Social Psychiatry and Psychiatric Epidemiology
https://doi.org/10.1007/s00127-023-02537-6

ORIGINAL PAPER

Associations between digital media use and psychotic experiences
in young adults of Quebec, Canada: a longitudinal study
Vincent Paquin1,2 · Frederick L. Philippe3 · Holly Shannon4,5 · Synthia Guimond4,6 · Isabelle Ouellet‑Morin7 ·Marie‑Claude Geoffroy1,2Received: 11 June 2022 / Accepted: 12 July 2023
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany 2023

Abstract
Purpose Digital media use has been associated with psychotic experiences in youth from the community, but the directionof association remains unclear. We aimed to examine between- and within-person associations of digital media use andpsychotic experiences in youth.
Methods The sample included 425 participants aged 18–25 years (82.5% female) from the community, followed between
May 2021 and January 2022 over 3 time points—of which 263 participants (61.9%) completed at least 2. Digital media use
was self-reported as time spent daily on TV and streaming platforms, social media, and video games over the past 3 months.
Psychotic experiences in the past 3 months were measured with the 15-item Community Assessment of Psychic Experiences.
Associations between digital media use and psychotic experiences were estimated using a random-intercept cross-lagged
panel model.
Results On average, individuals who reported greater digital media use also reported higher levels of psychotic experiences(r = 0.34, 95% CI 0.15, 0.53). However, a person’s variation in digital media use, relative to their personal average, was
not significantly associated with subsequent variations in their levels of psychotic experiences, or vice-versa. Results weresimilar across TV/streaming, social media and video game use, and after adjusting for age, sex, education, sleep, physicalactivity, and cannabis use.
Conclusion Individuals with a tendency for higher levels of digital media use also had a tendency for higher levels of psychotic experiences. Understanding this association may help personalize mental health interventions for people with psychotic
experiences, which may be offered digitally to promote their accessibility.
Keywords Psychotic-like experiences · Psychotic symptoms · Screen time · Technology use · Internet use · Chronotype* Vincent Paquinvincent.paquin2@mail.mcgill.ca
1

Department of Psychiatry, McGill University, Ludmer
Research and Training Building, 1033 Pine Avenue West,
Montreal, QC H3A 1A1, Canada

2

McGill Group for Suicide Studies, Douglas Mental Health
University Institute, Montreal, QC, Canada

3

Department of Psychology, University of Quebec
in Montreal, Montreal, QC, Canada

4

The Royal’s Institute of Mental Health Researchand Department of Psychiatry, University of Ottawa, Ottawa,
ON, Canada

5

Department of Neuroscience, Carleton University, Ottawa,
ON, Canada

6

Department of Psychoeducation and Psychology, Universityof Quebec in Outaouais, Gatineau, QC, Canada

7

School of Criminology, University of Montreal, and ResearchCenter of the Montreal Mental Health University Institute,
Montreal, QC, Canada

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Social Psychiatry and Psychiatric Epidemiology

Introduction
Psychotic experiences are disturbances of thought and perception that exist along a continuum within the community[1] and are more common in youth [2]. These experiences
include paranoid beliefs, hearing voices when alone, anda range of unusual perceptions. Psychotic experiences inadolescents and young adults are associated with higher
risk of subsequent psychotic disorders, as well as a rangeof other mental health problems including depression,anxiety, and suicide attempts [1, 3–5]. Thus, early identification of psychotic experiences can guide prognostic andtreatment targets to improve mental health [6, 7].
In recent years, the use of digital media has gained
attention as a potential environmental risk factor for psychotic experiences in youth [8–10]. Digital media, suchas TV and streaming platforms, social media, and video
games, have grown in popularity over the past decades,
and more rapidly during the COVID-19 pandemic [11,12]. In the province of Quebec, Canada, an estimated 97%of the population aged 18–24 years owns a smartphone,and 45% has reported that their screen time “substantially
increased” between 2020 and 2021 [12]. Digital media
use can reduce key protective factors of mental health,for example by taking time away from sleeping or exercising [13], or by becoming the source of addiction-likebehaviors [14]. Through these or other mechanisms, digital media use has been hypothesized to be associated withpsychotic experiences [10, 15]. Studies examining digital
media use and mental health in the general population
have found inconsistent associations, notably as a function of study design and outcomes of interest [16, 17]. In
addition, most studies have focused on general well-being
or depression as outcomes [14, 18], while little is known
about the association with psychotic experiences.

Associations between digital media use
and psychotic experiences
In adolescents and young adults from the community,
there is evidence that higher levels of psychotic experiences are associated with higher levels of internet addiction or maladaptive internet use; however, most studies
have been cross-sectional [8–10]. Thus, whether digital
media use preceded psychotic experiences, or vice-versa,
could not be determined. In fact, existing evidence points
to a bidirectional association. On the one hand, changes
in digital media use may precede changes in psychotic
experiences, for example, if digital media use induces
thought and perceptual disturbances [19, 20]. In an experience sampling study (44 participants with and without

13

psychosis), aspects of social media use, such as venting
about feelings and perceived low social rank, were associated with subsequent increases in paranoia, possibly due toundesirable responses from others and negative social selfcomparisons [21]. On the other hand, changes in psychotic
experiences may precede changes in digital media use,for example if psychotic experiences lead to preference
for digital media over other activities [22]. A systematic
review found that people with psychosis spend more time
on chat rooms and video games than people without psychosis, possibly as a means of fostering relationships [22].
To understand the temporal relationship between digital
media use and psychotic experiences, between-person andwithin-person associations should be untangled. Betweenperson associations indicate whether individuals’ propensity for higher digital media use is associated with theirpropensity for more psychotic experiences. Within-person
associations indicate whether a person’s increase or decreasein digital media use over time is associated with subsequent changes in their psychotic experiences, or vice-versa.
Within-person associations offer stronger evidence for putative causal effects because participants are analyzed as theirown comparators, thus indirectly controlling for many measured and unmeasured stable individual characteristics [23].
Other factors likely influence the association between
digital media use and psychotic experiences. Male andfemale individuals have different profiles of digital mediause [12], and some evidence points to stronger associations
with mental health among female individuals [16]. Whileprevious research has often aggregated different types ofdigital media use, such as TV and streaming platforms,social media, or video games, these platforms engage users
in different ways and may also be differently associated withpsychotic experiences [13, 24]. Sociodemographic characteristics and lifestyle-related behaviors may potentially confound the association between digital media use and mental
health [23, 25, 26]. For example, although digital media use
may affect sleep, a person may spend more time on digital
media at night because of their tendency to sleep late [27],a tendency which in turn is a potential risk factor for mental
ill-health [28]. Exploring how potential confounders suchas sociodemographic characteristics and lifestyle-related
behaviors impact the association of digital media use withpsychotic experiences may help uncover pathways of riskthat precede both outcomes.

Study aims
Using a longitudinal sample of youth, we aimed to examine the association between digital media use and psychotic
experiences. We investigated both between- and within-person associations. We hypothesized that, after adjusting forthe between-person association, there would be bidirectional

Social Psychiatry and Psychiatric Epidemiology

associations between digital media use and psychotic experiences at the within-person level. We also explored sex differences, associations of specific types of digital media use(TV or streaming, social media, and video games), and theinfluence of potential confounding variables (sociodemographic characteristics and lifestyle-related behaviors) on the
between-person association.

Methods
ParticipantsParticipants were from the Green/Screen Study (https://​osf.​i o/​y 2b9z/), a convenience sample of young adults(18–25 years) residing in the Canadian province of Quebec
and speaking one of the two official languages, French or
English. Participants were assessed over three time points
during the following periods: May 27 to June 14, 2021 (T1),
September 8 to October 13, 2021 (T2), and December 2 toJanuary 11, 2022 (T3). Questionnaires were administered
online through a custom survey platform (https://w​ ww.e​ labo​
rer.o​ rg). The study received ethical approval from the institutional review board of the University of Quebec at Montreal(#4552_e_2021).
Participants were recruited through sponsored advertisement which targeted the aforementioned age range and geographical region. Advertisement was placed on four popularsocial media platforms (Facebook, Instagram, Twitter andReddit) and two classified advertisement websites (Kijiji and
Craigslist). Individuals were directed to the study website
where the project and consent form were presented. All participants provided their consent via electronic signature. To
encourage participation and minimize attrition, we offereda draw of 20 lots of $25 CAD after the first study time pointand 10 lots of $100 CAD after the third time point.

Measures
Psychotic experiences were measured at each time pointwith the 15-item Community Assessment of Psychic Experiences [29, 30]. This version of the questionnaire captures 15
psychotic or psychotic-like experiences in the past 3 months,
including persecutory ideations (e.g., “Have you ever felt
as if people seem to drop hints about you or say things
with a double meaning?”), bizarre experiences (e.g., “Haveyou ever felt as if the thoughts in your head are not yourown?”), and perceptual abnormalities (e.g., “Have you ever
heard voices when you are alone?”). The frequency of eachexperience is rated on 4-point scale from “never” to “nearly
always”, and the global score is the averaged frequency of
all items (range 1–4). A screening cut-off of 1.75 has been
proposed for psychotic disorders [31]. The questionnaire has

good validity and reliability [29, 32, 33]. In accordance with
the World Mental Health Composite International Diagnostic Interview [34], we instructed participants not to include
experiences that occurred only while under the influenceof alcohol, drugs, or medications that were not prescribed.
Digital media use was measured at each time point withthree items adapted from the Coronavirus Health and Impact
Survey [35]. Items captured daily use in the past 3 monthsof (1) TV and streaming services, (2) social media, and (3)video games. For each item, the amount of use was rated
as 1 = “never/did not use”, 2 = “under 1 h”, 3 = “1–3 h”,
4 = “3–6 h”, and 5 = “more than 6 h”. Overall digital media
use was indexed by summing the three items (range: 3–15).
Potential confounding variables were measured at the firsttime point. These included age, sex (male or female), educational attainment, sleep phase delay, physical activity, andcannabis use. Educational attainment was dichotomized as
0 = “high school diploma or lower” and 1 = “some college
or higher”. Lower educational attainment has been associated with greater digital media use [36] and higher risk ofpsychotic experiences in the general population [25]. For
descriptive purposes, we also report employment and student status (none, part time or full time) at baseline. Sleep
phase delay was measured by assessing typical sleep habits
using the Munich Chronotype Questionnaire [27]. Sleep
phase delay, also known as evening chronotype, indicatesa tendency to initiate sleep later in the night, and to wakeup later in the morning. Greater sleep phase delay has been
associated with more psychotic experiences [37] and greater
computer use [38] in community-based samples. As recommended by the questionnaire creators [39], we calculated
sleep phase delay as the midsleep point (i.e., time of the
day corresponding to the middle of the sleep period; range
0–24 h) on days without engagements (i.e., without workor school), corrected for sleep debt accumulated duringdays with engagements. Physical activity was estimatedin Metabolic Equivalent Task (MET)–minutes/week using
the International Physical Activity Questionnaire—ShortForm [40]. Frequency of cannabis use in the past 2 weeks
was reported on a 4-point scale from “never” to “everyday”[41]. As aspects of lifestyle that predispose to screen-based
sedentary activities, both lower levels of physical activity
and higher levels of cannabis use could be associated withgreater digital media use and higher levels of psychotic
experiences [26].

Statistical analysis
Analyses were conducted in R version 4.1.2 (R Foundationfor Statistical Computing). Codes are available at https://o​ sf.​
io/​y2b9z/. Following recommendations regarding analysis
of internet surveys [42, 43], data of potentially lower quality from rushed or careless completion of the questionnaire13Social Psychiatry and Psychiatric Epidemiology

were identified, using criteria based on short completion
times, inconsistent responses, and implausible response patterns (see Supplementary Note 1).
Descriptive analyses were performed to report characteristics of the sample according to attrition status. To estimate
proportions of variance explained by between-person differences versus within-person fluctuations over the three time
points, we calculated the intraclass correlation coefficients(ICCs) of digital media use and psychotic experiences. The
ICC of a measure corresponds to the proportion of betweenperson variance, while the proportion of within-person variance is 1-ICC (i.e., the remaining variance).
Associations between digital media use and psychotic
experiences
To examine associations between digital media use and psychotic experiences, we used random-intercept cross-lagged
panel models [44, 45]. This type of model allowed us toconsider two types of associations: (1) stable differences
between persons (i.e., between-person associations, capturedby the correlation of random intercepts); and (2) associations
between digital media use and psychotic experiences related
to fluctuations over time (i.e., within-person associations,captured by cross-lagged regressions).
Here, random intercepts indicate individuals’ stable propensities for digital media use and psychotic experiences; thecorrelation between these two intercepts indicates whether
individuals with more psychotic experiences also differ intheir levels of digital media use compared with other individuals. Cross-lagged regressions identify whether individuals who increase their digital media use will subsequently
experience a change in psychotic symptoms, and vice-versa.
We estimated the model with the lavaan package [46],using robust standard errors to accommodate multivariate
non-normality. Missing data were handled with the Full
Information Maximum Likelihood: this method allows theinclusion of participants with missing data and is robust to
missingness at random [47]. At the within-person level, we
assumed that the strength of associations between digital
media use and psychotic experiences would be of similar
magnitude over time. We thus constrained the corresponding autoregressive and cross-lagged regression coefficientsto equality. To account for different means of digital media
use and psychotic experiences over the 3 time points, weallowed the intercepts to vary over time, which improved
model fit [45]. We defined statistical significance as twotailed p < 0.05, and the smallest effect size of interest as
correlation coefficient (r) or standardized regression coefficient (β) of 0.10 [48, 49]. We defined Comparative Fit
Index (CFI) > 0.90 as acceptable and > 0.95 as good fit; andboth root mean square error approximation (RMSEA) and13

standardized root mean squared residual (SRMR) < 0.08 as
acceptable and < 0.05 as good fit [50, 51].
We then tested three variations of the random-intercept
cross-lagged model. First, to explore how attrition may haveaffected the results, we restricted the sample to participants
who had completed at least two time points. Second, to test
for sex differences, we examined male and female as separate groups in the model. Third, to explore associations ofspecific types of digital media use, we separately examined
TV and streaming, social media, and video games.
Lastly, we explored the influence of potential confounding variables on the between-person association of overall
digital media use and psychotic experiences. To do so, we
separately regressed their random intercepts on age and sex,
educational attainment, sleep phase delay, physical activity,and cannabis use (i.e., we generated five models). We considered (1) whether the potential cofounding variables weresignificantly associated with the random intercepts of digital
media use and psychotic experiences, and (2) the extent towhich adjusting for these covariates accounted for the statistical association between the two random intercepts.

Results
Of 445 individuals who completed the assessments at thefirst time point, 20 were excluded due to being outside theeligible age range. Then, of the 425 eligible participants,
18 (4.24%) were identified as having data of potentiallylower quality because they completed the first assessmentin < 6 min or provided straight-line patterns of responses.
Due to being < 5% of the sample size, they were not removed
from analyses [42] (see Supplementary Note 1).
Table 1 presents the descriptive statistics of the total sample (N = 425) sociodemographic and key study variables at
the first time point. Of this sample, 263 (61.9%) completed
at least 2 of the 3 time points and 177 completed all 3 time
points (41.6%). Compared with participants who completed
2 or 3 time points, participants who completed only the firstassessment were more likely to report lower educational
attainment and endorsed higher levels of psychotic experiences (Table 1).
Mean (standard deviation) scores for psychotic experiences were 1.32 (0.35) at the first time point, 1.23 (0.27) atthe second time point, and 1.21 (0.29) at the third time point.
At baseline, 345 (81.3%) participants endorsed at least oneof the psychotic experience items, and 44 (10.4%) participants met the screening cut-off for psychotic disorders. Mean
(standard deviation) scores for overall digital media use were
8.88 (2.32) at the first time point, 8.30 (2.12) at the second
time point, and 8.23 (2.19) at the third time point.
Proportions of between-person variance, i.e., ICCs, were0.77 (95% CI 0.72, 0.80) for psychotic experiences, 0.55

Social Psychiatry and Psychiatric Epidemiology
Table 1  Descriptive statistics on sociodemographic and key study variables at baseline in the total sample and according to attrition status

Age in years, median [25th; 75th]
Sex, N (%)
FemaleMale
Educational attainment, N (%)
High school or lower
Some college or higher
Employment, N (%)
None
Part time
Full time
Studying, N (%):
None
Part time
Full time
Psychotic experiences, mean (SD)Overall digital media use, mean (SD)All included participants (N = 425)

Comparison of characteristics according to attrition status
Participants who completed ≥ 2 Participants who completed thetime points (N = 263)
first time point only (N = 162)p

22.0 [20.0;24.0]

22.0 [19.5;24.0]

22.0 [20.0;24.0]

348 (82.5%)
74 (17.5%)

222 (85.1%)
39 (14.9%)

126 (78.3%)
35 (21.7%)

0.66
0.10

112 (26.5%)
311 (73.5%)

60 (22.9%)
202 (77.1%)

52 (32.3%)
109 (67.7%)

127 (30.1%)
199 (47.2%)
96 (22.7%)

48 (29.6%)
77 (47.5%)
37 (22.8%)

79 (30.4%)
122 (46.9%)
59 (22.7%)

91 (21.7%)
47 (11.2%)
282 (67.1%)
1.32 (0.35)
8.88 (2.32)

41 (25.5%)
15 (9.32%)
105 (65.2%)
1.29 (0.34)
8.79 (2.31)

50 (19.3%)
32 (12.4%)
177 (68.3%)
1.38 (0.36)
9.03 (2.33)

0.04

0.99

0.26

0.008
0.22

Participants who completed 2 + time points were compared with completers of only 1 time point using Kruskal–Wallis and chi-squared tests.
Psychotic experiences were measured with the 15-item Community Assessment of Psychic Experiences (range 1–4) [29]. Overall digital media
use included TV and streaming platforms, social media, and video games based on the Coronavirus Health and Impact Survey (range 3–15) [35]SD standard deviation(95% CI 0.47, 0.61) for overall digital media use, 0.48 (95%
CI 0.40, 0.55) for TV or streaming use, 0.47 (95% CI 0.39,0.54) for social media use, and 0.70 (95% CI 0.65, 0.75) forvideo game use.

Associations between digital media use
and psychotic experiences
The random-intercept cross-lagged model produced acceptable fit: χ2(6) = 16.32, p = 0.01; CFI = 0.983; RMSEA = 0.064(90% CI 0.027, 0.102); SRMR = 0.052. Figure 1 presents
standardized estimates (see Supplementary Fig. 1 forunstandardized estimates). At the between-person level, therandom intercepts for overall digital media use and psychotic
experiences were significantly correlated at a magnitude
above the smallest effect size of interest. This finding indicates that the propensity of individuals for higher levels of
psychotic experiences was associated with their propensity
for higher levels of overall digital media use.
Cross-lagged associations between overall digital media
use and psychotic experiences were not statistically significant. In other words, individuals using digital media
more than their personal average at one time point did nothave significantly more psychotic experiences at the nexttime point. Similarly, individuals reporting more psychotic

experiences than their average at one time point did notreport significantly higher digital media use at the next time
point. However, although these cross-lagged associations
were not statistically significant, their confidence intervals
overlapped with the smallest effect sizes of interest in both
directions (− 0.10 and + 0.10), indicating that meaningful
associations could not be reliably ruled out.
After restricting to participants with complete data on
at least 2 time points (n = 263), the model produced similar
estimates and fit indices (Supplementary Fig. 2). Due to thesmall proportion of male participants, we did not examinegroup-based estimates of sex differences but rather restricted
the analysis to female participants. We obtained estimates
similar to those of the initial model (Supplementary Fig. 3).
With the complete sample of N = 425, separating digital
media use into TV and streaming, social media, and video
games yielded similar estimates with acceptable to good
fit indices (see Supplementary Figs. 4, 5 and 6). All three
types of digital media use were significantly correlated withpsychotic experiences at the between-person level; TV andstreaming: r = 0.25 (95% CI 0.07, 0.42); social media use:r = 0.28 (95% CI 0.08, 0.48); and video game use: r = 0.23
(95% CI 0.07, 0.38). Again, this indicates that the propensityof individuals for higher levels of psychotic experiences was
associated with their propensities for higher levels of each13

Social Psychiatry and Psychiatric Epidemiology

Psychotic
experiences(intercept)
1.00

Psychotic
experiences
(T1)
0.34
(0.15, 0.53)

0.13(-0.12, 0.39)

0.33(-0.26, 0.92)
0.10(-0.21, 0.40)

1.00

Psychotic
experiences
(T2)

0.05
(-0.20, 0.31)

0.04(-0.16, 0.24)
Digital m edia
(T1)

-0.21
(-0.49, 0.07)

1.00

1.00

0.14
(-0.30, 0.58)
0.04(-0.10, 0.19)

Psychotic
experiences
(T3)

0.05
(-0.19, 0.29)

0.03(-0.12, 0.17)
Digital media
(T2)

1.00

-0.16
(-0.38, 0.05)

Digital media
(T3)

1.00

Digitalmedia(intercept)

Fig. 1  Associations between overall digital media use and psychotic
experiences within and between persons. Random-intercept crosslagged panel model (N = 425), standardized estimates (95% confidence intervals). Participants’ propensities for psychotic experiences(up) and overall digital media use (bottom) are captured by time-var-

ying random intercepts (in bold). The correlation between intercepts(also in bold) is thus at the between-person level. Associations among
repeated measures at study time points T1, T2, and T3 are within persons. Paths from intercepts to repeated measures were constrained to
1.00 [73]type of digital media use. Cross-lagged associations were notstatistically significant, but their confidence intervals generally overlapped with the smallest effect sizes of interest(− 0.10 and + 0.10). Thus, after adjusting for between-person
differences, these estimates do not reliably rule in or out
whether a person’s increase in either type of digital media
is associated with a subsequent change in their levels of psychotic experiences. The same is true for the other directionof association, starting with an increase or decrease in thelevels of psychotic experiences to a subsequent change ineither type of digital media use.
Table 2 presents the regression estimates of overall digital
media use and psychotic experiences (random intercepts) on
potential confounding variables. Several associations wereboth statistically significant and above the smallest effectsize of interest. According to these criteria, older age wasassociated with lower levels of psychotic experiences but
was not associated with levels of digital media use. Malesex was associated with higher digital media use but not
psychotic experiences. Higher educational attainment was
associated with lower levels of both overall digital media
use and psychotic experiences. Greater sleep phase delay

was associated with higher levels of both digital media use
and psychotic experiences. Physical activity was not associated with either outcome. Cannabis use was associatedwith higher levels of psychotic experiences but not digital
media use.
Figure 2 presents the correlation between random intercepts for overall digital media use and psychotic experiences
after adjusting for those variables. The correlation coefficient remained significant and of similar magnitude acrossadjustments.

13

Discussion
Among young adults recruited from the community, thosewith higher levels of psychotic experiences also tended toreport higher use of digital media, an association that wasobserved across different types of digital media and independently of sociodemographic and lifestyle characteristics. At
the individual level, changes in digital media use over time
were not significantly associated with subsequent changes

Social Psychiatry and Psychiatric Epidemiology
Table 2  Associations ofpotential confounders withparticipants’ propensities foroverall digital media use andpsychotic experiences

Potential confounders

Standardized estimates (95% confidence intervals)
Association with random intercept of Association with random
overall digital media useintercept of psychotic experiences

Age in years
Male sex
Higher educational attainment
Sleep phase delay
Physical activity
Cannabis use− 0.07 (− 0.17, 0.04)
0.24 (0.12, 0.36)− 0.21 (− 0.33, − 0.10)
0.33 (0.22, 0.45)
0.00 (− 0.12, 0.13)
0.06 (− 0.05, 0.17)− 0.26 (− 0.36, − 0.16)
0.11 (− 0.03, 0.25)− 0.38 (− 0.49, − 0.28)
0.28 (0.14, 0.41)
0.01 (− 0.12, 0.14)
0.22 (0.09, 0.35)

Random-intercept cross-lagged panel model (N = 425 minus < 5% removed due to missing data on potential
confounders). Random intercepts for overall digital media use and psychotic experiences were regressedon potential confounders, each in separate models, except for age and sex which were included in the samemodel. Potential confounders were measured at baseline. Higher educational attainment (dichotomous) was
defined as some college or higher (reference: high school or lower). Sleep phase delay was measured with
the Munich Chronotype Questionnaire [27]; higher values indicate greater delay of the sleep phase, i.e.,evening chronotype. Physical activity was measured in metabolic equivalent of task (MET) per week withthe short-form International Physical Activity Questionnaire [40]; scores were divided by 1000 for rescaling. Cannabis use (dichotomous) was defined as 1–2 uses or more in the past 2 weeks (reference: no use inpast 2 weeks). In bold: statistically significant associations

Fig. 2  Unadjusted and adjusted association between overall digital
media use and psychotic experiences at the between-person level.
Random-intercept cross-lagged panel model (N = 425 minus < 5%removed due to missing data on potential confounders). Random
intercepts for overall digital media use and psychotic experienceswere regressed on potential confounders, each in separate models, except for age and sex which were included in the same model.
Potential confounders were measured at baseline. Higher educational
attainment (dichotomous) was defined as some college or higher (reference: high school or lower). Sleep phase delay was measured with
the Munich Chronotype Questionnaire [27]; higher values indicate
greater delay of the sleep phase, i.e., evening chronotype. Physicalactivity was measured in metabolic equivalent of task (MET) per
week with the short-form International Physical Activity Questionnaire [40]; scores were divided by 1000 for rescaling. Cannabis use(dichotomous) was defined as 1–2 uses or more in the past 2 weeks(reference: no use in past 2 weeks)in psychotic experiences, but small-effect associations could
not be reliably ruled out.
The between-person association of digital media use
and psychotic experiences is consistent with previous work
[8–10]. For example, in a population-based sample of 973
Irish adolescents, scores on the Young’s Diagnostic Questionnaire (which captures patterns of internet use resulting in
psychological or social distress) were cross-sectionally associated with the odds of having psychotic experiences [9]. Ina sample of adults from the UK Biobank, playing vs. notplaying computer games was associated with greater odds of
psychotic experiences [52]. The first study used self-reportsof “pathological” internet use, the second study employeda dichotomous assessment of usage, and the present study
investigated self-reports of time spent using digital media.
These constructs are different, and emerging evidenceshows that measures of “pathological use” correlate morestrongly with measures of mental health, while measures of“time spent using” correlate more strongly with objective
measures of digital media use such as those recorded by aphone activity tracker (albeit imperfectly, as discussed in
the limitations below) [14, 53, 54]. On its own, time spent
on digital media use does not provide information on functional impairment or distress arising from technology usage.
However, this self-reported behavior is commonly assessedin clinical practice, alongside other lifestyle factors such as
sleep and physical activity [55]. In this context, we suggestthat the identification of a propensity for higher levels ofdigital media use could provide information on the putativerisk for psychotic experiences. Stable individual characteristics likely explain, to some extent, the shared propensity
for digital media use and psychotic experiences.

13

Social Psychiatry and Psychiatric Epidemiology

Lower educational attainment and greater sleep phasedelay were significantly associated with individual propensities for digital media use and psychotic experiences. Previous work partially supports these associations. In a sample of US undergraduate students, higher levels of digital
media use were associated with subsequently lower grades
after adjusting for background educational and demographic
characteristics [36]. However, in a systematic review, socioeconomic status was not consistently associated with psychotic experiences [2]. In a survey of 1789 adults from thegeneral population in UK, sleep phase delay was associatedwith delusional mood (but no other types of psychotic experiences) [37], while in 439,933 adults from the UK Biobank,it was associated with higher levels of computer use [38]. Of
note, these studies and the present work cannot differentiate
academic performance or sleep as confounders (i.e., preceding digital media use and psychosis expression) versus
mediators (i.e., explaining a putative effect of digital media
use on psychotic experiences)—longer periods of follow-up
would be required to untangle the sequence of exposures.
Yet we found that neither sleep phase delay, educational
attainment, nor the other potential confounding variables
substantially explained the overall association of digital
media use and psychotic experiences. This leaves open thequestion of why individuals with higher psychosis expression tend to use digital media more. Rather than a reflectionof technology causing or exacerbating psychosis expression,
the explanation may lie in technological preference amongindividuals with psychotic experiences [15]. This multifactorial preference could be motivated for example by thefulfillment of psychological needs, such as leisure, social
connections or self-efficacy, in context of psychosis [56–58].
At the within-person level, changes in digital media
use were not significantly associated with changes in psychotic experiences. Limited research has examined theselongitudinal associations. In a sample of 44 individuals
with and without psychosis followed over 6 days, there
was no association between momentary reports of social
media use and subsequent increases in paranoia; however,specific behaviors such as posting about feelings, venting
on social media, or perceived low social rank during usage
were significant predictors [21]. Studies on other mental
health outcomes have generally found minimal or no withinperson associations with digital media use in the general
population [59–61]. For example, in 10,000 adults fromthe general Dutch population followed over 6 years, yearto-year changes in social media use were not prospectively
associated with changes in well-being after adjusting forthe between-person association [60]. In the present study,
cross-lagged associations were not statistically significant,but confidence intervals were relatively wide and overlappedwith the smallest effect size of interest of |β|= 0.10. Lack ofsignificant associations may reflect insufficient variance at13

the within-person level, for example if measures were notsensitive enough to changes over time, or if these changes
occurred over different time frames than the ~ 3-month intervals examined here. Other factors that were not measured
in the present study may moderate the within-person associations of digital media use with psychotic experiences:
examples include aspects of technology use (e.g., passive or
active, alone or with others) [62], need satisfaction (e.g., ofrelatedness) [63], distress, or functional impairments arising from digital media use [14]. It should be noted that thethree study time points coincided with different periods ofrestrictions related to the COVID-19 pandemic in Quebec,
Canada – from gradual deconfinement during the first study
time point [64], to abrupt re-confinement during the third[65]. Evolving restrictions likely yielded changes in stressand psychosocial functioning throughout, with different
impacts on mental health as a function of individual risk
factors (e.g., living alone) [66]. At the within-person level,
then, these factors may have been a source of unmeasured
variability, potentially limiting the study’s statistical power
to detect general associations between digital media use andpsychotic experiences.

Strengths and limitations
Repeated measures of digital media use and psychotic
experiences allowed us to examine, for the first time, theirbetween- and within-person associations. We considered
overall as well as specific types of digital media use andfound consistent correlations with psychotic experiences atthe between-person level. Building on previous work, wewere able to consider multiple potential confounders of
between-person associations, such as educational attainment and sleep phase delay, and showed they did not explain
participants’ shared propensities for digital media use andpsychotic experiences.
Our study had limitations. Self-reports of digital media
use only partially reflect objective use, and shared method
variance with self-reported psychotic experiences may
inflate their associations [53, 67]. However, from a translational standpoint, self-reports of digital media use are moreaccessible to the clinician’s questioning than objective measurement (such as smartphone monitoring), which is moreinvasive and may fail to capture digital media use acrossmultiple devices (computer, phone, TV, etc.). Although subjective and objective measures of digital media use are only
moderately correlated, these indices seem to have convergent predictive validity for mental health outcomes [68]. Our
study also suffered from attrition. Although the analyticalmethod we used is robust to missingness at random, bias ispossible if attrition occurred as a function of the strengthof association between psychotic experiences and digital
media use. Another limitation is that due to convenienceSocial Psychiatry and Psychiatric Epidemiology

sampling, our cohort was not representative of the general
population, notably with an overrepresentation of female
individuals and higher socioeconomic status. It is possiblethat individual characteristics such as male gender (which is
associated with video game addiction [69]) or other features
could lead to stronger associations between digital media use
and psychotic experiences [70], for example as a result ofgreater psychosis propensity and/or more maladaptive formsof digital media use. Lastly, online data collection is prone torushed or careless completion of questionnaires [42]. Aftersearching for low-quality data using systematic criteria, we
identified less than 5% of problematic entries which are thus
expected to have minimal impact on the analyses [42]. However, due to the difficulty of reliably identifying data of low
quality, there may be unexplained variability in the findings
stemming from additional problematic entries that were not
identified.
Future directions include replication in other populations,
including those at risk of psychosis or with psychotic disorders [71], with potentially distinct profiles of digital media
use. Examining digital media use over different time scales,from day-to-day variations to long-term trajectories over several years, may provide additional information on its association with psychotic experiences. Future research should
also aim to identify what underlies individuals’ shared propensity for psychosis expression and digital media use. If
compensatory strategies are at play (e.g., using technology
to foster relationships), then clinicians should be mindful of
those when counselling youth about optimal “screen time”.
Insights into mechanisms of shared propensity may also help
develop better digital interventions for individuals with psychotic experiences, such as mental health apps, virtual reality therapy, or online peer support [72]. Considering youth
with psychotic experiences have an elevated risk of subsequent psychopathology [4, 5], interventions that tap into
their technological preferences may provide accessible andscalable means of improving their mental health outcomes.
Supplementary Information The online version contains supplementary material available at https://d​ oi.o​ rg/1​ 0.1​ 007/s​ 00127-0​ 23-0​ 2537-6.
Funding This study received funding from the Stratas Foundation
awarded to VP and from the Canada Research Chair awarded to MCG
and IOM. The funders had no role in the design and conduct of thestudy; collection, management, analysis, and interpretation of the data;preparation, review, or approval of the manuscript; and decision tosubmit the manuscript for publication.
Data availability The data is not publicly available due to ethics
approval from participants not covering public sharing but is available
from the corresponding author on reasonable request.

Declarations
Conflict of interest On behalf of all authors, the corresponding author
states that there is no conflict of interest.

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