The Digital Stress Scale (DSS) is a 24-item self-report measure designed to assess the subjective experience of stress associated with digital technology use, particularly mobile and social media (Hall et al., 2021). The DSS can be used with adolescents or adults (ages 14+).
Developed within a communication and psychological framework, the DSS evaluates five distinct but related dimensions of digital stress:
Example DSS Items
For clinicians, the DSS offers several distinct advantages, particularly in understanding how digital technology use may be contributing to psychological distress in clients who use smartphones and social media. Research consistently demonstrates that digital stress—not merely time spent online—plays a crucial role in explaining the varied associations between digital media use and psychosocial outcomes (Steele et al., 2020; Hall et al., 2021; Khetawat & Steele, 2023).
The DSS aids in clinical formulation, treatment planning, and therapeutic interventions. As a formulation tool, it helps clinicians identify patterns of digital technology use that may be contributing to presenting problems, facilitating a more nuanced approach to case conceptualisation. This can be particularly valuable in understanding the aetiology of conditions such as depression, anxiety, and interpersonal difficulties, all of which have demonstrated associations with various dimensions of digital stress (Hall et al., 2021; Khetawat & Steele, 2023).
In treatment planning, elevated scores on specific DSS dimensions may indicate the need for targeted interventions addressing particular aspects of digital technology use. For example, high scores on the Connection Overload subscale might suggest interventions focused on digital boundaries and notification management, while elevated Approval Anxiety might indicate the need for addressing maladaptive cognitions related to social evaluation online.
During therapy, understanding a client’s DSS score can inform the focus of interventions. The DSS can also facilitate psychoeducation about the impact of digital technology use on psychological wellbeing, helping to normalise experiences, reduce self-blame, and validate the client’s experiences (Hall et al., 2021).
The Digital Stress Scale (DSS) items are typically averaged to provide subscale scores and a total score, with higher average scores (1-5) indicating greater digital stress.
Average scores for each subscale and the total score are converted to percentiles based upon a sample of 735 adolescents and young adults (Hall et al., 2021). These percentiles are then used to derive descriptive categories that aid in clinical interpretation. The descriptive categories for each subscale and the total score are:
Research indicates that the pattern and impact of digital stress may vary by developmental stage, with adolescents showing stronger associations between digital stress dimensions (particularly Approval Anxiety and FoMO) and psychosocial outcomes compared to young adults (Hall et al., 2021). When interpreting DSS scores for adolescents, consider the heightened importance of peer evaluation and social connectedness during this developmental period (Nesi et al., 2018).
On first administration a plot is presented displaying the total DSS and the subscale percentiles. The percentiles are presented with the qualitative descriptors in the background for ease of interpretation. When administered on multiple occasions, a longitudinal plot is displayed showing the subscale percentiles over time. When DSS scores are available from multiple timepoints, changes in scores can provide valuable information about the effectiveness of interventions or developmental changes in digital stress. For comparative interpretation, changes of at least 0.5 standard deviations in raw scores are considered clinically meaningful (the minimally important difference) (Norman et al., 2003; Turner et al., 2010). When interpreting changes, attention should be paid to both the total score and the patterns of change across subscales.
The DSS was developed through a four-phase process involving focus groups with adolescents and young adults, exploratory factor analyses with multiple samples, and confirmatory factor analyses with both adolescent and young adult populations (Hall et al., 2021).
The internal consistency of the DSS has been demonstrated, with Cronbach’s alpha coefficients for the total scale (α = 0.85) and subscales showing excellent reliability: Approval Anxiety (α = 0.93), Connection Overload (α = 0.91), Availability Stress (α = 0.88), Fear of Missing Out (α = 0.87), and Online Vigilance (α = 0.86) (Hall et al., 2021). These values indicate strong internal consistency across all dimensions of the measure.
Factor analysis supports the five-dimension structure of the DSS. Confirmatory factor analysis demonstrated excellent fit for the five-factor model (RMSEA = .044, CFI = .973, TLI = .969, SRMR = .040), with all items loading significantly on their respective factors (Hall et al., 2021). Furthermore, analysis supported a higher-order factor structure, indicating that the five dimensions contribute to an overall digital stress construct while maintaining their distinct characteristics.
Construct validity of the DSS is supported by its theoretically consistent relationships with measures of psychological distress and wellbeing. As predicted by theoretical models of digital stress, the DSS demonstrates significant positive associations with perceived stress (r = .47 for total score), anxiety (r = .45 for young adults, r = .52 for adolescents), and depressive symptoms (r = .35 for young adults, r = .50 for adolescents) (Hall et al., 2021). Khetawat amd Steele (2023) conducted a meta-analysis of the five-factors of digital stress and psychological distress, and reported weighted mean effect size estimates (Fisher’s Z) for each factor: Availability Stress (r = .29), Approval Anxiety (r = 30), FoMO (r = 35), Connection Overload (r = 26), and Online Vigilance (r = .34).
Convergent and divergent validity has been established through the differential patterns of associations between subscales and measures of psychosocial functioning. For example, the social dimensions of digital stress (Approval Anxiety and FoMO) show stronger negative correlations with measures of social relationships and functioning than do the technological dimensions (Connection Overload and Online Vigilance) (Hall et al., 2021).
Of particular note for clinical interpretation, research indicates that different dimensions of digital stress show different patterns of relationships with psychosocial outcomes. For example, approval anxiety and FoMO show stronger associations with depressive symptoms (r = .32 and r = .43, respectively) than do availability stress and connection overload (r = .09 and r = .21, respectively) among young adults. These differential patterns of associations support the clinical utility of examining specific dimensions of digital stress rather than relying solely on a total score.
The research also reveals developmentally sensitive relationships, with adolescents showing generally stronger associations between digital stress dimensions and measures of anxiety, depression, and social functioning compared to young adults (Hall et al., 2021). This finding is consistent with theoretical models suggesting that adolescents may be particularly vulnerable to the negative impacts of digital stress due to developmental factors including identity formation, heightened sensitivity to social evaluation, and ongoing development of self-regulation capacities (Nesi et al., 2018; Steele et al., 2020).
For clinical interpretation, DSS scores are typically evaluated dimensionally, with higher average scores indicating greater digital stress in specific domains. While formal clinical cutoffs have not been established, scores can be interpreted relative to sample means. Based on the combined sample from Hall et al. (2021), the following means and standard deviations provide reference points for interpretation:
These means and standard deviations are used to calculate percentiles which are then used to create descriptive categories for each subscale and the total score:
Hall, J. A., Steele, R. G., Christofferson, J. L., & Mihailova, T. (2021). Development and initial evaluation of a multidimensional digital stress scale. Psychological Assessment, 33(3), 230–242. https://doi.org/10.1037/pas0000979
Nesi, J., Choukas-Bradley, S., & Prinstein, M. J. (2018). Transformation of adolescent peer relations in the social media context: Part 1—A theoretical framework and application to dyadic peer relationships. Clinical Child and Family Psychology Review, 21(3), 267–294. https://doi.org/10.1007/s10567-018-0261-x
Reinecke, L., Aufenanger, S., Beutel, M. E., Dreier, M., Quiring, O., Stark, B., Wölfling, K., & Müller, K. W. (2017). Digital stress over the life span: The effects of communication load and Internet multitasking on perceived stress and psychological health impairments in a German probability sample. Media Psychology, 20, 90–115. https://doi.org/10.1080/15213269.2015.1121832
Norman, G. R., Sloan, J. A., & Wyrwich, K. W. (2003). Interpretation of changes in health-related quality of life: The remarkable universality of half a standard deviation. Medical Care, 41(5), 582–592. https://doi.org/10.1097/01.MLR.0000062554.74615.4C
Steele, R. G., Hall, J. A., & Christofferson, J. L. (2020). Conceptualizing digital stress in adolescents and young adults: Toward the development of an empirically based model. Clinical Child and Family Psychology Review, 23, 15–26. https://doi.org/10.1007/s10567-019-00300-5
Turner, D., Schünemann, H. J., Griffith, L. E., Beaton, D. E., Griffiths, A. M., Critch, J. N., & Guyatt, G. H. (2010). The minimal detectable change cannot reliably replace the minimal important difference. Journal of Clinical Epidemiology, 63(1), 28–36. https://doi.org/10.1016/j.jclinepi.2009.01.024
The DSS has been translated into several languages. The following translations used the original DSS to translate items and contacted the first author (J. A. Hall) for approval prior to translation.
Krägeloh, C.U., Medvedev, O.N., Alyami, H. et al. (2023). Translation and validation of the Arabic version of the Digital Stress Scale (DSS-A) with three Arabic-speaking samples. Middle East Current Psychiatry, 30, 118. https://doi.org/10.1186/s43045-023-00387-1
Kei, L. C., Jiabao, S., Shijuan, W., Xiaoyan, Y., & Guangyu, Z. (2023). Psychometric validation of the revised Chinese Digital Stress Scale in college students. Acta Scientiarum Naturalium Universitatis Pekinensis, 59(6), 1025-A2. https://doi.org/10.13209/j.0479-8023.2023.055
Xie, P., Mu, W., Li, Y. et al. (2023). The Chinese version of the Digital Stress Scale: Evaluation of psychometric properties. Current Psychology, 42, 20532–20542. https://doi.org/10.1007/s12144-022-03156-1
Zhang, C., Dai, B., & Lin, L. (2023). Validation of a Chinese version of the Digital Stress Scale and development of a short form based on item response theory among Chinese college students. Psychology Research and Behavior Management, 2897-2911. https://doi.org/10.2147/PRBM.S413162
Aracı, F. G. İ., Oyar, E., & Tan, Ş. A Cross-cultural validation of Multidimensional Digital Stress Scale in Türkiye. Kastamonu Education Journal, 32(2), 247-259. https://doi.org/10.24106/kefdergi.1473539
Sarıçam, H., & Günaydın, N. (2024). Üniversite Öğrencileri için Dijital Stres Ölçeğinin Türkçeye Uyarlanması: Geçerlilik ve Güvenirlik Çalışması. Yükseköğretim Dergisi, 14(3), 11-24. https://doi.org/10.53478/yuksekogretim.1381953
Khan, A., & Ilyas, U. (2024). Urdu adaptation and validation of Multidimensional Digital Stressor Scale. Media Asia, 52(2), 285–300. https://doi.org/10.1080/01296612.2024.2368344
For additional commentary on transnational application of the DSS:
Krägeloh, C. U. (2022). Digital Stress Scale (DSS). In International Handbook of Behavioral Health Assessment (pp. 1-12). Cham: Springer International Publishing.
