Background: A growing volume of mental health research is conducted with participants recruited and responding online. However, to date, few psychometric scales have been specifically validated for online research. Objective: We aimed to devise a brief, 12-item version of the Center for Epidemiological Studies Depression Scale (CES-D) in which first order factors are sufficiently measured. Methods: We recruited 218 adults with depression and 226 comparison participants with no mental health history. Both groups completed the original 20-item CES-D and measures of social support, psychological distress, and sociodemographic information (eg, age, gender, and household income). Measurement of social support included online support, and psychological distress included symptoms of social media use disorder along with loneliness and life dissatisfaction. Results: This brief, 12-item version of the CES-D was devised with persons with depression and replicated with comparison participants. For both, core sadness, somatic symptoms, interpersonal detachment, and absence of well-being each significantly contributed to measurement of a higher-order depression latent construct (P<.01). Structural equation modeling was performed to establish the construct validity of this 4-factor model in which depression is predicted by socioeconomic factors and depression predicts lower social support as well as greater psychological distress. Conclusions: Responses to this 12-item, online version of the CES-D demonstrate factorial and construct validity. Clinical research is required in future to ascertain whether scores greater than 11 (of 36) are suggestive of elevated depressive symptomology.

Background: In contrast to all previous generations, life today is lived both in-person and online. This creates both opportunities and risks to mental health and well-being. Social interaction is no longer geographically constrained, yet the anonymity and impersonality of social media create new problems. To quote Mike Tyson (July 2020), “Social media have made y’all way too comfortable with disrespecting people and not getting punched in the face for it.” Objective: This study set out to propose and test a hypothesized model to identify both direct and indirect predictors of life satisfaction. Independent or predictor variables included social media use, loneliness, and online and traditional social support. Methods: From March 2024 to October 2024, a total of 112 adults in the United States were recruited online and proceeded to complete study questionnaires. Participants were aged 42.62 (SD 12.74) years on average, had completed an average of 15.46 (SD 3.25) years of education, and reported an average household income of US $67,005 (SD US $41,560) per year. Results: Using path analysis, we found that social media use and online social support emerged as significant, indirect predictors of life satisfaction via loneliness and traditional in-person social support (P<.01). In total, 39% of variance in life satisfaction was explained by this path model (R2=0.39; P<.01). Conclusions: Contrary to hypothesis, these findings support the rich get richer hypothesis regarding online social support, not the social compensation theory, that is, online social support appears to function as an adjunct to in-person support, not as a substitute. The results of this study need to be replicated with more diverse, larger samples, with responses collected over multiple time points.

BACKGROUND A growing volume of mental health research is conducted with participants recruited and responding online. However, to date, few psychometric scales have been specifically validated for online research. OBJECTIVE We aimed to devise a brief, 12-item version of the Center for Epidemiological Studies Depression Scale (CES-D) in which first order factors are sufficiently measured. METHODS We recruited 218 adults with depression and 226 comparison participants with no mental health history. Both groups completed the original 20-item CES-D and measures of social support, psychological distress, and sociodemographic information (eg, age, gender, and household income). Measurement of social support included online support, and psychological distress included symptoms of social media use disorder along with loneliness and life dissatisfaction. RESULTS This brief, 12-item version of the CES-D was devised with persons with depression and replicated with comparison participants. For both, core sadness, somatic symptoms, interpersonal detachment, and absence of well-being each significantly contributed to measurement of a higher-order depression latent construct (P<.01). Structural equation modeling was performed to establish the construct validity of this 4-factor model in which depression is predicted by socioeconomic factors and depression predicts lower social support as well as greater psychological distress. CONCLUSIONS Responses to this 12-item, online version of the CES-D demonstrate factorial and construct validity. Clinical research is required in future to ascertain whether scores greater than 11 (of 36) are suggestive of elevated depressive symptomology.

BACKGROUND In contrast to all previous generations, life today is lived both in-person and online. This creates both opportunities and risks to mental health and well-being. Social interaction is no longer geographically constrained, yet the anonymity and impersonality of social media create new problems. To quote Mike Tyson (July 2020), “Social media have made y’all way too comfortable with disrespecting people and not getting punched in the face for it.” OBJECTIVE This study set out to propose and test a hypothesized model to identify both direct and indirect predictors of life satisfaction. Independent or predictor variables included social media use, loneliness, and online and traditional social support. METHODS From March 2024 to October 2024, a total of 112 adults in the United States were recruited online and proceeded to complete study questionnaires. Participants were aged 42.62 (SD 12.74) years on average, had completed an average of 15.46 (SD 3.25) years of education, and reported an average household income of US $67,005 (SD US $41,560) per year. RESULTS Using path analysis, we found that social media use and online social support emerged as significant, indirect predictors of life satisfaction via loneliness and traditional in-person social support (P<.01). In total, 39% of variance in life satisfaction was explained by this path model (R2=0.39; P<.01). CONCLUSIONS Contrary to hypothesis, these findings support the rich get richer hypothesis regarding online social support, not the social compensation theory, that is, online social support appears to function as an adjunct to in-person support, not as a substitute. The results of this study need to be replicated with more diverse, larger samples, with responses collected over multiple time points.

Background: As understanding of addiction extends beyond substance misuse, various behavioral disorders may emerge as recognized mental health conditions, including social media use disorder (SMUD). To date, however, empirical research supporting SMUD as a distinct disorder is limited and inconsistent. The present study set out to compare the relative prevalence of elevated symptoms suggestive of SMUD between those with no mental health history and affective disorders (both unipolar depression and bipolar disorder). Methods: Participants were recruited online and completed questionnaires measuring social media use (SMU), symptoms of SMUD, depression and various psychosocial factors associated with social media use (i.e., loneliness, social support). Among those with affective disorders, we set out to identify sociodemographic and clinical features that distinguish those with comorbid SMUD (i.e., above the cut-off on the SMUD screening measure). Results: Five main findings emerged from our analyses: (1) SMUD appears unrelated to known forms of SMU; (2) Among those with no mental health history, SMUD does not present independently of elevated depressive symptoms; (3), No sociodemographic or clinical features distinguish those with clinical depression reporting comorbid SMUD; (4), Among those with bipolar disorder (BD), symptoms of hypo/mania do not distinguish those also reporting elevated SMUD symptoms; (5) And SMUD symptoms no longer differ across clinical groups after adjusting for depressive symptoms. Conclusions: Our findings suggest that SMUD is unrelated to active or passive SMU. Symptoms suggestive of SMUD may instead be a contemporary manifestation of depressive symptomology. These findings do not support the existence of SMUD as a bona fide mental health condition.