,659 | CHN | Adolescents | Games >2 h/day | 1.78 | • | • | |||
| Jensen-Otsu and Austin (301) | 3,039 | USA | Adults 20–74 | Games >2 h/day | 1.77 | • | • | |||
| Zhu et al. (282) | 4,043 | CHN | Adult patients | Low PA | 1.74 | • | • | |||
| Cho et al. (302) | 15,146 | KOR | Older adults 60+ | Low PA | 1.73 | • | • | |||
| Zhang et al. (303) | 27,723 | CHN | University students | scr. t. >4 h/day | 1.72 | |||||
| Nam et al. (131) | 4,145 | KOR | Adults 20+ | >10 h/day | 1.71 | • | • | |||
| Rahe et al. (245) | 1,420 | GER | Patients and controls | Low PA | 1.71 | • | ||||
| da Costa et al. (304) | 610 | BRA | Adolescents | SM >4 h/day | 1.67 | • | ||||
| Silva et al. (305) | 88,509 | BRA | Adults 18+ | TV >6 h/day | 1.67 | • | ||||
| Lu et al. (306) | 965 | CHN | Adolescents | >4 h/day | 1.65 | • | ||||
| Wang and Peiper (228) | 13,526 | USA | High school students | scr. t. >3 h/day | 1.61 | • | • | • | ||
| Ma et al. (246) | 3,787 | SWE | Adolescents | PA < 1/month | 1.58 | • | • | |||
| Wu et al. (307) | 2,521 | CHN | University students | ST >2 h/day | 1.58 | • | • | |||
| Zhou et al. (308) | 584 | CHN | University students | scr. t. >1.4 h/day | 1.54 | • | • | |||
| Vancampfort et al. (309) | 67,077 | Int. | Adolescents | >8 h/day | 1.53 | • | ||||
| Hanna et al. (310) | 479 | QAT | University employees | >10 h/day | 1.41 | |||||
| Peltzer and Pengpid (272) | 20,222 | Int. | University students | Internet 6+ h/day | 1.40 | • | • | • | ||
| Schuch et al. (311) | 937 | BRA | Adults 18+ | >10 h/day | 1.40 | • | • | |||
| López-Sánchez et al. (133) | 4,157 | ESP | Adult patients | Inactivity | 1.37 | • | • | |||
| Kim and Han (312) | 54,603 | KOR | Adolescents | Phone >4 h/day | 1.37 | • | ||||
| Werneck et al. (189) | 60,202 | BRA | Adult pat. and contr. | TV >5 h/day | 1.34 | • | ||||
| Sedentary behavior (N = 61) | TO | CA | MI | TR | BV | |||||
| Apriliyasari et al. (313) | 3,234 | IDN | Adult patients | Low PA | 1.32 | • | • | |||
| Carriedo et al. (314) | 483 | ESP | Older adults 60–92 | Low PA | 1.29 | |||||
| Souza et al. (315) | 6,924 | BRA | Older adults 60+ | PA < 1/week | 1.28 | • | • | |||
| Liao et al. (316) | 2,914 | JPN | Adults 20–59 | SB >6 h/day | 1.25 | • | ||||
| Pengpid and Peltzer (317) | 72,262 | IND | Adults 45+ | Low PA | 1.24 | • | • | |||
| Masana et al. (261) | 2,718 | Int. | Older adults | Current | 1.23 | • | • | |||
| Kim (263) | 3,700 | KOR | Older adults 65+ | Low PA | 1.19 | • | • | |||
| Pengpid and Peltzer (318) | 3,201 | Int. | Adult patients | >8 h/day | 1.19 | • | • | |||
| Hruby et al. (281) | 12,708 | USA | Military service members | PA < 75 min/week | 1.17 | • | • | • | ||
| Taheri et al. (319) | 13,486 | IRN | Children 6–18 | scr. t. >2 h/day | 1.15 | • | • | |||
| Islam et al. (252) | 563 | BGD | Adolescents | Low PA | 1.10 | ø | ||||
| Zhang et al. (197) | 7,121 | CHN | Adults 18–81 | TV >3 h/day | 1.07 | • | • | |||
| Kim et al. (264) | 366,405 | KOR | Adolescents | Worst quartile | 1.07 | • | • | |||
| Draper et al. (320) | 1,719 | ZAF | Women 18–26 | Low PA | 0.84 | |||||
| Padmapriya et al. (321) | 863 | SGP | Pregnant women 18+ | TV >2 h/day | 0.75 | • | • | |||
| Diet quality (N = 33) | TO | CA | MI | TR | BV | |||||
| Sze et al. (152) | 424 | CHN | University students | Emotional eating | 28.19 | • | ||||
| ElBarazi and Tikamdas (322) | 509 | EGY | University students | Daily junk food | 7.90 | ø | ||||
| Khosravi et al. (323) | 330 | IRN | Adult patients and contr. | Worst quartile | 3.62 | • | ||||
| Islam et al. (129) | 600 | BGD | Older adults P and C | Poor diet | 3.44 | • | • | |||
| Gomes et al. (324) | 1,378 | BRA | Older adults 60+ | Worst tertile | 2.96 | • | • | |||
| Mutiso et al. (198) | 9,742 | KEN | High school students | Binge eating | 2.48 | • | • | • | ||
| Liu et al. (325) | 1,311 | CHN | Children 7–17 | Daily soda | 2.28 | • | ||||
| Lazarevich et al. (299) | 615 | MEX | Female uni. Students | Fast f. >1/week | 2.08 | |||||
| Hong and Peltzer (326) | 65,212 | KOR | Adolescents | Soda daily 3+ | 2.07 | • | • | |||
| Liu et al. (265) | 1,300 | USA | Adults 20–80 | Poor diet | 2.07 | • | • | |||
| Kim et al. (327) | 849 | KOR | Adolescent girls | Worst tertile | 2.03 | |||||
| Yu et al. (328) | 3,667 | CHN | Adults | Soda >3/w | 2.00 | • | • | |||
| Zhang et al. (303) | 27,723 | CHN | University students | Soda >4/day | 1.82 | |||||
| Liu et al. (329) | 906 | CHN | Postmenopaus. Women | Worst tertile | 1.79 | • | ||||
| Vicente et al. (330) | 406 | BRA | Adult patients | High soda | 1.73 | • | ||||
| Sangsefidi et al. (331) | 9,965 | IRN | Adults 20–70 | Fast food 1/week | 1.61 | • | • | |||
| Kim (263) | 3,700 | KOR | Older adults 65+ | Poor diet | 1.56 | • | • | |||
| Kim et al. (332) | 5,465 | KOR | Adults 20+ | Daily soda | 1.54 | • | • | |||
| Kang et al. (333) | 7,446 | KOR | Adults 18–65 | Worst quartile | 1.43 | • | • | |||
| Park et al. (334) | 65,528 | KOR | Adolescents | Fast f. >2/week | 1.42 | • | ||||
| Ra (335) | 24,006 | KOR | Adolescents | Worst tertile | 1.38 | • | • | • | ||
| Zheng et al. (336) | 13,637 | USA | Adults 20+ | Worst quartile | 1.34 | • | • | |||
| Werneck et al. (189) | 60,202 | BRA | Adult patients and contr. | Daily sweets | 1.34 | • | ||||
| Xia et al. (337) | 2,702 | CHN | Adult patients and contr. | Worst quartile | 1.33 | • | • | |||
| Diet quality (N = 33) | TO | CA | MI | TR | BV | |||||
| Tran et al. (338) | 3,670 | FRA | University students | Junk food | 1.30 | • | • | • | ||
| Miller et al. (339) | 3,430 | AUS | Adults 18+ | Daily soda | 1.30 | • | ||||
| Nouri Saeidlou et al. (340) | 510 | IRN | Adult female P and C | Western diet | 1.29 | • | • | |||
| Yim et al. (341) | 187,622 | KOR | Adolescents | Fast food 1/week | 1.20 | • | ||||
| Wang et al. (244) | 10,349 | USA | Adults 20+ | Poor diet | 1.18 | • | • | |||
| Knüppel et al. (342) | 8,087 | UK | Civil servants 35–55 | Worst tertile | 1.08 | • | • | |||
| Hall et al. (343) | 444 | MEX | University students | Worst half | 0.99 | • | ||||
| Rahe et al. (245) | 1,420 | GER | Patients and controls | Worst half | 0.98 | • | ||||
| Hosseinzadeh et al. (344) | 3,846 | IRN | Adults 20–55 | Worst quintile | 0.94 | • | • | |||
| Median | Unweighted mean (SD) | Weighted mean | ||||||||
| Cannabis | 1.49 | 1.88 (0.99) | 1.89 | |||||||
| Tobacco | 1.58 | 1.77 (0.70) | 1.70 | |||||||
| Sedentary behavior | 1.71 | 2.04 (1.16) | 1.62 | |||||||
| Diet quality | 1.56 | 2.72 (4.74) | 1.49 | |||||||
OR, odds ratio; SD, sedentary behavior and diet quality; TO, tobacco; CA, cannabis; MI, social misery; TR, psychological trauma; BV, bivariate (unadjusted); PA, physical activity; SB, sedentary behavior; SM, social media.
Table 2b.
Longitudinal depression studies (N = 43).
| Longitudinal: behavior before disorder (N = 30) | Covariate adjustments | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Cannabis (N = 7) | N | Area | Sample | Exposure | OR | SD | TO | MI | TR | BV |
| Matta et al. (150) | 37,192 | FRA | Adults 18–69 | Weekly + | 1.73 | |||||
| Hengartner et al. (345) | 591 | SWI | Adolescence->adulthood | Adolescence | 1.72 | • | ||||
| Agrawal et al. (346) | 13,986 | AUS | Twin pairs | >100 times | 1.53 | • | • | |||
| Cougle et al. (211) | 34,653 | USA | Adults | Weekly | 1.07 | • | ||||
| Copeland et al. (141) | 1,420 | USA | Childhood->adulthood | Cumul. daily | 1.00 | • | • | • | ||
| Blanco et al. (347) | 34,653 | USA | Adults 18+ | Past year | 0.90 | • | • | • | ||
| Feingold et al. (348) | 34,653 | USA | Adults 18+ | Weekly + | 0.67 | • | • | |||
| Tobacco (N = 12) | SD | CA | MI | TR | BV | |||||
| Bakhshaie et al. (349) | 2,101 | USA | Adults | Daily | 2.30 | • | ||||
| Raffetti et al. (237) | 3,139 | SWE | Adolescents | Past 30 days | 2.00 | • | ||||
| Rognli et al. (204) | 2,602 | NOR | Adolescence->adulthood | Low dep. | 1.91 | • | ||||
| Zhang et al. (350) | 1,196 | GER | Women 18–25 | Current | 1.55 | • | ||||
| Tsutsumimoto et al. (351) | 3,066 | JPN | Older adults 65+ | Current | 1.52 | • | • | |||
| Matta et al. (150) | 37,192 | FRA | Adults 18–69 | 1–19/day | 1.48 | |||||
| Cabello et al. (352) | 7,908 | Int. | Adults 18–49 and 50+ | Daily | 1.46 | • | • | |||
| Clyde et al. (267) | 1,614 | CAN | Adult patients | Daily | 1.33 | • | ||||
| Cougle et al. (211) | 34,653 | USA | Adults | Weekly | 1.31 | • | ||||
| Song et al. (353) | 8,842 | CHN | Pregnant women | Secondhand | 1.24 | • | ||||
| Song et al. (353) | 8,842 | CHN | Pregnant women | Daily | 1.20 | • | ||||
| Bolstad et al. (354) | 7,660 | FIN | Adolescence->adulthood | Daily | 1.07 | • | • | |||
| Sedentary behavior (N = 10) | TO | CA | MI | TR | BV | |||||
| Grøntved et al. (355) | 435 | DNK | Adolescence->adulthood | ST >3 h/day | 3.46 | • | • | |||
| Xie et al. (284) | 2,134 | CHN | University students | Worst quartile | 3.20 | • | • | |||
| Zhang et al. (350) | 1,196 | GER | Women 18–25 | PA < 1/mon. | 2.08 | • | ||||
| Wu et al. (307) | 2,521 | CHN | University students | ST increase | 1.77 | • | • | |||
| Sui et al. (356) | 4,802 | USA | Patients 18–80 | Worst tertile | 1.74 | • | • | |||
| Pavey and Brown (357) | 6,205 | AUS | Adult women | >10 h/day | 1.72 | • | • | |||
| Tsutsumimoto et al. (351) | 3,066 | JPN | Older adults 65+ | >8 h/day | 1.64 | • | • | |||
| Cabello et al. (352) | 7,908 | Int. | Adults 18–49 and 50+ | low PA | 1.23 | • | • | |||
| Zink et al. (358) | 2,525 | USA | High school students | >4 h/day | 1.23 | • | ||||
| Vancampfort et al. (296) | 5,483 | IRL | Adults 50+ | >8 h/day | 1.06 | • | • | |||
| Diet quality (N = 1) | TO | CA | MI | TR | BV | |||||
| Knüppel et al. (342) | 8,087 | UK | Civil servants 35–55 | Worst tertile | 1.47 | • | • | |||
| Median | Unweighted mean (SD) | Weighted mean | ||||||||
| Cannabis | 1.07 | 1.23 (0.42) | 1.14 | |||||||
| Tobacco | 1.47 | 1.53 (0.36) | 1.40 | |||||||
| Sedentary behavior | 1.73 | 1.91 (0.81) | 1.60 | |||||||
| Diet quality | 1.47 | 1.47 | 1.47 | |||||||
| Cannabis (N = 6) | N | Area | Sample | Outcome | OR | SD | TO | MI | TR | BV |
| Bolanis et al. (359) | 1,606 | CAN | Adolescents | Weekly | 2.30 | • | ||||
| Davies et al. (171) | 2,602 | UK | Adolescence->adulthood | Problem | 2.00 | • | ||||
| Feingold et al. (348) | 34,653 | USA | Adults 18+ | Any use | 1.72 | • | • | |||
| Cougle et al. (213) | 34,653 | USA | Adults | Dependence | 1.33 | • | ||||
| Bierhoff et al. (360) | 2,397 | USA | University students | Past 30 days | 1.02 | • | ||||
| Stapinski et al. (361) | 1,602 | CHL | Adolescents | Any use | 0.98 | • | • | |||
| Tobacco (N = 5) | SD | CA | MI | TR | BV | |||||
| Wiernik et al. (257) | 30,818 | FRA | Adults 18–69 | Curr. E-cig | 2.02 | • | ||||
| Davies et al. (171) | 2,627 | UK | Adolescence->adulthood | Weekly + | 1.23 | • | ||||
| Bierhoff et al. (360) | 2,397 | USA | University students | Past 30 days | 1.05 | • | ||||
| Lechner et al. (362) | 2,460 | USA | Adolescents | Any | 1.02 | • | ||||
| Cougle et al. (213) | 34,653 | USA | Adults | Dependence | 0.80 | • | ||||
| Sedentary behavior (N = 1) | TO | CA | MI | TR | BV | |||||
| Zink et al. (358) | 2,525 | USA | High school students | >4 h/day | 1.36 | • | ||||
| Diet quality (N = 1) | TO | CA | MI | TR | BV | |||||
| Knüppel et al. (342) | 8,087 | UK | Civil servants 35–55 | Increase | 0.98 | • | • | |||
| Median | Unweighted mean (SD) | Weighted mean | ||||||||
| Cannabis | 1.53 | 1.56 (0.54) | 1.53 | |||||||
| Tobacco | 1.05 | 1.22 (0.47) | 1.35 | |||||||
| Sedentary behavior | 1.36 | 1.36 | 1.36 | |||||||
| Diet quality | 0.98 | 0.98 | 0.98 | |||||||
OR, odds ratio; SD, sedentary behavior and diet quality; TO, tobacco; CA, cannabis; MI, social misery; TR, psychological trauma; BV, bivariate (unadjusted); PA, physical activity; SB, sedentary behavior; ST, screen time.
Anxiety
The review included 93 cross-sectional and 19 longitudinal figures for the association between unhealthy behaviors and anxiety (Tables 3a, 3b). The cross-sectional studies provide convincing evidence for associations between anxiety and cannabis, tobacco, sedentary behavior, and diet quality. The longitudinal studies provide some evidence that sedentary behavior and use of cannabis and tobacco may lead to subsequent anxiety and tenuous evidence that anxiety may lead to subsequent sedentary behavior and cannabis and tobacco use. No longitudinal evidence related to diet quality was included in this review.
Table 3a.
Cross-sectional anxiety studies (N = 93).
| N | Area | Sample | Exposure | OR | Covariate adjustments | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Cannabis (N = 23) | SD | TO | MI | TR | BV | ||||||
| Hasin et al. (136) | 36,309 | USA | Adults 18+ | Disorder | 3.70 | • | |||||
| Hasin and Walsh (20) | 36,309 | USA | Adults 18+ | Lifetime disorder | 3.20 | • | |||||
| Padwa et al. (151) | 8,940 | USA | Adult patients and contr. | Any | 2.74 | • | |||||
| Keith et al. (216) | 1,776 | USA | University students | Weekly 1–2 | 2.30 | ||||||
| Cougle et al. (211) | 43,093 | USA | Adults | Weekly | 2.20 | • | |||||
| Hines et al. (182) | 1,087 | UK | Adults | High-potency | 1.92 | • | |||||
| Young-Wolff et al. (123) | 196,022 | USA | Pregnant women | During pregnancy | 1.90 | • | |||||
| Cougle et al. (213) | 43,093 | USA | Adults | Weekly | 1.89 | • | |||||
| Hill et al. (215) | 3,157 | USA | Military veterans | Non-CUD | 1.52 | • | |||||
| López-Gil et al. (363) | 14,516 | ARG | Adolescents | Monthly | 1.46 | • | • | • | |||
| Mannes et al. (217) | 932 | USA | Adult patients | Past year | 1.28 | • | |||||
| Kerridge et al. (224) | 36,309 | USA | Adults 18+ | Disorder | 1.25 | • | • | ||||
| Hill et al. (229) | 4,069 | USA | Military veterans | Past 6 months | 1.23 | • | • | ||||
| Butler et al. (220) | 6,550 | CAN | Adolescents | Any | 1.20 | • | • | ||||
| Campbell et al. (176) | 101,405 | USA | Patients 18+ | Disorder | 1.16 | • | • | ||||
| Prestage et al. (223) | 3,017 | AUS | Gay and bisexual men | Past 6 months | 1.16 | ø | |||||
| Lekoubou et al. (230) | 400,391 | USA | Patients 18+ | Disorder | 1.14 | • | |||||
| Welsh et al. (234) | 483 | USA | Patients 11–24 | Disorder | 1.12 | ||||||
| Gaete et al. (236) | 935 | CHL | Young offenders 14–23 | Any | 1.09 | • | • | • | |||
| Chan et al. (183) | 181,870 | Int. | Adults 18+ | Potent herb | 1.05 | • | |||||
| Bonsaksen et al. (231) | 4,527 | NOR | Adults 18+ | Any | 0.96 | • | |||||
| Thompson et al. (233) | 662 | CAN | Adolescence->adulthood | >1/week | 0.94 | • | • | ||||
| Risal et al. (208) | 2,100 | NPL | Adults 18–65 | Any | 0.90 | ø | |||||
| Tobacco (N = 28) | SD | CA | MI | TR | BV | ||||||
| Klein et al. (239) | 11,785 | USA | Children 9–12 | Any | 2.97 | • | |||||
| Hajure et al. (364) | 411 | ETH | Adult patients | Current | 2.27 | • | |||||
| Nakie et al. (365) | 810 | ETH | High school students | Mod. risk | 2.03 | • | |||||
| Cougle et al. (213) | 43,093 | USA | Adults | Daily | 1.99 | • | |||||
| Peltzer and Pengpid (366) | 11,124 | IDN | Adolescents | Past 30 days | 1.95 | • | • | • | |||
| Sumbe et al. (273) | 2,439 | USA | Adolescents and adults | Past 30 days | 1.90 | • | • | ||||
| López-Sánchez et al. (133) | 4,157 | ESP | Adult patients | Current | 1.76 | • | • | ||||
| Prestage et al. (223) | 3,017 | AUS | Gay and bisexual men | Daily | 1.73 | • | • | ||||
| Mannes et al. (217) | 932 | USA | Adult patients | Past 30 days | 1.71 | • | |||||
| Kelishadi et al. (242) | 13,486 | IRN | Children 6–18 | Daily | 1.70 | • | |||||
| Hruby et al. (281) | 12,708 | USA | Military service members | Past 30 days | 1.70 | • | • | • | |||
| Cougle et al. (211) | 43,093 | USA | Adults | Weekly | 1.69 | • | |||||
| Islam et al. (252) | 563 | BGD | Adolescents | Current | 1.67 | • | • | ||||
| López-Gil et al. (363) | 16,872 | ARG | Adolescents | Monthly | 1.59 | • | • | • | |||
| Matcham et al. (253) | 7,878 | UK | Adult patients | Current | 1.44 | ||||||
| Bourbon et al. (125) | 10,985 | FRA | University students | Daily | 1.40 | • | • | • | |||
| Tobacco (N = 28) | SD | CA | MI | TR | BV | ||||||
| Kelishadi et al. (242) | 13,486 | IRN | Children 6–18 | Secondhand | 1.28 | • | |||||
| Kastaun et al. (274) | 11,937 | GER | Adults 18+ | Current | 1.22 | • | |||||
| Parker et al. (367) | 36,309 | USA | Adults | Past year | 1.20 | • | • | ||||
| Ellis et al. (275) | 3,468 | USA | Adults | Any | 1.18 | • | |||||
| Chou et al. (271) | 36,309 | USA | Adults 18+ | Disorder | 1.15 | • | |||||
| Abbasi-Ghahramanloo et al. (368) | 2,434 | IRN | Male workers | Current | 1.13 | • | • | ||||
| Li et al. (278) | 1,504 | CHN | University students | Current | 1.12 | ø | |||||
| Asfaw et al. (369) | 523 | ETH | University students | Current | 1.10 | ||||||
| Shahwan et al. (134) | 6,126 | SGP | Adults 18+ | Dependence | 1.00 | ||||||
| Chou et al. (280) | 36,309 | USA | Adults 18+ | Lifetime e-cig. | 1.00 | • | • | ||||
| Risal et al. (208) | 2,100 | NPL | Adults 18–65 | Any | 0.60 | ø | |||||
| Welsh et al. (234) | 483 | USA | Patients 11–24 | Disorder | 0.28 | ||||||
| Sedentary behavior (N = 29) | TO | CA | MI | TR | BV | ||||||
| Chen et al. (370) | 1,331 | CHN | Adolescents | Games >6 h/day | 5.25 | • | |||||
| Humer et al. (283) | 6,703 | AUT | Adolescents | Phone >8 h/day | 3.96 | ||||||
| Jiang et al. (289) | 28,298 | CHN | University students | Sedentary | 3.57 | • | • | ||||
| Wen et al. (371) | 900 | CHN | High school students | Scr. T. >2 h/day | 2.56 | • | |||||
| Vancampfort et al. (372) | 181,093 | Int. | Adolescents | >8 h/day | 2.27 | • | |||||
| Bu et al. (373) | 1,846 | CHN | University students | Low pa | 2.20 | • | |||||
| Kim et al. (196) | 6,510 | KOR | Adults 18–64 | Addiction | 2.19 | • | |||||
| Vadlin et al. (290) | 2,110 | SWE | Patients and controls | Problematic gaming | 2.06 | • | |||||
| Vancampfort et al. (374) | 42,469 | Int. | Adults 18+ | >8 h/day | 2.04 | • | • | ||||
| Kang et al. (375) | 1,204 | KOR | Older adults 65+ | Low PA | 1.77 | • | |||||
| Liu et al. (300) | 13,659 | CHN | Adolescents | Games >2 h/day | 1.59 | • | • | ||||
| Draper et al. (320) | 1,719 | ZAF | Women 18–26 | Low PA | 1.54 | ||||||
| Wu et al. (297) | 4,747 | CHN | University students | Scr. t. >2 h/day | 1.49 | • | |||||
| Lu et al. (306) | 965 | CHN | Adolescents | >4 h/day | 1.47 | • | |||||
| Vancampfort et al. (293) | 4,082 | Int. | Adult patients | >8 h/day | 1.41 | • | • | ||||
| Zhang et al. (303) | 27,723 | CHN | University students | scr. t. >4 h/day | 1.39 | ||||||
| López-Gil et al. (363) | 31,388 | ARG | Adolescents | >3 h/day | 1.33 | • | • | • | |||
| Islam et al. (252) | 563 | BGD | Adolescents | Low PA | 1.31 | ø | |||||
| Taheri et al. (319) | 13,486 | IRN | Children 6–18 | scr. t. >2 h/day | 1.28 | • | • | ||||
| López-Sánchez et al. (133) | 4,157 | ESP | Adult patients | Inactivity | 1.26 | • | • | ||||
| Wu et al. (307) | 2,521 | CHN | University students | scr. t. >2 h/day | 1.25 | • | • | ||||
| Wang et al. (376) | 59,587 | Int. | Adolescents | >2 h/day | 1.22 | • | • | ||||
| Hruby et al. (281) | 12,708 | USA | Military service members | PA < 75 min/week | 1.21 | • | • | • | |||
| Padmapriya et al. (321) | 863 | SGP | Pregnant women 18+ | TV >2 h/day | 1.21 | • | • | ||||
| Pengpid and Peltzer (318) | 3,201 | Int. | Adult patients | >8 h/day | 1.17 | • | • | ||||
| Schuch et al. (311) | 937 | BRA | Adults 18+ | >10 h/day | 1.17 | • | • | ||||
| Werneck et al. (377) | 99,791 | BRA | Adolescents | TV >4 h/day | 1.16 | • | |||||
| McDowell et al. (69) | 3,165 | IRL | Adults 50+ | Low PA | 1.06 | • | • | ||||
| Song and Lee (378) | 53,510 | KOR | Adolescents | Low PA | 1.06 | • | • | • | |||
| Diet quality (N = 13) | TO | CA | MI | TR | BV | ||||||
| ElBarazi and Tikamd (322) | 509 | EGY | University students | Daily junk food | 11.10 | ø | |||||
| Khan and Uddin (379) | 2,742 | BGD | Adolescents | Fast fo. 3+ day/week | 2.64 | • | • | ||||
| Hall et al. (343) | 444 | MEX | University students | Worst half | 2.35 | • | |||||
| Zhang et al. (303) | 27,723 | CHN | University students | Soda >4/day | 2.06 | ||||||
| Werneck et al. (380) | 100,648 | BRA | Adolescents | Daily fast food | 1.96 | ||||||
| Mutiso et al. (198) | 9,742 | KEN | High school students | Binge eating | 1.68 | • | • | • | |||
| Kaufman-Shriqui et al. (381) | 3,797 | Int. | Adults 18+ | Worsening diet | 1.61 | • | • | ||||
| Werneck et al. (377) | 99,791 | BRA | Adolescents | Daily poor | 1.47 | • | |||||
| López-Gil et al. (363) | 16,872 | ARG | Adolescents | Fast food 1/week | 1.24 | • | • | • | |||
| Sangsefidi et al. (331) | 9,965 | IRN | Adults 20–70 | Fast food 1/week | 1.19 | • | • | ||||
| Liu et al. (325) | 1,311 | CHN | Children 7–17 | Daily soda | 1.10 | • | |||||
| Sze et al. (152) | 424 | CHN | University students | Emotional eating | 0.99 | • | |||||
| Hosseinzadeh et al. (344) | 3,846 | IRN | Adults 20–55 | Worst quintile | 0.86 | • | • | ||||
| Median | Unweighted mean (SD) | Weighted mean | |||||||||
| Cannabis | 1.25 | 1.62 (0.76) | 1.50 | ||||||||
| Tobacco | 1.52 | 1.49 (0.53) | 1.51 | ||||||||
| Sedentary behavior | 1.41 | 1.81 (0.97) | 1.76 | ||||||||
| Diet quality | 1.61 | 2.33 (2.69) | 1.71 | ||||||||
OR, odds ratio; SD, sedentary behavior and diet quality; TO, tobacco; CA, cannabis; MI, social misery; TR, psychological trauma; BV, bivariate (unadjusted); PA, physical activity.
Table 3b.
Longitudinal anxiety studies (N = 19).
| Longitudinal: Behavior before disorder (N = 11) | Covariate adjustments | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Cannabis (N = 6) | N | Area | Sample | Exposure | OR | SD | TO | MI | TR | BV |
| Green et al. (382) | 330 | USA | Urban blacks | Before age 16 | 2.12 | • | • | • | ||
| Feingold et al. (383) | 34,653 | USA | Adults | Weekly + | 1.50 | • | • | |||
| Copeland et al. (141) | 1,420 | USA | Childhood->adulthood | Cumul. daily | 1.20 | • | • | • | ||
| Hengartner et al. (345) | 591 | SWI | Adolescence->adulthood | Adolescence | 1.12 | • | ||||
| Cougle et al. (211) | 34,653 | USA | Adults | Weekly | 1.09 | • | ||||
| Blanco et al. (347) | 34,653 | USA | Adults 18+ | Past year | 1.00 | • | • | • | ||
| Tobacco (N = 2) | SD | CA | MI | TR | BV | |||||
| Rognli et al. (204) | 2,602 | NOR | Adolescence->adulthood | Low dep. | 1.96 | • | ||||
| Cougle et al. (211) | 34,653 | USA | Adults | Weekly | 1.40 | • | ||||
| Sedentary behavior (N = 3) | TO | CA | MI | TR | BV | |||||
| Wu et al. (307) | 2,521 | CHN | University students | ST increase | 1.98 | • | • | |||
| Zink et al. (358) | 2,525 | USA | High school students | >4 h/day | 1.54 | • | ||||
| McDowell et al. (69) | 3,165 | IRL | Adults 50+ | Low PA | 1.06 | • | • | |||
| Median | Unweighted mean (SD) | Weighted mean | ||||||||
| Cannabis | 1.16 | 1.34 (0.42) | 1.20 | |||||||
| Tobacco | 1.68 | 1.68 (0.40) | 1.44 | |||||||
| Sedentary behavior | 1.54 | 1.53 (0.46) | 1.49 | |||||||
| Longitudinal: disorder before behavior (N = 8) | Covariate adjustments | |||||||||
| Cannabis (N = 5) | N | Area | Sample | Outcome | OR | SD | TO | MI | TR | BV |
| Tran et al. (384) | 1,230 | USA | Military veterans | Past 30 days | 6.53 | • | ||||
| Stapinski et al. (361) | 1,602 | CHL | Adolescents | Any use | 1.25 | • | • | |||
| Bierhoff et al. (360) | 2,397 | USA | University students | Past 30 days | 1.01 | • | ||||
| Cougle et al. (213) | 34,653 | USA | Adults | Dependence | 0.75 | • | ||||
| Feingold et al. (383) | 34,653 | USA | Adults | Any use | 0.49 | • | • | |||
| Tobacco (N = 2) | SD | CA | MI | TR | BV | |||||
| Cougle et al. (213) | 34,653 | USA | Adults | Dependence | 1.17 | • | ||||
| Bierhoff et al. (360) | 2,397 | USA | University students | Past 30 days | 1.02 | • | ||||
| Sedentary behavior (N = 1) | TO | CA | MI | TR | BV | |||||
| Zink et al. (358) | 2,525 | USA | High school students | Gaming >4 h/day | 1.36 | • | ||||
| Median | Unweighted mean (SD) | Weighted mean | ||||||||
| Cannabis | 1.01 | 2.01 (2.54) | 0.74 | |||||||
| Tobacco | 1.10 | 1.10 (0.11) | 1.16 | |||||||
| Sedentary behavior | 1.36 | 1.36 | 1.36 | |||||||
OR, odds ratio; SD, sedentary behavior and diet quality; TO, tobacco; CA, cannabis; MI, social misery; TR, psychological trauma; BV, bivariate (unadjusted); PA, physical activity.
Bipolar disorder
This review confirms that there is limited research on the association between unhealthy behaviors and bipolar disorder. A total of 25 cross-sectional and 10 longitudinal figures were included in the review (Tables 4a, 4b), many of which were based on the same NESARC dataset. The cross-sectional studies provide convincing evidence for associations between bipolar disorder and cannabis and tobacco use and tenuous evidence for associations with sedentary behavior and diet quality. The longitudinal studies provide some evidence that the use of cannabis and tobacco may lead to subsequent bipolar disorder, but no evidence that bipolar disorder may lead to subsequent cannabis and tobacco use. No longitudinal evidence related to sedentary behavior and diet quality was included in this review.
Table 4a.
Cross-sectional bipolar studies (N = 25).
| N | Area | Sample | Exposure | OR | Covariate adjustments | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Cannabis (N = 9) | SD | TO | MI | TR | BV | ||||||
| Hasin et al. (136) | 36,309 | USA | Adults 18+ | Disorder | 3.85 | • | |||||
| Hasin and Walsh (20) | 36,309 | USA | Adults 18+ | Lifetime disorder | 3.30 | • | |||||
| Cougle et al. (211) | 43,093 | USA | Adults | Weekly | 2.62 | • | |||||
| Padwa et al. (151) | 8,940 | USA | Adult patients and contr. | Any | 2.54 | • | |||||
| Patel et al. (385) | 380,265 | USA | Adult patients | Disorder | 1.53 | • | |||||
| Lekoubou et al. (230) | 400,391 | USA | Patients 18+ | Disorder | 1.45 | • | |||||
| Kerridge et al. (224) | 36,309 | USA | Adults 18+ | Disorder | 1.42 | • | • | ||||
| Cougle et al. (213) | 43,093 | USA | Adults | Weekly | 1.27 | • | |||||
| Campbell et al. (176) | 101,405 | USA | Patients 18+ | Disorder | 1.16 | • | • | ||||
| Tobacco (N = 13) | SD | CA | MI | TR | BV | ||||||
| Shahwan et al. (134) | 6,126 | SGP | Adults 18+ | Dependence | 3.70 | ||||||
| Molla et al. (186) | 422 | ETH | Adult patients | Dependence | 2.76 | • | |||||
| Li et al. (51) | 1,102 | CHN | Adult patients | Daily | 2.50 | • | |||||
| Dickerson et al. (187) | 1,938 | USA | Adult patients and contr. | Current | 2.18 | • | |||||
| Cougle et al. (213) | 43,093 | USA | Adults | Daily | 2.10 | • | |||||
| Cougle et al. (211) | 43,093 | USA | Adults | Weekly | 1.82 | • | |||||
| Yeh et al. (386) | 2,799 | TWN | Adolescents | Regular | 1.59 | • | |||||
| Bourbon et al. (125) | 10,985 | FRA | University students | Daily | 1.50 | • | • | • | |||
| Chou et al. (271) | 36,309 | USA | Adults 18+ | Disorder | 1.49 | • | |||||
| Chou et al. (280) | 36,309 | USA | Adults 18+ | Lifetime e-cig. | 1.30 | • | • | ||||
| Patel et al. (385) | 380,265 | USA | Adult patients | Disorder | 0.92 | • | |||||
| Werneck et al. (189) | 60,202 | BRA | Adult patients and contr. | Current | 0.89 | • | |||||
| Zolezzi et al. (188) | 196 | QAT | Patients 15+ | Current | 0.54 | ø | |||||
| Sedentary behavior (N = 2) | TO | CA | MI | TR | BV | ||||||
| Kim et al. (196) | 6,510 | KOR | Adults 18–64 | Addiction | 3.12 | • | |||||
| Werneck et al. (189) | 60,202 | BRA | Adult patients and contr. | TV >5 h/day | 0.41 | • | |||||
| Diet quality (N = 1) | TO | CA | MI | TR | BV | ||||||
| Werneck et al. (189) | 60,202 | BRA | Adult patients and contr. | Daily sweets | 2.20 | • | |||||
| Median | Unweighted mean (SD) | Weighted mean | |||||||||
| Cannabis | 1.53 | 2.13 (0.98) | 1.64 | ||||||||
| Tobacco | 1.59 | 1.79 (0.86) | 1.16 | ||||||||
| Sedentary behavior | 1.77 | 1.77 (1.92) | 0.67 | ||||||||
| Diet quality | 2.20 | 2.20 | 2.20 | ||||||||
OR, odds ratio; SD, sedentary behavior and diet quality; TO, tobacco; CA, cannabis; MI, social misery; TR, psychological trauma; BV, bivariate (unadjusted).
Table 4b.
Longitudinal bipolar studies (N = 10).
| Longitudinal: behavior before disorder (N = 7) | Covariate adjustments | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Cannabis (N = 4) | N | Area | Sample | Exposure | OR | SD | TO | MI | TR | BV |
| Feingold et al. (348) | 34,653 | USA | Adults 18+ | Weekly + | 2.47 | • | • | |||
| Marwaha et al. (387) | 3,370 | UK | Adolescence->adulthood | Weekly 2+ | 2.21 | • | • | |||
| Cougle et al. (211) | 34,653 | USA | Adults | Weekly | 1.37 | • | ||||
| Blanco et al. (347) | 34,653 | USA | Adults 18+ | Past year | 1.30 | • | • | • | ||
| Tobacco (N = 3) | SD | CA | MI | TR | BV | |||||
| Rognli et al. (204) | 2,602 | NOR | Adolescence->adulthood | Daily, low dep. | 4.21 | • | ||||
| Bolstad et al. (354) | 7,660 | FIN | Adolescence->adulthood | Daily | 1.83 | • | • | |||
| Cougle et al. (211) | 34,653 | USA | Adults | Weekly | 1.53 | • | ||||
| Median | Unweighted mean (SD) | Weighted mean | ||||||||
| Cannabis | 1.79 | 1.84 (0.59) | 1.73 | |||||||
| Tobacco | 1.83 | 2.52 (1.47) | 1.74 | |||||||
| Longitudinal: disorder before behavior (N = 3) | Covariate adjustments | |||||||||
| Cannabis (N = 2) | N | Area | Sample | Outcome | OR | SD | TO | MI | TR | BV |
| Feingold et al. (348) | 34,653 | USA | Adults 18+ | Any use | 0.61 | • | • | |||
| Cougle et al. (213) | 34,653 | USA | Adults | Dependence | 0.45 | • | ||||
| Tobacco (N = 1) | SD | CA | MI | TR | BV | |||||
| Cougle et al. (213) | 34,653 | USA | Adults | Dependence | 1.14 | • | ||||
| Median | Unweighted mean (SD) | Weighted mean | ||||||||
| Cannabis | 0.53 | 0.53 (0.11) | 0.53 | |||||||
| Tobacco | 1.14 | 1.14 | 1.14 | |||||||
OR, odds ratio; SD, sedentary behavior and diet quality; TO, tobacco; CA, cannabis; MI, social misery; TR, psychological trauma; BV, bivariate (unadjusted).
Personality disorders
The findings of this review confirm that the association between unhealthy behaviors and personality disorders has not been intensively studied. A total of 16 cross-sectional and three longitudinal figures were included in the review (Tables 5a, 5b), many of which were based on the same NESARC dataset. The cross-sectional studies provide convincing evidence for associations between personality disorder and cannabis and tobacco use, but no evidence related to sedentary behavior and diet quality was included in this review. Furthermore, no evidence indicating that unhealthy behaviors may lead to subsequent personality disorder and no evidence that personality disorder may lead to subsequent unhealthy behavior was included in this review.
Table 5a.
Cross-sectional personality disorder studies (N = 16).
| N | Area | Sample | Exposure | OR | Covariate adjustments | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Cannabis (N = 9) | SD | TO | MI | TR | BV | ||||||
| Hasin et al. (136) | 36,309 | USA | Adults 18+ | Disorder | 4.80 | • | |||||
| Hasin and Walsh (20) | 36,309 | USA | Adults 18+ | Lifetime disorder | 4.70 | • | |||||
| Jemal et al. (388) | 401 | ETH | Adult patients | Current | 4.38 | • | |||||
| Estévez et al. (221) | 5,677 | SWI | Male military conscripts | Weekly >1 | 4.00 | • | |||||
| Cougle et al. (213) | 43,093 | USA | Adults | Weekly | 3.37 | • | |||||
| Cougle et al. (211) | 43,093 | USA | Adults | Weekly | 3.27 | • | |||||
| Holzer et al. (389) | 31,765 | USA | Adults 50+ | Disorder | 2.94 | • | |||||
| Kerridge et al. (224) | 36,309 | USA | Adults 18+ | Disorder | 2.55 | • | • | ||||
| Fink et al. (232) | 392 | USA | Adult patients | Mod. disorder | 1.35 | • | |||||
| Tobacco (N = 7) | SD | CA | MI | TR | BV | ||||||
| Goldstein et al. (390) | 36,309 | USA | Adults 18+ | Disorder | 2.70 | • | • | ||||
| Estévez et al. (221) | 5,677 | SWI | Men 19 | Daily | 2.57 | • | |||||
| Holzer et al. (389) | 31,765 | USA | Adults 50+ | Disorder | 2.31 | • | |||||
| Cougle et al. (213) | 43,093 | USA | Adults | Daily | 1.93 | • | |||||
| Chou et al. (280) | 36,309 | USA | Adults 18+ | Lifetime e-cig. | 1.70 | • | • | ||||
| Cougle et al. (211) | 43,093 | USA | Adults | Weekly | 1.69 | • | |||||
| Chou et al. (271) | 36,309 | USA | Adults 18+ | Disorder | 1.55 | • | |||||
| Median | Unweighted mean (SD) | Weighted mean | |||||||||
| Cannabis | 3.37 | 3.48 (1.12) | 3.61 | ||||||||
| Tobacco | 1.93 | 2.06 (0.46) | 1.98 | ||||||||
OR, odds ratio; SD, sedentary behavior and diet quality; TO, tobacco; CA, cannabis; MI, social misery; TR, psychological trauma; BV, bivariate (unadjusted).
Table 5b.
Longitudinal personality disorder studies (N = 3).
| Longitudinal: behavior before disorder (N = 1) | Covariate adjustments | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Cannabis (N = 1) | N | Area | Sample | Exposure | OR | SD | TO | MI | TR | BV |
| Carpenter et al. (391) | 34,481 | USA | Adults 18+ | Disorder | 1.27 | • | ||||
| Median | Unweighted mean | Weighted mean | ||||||||
| Cannabis | 1.27 | 1.27 | 1.27 | |||||||
| Longitudinal: disorder before behavior (N = 2) | Covariate adjustments | |||||||||
| Cannabis (N = 1) | N | Area | Sample | Outcome | OR | SD | TO | MI | TR | BV |
| Cougle et al. (213) | 34,653 | USA | Adults | Dependence | 1.00 | • | ||||
| Tobacco (N = 1) | SD | CA | MI | TR | BV | |||||
| Cougle et al. (213) | 34,653 | USA | Adults | Dependence | 1.05 | • | ||||
| Median | Unweighted mean | Weighted mean | ||||||||
| Cannabis | 1.00 | 1.00 | 1.00 | |||||||
| Tobacco | 1.05 | 1.05 | 1.05 | |||||||
OR, odds ratio; SD, sedentary behavior and diet quality; TO, tobacco; CA, cannabis; MI, social misery; TR, psychological trauma; BV, bivariate (unadjusted).
Attention-deficit/hyperactivity disorder
This review included 36 cross-sectional and 19 longitudinal figures for the association between unhealthy behaviors and ADHD (Tables 6a, 6b). The cross-sectional studies provide convincing evidence for associations between ADHD and cannabis use, tobacco use, sedentary behavior, and diet quality. The longitudinal studies focused for the most part on how the disorder may lead to unhealthy behavior, probably because ADHD is a disorder often diagnosed in childhood. As such, only single studies provide any evidence that sedentary behavior and poor diet quality may lead to subsequent ADHD, and no evidence at all was included for cannabis and tobacco use. However, there was some evidence that ADHD may lead to subsequent cannabis and tobacco use and single-study evidence that the disorder may lead to sedentary behavior and poor diet quality.
Table 6a.
Cross-sectional ADHD studies (N = 36).
| N | Area | Sample | Exposure | OR | Covariate adjustments | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Cannabis (N = 8) | SD | TO | MI | TR | BV | ||||||
| Capusan et al. (392) | 17,779 | SWE | Adult twins | Any | 2.19 | • | |||||
| Welsh et al. (234) | 483 | USA | Patients 11–24 | Disorder | 2.10 | ||||||
| Karlsson et al. (393) | 4,666 | SWE | Adolescents | Any | 1.91 | • | |||||
| Estévez et al. (221) | 5,677 | SWI | Male military conscripts | Weekly >1 | 1.84 | • | |||||
| Moulin et al. (394) | 1,214 | FRA | Adults 18–35 | Abuse | 1.80 | • | • | • | |||
| Fuller-Thom et al. (395) | 6,872 | CAN | Adults 20–39 | Disorder | 1.46 | • | • | • | |||
| Thompson et al. (233) | 662 | CAN | Adolescence->adulthood | >1/week | 1.35 | • | • | ||||
| Leventhal et al. (222) | 3,177 | USA | Adolescents | Past-30 days | 1.27 | • | • | ||||
| Tobacco (N = 11) | SD | CA | MI | TR | BV | ||||||
| Riegler et al. (396) | 3,280 | AUT | Male military conscripts | Abuse | 3.01 | ø | |||||
| Moulin et al. (394) | 1,214 | FRA | Adults 18–35 | >10 cig./day | 2.00 | • | • | ||||
| Yeh et al. (386) | 2,799 | TWN | Adolescents | Regular | 1.69 | • | |||||
| Estévez et al. (221) | 5,677 | SWI | Men 19 | Daily | 1.62 | • | |||||
| Yeom et al. (397) | 3,441 | KOR | Male military conscripts | Dependence | 1.61 | • | |||||
| Galéra et al. (398) | 8,110 | FRA | University students | Daily | 1.52 | • | |||||
| Xu et al. (399) | 195,443 | USA | University students | Daily | 1.49 | • | |||||
| Capusan et al. (392) | 18,167 | SWE | Adult twins | Daily | 1.33 | • | |||||
| Welsh et al. (234) | 483 | USA | Patients 11–24 | Disorder | 0.89 | ||||||
| Weissenberg et al. (400) | 1,012 | CZE | Adults 18–65 | Current | 0.68 | • | • | ||||
| Abbasi-Ghahramanloo et al. (368) | 2,434 | IRN | Male workers | Current | 0.61 | • | • | ||||
| Sedentary behavior (N = 10) | TO | CA | MI | TR | BV | ||||||
| Kim et al. (196) | 6,510 | KOR | Adults 18–64 | Addiction | 8.90 | • | |||||
| Chowdhury (401) | 310 | BGD | Adults 18+ | Phone >4 h/day | 7.27 | • | |||||
| Gul et al. (402) | 289 | TUR | Adolescent patients | FB overuse | 5.48 | ø | |||||
| Alageel et al. (291) | 506 | Int. | University students | Addiction | 2.71 | • | |||||
| Vadlin et al. (290) | 2,110 | SWE | Patients and controls | Problematic gaming | 2.43 | • | |||||
| Liu et al. (300) | 13,659 | CHN | Adolescents | Games >2 h/day | 2.35 | • | • | ||||
| Moulin et al. (394) | 1,214 | FRA | Adults 18–35 | Games >1/week | 1.40 | • | • | • | |||
| Zhang et al. (197) | 7,121 | CHN | Adults 18–81 | TV >3 h/day | 1.16 | • | • | ||||
| Claesdotter-Knutsson et al. (403) | 17,006 | SWE | Adolescents | High phone | 1.14 | • | • | ||||
| Cook et al. (404) | 45,897 | USA | Youth 10–17 | SB >2 h/day | 1.05 | • | |||||
| Diet quality (N = 7) | TO | CA | MI | TR | BV | ||||||
| Ríos-Hernán et al. (405) | 120 | ESP | Child patients and contr. | Soda (w. tert.) | 3.89 | ø | |||||
| Yu et al. (406) | 332 | TWN | Child patients and contr. | Daily soda | 3.69 | • | |||||
| Abbasi et al. (407) | 500 | IRN | Child patients and contr. | Worst quintile | 3.45 | • | |||||
| Yan et al. (408) | 14,912 | CHN | Preschoolers 3–6 | Worst quintile | 1.76 | • | |||||
| Kim et al. (409) | 16,831 | KOR | Children 6–12 | Daily soda | 1.75 | • | |||||
| Weissenberg et al. (400) | 1,012 | CZE | Adults 18–65 | Daily sweets | 1.37 | • | • | ||||
| Zhou et al. (410) | 592 | CHN | Child patients and contr. | Worst tertile | 1.25 | • | • | ||||
| Median | Unweighted mean (SD) | Weighted mean | |||||||||
| Cannabis | 1.82 | 1.74 (0.34) | 1.89 | ||||||||
| Tobacco | 1.52 | 1.50 (0.67) | 1.50 | ||||||||
| Sedentary behavior | 2.39 | 3.39 (2.82) | 1.88 | ||||||||
| Diet quality | 1.76 | 2.45 (1.17) | 1.79 | ||||||||
OR, odds ratio; SD, sedentary behavior and diet quality; TO, tobacco; CA, cannabis; MI, social misery; TR, psychological trauma; BV, bivariate (unadjusted); PA, physical activity; SB, sedentary behavior.
Table 6b.
Longitudinal ADHD studies (N = 19).
| Longitudinal: behavior before disorder (N = 2) | Covariate adjustments | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Sedentary behavior (N = 1) | N | Area | Sample | Exposure | OR | TO | CA | MI | TR | BV |
| Poulain et al. (411) | 527 | GER | Preschool patients 3–6 | ST >0.5 h/day | 3.36 | • | ||||
| Diet quality (N = 1) | TO | CA | MI | TR | BV | |||||
| Del-Ponte et al. (412) | 2,924 | BRA | Child patients | Worst tertile | 1.67 | • | ||||
| Median | Unweighted mean | Weighted mean | ||||||||
| Sedentary behavior | 3.36 | 3.36 | 3.36 | |||||||
| Diet quality | 1.67 | 1.67 | 1.67 | |||||||
| Longitudinal: disorder before behavior (N = 17) | Covariate adjustments | |||||||||
| Cannabis (N = 6) | N | Area | Sample | Outcome | OR | SD | TO | MI | TR | BV |
| Jean et al. (413) | 4,270 | FRA | University students | Monthly >1 | 1.86 | • | • | |||
| Vogel et al. (414) | 5,103 | SWI | Male military conscripts | Past year | 1.85 | • | ||||
| Kim and Kim (415) | 2,449 | USA | Siblings | Any use | 1.77 | |||||
| Moggi et al. (416) | 4,602 | SWI | Male military conscripts | Past year | 1.70 | • | ||||
| Bierhoff et al. (360) | 2,397 | USA | University students | Past 30 days | 1.11 | • | ||||
| Estévez-Lamorte et al. (417) | 4,975 | SWI | Male military conscripts | Weekly >1 | 0.75 | • | ||||
| Tobacco (N = 9) | SD | CA | MI | TR | BV | |||||
| Kim and Kim (415) | 2,449 | USA | Siblings | Regular | 3.45 | |||||
| Mitchell et al. (418) | 709 | CAN | Patients and controls | Daily | 2.59 | • | ||||
| Jaisoorya et al. (116) | 5,145 | IND | University students | Any use | 1.96 | |||||
| Moggi et al. (416) | 4,602 | SWI | Male military conscripts | Past year | 1.62 | • | ||||
| Elkins et al. (419) | 3,762 | USA | Twin pairs 11 and 17 | Daily | 1.29 | • | ||||
| Estévez-Lamorte et al. (417) | 4,968 | SWI | Male military conscripts | Daily | 1.16 | • | ||||
| Vogel et al. (414) | 5,103 | SWI | Male military conscripts | Past year | 1.07 | • | ||||
| Bierhoff et al. (360) | 2,397 | USA | University students | Past 30 days | 1.04 | • | ||||
| Goldenson et al. (420) | 1,921 | USA | Adolescents | Any use | 1.03 | • | ||||
| Sedentary behavior (N = 1) | TO | CA | MI | TR | BV | |||||
| Bowling et al. (421) | 8,250 | USA | Children 6–15 | PA < 3 day/week | 1.17 | • | ||||
| Diet quality (N = 1) | TO | CA | MI | TR | BV | |||||
| Bowling et al. (421) | 8,250 | USA | Children 6–15 | Poor diet | 1.46 | • | ||||
| Median | Unweighted mean (SD) | Weighted mean | ||||||||
| Cannabis | 1.74 | 1.51 (0.47) | 1.51 | |||||||
| Tobacco | 1.29 | 1.69 (0.84) | 1.56 | |||||||
| Sedentary behavior | 1.17 | 1.17 | 1.17 | |||||||
| Diet quality | 1.46 | 1.46 | 1.46 | |||||||
OR, odds ratio; SD, sedentary behavior and diet quality; TO, tobacco; CA, cannabis; MI, social misery; TR, psychological trauma; BV, bivariate (unadjusted); PA, physical activity; ST, screen time.
Post-traumatic stress disorder
The review included 35 cross-sectional and only two longitudinal figures for the association between unhealthy behaviors and PTSD (Tables 7a, 7b). The cross-sectional studies provide convincing evidence for associations between PTSD and cannabis and tobacco use, as well as some evidence for an association with sedentary behavior, but none for diet quality. The longitudinal studies provide single-study evidence that the use of cannabis and tobacco may lead to subsequent PTSD. Nevertheless, it should be noted that sensitivity analyses for specialty samples, behavioral measurement disparities, and control variables introduced discrepancies related to the cannabis-PTSD relationship, indicating that these findings should be interpreted with extra carefulness.
Table 7a.
Cross-sectional PTSD studies (N = 35).
| N | Area | Sample | Exposure | OR | Covariate adjustments | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Cannabis (N = 18) | SD | TO | MI | TR | BV | ||||||
| Metrik et al. (205) | 301 | USA | Male military veterans | Weekly 2+ | 5.96 | ||||||
| Hasin et al. (136) | 36,309 | USA | Adults 18+ | Disorder | 4.30 | • | |||||
| de Silva et al. (422) | 671 | LKA | Male military personnel | Past year | 4.20 | • | |||||
| Hasin and Walsh (20) | 36,309 | USA | Adults 18+ | Lifetime disorder | 3.80 | • | |||||
| Padwa et al. (151) | 8,940 | USA | Adult patients and contr. | Any | 3.41 | • | |||||
| Young-Wolff et al. (123) | 196,022 | USA | Pregnant women | During pregnancy | 2.82 | • | |||||
| Ehlers et al. (423) | 614 | USA | Mexican Am. 18–30 | Dependence | 2.02 | ø | |||||
| Hill et al. (215) | 3,157 | USA | Military veterans | Non-CUD | 1.96 | • | |||||
| Bilevicius et al. (424) | 36,309 | USA | Adults 18+ | Disorder | 1.94 | • | • | ||||
| Kerridge et al. (224) | 36,309 | USA | Adults 18+ | Disorder | 1.65 | • | • | ||||
| Alenko et al. (425) | 398 | ETH | Male drivers | Moderate risk | 1.55 | • | • | ||||
| Welsh et al. (234) | 483 | USA | Patients 11–24 | Disorder | 1.52 | ||||||
| Gentes et al. (426) | 719 | USA | Male military veterans | Past 6 months | 1.30 | • | • | ||||
| Kevorkian et al. (427) | 2,990 | USA | Adults 18+ | Disorder | 1.22 | ||||||
| Lekoubou et al. (230) | 400,391 | USA | Patients 18+ | Disorder | 1.13 | • | |||||
| Campbell et al. (176) | 101,405 | USA | Patients 18+ | Disorder | 1.00 | • | • | ||||
| Hill et al. (229) | 4,069 | USA | Military veterans | Past 6 months | 1.00 | • | • | ||||
| Fink et al. (232) | 392 | USA | Adult patients | Mod. disorder | 0.57 | • | |||||
| Tobacco (N = 12) | SD | CA | MI | TR | BV | ||||||
| Kim et al. (428) | 5,075 | KOR | Adult patients | Disorder | 6.98 | • | |||||
| Bromet et al. (429) | 3,231 | USA | Disaster responders | Current | 2.50 | ø | |||||
| Welsh et al. (234) | 483 | USA | Patients 11–24 | Disorder | 2.19 | ||||||
| Peltzer and Pengpid (430) | 15,310 | ZAF | Adolescents and adults | Daily | 1.83 | • | |||||
| Smith et al. (431) | 3,119 | USA | Military veterans | Disorder | 1.59 | • | • | ||||
| Xu et al. (432) | 11,254 | CHN | University students | Any | 1.34 | • | |||||
| Chou et al. (271) | 36,309 | USA | Adults 18+ | Disorder | 1.27 | • | |||||
| Chou et al. (280) | 36,309 | USA | Adults 18+ | Lifetime e-cig. | 1.20 | • | • | ||||
| Sawchuk et al. (279) | 2,774 | USA | American Indians 15–54 | >100 cig. | 1.15 | • | |||||
| Whitworth et al. (433) | 1,140 | USA | Military veterans | Daily | 1.12 | • | |||||
| Ehlers et al. (423) | 614 | USA | Mexican Am. 18–30 | Dependence | 1.03 | ø | |||||
| Hruby et al. (281) | 12,708 | USA | Military service members | Past 30 days | 0.97 | • | • | • | |||
| Sedentary behavior (N = 4) | TO | CA | MI | TR | BV | ||||||
| Kim et al. (196) | 6,510 | KOR | Adults 18–64 | Addiction | 2.09 | • | |||||
| Whitworth et al. (433) | 1,140 | USA | Military veterans | Low PA | 1.84 | • | |||||
| Zhang et al. (197) | 7,121 | CHN | Adults 18–81 | TV >3 h/day | 1.28 | • | • | ||||
| Hruby et al. (281) | 12,708 | USA | Military service members | PA < 75 min/week | 1.22 | • | • | • | |||
| Diet quality (N = 1) | TO | CA | MI | TR | BV | ||||||
| Mutiso et al. (198) | 9,742 | KEN | High school students | Binge eating | 1.04 | • | • | • | |||
| Median | Unweighted mean (SD) | Weighted mean | |||||||||
| Cannabis | 1.80 | 2.30 (1.47) | 1.83 | ||||||||
| Tobacco | 1.31 | 1.93 (1.66) | 1.56 | ||||||||
| Sedentary behavior | 1.56 | 1.61 (0.43) | 1.47 | ||||||||
| Diet quality | 1.04 | 1.04 | 1.04 | ||||||||
OR, odds ratio; SD, sedentary behavior and diet quality; TO, tobacco; CA, cannabis; MI, social misery; TR, psychological trauma; BV, bivariate (unadjusted).
Table 7b.
Longitudinal PTSD studies (N = 2).
| Longitudinal: behavior before disorder (N = 2) | Covariate adjustments | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Cannabis (N = 1) | N | Area | Sample | Outcome | OR | SD | TO | MI | TR | BV |
| Lee et al. (434) | 674 | USA | Adolescence->adulthood | Weekly | 4.68 | • | • | |||
| Tobacco (N = 1) | SD | CA | MI | TR | BV | |||||
| Ibrahim et al. (435) | 7,561 | USA | Adults 18+ | Dependence | 1.59 | • | • | • | ||
| Median | Unweighted mean | Weighted mean | ||||||||
| Cannabis | 4.68 | 4.68 | 4.68 | |||||||
| Tobacco | 1.59 | 1.59 | 1.59 | |||||||
OR, odds ratio; SD, sedentary behavior and diet quality; TO, tobacco; CA, cannabis; MI, social misery; TR, psychological trauma; BV, bivariate (unadjusted).
Analyses
Aim 1: understand individual associations between a given unhealthy behavior and a given mental disorder in relation to other such disorders
The general finding of this review was that associations between a given unhealthy behavior and mental disorders tended to resemble each other in terms of their strength (Figure 2 and Supplementary Figures A2a–A2d), although with some exceptions. In studies of tobacco use, associations with psychosis tended to be stronger than associations with other mental disorders, with the differences reaching statistical significance for cross-sectional studies in t-test comparisons with depression (t = 2.34, df = 88, two-sided p = 0.022), anxiety (t = 2.83, df = 24.07, two-sided p = 0.009), and ADHD (t = 2.09, df = 28, two-sided p = 0.046). Associations with anxiety for their part tended to be weaker than associations with other mental disorders, reaching statistical significance for cross-sectional studies in t-test comparisons with personality disorder (t = 2.61, df = 33, two-sided p = 0.014) and marginal significance (t = 1.86, df = 97, two-sided p = 0.066) with depression. In studies of sedentary behavior and diet quality, associations with PTSD tended to be weaker than associations with other mental disorders, although low numbers of studies for many disorders entailed that the differences reached marginal statistical significance only in cross-sectional t-test comparisons with psychosis (t = 2.03, df = 9, two-sided p = 0.07).
Figure 2.
Odds ratios for cross-sectional studies of cannabis and psychiatric disorders.
Because of constraints on space, it is not possible to report more detailed analyses for all the behaviors here reviewed. However, since the association between cannabis use and psychosis was previously noted as especially controversial, it will be analyzed in further detail. Two analyses compared the strength of the cannabis-psychosis association with that of associations between cannabis use and other mental disorders. The first such analysis compared the means and spreads for each disorder between different studies, with the boxplots of Figure 2 summing up this analysis for cross-sectional studies of cannabis use. These studies indicate that cannabis use was not more strongly associated with psychosis than with the other mental disorders here under purview. T-tests indicated that only the figures for personality disorder were significantly different from those for psychosis (t = 4.18, df = 27, two-sided p < 0.001), and indeed the figures for personality disorder were significantly higher than those for all other diagnoses (depression t = 4.33, df = 50, two-sided p < 0.001; anxiety t = 5.44, df = 30, two-sided p < 0.001; bipolar disorder t = 2.73, df = 16, two-sided p = 0.015; ADHD t = 4.44, df = 9.66, two-sided p < 0.001; PTSD t = 2.13, df = 25, two-sided p = 0.043). In addition, the association with anxiety was marginally weaker than the association with PTSD (t = 1.78, df = 24.05, two-sided p = 0.06). No differences were detected for longitudinal studies or specifically for longitudinal (behavior before disorder) studies (Supplementary Figures A2a, A2b), but there were not many such studies to compare.
As can be seen in Tables 1a–7a, furthermore, the weighted means of odds ratios for cross-sectional associations between cannabis use and the seven mental disorders tended to converge even more closely than the unweighted means to the area of 1.5–2.0, with the only exception relating to personality disorders. Longitudinal means (Tables 1b–7b) were somewhat more varied, but the weighted means for longitudinal (behavior before disorder) studies converged to the area of 1.0–2.0 except for ADHD, for which there was no evidence, and for a single high-OR PTSD study. Weighted means for the small number of longitudinal (disorder before behavior) studies for their part converged to the area of 0.5–1.5.
The second analysis focused on within-study comparisons. Some of the studies included in this review reported comparable figures for associations between cannabis use and psychosis as well as other disorders, and these figures are presented in Table 8 with an OR cut-off of 0.25. Longitudinal results related to having a mental disorder at baseline and subsequent initiation of cannabis or tobacco use have been ignored as they are not relevant for the comparison of putative harms resulting from the behavior. Unfortunately, only a few studies reported comparable figures, and there were no figures at all for ADHD and personality disorder. According to the available data, however, the strength of the association between cannabis use and psychosis seems to be at the same level as the strength of the associations between cannabis use and depression, anxiety, bipolar disorder, and PTSD. It appears that no reviews have investigated the associations between cannabis and psychosis as well as cannabis and other disorders at the same time.
Table 8.
Cannabis-psychosis compared to cannabis-other disorders.
| Depression | Anxiety | Bipolar disorder | PTSD | |
|---|---|---|---|---|
| Psychosis stronger (OR + 0.25) | Campbell et al. (176) | Campbell et al. (176) | Campbell et al. (176) | Campbell et al. (176) |
| Psychosis and other disorders on same level | Chan et al. (183) Davies et al. (171) Hines et al. (182) | Chan et al. (183) | ||
| Other disorders stronger (OR + 0.25) | Padwa et al. (151) | Hines et al. (182) Padwa et al. (151) | Padwa et al. (151) | Padwa et al. (151) |
It is worth mentioning that the study by Padwa et al. (151) found that cannabis use was also associated with a broad range of general medical disorders including circulatory system disease, digestive system disease, and musculoskeletal disease. The strength of these associations was broadly at the same level as the strength of the associations with mental disorders.
Aim 2: understand individual associations between a given unhealthy behavior and a given mental disorder in relation to other such behaviors
Two analyses compared the strength of associations between different unhealthy behaviors and a given mental disorder. The first such analysis compared the means and spreads for each disorder between different studies. The boxplots of Figures 3–9 sum up this analysis for each disorder in cross-sectional studies, while boxplots for longitudinal (behavior before disorder) studies are available in the Supplementary Figures A3–A6. Note that in order to improve the quality of presentation, one outlier study (152) with a very high OR of 28.19 has been removed from the figures for depression.
Figure 3.
Odds ratios for psychosis in cross-sectional studies.
Figure 9.
Odds ratios for PTSD in cross-sectional studies.
Figure 4.
Odds ratios for depression in cross-sectional studies. In order to improve the quality of presentation, one outlier study (152) with a very high OR of 28.19 for diet quality and depression has been removed.
Figure 5.
Odds ratios for anxiety in cross-sectional studies.
Figure 6.
Odds ratios for bipolar disorder in cross-sectional studies.
Figure 7.
Odds ratios for personality disorder in cross-sectional studies.
Figure 8.
Odds ratios for ADHD in cross-sectional studies.
For the overall comparisons between cannabis and tobacco, t-tests indicated that only the figures for personality disorder were significantly different from each other (t = 3.13, df = 4, two-sided p = 0.007) in cross-sectional studies. There were no significant differences for longitudinal (behavior before disorder) findings. There were also no significant differences between cannabis and diet quality studies, although the number of studies available for each analysis was sometimes very small. For comparisons between cross-sectional studies of cannabis and sedentary behavior, t-tests indicated that the figures for ADHD (t = 1.83, df = 9.33, two-sided p = 0.099) were marginally significant. Tobacco use tended toward lower-strength associations with mental disorders than sedentary behavior and poor diet quality in cases where there were enough studies to make meaningful comparisons. These comparisons (with sedentary behavior and diet quality combined) reached significance for ADHD (t = 2.56, df = 19.94, two-sided p = 0.019). There were no significant differences between sedentary behavior and diet quality.
Weighted means gave emphasis to a single large-N, high-OR study of sedentary behavior and psychosis that made the cross-sectional association between sedentary behavior and psychosis appear to be substantially stronger especially than the association for cannabis. The weighing also gave emphasis to a single large-N study of sedentary behavior and bipolar disorder that made sedentary behavior seem protective against this disorder. Weighted means otherwise supported a difference between cannabis and tobacco with regard to personality disorder, but tended to mitigate the differences between cannabis use and sedentary behavior with regard to ADHD and PTSD. For longitudinal studies, weighing tended to reduce differences in odds ratio means between the different behaviors.
The second analysis focused on within-study comparisons. Some of the studies included in this review reported comparable figures for associations between different behaviors and a specific mental disorder, and these figures are presented in Table 9 with an OR cut-off of 0.25. In order to qualify as comparable, figures should reflect light, moderate or high use of both cannabis and tobacco, although exceptions were made for cases where differences in association strength were maintained despite a divergence in behavior intensity that presumably weakened the strongest association. In Magklara et al. (153), for instance, ever-use of cannabis incurred a higher OR for depression than daily use of tobacco, and this finding has been added to Table 9 despite the sub-moderate threshold for inclusion in the cannabis group. Longitudinal results related to having a mental disorder at baseline and subsequent initiation of cannabis or tobacco use have been ignored as they are not relevant for the comparison of putative harms resulting from the behavior.
Table 9.
Associations between cannabis and psychiatric disorders compared to tobacco.
| Psychosis | Depression | Anxiety | |
|---|---|---|---|
| Cannabis stronger (OR + 0.25) | Bhavsar et al. (172) Davies et al. (171) Degenhardt et al. (121) Jones et al. (200) | Cougle et al. [(213); CS] Davies et al. (171) Esmaeelzadeh et al. (18) Magklara et al. (153) Mannes et al. (217) Matta et al. (150) Porras-Segovia et al. (114) Risal et al. (208) | Risal et al. (208) Welsh et al. (234) |
| Cannabis and tobacco on same level | Carney et al. (179) Degenhardt et al. (202) McMahon et al. (120) | Chadi et al. (218) Estévez et al. (221) Wang and Peiper (228) Welsh et al. (234) | Cougle et al. [(213); CS] López-Gil et al. (363) |
| Tobacco stronger (OR + 0.25) | Ferraro et al. (177) Fusar-Poli et al. (8) Ryan et al. (203) | Prestage et al. (223) Seaman et al. (226) Tiburcio Sainz et al. (235) | Esmaeelzadeh et al. (18) Mannes et al. (217) Prestage et al. (223) |
Meta-analyses indicated by italics.
CS, cross-sectional; LN, longitudinal.
We see in Table 9 that cannabis and tobacco appear to be associated with psychosis and anxiety at about the same level. For depression, the association for cannabis appears to be slightly stronger than that for tobacco. Other disorders were less commonly studied, but Supplementary Table A9 indicates that cannabis may be more strongly associated with personality disorder and perhaps ADHD than tobacco is, while the very limited evidence available indicates that their association with bipolar disorder and PTSD is about equally strong.
Table 10 compares diet quality and sedentary behavior to cannabis and (most commonly) tobacco use. Depression is fairly well-studied, and it appears that the association with diet quality and sedentary behavior is at the same level as the association with cannabis/tobacco use. Less evidence is available for psychosis and anxiety, but the evidence for psychosis seems to indicate that the association with diet quality and sedentary behavior is at least as strong as the association with cannabis/tobacco use, while the opposite may be true for anxiety. Very limited evidence for ADHD and PTSD indicates that the associations with diet quality and sedentary behavior are as strong or stronger than the associations with cannabis/tobacco use (Supplementary Table A10).
Table 10.
Associations between cannabis/tobacco and psychiatric disorders compared to diet quality and sedentary behavior.
| Psychosis | Depression | Anxiety | |
|---|---|---|---|
| Cannabis/tobacco stronger (OR + 0.25) | Albasara et al. (130) Kim et al. (264) López-Sánchez et al. (133) Ma et al. (246) Masana et al. (261) Rahe et al. (245) Wang et al. (244) | Hruby et al. (281) López-Sánchez et al. (133) | |
| Cannabis/tobacco and diet quality/sedentary behavior on same level | Werneck et al. [(189); DQ] | Cabello et al. (352) Hruby et al. (281) Kim [(263); DQ] Melin et al. (255) Nam et al. (131) Peltzer and Pengpid (272) Tsutsumimoto et al. (351) | López-Gil et al. (363) |
| Diet quality/sedentary behavior stronger (OR + 0.25) | Fusar-Poli et al. (8) McMahon et al. (120) Werneck et al. [(189); SB] | Kim [(263); PA] Liu et al. (265) Luo et al. (258) Pengpid and Peltzer (276) Wang and Peiper (228) Zhang et al. (350) Zhu et al. (282) |
Meta-analyses indicated by italics.
DQ, diet quality; PA, physical activity; SB, sedentary behavior.
Discussion
Aim 1: understand individual associations between a given unhealthy behavior and a given mental disorder in relation to other such disorders
According to the findings of this comparative review, the association between a given behavior and a given disorder tended to resemble similar associations with other disorders. Some disorders distinguished themselves as particularly weakly or strongly associated with a given behavior, such as psychosis and anxiety for tobacco use and PTSD for sedentary behavior and diet quality, but even in these cases the differences were of moderate magnitude. Taking the cannabis-psychosis association as a basis for comparison, it appears that cannabis use is not more strongly associated with psychosis than with the other disorders. Comparisons across studies and within studies both supported this conclusion. However, personality disorder appeared to be more strongly associated with cannabis use than what is the case for psychosis and other disorders. Furthermore, in some of the sensitivity analyses that removed specific groups of studies from the dataset, cannabis use appeared to be more strongly associated with PTSD than with psychosis. Thus, in contrast to the consensus paper by D'Souza et al. (1), which used a single study by Starzer et al. (137) as the basis for inferring that “[c]annabis is more likely to be associated with psychosis outcomes than other psychiatric diagnoses” (p. 732), this review found that cannabis use is equally strongly associated with a range of different mental disorders, with only personality disorder (and perhaps PTSD) distinguishing themselves in terms of being especially strongly associated with cannabis use. D'Souza et al.'s (1) identification of the specificity of the cannabis-psychosis relationship as an indication that this relationship may reflect a causal effect from cannabis use, as per the classic criteria by Hill (112), is therefore unsupported by the present review.
This conclusion should not be especially susceptible to variation in behavior intensity, as moderate (weekly) cannabis use was emphasized in findings across different disorders. Nevertheless, sensitivity analyses did identify disparities related to sample groups, behavioral measurement, and control variables that may have impacted findings. Within-study analyses should be immune to such effects as the studies included in these analyses utilized the same participant samples and control variables, however.
Aim 2: understand individual associations between a given unhealthy behavior and a given mental disorder in relation to other such behaviors
The general finding from this review was that the association between a given behavior and a given disorder tended to resemble similar associations for other behaviors. Thus, it appears that moderate use of cannabis is about as strongly associated with mental disorders as is moderate use of tobacco and high use of fast food, sugary beverages, and television. In terms of their respective behavior intensity, conceptualized as the behavior related to the median-area odds ratio in cross-sectional studies, cannabis use ranging from any exposure via weekly or current use to use disorder appears to be about as harmful as tobacco use ranging from any exposure via daily, regular or current use to use disorder. Both of these, in turn, appear to be about as harmful as behavior ranging from 2 to 4 h of daily screen time, 4 to 10 h of daily inactivity, or daily ingestion of sweets, sodas, and fast food up to binge eating or the disordered use of computer games, Facebook or the internet. The only statistically significant divergence at the 0.05 level related to personality disorder, which was more strongly associated with moderate cannabis use than with moderate tobacco use.
Within-study comparisons generally supported this conclusion, although with some variation for depression and ADHD. Thus, in contrast to the consensus paper by D'Souza et al. (1) that drew the inference that “[t]he risk for a psychosis outcome is highest for cannabis relative to other drugs” (p. 732), this review found that the association between cannabis use and psychosis is about equally strong as the association between psychosis and tobacco use as well as the associations with sedentary behavior and poor diet quality. It should be acknowledged, however, that the latter two seem not to have been intensively studied, and that variation in behavior intensity may have affected overall findings. This review emphasized moderate cannabis and tobacco use, operationalized as weekly cannabis use and daily tobacco use of around 10 cigarettes, as compared to high levels of sedentary behavior and poor diet quality. If the review had instead focused on high levels of cannabis use, the comparison would probably have favored tobacco use, sedentary behavior, and poor diet quality as less harmful behaviors.
There are, however, several reasons to focus on moderate (weekly) rather than high (daily or more) cannabis use in harms assessments. Heavy use of intoxicants typically reflects motivations related to coping, and such motivations are normally related to underlying problems that are themselves associated with risk for mental disorder (13). Furthermore, the frequent use of intoxicants may exacerbate such underlying problems via social mechanisms that are not directly related to the pharmacological effects of the intoxicant itself. A person who engages in frequent daytime intoxicant use will incur a range of negative consequences in terms of employment and social relations irrespective of whether the intoxicant is cannabis, alcohol, or some other substance, and these negative consequences will often exacerbate the underlying problems that motivate the frequent intoxicant use in the first place. Studies of such behaviors will therefore tend to conflate negative consequences incurred via social mechanisms and negative consequences incurred via pharmacological mechanisms. In order to avoid such conflation of effects and constrain the measurement of consequences toward those that are of a pharmacological nature, it would be advisable to focus on moderate intoxicant use.
The findings of this review could be understood in relation to Nutt's (154) comparison of the harms related to MDMA use and horse riding. Both studies related the risks incurred by substance use to risks incurred by ordinary and socially acceptable activities, and found that despite their negative reputation, use of substances such as MDMA, cannabis, and tobacco do not appear to be more harmful than these ordinary activities.
Aim 3: understand the association between unhealthy behaviors and mental disorders in relation to statistical control for covariates
This review constructed dichotomous indicators for covariate adjustment in order to better understand the overall impact of such adjustment on studies of associations between unhealthy behaviors and mental disorders. The most influential of these adjustment indicators were those for cannabis and tobacco use, which reached significance at the 0.05 level in overall correlation with odds ratios across disorders in non-cannabis and non-tobacco studies, respectively. This finding allows for the conclusion that failing to control for cannabis and tobacco use, both of which tend toward collinearity both with each other and with other unhealthy behaviors as well as with a number of mental disorders, entails an exaggeration of the strength of the association under scrutiny. In other words, failing to control for cannabis and tobacco use in studies of unhealthy behaviors and mental disorders may amount to analytical malpractice. At a minimum, such studies should report comparable figures for cannabis and/or tobacco so that readers may calibrate the results presented for the unhealthy behavior under scrutiny in comparison to figures for cannabis and tobacco use.
The indicator for adjustment for diet quality and/or sedentary behavior was not correlated with odds ratios in cannabis and tobacco studies, but the inclusion of such adjustment was also so uncommon that the overall statistics of its impact may not be trustworthy. This applies especially to cannabis, as only 1% of cannabis studies controlled for diet quality and/or sedentary behavior and only 8% of studies of the latter behaviors controlled for cannabis use. It thus appears that researchers tend to understand cannabis use and poor diet quality/sedentary behavior as conceptually distinct, with cannabis use constituting a form of very harmful drug use whereas frequent consumption of fast food or television merely constitutes an unhealthy lifestyle choice. The findings of this review indicate that any such distinction, at least with regard to moderate cannabis use, is probably illusory, as weekly cannabis use appears to be about equally harmful as weekly to daily consumption of cheeseburgers.
Tobacco use appears to be positioned somewhat more closely to poor diet quality/sedentary behavior on a conceptual level. A majority (55%) of studies of the latter behaviors controlled for tobacco use, and the 14% of tobacco studies which controlled for diet quality and/or sedentary behavior at least outnumber the corresponding number of cannabis studies (1%) by a wide margin. Thus, there is some evidence of a tendency among researchers to understand tobacco use as a form of unhealthy behavior that is conceptually related to other unhealthy behaviors that do not involve substance use.
The indicator for bivariate analysis was also fairly influential on overall odds ratios, although perhaps not to the extent that one would expect. Only a small number of studies reported bivariate figures, and in comparative analyses with many included studies we would expect the influence from bivariate figures to cancel each other out on average. Sensitivity analyses that removed the studies with bivariate figures from the dataset introduced differences, albeit of marginal significance, relating to the association between cannabis use and PTSD. It would therefore appear that studies investigating the cannabis-PTSD relationship were especially impacted by figures lacking statistical control.
Indicators for trauma and misery adjustment had some influence on overall odds ratios and localized impact of a more substantial nature on findings related to specific disorders and behaviors. It is therefore true to say that the methods used to analyze the impact from trauma and misery on the studies included in this review did not identify a major impact on association strengths. Nevertheless, since this review focused on behaviors of moderate intensity for tobacco and cannabis use, the finding of a tentative or light influence could be said to reflect expectations. Moderate use was here conceptualized as use driven by a broad range of motivations, in contradistinction to the heavy use understood as being more often driven by coping motivations related to underlying trauma and misery. As such, one would expect that indicators for trauma and misery adjustment would have a more substantial impact on analyses of heavy substance use than what was the case in the present review.
It is important to note that if the association between substance use and mental disorder reflects, to some meaningful extent, an association between substance use and underlying factors of trauma and misery, then overall changes in the user population toward or away from more widespread use should be accompanied by corresponding changes to the strength of the association between the substance use and mental disorder. When the use of a given substance becomes more prevalent, at least in terms of large-scale changes, this normally reflects an acceptance of this substance among people who are driven primarily not by coping motives but by recreational, social, enhancement or expansion motives. Similarly, when the use of a previously prevalent substance is reduced, this normally reflects reduced acceptance of the given substance among people driven primarily by non-coping motives. In the western world, recent decades have seen reduced acceptance of tobacco use and increased acceptance of cannabis use, and we would therefore expect an increasing association between mental disorder and tobacco use along with a decreasing association for cannabis use.
These expectations are supported by research findings. Talati et al. (155) found that while overall tobacco use declined in the U.S. from the 1940s to 1980s, the proportion of smokers with nicotine dependence increased from 31% to 70% and the number of dependent smokers with depression, ADHD, bipolar disorder, and personality disorder increased significantly more than among non-smokers. In Canada, Yang and D'Arcy (156) similarly found that depression among older adults smokers increased substantially more than among non-smokers over the years 1994–2014. For cannabis, several studies have found increases in overall use without corresponding increases in cannabis use disorder (157–165), although there is also some conflicting evidence (166–168). Changes in associations with mental disorders have not been intensively explored, but one study by Livne et al. (169) found that “the association between cannabis use (but not CUDs) and dysthymia has weakened over time” (p. 327). If ongoing policy liberalization continues, the proportion of users with use disorder might be expected to further weaken, and the same could be said for the association between overall cannabis use and mental disorders.
Limitations
Beyond the classification of specialty samples, behavioral measurement disparities, and statistical control for social misery and psychological trauma, this review has not assessed the methodological validity of the studies included. In particular, their utilization of healthy controls for patient samples and their construction of samples putatively representing the general population have not been assessed. Relatedly, the review has not accounted for variation in the measurement of mental disorders, which is a possible confounder to the comparative analyses. It is also possible that the focus on studies presenting their findings in terms of odds ratios may constitute a source of bias, although there is no particular reason to believe that findings presented as odds ratios should be stronger or weaker than findings presented as hazard ratios or in some other way.
The dichotomous indicator variables for trauma and misery adjustment constructed in this review might be criticized for being overly simplistic. It is possible that a more complex set of adjustment indicators would have been more sensitive for nuanced variations in covariate regimes in the included studies. A high percentage of these studies controlled for education, but although education is correlated with poverty and unemployment, adjustment for this single variable by no means exhausts the impact from social misery on unhealthy behaviors and mental disorders. Adjustment for psychological trauma was far less common, making a single dichotomous indicator more appropriate.
This review was limited to records from the Pubmed database published in 2015 or later. Such a selection of recent studies registered in Pubmed may have served to introduce bias in overall findings, although the author is not aware of any reason to believe that the Pubmed database is systematically different from other databases. As such, the present selection of studies could arguably be regarded as representative of the full population of such studies. Nevertheless, the present findings should be regarded as tentative and explorative until confirmed by more comprehensive investigations.
Furthermore, this review has analyzed and compared included studies at the level of their published findings rather than in terms of their underlying data. While this approach arguably yielded noteworthy results, it would undoubtedly be interesting to perform a comparative meta-analysis of the datasets for the behaviors and disorders here under purview. Such a meta-analysis would probably have to limit its scope as compared to the present review, but would allow for a more comprehensive understanding of the tentative findings obtained here. It should also be noted that the author of the present review performed all the assessments and analyses singlehandedly, increasing the risk of bias. Inter-rater reliability therefore could not be established. Moreover, the comparisons reported in this review were not Bonferroni corrected, and because 252 separate analyses were performed it is unlikely that any association would have maintained statistical significance after such correction.
Publication bias should be assumed to have affected the results presented in this review. Indeed, it was not infrequently the case that studies which investigated several relationships between unhealthy behaviors and disorders only reported odds ratios for those relationships that were statistically significant. Unless there is reason to believe that such bias should have affected some relationships substantially more than others, however, the comparative perspective of this review probably entails that publication bias on each side of the comparison would tend to cancel each other out.
It must be acknowledged that this review compared a broad range of studies across different populations and participant samples, although the same is true for more narrow reviews examining only one of these behavior-disorder relationships (17). Variation related to specialty samples, behavioral measurement disparities, and control variables did not appear to have a major impact on overall findings, which were largely robust across relevant sensitivity analyses. The findings should nevertheless be regarded as explorative and interpreted with caution. While further research is necessary in order to confirm the results obtained in this review, the apparent convergence of the strength of associations between four different types of unhealthy behaviors and seven different mental disorders is a noteworthy if tentative basis for further investigations.
A useful extension to the comparisons in this review would be to include studies of alcohol. As an intoxicant, alcohol may constitute a closer parallel to cannabis than the other unhealthy behaviors under scrutiny in this review, and it seems possible to compare the two in terms of both moderate use and heavy chronic use. It would also be interesting to extend such comparisons to other illicit drugs, although the associations between such drug use and mental disorder may not yet have been studied to such an extent that it is meaningful to include them in a comparative review.
Conclusion
According to this review, the association between moderate cannabis use and mental disorder is about equally strong for psychosis, depression, anxiety, bipolar disorder, ADHD, and PTSD, while the less intensively studied association with personality disorder appears to be somewhat stronger than the others. Furthermore, these associations with cannabis use appear to be paralleled by associations with tobacco use, poor diet quality, and sedentary behavior at about the same strength, although once more with personality disorder as an exception. As such, the often-emphasized association between cannabis use and psychosis appears to lack specificity both across disorders and across behaviors.
When a broad range of different unhealthy behaviors appear to be about equally strongly associated with a broad range of mental disorders, we should probably understand them to reflect a general and shared etiology. The best candidate for a general explanation is arguably that this broad range of associations reflect underlying psychological trauma and social misery, which frequently appear together. Thus, the associations identified in this review may not represent a causal effect from the unhealthy behavior itself so much as a spurious relationship where both the behavior and the disorder are caused by underlying factors such as psychological trauma and social misery. Nevertheless, it should be noted that the analyses employed in the present review did not identify a major impact from variation in statistical control for such factors. Because of its exceptional strength, it is possible that the association between cannabis use and personality disorder constitutes an exception to this general explanation, although more research is required.
Data availability statement
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.
Author contributions
PJ: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing—original draft, Writing—review & editing.
Funding Statement
The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.
Conflict of interest
The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2023.1268339/full#supplementary-material
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Data Availability Statement
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.