Higher Social Media Use Associated with Higher Hemoglobin A1C and Greater Diabetes Distress in Young Adults with Type 1 Diabetes

Abstract

Objective:

Young adults (YA) with type 1 diabetes struggle with glycemic control and diabetes distress. As social media (SM) is highly used by YA, we explored how YA with diabetes use SM, identifying associations with demographics and diabetes measures.

Research Design and Methods:

We developed and emailed a survey to YA seen in clinic, aged 18–25 years with diabetes ≥1 year, querying SM, demographic, and diabetes characteristics, as well as diabetes distress (Problem Areas in Diabetes [PAID]). We divided the sample into lower (<3 h/day) and higher (3+ h/day) SM use and compared characteristics using t-tests and Chi-square.

Results:

Of 1176 YA approached, 385 (33%) provided evaluable responses. Mean age was 22 years; 61% were female and 86% non-Hispanic white (NHW); 83% used a pump and 96% used a continuous glucose monitor. Mean HbA1c was 7.4% ± 1.3% (56.3 ± 14.2 mmol/mol). Almost all (98%) used SM; Instagram (56%) and TikTok (24%) were most popular. The lower (65%) and higher (35%) SM use groups differed by age (23 vs. 22.3 years, P = 0.02), race/ethnicity (90 vs 78.5% NHW, P = 0.002), HbA1c (7.3% vs. 7.6% [55.2 vs. 59.6], P = 0.007), and PAID score (24.5 vs. 29.7, P = 0.02), respectively. Diabetes content represented <12% of overall SM engagement, while 27.4% of sample reported SM as their primary source of diabetes technology knowledge.

Conclusions:

YA with higher SM use were younger, had higher HbA1c, reported more diabetes distress, and were less likely to be NHW. Engagement with diabetes SM was relatively low, representing a potential opportunity to disseminate education and support to YA with diabetes.

Keywords

type 1 diabetes social media diabetes distress glycemic control diabetes self-care

Introduction

Young adults (YA), defined by the Society for Adolescent Health and Medicine as those aged 18–25 years old, face multiple health challenges unique to their age and stage of development¹ YA with type 1 diabetes are no exception and may experience additional challenges related to the demands of diabetes self-management competing with the multitude of other tasks of young adulthood related to education, occupation, socialization, finances, among others.² It is well-established that adolescents and YA with type 1 diabetes have the highest hemoglobin A1c (HbA1c) across the lifespan^3–5 with mean HbA1c levels rising and not approaching goal again until after age 25 years old. These differences can have clinical significance, as HbA1c levels above 7% (53 mmol/mol) are associated with increased risk of future microvascular complications⁶ and cardiovascular disease.⁷

In addition to challenges related to self-care and achieving target glycemic control, YA with type 1 diabetes deal with elevated rates of distress and mental health concerns (one study citing over 1/3 of YA with type 1 diabetes reporting depressive symptoms)⁸ as well as lower health related quality of life⁹ Diabetes distress, defined as the emotional distress resulting from living with diabetes and the burden of self-management¹⁰ is highly prevalent in YA.¹¹ A qualitative study of YA with type 1 diabetes found that most study participants considered diabetes to be emotionally challenging at least some of the time; sources of their distress included stigma, day-to-day management difficulties, difficulties navigating health care, and concerns about the future including diabetes complications and pregnancy.¹² Mitigating factors cited by participants included interaction with health care providers, diabetes education opportunities, and peer support.

In light of these challenges, researchers have investigated creative means to support these vulnerable young people.¹³ Given their robust use of digital communication technologies, the use of m-health interventions using apps^14,15 and text messaging^16,17 have demonstrated some benefits to self-care. However, in recent years, especially following the COVID-19 pandemic, social media (SM) has shifted to becoming a predominant vehicle for interactions between YA’s, and may present an opportunity to provide support for young people with type 1 diabetes. Indeed, recent studies demonstrate young people are some of the most frequent users of SM.¹⁸ While there are some concerns about negative or detrimental effects of SM on young people^19,20 other studies^21–23 and recommendation from the 2023 Surgeon General’s report²⁴ suggest a more nuanced relationship with SM, especially for marginalized young people, and the need for further research in the area.

With regards to SM use in people with diabetes, preliminary investigations using qualitative methods^25–28 and automated natural language processing of SM posts^29,30 highlight potential benefits, noting themes of building community, humor, venting, social support via self-disclosure, and overall positivity. However, concerns about SM remain. What appears to be deficient in the literature is a detailed, quantitative assessment of the SM habits of YA with type 1 diabetes and an evaluation of how this use may be associated with factors such as glycemic control and diabetes distress. The aim of the current study was to better understand how SM is used by YA with type 1 diabetes and to identify associations of SM use with participant characteristics, glycemic control, and self-reported diabetes distress. Furthermore, we hoped to consider opportunities by which SM might aid YA with type 1 diabetes in their struggles with self-management, diabetes distress, and need for diabetes peer support.

Methods

The survey was created by a multidisciplinary team of pediatric endocrinologists, nurse practitioners, diabetes mental health specialists, and research team members to capture self-reported information regarding frequency and type of SM use, diabetes distress (using the previously validated Problem Areas in Diabetes [PAID³¹] survey), demographics, and diabetes management characteristics including diabetes technology use and self-reported HbA1c. A PAID score of 40 or above indicated significant distress. It also included queries about the details of general SM use and diabetes-specific SM use including details about posts the YA viewed, reacted to, or posted themselves, if applicable. Queries were a combination of checkboxes, slider bars, and open-ended questions. The survey required less than 30 min to complete and was formatted for use in Research Electronic Data Capture (REDCap).³² This study was reviewed and approved by the Joslin Diabetes Center Committee on Human Studies (CHS# 132).

An automated electronic health record (EHR) extraction identified YA aged 18–25 years and with type 1 diabetes for at least 1 year receiving care at the Joslin Diabetes Center Pediatric and Adult Diabetes Clinics at least once within the past year. Standard care involved multidisciplinary in-person or virtual clinic visits including monitoring of glucose levels, counseling regarding comorbidities, mental health assessments, nutritional counseling, and diabetes education. We used REDCap to approach potential participants via email, obtain informed consent, and administer the survey. The email survey was fielded three times, 5 days apart, with repeat emails sent to those who did not respond previously. Surveys were disseminated over two 10-day periods in February and April 2024. If the EHR-listed contact email address was still that of the parent/guardian, and the parent responded to the survey invitation, they were asked to forward the email to their adult child for their consideration to complete the survey.

Once electronic consent was provided, the survey began by confirming the individual qualified for enrollment based upon age and duration of type 1 diabetes. Next, it queried participants’ primary sources of diabetes information, support, and knowledge about new diabetes technology. Response options included medical team, friends/family, SM, websites, or other. They were asked if they have at least one active social media account. If they did, they were then asked to consider their SM interactions in the past 30 days. They were asked the amount of time spent on SM by selecting one of multiple options ranging from “I don’t use social media at all” or 1 h per month, to over 8 h per day or “constantly.” If they reported they did not have an active account or did not use SM at all, they were asked why not. If they reported they did use SM, they were asked specific questions about the platforms used and the content with which they interacted. They were asked what proportion of SM use included diabetes-specific content with the response options given via a slider bar. Specifically, three groups of questions asked what percent of the total posts viewed, reacted to, and posted by the YA on SM were about diabetes. To address issues specific to YA, all participants were also asked the degree to which it is hard to get into a routine with their diabetes care and how difficult it is to prioritize their diabetes care using the same 5-point Likert scale used for the PAID questions. For these questions, we dichotomized questions using a 5-point Likert response scale into those rated as not a problem or minor problem versus those rated as a moderate, somewhat serious, or serious problem.

Following survey completion, diabetes care and technology related outcomes were extracted from the EHR. Clinician-entered data included most recent HbA1c from the previous 6 months and diabetes technology use.

The sample was divided into lower (1–3 h per day or less) and higher (3–5 h a day or more) SM use based on self-reported time spent on SM according to previous report of average self-reported SM use by YA as being between 2.5–3 h per day.³³ We also performed three-way comparisons of the lower (less than 1–3 h per day), average (1–3 h per day) and higher (3–5 h per day or more) and compared using ANOVA. The sample was then categorized into two groups based upon those for whom SM was the primary source of diabetes knowledge versus those using other sources. Statistical comparisons between groups included t-tests for continuous variables, chi-square tests for categorical variables, ANOVA for multi-group comparisons, and generalized linear modeling for adjusted analyses. All analyses were performed using STATA 18 statistical software (StataCorp LLC, College Station, TX).

Results

There were 1195 potential participants identified from the EHR (Fig. 1). Of these, 19 did not have an email address on file. Only 1 participant declined participation by email. There were 18 who were ineligible, either through the initial screening questions or disqualified in data analysis stage. There were 3 participants who completed the survey but did not answer the main SM questions. The final sample comprised 385 participants, with the majority excluded due to nonresponsiveness.

FIG. 1.

Survey responses. Overall response rate was 32.8%. Noneligible respondents included those found to be ineligible due to age during data check or who selected “No” to one of two eligibility questions confirming age 18–25, TYPE 1 DIABETES diagnosis, and diabetes duration of 1 year or more.

Baseline characteristics are displayed in Table 1. The mean age of the sample was 22.7 years, with a diabetes duration of 9.9 years. The sample was 60.6% female and 86.0% non-Hispanic white (NHW). Most of the sample spent the majority of their time at work or school (94.0%). The mean HbA1c from the EHR was 7.38%, whereas self-reported was 7.08%. The majority of the sample used CGM (96.1%) and/or insulin pump (82.6%). The mean PAID score was 26.3, including 21.5% whose scores were at or above the threshold of 40.

Table 1.

Baseline Characteristics and Comparisons by Hours of Self-Reported Social Media Use per Day

N = 385	Total sample	Lower SM use (≤1–3 h/day) n = 250, 65%	Higher SM use (3–5+ h/day) n = 135, 35%	P
Age, years (mean ± SD)	22 ± 2.2	23 ± 2.2	22.35 ± 2.15	0.02
Diabetes duration, years (mean ± SD)	9.9 ± 5.4	9.9 ± 5.7	9.7 ± 5.0	0.76
Gender, (%female)	60.6%	56.8%	67.4%	0.24
Race ethnicity (%NHW)	86.0%	90.0%	78.5%	0.002
Majority of time spent:				0.02
Work	54.2%	58.8%	45.2%
School	39.9%	36.8%	45.9%
Other	6.0%	11%	8.9%
HbA1c (EHR), % (mmol/mol)	7.4 ± 1.3 (56.3 ± 14.2)	7.3 ± 1.2 (55.2 ± 13.1)	7.6 ± 1.4 (59.6 ± 15.3)	0.007
HbA1c (self-report)	7.1 ± 0.9 (54.1 ± 9.8)	7.0 ± 0.9 (53.0 ± 9.8)	7.3 ± 0.9 (55.2 ± 9.8)	0.004
CGM (%use)	96.1%	96.4%	98.5%	0.23
Pump (%use)	82.6%	80.8%	85.9%	0.21
PAID score (mean ± SD)	26.31 ± 20.3	24.5 ± 18.1	29.7 ± 23.4	0.02
PAID high distress (% ≥40)	21.5%	18.8%	26.7%	0.07

Comparisons were performed using t-test and Chi-square, and P < 0.05 was considered significant.

Gender, race/ethnicity, time spent, CGM use, pump use, PAID score, and routine/priority questions (reported moderate, somewhat serious or serious problem) were self-reported from the survey.

Age and diabetes duration were extracted from the EHR only.

HbA1c was queried both from the EHR and via survey questions. Correlation between self-reported and EHR HbA1c was r = 0.8793, P < 0.0001.

SM, social media; TYPE 1 DIABETES, type 1 diabetes; NHW, non-Hispanic white; HbA1c, hemoglobin A1c; CGM, continuous glucose monitor; SD, standard deviation.

Most YA reported their primary source of diabetes information (82.1%) and knowledge about new diabetes technology (55.6%) was their medical team (Fig. 2A,B). Their primary source of diabetes support was friends/family (68%) (Fig. 2C). In terms of SM use, the majority (47%) reported they used SM for 1–3 h per day (Fig. 3A), and the most-used platforms in general were Instagram (56%) and TikTok (24%) (Fig. 3B). Instagram and TikTok were also the most-commonly reported sources of diabetes SM content. For those who did not use SM (n = 7, 1.8%) the most common reason was that SM took up too much time, followed by SM providing too much information; no interest in what others convey on SM; and no desire to share anything about themselves on SM. For those who reported needing a break from SM (n = 24, 6.4%), the most-endorsed reasons were that SM was negatively affecting mental health, feeling fake, and left them feeling judged.

FIG. 2.

Reported sources of information and support. “Websites” category was specified to be educational/informational. (A) “Which of the following would you consider to be the PRIMARY source of your diabetes INFORMATION? “Other” included: knowing all information already (1), diabetes camp (1), medical/nursing school (3). (B) “Which of the following would you consider to be the PRIMARY source of knowledge about NEW DIABETES TECHNOLOGY?” “Other” included diabetes camp (1), JDRF (1), email (2), medical team (2). (C) “Which of the following would you consider to be the PRIMARY source of your diabetes SUPPORT?” “Other” included therapist (1), myself (2), diabetes camp (1).

FIG. 3.

Social media (SM) use characteristics. For each question, participants could only select one option. Dark arrow indicates SM as a response. (Left) “On average, how much time do you spend on SM?” (Right) “Which platform do you interact with the most?” “Other” responses included Tumblr (2) and LinkedIn (1).

When we compared the higher SM users (n = 135) with lower SM users (n = 250) (Table 1), we found higher SM users included a lower proportion of NHW participants (79% vs. 90%, P = 0.002), younger aged participants (22.4 vs. 22.9 years, P = 0.02), higher HbA1c levels (both by EHR and self-report: 7.6 vs. 7.3% [5.96 vs 55.2 mmol/mol], P = 0.007 and 7.3 vs. 7.0% [55.2 vs. 53.0 mmol/mol], P = 0.004, respectively), and higher PAID scores (29.7 vs. 24.5, P = 0.02). Occurrence of self-reported diabetic ketoacidosis episodes in the past 6 months, although low in 6% of the sample, did not differ between higher and lower SM users. In terms of queries related to YA-specific diabetes concerns, there was a significant difference in responses to the query about significant problems getting into a routine with diabetes care (higher SM use 28%, lower SM use 40%, P = 0.03) but no significant differences in responses to difficulty prioritizing diabetes question (P = 0.42).

When three-way comparisons were performed between low (n = 109), average (n = 142), and high (n = 135) users, demographic and diabetes care characteristics among these groups differed similarly by race/ethnicity with lower proportion of NHW participants in the high SM users (P = 0.01), higher HbA1c in the high SM users (both EHR-extracted and self-reported, P = 0.02), and higher mean PAID score (P = 0.02). Additionally, the three-way comparison analysis revealed lower pump use in the low SM group (69.1% in low, 85.2% average, and 85.9% high, P = 0.005). There were significant differences in reported problems with getting into a routine with diabetes care (34% low, 36% average, 40% high, P = 0.03) but not to the query about difficulty prioritizing diabetes question (P = 0.42).

There was a significant, positive correlation between both EHR-extracted and self-reported HbA1c and the PAID score (β = 5.86, P = 0.002 [EHR] and β = 3.39, P = 0.001 [self-reported]).

The prevalence of diabetes-focused dialogs in total SM interactions was low. Overall, the median (interquartile range) percentage of self-reported diabetes SM use was 11% (5–25), with reactions and/or comments occurring in 9% (3–19) of SM use. Participants reported rarely posting about diabetes on SM, only 3% (1–13) of the time. The only significant differences between high and low users were in reactions/comments, with low users having a lower median percentage of posts about diabetes (8%[2–17]) than higher SM users (11%[4–24]), P = 0.03. Of individuals posting diabetes content, the majority (69%) reported their diabetes content was private or mixed private/public, with no significant differences between high and low SM users (P = 0.14). When asked to check all applicable categories for viewed diabetes SM content, the most frequently reported were informative posts and those involving personal stories. When asked what posts about diabetes they would like to see more of, most frequently selected were informative and funny posts. When asked how often SM changed the way they managed their diabetes, 27.3% reported SM changed the way they managed their diabetes at least monthly.

When we compared those for whom SM was their primary source of diabetes knowledge (n = 105, 27%) with all others, we found differences in self-reported gender (80% males in the SM as primary source group, 53% males in others, P < 0.001) and higher PAID scores (30.99 ± 20.5 in the SM as primary source group 24.59 ± 20.0, in all others, P = 0.006).

Discussion

In this study of the self-reported SM habits of 385 YA with type 1 diabetes, we demonstrated higher HbA1c and greater diabetes distress in those with more SM use. Many YA reported that SM was an important source of information about new diabetes technology. Higher SM users were also more likely to spend the majority of their time in school rather than work, be part of racial/ethnic minority groups, and report challenges getting into a routine with diabetes care. While the overall engagement with SM was high, engagement with diabetes-related SM was low, representing a potential opportunity for the creation and dissemination of supportive, educational diabetes SM content geared to YAs.

In terms of general SM use in this sample of YA with type 1 diabetes, the use in hours per day and most frequently used platforms were consistent with a recent study of over 1000 young people aged 18–27.³⁴ While sources of information and support were intuitive—YA cited most frequently getting medical knowledge from medical professionals and support from friends and family—over one quarter of respondents noted SM as their primary source of information about new diabetes technology. This emphasizes the importance of access to accurate health information about diabetes on SM, especially as type 1 diabetes clinicians cite misinformation as a major concern for patients using SM.³⁵

Our observations of higher SM use with both higher HbA1c and diabetes distress appear to be novel. SM has not typically been included as a risk factor for uncontrolled diabetes or diabetes distress.^36,37 However, these associations do not provide insight on causality—that is, whether high SM use might lead to higher HbA1c or diabetes distress or whether individuals with high HbA1c or diabetes distress seek out SM for information and/or support. Notably, there are reports of higher SM use in young people who report adverse mental health concerns.^38,39 However, over one-quarter of participants reported SM has changed the way they managed their diabetes. Although not specified positively or negatively, it behooves diabetes clinicians and researchers to consider their recommendations as reported in the survey to have more informative and humorous posts promoting consistent and evidence-based diabetes self-care.

In our sample, those in racial and ethnic minority groups were more likely to spend more time on SM. This is consistent with Pew Foundation findings, that Black and Hispanic teens spent more time online than other groups.⁴⁰ Disparities in glycemic control and diabetes distress in YA with type 1 diabetes from racial and ethnic minority groups compared with NHW are well-established.^41,42 Applying the findings from these reports to our own opens the door for further investigations on how SM may be able to play a role in providing targeted support to those individuals at higher risk of diabetes complications, including those from racial and ethnic minority groups.

The findings should be interpreted in the context of limitations. The analysis used self-reported SM use, which may have under- or overrepresented participants’ actual SM use. The study used email addresses to approach patients, and thus people who were connected to their technology would be more likely to respond. For those who had recently turned 18 years of age, the email address on file may have been that of a parent or guardian. Participants were informed in the subject line of the recruitment email that the topic of the survey was SM; those who were not SM users or for some reason were not interested in discussing the topic may have been discouraged from participating, leading to a biased sample. Distributing this survey to a larger national or international sample of respondents using multiple approaches could address some of these limitations. Despite limitations, the findings of our studies suggest benefits of further studies of SM use, including potential development of interventions targeting glycemic control, distress, and technology use, particularly when providing a source of medically sound information and support.

Conclusions

As SM becomes an increasingly important aspect to the lives of young people, this study confirms robust SM use in YA with type 1 diabetes, and presents a potential opportunity for type 1 diabetes clinicians and other experts to use SM to connect with individuals with elevated HbA1c and diabetes distress. SM certainly warrants further research as a potential tool to support young people with type 1 diabetes in efforts to optimize self-care, improve glycemic control, and reduce diabetes distress.

Authors’ Contributions

T.K.M., E.R.W., L.B., and L.M.L. designed the survey. T.K.M. and L.B. extracted data. A.A. and T.K.M. performed statistical analyses. T.K.M. wrote the first draft of the article. E.R.W., L.B., A.A., and L.M.L. reviewed and edited the article. All authors approved the final version of the article. T.K.M. is the guarantor of this article.

Footnotes

Acknowledgments

Patients who participated in this study.

Author Disclosure Statement

The authors have no conflicts of interest.

Funding Information

This work was supported by the Thomas J. Beatson, Jr. Foundation Grant, 2023-019 (T.K.M.) and NIH/NIDDK 5K12DK094721-09 (L.M.L./T.K.M.).

Abbreviations

References

Walker-Harding

, Christie

, Joffe

, et al. Young adult health and well-being: A position statement of the society for adolescent health and medicine. J Adolesc Health, 2017; 60(6):758–759.

Monaghan

, Helgeson

, Wiebe

. Type 1 diabetes in young adulthood. Curr Diabetes Rev, 2015; 11(4):239–250.

Miller

, Foster

, Beck

, et al.; T1D Exchange Clinic Network. Current state of type 1 diabetes treatment in the U.S.: Updated data from the t1d exchange clinic registry. Diabetes Care, 2015; 38(6):971–978.

Smith

, Tamborlane

, DiMeglio

, et al. State of type 1 diabetes management and outcomes from the T1D exchange in 2016–2018. Diabetes Technol Ther, 2019; 21(2):66–72.

Ebekozien

, Mungmode

, Sanchez

, et al. Longitudinal trends in glycemic outcomes and technology use for over 48,000 people with type 1 diabetes (2016–2022) from the T1D exchange quality improvement collaborative. Diabetes Technol Ther, 2023; 25(11):765–773.

Nathan

, DCCT/EDIC Research Group. The diabetes control and complications trial/epidemiology of diabetes interventions and complications study at 30 years: Overview. Diabetes Care, 2014; 37(1):9–16.

Nathan

, Cleary

, Backlund

J-YC

, et al.; Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) Study Research Group. Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes. N Engl J Med, 2005; 353(25):2643–2653.

Hislop

, Fegan

, Schlaeppi

, et al. Prevalence and associations of psychological distress in young adults with type 1 diabetes. Diabet Med, 2008; 25(1):91–96.

Bronner

, Peeters

MAC

, Sattoe

JNT

, et al. The impact of type 1 diabetes on young adults’ health-related quality of life. Health Qual Life Outcomes, 2020; 18(1):137.

10.

Hendrieckx

, Halliday

, Beeney

, et al. Diabetes and emotional health a practical guide for health professionals supporting adults with type 1 or type 2 diabetes. In: American Diabetes Association, 3rd ed.. American Diabetes Association; 2021.

11.

Wentzell

, Alamarie

, Herndon

, et al. 1170-P: Comparative measurement utility of diabetes distress (DD) surveys in adolescents and young adults (AYA) with type 1 diabetes (T1D). Diabetes, 2024; 73(Supplement_1).

12.

Balfe

, Doyle

, Smith

, et al. What’s distressing about having type 1 diabetes? A qualitative study of young adults’ perspectives. BMC Endocr Disord, 2013; 13:25.

13.

O’Hara

, Hynes

, O’Donnell

, et al. A systematic review of interventions to improve outcomes for young adults with type 1 diabetes. Diabet Med, 2017; 34(6):753–769.

14.

Castensøe-Seidenfaden

, Husted

, Jensen

, et al. Testing a smartphone app (young with diabetes) to improve self-management of diabetes over 12 months: Randomized controlled trial. JMIR Mhealth Uhealth, 2018; 6(6):e141.

15.

Goyal

, Nunn

, Rotondi

, et al. A mobile app for the self-management of type 1 diabetes among adolescents: A randomized controlled trial. JMIR Mhealth Uhealth, 2017; 5(6):e82.

16.

Kaushal

, Katz

LEL

, Joseph

, et al. A text messaging intervention with financial incentive for adolescents with type 1 diabetes. J Diabetes Sci Technol, 2022; 16(1):120–127.

17.

Markowitz

, Cousineau

, Franko

, et al. Text messaging intervention for teens and young adults with diabetes. J Diabetes Sci Technol, 2014; 8(5):1029–1034.

18.

Pew Research Center. Americans’ social media use. Pew Research Center; 2024. Available from: https://www.pewresearch.org/internet/2024/01/31/americans-social-media-use/?gad_source=1&gad_campaignid=22378837192&gbraid=0AAAAA-ddO9GisHdcscM3V6Q-XkqXqnRid&gclid=EAIaIQobChMI3N_I_-SjkQMVgKJmAh2GFTk6EAAYASAAEgK61_D_BwE

19.

Olvera

, Stebbins

, Goetz

, et al. TikTok tics: A pandemic within a pandemic. Mov Disord Clin Pract, 2021; 8(8):1200–1205.

20.

Tiggemann

, Slater

. NetGirls: The internet, facebook, and body image concern in adolescent girls. Int J Eat Disord, 2013; 46(6):630–633.

21.

Bekalu

, McCloud

, Viswanath

. Association of social media use with social well-being, positive mental health, and self-rated health: Disentangling routine use from emotional connection to use. Health Educ Behav, 2019; 46(2_suppl):69–80.

22.

Gudka

, Gardiner

KLK

, Lomas

. Towards a framework for flourishing through social media: A systematic review of 118 research studies. J Posit Psychol, 2023; 18(1):86–105.

23.

Beyens

, Pouwels

, van Driel

, et al. The effect of social media on well-being differs from adolescent to adolescent. Sci Rep, 2020; 10(1):10763.

24.

Murthy

. Social Media and Youth Mental Health: The U.S. Surgeon General’s Advisory. US Department of Health and Human Services: Washington, DC; 2023.

25.

Tenderich

, Tenderich

, Barton

, et al. What are PWDs (People with Diabetes) doing online? A netnographic analysis. J Diabetes Sci Technol, 2019; 13(2):187–197.

26.

Holtz

, Kanthawala

. #T1DLooksLikeMe: Exploring self-disclosure, social support, and type 1 diabetes on instagram. Front Commun, 2020; 5:510278.

27.

Stellefson

, Paige

, Apperson

, et al. Social media content analysis of public diabetes facebook groups. J Diabetes Sci Technol, 2019; 13(3):428–438.

28.

Kaushal

, Branka

, Wentzell

, et al. 625-P: Exploring social media as potential support for young adults (YA) with T1D. Diabetes, 2022; 71(Supplement_1):625-P.

29.

Gabarron

, Dorronzoro

, Rivera-Romero

, et al. Diabetes on twitter: A sentiment analysis. J Diabetes Sci Technol, 2019; 13(3):439–444.

30.

Liu

, Mei

, Hanauer

, et al. Use of social media in the diabetes community: An exploratory analysis of diabetes-related tweets. JMIR Diabetes, 2016; 1(2):e4–e8.

31.

Polonsky

, Anderson

, Lohrer

, et al. Assessment of diabetes-related distress. Diabetes Care, 1995; 18(6):754–760.

32.

Harris

, Taylor

, Minor

, et al.; REDCap Consortium. The REDCap consortium: Building an international community of software platform partners. J Biomed Inform, 2019; 95:103208.

33.

Simon Kemp. The time we spend on social media. DataReportal; 2024. Available from: https://datareportal.com/reports/digital-2024-deep-dive-the-time-we-spend-on-social-media

34.

Skiera

. What Gen Z thinks about its social media and smartphone usage. The Harris Poll; 2024. Available from: https://theharrispoll.com/briefs/gen-z-social-media-smart-phones/

35.

Chatty

, Laffel

, Maxwell

. Clinician-percieved benefits and harms of social media use in young people with type 1 diabetes. In: Pediatric Endocrine Society 2025 Annual Meeting. National Harbor, MD; 2025.

36.

Demeterco-Berggren

, Ebekozien

, Noor

, et al. Factors associated with achieving target A1C in children and adolescents with type 1 diabetes: Findings from the T1D exchange quality improvement collaborative. Clin Diabetes, 2022; 41(1):68–75.

37.

Akturk

, Rompicherla

, Rioles

, et al. Factors associated with improved A1C among adults with type 1 diabetes in the United States. Clin Diabetes, 2022; 41(1):76–80.

38.

Shannon

, Bush

, Villeneuve

, et al. Problematic social media use in adolescents and young adults: Systematic review and meta-analysis. JMIR Ment Health, 2022; 9(4):e33450.

39.

Khalaf

, Alubied

, Khalaf

, et al. The impact of social media on the mental health of adolescents and young adults: A systematic review. Cureus, 2023; 15(8):e42990.

40.

Vogels

, Gelles-Watnick

, Massarat

. Teens, social media and technology 2022. Pew Research Center; 2022. Available from: https://www.pewresearch.org/internet/2022/08/10/teens-social-media-and-technology-2022/?gad_source=1&gad_campaignid=22378837192&gbraid=0AAAAA-ddO9GisHdcscM3V6Q-XkqXqnRid&gclid=EAIaIQobChMI4NnUgOyjkQMV56dmAh37Bzb7EAAYAiAAEgI-zfD_BwE

41.

Agarwal

, Kanapka

, Raymond

, et al. Racial-ethnic inequity in young adults with type 1 diabetes. J Clin Endocrinol Metab, 2020; 105(8):E2960–E2969.

42.

Agarwal

, Schechter

, Gonzalez

, et al. Racial-ethnic disparities in diabetes technology use among young adults with type 1 diabetes. Diabetes Technol Ther, 2021; 23(4):306–313.