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Social Media and Quality of Life: A Study on Digital Divide across Generations

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02 May 2025

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05 May 2025

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Abstract
Objectives: This study aims to examine the relationship between Social Media Use (SMU), Social Media Needs (SMN), and Quality of Life (QoL) among middle-aged and older adults in Nepal, highlighting generational aspects of the digital divide.Study Design: A cross-sectional quantitative study design was employed to assess generational differences in social media usage and perceived quality of life among middle-aged and older adults in an urban Nepali setting.Methods: A purposive sample of 1,000 individuals aged 45 and above, residing in Kathmandu Metropolitan City, was surveyed. Standardized instruments, including the World Health Organization Quality of Life-BREF (WHOQOL-BREF) and the Social Networking Sites Uses and Needs (SNSUN) questionnaire, were administered in Nepali and English. Descriptive and inferential statistics, including regression analysis, were performed using SPSS. Ethical approval was obtained from the Nepal Health Research Council (Ref no. 3039). Results: Among participants, 60% used Facebook and 76.5% used YouTube daily. However, over 60% scored low on diversion, cognitive, affective, personal integrative, and social integrative needs. While 64.4% rated their overall QoL as good, 63% were dissatisfied with physical health, and 51% reported poor psychological well-being. Environmental quality was rated fair or poor by 55%. Weak correlations were found between socio-demographic factors and QoL or SMU/SMN patterns. Conclusions: Despite frequent use of popular social platforms, older adults in Nepal report low social media needs and dissatisfaction in key health domains. Addressing physical, psychological, and environmental deficits alongside digital literacy and tailored content may improve QoL outcomes. Findings suggest targeted interventions across generations.
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1. Introduction

The Department of Statistics Nepal (2021) revealed that the elderly population in Nepal is increasing,¹,² and that older adults are increasingly engaging with social media (SM). It is crucial to improve their quality of life (QoL) and overall well-being (WB).³,⁴ Numerous studies have demonstrated that social media use (SMU) can influence QoL both positively and negatively, depending on usage patterns.⁵⁻⁷ Older adults often face challenges such as aging, illness, retirement, and reduced social contact, which may lead them to use SMU to alleviate loneliness and maintain communication.³,
To better understand the impact of SMU on QoL among senior citizens, this study focuses on generational differences in digital engagement. Although SMU is increasingly recognized as a tool for addressing societal needs and enhancing living standards, research focusing on its effects among older populations remains limited, as most studies emphasize youth.⁴,⁹⁻¹¹
Therefore, aim and scope of this study are to examines patterns of SMU and their relationship with QoL among middle-aged and older adults, offering insights into bridging the digital generation gap and enhancing well-being across age groups.

2. Methods

This study employed a quantitative cross-sectional design to examine the relationship between SMU, SMN, and QoL among middle-aged and older adults. The research was conducted in Ward No. 3, Maharajgunj, within Kathmandu Metropolitan City, Nepal. The inclusion criteria specified individuals aged 45 years and above who were capable of completing a self-administered questionnaire. The sample size was calculated using Cochran’s formula for an unknown population, yielding a total of 1,000 participants. A purposive sampling method was used for data collection. To ensure accessibility and inclusivity, both Nepali and English versions of the questionnaire were administered.
The questionnaire consisted of three sections. The first section collected socio-demographic data, including age, sex, marital status, educational background, and sources of income. The second section measured participants’ QoL using the World Health Organization Quality of Life-BREF (WHOQOL-BREF) instrument. This tool includes 26 items covering four domains: physical health (7 items), psychological well-being (6 items), social relationships (3 items), and environmental conditions (8 items). The first two items assess general QoL and overall health satisfaction on a 5-point Likert scale. Based on scoring guidelines, QoL outcomes were categorized as Very Good (76–100%), Good (51–75%), Medium/Fair (26–50%), or Poor (0–25%).
The third section assessed social media behavior using the validated Social Networking Sites Use and Needs (SNSUN) Scale. This section included 27 items: eight measuring social media use frequency, nine on general needs, and ten items divided among five need domains—diversion, cognitive, affective, personal integrative, and social integrative. Items were rated on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Questions 4, 6, and 8 were open-ended and asked in a numeric format. SMU and SMN scores were categorized as low (≤50%) and high (>51%).
The SNSUN Scale demonstrated strong internal consistency with a Cronbach’s alpha of 0.92. The WHOQOL-BREF instrument also showed high reliability across domains, with alpha values of 0.71 for physical health, 0.77 for psychological well-being, 0.80 for social relationships, and 0.89 for environmental conditions. Statistical analyses were performed using SPSS. Descriptive statistics, such as frequencies and percentages, were used to describe the sample. Inferential statistics, including regression analysis, were employed to explore associations between SMU, SMN, and QoL. Ethical approval was obtained from the Nepal Health Research Council (Ref. No. 3039, May 2022). All participants provided informed consent, and confidentiality and autonomy were maintained throughout the study.

Reliability and Validity

The Social Networking Sites Use and Needs (SNSUN) Scale demonstrated high internal consistency, with a Cronbach’s alpha of 0.92 across its five dimensions: diversion, cognitive, affective, personal integrative, and social integrative needs. These dimensions were evaluated to understand the underlying motivations and patterns of social media use among participants.¹⁴,¹⁵
The WHOQOL-BREF instrument showed strong reliability and discriminatory validity. Domain-specific Cronbach’s alpha values were 0.71 for physical health, 0.77 for psychological well-being, 0.80 for social relationships, and 0.89 for environmental quality, indicating good internal consistency.¹²,¹³

Data Analysis

Data were analyzed using IBM SPSS Statistics (version 29.0.2). Descriptive statistics were applied to summarize participant characteristics and distribution patterns. Inferential statistical analyses, including regression models, were employed to explore relationships between social media use, social media needs, and quality of life variables.

Ethical Considerations

The study procedure was reviewed by the IRB or ethics committee of the Nepal Health Research Council (Ref no. 3039, May 2022). Informed consent ensured voluntary participation, confidentiality, and respect for autonomy. All participants provided informed consent prior to data collection. The study ensured participant confidentiality, voluntary participation, and full respect for autonomy in compliance with ethical research standards.

3. Results

This study demonstrated reliability and validity, and conducted data analysis using descriptive and inferential statistics, such as regression, to examine social media needs and use, as well as quality of life, to achieve the study's objectives.
Table 1 shows that 78.1% of participants were aged 45-64, and 58.2% were female. Most were married (92.6%) and had secondary education (33.6%). The primary income source was work (33.6%).
Based on Figure 1, the results demonstrate that the majority of participants use mobile devices for social media.
Table 2: The results indicate that 60% of participants use Facebook daily, while 76.5% use YouTube daily. In contrast, Instagram and Twitter see much lower daily usage, with most participants never using Instagram (88.5%) and Twitter (49.1%).
Figure 2 shows the Participants indicated varying frequencies of notification checks: 75.4% reported doing so 1-9 times, 11.7% more than 9 times, and 3.7% checked on every notification beep.
Table 3 shows that the majority of participants reported low levels of social media needs and use, with more than 50% scoring low across all domains: 68.2% for diversion, 71.0% for cognitive needs, 62.9% for affective needs, 60.9% for personal integrative needs, and 62.1% for social integrative needs. High levels of need were considerably lower, with no domain exceeding 40%, suggesting that most participants engage with social media only minimally for these specific purposes.
Table 4 indicates a generally positive perception of the overall quality of life, with 64.4% of participants rating it as very good or good. However, this contrasts with notable dissatisfaction in health-related domains: 63% rated their physical health and 51% their psychological health as fair or poor. Additionally, 55% of participants assessed their environmental quality as fair or poor, highlighting significant room for improvement in this area.
Table 5 presents the results of the regression analysis, showing that age had a small positive correlation with physical health (r = 0.064) and environmental health (r = 0.052). Gender demonstrated a modest positive correlation with environmental health (r = 0.075), while income source showed a slight positive correlation with physical health (r = 0.032).

4. Discussion

The findings of this study offer valuable insights into the relationship between SMU, SMN, and QoL among middle-aged and older adults in Nepal. Using validated tools and robust statistical methods, this research contributes to the growing body of evidence exploring digital engagement in aging populations. It provides context-specific data relevant to South Asia. The results on SMU, and SMN show that most participants primarily accessed social media through mobile devices, with Facebook (60%) and YouTube (76.5%) being the most frequently used platforms. This pattern aligns with global studies, including those by James and Harville (2018) and Marzo et al. (2024), which identified Facebook and YouTube as the dominant platforms among older adults.¹³,¹⁶ In contrast, Instagram and Twitter were significantly underutilized, with 88.5% and 49.1% of participants, respectively, reporting no use. These differences may stem from age-related preferences, perceived relevance, and usability concerns associated with specific platforms.³,¹⁷,¹⁸
Participants also demonstrated consistently low social media needs across all dimensions: diversion (68.2%), cognitive (71.0%), affective (62.9%), personal integrative (60.9%), and social integrative (62.1%). These findings are consistent with prior research indicating that older adults often engage with social media for specific, limited purposes rather than broad social interaction or entertainment.⁸,¹³,¹⁹ The pattern observed suggests a selective and goal-oriented use of social media, as previously noted in studies of aging populations.⁴,
The perceived overall QoL was relatively positive, with 64.4% of participants rating it as very good or good. However, there was notable dissatisfaction in the domains of physical health (63% rated it as fair or poor) and psychological health (51% rated it as fair or poor). This divergence highlights the complexity of aging: while older adults may express general satisfaction with life, underlying health challenges often diminish specific aspects of well-being. This observation is consistent with prior literature suggesting that subjective well-being may not fully capture latent health issues.⁶,²⁰,²¹
The Environmental Quality shows the results from 55% of participants rated environmental quality as fair or poor, suggesting that many older adults perceive deficiencies in their immediate surroundings. These results align with research from both urban and rural settings in China, where environmental conditions significantly influenced older adults’ QoL.²²,²³ This highlights the need for improved infrastructure, safer neighborhoods, and more age-friendly community services. Regression analysis findings revealed minimal but noteworthy correlations between socio-demographic factors and both SMU and QoL outcomes. Age showed small positive correlations with physical health (r = 0.064) and environmental health (r = 0.052), possibly reflecting the stability and adaptation found in older cohorts with established routines and social support.²² Gender also exhibited a slight positive correlation with environmental health (r = 0.075), echoing previous findings that suggest gender differences in health-related perceptions.²²
Marital status displayed a positive correlation with diversion (r = 0.041) and a negative correlation with personal integrative needs (r = –0.070), implying that married individuals may use social media more for social interaction than for enhancing status or self-promotion.²³,²⁴ These findings are supported by prior research demonstrating that relationship status influences both the frequency and motivation behind social media use.25,26 Research on climate change and extreme weather events with dust transport reveals that airborne particulate matter can exacerbate chronic inflammatory conditions and modulate immune responses, 27, 28 potentially contributing to hematologic malignancies like adverse effects on QoL via indirect pathways. 29 AI-driven assessments of occupational hazards highlight the complex burden faced by at-risk professionals, emphasizing the need to investigate social life as a direct risk factor on public health, especially students and the healthcare workforce. 30 The sensitive population at educational institutions is part of a complex exposure correlated with the link between emotional intelligence, education level, and quality of life. 30, 31

Future research and Suggestions

Social media platforms have the potential to play a significant role in promoting health and wellness among middle-aged and older adults. By developing tailored content, integrating digital health programs, and addressing environmental challenges, these platforms can help improve user engagement and overall quality of life. Such initiatives include virtual exercise classes, mental health resources, and online community support groups. Additionally, educational campaigns can empower older adults to use social media safely and effectively, thereby enhancing their digital participation and access to beneficial content. Policy interventions that promote inclusivity and protect against ageism are also essential for creating a more supportive and equitable online environment for older users.

Limitations of the Study's

This study has several limitations that should be acknowledged. First, the sample was predominantly composed of middle-aged and older adults, which may limit generalizability to younger populations. Second, the study relied on self-reported data, which may introduce response bias. Third, the cross-sectional design precludes any inference of causality between social media use and quality of life. Additionally, the study did not investigate platform-specific features that may influence user satisfaction or engagement. Future research should consider longitudinal designs to explore causal relationships and include comparative analyses across different social media platforms. Cross-cultural studies are also recommended to broaden the understanding of digital behavior and its effects on quality of life in diverse sociocultural contexts.

5. Conclusions

This study examined the relationship between social media use (SMU), social media needs (SMN), and quality of life (QoL) among middle-aged and older adults. Although engagement with platforms such as Facebook and YouTube was high, overall SMN within this demographic remained relatively low. Despite a generally positive perception of QoL, participants reported notable challenges in physical and psychological health as well as environmental quality. The complexity of these associations, particularly in relation to age, gender, and marital status, underscores the need for targeted interventions. These may include age-sensitive social media content, health and wellness programs, environmental improvements, and digital literacy campaigns aimed at enhancing well-being among older adults. Future research should incorporate more diverse populations, utilize objective assessment tools, and employ longitudinal designs to explore further how social media engagement can support QoL in aging communities.

Author Contributions

PT, AV, PS, RB, AC, RK, SKS, GB, PTS, and IA screened the titles, abstracts, and full texts and extracted data. Assessment and statistical analysis and wrote the initial draft of the manuscript PT, AV, PS, RB, AC, RK, SKS, GB, PTS, and IA contributed to the revision of the manuscript, provided critical feedback. Conceptualization and writing of the first draft edited and reviewed the manuscript, designed the research question and analytical approach from PT, AV, PS, RB, AC, RK, SKS, GB, PTS, and IA. Supervision PP, and IA. Project Administration IA. All authors had full access to all the data in the study and approved the final version, reviewed, edited, and approved the final manuscript.

Funding

This study did not get any funding.

Acknowledgement

We express our gratitude to the Heal-Link for their direct support to cover the APC for Open Gold Access of this article if published the article under agreement with Elsevier Publisher Additionally, we would like to express our appreciation to the, Editor-in-Chief, Editors, and reviewers for their valuable feedback and insightful suggestions for improving this article.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The Device you use for social media.
Figure 1. The Device you use for social media.
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Figure 2. Participants indicated varying frequencies of notification checks.
Figure 2. Participants indicated varying frequencies of notification checks.
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Table 1. Socio-demographic characteristics of the study participants.
Table 1. Socio-demographic characteristics of the study participants.
Variables Number Percentage
Age
45- 64 781 78.1
>65 (older adulthood) 219 21.9
Sex
Male 417 41.7
Female 582 58.2
Marital Status
Single 2 0.2
Married 926 92.6
Divorced 7 0.7
Widowed 62 6.2
Educational Status
Informal education 85 8.5
Primary 161 16.1
Secondary 336 33.6
Higher secondary 156 15.6
Graduation and above 262 26.1
Income Source
Work 519 33.6
Business / Investment 298 29.8
Foreign employment 53 5.3
Agriculture 35 5.3
Others 93 9.3
Table 2. Illustration of Social Networking Sites Used by Participants.
Table 2. Illustration of Social Networking Sites Used by Participants.
Never Rarely (a few times in a year Occasionally (a few times in a month 3 to 5 times a week Everyday
Facebook 51 84 89 176 600
Twitter 491 127 120 155 107
Instagram 885 26 25 39 25
YouTube 29 47 53 106 765
WhatsApp 490 127 121 155 107
Google+ 49 246 645 28 32
25 65 99 63 748
Table 3. Social Media Needs and Uses.
Table 3. Social Media Needs and Uses.
Social Media Use/Needs Number Percentage
Diversions: (Escapism and Tension Release)
Low 682 68.2
High 318 31.8
Cognitive Needs (Acquire Information & Knowledge)
Low 710 71.0
High 290 29.0
Affective Needs (Emotions, Pleasure & Feelings)
Low 629 62.9
High 371 37.1
Personal Integrative (Enhance credibility, status)
Low 609 60.9
High 391 39.1
Social Integrative Needs (Interaction with Friends / Family) (n=994)
Low 617 62.1
High 377 37.9
Table 4. Participant Perspectives on Quality of Life Variations.
Table 4. Participant Perspectives on Quality of Life Variations.
Quality of Life Number Percentage
Overall quality of life
Very good 43 4.3
Good 551 55.1
Neither poor nor good 93 9.3
Poor 198 19.8
Very poor 115 11.5
General health quality
Very Satisfied 31 3.1
Satisfied 584 58.4
Neither Satisfied/dissatisfied 89 8.9
Dissatisfied 229 22.9
Very Dissatisfied 67 6.7
Physical health quality
Very good (76-100) 131 13
Good (51-75) 239 24
Medium/ Fair (26-50) 297 30
Poor (0-25) 333 33
Psychological health quality
Very good (76-100) 214 21
Good (51-75) 275 28
Medium /Fair (26-50) 151 15
Poor (0-25) 360 36
Environment quality
Very good (76-100) 232 23
Good (51-75) 217 22
Medium/ Fair (26-50) 232 23
Poor (0-25) 319 32
Table 5. Correlation of Socio-demographic Factors with Quality of Life.
Table 5. Correlation of Socio-demographic Factors with Quality of Life.
Variables Physical health Psychological health Environmental health
Age 0.064 0.041 0.052
Gender 0.059 0.020 0.075
Marital status -0.033 0.009 0.036
Qualification level -0.009 -0.013 -0.075
Income Source 0.032 -0.014 0.010
Notes: (p<0.05) Abbreviations: WHOQOL-BREF, World Health Organization QOL Questionnaire- BREF.
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