Content Server
Content Server
Content Server
Abstract
Background: Psychological factors such as depression, anxiety, stress and insomnia and problematic social media
use are able to alter our memories and might have an impact on memory function and retrieval. More studies are
needed to better understand the relationship between memory performance and mental health disorders,
especially the ones that could be related to problematic social media use. The objective of this study was to
evaluate any association between problematic social media use, depression, anxiety, stress, and insomnia vs
memory performance among a representative sample of Lebanese people.
Methods: This cross-sectional study, conducted between January and May 2019, enrolled 466 community dwelling
participants using a proportionate random sample from all Lebanese governorates. The questionnaire consisted of
the following measures: the Memory Awareness Rating Scale (MARS) to assesses views of memory performance, the
problematic social media use scale to measure the degree of addiction to social media, the Hamilton depression
rating scale and Hamilton anxiety scale to assess depression and anxiety respectively, the Beirut Distress Scale to
assess stress and the Lebanese Insomnia sale to assess insomnia. The data analysis was performed using the SPSS
software version 25. A linear regression was conducted, taking the memory performance scale as the dependent
variable. A mediation analysis was performed to test the effect of problematic social media use on memory
performance mediated by depression, anxiety, stress and insomnia.
(Continued on next page)
© The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if
changes were made. The images or other third party material in this article are included in the article's Creative Commons
licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons
licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain
permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the
data made available in this article, unless otherwise stated in a credit line to the data.
Dagher et al. Head & Face Medicine (2021) 17:6 Page 2 of 12
These findings applied to both quality/clarity of memory enhanced [25]. Nevertheless, more studies are needed to
recollection as well as quantity of memories stored [16– better understand the relationship between memory per-
18]. Second, emotional disorders such as depression, in- formance and certain mental health disorders, particu-
somnia and stress were also shown to have deleterious larly depression, anxiety, stress and insomnia. What’s
consequences on our memory function [19, 20]. That more, considering our society’s newly found interest in
being said, the effects of stress are regulated by numer- social networking and given the progressive unraveling
ous significant factors and thus it is of importance to of its adverse effects on our psyche and cognitive skills
note that according to recent studies, stress adversely af- [26], we found great potential in analyzing the relation-
fects some episodic memory processes whilst improving ship between memory function and Problematic Social
others [21]. Concerning anxiety, controversial results Media Use (PSMU). In addition, research projects taking
were elucidated: some studies emphasis that anxiety did on similar subjects in Lebanon are still scarce, hence
not induce lower memory performance (in contrast to underlining the necessity of diving deeper into this mat-
depression), whilst others attempt to explain how anx- ter and making the pursuit of this topic ever so compel-
iety has the same repercussions on memory as the other ling. Finally, bearing in mind that mental disorders are
mood disorders [22]. Finally, people suffering from these among the most common health problems in Lebanon
psychiatric disorders, more specifically emotional disor- [27], it is of great importance to highlight and expose
ders, are more prone to develop addictive disorders such their potential repercussions, notably on our memory.
as PSMU [23]. To boot, previous results indicated that For all of that, the objective of this study was to evaluate
social anxiety lead to favoring computer mediated over any association between problematic social media use,
face to face communication [24]. Reciprocally, individ- depression, anxiety, stress, and insomnia vs memory per-
uals with greater PSMU levels neglect some aspects of formance among a representative sample of Lebanese
their lives thus contributing to the development of de- people.
pressive symptoms. With all that in mind, given how the
literature illustrates this complex relationship suggests Methods
that certain mental health disorders like anxiety, depres- Study design and participants
sion, stress and insomnia might play an intermediate This cross-sectional study was carried out between
role between PSMU and memory performance, hence January and May 2019. It enrolled 466 residents of the
mediating their correlation. community randomly selected from Lebanon’s Mohafa-
To summarize, memory is fundamentally a vital bio- zat in a proportionate rate. The Mohafazat are divided
logical function crucial for our survival. The progress into Caza (stratum), divided into villages. From a list
that has been made in the past few decades elucidating provided by the Central Agency of Statistics in Lebanon,
the factors affecting memory and forgetfulness has pro- we chose two villages per Caza where the questionnaire
vided useful insight into how memories might be was distributed randomly to the households, based on a
Dagher et al. Head & Face Medicine (2021) 17:6 Page 4 of 12
Fig. 2 Structural equation model in adult population. —observed variable; —latent variable; —impact of one variable on
another; e—residual error in the prediction of an unobserved factor; * p < 0.001. Numbers without a * indicate non-significant associations. The
values of standardized coefficients are presented
random sampling technique to select the included questionnaire were distributed to the participants. The
house. Those who agreed to take part in the study were required time to complete the questionnaire was around
invited to complete the questionnaire via a face-to-face 10–15 min. The methodology used in this study is simi-
interview. All individuals over the age of 18 were eligible lar to the one used in previous papers [28–31].
to participate. Excluded were those with dementia (ac- Out of 500 questionnaires distributed, 466 (93.2%)
cording to one of the family members), and those who were completed and collected back. The sociodemo-
refused to complete the questionnaire. The distribution graphic characteristics of the participants are summa-
of the questionnaire as well the data collection were per- rized in Table 1. The results showed that the mean age
formed by a one study-independent well-trained clinical of the participants was 27.29 ± 11.46 years and the mean
psychologist. The clinical psychologist has gone through number of hours spent on social media per day was
a training with the researcher before starting data collec- 6.22 ± 4.92. The majority of the participants were fe-
tion. Any queries or doubts that were encountered dur- males (61.8%), had a university level of education
ing the data collection, the psychologist had the capacity (66.7%), single (68.1%), with a low monthly income
to contact and refer to the researcher. Paper-based (61.4%). Almost all participants use their cellular as the
Dagher et al. Head & Face Medicine (2021) 17:6 Page 5 of 12
Table 1 Sociodemographic characteristics of the sample assessed the sociodemographic characteristics of the par-
population (N = 466) ticipants (age, number of kids, gender, education level,
Frequency Percentage socioeconomic level and marital status). The second part
Gender of the questionnaire consisted of measures used in this
Male 176 38.2% study as follows:
Female 285 61.8%
The memory performance scale of the memory
Education level
awareness rating scale (MARS-MPS)
Illiterate 8 1.8%
The MARS-MPS scale was used to assesses views of
Primary 17 3.7% memory performance on specific aspects of memory
Complementary 32 7.0% functioning following direct experience in each case of
Secondary 95 20.8% an analogue task assessing the given aspect of memory
University 304 66.7% functioning. These tasks are similar to the tasks used in
Monthly income
the sub-tests of the Rivermead Behavioural Memory
Test Examples of the situations include remembering a
< 1000 $ 266 61.4%
person’s name, remembering a short route and recogniz-
1000–2000 $ 122 28.2% ing familiar objects. Ratings are made on a 0–4 scale
> 2000 $ 45 10.4% where 0 = very poor and 4 = very good. The maximum
Marital status possible score is 52 and a higher score indicates a more
Single 309 68.1% positive perception of functioning [32]. In this study, the
Married 140 30.8%
Cronbach alpha for MPS was 0.912.
Widowed 3 0.7%
Social media use disorder scale (SMUD)
Divorced 2 0.4%
The SMDS is a 27-item scale that measure the degree of
Kind of device mostly used on social media problematic social media use [33]. Higher scores indi-
Laptop 22 5.2% cated higher problematic social media use. The Cron-
Cellular 394 92.9% bach’s alpha for this scale was very good (0.847).
PC 3 0.7%
Tablet 5 1.2% Hamilton depression rating scale (HDRS)
Smoking The validated Arabic version of the HDRS was used in
this study [34]. The first 17 items of the HDRS are
Yes 89 19.4%
scored and measure the severity of depressive symptoms
No 370 80.6%
[35]. The total depression score was calculated by sum-
Mean SD ming the answers of these seventeen items. Higher
Age (in years) 27.29 11.46 scores indicated higher depression. The Cronbach’s
Number of kids 0.76 1.55 alpha for this scale was good (0.873).
Number of hours spent on social media 6.22 4.92
per day Hamilton anxiety scale (HAM-A)
The HAM-A [36], validated in Lebanon [37], is a com-
mostly used device on social media (92.9%) and 19.4% monly used scale to measure anxiety in medical and
were smokers. research sites. It consists of 14 items, rated according to
a four-point Likert scale (0 = symptoms not present to
Minimal sample size calculation 4 = very severe symptoms). Higher scores indicated
According to the G-power software, and based on an ef- higher anxiety. The Cronbach’s alpha for this scale was
fect size f2 = 2%, an alpha error of 5%, a power of 80%, good (0.914).
and taking into consideration 14 factors to be entered in
the multivariable analysis, and taking a 15% as a refusal Beirut distress scale (BDS-10)
rate the results showed that a minimal number of 465 It is a 10-item classic stress assessment instrument [38].
was needed. The questions in this scale ask about your feelings and
thoughts during the last month, with the answers mea-
Questionnaire sured on a 4-point Likert scale: 0 (never) up to 3 (very
The questionnaire used during the interview was in often). Higher scores indicated higher perceived stress.
Arabic, the native language of Lebanon. The first part The Cronbach’s alpha for this scale was good (0.743).
Dagher et al. Head & Face Medicine (2021) 17:6 Page 6 of 12
Lebanese insomnia scale (LIS-18) fit of the model was verified. P < 0.05 was considered
This 18-item scale is used for the diagnosis of insomnia significant.
on the basis of several validated/universally applicable
self-report scales [39]. Answers are graded on a 5-point Mediation analysis
Likert scale (1 = Never to 5 = Always), and items 4, 18 The PROCESS SPSS Macro version 3.4, model four [41]
and 22 reversed, with higher scores indicating higher in- was used to calculate three pathways. Pathway A deter-
somnia. The Cronbach’s alpha for this scale was good mined the regression coefficient for the effect of
(0.815). problematic social media use on depression/anxiety/
stress/insomnia; Pathway B examined the association
between depression/anxiety/stress/insomnia and mem-
Translation procedure of the questionnaire
ory performance, independent of the problematic social
A forward and backward translation was conducted for
media use, and Pathway C′ estimated the total and dir-
the MARS-MPS and PSMU scales. One translator was
ect effect of problematic social media use and memory
in charge of translating the scales from English to
performance respectively. Pathway AB calculated the in-
Arabic, and another one was involved in the translation
direct intervention effects. To test the significance of the
from Arabic back to English. Discrepancies between the
indirect effect, the macro generated bias-corrected boot-
original and translated English versions were resolved by
strapped 95% confidence intervals (CI) [41]. A significant
consensus.
mediation was determined if the CI around the indirect
effect did not include zero [41]. The covariates that were
Statistical analysis included in the mediation model were those that showed
SPSS software version 25 was used to conduct data ana- significant associations with memory performance in the
lysis. Cronbach’s alpha values were recorded for reliabil- bivariate analysis.
ity analysis for all the scales. The first sample (n = 270)
was used to conduct the MPS and PSMU items’ factor Results
analysis, whereas the second was used for the confirma- The mean memory performance score was 31.71 ± 9.88
tory analysis. A factor analysis was initiated using the (median = 31; minimum = 6; maximum = 52), problem-
“principal component analysis” technique to confirm the atic social media use (PSMU) scale 8.15 ± 5.71, depres-
legitimacy of the construct of both scales in our sample. sion 9.84 ± 9.14, anxiety 15.00 ± 10.73, stress 18.24 ± 5.28
The Kaiser-Meyer-Olkin (KMO) value and the Bartlett’s and insomnia 68.99 ± 12.79.
sphericity test were checked for sampling adequacy. The
factors with Eigen values > 1 were kept. The Statistica Factor analysis of the PSMU and MPS scales
software was used to conduct confirmatory factor ana- Since the PSMU and MPS scales are not validated in
lysis on subsample 2 (n = 196). Multiple indices of Lebanon, an exploratory factor analysis (EFA) using the
goodness-of-fit were described for each scale: the Rela- principal component analysis was conducted for both
tive Chi-square (χ2/df) (cut-off values:< 2–5), the Root scales. A confirmatory factor analysis followed the EFA.
Mean Square Error of Approximation (RMSEA) (close Results are summarized in Supplementary Tables 1 and
and acceptable fit are considered for values < 0.05 and < 2. The SMUD and MPS items converged over a solution
0.11 respectively), the Goodness of Fit Index (GFI) and of 3 and 2 factors respectively.
the Adjusted Goodness of Fit Index (AGFI) (acceptable
values are ≥0.90) [40]. Bivariate analysis
The Student t-test was used to compare continuous Bivariate analysis taking the memory performance scale
variables in two groups. Pearson correlation was used as the dependent variable showed a negative weakly cor-
for linear correlation between continuous variables. The relation between number of hours spent on social media
Student t-test was used to compare the means of 2 per day (r = − 0.16, p = 0.001), depression (r = − 0.16,
groups. A stepwise linear regression was conducted, tak- p = 0.001), perceived stress (r = − 0.14, p = 0.003), anx-
ing the memory performance scale as the dependent iety (r = − 0.28, p < 0.001), problematic Social media use
variable. All variables that showed a p < 0.1 in the bivari- (r = − 0.15, p = 0.001), insomnia(r = − 0.17, p < 0.001)
ate analysis were considered as important variables to be and memory performance scale (Table 2).
entered in the model in order to eliminate potentially
confounding factors as much as possible. Structural Multivariable analysis
equation modeling (SEM) was performed (using SPSS The results of a stepwise linear regression, taking the
AMOS) to assess the structural association between memory performance scale as the dependent variable,
problematic social media use, stress, insomnia, anxiety, showed that higher problematic social media use
depression and memory performance. The goodness of score (Beta = − 0.21, p = 0.008) and higher anxiety
Dagher et al. Head & Face Medicine (2021) 17:6 Page 7 of 12
Table 2 Bivariate analysis taking the memory performance scale t = − 3.162.24, p = 0.001025 (R2 = 0.0705, df = 358).
as the dependent variable The mediating effect of anxiety was 21.19% (partial
Memory performance p-value mediation). Also, a mediation effect of the insomnia
scale was found at 10.52%.
Mean ± SD It is noteworthy that depression and, stress or in-
Gender somnia did not mediate the association between prob-
Male 30.66 ± 9.34 0.095 lematic social media use and memory performance
Female 32.24 ± 10.07 (Table 4, Fig. 2).
Correlation coefficient
Structural equation modeling
Age 0.156 0.001
The structural relationships between PSMU, insomnia,
Number of kids 0.086 0.071 stress, anxiety, depression and memory performance are
Number of hours spent on −0.163 0.001 displayed in Fig. 2. The path coefficients for the paths
social media per day from insomnia to memory performance, from PSMU to
Depression (HAMD scale) −0.160 0.001 anxiety and from PSMU to depression were the only
Perceived stress (PSS scale) −0.141 0.003 ones that showed significance; higher insomnia was sig-
Anxiety (HAMA scale) − 0.289 < 0.001 nificantly associated with less memory performance
Problematic social media use −0.153 0.001
(SB = -0.16; p < 0.001), whereas higher PSMU was sig-
nificantly associated with higher anxiety (SB = 0.18; p <
Insomnia (LIS scale) −0.175 < 0.001
0.001) and higher depression (SB = 0.16; p < 0.001)
Numbers in bold indicate significant p-values all other variables not displayed
in the table showed a p > 0.1 in the bivariate analysis and were not included
respectively.
Table 3 Multivariable analysis: Linear regression taking the memory performance scale variable as the dependent variable
Unstandardized Standardized p- 95% Confidence Interval
Beta Beta value
Lower bound Upper bound
Anxiety (HAMA scale) −0.254 −0.275 < 0.001 − 0.338 − 0.169
Problematic social media use score −0.216 − 0.124 0.008 − 0.376 − 0.056
Perceived stress scale − 0.102 − 0.055 0.267 − 0.282 0.078
Insomnia −0.049 − 0.062 0.254 − 0.132 0.035
Depression scale 0.038 0.035 0.528 −0.080 0.157
Variables entered in the model: Problematic social media use score, anxiety (HAMA scale), perceived stress (PSS scale), insomnia (LIS scale) and depression (HAMD
scale), gender, age, number of kids.
Table 4 Mediation analysis
Dagher et al. Head & Face Medicine
Effect of problematic social Effect of problematic social media use and Direct effect of problematic social media Mediating effect of anxiety
media use on anxiety anxiety on memory performance use on memory performance
Beta t p Beta t p Beta t p
Problematic social media use 0.25 [0.04–0.46] 2.34 0.01 −0.15 [−0.34–0.02] −1.67 0.09 −0.21 [− 0.40—0.02] −2.24 0.025 21.19%
(2021) 17:6
coupling higher social network sites (SNS) use to lower relationship. People suffering from anxiety are more at
memory work [16, 17, 43–46]. Literature displayed a risk of developing addictive disorders such as PSMU
modest amount of articles that interpreted this relation- [23], and PSMU leads to increased anxiety due to the
ship. De facto, this occurrence was primarily linked to famous “fear of missing out” [53]. Overall, all this infor-
“semantic memory” which is the memory of facts and mation could also explain anxiety’s partial mediation
common knowledge, not gained by personal experiences. role between PSMU and memory performance. Provided
The constant access to information via the internet will the divergence in results across the literature, and con-
replace the need for certain types of memory, predomin- sidering that we cannot ignore the potential threat of
antly, the semantic memory. Furthermore, it was dem- anxiety influencing our youth’s memory and cognitive
onstrated that increased handling of all activities on the functions, we find it ever so important to encourage dee-
internet will lead to anatomical changes in the grey mat- per investigations concerning this topic with the purpose
ter of the brain’s cortex, therefore influencing not only of unraveling the truth behind these interactions. For
memory but also impulse control and decision making that, we believe our findings will play a role in clearing
[17]. In other words, social media consumption, and and detailing the extremely heterogeneous image of anx-
more specifically PSMU, can alter our body’s biological iety disorders and their correlations with cognitive devia-
composition in a way that suits the technology most. tions, all whilst refining our diagnostic skills.
Given our headed direction in the tech world and the Finally, multivariable analysis results did not associate
high probability that SNSs are only going to get more depression, stress and insomnia to lower memory per-
popular in the coming years, these findings could repre- formance. These findings oppose previous literature [47,
sent the starting point of a long-term evolutionary 50, 51]. In fact, it has been suggested that depressive
change in the way our minds are structured and the way symptoms alone (excluding symptoms of anxiety) affect
they process information. the prompt recall of new information and their amount
Moreover, our study demonstrated an essential factor of acquisition, but did not affect either retrieval nor re-
that affected memory as well as played a mediating role tention of memory [47]. The latter study suggested that
between our two main variables. This subject was previ- memory difficulties are mostly reported in individuals
ously discussed and various contradictory theories were who suffers from coexistent mood disorders [47]. How-
given across the literature. On one hand, our results ever, our results did not demonstrate such relationship.
linked anxiety to decreased memory work and this was This can be explained by the fact that the depression,
in opposite to some of the previously published studies anxiety, stress and insomnia scales were all self-reported
[47, 48]. For instance, some demonstrate that symptoms questionnaires and were not administered by a psycholo-
of anxiety alone (excluding symptoms of depression) did gist or psychiatrist, therefore possibly affecting said re-
not display any significant detrimental consequences on sults’ reliability. Similarly, the memory awareness rating
any aspects of memory functioning, in contrast to other scale (MARS) was not carried out by specialists, which
types of mood disorders [47]. This could be interpreted might explain the negative association concluded.
through the “lack of motivation” that other types of
mood disorders have. Actually, some even go to say that Clinical implications
a moderate level of anxiety may have a beneficial out- If correlation can be made through this study in
come on cognition and memory [47]. On the other Lebanon, awareness about social media should be spread
hand, in correspondence with our results, some studies among parents and children in order to reduce its use
emphasized that anxiety correlates with lower memory and prioritize activities that promote teenagers executive
and thus decreased academic performances [11, 49, 50]. functioning and well-being such as sleep, physical activ-
This was justified by the possible influence of different ity along with positive interactions with family and
mood disorders including anxiety on the verbal working friends. In addition, this study can open the gate for fu-
memory and the central executive memory which both ture research that should focus on prevention programs
are mandatory in educational performance [51]. In fact, and possible interventions to mitigate the potential ad-
worrying takes up a portion of our working memory verse consequences among those that suffer from prob-
capacity which means that less residual working memory lematic SNSs overuse. Plus, if an individual is already
capacity will be available whenever we are thinking of known to have a PSMU, it would be of a great import-
worrisome affairs [50]. Moreover, a characteristic aspect ance to look for signs and symptoms of other comorbid
of anxiety is narrow control on worrying ideas and at- mood disorders such as anxiety in order to treat it and
tentional biases, which can lead to greater focus on control it in early stages and prevent further damage. Fi-
negative stimuli disrupting memory and cognitive per- nally, in Lebanon, the proportion of people being treated
formances [52]. Furthermore, the relationship between for mental disorders turned out to be much lower than
anxiety and addictive disorders is a bidirectional more developed countries [27] and since our study calls
Dagher et al. Head & Face Medicine (2021) 17:6 Page 10 of 12
Author details
Conclusion 1
Faculty of Medicine and Medical Sciences, Holy Spirit University of Kaslik
To conclude, concerning problematic social media (USEK), Jounieh, Lebanon. 2Univ. Limoges, UMR 1094, Neuroépidémiologie
use, a clear correlation was demonstrated in this Tropicale, Institut d’Epidémiologie et de Neurologie Tropicale, GEIST, 87000
Limoges, France. 3Research and Psychology Departments, Psychiatric
study linking it to lower memory performance, with Hospital of the Cross, Jal Eddib, Lebanon. 4INSPECT-LB: Institut National de
this association being partially mediated by anxiety. Sante Publique, Epidemiologie Clinique et Toxicologie-Liban, Building 560,
Future studies should evaluate the possible mecha- Street 8, Biakout, Beirut, Lebanon. 5School of Pharmacy, Lebanese
International University, Beirut, Lebanon. 6Faculty of Arts and Sciences, Holy
nisms and methods for effective awareness especially Spirit University of Kaslik (USEK), Jounieh, Lebanon.
towards the younger generation. Concerning anxiety,
it obviously affected certain types of memory leading Received: 7 September 2020 Accepted: 16 February 2021
to lower memory functioning and thus decreased aca-
demic performance. Therefore, it is a must to high- References
light the importance of diagnosing and treating 1. Shukal OP. Excellence in life: Gyan publishing house; 2007.
anxiety and depression that are recently increasing 2. Aguirre GK, D'Esposito M. Topographical disorientation: a synthesis and
taxonomy. Brain. 1999;122(9):1613–28.
over the decades in order to avoid their negative 3. Eysenck M. Attention and arousal: cognition and performance: Springer
sequels. Science & Business Media; 2012.
Dagher et al. Head & Face Medicine (2021) 17:6 Page 11 of 12
4. Eleven Factors that Influence Memory Process in Humans. Available from: 28. Youssef L, Hallit R, Kheir N, Obeid S, Hallit S. Social media use disorder and
http://www.psychologydiscussion.net/memory/11-factors-that-influence- loneliness: any association between the two? Results of a cross-sectional
memory-process-in-humans/582 study among Lebanese adults. BMC Psychol. 2020;8(1):56.
5. Hussain Z, Griffiths MD. Problematic social networking site use and 29. Youssef L, Hallit R, Akel M, Kheir N, Obeid S, Hallit S. Social media use
comorbid psychiatric disorders: a systematic review of recent large-scale disorder and alexithymia: any association between the two? Results of
studies. Front Psychiatry. 2018;9:686. a cross-sectional study among Lebanese adults. Perspect Psychiatr
6. Number of Social Media Users Worldwide From 2010 to 2021 (in billions). Care. 2020.
Available online at: https://www.statista.com/statistics/278414/number-of- 30. Barbar S, Haddad C, Sacre H, Dagher D, Akel M, Kheir N, Salameh P, Hallit S,
worldwide-social-network-users/ (Accessed 26 Sept 2018). Obeid S. Factors associated with problematic social media use among a
7. Thorisdottir IE, Sigurvinsdottir R, Asgeirsdottir BB, Allegrante JP, Sigfusdottir sample of Lebanese adults: The mediating role of emotional intelligence.
ID. Active and passive social media use and symptoms of anxiety and Perspect Psychiatr Care. 2020. https://doi.org/10.1111/ppc.12692.
depressed mood among Icelandic adolescents. Cyberpsychol Behav Soc 31. Malaeb D, Salameh P, Barbar S, Awad E, Haddad C, Hallit R, Sacre H, Akel M,
Netw. 2019;22(8):535–42. Obeid S, Hallit S. Problematic social media use and mental health
8. Liu X, Lin X, Zheng M, Hu Y, Wang Y, Wang L, Du X, Dong G. Internet (depression, anxiety, and insomnia) among Lebanese adults: Any mediating
search alters intra-and inter-regional synchronization in the temporal Gyrus. effect of stress? Perspect Psychiatr Care. 2020. https://doi.org/10.1111/ppc.12
Front Psychol. 2018;9:260. 576.
9. Hidaka BH. Depression as a disease of modernity: explanations for 32. Clare L, Wilson BA, Carter G, Roth I, Hodges JR. Assessing awareness in
increasing prevalence. J Affect Disord. 2012;140(3):205–14. earlystage Alzheimer’s disease: development and piloting of the Memory
10. Schweizer S, Kievit RA, Emery T, Cam CAN, Henson RN. Symptoms of Awareness Rating Scale. Neuropsychol Rehabil. 2002;12:341–62.
depression in a large healthy population cohort are related to subjective 33. van den Eijnden RJ, Lemmens JS, Valkenburg PM. The social media disorder
memory complaints and memory performance in negative contexts. scale. Comput Hum Behav. 2016;61:478–87.
Psychol Med. 2018;48(1):104–14. 34. Obeid S, Abi Elias Hallit C, Haddad C, Hany Z, Hallit S. Validation of
11. Balderston NL, Vytal KE, O'Connell K, Torrisi S, Letkiewicz A, Ernst M, Grillon the Hamilton depression rating scale (HDRS) and sociodemographic
C. Anxiety patients show reduced working memory related dlPFC activation factors associated with Lebanese depressed patients. Encephale. 2018;
during safety and threat. Depress Anxiety. 2017;34(1):25–36. 44:397.
12. Keles B, McCrae N, Grealish A. A systematic review: the influence of social 35. Hamilton M. A rating scale for depression. J Neurol Neurosurg Psychiatry.
media on depression, anxiety and psychological distress in adolescents. Int 1960;23:56–62.
J Adolesc Youth. 2020;25(1):79–93. 36. Hamilton M. The assessment of anxiety states by rating. Br J Med Psychol.
13. Mantua J, Simonelli G. Sleep duration and cognition: is there an ideal 1959;32(1):50–5.
amount? Sleep. 2019;42(3):zsz010. 37. Hallit S, Haddad C, Hallit R, Akel M, Obeid S, Haddad G, Soufia M, Khansa W,
14. Cohen-Zion M, Stepnowsky C, Marler ST, Kripke DF, Ancoli-Israel S. Changes Khoury R, Kheir N. Validation of the Hamilton anxiety rating scale and state
in cognitive function associated with sleep disordered breathing in older trait anxiety inventory a and B in Arabic among the Lebanese population.
people. J Am Geriatr Soc. 2001;49(12):1622–7. Clin Epidemiol Global Health. 2019;7(3):464–70.
15. Roozendaal B. Stress and memory: opposing effects of glucocorticoids on 38. Malaeb D, Farchakh Y, Haddad C, Sacre H, Obeid S, Hallit S, Salameh P.
memory consolidation and memory retrieval. Neurobiol Learn Mem. 2002; Validation of the Beirut distress scale (BDS-10), a short version of BDS-
78(3):578–95. 22, to assess stress among the Lebanese population. Perspect Psych
16. Sharifian N, Zahodne L. Social media bytes: daily associations between Care. 2021.
social media use and everyday memory failures across the adult life span. J 39. Hallit S, Sacre H, Haddad C, Malaeb D, Al Karaki G, Kheir N, Hajj A, Hallit R,
Gerontol B Psychol Sci Soc Sci. 2020;75(3):540–8. Salameh P. Development of the Lebanese insomnia scale (LIS-18): a new
17. Tamir DI, Templeton EM, Ward AF, Zaki J. Media usage diminishes memory scale to assess insomnia in adult patients. BMC Psychiatry. 2019;19(1):421.
for experiences. J Exp Soc Psychol. 2018;76:161–8. https://doi.org/10.1186/s12888-019-2406-y.
18. Sparrow B, Liu J, Wegner DM. Google effects on memory: cognitive 40. Marsh HW, Hau K-T, Wen Z. In search of golden rules: comment on
consequences of having information at our fingertips. Science. 2011; hypothesis-testing approaches to setting cutoff values for fit indexes and
333(6043):776–8. dangers in overgeneralizing Hu and Bentler's (1999) findings. Struct Equ
19. Dillon DG, Pizzagalli DA. Mechanisms of memory disruption in depression. Model. 2004;11(3):320–41.
Trends Neurosci. 2018;41(3):137–49. 41. Hayes AF. Introduction to mediation, moderation, and conditional process
20. Wardle-Pinkston S, Slavish DC, Taylor DJ. Insomnia and cognitive analysis: a regression-based approach: Guilford publications; 2017.
performance: a systematic review and meta-analysis. Sleep Med Rev. 2019; 42. Teens, Social Media, and Technology 2018. Available online at: http://www.
48:101205. pewinternet.org/2018/05/31/teens-socialmedia-technology-2018/50/
21. Shields GS, Sazma MA, McCullough AM, Yonelinas AP. The effects of acute (Accessed 26 Sept 2018).
stress on episodic memory: a meta-analysis and integrative review. Psychol 43. Firth J, Torous J, Stubbs B, Firth JA, Steiner GZ, Smith L, Alvarez-Jimenez M,
Bull. 2017;143(6):636–75. Gleeson J, Vancampfort D, Armitage CJ. The “online brain”: how the internet
22. Moran TP. Anxiety and working memory capacity: a meta-analysis and may be changing our cognition. World Psychiatry. 2019;18(2):119–29.
narrative review. Psychol Bull. 2016;142(8):831–64. 44. Hadlington L. Cognitive failures in daily life: exploring the link with
23. Schou Andreassen C, Billieux J, Griffiths MD, Kuss DJ, Demetrovics Z, internet addiction and problematic mobile phone use. Comput Hum
Mazzoni E, Pallesen S. The relationship between addictive use of Behav. 2015;51:75–81.
social media and video games and symptoms of psychiatric disorders: 45. Brand M, Young KS, Laier C. Prefrontal control and internet addiction: a
a large-scale cross-sectional study. Psychol Addict Behav. 2016;30(2): theoretical model and review of neuropsychological and neuroimaging
252–62. findings. Front Hum Neurosci. 2014;8:375.
24. Zsido AN, Arato N, Lang A, Labadi B, Stecina D, Bandi SA. The connection 46. Liu H, Chen Z, Ke W, Chen X. The impact of Enterprise social networking
and background mechanisms of social fears and problematic social use on team performance: transactive memory system as an explanation
networking site use: a structural equation modeling analysis. Psychiatry Res. mechanism. PACIS. 2014;2014:70.
2020;292:113323. 47. Kizilbash AH, Vanderploeg RD, Curtiss G. The effects of depression and
25. Stern SA, Alberini CM. Mechanisms of memory enhancement. Wiley anxiety on memory performance. Arch Clin Neuropsychol. 2002;17(1):57–67.
Interdiscip Rev Syst Biol Med. 2013;5(1):37–53. 48. Christopher G, MacDonald J. The impact of clinical depression on working
26. Lodge JM, Harrison WJ. The role of attention in learning in the digital age. memory. Cognitive Neuropsychiatry. 2005;10(5):379–99.
Yale J Biol Med. 2019;92(1):21–8. 49. Sari BA, Koster EH, Derakshan N. The effects of active worrying on working
27. Karam EG, Mneimneh ZN, Karam AN, Fayyad JA, Nasser SC, Chatterji S, memory capacity. Cognit Emot. 2017;31(5):995–1003.
Kessler RC. Prevalence and treatment of mental disorders in Lebanon: a 50. Hayes S, Hirsch C, Mathews A. Restriction of working memory capacity
national epidemiological survey. Lancet. 2006;367(9515):1000–6. during worry. J Abnorm Psychol. 2008;117(3):712.
Dagher et al. Head & Face Medicine (2021) 17:6 Page 12 of 12
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
BioMed Central publishes under the Creative Commons Attribution License (CCAL). Under
the CCAL, authors retain copyright to the article but users are allowed to download, reprint,
distribute and /or copy articles in BioMed Central journals, as long as the original work is
properly cited.