Abstract
Introduction
This study aimed to assess the levels and patterns of social media use among Saudi adolescents and middle-aged adults, evaluate associations with self-esteem and identify which usage modalities (image-based, comparison-based, belief-based, consumption-based) are differentially linked to low self-esteem.
Methods
A cross-sectional online survey was conducted among 600 Saudi participants aged 14–50 years. The questionnaire included demographic and lifestyle items, medical history, the Rosenberg Self-Esteem Scale (RSES) and the Social Media Use Scale (SMUS). Logistic regression models assessed predictors of low self-esteem and comparison-based use.
Results
Participants reported high social media engagement, with 68.5% using it over 4 hours per day. Comparison-based and consumption-based modalities were most frequent. Low self-esteem was observed in 16.2% of participants and independently predicted by younger age (OR=0.96, 95% CI 0.92 to 0.99), low socioeconomic status (OR=3.00, 1.45 to 6.19), mental health history (OR=2.02, 1.15 to 3.53), unhealthy diet (OR=2.10, 1.01 to 4.34) and high comparison-based use (OR=4.93, 2.58 to 9.43). Moderate and high Snapchat use were associated with lower odds of low self-esteem. Comparison-based use was further associated with unhealthy diet, low spirituality, thyroid disorders and >8 hours of daily use.
Conclusion
Comparison-based social media use is a strong and independent predictor of low self-esteem, underscoring the importance of analysing usage patterns over mere frequency or platform type. These findings support the need for targeted interventions focusing on at-risk modalities of social media engagement. The results hold implications for youth-focused digital literacy, mental health prevention strategies and culturally informed psychosocial support in the Saudi context.
Keywords: Mental Health, Adolescent, Public Health
WHAT IS ALREADY KNOWN ON THIS TOPIC
Social media use has been associated with a broad range of adverse psychological outcomes, including depression, anxiety, body image concerns, disordered eating and reduced well-being. However, findings based solely on time spent online, platform type or content exposure remain inconsistent, suggesting that specific patterns of engagement may be more relevant.
WHAT THIS STUDY ADDS
Comparison-based social media use was the only modality independently associated with low self-esteem, whereas overall use duration, platform type, content preferences and other usage modalities were not independently associated. These findings highlight the importance of examining how individuals use social media rather than exposure alone.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
Research and preventive strategies should move beyond time-based measures towards modality-specific assessment. Screening and digital literacy interventions may benefit from identifying comparison-based behaviours and promoting more adaptive engagement with socially comparative content.
Introduction
Social media has deeply impacted modern life, encompassing both beneficial and harmful effects.1,2 Greater exposure to digital platforms has been associated with severe mental morbidity such as depression, anxiety, disordered eating and lowered overall well-being, with such effects following a dose–response relationship.3,4 Furthermore, recent observations underscore the importance of usage modality in shaping mental health outcomes. For instance, prolonged use of image-centric platforms has been linked to greater vulnerability, especially among the youth.5–7
Among the various psychological domains associated with social media use, self-esteem represents an important factor linked to psychological morbidity and a range of psychiatric symptoms.8 The structural features of social media that facilitate constant self-presentation and social comparison, along with elements such as curated profiles and quantifiable indicators of social approval (likes, shares, comments), can be associated with both positive and negative self-evaluations, with potential implications for psychological well-being.9 The negative interplay between detrimental digital practices and diminished self-esteem may contribute to the emergence and maintenance of psychological morbidity.8,10 Thus, self-esteem may represent an important factor associated with vulnerability to psychological morbidity. Several theoretical frameworks have proposed directional, reciprocal or mutually reinforcing relationships between self-esteem and psychiatric disorders.11–14 These theories are further supported by clinical research, demonstrating that interventions targeting self‐esteem can lead to improvements in mental health outcomes illnesses.15,16
Advances in understanding suggest that social media usage modality can be conceptualised within a multidimensional framework, with each mode potentially showing different associations with psychiatric symptoms and psychological well-being.7,17 Following this approach, Tuck and Thompson18 developed and validated the Social Media Use Scale (SMUS), distinguishing four distinct modalities of social media use: comparison-based (Viewing others’ profiles/lives and comparing them to own), image-based (editing, posting or curating personal content), belief-based (engaging with emotionally charged, ideological or controversial content) and consumption-based (passive scrolling, viewing stories, watching influencers, with no interaction). Each modality was shown to differentially correlate with psychological traits such as self-esteem, depression and emotion regulation goals.18 This scale provides a validated framework to assess the psychological correlates of specific social media use modalities rather than relying on time-based or passive-active dichotomies, offering a more precise approach to identifying at-risk usage behaviours. Nevertheless, much of the existing evidence has focused on overall exposure, time spent on social media or broad passive-active classifications. Consequently, comparatively little is known about how the four distinct SMUS modalities are independently and differentially associated with self-esteem. Clarifying these modality-specific associations may help identify which patterns of engagement are most closely related to lower self-esteem beyond the duration or frequency of social media use.
The present study investigated the levels and patterns of social media use, encompassing usage duration, types of content engagement and distinct modalities of use, and examined their associations with self-esteem levels. It particularly focused on analysing the differential associations between specific modalities and self-esteem to identify potentially maladaptive usage patterns.
Accordingly, the study sought to answer the following research questions: (1) What are the prevailing levels and patterns of social media use among the study population? (2) How are overall usage duration, platform and content preferences, and the four SMUS modalities associated with self-esteem? (3) Which social media use modalities are independently associated with low self-esteem after accounting for relevant sociodemographic and behavioural factors?
Such insights are essential for mental health professionals, educators and policymakers aiming to mitigate the adverse psychological effects of social media while promoting more adaptive and constructive use.
Methods
Design and setting
A community-based, cross-sectional study was conducted in Saudi Arabia, between February 2024 and December 2024. Saudi Arabia represents a particularly relevant setting for this investigation, given its young and highly digitally connected population. This provides an appropriate context for examining whether specific patterns and modalities of social media engagement are associated with self-esteem.
Population
We targeted adolescents (aged 14–17 years) and young (18–39 years) and middle-aged adults up to the age of 50. Both genders were included. These age groups represent generations among whom social media is widely integrated into daily communication, entertainment and information seeking. Participants were eligible if they held Saudi nationality and were residing in Saudi Arabia during the study period. This selection aimed to ensure cultural specificity and contextual relevance of the data, as social media use and its psychological impact may vary significantly across cultural environments.
Sampling
The target sample size was calculated to detect an unknown proportion (p=0.50) of participants with low self-esteem in an infinite population, using a 95% CI, 80% statistical power and a 5% margin of error. The calculated sample size was increased by 30% to account for potential incomplete or ineligible responses, yielding a final target of 490 participants. As this was an internet-based study, a convenience sampling method was employed to include all eligible and consenting individuals with complete participation.
Tool and outcomes
The study data were collected using an online structured questionnaire (online supplemental appendix), comprising the following parts:
Demographic and lifestyle data.
These include sociodemographic data such as age, gender, physical activity frequency, etc.
Health-related data and medical history:
This part covered self-reported weight change over the past year, history of eating disorders, mental health conditions and other relevant chronic conditions.
Levels and patterns of social media use.
This part of the questionnaire assessed various aspects of social media use habits, including the daily duration of use, the type of content frequently engaged with (using a list of 13 content categories such as social networking, entertainment, fashion and beauty, etc) and self-reported usage levels across major platforms (eg, Instagram, Snapchat, TikTok) based on a 6-point scale ranging from ‘not a user’ to ‘intensive user’.
Social media use modalities
Social media use modalities have been assessed using the SMUS, which evaluates the frequency of engagement in 17 distinct social media activities over the past 7 days.18 Participants reported their activity on a 9-point scale ranging from ‘never’ to ‘hourly or more’, excluding direct messaging. The SMUS comprises four subscales: image-based (eg, posting, monitoring, editing content; items 1–5), comparison-based (eg, body and life comparisons; items 6–8), belief-based (eg, posting or reacting to negative or controversial content; items 9–12) and consumption-based (eg, passive browsing, viewing stories or influencer content; items 13–17). Subscale scores were calculated as the mean of corresponding item responses.
Self-esteem
The level of self-esteem was assessed using the Arabic version of the Rosenberg Self-Esteem Scale (RSES).19 RSE scale is a validated 10-item self-report questionnaire including positive and negative statements about self-worth. A total score is computed (range from 10 to 40), where higher scores indicate higher self-esteem and a score ≤15 indicates low self-esteem.20
Eating disorders.
The questionnaire also included the Eating Disorder Examination Questionnaire (EDE-Q) (EDE-Q).21 However, this component will be analysed separately in a forthcoming publication, focusing specifically on the interplay between social media use modalities and disordered eating.
Procedure
The questionnaire was first developed in English and underwent face and content validity assessment by a panel of three academic psychiatrists and one research methodologist, who reviewed it for clarity, relevance and completeness. Based on their feedback, modifications were made to enhance the questionnaire’s validity. It was then translated into Arabic by a bilingual expert, especially the SMUS subscales, and a back-translation process was conducted by an independent translator to ensure linguistic and conceptual equivalence. The final version was adapted for online dissemination using a user-friendly format that included an informed consent statement. Before data collection, the final questionnaire was pretested with a small group of individuals to assess the clarity, comprehensibility and suitability of its wording and response options.
Participants aged 18 years or older provided electronic informed consent embedded in the questionnaire. For participants under the age of 17, the legal age of consent in Saudi Arabia, an additional parental consent statement was provided in accordance with ethical requirements. Confidentiality and anonymity of responses were guaranteed, and no personally identifiable information was collected.
The questionnaire was disseminated digitally via various social media platforms and groups. Trained medical students supported the distribution process after receiving a short orientation on ethical participation recruitment and communication. As the total number of individuals who received or viewed the survey invitation was unknown, a response rate could not be calculated. The data collection period was planned for 8 weeks; however, the target sample size was successfully achieved within 7 weeks.
Statistical methods
Data were analysed using IBM SPSS Statistics software, V.21 for Windows. Descriptive statistics were used to summarise the sample characteristics, expressed as means with SD and median (75th centile (P75)) for continuous variables and frequencies with percentages for categorical variables. Internal consistency of the included scales including RSE and the 4 SMUS subscales was evaluated using Cronbach’s alpha.
Group comparisons between participants with and without low self-esteem, as well as factors associated with comparison-based social media use (defined by a subscale score ≥3.33, the upper quartile), were assessed using independent-sample t-tests for continuous variables and Pearson’s χ² tests for categorical variables.
To explore predictors of low self-esteem, a multivariate binary logistic regression model was constructed, including variables found significant at p<0.10 in bivariate analyses. Another logistic regression was conducted to identify factors independently associated with comparison-based social media use. Both models reported ORs with 95% CIs as well as model fit using Nagelkerke R².
A p value <0.05 was considered statistically significant throughout inferential analyses.
Results
Participants characteristics
The study sample consisted of 600 participants, predominantly females (60.8%), with a mean (SD) age of 27.40 (8.10) years. The participants’ characteristics are presented in detail in table 1.
Table 1. Participants’ demographic and lifestyle characteristics (N=600).
| Parameter | Level | Mean | SD |
|---|---|---|---|
| Age (years) | 27.40 | 8.10 | |
| Parameter | Level | Frequency | Percentage |
| Gender | Male | 235 | 39.2 |
| Female | 365 | 60.8 | |
| Professional status | Student | 222 | 37 |
| Employed | 230 | 38.3 | |
| Housewife | 54 | 9 | |
| Unemployed | 94 | 15.7 | |
| Highest educational level | Intermediate | 17 | 2.8 |
| Secondary | 139 | 23.2 | |
| University+ | 444 | 74 | |
| Marital status | Single | 402 | 67 |
| Married | 179 | 29.8 | |
| Divorced | 17 | 2.8 | |
| Widowed | 2 | 0.3 | |
| Socioeconomic status | Low (<5K SAR) | 157 | 26.2 |
| Moderate (5–10K SAR) | 230 | 38.3 | |
| High (10–20K SAR) | 154 | 25.7 | |
| Very high (>20K SAR) | 59 | 9.8 | |
| Physical activity | Daily | 48 | 8 |
| 3-4 times a week | 128 | 21.3 | |
| 1-2 times a week | 122 | 20.3 | |
| Rarely | 232 | 38.7 | |
| Never | 70 | 11.7 | |
| Diet | Very healthy | 13 | 2.2 |
| Somewhat healthy | 256 | 42.7 | |
| Somewhat unhealthy | 263 | 43.8 | |
| Unhealthy | 68 | 11.3 | |
| Sleep duration Bed time |
<5 hours | 60 | 10 |
| 5-6 hours | 273 | 45.5 | |
| 7-8 hours | 198 | 33 | |
| >8 hours | 69 | 11.5 | |
| Bed time | Before 22:00 | 40 | 6.7 |
| 22:00–24:00 | 189 | 31.5 | |
| 24:00–02:00 | 216 | 36 | |
| After 02:00 | 155 | 25.8 | |
| Spiritual lifestyle | Regular and highly committed | 185 | 30.8 |
| Regular and moderately committed | 263 | 43.8 | |
| Frequently and moderately committed | 109 | 18.2 | |
| Rarely or lowly committed | 43 | 7.2 | |
| Average daily use of social media | <2 hours | 39 | 6.5 |
| 2–4 hours | 150 | 25.0 | |
| 4–6 hours | 188 | 31.3 | |
| 6–8 hours | 118 | 19.7 | |
| >8 hours | 105 | 17.5 |
Medical history and physical health
About one-third of participants reported stable weight over the past year (31.7%), while 36.3% experienced weight loss (12.0% considerable, 24.3% slight) and 32.0% reported weight gain (25.2% slight, 6.8% considerable). A history of eating disorders was noted in 9.8%, and 21.2% had other psychiatric conditions, while 5.3% were on psychiatric medication (table 2).
Table 2. Participants’ health data and medical history.
| Parameter | Level | Mean | SD |
|---|---|---|---|
| BMI | Kg/m2 | 25.14 | 6.46 |
| Parameter | Level | Frequency | Percentage |
| Weight change over the past year | Considerably lost weight | 72 | 12.0 |
| Slightly lost weight | 146 | 24.3 | |
| Remained the same | 190 | 31.7 | |
| Slightly gained weight | 151 | 25.2 | |
| Considerably gained weight | 41 | 6.8 | |
| Psychiatric morbidity | History of eating disorders | 59 | 9.8 |
| Other psychiatric history | 127 | 21.2 | |
| Current medication | 32 | 5.3 | |
| Somatic morbidity | GI disorders | 166 | 27.7 |
| Diabetes | 30 | 5.0 | |
| Thyroid disorders | 46 | 7.7 | |
| Other hormonal disorders | 61 | 10.2 | |
| Other condition affecting appetite, weight of energy levels | 69 | 11.5 | |
| Missed menstrual period over the past 3–4 months | No | 324 | 54.0 |
| Yes | 49 | 8.2 | |
| Does not remember | 227 | 37.8 | |
| Contraceptive use | No | 334 | 55.7 |
| Yes | 40 | 6.7 | |
| Does not remember | 226 | 37.7 |
Patterns and levels of social media use
Most participants reported moderate to high daily social media use, with 31.3% using it for 4–6 hours, 19.7% for 6–8 hours and 17.5% exceeding 8 hours per day (table 1). Regarding the platform, Snapchat had the highest mean usage score (3.25 ± 1.33), with 74.8% classified as active users or above (active, highly engaged or intensive user), followed by WhatsApp (mean = 2.91 ± 1.16) and TikTok (2.69 ± 1.61) (table 3).
Table 3. Levels of use across social media platform (N=600).
| Platform | Not a user n (%) |
Occasional n (%) |
Moderate n (%) |
Active n (%) |
Highly engaged n (%) |
Intensive user n (%) |
Mean (SD) |
|---|---|---|---|---|---|---|---|
| 531 (88.5) | 51 (8.5) | 13 (2.2) | 4 (0.7) | 1 (0.2) | 0 (0.0) | 0.16 (0.48) | |
| YouTube | 24 (4.0) | 120 (20.0) | 228 (38.0) | 116 (19.3) | 57 (9.5) | 55 (9.2) | 2.38 (1.27) |
| 6 (1.0) | 48 (8.0) | 172 (28.7) | 222 (37.0) | 70 (11.7) | 82 (13.7) | 2.91 (1.16) | |
| 83 (13.8) | 134 (22.3) | 159 (26.5) | 140 (23.3) | 45 (7.5) | 39 (6.5) | 2.08 (1.38) | |
| TikTok | 83 (13.8) | 65 (10.8) | 91 (15.2) | 185 (30.8) | 67 (11.2) | 109 (18.2) | 2.69 (1.61) |
| X Twitter | 81 (13.5) | 132 (22.0) | 149 (24.8) | 138 (23.0) | 50 (8.3) | 50 (8.3) | 2.16 (1.43) |
| Telegram | 75 (12.5) | 206 (34.3) | 174 (29.0) | 93 (15.5) | 26 (4.3) | 26 (4.3) | 1.78 (1.23) |
| Snapchat | 18 (3.0) | 41 (6.8) | 92 (15.3) | 218 (36.3) | 83 (13.8) | 148 (24.7) | 3.25 (1.33) |
| 559 (93.2) | 16 (2.7) | 12 (2.0) | 8 (1.3) | 1 (0.2) | 4 (0.7) | 0.15 (0.63) |
Values are frequency (percentage) among the total participants.
Regarding content engagement, the highest engagement rates were observed for social networking (68.3%), spirituality (67.0%) and entertainment (63.5%) (figure 1).
Figure 1. Social media content engagement. Bars represent the percentage of participants who declared engaging with the given type of content, on social media, on a frequent or regular basis.
Modalities of social media use
All SMUS subscales showed good internal consistency, including image-based (Cronbach’s α=0.739), comparison-based (0.730), belief-based (0.750) and consumption-based (0.732). The highest weighted scores were observed for consumption-based use (mean=2.70, median=2.40 (P75=3.40)), followed by comparison-based (2.67, 2.33 (3.33)) and image-based use (2.02, 1.60 (2.60)), while belief-based use scored lowest (1.49, 1.25 (1.50)) (figure 2).
Figure 2. Patterns of social media use modalities across percentiles.
Levels and factors associated with self-esteem
The RSE scale showed excellent internal consistency (Cronbach’s α=0.809) and had a mean score of 21.06 (SD=5.26). Low self-esteem (RSE score 0–15) was observed in 16.2% of the participants (95% CI 19.7 to 26.3%). Of the remainders, 60.8% had normal (16–25) while 23.0% had high (26–30) self-esteem levels (Results not presented in tables).
We analysed the associations of self-esteem with sociodemographic and lifestyle factors (online supplemental table 1), medical history (online supplemental table 2) and patterns of social media use (online supplemental table 3). Participants with low self‐esteem were significantly younger than those with normal or high self-esteem (mean 25.23 vs 27.82 years, p=0.004). Low self‐esteem was associated with low socioeconomic status (23.6% vs 9.7%, p=0.011) with reference to high-income groups, an unhealthy (32.4% vs 11.2%, p<0.001) with reference to healthy nutrition, and poor spiritual commitment (32.6% vs 9.7%, p=0.001) with reference to more regular or committed individuals, respectively. Low self-esteem was also associated with a history of eating disorders (25.4% vs 15.2%, p=0.042) and mental health disorders (26.0% vs 13.5%, p=0.001) with reference to absence of such history.
Regarding social media use modalities, comparison-based use (31.5% vs 8.1%, p<0.001) was significantly associated with low self-esteem, along with belief-based use (21.8% vs 13.6%, p=0.022) and consumption-based use (21.6% vs 12.5%, p=0.041), compared with the absence of such use modalities, respectively. In contrast, no significant difference was observed for image-based use (19.3% vs 16.3%, p=0.314).
Among the studied platforms, only Snapchat engagement showed a significant association with low self‐esteem. Specifically, 28.8% of Snapchat nonusers/occasional users (17/59) had low self‐esteem, compared with 13.9% of moderate to active users (43/310) and 16.0% of highly engaged to intensive users (37/231) (p=0.017). None of the remaining social media use parameters showed significance. Table 4 recapitulates the significant factors associated with low self-esteem.
Table 4. Significant factors associated with self-esteem (N=600).
| Parameter | Level | Self-esteem | P value | |
|---|---|---|---|---|
| Normal n (%) |
Low n (%) |
|||
| Age (years) | Mean (SD) | 27.82 (8.40) | 25.23 (5.88) | 0.004* |
| Socioeconomic status | Low | 120 (76.4) | 37 (23.6) | 0.011* |
| Moderate | 194 (84.3) | 36 (15.7) | ||
| High | 139 (90.3) | 15 (9.7) | ||
| Very high | 50 (84.7) | 9 (15.3) | ||
| Diet | Healthy | 239 (88.8) | 30 (11.2) | <0.001* |
| Somewhat unhealthy | 218 (82.9) | 45 (17.1) | ||
| Unhealthy | 46 (67.6) | 22 (32.4) | ||
| Spiritual lifestyle | Regular and highly committed | 167 (90.3) | 18 (9.7) | 0.001* |
| Regular and moderately committed | 222 (84.4) | 41 (15.6) | ||
| Frequently and moderately committed | 85 (78.0) | 24 (22.0) | ||
| Rarely or lowly committed | 29 (67.4) | 14 (32.6) | ||
| Eating disorders history | No | 459 (84.8) | 82 (15.2) | |
| Yes | 44 (74.6) | 15 (25.4) | 0.042* | |
| Mental health history | No | 409 (86.5) | 64 (13.5) | |
| Yes | 94 (74.0) | 33 (26.0) | 0.001* | |
| Spirituality content engagement | Unlikely or rarely | 158 (79.8) | 40 (20.2) | |
| Frequently or regularly | 345 (85.8) | 57 (14.2) | 0.006* | |
| Snapchat use level | Non-use or occasional | 42 (71.2) | 17 (28.8) | |
| Moderate to active | 267 (86.1) | 43 (13.9) | ||
| Highly engaged to intensive | 194 (84.0) | 37 (16.0) | 0.017* | |
| Image-based | Not likely | 221 (83.7) | 43 (16.3) | |
| Low likelihood | 152 (86.9) | 23 (13.1) | ||
| High likelihood | 130 (80.7) | 31 (19.3) | 0.314 | |
| Comparison-based | Not likely | 262 (91.9) | 23 (8.1) | |
| Low likelihood | 126 (85.7) | 21 (14.3) | ||
| High likelihood | 115 (68.5) | 53 (31.5) | < 0.001* | |
| Belief-based | Not likely | 247 (86.4) | 39 (13.6) | |
| Low likelihood | 95 (88.0) | 13 (12.0) | ||
| High likelihood | 161 (78.2) | 45 (21.8) | 0.022* | |
| Consumption-based | Not likely | 245 (87.5) | 35 (12.5) | |
| Low likelihood | 131 (82.9) | 27 (17.1) | ||
| High likelihood | 127 (78.4) | 35 (21.6) | 0.041* | |
Only statistically significant factors are presented in this table. Non-significant factors are presented in online supplemental tables 1–3.
Statistically significant (p<0.05).
Predictors of low self-esteem
In the multivariate model (table 5), low self-esteem was independently predicted by age (OR=0.96, 95% CI 0.92 to 0.99, p=0.021), low socioeconomic status (OR=3.00, 95% CI 1.45 to 6.19, p=0.003), a history of mental health disorders (OR=2.02, 95% CI 1.15 to 3.53, p=0.014) and high comparison-based social media use (OR=4.93, 95% CI 2.58 to 9.43, p<0.001). Additionally, having an unhealthy diet was marginally associated with low self-esteem (OR=2.10, 95% CI 1.01 to 4.34, p=0.046). Notably, moderate (OR=0.43, 95% CI 0.20 to 0.91, p=0.027) and high (OR=0.43, 95% CI 0.20 to 0.94, p=0.033) levels of Snapchat use, both were significantly associated with lower odds of low self-esteem. Belief-based and consumption-based modalities were not significant predictors. The model accounted for 24.5% of the variance in self-esteem (Nagelkerke R²=0.245).
Table 5. Predictors of low self-esteem.
| Predictor | Level | OR | 95% CI | P value | |
|---|---|---|---|---|---|
| Age (years) | 0.96 | 0.92 to 0.99 | 0.021* | ||
| Socioeconomic level | High | Ref | – | – | 0.026* |
| Low | 3.00 | 1.45 to 6.19 | 0.003* | ||
| Moderate | 1.76 | 0.86 to 3.60 | 0.121 | ||
| Very high | 1.85 | 0.70 to 4.92 | 0.217 | ||
| Diet | Healthy | Ref | – | – | 0.133 |
| Somewhat unhealthy | 1.22 | 0.70 to 2.13 | 0.485 | ||
| Unhealthy | 2.10 | 1.01 to 4.34 | 0.046* | ||
| Spiritual lifestyle | Regular/high | Ref | – | – | 0.153 |
| Regular/moderate | 1.59 | 0.82 to 3.08 | 0.171 | ||
| Frequently/moderate | 2.16 | 1.00 to 4.62 | 0.049 | ||
| Lowly committed | 2.51 | 0.98 to 6.39 | 0.054 | ||
| Spirituality content | Frequently or regularly | 0.73 | 0.44 to 1.24 | 0.245 | |
| History of eating disorder | Yes | 1.50 | 0.71 to 3.17 | 0.284 | |
| Mental health history | Yes | 2.02 | 1.15 to 3.53 | 0.014* | |
| Image-based† | Not likely | Ref | – | – | 0.101 |
| Low likelihood | 0.56 | 0.30 to 1.04 | 0.064 | ||
| High likelihood | 0.57 | 0.30 to 1.08 | 0.083 | ||
| Comparison-based† | Not likely | Ref | – | – | <0.001* |
| Low likelihood | 1.69 | 0.85 to 3.36 | 0.135 | ||
| High likelihood | 4.93 | 2.58 to 9.43 | <0.001* | ||
| Belief-based† | Not likely | Ref | – | – | 0.742 |
| Low likelihood | 0.75 | 0.35 to 1.61 | 0.461 | ||
| High likelihood | 0.98 | 0.53 to 1.81 | 0.952 | ||
| Consumption-based† | Not likely | Ref | – | – | 0.946 |
| Low likelihood | 1.11 | 0.59 to 2.09 | 0.743 | ||
| High likelihood | 1.04 | 0.54 to 2.00 | 0.912 | ||
| Snapchat use | Occasional or nonuser | Ref | – | – | 0.067 |
| Moderately active | 0.43 | 0.20 to 0.91 | 0.027* | ||
| High-to-intensive | 0.43 | 0.20 to 0.94 | 0.033* | ||
Model Goodness-of-fit: Nagelkerke R2=0.245.
Statistically significant result (p<0.05).
Levels of social media use modalities were defined based on percentile statistics as follows: Not likely (< median); low likelihood (> median to <P75), high likelihood (>P75). Ref: category used as reference to calculate the OR.
Profiling comparison-based use modality
Independent t-tests (for age, BMI) and Pearson χ² tests (for all categorical factors) evaluated associations of comparison-based social media use (Comparison-based SMUS score ≥3.33, corresponding to the 75th centile) with sociodemographic and lifestyle factors (online supplemental table 4), medical history (online supplemental table 5) and patterns of social media use (online supplemental table 6). Among lifestyle factors, both unhealthy diet (45.6% vs 19.3%, p<0.001) and low spiritual commitment (53.5% vs 22.7%, p=0.001) were linked to comparison-based use with reference to healthy diet and regular/highly engaged spiritual lifestyle, respectively. Regarding medical history, participants with a history of mental health disorders (35.4% vs 26.0%, p=0.036) or thyroid disorders (45.7% vs 26.5%, p=0.006) were more likely to engage in comparison-based social media use compared with their respective counterparts. Associations with other social media use parameters showed only extended duration of social media use (>8 hours daily) to be associated with greater likelihood for comparison-based use (44.8%) compared with lower usage durations (17.9%–27.1%), and the difference was statistically significant (p<0.001).
The multivariate logistic regression model (table 6) showed that comparison-based use profile is independently associated with having a somewhat unhealthy diet (OR=1.89, 95% CI 1.24 to 2.88, p=0.003) or an unhealthy diet (OR=2.70, 95% CI 1.47 to 4.95, p=0.001), a rarely or lowly committed spiritual lifestyle (OR=2.49, 95% CI 1.19 to 5.21, p=0.016), a history of thyroid disorder (OR=2.82, 95% CI 1.47 to 5.39, p=0.002), and spending more than 8 hours per day on social media (OR=2.29, 95% CI 1.30 to 4.04, p=0.004). The model accounted for 12.3% of the variance in comparison-based use (Nagelkerke R²=0.123).
Table 6. Independent factors associated with comparison-based social media use.
| Predictor | Level | OR | 95% CI | P value | |
|---|---|---|---|---|---|
| Dietary habit | Healthy | Ref | – | – | 0.001 |
| Somewhat unhealthy | 1.89 | 1.24 to 2.88 | 0.003 | ||
| Unhealthy | 2.70 | 1.47 to 4.95 | 0.001 | ||
| Spiritual lifestyle | Regular/highly committed | Ref | – | – | 0.031 |
| Regular/moderate | 1.28 | 0.81 to 2.02 | 0.285 | ||
| Frequent/moderate | 0.82 | 0.46 to 1.47 | 0.502 | ||
| Rarely or lowly committed | 2.49 | 1.19 to 5.21 | 0.016 | ||
| Psychiatric history | Yes | 1.35 | 0.86 to 2.11 | 0.187 | |
| Thyroid disorder | Yes | 2.82 | 1.47 to 5.39 | 0.002 | |
| Daily duration of social media use | Up to 4 hours | Ref | – | – | 0.017 |
| >4–8 hours | 1.45 | 0.92 to 2.29 | 0.109 | ||
| >8 hours | 2.29 | 1.30 to 4.04 | 0.004 | ||
Multivariate logistic regression. Dependent factor: comparison-based social media use (‘Likely’ score≥3.33).
The model explains 12.3% of the variable variance (Nagelkerke R2=0.123).
Statistically significant result (p<0.05).
Ref, category used as reference to calculate the OR.
Discussion
This study builds on the premise that social media’s psychological associations may not be solely determined by time spent online but also by the specific modalities of use. Drawing on multidimensional frameworks and theories, the study investigated social media use patterns and modalities among 600 Saudi adolescents and adults aged up to 50 years and explored how distinct modes of social media engagement differentially relate to self-esteem.
Levels and patterns of social media use and related health impact
Overall, we observed high levels of social media use, indicated by long daily exposures along with high engagement rates with a multitude of platforms and content types. However, neither the overall daily duration of use, nor the specific platform or type of content engaged with, showed a notable effect on self-esteem. This is somewhat contrasting with previous data among the Saudi population, where several studies highlighted the differential impact of some platforms both on lifestyle and health outcomes. While some platforms were associated with positive lifestyle behaviours, such as healthy diet and physical exercise, others were associated with negative behaviours such as tobacco smoking, poor sleep quality and depression.22,23 Other studies observed multiple adverse outcomes associated with prolonged social media use, especially among students and younger population, including low academic performance, poor social interaction and sedentary lifestyle.22,24,25 However, a recent study agreed with our findings showing no association between daily use duration on self-esteem, while modalities of use such as comparison with others, posting frequency, motivation for use and perceived impact were significantly associated.26
Notwithstanding the difference in outcome between these studies, the discrepancy in findings may indicate a shift in the mechanisms by which social media may interfere with health outcomes, potentially resulting in a generational effect. Younger users may show stronger associations between social media use and adverse psychological outcomes, while older generations appear to engage in more adaptive or resilient use patterns, reflecting a more ‘old-school’ approach to social media. Over the past decade, social media use has shown a marked global increase in both penetration and intensity, particularly among younger populations. Transgenerational analysis indicates that, while older age groups are increasingly engaged in social media, the levels of addiction among younger generations remain significantly higher and are associated with greater vulnerability to adverse mental health outcomes.27 This phenomenon is observed in several countries and cultures and is likely to be influenced by goals and modes of social media use, among other covariates.28 In other terms, while social media use has become pervasive, its mere frequency or intensity is no longer necessarily a reliable indicator of mental health outcomes. Rather, the mode or pattern of use may be more closely associated with these outcomes. Understanding these patterns is essential for elucidating the potential pathways linking social media use to mental health and for informing eventual prevention and intervention strategies.
Social media use modalities and self-esteem
The use of the SMUS enabled distinguishing four modalities of social media use among our study population, with comparison-based and consumption-based modalities being the most frequent. However, of all four modalities, comparison-based use was the only mode significantly associated with self-esteem, independently increasing the likelihood of low self-esteem by fivefold. This is the major finding of this study. It highlights the greater relevance of use modality—over other aspects of use—in determining mental health outcomes, further validating the SMUS tool and concept. Nevertheless, given the cross-sectional design, temporal sequence and causality cannot be established, and the observed relationship may be bidirectional. Consistently, Tuck and Thompson observed that comparison-based mode showed unique associations with poorer psychological well-being, including low self-esteem, depressive symptoms, body appearance anxiety. They further observed that comparison-based use was associated with adverse emotion regulation. That is, individuals using this mode combined a social desire to present themselves favourably and connect with others, with a tendency to regulate emotions in maladaptive ways; namely, by amplifying negative emotions or suppressing positive ones.18 These observations align with the concept of social comparison, which is consistently associated with adverse psychological outcomes, notably low self-esteem, and is recognised as a potential pathway linking social media use and mental health.29,30
In bivariate analyses, low self-esteem was associated with both belief-based and consumption-based modalities, while no significant association was observed with image-based mode. Although these associations were not confirmed in the multivariate model, they highlight a potential relevance of belief-based and consumption-based modalities in specific contexts or subgroups, warranting further exploration. In both their studies 2 and 3, Tuck and Thompson found that belief-based social media use was consistently correlated with ‘lower self-esteem, greater depression, and a contra-hedonic emotion regulation cycle’, along with high levels of boredom and drama engagement. On the other hand, belief-based use was associated with low levels of ‘agreeableness and conscientiousness’ personality traits.18 The latter observation is relevant as these two personality traits are associated with positive emotion regulation and psychological well-being, and their negative association with the belief-based mode may indicate greater vulnerability to adverse mental health outcomes through its relationship with negative emotion regulation and maladaptive social functioning in response to specific social media content.31,32
Regarding consumption-based mode, our findings show some divergence from the literature. Tuck and Thompson observed that this mode of social media use was associated with positive emotional well-being including greater self-esteem and lower risk of depressive symptoms.18 This mode is considered an ‘enjoyment’ type of social media use, aligned with entertainment goals and characterised by interest-driven exploration and browsing aimed at seeking inspiration and new ideas, rather than deep emotional engagement.33,34
The relevance and implications of comparison-based use
The strong association between comparison-based social media use and low self-esteem is consistently supported across various studies and populations. Vogel et al35 demonstrated that frequent Facebook users reported lower trait self-esteem, largely mediated by greater exposure to upward social comparisons, that is, viewing others display idealised lives, achievements or appearances. In experimental settings, even temporary exposure to such content reduced participants’ state self-esteem and led to negative self-evaluations, especially when users encountered profiles showcasing socially desirable traits like high activity or healthy habits.35 Another study by Vogel et al36 found that individuals with high social comparison orientation were more vulnerable to self-esteem deterioration, having greater engagement with social media and stronger emotional responses to comparisons. This indicates how individual predispositions and modes of use may shape individuals’ psychological responses to social media.36 These findings are further supported by qualitative data from Tosun et al,37 who explored mixed emotional responses to social comparisons on social networks among mothers. Interestingly, many participants described simultaneous feelings of contrast and assimilation, highlighting the complexity of emotional processing associated with social media comparisons.37
Together, these studies emphasise that comparison-based use is not a neutral activity; rather, it may be associated with patterns of internal self-evaluation linked to lower self-worth and emotional stability. Clinically, this underscores the potential value of healthcare providers and educators assessing patterns of social comparison among social media users, particularly in youth and vulnerable groups. Preventive strategies may include awareness campaigns, digital literacy education and targeted psychoeducation focused on helping individuals understand and critically navigate comparison-triggering content.
The development of an online-based SMUS screening version may be a practical solution for self-evaluation, early detection and monitoring of potentially high-risk social media use patterns. Such a tool could empower individuals to identify potentially harmful usage modalities and take self-corrective action before psychological symptoms emerge or worsen. It could also serve as a preventive resource for schools, universities and healthcare settings to promote healthier digital habits and mental well-being.
One of the potential applications of this tool is to identify high-risk profiles associated with problematic social media use modalities. In the present study, further analysis of comparison-based use showed strong associations with unhealthy diet, poor spirituality and extensive daily use of social media. Research indicates that spirituality and a strong perceived relationship with God are associated with lower levels of social comparison and body image dissatisfaction, while also being positively associated with self-esteem.38,39 In the context of the Saudi population, spirituality holds cultural relevance and may represent a potential area for culturally appropriate preventive interventions addressing the adverse psychological correlates of social media use. Strengthening spiritual commitment may potentially support resilience by reinforcing self-worth and identity stability, although this hypothesis requires prospective investigation. Dietary behaviour is also recognised as being associated with self-esteem, potentially through body image,40 and may ultimately be related to the risk of disordered eating.41 While the interaction between self-esteem and eating disorders is an important area of investigation, it falls beyond the scope of the present analysis and will be addressed in greater detail in a forthcoming study based on the same population.
The significance of self-esteem in mental health impact of social media use
One of the key premises of the present study is that self-esteem may represent an important psychological factor in the relationship between extensive social media use and mental health. This is supported by several theoretical frameworks and clinical studies proposing directional or reciprocal relationships between low self-esteem and psychiatric morbidity. Cognitive models, such as Fennell’s adaptation of Beck’s schema theory, posit that low self-esteem stems from early negative experiences, leading to maladaptive self-schemas that may predispose individuals to depression and anxiety.11 A refined model of this theory posits that individuals with low self-esteem are more susceptible to cognitive biases that reinforce negative beliefs about the self and others, increasing their vulnerability to psychological distress, particularly in response to triggering situations such as social media interactions.12 Self-discrepancy theory suggests that conflicts between one’s actual self and ideal can result in emotional distress, contributing to depression and anxiety.13 In the context of eating disorders, the transdiagnostic model identifies low self-esteem as a core maintaining factor associated with behaviours like restrictive eating and body dissatisfaction.14
These theoretical perspectives are supported by clinical studies demonstrating that interventions targeting self‐esteem, including cognitive-behavioural and psychoeducational interventions, can lead to improvements in mental health outcomes.15,16 This suggests that self‐esteem is both a marker and a potentially modifiable risk factor in the course of psychiatric illnesses. An interesting clinical trial by Gordon et al42 tested the SoMe social media literacy programme among adolescents and found reduced dietary restraint and depressive symptoms in girls at 6 months.42 While effects were limited overall, the findings suggest that interventions fostering critical engagement with social media may help mitigate its negative mental health impact, especially when tailored to individual and gender-specific vulnerabilities.
Nonetheless, self-esteem is only one among several potential pathways through which social media use may be associated with mental health. One critical and increasingly pervasive mechanism in modern digital interactions is self-disclosure, defined as the communication of personal information to others. This practice may be associated with changes in self-concept and emotion regulation, especially among youth, increasing vulnerability to emotional strain.43 An interesting research perspective is to explore the combination of comparison-based use and online self-disclosure practices to further refine our understanding. Other mechanisms particularly relevant to adolescents involve developmental changes in behaviour, cognition and neurobiological pathways related to stress and reward processing. These changes can exert a deeper influence on personality traits such as self-concept, emotion regulation and social interaction, potentially predisposing individuals to comparison-based use and amplifying its detrimental impact on mental health.44 This is consistent with our findings indicating younger age to be an independent factor of low self-esteem, further supporting the particular vulnerability of youth.
These mechanisms represent compelling and largely unexplored research perspectives that merit greater attention in future investigations. This line of research holds significant potential for identifying developmental windows of vulnerability, refining psychosocial models of risk and informing the design of age-sensitive interventions. Longitudinal and experimental studies are needed to clarify temporal direction, potential bidirectional relationships and the mechanisms underlying the observed associations.
Limitations
This study has some limitations that should be considered in interpreting the findings. First, its cross-sectional design prevents any inference of causality between social media use modalities and self-esteem outcomes. Second, reliance on self-reported data introduces potential biases, including social desirability and recall bias. Additionally, participants’ identities, ages and declarations of parental permission could not be independently verified because the survey was anonymous and conducted online. Therefore, the possibility of inaccurate age reporting or unverified parental permission cannot be excluded. Third, the tools used, while validated, may have variable sensitivity and specificity in accurately diagnosing low self-esteem and discriminating between different social media use modalities. Additionally, the generalisability of the findings is limited by the convenience sampling method, which relied on online dissemination through social media platforms, potentially over-representing certain users who were more active on social media or more willing to participate in online research. Finally, because the survey link was circulated through open and shareable online channels, the number of individuals exposed to the invitation was unknown, precluding calculation of a valid response rate.
Conclusion
This study demonstrates that the psychological correlates of social media use may depend more on the way individuals engage with social media than on the duration of use or the platforms accessed. Comparison-based and consumption-based use were the most common modalities in the study population; however, comparison-based use was the only modality independently associated with low self-esteem. Younger age was also independently associated with lower self-esteem, suggesting greater vulnerability across younger individuals.
These findings support moving beyond exposure-based indicators towards a multidimensional assessment of social media behaviour. In clinical, educational and public health settings, greater attention should be directed towards patterns involving social comparison, idealised self-presentation and repeated evaluation of oneself against others. Modality-based assessment may help identify individuals who could benefit from digital literacy, psychoeducation and interventions promoting healthier engagement with comparative content.
Given the cross-sectional design, the temporal direction of these relationships cannot be established, and low self-esteem may also increase susceptibility to comparison-based social media use. Longitudinal and intervention studies are, therefore, needed to clarify directionality, identify vulnerable subgroups and determine whether modifying comparison-based engagement can improve psychological well-being.
Supplementary material
Acknowledgements
The author thanks Taif University, Saudi Arabia, for their support in conducting this study. The author would also like to express his sincere gratitude to the senior medical students from various medical schools across all regions of Saudi Arabia who served as data collectors for this study. Their dedication and professionalism were vital to the success of the project. Prior to data collection, all data collectors received thorough training through multiple sessions conducted by expert psychiatrists. This training covered essential aspects of administering the questionnaire, as well as effective communication techniques to approach participants, explain the study objectives, answer queries, and obtain informed consent in accordance with ethical standards.
Footnotes
Funding: This research was funded by Taif University, Saudi Arabia, Project No. (TU-DSPP-2025-25).
Provenance and peer review: Not commissioned; externally peer-reviewed.
Patient consent for publication: Not applicable.
Ethics approval: The study was reviewed and approved by the Ethics Committee of Taif University (approval number HAO-02-T-105/45-284). The funder did not influence the results/outcomes of the study despite author affiliations with the funder. Participants gave informed consent to participate in the study before taking part.
Data availability free text: Data associated with this project are available upon reasonable request from the corresponding author, subject to approval from the relevant Human Research Ethics Committee.
Patient and public involvement: Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Data availability statement
Data are available upon reasonable request.
References
- 1.Schønning V, Hjetland GJ, Aarø LE, et al. Social Media Use and Mental Health and Well-Being Among Adolescents - A Scoping Review. Front Psychol. 2020;11:1949. doi: 10.3389/fpsyg.2020.01949. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Odgers CL, Jensen MR. Annual Research Review: Adolescent mental health in the digital age: facts, fears, and future directions. J Child Psychol Psychiatry. 2020;61:336–48. doi: 10.1111/jcpp.13190. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Barthorpe A, Winstone L, Mars B, et al. Is social media screen time really associated with poor adolescent mental health? A time use diary study. J Affect Disord. 2020;274:864–70. doi: 10.1016/j.jad.2020.05.106. [DOI] [PubMed] [Google Scholar]
- 4.Abi-Jaoude E, Naylor KT, Pignatiello A. Smartphones, social media use and youth mental health. CMAJ. 2020;192:E136–41. doi: 10.1503/cmaj.190434. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Nagata JM, Iyer P, Chu J, et al. Contemporary screen time modalities among children 9-10 years old and binge-eating disorder at one-year follow-up: A prospective cohort study. Int J Eat Disord. 2021;54:887–92. doi: 10.1002/eat.23489. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Cataldo I, Lepri B, Neoh MJY, et al. Social Media Usage and Development of Psychiatric Disorders in Childhood and Adolescence: A Review. Front Psychiatry. 2020;11:508595. doi: 10.3389/fpsyt.2020.508595. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Christensen KA, Forbush KT, Cushing CC, et al. Evaluating associations between fitspiration and thinspiration content on Instagram and disordered-eating behaviors using ecological momentary assessment: A registered report. Int J Eat Disord. 2021;54:1307–15. doi: 10.1002/eat.23518. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Prieler M, Choi J, Lee HE. The Relationships among Self-Worth Contingency on Others’ Approval, Appearance Comparisons on Facebook, and Adolescent Girls’ Body Esteem: A Cross-Cultural Study. Int J Environ Res Public Health. 2021;18:901. doi: 10.3390/ijerph18030901. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Bayer JB, Triệu P, Ellison NB. Social Media Elements, Ecologies, and Effects. Annu Rev Psychol. 2020;71:471–97. doi: 10.1146/annurev-psych-010419-050944. [DOI] [PubMed] [Google Scholar]
- 10.Bellis MA, Sharp CA, Hughes K, et al. Digital Overuse and Addictive Traits and Their Relationship With Mental Well-Being and Socio-Demographic Factors: A National Population Survey for Wales. Front Public Health. 2021;9:585715. doi: 10.3389/fpubh.2021.585715. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Fennell MJV. Low Self-Esteem: A Cognitive Perspective. Behav Cogn Psychother. 1997;25:1–26. doi: 10.1017/S1352465800015368. [DOI] [Google Scholar]
- 12.Rimes KA, Smith P, Bridge L. Low self-esteem: a refined cognitive behavioural model. Behav Cogn Psychother. 2023;51:579–94. doi: 10.1017/S1352465823000048. [DOI] [PubMed] [Google Scholar]
- 13.Higgins ET. Self-discrepancy: a theory relating self and affect. Psychol Rev. 1987;94:319–40. doi: 10.1037/0033-295X.94.3.319. [DOI] [PubMed] [Google Scholar]
- 14.Fairburn CG, Cooper Z, Shafran R. Cognitive behaviour therapy for eating disorders: a “transdiagnostic” theory and treatment. Behav Res Ther. 2003;41:509–28. doi: 10.1016/s0005-7967(02)00088-8. [DOI] [PubMed] [Google Scholar]
- 15.Lee S, Lee E. Effects of Cognitive Behavioral Group Program for Mental Health Promotion of University Students. IJERPH. 2020;17:3500. doi: 10.3390/ijerph17103500. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Sáez G, López-Nuñez C, Carlos-Vivas J, et al. A Multicomponent Program to Improve Self-Concept and Self-Esteem among Intimate Partner Violence Victims: A Study Protocol for a Randomized Controlled Pilot Trial. Int J Environ Res Public Health. 2021;18:4930. doi: 10.3390/ijerph18094930. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Kvardova N, Machackova H, Smahel D. The Direct and Indirect Effects of Online Social Support, Neuroticism, and Web Content Internalization on the Drive for Thinness among Women Visiting Health-Oriented Websites. Int J Environ Res Public Health. 2020;17:2416. doi: 10.3390/ijerph17072416. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Tuck AB, Thompson RJ. The Social Media Use Scale: Development and Validation. Assessment. 2024;31:617–36. doi: 10.1177/10731911231173080. [DOI] [PubMed] [Google Scholar]
- 19.Oweis A, Gharaibeh M, Alhourani R. Rosenberg Self-Esteem Scale--Modified Arabic Version. PsycTESTS Dataset. 2014 [Google Scholar]
- 20.Rosenberg M. Rosenberg self-esteem scale (RSE) Acceptance and Commitment Therapy Measures Package. 1965;61:18. [Google Scholar]
- 21.Fairburn CG, Beglin SJ. Assessment of eating disorders: interview or self-report questionnaire? Int J Eat Disord. 1994;16:363–70. [PubMed] [Google Scholar]
- 22.Abdel-Salam DM, Alrowaili HI, Albedaiwi HK, et al. Prevalence of Internet addiction and its associated factors among female students at Jouf University, Saudi Arabia. J Egypt Public Health Assoc. 2019;94:12. doi: 10.1186/s42506-019-0009-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.BinDhim NF, Althumiri NA, Al-Duraihem RA, et al. Association between daily use of social media and behavioral lifestyles in the Saudi community: a cross-sectional study. Front Public Health. 2023;11:1254603. doi: 10.3389/fpubh.2023.1254603. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Alshwiah A, Alaulamie L. Social media usage and its association with students’ performance and attitude in Saudi Arabia. JARHE . 2023;15:355–68. doi: 10.1108/JARHE-11-2021-0417. [DOI] [Google Scholar]
- 25.Kolhar M, Kazi RNA, Alameen A. Effect of social media use on learning, social interactions, and sleep duration among university students. Saudi J Biol Sci. 2021;28:2216–22. doi: 10.1016/j.sjbs.2021.01.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Alshaikhi OAM, Alshaikhi SA, AlZubaidi HAA, et al. Social Media Effect on Personal Self-Esteem Among the Population in Saudi Arabia. Cureus. 2023;15:e49543. doi: 10.7759/cureus.49543. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Koçak O, Arslan H, Erdoğan A. Social Media use Across Generations: from Addiction to Engagement. EIS . 1:63–77. doi: 10.5755/j01.eis.1.15.29080. n.d. [DOI] [Google Scholar]
- 28.Mulvey MS, Lever MW, Elliot S. A Cross-National Comparison of Intragenerational Variability in Social Media Sharing. Journal of Travel Research . 2020;59:1204–20. doi: 10.1177/0047287519878511. [DOI] [Google Scholar]
- 29.Verduyn P, Gugushvili N, Massar K, et al. Social comparison on social networking sites. Curr Opin Psychol. 2020;36:32–7. doi: 10.1016/j.copsyc.2020.04.002. [DOI] [PubMed] [Google Scholar]
- 30.Wirtz D, Tucker A, Briggs C, et al. How and Why Social Media Affect Subjective Well-Being: Multi-Site Use and Social Comparison as Predictors of Change Across Time. J Happiness Stud. 2021;22:1673–91. doi: 10.1007/s10902-020-00291-z. [DOI] [Google Scholar]
- 31.Soto CJ. Is happiness good for your personality? Concurrent and prospective relations of the big five with subjective well-being. J Pers. 2015;83:45–55. doi: 10.1111/jopy.12081. [DOI] [PubMed] [Google Scholar]
- 32.Sharma B, Lee SS, Johnson BK. The dark at the end of the tunnel: Doomscrolling on social media newsfeeds. Technology, Mind, and Behavior . 2022;3:144–56. doi: 10.1037/tmb0000059. [DOI] [Google Scholar]
- 33.Valkenburg PM, Beyens I, Pouwels JL, et al. Social Media Browsing and Adolescent Well-Being: Challenging the “Passive Social Media Use Hypothesis”. J Comput Mediat Commun. 2022;1 doi: 10.1093/jcmc/zmab015. [DOI] [Google Scholar]
- 34.Weinstein E. The social media see-saw: Positive and negative influences on adolescents’ affective well-being. New Media & Society . 2018;20:3597–623. doi: 10.1177/1461444818755634. [DOI] [Google Scholar]
- 35.Vogel EA, Rose JP, Roberts LR, et al. Social comparison, social media, and self-esteem. Psychol Pop Media Cult. 2014;3:206–22. doi: 10.1037/ppm0000047. [DOI] [Google Scholar]
- 36.Vogel EA, Rose JP, Okdie BM, et al. Who compares and despairs? The effect of social comparison orientation on social media use and its outcomes. Pers Individ Dif. 2015;86:249–56. doi: 10.1016/j.paid.2015.06.026. [DOI] [Google Scholar]
- 37.Tosun LP, Öztürk A, Özdemir G. Mother to Mother: Mothers’ Social Comparison-Based Emotions on Social Networking Sites. Eur J Psychol. 2020;16:602–18. doi: 10.5964/ejop.v16i4.2159. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.J. Homan K, A. Lemmon V. Perceived relationship with God moderates the relationship between social comparison and body appreciation. Mental Health, Religion & Culture. 2015;18:425–39. doi: 10.1080/13674676.2015.1075481. [DOI] [Google Scholar]
- 39.Ollivaud J, Galharret J, Roussiau N. Spirituality, self-esteem and the mechanisms of social and temporal comparison. Spiritual Psychology and Counseling. 2023;8:83–102. doi: 10.37898/spc.2023.8.1.187. [DOI] [Google Scholar]
- 40.So B, Kwon KH. The influence of diet behavior on self-esteem according to the body image of workers in Republic of Korea: Mediating role of diet behavior. J Hum Behav Soc Environ. 2024;34:584–604. doi: 10.1080/10911359.2023.2177229. [DOI] [Google Scholar]
- 41.Krauss S, Dapp LC, Orth U. The Link Between Low Self-Esteem and Eating Disorders: A Meta-Analysis of Longitudinal Studies. Clinical Psychological Science. 2023;11:1141–58. doi: 10.1177/21677026221144255. [DOI] [Google Scholar]
- 42.Gordon CS, Jarman HK, Rodgers RF, et al. Outcomes of a Cluster Randomized Controlled Trial of the SoMe Social Media Literacy Programme for Improving Body Image-Related Outcomes in Adolescent Boys and Girls. Nutrients. 2021;13:3825. doi: 10.3390/nu13113825. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Luo M, Hancock JT. Self-disclosure and social media: motivations, mechanisms and psychological well-being. Curr Opin Psychol. 2020;31:110–5. doi: 10.1016/j.copsyc.2019.08.019. [DOI] [PubMed] [Google Scholar]
- 44.Orben A, Meier A, Dalgleish T, et al. Mechanisms linking social media use to adolescent mental health vulnerability. Nat Rev Psychol . 2024;3:407–23. doi: 10.1038/s44159-024-00307-y. [DOI] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data are available upon reasonable request.