Precarious work and methodological challenges to study hard-to-reach populations.

OBJECTIVE: To describe the methodological challenges and strategies of a web survey on the working conditions and health among delivery workers. METHODS: The study population consisted of Brazilian delivery workers operating in the national territory. Procedures include building solid and ongoing collaboration with worker representatives and conducting a four-month data collection from February to May 2022, sharing the link to the online questionnaire on social media such as social networks (Facebook, Instagram) and messaging apps (WhatsApp, Telegram). RESULTS: The recruitment of 41 leaders or influencers of delivery workers increased the dissemination of the study, some of whom participated in the consensual validation of the questionnaire; the production of content for social media for the dissemination of the questionnaire link on social networks and applications, and the in-person dissemination of the study at the delivery workers' meeting points during the workday played a fundamental role, totaling around 132 hours in 45 shifts. The strategies adopted for data collection with a hybrid approach to dissemination made it possible to carry out the web survey. After four months of the web survey, 564 delivery workers, 543 men and 18 women, responded to the online questionnaire. CONCLUSION: The web survey presented methodological strategies to overcome the challenge of reaching workers, including hybrid work, to increase the participation of workers, on whom epidemiological research is still scarce.

Patients Are Turning to TikTok for Health Information-Here's What Clinicians Need to Know.

This Medical News article discusses research on health information being shared on the social media platform.

Persistent interaction patterns across social media platforms and over time.

Growing concern surrounds the impact of social media platforms on public discourse1-4 and their influence on social dynamics5-9, especially in the context of toxicity10-12. Here, to better understand these phenomena, we use a comparative approach to isolate human behavioural patterns across multiple social media platforms. In particular, we analyse conversations in different online communities, focusing on identifying consistent patterns of toxic content. Drawing from an extensive dataset that spans eight platforms over 34 years-from Usenet to contemporary social media-our findings show consistent conversation patterns and user behaviour, irrespective of the platform, topic or time. Notably, although long conversations consistently exhibit higher toxicity, toxic language does not invariably discourage people from participating in a conversation, and toxicity does not necessarily escalate as discussions evolve. Our analysis suggests that debates and contrasting sentiments among users significantly contribute to more intense and hostile discussions. Moreover, the persistence of these patterns across three decades, despite changes in platforms and societal norms, underscores the pivotal role of human behaviour in shaping online discourse.

Electronic Media Use and Sleep Quality: Updated Systematic Review and Meta-Analysis.

BACKGROUND: This paper explores the widely discussed relationship between electronic media use and sleep quality, indicating negative effects due to various factors. However, existing meta-analyses on the topic have some limitations. OBJECTIVE: The study aims to analyze and compare the impacts of different digital media types, such as smartphones, online games, and social media, on sleep quality. METHODS: Adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the study performed a systematic meta-analysis of literature across multiple databases, including Web of Science, MEDLINE, PsycINFO, PubMed, Science Direct, Scopus, and Google Scholar, from January 2018 to October 2023. Two trained coders coded the study characteristics independently. The effect sizes were calculated using the correlation coefficient as a standardized measure of the relationship between electronic media use and sleep quality across studies. The Comprehensive Meta-Analysis software (version 3.0) was used to perform the meta-analysis. Statistical methods such as funnel plots were used to assess the presence of asymmetry and a p-curve test to test the p-hacking problem, which can indicate publication bias. RESULTS: Following a thorough screening process, the study involved 55 papers (56 items) with 41,716 participants from over 20 countries, classifying electronic media use into "general use" and "problematic use." The meta-analysis revealed that electronic media use was significantly linked with decreased sleep quality and increased sleep problems with varying effect sizes across subgroups. A significant cultural difference was also observed in these effects. General use was associated with a significant decrease in sleep quality (P<.001). The pooled effect size was 0.28 (95% CI 0.21-0.35; k=20). Problematic use was associated with a significant increase in sleep problems (P≤.001). The pooled effect size was 0.33 (95% CI 0.28-0.38; k=36). The subgroup analysis indicated that the effect of general smartphone use and sleep problems was r=0.33 (95% CI 0.27-0.40), which was the highest among the general group. The effect of problematic internet use and sleep problems was r=0.51 (95% CI 0.43-0.59), which was the highest among the problematic groups. There were significant differences among these subgroups (general: Qbetween=14.46, P=.001; problematic: Qbetween=27.37, P<.001). The results of the meta-regression analysis using age, gender, and culture as moderators indicated that only cultural difference in the relationship between Eastern and Western culture was significant (Qbetween=6.69; P=.01). All funnel plots and p-curve analyses showed no evidence of publication and selection bias. CONCLUSIONS: Despite some variability, the study overall confirms the correlation between increased electronic media use and poorer sleep outcomes, which is notably more significant in Eastern cultures.

Instagram Stories Unveiled: Exploring Links with Psychological Distress, Personality, and Gender.

Instagram is one of the most used platforms, and ephemeral stories are proving to be the most used medium for users to share content on the platform. However, there have been few studies examining this type of content in relation to emotional well-being. This study examined the association between the number of published Instagram stories, psychological well-being, personality traits, and gender in a sample of 734 Instagram users from Italy, including 281 men and 453 women, with a mean age of 25.19 years (SD = 7.08). Participants were recruited online and asked to complete an online questionnaire. Differences were found between genders in terms of time spent on Instagram, but not in terms of the number of stories posted in the past week. In the overall sample, a small positive correlation was found between the number of Instagram stories posted and extraversion. When considering gender differences, small effect sizes were observed for emotional dysregulation, agreeableness, and neuroticism, indicating a stronger association with Instagram stories in the female group, and for openness, indicating a stronger association in the male group. Results of multiple regression analyses suggest that among females, psychological variables, including personality and emotional distress, may have a stronger association with Instagram stories. To our knowledge, this is the first study to report these differences. The findings help to clarify how certain characteristics of social media platforms relate to psychological well-being and personality differently in men and women in their journey to using social media.

Online images amplify gender bias.

Each year, people spend less time reading and more time viewing images1, which are proliferating online2-4. Images from platforms such as Google and Wikipedia are downloaded by millions every day2,5,6, and millions more are interacting through social media, such as Instagram and TikTok, that primarily consist of exchanging visual content. In parallel, news agencies and digital advertisers are increasingly capturing attention online through the use of images7,8, which people process more quickly, implicitly and memorably than text9-12. Here we show that the rise of images online significantly exacerbates gender bias, both in its statistical prevalence and its psychological impact. We examine the gender associations of 3,495 social categories (such as 'nurse' or 'banker') in more than one million images from Google, Wikipedia and Internet Movie Database (IMDb), and in billions of words from these platforms. We find that gender bias is consistently more prevalent in images than text for both female- and male-typed categories. We also show that the documented underrepresentation of women online13-18 is substantially worse in images than in text, public opinion and US census data. Finally, we conducted a nationally representative, preregistered experiment that shows that googling for images rather than textual descriptions of occupations amplifies gender bias in participants' beliefs. Addressing the societal effect of this large-scale shift towards visual communication will be essential for developing a fair and inclusive future for the internet.

Social Acceptability of Health Behavior Posts on Social Media: An Experiment.

INTRODUCTION: Social media sites like Twitter (now X) are increasingly used to create health behavior metrics for public health surveillance. Yet little is known about social norms that may bias the content of posts about health behaviors. Social norms for posts about four health behaviors (smoking tobacco, drinking alcohol, physical activity, eating food) on Twitter/X were evaluated. METHODS: This was a randomized experiment delivered via web-based survey to adult, English-speaking Twitter/X users in three Michigan, USA, counties from 2020 to 2022 (n=559). Each participant viewed 24 posts presenting experimental manipulations regarding four health behaviors and answered questions about each post's social acceptability. Principal component analysis was used to combine survey responses into one perceived social acceptability measure. Linear mixed models with the Benjamini-Hochberg correction were implemented to test seven study hypotheses in 2023. RESULTS: Supporting six hypotheses, posts presenting healthier (CI: 0.028, 0.454), less stigmatized behaviors (CI: 0.552, 0.157) were more socially acceptable than posts regarding unhealthier, stigmatized behaviors. Unhealthy (CI: -0.268, -0.109) and stigmatized behavior (CI: -0.261, -0.103) posts were less acceptable for more educated participants. Posts about collocated activities (CI: 0.410, 0.573) and accompanied by expressions of liking (CI: 0.906, 1.11) were more acceptable than activities undertaken alone or disliked. Contrary to one hypothesis, posts reporting unusual activities were less acceptable than usual ones (CI: -0.472, 0.312). CONCLUSIONS: Perceived social acceptability may be associated with the frequency and content of health behavior posts. Users of Twitter/X and other social media platform posts to estimate health behavior prevalence should account for potential estimation biases from perceived social acceptability of posts.

Digital Interventions to Support Lung Cancer Screening: A Systematic Review.

INTRODUCTION: Lung cancer remains a leading cause of cancer-related deaths globally. Lung cancer screening (LCS) with low-dose computed tomography (LDCT) can reduce lung cancer mortality, but its adoption in the U.S. has been limited. Digital interventions have the potential to improve uptake of LCS. This systematic review aims to summarize the evidence for the effectiveness of digital interventions in promoting LCS. METHODS: A systematic search of three electronic databases (PubMed, Embase, and Medline) was conducted to identify studies published between January 2014 and May 2023. Studies were reviewed and abstracted between February 2023 and July 2023. Outcomes related to knowledge, decision-making and screening were measured. Study quality was assessed using the Joanna Briggs Institute (JBI) critical appraisal tools. RESULTS: Of 1,979 screened articles, 30 studies were included in this review. Digital interventions evaluated included decision aids (n=20), electronic health record (EHR)-based interventions (n=7), social media campaigns and mobile applications (n=3). Decision aids were the most commonly studied digital interventions, with most studies showing improved knowledge (13/13) and reduced decisional conflict (7/9) but most did not show a substantial change in screening use. Fewer studies tested clinician-facing or multi-level interventions. DISCUSSION: Digital interventions, particularly decision aids, have shown promise in improving knowledge and the quality of decision-making around LCS. However, few interventions have been shown to substantially alter screening behavior and few clinician-facing or multi-level interventions have been rigorously tested. Further research is needed to develop effective tools for engaging patients in LCS, to compare the efficacy of different interventions, and evaluate implementation strategies in diverse healthcare settings.

Online searches to evaluate misinformation can increase its perceived veracity.

Considerable scholarly attention has been paid to understanding belief in online misinformation1,2, with a particular focus on social networks. However, the dominant role of search engines in the information environment remains underexplored, even though the use of online search to evaluate the veracity of information is a central component of media literacy interventions3-5. Although conventional wisdom suggests that searching online when evaluating misinformation would reduce belief in it, there is little empirical evidence to evaluate this claim. Here, across five experiments, we present consistent evidence that online search to evaluate the truthfulness of false news articles actually increases the probability of believing them. To shed light on this relationship, we combine survey data with digital trace data collected using a custom browser extension. We find that the search effect is concentrated among individuals for whom search engines return lower-quality information. Our results indicate that those who search online to evaluate misinformation risk falling into data voids, or informational spaces in which there is corroborating evidence from low-quality sources. We also find consistent evidence that searching online to evaluate news increases belief in true news from low-quality sources, but inconsistent evidence that it increases belief in true news from mainstream sources. Our findings highlight the need for media literacy programmes to ground their recommendations in empirically tested strategies and for search engines to invest in solutions to the challenges identified here.

Predicting the drop out from the maternal, newborn and child healthcare continuum in three East African Community countries: application of machine learning models.

BACKGROUND: For optimal health, the maternal, newborn, and child healthcare (MNCH) continuum necessitates that the mother/child receive the full package of antenatal, intrapartum, and postnatal care. In sub-Saharan Africa, dropping out from the MNCH continuum remains a challenge. Using machine learning, the study sought to forecast the MNCH continuum drop out and determine important predictors in three East African Community (EAC) countries. METHODS: The study utilised Demographic Health Surveys data from the Democratic Republic of Congo (DRC) (2013/14), Kenya (2014) and Tanzania (2015/16). STATA 17 was used to perform the multivariate logistic regression. Python 3.0 was used to build five machine learning classification models namely the Logistic Regression, Random Forest, Decision Tree, Support Vector Machine and Artificial Neural Network. Performance of the models was assessed using Accuracy, Precision, Recall, Specificity, F1 score and area under the Receiver Operating Characteristics (AUROC). RESULTS: The prevalence of the drop out from the MNCH continuum was 91.0% in the DRC, 72.4% in Kenya and 93.6% in Tanzania. Living in the rural areas significantly increased the odds of dropping out from the MNCH continuum in the DRC (AOR:1.76;95%CI:1.30-2.38), Kenya (AOR:1.23;95%CI:1.03-1.47) and Tanzania (AOR:1.41;95%CI:1.01-1.97). Lower maternal education also conferred a significant increase in the DRC (AOR:2.16;95%CI:1.67-2.79), Kenya (AOR:1.56;95%CI:1.30-1.84) and Tanzania (AOR:1.70;95%CI:1.24-2.34). Non exposure to mass media also conferred a significant positive influence in the DRC (AOR:1.49;95%CI:1.15-1.95), Kenya (AOR:1.46;95%CI:1.19-1.80) and Tanzania (AOR:1.65;95%CI:1.13-2.40). The Random Forest exhibited superior predictive accuracy (Accuracy = 75.7%, Precision = 79.1%, Recall = 92.1%, Specificity = 51.6%, F1 score = 85.1%, AUROC = 70%). The top four predictors with the greatest influence were household wealth, place of residence, maternal education and exposure to mass media. CONCLUSIONS: The MNCH continuum dropout rate is very high in the EAC countries. Maternal education, place of residence, and mass media exposure were common contributing factors to the drop out from MNCH continuum. The Random Forest had the highest predictive accuracy. Household wealth, place of residence, maternal education and exposure to mass media were ranked among the top four features with significant influence. The findings of this study can be used to support evidence-based decisions in MNCH interventions and to develop web-based services to improve continuity of care retention.

How social media usage affects psychological and subjective well-being: testing a moderated mediation model.

BACKGROUND: A growing body of literature demonstrates that social media usage has witnessed a rapid increase in higher education and is almost ubiquitous among young people. The underlying mechanisms as to how social media usage by university students affects their well-being are unclear. Moreover, current research has produced conflicting evidence concerning the potential effects of social media on individuals' overall well-being with some reporting negative outcomes while others revealing beneficial results. METHODS: To address the research gap, the present research made an attempt to investigate the crucial role of social media in affecting students' psychological (PWB) and subjective well-being (SWB) by testing the mediating role of self-esteem and online social support and the moderation effect of cyberbullying. The data in the study were obtained from a sample of 1,004 college students (483 females and 521 males, Mage = 23.78, SD = 4.06) enrolled at 135 Chinese universities. AMOS 26.0 and SPSS 26.0 as well as the Process macro were utilized for analyzing data and testing the moderated mediation model. RESULTS: Findings revealed that social media usage by university students was positively associated with their PWB and SWB through self-esteem and online social support, and cyberbullying played a moderating role in the first phase of the mediation process such that the indirect associations were weak with cyberbullying reaching high levels. CONCLUSION: These findings highlight the importance of discerning the mechanisms moderating the mediated paths linking social media usage by young adults to their PWB and SWB. The results also underline the importance of implementing measures and interventions to alleviate the detrimental impacts of cyberbullying on young adults' PWB and SWB.

Do indesejável ao (in)evitável: velhice estigmatizada na pandemia da Covid-19 / From the undesirable to the (in) preventable: old age stigmatized in the Covid-19 pandemic / De lo indeseable a lo (in) evitable: vejez estigmatizada enla pandemia de Covid-19

O artigo tem por objetivo analisar as representações sociais sobre a imagem da pessoa idosa associadas a pandemia de COVID-19 nas publicações do Instagram. Caracteriza-se como um estudo exploratório, descritivo, de abordagem qualitativa, com estratégia de investigação pautada no estudo de caso. Os dados consistiram em 19 memes publicados na internet na rede social Instagram por meio da busca aleatória do maior número possível de memes, a partir do buscador "idoso" e "quarentena". A partir da Técnica de Análise de Conteúdo proposta por Bardin emergiram duas categorias analíticas: Categoria 1. Recolhimento/enclausuramento da pessoa idosa (52,63%) e Categoria 2. Impedimento/limitação da mobilidade das pessoas idosas fora de casa (47,37%). As representações sociais acerca da pessoa idosa publicadas nas redes sociais relacionadas ao caso da COVID-19 desvelaram um sentido negativo atribuído ao envelhecer e demonstram como há um processo de naturalização da estigmatização e invisibilidade da violência contra pessoas idosas. (AU)

The article aims to analyze the social representations of the image of the elderly associated with the COVID-19 pandemic in Instagram publications. It is characterized as an exploratory, descriptive study with a qualitative approach, with an investigation strategy based on the case study.The data consisted of 19 memes published on the internet on the social network Instagramthrough the random search of the largest possible number of memes, using the search engine "elderly" and "quarantine". From the Content Analysis Technique proposed by Bardin, two analytical categories emerged: Category 1. Withdrawal/enclosure of the elderly person (52.63%) and Category 2. Impediment/limitation of mobility of elderly people outside the home (47.37 %). The social representations about the elderly person published on social networks related to the case of COVID-19 revealed a negative meaning attributed to aging and demonstrate how there is a process of naturalization of stigmatization and invisibility of violence against the elderly. (AU)

El artículo tiene como objetivo analizar las representaciones sociales de la imagen del anciano asociado a la pandemia COVID-19 en publicaciones de Instagram. Se caracteriza por ser un estudio exploratorio, descriptivo con enfoque cualitativo, con una estrategia de investigación basada en el estudio de caso. Los datos consistieron en 19 memes publicados en Internet en la red social Instagram através de la búsqueda aleatoria del mayor número posiblede memes, utilizando el motor de búsqueda "ancianos" y "cuarentena". De la Técnica de Análisis de Contenidos propuesta por Bardin surgieron dos categorías analíticas: Categoría 1. Retiro/encerramiento del anciano (52,63%) y Categoría 2. Impedimento/limitación de la movilidad de los ancianos fuera del hogar (47,37%). Las representaciones sociales sobre las personas mayores publicadas en las redes sociales relacionadas con el caso de COVID-19 revelaron un significado negativo atribuido al envejecimiento y demuestran cómo hay un proceso de naturalización de la estigmatización e invisibilidad de la violencia contra las personas mayores. (AU)

On the use of aspect-based sentiment analysis of Twitter data to explore the experiences of African Americans during COVID-19.

According to data from the U.S. Center for Disease Control and Prevention, as of June 2020, a significant number of African Americans had been infected with the coronavirus disease, experiencing disproportionately higher death rates compared to other demographic groups. These disparities highlight the urgent need to examine the experiences, behaviors, and opinions of the African American population in relation to the COVID-19 pandemic. By understanding their unique challenges in navigating matters of health and well-being, we can work towards promoting health equity, eliminating disparities, and addressing persistent barriers to care. Since Twitter data has shown significant promise as a representation of human behavior and for opinion mining, this study leverages Twitter data published in 2020 to characterize the pandemic-related experiences of the United States' African American population using aspect-based sentiment analysis. Sentiment analysis is a common task in natural language processing that identifies the emotional tone (i.e., positive, negative, or neutral) of a text sample. Aspect-based sentiment analysis increases the granularity of sentiment analysis by also extracting the aspect for which sentiment is expressed. We developed a machine learning pipeline consisting of image and language-based classification models to filter out tweets not related to COVID-19 and those unlikely published by African American Twitter subscribers, leading to an analysis of nearly 4 million tweets. Overall, our results show that the majority of tweets had a negative tone, and that the days with larger numbers of published tweets often coincided with major U.S. events related to the pandemic as suggested by major news headlines (e.g., vaccine rollout). We also show how word usage evolved throughout the year (e.g., outbreak to pandemic and coronavirus to covid). This work also points to important issues like food insecurity and vaccine hesitation, along with exposing semantic relationships between words, such as covid and exhausted. As such, this work furthers understanding of how the nationwide progression of the pandemic may have impacted the narratives of African American Twitter users.

Lexicon-based sentiment analysis to detect opinions and attitude towards COVID-19 vaccines on Twitter in Italy.

The paper proposes a methodology based on Natural Language Processing (NLP) and Sentiment Analysis (SA) to get insights into sentiments and opinions toward COVID-19 vaccination in Italy. The studied dataset consists of vaccine-related tweets published in Italy from January 2021 to February 2022. In the considered period, 353,217 tweets have been analyzed, obtained after filtering 1,602,940 tweets with the word "vaccin". A main novelty of the approach is the categorization of opinion holders in four classes, Common users, Media, Medicine, Politics, obtained by applying NLP tools, enhanced with large-scale domain-specific lexicons, on the short bios published by users themselves. Feature-based sentiment analysis is enriched with an Italian sentiment lexicon containing polarized words, expressing semantic orientation, and intensive words which give cues to identify the tone of voice of each user category. The results of the analysis highlighted an overall negative sentiment along all the considered periods, especially for the Common users, and a different attitude of opinion holders towards specific important events, such as deaths after vaccination, occurring in some days of the examined 14 months.

Cascading bidirectional influences of digital media use and mental health in adolescence.

A substantial portion of critical adolescent development is occurring within digital environments. However, certain individual differences may lead adolescents to use digital media in diverse ways. In this chapter we suggest that the way teens use digital media influences how digital media affects their mental health. Further, we propose a model in which these influences, in the context of ongoing development, may have feedback effects on how digital media is subsequently used, thus resulting in a self-perpetuating cycle. Our model suggests that certain developmental risk/protective factors and maladaptive/adaptive digital media behaviors likely perpetuate each other in a cyclical manner each serving to maintain and/or escalate the other. We discuss existing evidence of these processes in psychosocial, identity, incentive processing, and physical health development. Future research focusing on individual differences and self-reinforcing digital media behaviors that manifest these feedback loops may portray a more complete picture of cascading digital media influences across adolescent development.

Ethical considerations and methodological uses of Facebook data in public health research: A systematic review.

OBJECTIVE: Since 2016, around seven in 10 adults in the United States (U.S.) actively use Facebook. While much Facebook data is publicly available for research, many users may not understand how their data are being used. We sought to examine to what extent research ethical practices were employed and the research methods being used with Facebook data in public health research. METHODS: We conducted a systematic review (PROSPERO registration CRD42020148170) of social media-based public health research focused on Facebook published in peer-reviewed journals in English between January 1, 2006 and October 31, 2019. We extracted data on ethical practices, methodology, and data analytic approaches. For studies that included verbatim user content, we attempted to locate users/posts within a timed 10-min period. RESULTS: Sixty-one studies met eligibility criteria. Just under half (48%, n = 29) sought IRB approval and six (10%) sought and obtained informed consent from Facebook users. Users' written content appeared in 39 (64%) papers, of which 36 presented verbatim quotes. We were able to locate users/posts within 10 min for half (50%, n = 18) of the 36 studies containing verbatim content. Identifiable posts included content about sensitive health topics. We identified six categories of analytic approaches to using these data: network analysis, utility (i.e., usefulness of Facebook as a tool for surveillance, public health dissemination, or attitudes), associational studies of users' behavior and health outcomes, predictive model development, and two types of content analysis (thematic analysis and sentiment analysis). Associational studies were the most likely to seek IRB review (5/6, 83%), while those of utility (0/4, 0%) and prediction (1/4, 25%) were the least likely to do so. CONCLUSIONS: Stronger guidance on research ethics for using Facebook data, especially the use of personal identifiers, is needed.

Digital self-harm and suicidality among adolescents.

BACKGROUND: Research on digital self-harm - the anonymous online posting, sending, or otherwise sharing of hurtful content about oneself - is still in its infancy. Yet unexplored is whether digital self-harm is related to suicidal ideation or suicide attempts. METHODS: In the current study, survey data were collected in 2019 from a national sample of 4972 American middle and high school students (Mage = 14.5; 50% female). Logistic regression analysis was used to assess whether lifetime engagement in two different indicators of digital self-harm was associated with suicidal thoughts and attempts within the past year. RESULTS: Logistic regression analysis showed that engagement in digital self-harm was associated with a five- to sevenfold increase in the likelihood of reporting suicidal thoughts and a nine- to 15-fold increase in the likelihood of a suicide attempt. CONCLUSIONS: Results suggest a connection between digital self-harm and suicidality. As such, health professionals must screen for digital self-harm to address underlying mental health problems among youth that may occur prior to or alongside suicidality, and parents/caregivers must convey to children that they are available to dialog, support, and assist with the root issues that may eventually manifest as digital self-harm.

Examination of the temporal sequence between social media use and well-being in a representative sample of adults.

Given insufficient prospective evidence for relationships between social media use and well-being among adults, the present study examined the temporal sequence between social media use and psychological distress and life satisfaction, and explored age and gender differences. A representative sample of adults (N = 7331; 62.4% women; Mage = 51.94; SD = 13.48; 15-94 years) were surveyed annually across four waves. Cross-lagged panel models demonstrated bidirectional relationships between social media use and well-being. Higher psychological distress and lower life satisfaction predicted higher social media use more strongly than the reverse direction, with effects particularly pronounced for the impact of psychological distress. Although the patterns of findings were relatively consistent across age and gender, results suggested that women and middle- and older-aged adults experience detrimental effects of social media use on well-being, which may drive subsequent increased use of social media. The bidirectional relationships suggest that adults who experience psychological distress or lower life satisfaction may seek to use social media as a way to alleviate poor well-being. However, paradoxically, this maladaptive coping mechanism appears to drive increased social media use which in turn can exacerbate poor well-being. Clinicians should be aware of these bidirectional relationships and work with clients towards replacing ineffective strategies with more helpful coping approaches. As this study used a simplistic measure of social media use, future research should address this limitation and explore nuanced relationships afforded by assessing specific social media activities or exposure to certain types of content.