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Factores sociodemográficos, salud mental y redes sociales: un estudio
comparativo sobre la conducta suicida
Sociodemographic factors, mental health and social media: a comparative
study on suicidal behavior
Claudia García-Martínez 1,2* , Séfora Ene Gimeno1 & Samara Sáez1
1Asociación de Trastornos Depresivos de Aragón (AFDA)
2Universidad San Jorge (Villanueva de Gállego).
*Corresponding author: claudiagarmar@gmail.com
Recibido 2023-05-20. Aceptado 2023-06-05
Resumen
Introducción: El suicidio es un creciente problema de salud pública. Este estudio tiene como objetivo
analizar la ideación, planificación y/o conducta suicida en relación a factores sociodemográficos,
estado de salud mental y uso de redes sociales. Se comparó un grupo de personas con conducta
suicida con otro grupo sin conducta suicida, emparejados por sexo y edad. Metodología: Se realizó un
estudio descriptivo longitudinal comparativo. La variable de resultado fue la conducta suicida. Se
recopilaron datos sobre variables sociodemográficas, salud mental y uso de redes sociales. Se
utilizaron los estadísticos chi-cuadrado y T de student y se llevó a cabo un análisis de regresión logística
multivariante. Resultados: Se contó con la participación de 102 sujetos, 51 de ellos con ideación o
intento suicida, y 51 de ellos sin ideación suicida en el último año. Se encontraron diferencias
significativas entre estos grupos en cuanto a variables sociodemográficas e historial de diagnósticos
psiquiátricos. Según el análisis multivariante, los factores asociados con la conducta autolítica incluyen
no estar trabajando (estudiar, desempleo, jubilado, o con incapacidad temporal o permanente) (B:
1,193, Odds ratio: 3,298), haber presentado un episodio depresivo el año anterior (B: -2,350, Odds
ratio: 0,095), y haber experimentado un trastorno de ansiedad (B: -1,659, Odds ratio: 0,190). El
modelo muestra una Rcuadrado de Cox y Snell de 0,418. Conclusiones: Estos resultados confirman la
existencia de un perfil de personas en riesgo de conducta suicida. El uso diferencial de redes sociales
puede servir para la evaluación ecológica instantánea de riesgo.
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Palabras clave: Suicidio; determinantes sociales de la salud; redes sociales; depresión, comorbilidad
psiquiátrica.
Abstract
Introduction: Suicide is a growing public health problem. This study aims to analyze suicidal ideation,
planning, and/or behavior in relation to sociodemographic factors, mental health status, and use of
social media, comparing a group of people with suicidal behavior and those without suicidal behavior,
matched by sex and age. Methodology: A comparative longitudinal descriptive study was carried out.
The outcome variable was suicidal behavior. Sociodemographic, mental health, and social media use
variables were also collected. Chi-square and Student's T statistics were used and multivariate logistic
regression was performed. Results: 102 subjects participated, 51 of them with suicidal ideation or
attempt, and 51 of them without suicidal ideation. Significant differences were found between these
groups in terms of sociodemographic variables and history of psychiatric diagnoses. According to the
multivariate analysis, the factors associated with self-injurious behavior are: not being working
(studying, unemployed, retired, or with a temporary or permanent disability) (B: 1.193, Odds ratio:
3.298), having a depressive episode in the previous year (B: -2.350, Odds ratio: 0.095), and an anxiety
disorder (B: -1.659, Odds ratio: 0.190). The model has a Cox and Snell R-squared of 0.418. Conclusions:
These results confirm the existence of a profile of people who may exhibit suicidal behavior. The
different patterns of use of social media can be useful for instantaneous ecological risk assessment.
Keywords: Suicide; Social Determinants of Health; social media; depression, psychiatric comorbidity.
INTRODUCTION
Suicide has become a public health problem. There are more than 800,000 deaths each year, and it is
the second leading cause of death in young people aged 15-29 years (World Health Organization,
2016). In addition, it is estimated that, for every self-inflicted death, there are another 20 suicide
attempts (World Health Organization, 2016). In Spain, suicide has been the leading cause of non-
natural death since 2012 (Instituto Nacional de Estadística, 2020).
Suicidal behavior is defined as a variety of behaviors including thinking about suicide (or ideation),
planning to commit suicide, attempting suicide, and suicide itself. Research has consistently
associated suicidal behavior with emotional states such as depression and hopelessness (Large, 2018;
World Health Organization, 2014).
Several studies have examined risk factors for an attempt or completed suicide to occur. Among them,
Bryan and Rudd's study (Bryan & Rudd, 2006; Pisani, Murrie, & Silverman, 2016) collects different
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variables that have been empirically shown to be essential for risk assessment. These factors are
predisposition to suicidal behavior (i.e., prior suicidal behavior, or psychiatric diagnoses,); presenting
symptomatology (e.g., sadness, anhedonia, dyssomnia, low self-esteem, fatigue); presence of
hopelessness; nature of suicidal thinking (e.g.., ideation, suicidal plan, lethality of means, explicit
suicidal intent); impulsivity and self-control; and identifiable precipitating factors or stressors (e.g.,
significant loss, relationship instability). Emotional dysregulation also appears to be an important
predictor of suicidal outcomes (Berman & Silverman, 2014). On the other hand, protective factors
such as social support or life satisfaction have also been identified.
Recent studies have concluded that the time factor may be a relevant variable in suicidal behavior.
Some research using ecological momentary assessments through mobile devices has shown that
suicidal ideation can vary over short periods (Kleinman et al., 2017). In this sense, assessments
conducted in a natural environment for the person and in real time could be a crucial approach to
suicide prevention (Bryan & Rudd, 2006; Sedano-Capdevila, Porras-Segovia, Bello, Baca-García, &
Barrigon, 2021; Wortzel, Homaifar, Matarazzo, & Brenner, 2014). Therefore, the role of social media
as a tool in suicide prevention is being analyzed in recent years.
There is no doubt that social media have transformed the world and the way we communicate, with
more than 3.8 billion users worldwide (Kessler, Bossarte, Luedtke, Zaslavsky, & Zubizarreta, 2020). It
is common for people to express their emotions, beliefs, and thoughts through social media (Beck,
Brown, Berchick, Stewart, & Steer, 1990). Therefore, these new forms of social interaction with
suicidal behavior have been analyzed from a causal perspective; however, recent studies highlight the
potential of social media to prevent and offer assistance at the time of self-injurious behavior (Ribeiro,
Huang, Fox, & Franklin, 2018).
Online interventions for suicide prevention have focused primarily on three directions (Christensen,
Batterham, & O'Dea, 2014): 1) online therapeutic interventions for suicide prevention; 2) online
detection of suicidal tendencies; and 3) real-time identification of individuals at risk, either by others
or by computer language processing systems using artificial intelligence (AI).
Social media have already demonstrated their potential in the real-time detection of mental health
problems. For example, several studies have shown the effectiveness of Twitter in predicting users
who will suffer from a depressive episode (Kleiman et al., 2017), postpartum depression, and even
post-traumatic stress disorder (Luxton, June, & Fairall, 2012). Machine learning algorithms are now
being used to assess suicide risk and identify suicidal individuals. Regarding suicidal behavior, social
media can be considered a useful tool in its detection. Some studies have developed automatic (via
AI) machine learning classification systems that can effectively differentiate people who are at risk of
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suicide from those who are not (Braithwaite, Giraud-Carrier, West, Barnes, & Hanson, 2016). They are
even able to identify temporal patterns in postings before suicide deaths. Reviews on the subject
(Sedano-Capdevila et al., 2021) yield similar results. However, further validation of these systems is
needed.
To deepen and shed light on the relationship between suicide, the use of social media, and the
comorbidity of mental health problems, this study aims to analyze suicidal behavior in relation to
sociodemographic factors, mental health status, and the use of social media. To this end, the aim is
to compare a group of people with suicidal behavior and another group without suicidal behavior,
matched by sex and age. Finally, it is intended to identify the predictive factors of suicidal behavior.
METHOD
Design
Descriptive comparative study of people with suicidal ideation or attempt in the last year and people
without suicidal ideation, matched by sex and age, in relation to sociodemographic variables, mental
disorders, and social media use factors.
Participants
Individuals who had presented suicidal behavior (suicidal ideation, planning, or attempt) during the
last 12 months were recruited at the Aragon Association of Depressive Disorders (AFDA) and were
matched by sex and age with people who had not experienced suicidal behavior.
The inclusion criteria for both groups (suicidal behavior and no suicidal behavior in the last year) were:
being over 18 years of age, being able to read and write in Spanish, and being users of social media
such as Twitter and/or Instagram.
Based on the study by Bellón et al. (Bellón et al., 2010), and considering the prevalence of depression,
suicidal ideation, planning, and attempt in people with depression, the sample size was calculated for
the bilateral hypothesis test in both groups. Assuming an error of 5%, with a confidence interval of
95%, a precision of 5%, and adding 25% for incomplete data and a lower prevalence in the control
group due to the inclusion criteria, the required sample size was 40 subjects in each group. Finally, 51
persons participated in each group, with a total sample size of 102 subjects.
Instruments
The outcome variable was suicidal behavior understood as ideation, planning, and/or suicide
attempts reported in the last 12 months. It was collected through the question: Have you experienced
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major suicidal thoughts, planned a possible attempt, or attempted suicide in the last year? In which
month was it? If the person answered affirmatively, indicating a time frame, it was considered that
he/she had had suicidal ideation, planning, or attempt.
Sociodemographic, mental health status, and social media use variables were also collected. A
questionnaire developed ad hoc for the study was used for this purpose.
Sociodemographic variables that act as social determinants of health: age; sex; marital status (single,
married or partnered, divorced or separated); cohabitation at home (alone, with a partner, with
partner and/or children, with my family of origin, with friends in a shared apartment, in a room in a
shared apartment); level of education (basic education, compulsory education, vocational training,
university studies); employment status (student, unemployed, employed, retired, temporary
disability, permanent disability) were collected.
Use of social media: Use of social media and which ones; frequency of use (daily, every 2-3 days, every
week, every 2 weeks, every 3-4 weeks, at least once a month); the number of hours of use if the
frequency is daily; frequency of content posting (daily, every 2-3 days, weekly, every 2 weeks, every
2 weeks, every 3-4 weeks, less than once a month); frequency of use of social media in times of
emotional distress (always, frequently, sometimes, rarely, never).
Mental health status: lifetime and past year diagnosis of depression; other mental health diagnosis
and, if yes, specify which one (anxiety disorder, anxious-depressive adaptive disorder, sleep disorder,
personality disorder (PD), eating disorder (ED), obsessive-compulsive disorder (OCD), substance
abuse disorder, somatization disorder, specific phobia, other).
Data collection took place between May 2022 and April 2023.
Procedure
Dissemination was carried out mainly through the WhatsApp diffusion lists of people associated with
the AFDA entity and information leaflets. For the recruitment of people without suicidal behavior, it
was disseminated through triptychs in other social entities and centers of the University of Zaragoza
and sent information about the study through WhatsApp among contacts.
Those interested in participating had access to an online questionnaire (through Google Forms) where
information about the study was reflected and informed consent was requested through the same
web application. The duration of the questionnaire was 15 minutes. Once recruited, they accessed
the questionnaire. Given the characteristics of the study, a study cell phone and WhatsApp line were
available throughout the duration of recruitment, attended by a trained health professional, to
resolve doubts and deal with possible emergency situations.
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Data analysis
IBM® SPSS® Statistics version 26 and Microsoft Excel were used to perform the statistical analyses. P-
values lower than 0.05 were considered statistically significant. First, a descriptive analysis of the
sample was performed showing means and standard deviations of the continuous variables, and the
frequency and percentages of the categorical variables. Given the normal distribution of the
continuous variables, parametric statistics were used. The study variables were compared according
to whether or not suicidal behavior or ideation had occurred. For this purpose, Student's t-statistics
and Chi-square were used depending on whether the variables were continuous or categorical,
respectively. Finally, a multivariate logistic regression analysis was developed, keeping in the model
the variables that obtained a p-value lower than 0.10. The dependent variable was suicide ideation,
planning, and attempt, and the independent variables were the rest of the study variables. The
following variables were categorized: age over or under 40 years; marital status married or
single/divorced; basic education or vocational training/university studies; living alone or with non-
affective partners, and living with affective partners; occupation working or not working
(unemployed, studying, retired, or on temporary or permanent disability); frequency of social media
use (above or below average) and posting daily or several days a week versus less frequently.
Ethical issues
All the procedures developed in this work complied with the ethical standards of the Clinical Research
Ethics Committee of Aragón and with the Declaration of Helsinki of 1975, and its version revised in
2008. The study protocol was approved by the Clinical Research Ethics Committee of Aragón (Spain)
(PI20/302). All participants signed the informed consent, and their data were anonymized.
RESULTS
A total of 102 subjects participated, 51 of them with suicidal ideation or attempt, and 51 of them
without suicidal ideation. Table 1 shows the characteristics of the total sample. As can be seen, 88,2%
of the participants were women, with a mean age of 35,21 years (SD: 0,734, range between 18 and
68 years). The profile of the participant is female, with university studies, living with people (with
partner, partner and children, or family of origin), who is working, uses social media daily for about 3
hours on average per day, and rarely or never uses them in moments of emotional distress. The most
used social media are WhatsApp, Youtube, and Facebook. Regarding mental health diagnoses, 59.8%
have had depression at some time in their lives, and 55.9% also have another diagnosed psychiatric
disorder, the most frequent being anxiety disorder (50%).
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Table 1
Characteristics of the sample in the study variables.
VARIABLES
TOTAL SAMPLE n= 102
Frequency (%)
Mean (SD)
Sex
Female
Male
90 (88,2%)
12 (11,8%)
Age*
35,21 (0,734)
Marital status
Married or partnered
Single
Separated or divorced
58 (56,8%)
38 (37,3%)
6 (5,9%)
Coexistence at home
I live alone
I live with my partner
I live with my partner and/or children
I live with my family of origin
I live with friends in a shared apartment
I live in a room in a shared apartment
18 (17,6%)
23 (22,5%)
27 (26,5%)
23 (22,5%)
6 (5,9%)
5 (4,9%)
Educational level
Compulsory education
Vocational training
University studies
6 (5,9%)
26 (25,5%)
70 (68,6%)
Occupation
Student
Unemployed
Employed
Retired
Temporary disability
Permanently disabled
15 (14,7%)
17 (16,7%)
61 (59,8%)
1 (1,0%)
5 (4,9%)
3 (2,9%)
Frequency of use of social media
Daily
Every 2-3 days
Every week
Every 2 weeks
Every 3-4 weeks
Less than once a month
97 (95,1%)
3 (2,9%)
2 (2,0%)
0
0
0
Hours/day in social media*
3,02 (2,153)
Frequency of publication
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Daily
Every 2-3 days
Every week
Every 2 weeks
Every 3-4 weeks
Less than once a month
11 (10,8%)
18 (17,6%)
12 (11,8%)
7 (6,9%)
13 (12,7%)
41 (40,2%)
Use of social media in moments of discomfort
Always
Frequently
Occasionally
Rarely
Never
0
9 (8,8%)
30 (29,4%)
34 (33,3%)
29 (28,4%)
History of depression
No
Yes
41 (40,2%)
61 (59,8%)
Depression in the last year
No
Yes
64 (62,7%)
38 (37,3%)
Other psychiatric diagnoses
No
Yes
45 (44,1%)
57 (55,9%)
Psychiatric diagnosis (yes%)
Anxiety disorder
Anxious-depressive adaptive disorder
Sleep disorder
Personality disorder
Eating behavior disorder
Obsessive-compulsive disorder
Substance abuse disorder
Somatization disorder
Specific phobia
51 (50%)
15 (14,7%)
15 (14,7%)
6 (5,9%)
11 (10,8%)
5 (4,9%)
1 (1%)
1 (1%)
2 (2%)
*Continuous variables are shown as means and standard deviations (SD).
Table 2 shows the comparison between subjects who have had suicidal ideation or attempts and those
who have not had suicidal ideation or attempts in the last year. As can be seen, there are significant
differences in the marital status variable, with people with suicidal behavior or attempts being mostly
single or separated/divorced; in relation to occupation, with people with suicidal behavior or attempts
being mostly unemployed (unemployed, retired, with a disability, etc.); in the variable hours of use of
social media and use in moments of emotional distress, being higher in people with suicidal behavior
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or attempt; and finally regarding mental health diagnoses, with a higher percentage of people with
suicidal ideation or attempt with a previous diagnosis or in the last year of depression and other
psychiatric comorbidities such as anxiety disorders, anxious-depressive adaptive disorder, sleep
disorder, and an eating disorder.
Table 2
Comparison of subjects with suicidal ideation or attempt and those without suicidal ideation or attempt on the study
variables.
VARIABLES
SUBJECTS WITHOUT
SUICIDAL IDEATION
OR SUICIDE ATTEMPT
N=51
Frequency (%)
Mean (SD)
SUBJECTS WITH SUICIDAL
IDEATION
OR SUICIDE ATTEMPT
N=51
Frequency (%)
Mean (SD)
P-VALUE
Sex
Female
Male
45 (88,2%)
6 (11,8%)
45 (88,2%)
6 (11,8%)
Age*
36,10 (10,54)
34,31 (10,95)
0,404
Marital status
Married or partnered
Single
Separated or divorced
33 (64,7%)
18 (35,3%)
0
25 (49%)
20 (39,2%)
6 (11,8%)
0,027
Coexistence at home
I live alone
I live with my partner
I live with my partner and/or children
I live with my family of origin
I live with friends in a shared apartment
I live in a room in a shared apartment
8 (15,7%)
13 (25,5%)
16 (31,4%)
8 (15,7%)
4 (7,8%)
2 (3,9%)
10 (19,6%)
10 (19,6%)
11 (21,6%)
15 (29,4%)
2 (3,9%)
3 (5,9%)
0,475
Educational level
Compulsory education
Vocational training
University studies
1 (2%)
10 (19,6%)
40 (78,4%)
5 (9,8%)
16 (31,4%)
30 (58,8%)
0,065
Occupation
Student
Unemployed
Employed
Retired
Temporary disability
Permanently disabled
5 (9,8%)
6 (11,8%)
38 (74,5%)
1 (2,0%)
1 (2,0%)
10 (19,6%)
11 (21,6%)
23 (45,1%)
4 (7,8%)
3 (5,9%)
0,027
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Frequency of use of social media
Daily
Every 2-3 days
Every week
Every 2 weeks
Every 3-4 weeks
Less than once a month
58 (94,1%)
1 (2%)
2 (3,9%)
0
0
0
49 (96,1%)
2 (3,9%)
0
0
0
0
0,310
Hours/day in social media*
2,65 (2,11)
3,39 (2,14)
0,02
Frequency of publication
Daily
Every 2-3 days
Every week
Every 2 weeks
Every 3-4 weeks
Less than once a month
5 (9,8%)
6 (11,8%)
8 (15,7%)
2 (3,9%)
8 (15,7%)
22 (43,1%)
6 (11,8%)
12 (23,5%)
4 (7,8%)
5 (9,8%)
5 (9,8%)
19 (37,3%)
0,345
Use of social media in moments of discomfort
Always
Frequently
Occasionally
Rarely
Never
0
4 (7,8%)
8 (15,7%)
19 (37,3%)
20 (39,2%)
5 (9,8%)
22 (43,1%)
15 (29,4%)
9 (17,6%)
0,010
History of depression
No
Yes
31 (60,8%)
20 (39,2%)
10 (19,6%)
41 (80,4%)
<0,001
Depression in the last year
No
Yes
45 (88,2%)
6 (11,8%)
19 (37,3%)
32 (62,7%)
<0,001
Other psychiatric diagnoses
No
Yes
31 (60,8%)
20 (39,2%)
14 (27,5%)
37 (72,5%)
0,001
Psychiatric diagnosis (yes%)
Anxiety disorder
Anxious-depressive adaptive disorder
Sleep disorder
Personality disorder
Eating behavior disorder
Obsessive-compulsive disorder
Substance abuse disorder
Somatization disorder
Specific phobia
14 (27,5%)
4 (7,8%)
3 (5,9%)
1 (2%)
2 (3,9%)
1 (2%)
1 (2%)
0
0
37 (72,5%)
11 (21,6%)
12 (23,5%)
5 (9,8%)
9 (17,6%)
4 (7,8%)
0
1 (2%)
2 (3,9%)
<0,001
0,050
0,012
0,092
0,025
0,169
0,315
0,315
0,153
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*Continuous variables are shown as means and standard deviations (SD). Student's t-statistic is used in the comparison between groups.
For the rest of the variables, which are shown in frequencies and percentages, the Chi-Square statistic is used in the comparison between
groups.
The results of the multivariate analysis are shown in Table 3, where only the factors that obtained a
p-value of less than 0.10 are shown. As can be seen, the factors associated with self-injurious behavior
are not working (studying, unemployed, retired, or on temporary or permanent disability) (B: 1.193,
Odds ratio: 3.298), having a depressive episode in the previous year (B: -2.350, Odds ratio: 0.095), and
an anxiety disorder (B: -1.659, Odds ratio: 0.190). The model has a Cox and Snell R-squared of 0.418.
Table 3.
Multivariate logistic regression of factors associated with suicide ideation or attempt.
DISCUSSION
The results of this study shed light on the profile of the person who has suicidal ideation, planning, or
attempt in relation to sociodemographic variables, use of social media, and mental health diagnoses.
The relevant variables are sex, the presence of a depressive disorder, and people who present an
anxiety disorder.
In relation to sex differences, we know that women have twice the risk of developing major depression
compared to men (Gili et al., 2011; Haro et al., 2006; Kim et al., 2016; Vives et al., 2013). In the case
of suicidal behavior, there is likewise a higher percentage of women with suicide attempts compared
to men, even though deaths are more frequent in men (Canetto & Sakinofsky, 1998).
Both facts, in addition to the known comorbidity between depression and suicidal behavior
(Henriksson et al., 1993), would explain this higher profile of women. The difference in the prevalence
of depression as a function of gender can be explained by biological, psychological, and social factors
(Janet S. Hyde & Mezulis, 2020; Janet Shibley Hyde, Mezulis, & Abramson, 2008), but it is relevant to
take into account the evolutionary perspective when explaining the risk factors for depression (Salk,
Hyde, & Abramson, 2017). In this sense, this perspective points out that the differences would begin
B
Exp (B)
Odds ratio
95% Intervalo de
confianza Exp(B)
p-valor
Not working
1,193
3,298
0,962 11,310
0,058
Working
0b
Ref
.
Not having depression in the
previous year
-2,350
0,095
0,023 0,397
0,001
Having depression in previous year
0b
Ref
.
No diagnosis of anxiety disorder
-1,659
0,190
0,052 0,700
0,013
Anxiety disorder
0b
Ref
.
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to occur from adolescence onwards at which time there may be the appearance of certain stressors
or role development, both based on gender, which would explain a greater vulnerability to develop
depression in the future.
On the other hand, these results confirm the relationship evidenced in the literature between suicidal
behavior and the presence of depression and anxiety (Beck et al., 1990; Gouin et al., 2023; Hu et al.,
2023; Laghaei, Mehrabizadeh Honarmand, Jobson, Abdollahpour Ranjbar, & Habibi Asgarabad, 2023;
Misiak, Samochowiec, Gawęda, & Frydecka, 2023; Nawaz, Shah, & Ali, 2023; Ribeiro et al., 2018; Souza
et al., 2023; Wilk, Falk, Joseph, & Smith, 2023), as well as with other social determinants of health.
Other significant mental health diagnoses such as anxious-depressive adaptive disorder, sleep
disorder, and eating disorder also appear in the bivariate analysis. The relationship between suicide
and insomnia and adaptive disorders has been confirmed in the literature (Kramer et al., 2023; Misiak
et al., 2023). The relationship between suicide and having an ED should be studied further since
several studies (Sohn et al., 2023; Velkoff, Brown, Kaye, & Wierenga, 2023) have found that people
with ED have a higher prevalence of non-suicidal self-injury, suicide ideation, and attempt but not
death by suicide compared to controls and emphasize the need for effective clinical strategies to
address these behaviors in patients with ED. A difference close to significance has been found between
people presenting personality disorder and suicide (p-value is 0.09), as exists in the literature (Kaurin,
Dombrovski, Hallquist, & Wright, 2023), but it may be due to the design and the small percentage of
patients presenting PD in this study.
In relation to sociodemographic variables, significant differences have been found between suicide,
educational level, marital status, or being employed. These factors are in turn risk factors for
depression. These results have also been found in the literature (Bjelland et al., 2008; Golden et al.,
2009; Koster et al., 2006; Lino, Portela, Camacho, Atie, & Lima, 2013; Lorant et al., 2007; Phelan, Link,
& Tehranifar, 2010; Plaisier et al., 2007; Weich, Nazareth, Morgan, & King, 2007).
Regarding the use of social media, people with suicide ideation, planning, or attempts use social media
more hours per day, and more frequently when they present emotional distress. Beyond the impact
that the use of social media may have on people's mental health, this fact allows us to know that
people at risk of suicidal behavior use social media at times of distress. Therefore, social media can be
a key means for the early detection of suicide, as established by several studies (Aladag, Muderrisoglu,
Akbas, Zahmacioglu, & Bingol, 2018; Braithwaite et al., 2016; Cheng, Li, Kwok, Zhu, & Yip, 2017;
Coppersmith, Leary, Crutchley, & Fine, 2018).
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This study presents strengths and limitations. Among the strengths, we can highlight the novelty of
the study in shedding light on the profile of people with suicidal ideation, planning, and attempt
through a comparison of sex- and age-matched cohorts.
On the other hand, this study also has limitations. The first of these is that, as one of the study's
objectives is to analyze the relationship between suicide and social media, one exclusion criterion is
not using social media. The digital divide affects the sample of this study and people who do not use
social media, such as older people, are not reflected. It would be interesting to know the risk profile
of people who do not use social media and to explore through what alternative means they
communicate their psychological state. Another limitation is that the source of information was the
patients themselves with self-reported measures. In the future, it would be interesting to include
validated instruments to measure suicidal behavior in the study.
CONCLUSION
Given the relevance of suicide as a public health problem, it is important to shed light on the
relationship between suicide, sociodemographic variables, other mental health diagnoses, and social
media use. People with suicidal behavior or attempts present mental health disorders such as
depression, anxiety, sleep disorder, or adaptive disorders. In addition, they use social media more
times per day and specifically when they present emotional distress. The development and knowledge
of a risk profile may allow us to more accurately tailor suicide preventive actions towards this specific
population and thus improve the effectiveness of interventions.
AVAILABILITY OF DATA AND MATERIALS
Data supporting the findings of this study are available, upon reasoned request, from the
corresponding author.
CONFLICT OF INTEREST
The authors declare that they have no conflict of interest.
FUNDING
This study has been financed by the Carlos III Health Institute, project FIS PI021/01356, and FEDER
funds "Otra forma de hacer Europa". The funding institution has not intervened at any time in the
development, analysis, and publication of this study.
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AUTHOR CONTRIBUTIONS
CGM was responsible for the following contributions: conceptualization, data curation, formal
analysis, drafting-original manuscript, and editing. SEG and SSM were responsible for
conceptualization, writing, editing, and revising the manuscript.
ACKNOWLEDGMENTS
The authors would like to thank Selene Fernández and the Association of Depressive Disorders of
Aragon (AFDA), their workers and associates for their collaboration in the development of this study.
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