Perceived social support, subjective well-being, coping styles, personality traits, and social media addiction among patients with depression

Abstract

BACKGROUND

Major depressive disorder (MDD) is a debilitating and commonly prevalent mental health condition that impacts around 5% of the global population. It is recognised as one of the leading causes of disability worldwide. Understanding the factors that influence the severity and progression of MDD, such as perceived social support, subjective well-being, coping mechanisms, personality traits, and social media addiction, can help enhance the development of effective treatment and prevention strategies.

AIM

To analyse the relationship between perceived social supports, subjective well-being, coping styles, personality traits, and social media addiction among adult patients with MDD, and compare these factors with those of adult patients with MDD who are in remission.

METHODS

All participants aged 18 to 60 years who were attending the adult psychiatry outpatient department were initially screened for eligibility. The investigator has obtained informed consent. The participants were divided into two groups: The study group [Hamilton Depression Rating Scale (HAM-D) score > 7] and the comparator group (HAM-D score ≤ 7). Standardised assessment tools, Multidimensional Scale of Perceived Social Support, World Health Organization-5 Wellbeing Index, Brief COPE Inventory, HAM-D, Social Networking Addiction Scale (SNAS), and Personality Inventory for the Diagnostic and Statistical Manual of Mental Disorders - Brief Form, were applied to all the participants in both groups.

RESULTS

A total of 140 patients were recruited in the study (70 symptomatic and 70 in remission). Patients who were in remission showed significantly higher mean Multidimensional Scale of Perceived Social Support scores (48.70 ± 11.01) as compared to symptomatic patients (33.00 ± 15.37). The World Health Organization wellbeing was significantly lower in symptomatic patients (7.93 ± 1.75) as compared with those who were in remission (13.90 ± 1.61). The mean SNAS scores were higher in symptomatic patients (72.37 ± 19.83) compared to patients in remission (67.03 ± 28.09). Problem-focused coping showed a significant negative correlation with SNAS scores (r = -0.334, P = 0.005) and HAM-D scores (r = -0.273, P = 0.022). Among personality trait domains, disinhibition had a strong positive and significant correlation (r = 0.515, P < 0.001) with SNAS scores. Detachment and psychoticism were significantly higher in symptomatic patients. In social media addiction, tolerance, withdrawal, and relapse were significantly higher in symptomatic patients in comparison to those in remission.

CONCLUSION

This research emphasises the rising importance of digital behaviour patterns among psychiatric groups. More screen time and problematic social media use were linked to depression symptoms and reduced psychosocial functioning. Adding behavioural interventions that focus on digital hygiene, improving coping skills, and re-engaging social abilities could serve as useful complements to conventional drug and therapy approaches.

Key Words: Major depressive disorder; Perceived social support; Coping styles; Social media addiction; Subjective well-being; Personality traits

Core Tip: The perceived social support is significantly higher among patients of depression who are in remission than those who are symptomatic. The symptomatic patients with major depressive disorder have higher social networking use than those in remission. Social networking addiction has a significant positive correlation with disinhibition personality traits. Social networking addiction has a significant negative correlation with problem-focused coping strategies.

INTRODUCTION

Major depressive disorder (MDD) has an estimated prevalence of about 5% among adults, as per the report of the World Health Organization (WHO)[1]. There are many psychological, social, and behavioural factors that influence its onset, course, and outcomes. Addiction to social media also emerged as an important factor associated with depression[2-4]. Depression produces many negative consequences, including compromised social functioning, decreased quality of life, and risk of suicide. With the rising prevalence of depression, it becomes crucial to understand the factors that affect the severity and course of the disorder in order to improve treatment and prevention strategies[5,6]. Some of the psychosocial factors related to depression are perceived social support, subjective well-being, coping styles, and personality traits, which have been found to be the most important in the experience and management of depression symptoms. Social support is one of the most important factors that lessens the impact of stress and improves health outcomes[7]. People with higher subjective well-being seem to be in a better state of mental health than others with lower subjective well-being, who appear to be more susceptible to experiencing depressive symptoms. While subjective well-being and perceived social support work independently, an individual's psychological distress and ability to cope with it are greatly benefited by the combination of both[8,9]. Coping mechanisms have a big impact on mental health. People with depression who use maladaptive coping mechanisms, including avoidance and rumination, experience worse treatment outcomes and more severe symptoms. Adaptive coping strategies, on the other hand, such as solving problems and looking for social support, are associated with improved mental health outcomes[10]. Too much usage of social media sites is detrimental to mental health. These platforms promote social comparison, which fosters negative feelings, inadequate social support, and lowers self-esteem[11,12]. Personality traits play a significant role in how individuals with depression experience and respond to stressors. Traits such as negative affect, detachment, psychoticism, antagonism, and disinhibition influence emotional regulation, social interactions, and coping strategies, which in turn affect the progression of depressive symptoms. Also, excessive use of social media may serve as both an escape and a source of exacerbated emotional distress[13,14]. There is a close and complex relationship between perceived social support, subjective well-being, coping styles, personality traits, and social media addiction among patients with depression. Personality trait is a vulnerability factor for depression, and coping styles are primarily influenced by personality traits. Social support is an external factor that has a moderating role in depression. Subjective well-being is critically compromised in depression, and the psychological distress often triggers social media use. It’s important to figure out how these psychological and social elements work with MDD to generate new and better ways to prevent and treat the disorder. This can lead to better outcomes and improved management of MDD[15-17]. Understanding the impact of social support, coping styles, personality traits, and social media addiction can improve prevention and intervention strategies, ultimately enhancing mental health outcomes[18].

MATERIALS AND METHODS

Study design

It is a cross-sectional study, conducted at a tertiary care centre in Northern India. While this design enabled assessment of associations among key psychosocial and behavioural variables, it precludes any inference of causality. Longitudinal or experimental approaches were not used due to practical feasibility and resource constraints. All participants aged 18 years to 60 years who were attending the adult psychiatry outpatient department were initially screened for eligibility. Individuals who met the predetermined selection criteria and provided written informed consent were subsequently included in the study. The diagnosis of each participant was confirmed through a comprehensive clinical assessment, utilising the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria. The participants were divided into two groups: The symptomatic group [Hamilton Depression Rating Scale (HAM-D) score > 7] and the remission group (HAM-D score ≤ 7). At the baseline of the study, a semi-structured questionnaire was administered to collect comprehensive socio-demographic information as well as their patterns of social media use. Multidimensional Scale of Perceived Social Support (MPSS) to assess the participants’ perceived levels of social support across various domains, WHO-5 Wellbeing Index to evaluate the general wellbeing of participants, Brief COPE Inventory to assess the coping strategies employed by participants in response to stress, HAM-D to quantify the severity of depressive symptoms, Social Networking Addiction Scale (SNAS) to evaluate the degree of addictive behaviours related to social media use, Personality Inventory for DSM-5 - Brief Form to assess participant’s personality traits in line with the DSM-5 criteria. Ethical approval was taken from the Institutional Ethics Committee vide letter No. 2609/Ethics/2024 with reference code No. XXII-PGTSC-IIA/P37.

Sample size

Sample size was calculated using "G Power": Statistical Power analyses 3.1.9.7 Application[19]. The sample size that was determined was 140, taking into account an effect size of 0.30 and a power of 95%.

Sampling technique

A convenience sampling method was used to select participants who met the inclusion criteria.

Statistical analysis

To analyse the data, IBM SPSS Software, version 24.0, was used[20]. Demographic and clinical variables were reported using descriptive statistics. The data was analysed for normality using the Shapiro-Wilk test and was found to be normal. The Mann-Whitney U test is used to determine whether there is a statistically significant difference between the distributions of two independent groups. The χ²d test was used for categorical data; baseline characteristics were compared between groups. P-values < 0.05 were taken as statistically significant.

RESULTS

This study was conducted in the year 2024-25, after obtaining ethical approval from the Institutional Ethics Committee. The total enrolled patients were divided into two groups. The two groups were the symptomatic group (symptomatic patients of MDD) and the remission group (patients of MDD currently in remission). For the symptomatic group, a total of 86 cases were screened to enrol 70 participants in this group. Out of the cases screened, 16 were excluded. For the remission group, a total of 85 cases were screened to enrol 70 participants in this group. Out of the cases screened, 15 were excluded (Figure 1). The most common reason for the exclusion was not having personal phones/access to social media platforms for both groups. Baseline socio-demographic characteristics of both groups were comparable (Table 1). On comparing clinical variables, the symptomatic group had a significantly lower HAM-D score; other variables of both groups were comparable. The symptomatic group also had significantly lower well-being scores on the WHO-5 Well-being Index compared to the remission group (P < 0.001) (Table 2). The results showed that subjects in the symptomatic group had much lower perceived social support (33.00 ± 15.37) when compared to subjects in the remission group (48.70 ± 11.01) (P-value < 0.001) (Figure 2, Tables 1 and 2).

Figure 1  Participant flow diagram.

Figure 2 Scale survey statistical chart. A: World Health Organization-5 Wellbeing scores; B: Perceived social support (Multidimensional Scale of Perceived Social Support); C: Screen time (hours/day); D: Brief COPE subscales; E: Social Networking Addiction Scale domains; F: Social Media Addiction prevalence (Social Networking Addiction Scale > 84). WHO: World Health Organization; MSPSS: Multidimensional Scale of Perceived Social Support; SNAS: Social Networking Addiction Scale; SMA: Social media addiction.

Table 1 Socio-demographic profile of the study participants in both the groups, n (%).

Variables		Group A (n = 70)	Group B (n = 70)	χ2 value	P value
Gender	Male	45 (64.29)	47 (67.14)	0.032	0.861
	Female	25 (35.71)	23 (32.86)
Age	18-30	33 (47.14)	26 (37.14)	2.55	0.279
	> 30-45	21 (30.0)	30 (20.0)
	> 45-60	16 (22.6)	14 (20.0)
Domicile	Rural	42 (60.0)	39 (55.71)	0.116	0.732
	Urban	28 (40.0)	31 (44.29)
Education	Illiterate	6 (8.57)	11 (15.71)	7.44	0.114
	Primary	26 (37.14)	24 (34.28)
	High School	18 (25.71)	8 (11.42)
	Intermediate	16 (22.85)	18 (25.71)
	Graduate and above	4 (5.71)	9 (12.85)
Occupation	Unskilled/semiskilled	33 (47.14)	35 (50.00)	1.86	0.692
	Unemployed	16 (22.86)	10 (14.29)
	Housewife	14 (20.00)	16 (22.86)
	Skilled	7 (10.00)	9 (12.86)
Family Income	< 10000	11 (15.7)	7 (10.0)	1.02	0.601
	10000-20000	29 (41.42)	31 (44.28)
	> 20000	30 (42.87)	32 (45.71)
Family type	Joint	42 (59.42)	36 (51.43)	0.606	0.437
	Nuclear	28 (40.58)	32 (48.57)
Marital status	Married	41 (58.57)	44 (62.85)	0.269	0.604
	Unmarried	29 (41.43)	26 (37.15)
Religion	Hindu	50 (71.42)	48 (68.57)	0.136	0.712
	Muslim	20 (28.58)	22 (31.43)

Table 2 Comparison of clinical variables and World Health Organization wellbeing scores, multidimensional perceived social support (Multidimensional Scale of Perceived Social Support) score and screen time per day in hours among both the groups, n (%).

Clinical variables		Group A (n = 70)	Group B (n = 70)	χ2 value	P value
Number of episodes	Single	53 (75.71)	59 (84.29)	1.12	0.291
	Multiple	17 (24.29)	11 (15.71)
Family history of any psychiatric illness	Present	12 (17.14)	8 (11.43)	1.02	0.312
	Absent	58 (82.85)	62 (88.57)
Duration of current episode	< 6 months	42 (47.14)	39 (37.14)	3.11	0.212
	6-12 months	17 (30.00)	25 (20.0)
	> 12 months	11 (22.62)	6 (20.0)
Clinical variables				U value
Mean duration of current episodes		3.54 ± 3.01	3.26 ± 2.18	2686.0	0.318
HAM-D score		19.24 ± 1.89	5.13 ± 1.54	22.99	< 0.001
WHO-5 Well-Being Index Score		7.93 ± 1.75	13.90 ± 1.61	22.99	< 0.001
MPSS Scores				χ2/U valueb
Low (12-35)		45 (64.29)	6 (08.57)	47.57	< 0.001a
Medium (36-60)		20 (28.57)	59 (80.00)
High (61-84)		5 (7.14)	8 (11.43)
mean ± SD		33.00 ± 15.37	48.70 ± 11.01	3814.14	< 0.001a
Screen time usage				U value
Hours/day		3.49 ± 1.20	2.57 ± 1.36	3312.52	< 0.001
Daily use (weekly average) (hours/day)				χ2 value
< 2		6 (8.57)	25 (35.71)	17.24	< 0.001
2-4		41 (58.57)	35 (50.00)
> 4		23 (32.86)	10 (14.29)

^aP value < 0.01.

^bP value < 0.05, Mann-Whitney U test and χ² test significant at ^bP value < 0.05.

HAM-D: Hamilton Depression Rating Scale; WHO: World Health Organization; MPSS: Multidimensional Scale of Perceived Social Support .

Symptomatic group subjects have demonstrated less utilisation of problem-focused and emotion-focused coping as compared to the remission group (P-value < 0.001). Although avoidant coping was higher in the remission group, but, it was not statistically significant (P-value = 0.478). The personality inventory for DSM-5 (PID-5) domain of detachment was significantly higher in the subjects of the symptomatic group (7.5 ± 2.7) as compared to the remission group (5.63 ± 3.08) (Table 3). Psychoticism was also significantly higher in subjects of the symptomatic group (5.34 ± 3.34) as compared to the remission group (3.19 ± 3.3). Smartphone usage was universal across both groups for non-essential purposes, and all the subjects had personal smartphones. Total screen time was significantly higher in the symptomatic group (3.49 ± 1.20) as compared to the remission group (2.57 ± 1.36). Subjects having screen time > 4 hours/day were significantly higher in the symptomatic group (32.86%) as compared to the remission group (14.29%).

Table 3 Comparison of personality inventory for Diagnostic and Statistical Manual of Mental Disorders brief form scores between both groups, mean ± SD.

Personality trait domain	Group A (n = 70)	Group B (n = 70)	U value	P value
Negative affect	8.23 ± 2.97	7.49 ± 2.29	2677	0.345
Detachment	7.5 ± 2.7	5.63 ± 3.08	3503	< 0.001a
Antagonism	1.58 ± 1.08	1.84 ± 1.79	2104	0.149
Disinhibition	6.46 ± 3.23	5.99 ± 4.73	2838	0.106
Psychoticism	5.34 ± 3.34	3.19 ± 3.3	3601	< 0.001a

^aP value < 0.01, Mann-Whitney U test is significant at ^aP value < 0.01.

Social media addiction scores were significantly higher in subject of symptomatic group, in total scores (72.37 ± 19.83) and in domains of tolerance (11.76 ± 3.08), withdrawal (13.46 ± 10.77), and relapse (13.61 ± 4.28) as compared to remission group total scores (67.03 ± 28.09), tolerance (10.39 ± 4.49), withdrawal (10.77 ± 5.57), and relapse (10.23 ± 6.91). Social media addiction (SNAS score > 84) was present in 13 patients (18.57%) of the symptomatic group as compared to 19 patients (27.14%) of the remission group. However, this difference was not statistically significant (P-value = 0.314) (Table 4).

Table 4 Domain wise comparison of Brief COPE score, score of social networking addiction scale between both groups and comparison of social networking addiction scale score between both groups.

Brief COPE	Group A (n = 70)	Group B (n = 70)	U value	P value
Problem focused	14.77 ± 3.93	40.33 ± 9.52	3790	< 0.001b
Emotion focused	21.53 ± 3.94	27.47 ±6.34	3799.5	< 0.001b
Avoidant coping	12.41 ± 2.1	13.34 ± 3.3	2618.5	0.478
SNAS domains
Salience	14.67 ± 4.2	15.37 ± 5.13	2812	0.13
Mood modification	12.39 ± 2.71	12.64 ± 3.43	2706.51	0.28
Tolerance	11.76 ± 3.08	10.39 ± 4.49	1788.52	0.005a
Withdrawal	13.46 ± 10.77	10.77 ± 5.57	1619.01	0.001a
Conflict	6.47 ± 4.07	7.63 ± 4.84	2774.02	0.169
Relapse	13.61 ± 4.28	10.23 ± 6.91	1494.11	< 0.001b
Total score	72.37 ± 19.83	67.03 ± 28.09	1883.5	0.018a
SNAS score			χ2 value
SMA present (> 84)	13 (18.57)	19 (27.14)	1.01	0.314
SMA not present (≤ 84)	57 (81.43)	51 (72.86)

^aP value < 0.05.

^bP value < 0.01.

SNAS: Social Networking Addiction Scale; SMA: Social media addiction.

On applying Spearman’s correlation in symptomatic patients (symptomatic group)

Depression severity (HAM-D) has a significantly weak and negative correlation with problem-focused coping (r = -0.273, P = 0.022), and has a significantly weak but positive correlation with emotion-focused coping (r = 0.341, P = 0.004) in brief COPE (Table 5). HAM-D has a weak but positive correlation with negativism (r = 0.261, P = 0.029) and disinhibition (r = 0.293, P = 0.014) domains of PID-5. Social media score (SNAS) use has a significantly weak negative correlation with problem-focused coping (r = -0.334, P = 0.005) and a significantly weak positive correlation with the avoidant coping styles (r = 0.277, P = 0.020) on brief COPE, and a significant positive correlation with PID-5 disinhibition personality domain (r = 0.515, P < 0.001) (Table 6).

Table 5 Correlation of Hamilton Depression Rating Scale with Perceived social support, coping, well-being, and personality domains among symptomatic patients group A (n = 70).

Variables		HAM-D
		Correlation coefficient1	P value
MPSS		-0.060	0.619
Brief COPE	Problem focused	-0.273	0.022a
	Emotion focused	0.341	0.004b
	Avoidant	0.022	0.859
WHO Well-being		-0.051	0.674
PID	Negativism	0.261	0.029a
	Detachment	0.071	0.557
	Antagonism	0.097	0.426
	Disinhibition	0.293	0.014a
	Psychoticism	-0.050	0.682

^aP < 0.05.

^bP < 0.01.

¹Spearman’s correlation analysis.

HAM-D: Hamilton Depression Rating Scale; WHO: World Health Organization; MPSS: Multidimensional Scale of Perceived Social Support; PID: Personality Inventory for the Diagnostic and Statistical Manual of Mental Disorders.

Table 6 Correlations of Social Networking Addiction Scale Score with Perceived social support, coping, and personality domains among symptomatic patients group A (n = 70).

Variables		SNAS total score
		Correlation coefficient1	P value
Total MPSS score		-0.060	0.684
Brief COPE	Problem focused	-0.334	0.005b
	Emotion Focused	-0.018	0.882
	Avoidant	0.277	0.020a
PID-5	Negativism	0.054	0.659
	Detachment	0.209	0.082
	Antagonism	-0.082	0.500
	Disinhibition	0.515	< 0.001b
	Psychoticism	-0.102	0.403

^aP value < 0.05.

^bP value < 0.01.

¹Spearman’s correlation analysis.

Screen time per day has a significant positive correlation with SNAS score (r = 0.751, P = 0.010) and disinhibition scores (r = 0.381, P = 0.001) on PID-5, a significant negative correlation with WHO wellbeing scores (r = -0.308, P = 0.010), and problem-focused coping (r = -0.245, P = 0.041). On correlation analysis, MPSS scores showed no significant association with SNAS scores (r = -0.060, P = 0.684). A moderate negative correlation was observed between MPSS and daily screen time (r = -0.513), suggesting that lower perceived social support may be linked to higher screen time; however, this did not reach statistical significance (P = 0.205) (Table 7).

Table 7 Correlations of screen time (per day) among group A with social media addiction severity, well-being, and personality domains, and problem-focused coping, among symptomatic patients group A (n = 70).

Variables	Screen time/day (hours)
	Correlation coefficient1	P valuea
SNAS score	0.751	0.010
WHO Well-Being score	-0.308	0.010
PID disinhibition	0.381	0.001
Problem-focused coping	-0.245	0.041
MPSS	-0.153	0.205

^aP value < 0.05.

¹Spearman’s correlation analysis.

SNAS: Social Networking Addiction Scale; WHO: World Health Organization; MPSS: Multidimensional Scale of Perceived Social Support; PID: Personality Inventory for the Diagnostic and Statistical Manual of Mental Disorders.

DISCUSSION

This cross-sectional study was conducted at a tertiary psychiatry centre in North India to explore and analyse the relationships among perceived social support, subjective well-being, coping styles, personality traits, and social media addiction in patients with MDD. Additionally, the study aimed to compare these dimensions between symptomatic patients (symptomatic group ) and those in remission (remission group) as per DSM-5. A total of 140 participants were included in this study, 70 in each group. Both groups were comparable in the socio-demographic variables, including age, gender, education, occupation, income, marital status, and religion. Both groups were comparable in terms of clinical variables such as mean duration of current episode, number of episodes, and family history of psychiatric illness. The mean score of the HAM-D score was significantly higher in the symptomatic group (19.24 ± 1.89) as compared to the remission group (5.13 ± 1.54), representing moderate to severe illness in symptomatic group patients, as ours is a tertiary institute. Patients with less severe episodes are either treated at the primary care physician level or do not seek treatment.

In the present study, the WHO Wellbeing was significantly lower in symptomatic patients (symptomatic group) (mean ± SD = 7.93 ± 1.75) as compared with those who were in remission (remission group) (mean ± SD = 13.90 ± 1.61), with a P-value of < 0.001. Additionally, this study also found that screen time per day had a moderate negative correlation with well-being scores (r = -0.308, P = 0.01), suggesting that individuals who spent more time on screens tended to report lower levels of well-being. These findings are in line with the existing literature as observed by Li et al[21], who reported a strong inverse relationship between self-reported depressive symptoms and subjective well-being using validated measures. It is also obvious that symptomatic patients have lower subjective well-being than their counterparts, who are in remission.

In the present study, a significantly (P-value < 0.001) very high proportion of symptomatic group subjects (64.29%) reported low perceived social support compared to only 8.57% of those in the remission group. Also, Remission Group showed significantly higher average perceived social support (80.00% vs 28.57%) and higher mean MPSS scores (48.70 ± 11.01 vs 33.00 ± 15.37) compared to symptomatic patients (P-value < 0.001). This is consistent with the findings of a systematic review by Wang et al[22] that among people with clinical depression, those who felt they had lower social support were associated with worse depression outcomes over time. The above two findings, related to perceived social support and subjective well-being, can be inferred in line with previous findings, which suggest that major depression is often accompanied by feelings of isolation and diminished perception of social connectedness[22]. Low perceived social support also acts as a precipitating and perpetuating factor for depressive symptoms, thereby hindering remission and recovery[23]. Similarly, subjective well-being, which shows positive affect and life satisfaction, is found to be impaired in symptomatic individuals[24].

The symptomatic group demonstrated significantly lower use of both problem-focused (14.77 ± 3.93 vs 40.33 ± 9.52; P < 0.001) and emotion-focused coping strategies (21.53 ± 3.94 vs 27.47 ± 6.34; P < 0.001) as compared to the remission group. This study also shows that the severity of depression on HAM-D scores showed a weak negative correlation (r = -0.273, P = 0.022) with problem-focused coping style and a weak positive correlation with emotion-focused coping style scores (r = 0.341, P = 0.004), and these correlations were significant. Similarly, in a meta-analysis, researchers observed that depressed individuals struggle with effective problem-solving and predominantly use avoidance and emotion-focused coping[25,26]. These findings suggest that greater severity of depressive symptoms is linked to reduced engagement in solution-oriented coping strategies and increased reliance on emotion-driven responses. It has also been reported that emotion-focused coping in depressive states may also function as maladaptive, which leads to rumination and avoidance[27]. This study also shows that certain personality dimensions do very differently in both groups. On PID-5, subjects in the symptomatic group showed significantly higher scores in domains of detachment (symptomatic group vs remission group; mean ± SD = 7.5 ± 2.7 vs mean ± SD = 5.63 ± 3.08; P < 0.001) and psychoticism (symptomatic group vs remission group; mean ± SD = 5.34 ± 3.34 vs mean ± SD = 3.19 ± 3.3; P < 0.001). This indicates that patients with active depressive symptoms tend to display more social withdrawal and psychotic-like experiences than those in remission. It aligns with the study of Fowler et al[28], which found that detachment and neuroticism showed a substantial independent correlation with depression severity, indicating social withdrawal tendencies are highly associated with more severe depressive symptoms. The finding of higher psychoticism scores in patients with active depression (symptomatic group) may reflect an underlying cognitive-perceptual dysregulation associated with severe depressive states. Depression severity (HAM-D score) has a weak but positive correlation (r = 0.293, P = 0.014) with disinhibition and negativism domains (r = 0.26, P = 0.029) of PID-5.

Screen time (mean hours per day) is considered an important clinical variable that has been found to be significantly higher in the symptomatic group (3.49 ± 1.20 hours) compared to the remission group (2.57 ± 1.36 hours, P < 0.001). Additionally, a significantly higher proportion, 32.86% of symptomatic patients (symptomatic group ) reported longer screen use time exceeding 4 hours daily, compared to only 14.29% of those in remission. These findings indicate that participants who had depressive symptoms engaged in significantly higher screen time compared to those in remission, which aligns with previous literature indicating an association of excessive screen use with depressive symptoms[29]. This study found significantly higher (P-value = 0.018) social media addiction score in the symptomatic group, with mean SNAS scores of (72.37 ± 19.83), compared to the remission group (67.03 ± 28.09). It aligns with the findings of Kim et al[30], who say individuals with depression use their phones to alleviate their negative feelings and spend more time on communication activities and platforms via mobile phone, which in turn can deteriorate into problematic use of smartphones.

An interesting paradox emerged in our findings: Although symptomatic patients scored significantly higher in addiction severity dimensions (tolerance, withdrawal, relapse), the overall prevalence of social media addiction was numerically higher in the remission group (27.14% vs 18.57%), albeit non-significantly. One possible explanation is that patients in remission could push a higher percentage above the diagnostic cutoff because patients in remission might, whether intentionally or inadvertently, rely more on social media to sustain social connectivity, emotional regulation, and engagement during recovery. Higher severity scores, however, do not always correspond to higher categorical prevalence. On the other hand, symptomatic patients may use social media in more intense but erratic ways. Improved motivation, focus, and energy levels, as well as neurocognitive and psychosocial adaptations in remission, could be another explanation. These findings underscore the complexity of digital behaviour in depression and point toward the possibility of group-dependent thresholds of “problematic use”. Longitudinal research is warranted to disentangle whether remission facilitates compensatory but potentially maladaptive digital engagement, or whether these patterns represent a transitional phenomenon in recovery.

On correlation analysis, social media score (SNAS) use has a significantly weak negative correlation with problem-focused coping (r = -0.334, P = 0.005), and a significantly weak positive correlation (r = 0.277, P = 0.020), with the avoidant coping styles on brief COPE. The association of adaptive coping with screen time (r = -0.245, P = 0.041), indicats that adaptive coping mechanisms are associated with reduced screen engagement. This finding aligns with the previous studies, indicating people with problematic use of social media tend to use maladaptive patterns of coping such as avoidance and disengagement[31]. It is also consistent with a systematic review done in 2018 showing that SNAS severity reflects poor problem coping and increased avoidance[32].

Among PID-5, our study showed that the disinhibition domain had a moderate positive correlation (r = 0.515, P < 0.001) with SNAS scores and a weak but positive correlation with total screen time per day (r = 0.381, P = 0.001), suggesting that individuals with higher impulsivity are more likely to engage in excessive screen use[33]. This indicates that individuals with impulsivity and poor self-control are more likely to exhibit addictive use of social media. Impulsivity reflects a tendency to act quickly with little forethought, especially under conditions of heightened affect. In individuals with MDD, this trait often interacts with emotional dysregulation - difficulties in understanding, tolerating, and modulating negative emotions. A key expression of this interaction is negative urgency, the propensity to engage in rash behaviors when distressed. Social media provides an immediate and easily accessible means of distraction or mood repair, thereby serving as a maladaptive emotion-regulation strategy for disinhibited individuals. While such use offers short-term relief, it reinforces avoidance, sustains depressive symptoms, and contributes to compulsive checking patterns. Thus, impulsivity and emotional dysregulation together form a central vulnerability pathway linking MDD with social media addiction, underscoring the importance of integrating impulse-control training and emotion-regulation skills into clinical interventions. Also, the study found that social media use severity showed a non-significant negative correlation with subjective well-being (r = -0.205, P = 0.089) among patients with depression. The trend points toward higher social media use being linked to poorer well-being. This finding is also in line with prior literature[34,35].

In the present study, Screen time per day showed a strong positive correlation with SNAS scores (r = 0.751, P = 0.010) among patients with depression, which is in line with previous literature[36]. These findings suggest that in clinical populations like MDD patients, screen time could be a strong predictor of problematic smartphone and social media use, as in the general population. The significantly lower well-being scores in the symptomatic group likely reflect the affective and cognitive disruptions inherent to depressive episodes. However, the co-occurrence of elevated SNAS scores and increased screen time, both of which were negatively associated with WHO-5 wellbeing scores, suggests that problematic digital behaviour may further impair subjective wellbeing. While causality cannot be inferred, the findings highlight the importance of addressing digital behaviour patterns in the comprehensive assessment and management of depression[24,37-39].

Association between multidimensional perceived social support (MPSS) and social media engagement patterns. The results showed no significant correlation between MPSS and SNAS scores (r = –0.060, P = 0.684), and a moderate but non-significant negative correlation between MPSS and daily screen time (r = –0.513, P = 0.205). These findings suggest that in our sample, lower perceived social support was not directly associated with higher levels of social media addiction or screen time. While our data did not support the digital disconnection hypothesis, the observed negative trend with screen time highlights the need for further exploration in larger, adequately powered studies.

While personality traits and coping styles are conceptually framed as stable characteristics, their expression may be modulated by current affective states. Research supports both perspectives: Trait-based models explain enduring patterns of maladaptation[40], while state-dependent models highlight how depressive symptoms can bias self-report and coping engagement[26,41,42]. In the present study, both personality detachment and disinhibition and coping impairments were evident in the symptomatic group, suggesting that the interplay of enduring traits and acute symptomatology likely contributes to the observed psychosocial dysfunctions, including excessive digital engagement and reduced well-being.

Implications for future research

This study points to several important directions for future research. First, longitudinal designs are needed to better differentiate trait-based vulnerabilities (e.g., personality structure, habitual coping style) from state-dependent distortions that emerge during active depressive episodes. Repeated measures across acute, remission, and recovery phases would help clarify which psychosocial variables are stable and which are dynamic. Second, future studies should include a healthy control group to establish normative baselines for constructs like PID-5 personality traits, coping strategies, and social media use patterns. This would enhance the interpretive value of case-control comparisons and enable more accurate trait-state distinction. Third, advanced statistical modelling approaches such as mediation and moderation analyses could be employed to explore how personality traits and coping styles interact to influence digital behaviour and subjective well-being. Investigating these multivariate pathways would offer richer insights into the complex interplay of psychological and behavioural factors in depression. Finally, while the current study did not find support for the “digital disconnection hypothesis”, this concept remains theoretically compelling. Future research with larger sample sizes and objective tracking of screen behaviour (e.g., app usage logs) could reduce self-report bias and better elucidate how digital environments shape perceived support and mental health outcomes.

Strengths

The present study established multiple domains, including perceived social support, well-being, coping mechanisms, and personality traits, and uniquely incorporated the assessment of social media use to explore its interaction with depressive phenomenology and overall well-being. By comparing symptomatic and remission groups, the study provides valuable insights into how these variables differ across different phases of depression. Unlike prior research that often relied on college or community samples, this study examined social media behaviour in clinically diagnosed patients, thereby enhancing ecological validity.

Limitations

As an observational and cross-sectional study, causality between depression severity and other study variables cannot be established; only associations were determined. The study was limited to a single centre, adult population, and patients with unipolar depression. It limits the generalizability of the study to populations with other socio-economic and other settings or broader populations. This study utilized a single-center convenience sample from Northern India, which may limit the generalizability of the findings. Cultural and geographic factors - such as regional variations in internet access, platform preferences, and social norms around online interaction - can influence social media usage patterns and coping styles. Therefore, while the present findings provide important insights into patients with MDD in this cultural setting, future research using multi-center and cross-cultural samples is needed to enhance external validity.

As participants were recruited from a tertiary psychiatric outpatient clinic, a substantial proportion were on pharmacological treatment (e.g., antidepressants). Medication status was not controlled for statistically; hence, it may represent a potential confounding factor, particularly with regard to behavioural and motivational outcomes. Furthermore, although several correlations were statistically significant, most effect sizes were weak. These results should thus be interpreted with caution; even weak associations may still be relevant to understanding complex, multifactorial conditions such as MDD. Future studies with longitudinal designs and larger samples are needed to clarify causal pathways and establish stronger clinical relevance.

CONCLUSION

In the case of patients with symptomatic MDD, they experience significantly lower social support, subjective well-being, and adaptive coping, coupled with higher screen time and more social media addiction, when compared with patients who are in remission. The personality vulnerabilities, especially disinhibition, seem to correlate with problematic digital use. Considering the unfurling multi-faceted comorbidity of depression, there is a need to unfurl the range of clinical management of depression beyond the reduction of depression symptoms. The management should include digital hygiene education (screen-time monitoring and alerting strategies) alongside controlled coping strategies (problem-solving techniques, mindfulness, adaptive emotional regulation) and social re-engagement programs (peer support initiatives, family-based involvement, rebuilding social support networks), which would function to decrease psychosocial depression.

At a systems level, digital health behavioural protocols could be operationalized through mobile applications, brief psychoeducation, and digital follow-up scheduling for outpatient management, integrated with apps and short modules would facilitate the systematic integration. Future research needs to use longitudinal designs, include parameters for healthy control groups, and a strategy for objective digital tracking to clarify causation and to better capture the dynamics of digital behaviour in depression. By embedding psychosocial and digital considerations into psychiatric care, treatment planning can become more personalized, holistic, and effective for patients with depression.