This paper is in the following e-collection/theme issue:

Involvement of Pediatric Populations in Health and Health Services Research (18) Patient-Reported Outcome Measures (PROMs) (294) Pediatrics (205) Electronic/Mobile Data Capture, Internet-based Survey & Research Methodology (847) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1666) Patient-Reported Outcome Measures (PROMs) (294) Pediatrics (205) Electronic/Mobile Data Capture, Internet-based Survey & Research Methodology (847) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1666) Patient-Reported Outcome Measures (PROMs) (294) Electronic/Mobile Data Capture, Internet-based Survey & Research Methodology (847) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1666) Pediatrics (205)

Published on in Vol 6 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/46555, first published .
Associations Between Patient-Reported Outcome Measures of Physical and Psychological Functioning and Willingness to Share Social Media Data for Research Among Adolescents With a Chronic Rheumatic Disease: Cross-Sectional Survey

Associations Between Patient-Reported Outcome Measures of Physical and Psychological Functioning and Willingness to Share Social Media Data for Research Among Adolescents With a Chronic Rheumatic Disease: Cross-Sectional Survey

Associations Between Patient-Reported Outcome Measures of Physical and Psychological Functioning and Willingness to Share Social Media Data for Research Among Adolescents With a Chronic Rheumatic Disease: Cross-Sectional Survey

Authors of this article:

Elissa R Weitzman1, 2, 3 Author Orcid Image ;   Machiko Minegishi1, 3 Author Orcid Image ;   Rachele Cox1, 3 Author Orcid Image ;   Lauren E Wisk4 Author Orcid Image
Article Authors Cited by (1) Tweetations (2) Metrics

    1Division of Adolescent/Young Adult Medicine, Boston Children’s Hospital, , Boston, MA, , United States

    2Department of Pediatrics, Harvard Medical School, , Boston, MA, , United States

    3Division of Addiction Medicine, Boston Children’s Hospital, , Boston, MA, , United States

    4Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine, University of California Los Angeles, , Los Angeles, CA, , United States

    Corresponding Author:

    Elissa R Weitzman, MSc, ScD


    Background: Social media data may augment understanding of the disease and treatment experiences and quality of life of youth with chronic medical conditions. Little is known about the willingness to share social media data for health research among youth with chronic medical conditions and the differences in health status between sharing and nonsharing youth with chronic medical conditions.

    Objective: We aimed to evaluate the associations between patient-reported measures of disease symptoms and functioning and the willingness to share social media data.

    Methods: Between February 2018 and August 2019, during routine clinic visits, survey data about social media use and the willingness to share social media data (dependent variable) were collected from adolescents in a national rheumatic disease registry. Survey data were analyzed with patient-reported measures of disease symptoms and functioning and a clinical measure of disease activity, which were collected through a parent study. We used descriptive statistics and multivariate logistic regression to compare patient-reported outcomes between youth with chronic medical conditions who opted to share social media data and those who did not opt to share such data.

    Results: Among 112 youths, (age: mean 16.1, SD 1.6 y; female: n=72, 64.3%), 83 (74.1%) agreed to share social media data. Female participants were more likely to share (P=.04). In all, 49 (43.8%) and 28 (25%) participants viewed and posted about rheumatic disease, respectively. Compared to nonsharers, sharers reported lower mobility (T-score: mean 49.0, SD 9.4 vs mean 53.9, SD 8.9; P=.02) and more pain interference (T-score: mean 45.7, SD 8.8 vs mean 40.4, SD 8.0; P=.005), fatigue (T-score: mean 49.1, SD 11.0 vs mean 39.7, SD 9.7; P<.001), depression (T-score: mean 48.1, SD 8.9 vs mean 42.2, SD 8.4; P=.003), and anxiety (T-score: mean 45.2, SD 9.3 vs mean 38.5, SD 7.0; P<.001). In regression analyses adjusted for age, sex, study site, and Physician Global Assessment score, each 1-unit increase in symptoms was associated with greater odds of willingness to share social media data, for measures of pain interference (Adjusted Odds Ratio [AOR] 1.07, 95% CI 1.001-1.14), fatigue (AOR 1.08, 95% CI 1.03-1.13), depression (AOR 1.07, 95% CI 1.01-1.13), and anxiety (AOR 1.10, 95% CI 1.03-1.18).

    Conclusions: High percentages of youth with rheumatic diseases used and were willing to share their social media data for research. Sharers reported worse symptoms and functioning compared to those of nonsharers. Social media may offer a potent information source and engagement pathway for youth with rheumatic diseases, but differences between sharing and nonsharing youth merit consideration when designing studies and evaluating social media–derived findings.

    JMIR Pediatr Parent 2023;6:e46555

    doi:10.2196/46555

    Keywords

    patient-reported outcomes; PROM; outcome measure; outcome measures; patient reported; patient data; social media; sharing; personally generated data; chronic illness; quality of life; rheumatic disease; rheumatic; rheumatoid; adolescent; adolescents; youth; research involvement; privacy; confidentiality; confidential; personal 


    Nearly 1 in 4 US youths are growing up with a chronic illness [1]. Many experience significant levels of disease and treatment burden (eg, pain and medication side effects) that undermine well-being, leading to frequent and costly health care utilization and family financial problems [2]. By adulthood, youth with a chronic illness face increased risks of poor educational, relationship, economic, and health outcomes [3,4]. Life-course risks reflect the complex interplay of disease and treatment experiences and the cumulative effects of social isolation, victimization, school disruption, psychological injury, and home life strain that can accompany chronic illness [5]. Capturing patients’ perspectives about these issues is vital to creating supportive interventions. This is especially true for adolescents, as the biopsychosocial processes of puberty, maturation, and development can impact the course and experience of chronic illness just as chronic illness experiences can impact these processes [6,7]. Patient-centered research with adolescents may advance understanding of these issues [8-10].

    Psychosocial factors contribute to disease symptoms, such as pain and fatigue, among adolescents with a chronic illness [11]. However, we do not know, with regard to the day-to-day lives of adolescents, what issues are the most important to address to disrupt feedback between disease activity and psychosocial health [12-14]. For example, the experience of pain may be exacerbated by feelings of stress and isolation related to a chronic illness, which can be missed by clinicians when making a treatment decision. Discordance between a young patient’s disease experience, including their sense of well-being, and clinical manifestations of disease can stymie and misdirect treatment. This is important for chronic relapsing conditions that may have an unpredictable disease course with periods of flare and dormancy, such as pediatric-onset rheumatic diseases [15-18], which affect 1 in 250 US children younger than 18 years and account for an estimated US $8.3 billion in annual hospital charges [19,20]. Juvenile idiopathic arthritis (JIA) and systemic lupus erythematosus (SLE) are two common forms of pediatric-onset rheumatic disease. JIA is the most common cause of acquired disability in the United States and the fifth most common chronic childhood disease [21]. Youth with JIA report poorer health-related quality of life than that of their peers, even in the setting of low disease activity and after treatment with biologic agents [22-24]. The impacts of JIA persist into adulthood, by which time nearly half of affected youth still experience recurrent or ongoing disease activity, active arthritis, progressive joint destruction, and decreased health-related quality of life [22,25-28]. SLE is a lifelong, chronic, multisystem autoimmune disease; around 15% of persons with SLE developed it in childhood [29], and these persons typically experience severe phenotypes, including organ disease. Youth with SLE may experience secondary morbidities and psychosocial difficulties (eg, mood disorders, body image problems, and academic and social challenges [30]) because, in addition to life-threatening disease manifestations, treatment includes exposure to high doses and prolonged courses of glucocorticoids, as well as cytotoxic agents [31-33].

    Patient-reported outcome (PRO) measures that capture dimensions of well-being can inform understanding of treatment experience and efficacy [33], and studies on the clinical validity of PROs are underway among youth with rheumatic diseases and other conditions [10,33]. Data gleaned from youths’ social media use may serve as an additional source of information about young patients’ experiences of disease and treatment.

    Engaging youth with JIA and SLE in reporting about their health via social media and in sharing their social media data with investigators may provide a channel for learning about youth psychosocial status and physical functioning to complement clinical observations and PROs. Social media data may be obtained passively or actively. In the passive case, social media data may be obtained without youths’ permission or even without their awareness (as when data are programmatically collected and even sold by social media platforms). Additionally, social media data may be obtained actively via approaches that involve permissioned sharing, opt-in settings, and explicit notifications [34-38].

    Regulatory efforts are being enacted to protect the privacy and autonomy of youth in web-based spaces [39,40]. As such, it is vital to understand whether young cohorts are willing to actively share their social media data for health research and whether health status differs between sharing and nonsharing groups [41]. Such insight would (1) clarify the feasibility of engaging youth with rheumatic diseases in sharing social media data for research and (2) help to quantify biases that could arise when relying on active models of collecting data from web-based cohorts. We sought to describe social media use among a clinically characterized cohort of youth with rheumatic diseases and to understand their passive (reading and viewing activities) and active (posting text or images) social media use in relation to these diseases. We further sought to quantify willingness to share social media data for health research and associations between willingness to share and patient-reported experiences of disease symptoms and functioning. We hypothesized that there would be equivalent percentages of youth who would and youth who would not agree to share their social media data for health research under a model of direct observation (ie, friending or otherwise providing access to social media data). Additionally, we hypothesized that the sharing cohort would report fewer symptoms and better functioning compared to those of the nonsharing cohort, potentially reflecting a greater sense of comfort and ease with their health and activities and fewer inhibitions about revealing any vulnerabilities. To date, few studies have been able to link personally generated data from social media with PROs and clinical data [42] to elucidate biases relevant to establishing the validity of social media data for health research—a recognized need [43,44].


    Overview

    Among adolescents with JIA or SLE who were members of the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry [45,46] and enrolled in a prospective multisite study to clinically validate PRO measures [33], we investigated associations between social media use and willingness to share social media data for research. Survey reports were collected via a tablet computer by using the REDCap (Research Electronic Data Capture; Vanderbilt University) secure web application [47,48] at regularly scheduled clinics visits, during which PRO and clinical data were also collected.

    Ethical Considerations

    A small stipend was provided to participants in the form of a US $20 gift card. Trained research assistants obtained in-person informed assent and assigned participants a unique study ID that was linked to the ID used for the clinical validation study and registry to ensure confidentiality. The study protocol was reviewed and approved by the Boston Children’s Hospital institutional review board (protocol number: IRB-P00025665).

    Setting and Sample

    Adolescents were eligible if they were members of the CARRA Registry, were diagnosed with JIA or SLE, enrolled in the parent prospective clinical validation study [33], and were at 1 of 3 validation study sites that participated in this substudy. Additional eligibility criteria were an age of 13 to 18 years, the ability to complete the survey in English on a tablet computer, and the reported use of at least 1 of 4 popular social media platforms (Facebook, Twitter, Instagram, or Snapchat) in the past 30 days. Patients were ineligible if they were medically or emotionally unstable or were otherwise unable to assent, as determined by a clinician or site research team member; were unable to speak or read English at an eighth-grade reading level; or did not attend the data collection visit (absent at recruitment).

    Of the 145 patients approached, 123 consented (84.8%), of whom 6 were excluded because they were absent during data collection or reported Patient-Reported Outcomes Measurement Information System (PROMIS) measures at a time that did not overlap social media data collection. Of the remaining 117 patients, 5 did not use Facebook, Twitter, Instagram, or Snapchat and were excluded, leaving an analytic sample of 112 (91.1%; Figure 1).

    ‎
    Figure 1. CONSORT (Consolidated Standards of Reporting Trials) diagram of study sample.

    Sources of Study Data and Measures

    Clinical and demographic data were drawn from the CARRA Registry. PRO and social media survey data were collected electronically by using wireless touch screen tablets during the clinic visit. Social demographic measures included race, Hispanic ethnicity, sex at birth, date of birth, insurance status, and the highest education attained by a parent. Clinical characteristics included the study recruitment and treatment site, the Physician Global Assessment (PGA) score [49], a 10-point visual analog scale score (a value of ≥1 represented active disease), BMI, and disease duration in months.

    PROMIS Pediatric measures [50,51] included short-form measures of fatigue, mobility, pain interference, depressive symptoms, anxiety, and meaning and purpose, which were administered by using computer-assisted technology [10,33]. Higher PROMIS symptom T-scores reflect worse symptom levels, and higher functioning scores reflect better functioning. PROMIS measures are designed such that the mean score of the relevant reference population (ie, healthy youth) is 50, with an SD of 10 [52]. A 3-point difference in the PROMIS Pediatric T-score metric is considered a minimally important difference [53].

    Willingness to share social media data via direct observation by the research team was assessed for Facebook, Instagram, Twitter, and Snapchat. Willingness measures are summarized in Table 1, along with related measures of motivation to share social media data, reasons for not sharing, and passive and active use patterns.

    Table 1. Measures of willingness to share social media data.
    QuestionResponse optionsAnswers
    Willingness to share social media
    “Are you willing to share your social media posts from the following site(s) for the two-week time interval around this study visit and your next study visit? This involves ‘friending’ the study account so the study team can view your posts. We will not message, ‘like,’ post on, or interact with your accounts”
    • Facebook
    • Twitter
    • Instagram
    • Snapchat
    1. Yes
    2. No
    3. Prefer not to answer
    “(If YES to sharing any SM with the study) How much do you agree with the following statements regarding your motivations for sharing your social media data with this study?”
    • I will be able to help other patients with rheumatic conditions
    • I am interested in research
    • I am interested in social media and technology
    • Participating in this research makes me feel valued
    • The $20a gift card incentivized me to participate in this research
    • I have some other motivation for sharing my social media data with this study
    1. Strongly agree
    2. Agree
    3. Disagree
    4. Strongly disagree
    5. Prefer not to answer
    Frequency of social media use
    “About how often do you visit OR use the following social media sites?”
    • Facebook
    • Twitter
    • Instagram
    • Snapchat
    1. Several times a day
    2. About once a day
    3. A few times a week
    4. Every few weeks
    5. Less often
    6. I do not use this site
    7. Prefer not to answer
    “How often do you VIEW/READ about other people who have a rheumatic condition on any of the following sites?”
    • Facebook
    • Twitter
    • Instagram
    • Snapchat
    1. Often
    2. Sometimes
    3. Rarely
    4. Never
    5. Prefer not to answer
    “Have you ever POSTED about your rheumatic condition on social media?”
    1. Yes
    2. No
    3. Prefer not to answer
    Benefits of viewing/reading about others with RDc
    “VIEWING/READING about other people who have a rheumatic condition on social media…”
    • Helps me to feel less alone with my rheumatic condition
    • Helps me to talk to my friends about my rheumatic condition
    • Helps me to feel more prepared when talking to my doctor/care team about my rheumatic condition
    • Provides me with information about my condition that I can understand
    • Helps me to talk to my parents or guardians about my rheumatic condition
    • Provides me with information about treatments for my rheumatic condition
    • Provides me with health information that my doctors/care team cannot provide
    • Provides me with health information that I cannot find anywhere else
    1. Strongly agree
    2. Agree
    3. Disagree
    4. Strongly disagree
    5. Prefer not to answer
    Motivations for posting
    “How much do the following reasons motivate you to POST/SHARE about your condition on social media?”
    • I want to feel understood
    • I want to help or provide support to other people living with a rheumatic condition or any chronic health condition
    • I want to connect with others living with a rheumatic condition or any chronic health condition
    • I want to share my experiences with a community that believes me
    • I want to update my friends/family members about my rheumatic condition
    • I want to get help or support from others who are living with a rheumatic condition or any chronic health condition
    • I want to share my thoughts/feelings when my rheumatic condition is under good control
    • I want to share my thoughts/feelings when I am experiencing disease symptoms
    1. A great deal
    2. Somewhat
    3. Very little
    4. Not at all
    5. Prefer not to answer
    “How important to you are the following reasons when you are making decisions NOT to POST/SHARE about your rheumatic condition?”
    • My rheumatic condition does not define me
    • I do not want others to feel bad for me because of my rheumatic condition
    • I do not want to disclose my diagnosis public on the internet
    • My rheumatic condition is not serious enough for me to post about it on social media
    • I worry about others knowing too much about my health
    • I do not want my friends to find out how I am feeling or doing
    • People might make fun of me, or I might get teased/bullied
    • I do not want my parents or guardians to find out how I am feeling or doing
    1. A great deal
    2. Somewhat
    3. Very little
    4. Not at all
    5. Prefer not to answer

    aUS $20.

    bN/A: not applicable.

    cRD: rheumatic disease.

    Data Analyses

    Summary statistics were computed to characterize the study sample overall and by willingness to share social media data for research. The differences in demographic characteristics based on willingness to share social media data were analyzed by using appropriate statistical tests, including the Kruskal-Wallis test, 2-tailed t test, 2-sided Fisher exact test, and chi-square test. For 2 participants with JIA and 1 patient with SLE, we imputed missing values on the PGA by using the median score for their disease group and similarly imputed missing BMI values for 2 participants with JIA. In separate multivariable logistic regressions, we assessed the associations between willingness to share social media data for research (the dependent variable) and each PROMIS measure. All models controlled for age, biological sex, study site, and PGA score. The analyses were conducted by using SAS 9.4 (SAS Institute) [54]. Statistical significance was considered at P<.05.


    Sample Characteristics

    Of the 112 participants, 98 (87.5%) were persons with JIA and 14 (12.5%) were persons with SLE. Overall, participants were aged 13 to 18 (mean 16.1, SD 1.6) years, 72 (64.3%) were female, 86 (76.8%) were White, 105 (93.8%) were non-Hispanic, and 101 (90.2%) had private insurance. For all, the average BMI was 23.2 (SD 4.3) kg/m2, and the average PGA score was 0.8 (SD 1.5), indicating inactive disease.

    Willingness to share social media data was reported by a majority (83/112, 74.1%) of participants. In all, 43.8% (49/112) reported viewing or reading about others with rheumatic diseases on social media, and 25% (28/112) reported posting about rheumatic disease (Table 1).

    Association Between Willingness to Share Social Media Data and Health Status

    Willingness to share social media data was associated with female sex (P=.04) and greater disease activity (P=.04), which was measured as a mean PGA score (Table 2). Compared to nonsharers, sharers reported lower mobility (T-score: mean 49.0, SD 9.4 vs mean 53.9, SD 8.9; P=.02), greater pain interference (T-score: mean 45.7, SD 8.8 vs mean 40.4, SD 8.0; P=.005), more fatigue (T-score: mean 49.1, SD 11.0 vs mean 39.7, SD 9.7; P<.001), more depression (T-score: mean 48.1, SD 8.9 vs mean 42.2, SD 8.4; P=.003), and greater anxiety (T-score: mean 45.2, SD 9.3 vs mean 38.5, SD 7.0; P<.001).

    In logistic regression analyses that controlled for age, sex, study site, and PGA score, each 1-unit increase in symptoms was associated with greater odds of willingness to share social media data, for measures of pain interference (Adjusted Odds Ratio [AOR] 1.07, 95% CI 1.001-1.14), fatigue (AOR 1.08, 95% CI 1.03-1.13), depression (AOR 1.07, 95% CI 1.01-1.13), and anxiety (AOR 1.10, 95% CI 1.03-1.18; Table 3).

    Table 2. Characteristics of the sample by willingness to share social media.
    CharacteristicsAll participants (N=112)Social media sharingP value
    Yes (n=83, 74.1%)No (n=29, 25.9%)
    Age (years), mean (SD)16.1 (1.6)16.2 (1.6)15.9 (1.5).34
    Biological sex, n (%a).04
    Male40 (35.7)25 (30.1)15 (51.7)
    Female72 (64.3)58 (69.9)14 (48.3)
    Race, n (%a).51
    White86 (76.7)65 (78.3)21 (72.4)
    Asian5 (4.5)2 (2.4)3 (10.3)
    African American2 (1.8)2 (2.4)0 (0)
    Mixed race4 (3.6)3 (4.6)1 (3.4)
    Other raceb6 (5.4)5 (6)1 (3.4)
    Unknown9 (8)6 (7.2)3 (10.3)
    Ethnicity, n (%a).67
    Hispanic7 (6.3)6 (7.2)1 (3.4)
    Non-Hispanic105 (93.8)77 (92.8)28 (96.6)
    Parental education, n (%a).10
    Less than a college degree2 (1.8)0 (0)2 (6.9)
    College degree or higher39 (34.8)29 (34.9)10 (34.5)
    Prefer not to answer or missing71 (63.4)54 (65.1)17 (58.6)
    Insurance, n (%a).15
    Private health insurance101 (90.2)77 (92.8)24 (82.8)
    Government insurance or other11 (9.8)6 (7.2)5 (17.2)
    Rheumatic disease diagnosis, n (%a).19
    Systemic lupus erythematosus14 (12.5)8 (9.6)6 (20.6)
    Juvenile idiopathic arthritis98 (87.5)75 (90.4)23 (79.3)
    Health characteristics, mean (SD)
    BMI (kg/m2)23.2 (4.3)23.3 (4.3)22.7 (4.6).39
    Disease duration (months)84.6 (53.8)84.2 (52.9)85.6 (57.1)>.99
    Physician Global Assessment (score)0.8 (1.5)1.0 (1.6)0.5 (1.1).04
    PROMIScPediatric measure (T-score)
    Mobility50.3 (9.5)49.0 (9.4)53.9 (8.9).02
    Pain interference44.3 (8.9)45.7 (8.8)40.4 (8.0).005
    Fatigue46.7 (11.4)49.1 (11.0)39.7 (9.7)<.001
    Depressive symptoms46.6 (9.1)48.1 (8.9)42.2 (8.4).003
    Anxiety43.5 (9.2)45.2 (9.3)38.5 (7.0)<.001
    Meaning and purposed47.4 (8.3)47.4 (8.6)47.2 (7.4).91
    Passive social media use, n (%a)49 (43.8)39 (47)10 (34.5).24
    Active social media use, n (%a)28 (25)23 (27.7)5 (17.2).26

    aColumn percentages are shown (ie, the percentages were calculated by using the n values presented in the “All Participants” [N=112], “Yes” [n=83], and “No” [n=29] headings as the denominators).

    bIncludes Middle Eastern or North African, Native American, American Indian, Alaska Native, Native Hawaiian, or other Pacific Islander.

    cPROMIS: Patient-Reported Outcomes Measurement Information System.

    dPROMIS measures for meaning and purpose were assessed among a total of 105 participants, including 80 (76.2%) in the “Yes” social media sharing group. Out of 112 total participants, 7 were not administered the PROMIS Meaning and Purpose survey during the parent study visit for reasons of timing.

    Table 3. Associations between PROMISa Pediatric measures of physical functioning, symptoms, and psychosocial well-being and social media sharing (N=112).b
    PROMIS Pediatric measureAdjusted odds ratioc (95% CI)
    Mobility0.95 (0.89-1.003)
    Pain inference1.07 (1.001-1.14)
    Fatigue1.08 (1.03-1.13)
    Depressive symptoms1.07 (1.01-1.13)
    Anxiety1.10 (1.03-1.18)
    Meaning and purposed1.01 (0.95-1.07)

    aPROMIS: Patient-Reported Outcomes Measurement Information System.

    bOutcome: sharing social media contents (reference: not sharing social media); exposure: 1-unit increase in the PROMIS.

    cAdjusted models controlled for the participants’ age, sex, study sites, and Physician Global Assessment score. The reference group for the model is the participant group that was not willing to share the contents of any of their social media platforms with the researchers for this study. For each model, each individual PROMIS measure was entered independently, so these estimates do not reflect adjustment for other PROMIS measures.

    dPROMIS measures for meaning and purpose were assessed among a total of 105 participants, including 80 (76.2%) in the “Yes” social media sharing group. Out of 112 total participants, 7 were not administered the PROMIS Meaning and Purpose survey during the parent study visit for reasons of timing.

    Social Media Use and Value for Youth With Rheumatic Disease

    The use of Instagram, Snapchat, Facebook, and Twitter was reported by 94.6% (106/112), 83.9% (94/112), 42.9% (48/112), and 31.3% (35/112) of participants, respectively, and poly-platform use was reported by 84.8% (95/112) of participants. More than two-fifths of participants (49/112, 43.8%) reported passive social media use, that is, reading about others with rheumatic diseases, while one-quarter (28/112, 25%) reported active social media use, that is, posting about rheumatic disease. Passive and active use patterns did not differ by age or by diagnosis; however, larger percentages of female participants than male participants reported both passive and active rheumatic disease–related social media activity (Table 4).

    Table 4. Sample characteristics by passive or active social media use.
    CharacteristicsAll participants (N=112)Read about others with RDa on SMbPost about RD on SM
    Yes (n=49, 43.8%)No (n=63, 56.3%)P valueYes (n=28, 25%)No (n=84, 75%)P value
    Age (years), mean (SD)16.1 (1.6)16.3 (1.6)16.0 (1.6).3216.3 (1.6)16.1 (1.6).61
    Biological sex, n (%)
    Male40 (35.7c)9 (22.5d)31 (77.5d)<.0012 (5d)38 (95d)<.001
    Female72 (64.3c)40 (55.6d)32 (44.4d)N/Ae26 (36.1d)46 (63.9d)N/A
    Rheumatic disease diagnosis, n (%)
    Systemic lupus erythematosus14 (12.5c)9 (64.3d)5 (35.7d).105 (35.7d)9 (64.3d).33
    Juvenile idiopathic arthritis98 (87.5c)40 (40.8d)58 (59.2d)N/A23 (23.5d)75 (76.5d)N/A
    BMI (kg/m2), mean (SD)23.2 (4.3)23.3 (4.3)23.0 (4.4).7824.4 (4.8)22.7 (4.1).12
    Disease duration (months), mean (SD)84.6 (53.8)76.9 (49.2)90.5 (56.7).2183.2 (48.7)85.0 (55.6).96
    Physical Global Assessment (score), mean (SD)0.8 (1.5)0.8 (1.2)0.8 (1.7).230.6 (0.7)0.9 (1.6).99

    aRD: rheumatic disease.

    bSM: social media.

    cThis percentage was calculated by using the total number of participants (N=112) as the denominator.

    dA row percentage is presented (ie, the n value in the corresponding “All Participants” row total was used as the denominator).

    eN/A: not applicable.

    Among 49 youths who reported passive disease-related use of social media, there were high levels of agreement that such use is helpful for observational learning from others and for alleviating feelings of isolation (Figure 2). Similarly, many reported that passive use increases their access to understandable information and helps them feel prepared in speaking with family and their care team about rheumatic disease. Of the 28 social media users who reported that they use social media actively to post about rheumatic disease, many reported doing so to feel understood, support and connect with others with rheumatic diseases, and share experiences with family and the rheumatic disease community (Figure 3). Among all social media users, a plurality endorsed the importance of not defining themselves by their condition and not wanting others to feel badly for them because of their condition, reporting these as reasons for not posting about their condition. Others refrained from posting about their condition to avoid public disclosure, retain privacy, and protect themselves from others’ attention or because they considered their condition insufficiently serious to merit attention (Figure 4). Finally, participants who shared their social media data reported they were motivated to do so because they were interested in research or technology, out of altruism toward other patients with the same conditions, to feel valued, and to receive a stipend (Figure 5).

    ‎
    Figure 2. Responses for the following measure: “VIEWING/READING about other people who have a rheumatic condition on social media.” RD: rheumatic disease.
    ‎
    Figure 3. Responses for the following measure: “How much do the following reasons motivate you to POST/SHARE about your condition on social media?” RD: rheumatic disease.
    ‎
    Figure 4. Responses to the following measure: “How important to you are the following reasons when you are making decisions NOT to POST/SHARE about your rheumatic condition?” RD: rheumatic disease.
    ‎
    Figure 5. Responses to the following measure: “How much do you agree with the following statements regarding your motivations for sharing your social media data with this study?” RD: rheumatic disease.

    Principal Findings

    In this multisite cohort study centered on adolescents with rheumatic diseases, we found high levels of willingness to share social media data for health research. Contrary to our original hypothesis that willingness to share would be associated with lower levels of symptoms and better psychosocial status, sharing was associated with higher levels of disease-related symptoms, specifically pain interference and fatigue, and higher levels of depression and anxiety. Both passive and active disease-related use of social media were reported, and larger percentages of female participants than male participants engaged in these activities. Passive and active social media use patterns were motivated by participants’ goals regarding observational learning about their condition and connection to others with similar conditions, as well as by the exchange of social support. Motivations to refrain from posting about rheumatic disease reflected participants’ goals of wanting to avoid having their condition define them, remaining private, and protecting against disclosure and ridicule. Among participants who agreed to share their social media data for research (n=83), almost all did so out of interest in research (83/83, 100%), to help others with a rheumatic condition (80/83, 96%), and because they felt it was of value personally (70/83, 84%) and financially (ie, for compensation; 59/83, 71%).

    A growing body of research indicates the potential for improving adolescent and young adult health behaviors and outcomes (eg, health food consumption, reduced BMI, and reduced tobacco use) through engagement with social media and related features for peer groups and messaging [55-57]. Nevertheless, understanding of the potential for using social media as a source of health information and as a platform for research engagement is constrained by the lack of insight into the differences in health status between persons who are and persons who are not willing to share their data. For participatory surveillance models, some evidence shows that greater sharing and openness exist among early adopters of research apps and among persons whose disease is better controlled [58]. Other studies have found less reticence to share digital health data for care improvement among technology users who report lower incomes when compared to those who report higher incomes [59].

    This study adds to what is known about willingness to share social media, with the added advantage of assessing reports from a clinically characterized cohort whose actual sharing was directly observed, differing from studies on hypothetical willingness, which are more common [60-62]. Few studies of social media use and social media data sharing provide access to linked clinical data [42] or structured PROs, and to our knowledge, none have been undertaken among youth with rheumatic diseases. Research with teenagers is vital, since this group is assuming control over their own health care, health information, and social media, and as a group, teenagers are both heavily engaged with social media [63] and uniquely vulnerable to social influences communicated through web-based channels [58,59,64,65].

    Our findings of differences between sharing and nonsharing cohorts have implications for the use of personally generated data from web-based cohorts. In this study, sharing was associated with worse health. It is not clear what explains differences in health status between sharing and nonsharing youth. It may be that youth with rheumatic diseases who are unwilling to share their social media data are more socially engaged in ways that they consider unsuitable or too sensitive to allow sharing. Alternatively, they may be less desirous of research attention if they feel well and are able to satisfy their social needs through offline means. Future work may help elucidate reasons for observed differences. Similarly, future work with youth affected by other conditions is merited to understand whether differences between sharing and nonsharing groups hold.

    The findings from this study have larger implications for social media–related research. First, investigators who use social media platforms to engage adolescents in health research should be explicit about the potential for biases related to inferences when denominators are poorly specified or are unknown [41,66]. The results from this study suggest that observations about the health of youth with rheumatic diseases drawn from social media–engaged cohorts may be skewed toward describing youth with greater symptoms and worse psychosocial health. This is a limitation when the goal of a study is to understand the entirety of a patient population but may be an advantage when the aim of a study is to engage youth who are struggling. The internet and social media serve important supportive functions for youth with a chronic illness, of whom many have been disproportionately adversely affected by social isolation and the hardships of the recent COVID-19 pandemic [67]. Second, findings regarding the high value placed by youth with rheumatic diseases on obtaining social and informational support related to their condition from others on the web is revealing of the seriousness of the gaps in support available from organized health care systems, as others have reported [68]. As youth turn to social media for support to fill these gaps, it remains important to consider the accuracy and safety of information offered by web-based peers, accessibility to youth across a range of health literacy and technology access levels, and the potential for harm from exposure to misinformation. Steps for detecting and addressing this during clinical visits might include asking young patients about their need for information and support and their ability to access web-based resources, as well as providing links and pointers to reliable, vetted sources of web-based guidance. These “low-tech” strategies can be implemented among even more computationally sophisticated systems for evaluating and improving the quality and accuracy of web-based information.

    Limitations

    This report draws from a convenience sample of youth, and generalizability is limited by several factors, including the focus on youth with rheumatic diseases who have access to and use the internet and social media. The study cohort consisted of youth who were previously enrolled in research, and our findings may not generalize to youth who refrain from or have limited access to research opportunities. Sociodemographic diversity is also limited. However, the clinical confirmation of disease status and availability of validated health measures (eg, PROMIS measures) are strengths. Nevertheless, the results may not generalize to youth at other clinical sites, a broader sample of youth with rheumatic diseases, youth with other chronic conditions, or youth who do not use social media. This study did not assess participants’ understanding of health information or their ability to discern information quality or misinformation. The cross-sectional nature of this study precludes causal interpretation.

    Conclusions

    We found high willingness to share social media data for health research among a clinically characterized cohort of adolescents with rheumatic diseases and substantial use of social media for disease-related observational learning and social connection. Differences in health status between sharing and nonsharing youth (as well as between social media users and nonusers) underscore the importance of considering the potential for biases in research results that rely on social media data and the importance of identifying opportunities to engage and improve the health of youth who may be on the web and in need of support.

    Acknowledgments

    The authors would like to acknowledge Kara Magane and Alexandra Marin for help with study planning and Sydney Pierce and Laura Blakemore for help with manuscript preparation. We thank Dr Fatma Dedeoglu, Dr Bryce B Reeve, Dr Sarah Ringold, Dr Laura E Schanberg, Dr Richard Vehe, and Dr Emily von Scheven for providing access to study registry and patient-reported outcome data. The authors wish to acknowledge the Childhood Arthritis and Rheumatology Research Alliance (CARRA) and the ongoing Arthritis Foundation financial support of CARRA. This work was funded by the National Institute of Arthritis and Musculoskeletal and Skin Diseases/National Institutes of Health (NIAMS/NIH; R21AR070944-01A1).

    Conflicts of Interest

    None declared.

    1. NSCH 2018 19: number of current or lifelong health conditions, nationwide, age in 3 groups. National Survey of Children’s Health Data Resource Center for Child & Adolescent Health. URL: nschdata.org/browse/survey/results?q=7625&g=787 [accessed 2023-10-23]
    2. /pub/Health_Statistics/NCHS/slaits_cshcn_survey/2009_2010/datasets/. Centers for Disease Control and Prevention. URL: ftp.cdc.gov/pub/Health_Statistics/NCHS/slaits_cshcn_survey/2009_2010/datasets/ [accessed 2023-11-14]
    3. Wisk LE, Weitzman ER. Expectancy and achievement gaps in educational attainment and subsequent adverse health effects among adolescents with and without chronic medical conditions. J Adolesc Health. 2017 Oct;61(4):461-470 [CrossRef] [Medline]
    4. Wisk LE, Weitzman ER. Social relationships and associated health effects among adolescents with and without chronic medical conditions. J Adolesc Health. 2017 Feb;60(2 suppl 1):S19-S20 [CrossRef]
    5. Compas BE, Jaser SS, Dunn MJ, Rodriguez EM. Coping with chronic illness in childhood and adolescence. Annu Rev Clin Psychol. 2012;8:455-480 [CrossRef] [Medline]
    6. Michaud PA, Suris JC, Viner R. The adolescent with a chronic condition: epidemiology, developmental issues and health care provision. World Health Organization. 2007 Mar 28. URL: iris.who.int/bitstream/handle/10665/43775/9789241595704_eng.pdf?sequence=1 [accessed 2023-10-23]
    7. April KT, Cavallo S, Feldman DE. Children with juvenile idiopathic arthritis: are health outcomes better for those diagnosed younger? Child Care Health Dev. 2013 May;39(3):442-448 [CrossRef] [Medline]
    8. Lavallee DC, Chenok KE, Love RM, et al. Incorporating patient-reported outcomes into health care to engage patients and enhance care. Health Aff (Millwood). 2016 Apr;35(4):575-582 [CrossRef] [Medline]
    9. Nap-van der Vlist MM, Hoefnagels JW, Dalmeijer GW, et al. The PROactive cohort study: rationale, design, and study procedures. Eur J Epidemiol. 2022 Sep;37(9):993-1002 [CrossRef] [Medline]
    10. Forrest CB, Schuchard J, Bruno C, et al. Self-reported health outcomes of children and youth with 10 chronic diseases. J Pediatr. 2022 Jul;246:207-212.e1 [CrossRef] [Medline]
    11. Fair DC, Rodriguez M, Knight AM, Rubinstein TB. Depression and anxiety in patients with juvenile idiopathic arthritis: current insights and impact on quality of life, a systematic review. Open Access Rheumatol. 2019 Nov 1;11:237-252 [CrossRef] [Medline]
    12. Weiss JE, Luca NJC, Boneparth A, Stinson J. Assessment and management of pain in juvenile idiopathic arthritis. Paediatr Drugs. 2014 Dec;16(6):473-481 [CrossRef] [Medline]
    13. Luca NJC, Feldman BM. Health outcomes of pediatric rheumatic diseases. Best Pract Res Clin Rheumatol. 2014 Apr;28(2):331-350 [CrossRef] [Medline]
    14. La Hausse de Lalouvière L, Ioannou Y, Fitzgerald M. Neural mechanisms underlying the pain of juvenile idiopathic arthritis. Nat Rev Rheumatol. 2014 Apr;10(4):205-211 [CrossRef] [Medline]
    15. Wallace CA, Huang B, Bandeira M, Ravelli A, Giannini EH. Patterns of clinical remission in select categories of juvenile idiopathic arthritis. Arthritis Rheum. 2005 Nov;52(11):3554-3562 [CrossRef] [Medline]
    16. Ringold S, Seidel KD, Koepsell TD, Wallace CA. Inactive disease in polyarticular juvenile idiopathic arthritis: current patterns and associations. Rheumatology (Oxford). 2009 Aug;48(8):972-977 [CrossRef] [Medline]
    17. Magni-Manzoni S, Pistorio A, Labò E, et al. A longitudinal analysis of physical functional disability over the course of juvenile idiopathic arthritis. Ann Rheum Dis. 2008 Aug;67(8):1159-1164 [CrossRef] [Medline]
    18. Kamphuis S, Silverman ED. Prevalence and burden of pediatric-onset systemic lupus erythematosus. Nat Rev Rheumatol. 2010 Sep;6(9):538-546 [CrossRef] [Medline]
    19. Hersh AO, Watkins-Castillo SI. Juvenile arthritis. The Burden of Musculoskeletal Diseases in the United States. URL: www.boneandjointburden.org/fourth-edition/iiib60/juvenile-arthritis [accessed 2023-02-15]
    20. Juvenile arthritis treatment & diagnosis. National Institute of Arthritis and Musculoskeletal and Skin Diseases. URL: www.niams.nih.gov/health-topics/juvenile-arthritis [accessed 2023-02-15]
    21. Sacks JJ, Helmick CG, Luo YH, Ilowite NT, Bowyer S. Prevalence of and annual ambulatory health care visits for pediatric arthritis and other rheumatologic conditions in the United States in 2001-2004. Arthritis Rheum. 2007 Dec 15;57(8):1439-1445 [CrossRef] [Medline]
    22. Seid M, Opipari L, Huang B, Brunner HI, Lovell DJ. Disease control and health-related quality of life in juvenile idiopathic arthritis. Arthritis Rheum. 2009 Mar 15;61(3):393-399 [CrossRef] [Medline]
    23. Haverman L, Grootenhuis MA, van den Berg JM, et al. Predictors of health-related quality of life in children and adolescents with juvenile idiopathic arthritis: results from a web-based survey. Arthritis Care Res (Hoboken). 2012 May;64(5):694-703 [CrossRef] [Medline]
    24. Gutiérrez-Suárez R, Pistorio A, Cruz AC, et al. Health-related quality of life of patients with juvenile idiopathic arthritis coming from 3 different geographic areas: the PRINTO multinational quality of life cohort study. Rheumatology (Oxford). 2007 Feb;46(2):314-320 [CrossRef] [Medline]
    25. Minden K, Niewerth M, Listing J, et al. Long-term outcome in patients with juvenile idiopathic arthritis. Arthritis Rheum. 2002 Sep;46(9):2392-2401 [CrossRef] [Medline]
    26. Oen K, Malleson PN, Cabral DA, Rosenberg AM, Petty RE, Cheang M. Disease course and outcome of juvenile rheumatoid arthritis in a multicenter cohort. J Rheumatol. 2002 Sep;29(9):1989-1999 [Medline]
    27. Packham JC, Hall MA, Pimm TJ. Long-term follow-up of 246 adults with juvenile idiopathic arthritis: predictive factors for mood and pain. Rheumatology (Oxford). 2002 Dec;41(12):1444-1449 [CrossRef] [Medline]
    28. Zak M, Pedersen FK. Juvenile chronic arthritis into adulthood: a long-term follow-up study. Rheumatology (Oxford). 2000 Feb;39(2):198-204 [CrossRef] [Medline]
    29. Tucker LB, Menon S, Schaller JG, Isenberg DA. Adult- and childhood-onset systemic lupus erythematosus: a comparison of onset, clinical features, serology, and outcome. Br J Rheumatol. 1995 Sep;34(9):866-872 [CrossRef] [Medline]
    30. Meacock R, Dale N, Harrison MJ. The humanistic and economic burden of systemic lupus erythematosus: a systematic review. Pharmacoeconomics. 2013 Jan;31(1):49-61 [CrossRef] [Medline]
    31. Vazzana KM, Daga A, Goilav B, et al. Principles of pediatric lupus nephritis in a prospective contemporary multi-center cohort. Lupus. 2021 Sep;30(10):1660-1670 [CrossRef] [Medline]
    32. Chaigne B, Chizzolini C, Perneger T, et al. Impact of disease activity on health-related quality of life in systemic lupus erythematosus - a cross-sectional analysis of the Swiss Systemic Lupus Erythematosus Cohort Study (SSCS). BMC Immunol. 2017 Mar 28;18(1):17 [CrossRef] [Medline]
    33. Weitzman ER, Gaultney A, von Scheven E, et al. Construct validity of patient-reported outcomes measurement information system paediatric measures in juvenile idiopathic arthritis and systemic lupus erythematosus: cross-sectional evaluation. BMJ Open. 2023 Jan 27;13(1):e063675 [CrossRef] [Medline]
    34. Ancker JS, Witteman HO, Hafeez B, Provencher T, Van de Graaf M, Wei E. “You get reminded you're a sick person”: personal data tracking and patients with multiple chronic conditions. J Med Internet Res. 2015 Aug 19;17(8):e202 [CrossRef] [Medline]
    35. Wong CA, Merchant RM, Moreno MA. Using social media to engage adolescents and young adults with their health. Healthc (Amst). 2014 Dec;2(4):220-224 [CrossRef] [Medline]
    36. Hausmann JS, Weitzman ER. The promises and perils of social media for pediatric rheumatology. Rheum Dis Clin North Am. 2022 Feb;48(1):233-243 [CrossRef] [Medline]
    37. Wong CA, Madanay F, Ozer EM, et al. Digital health technology to enhance adolescent and young adult clinical preventive services: affordances and challenges. J Adolesc Health. 2020 Aug;67(2S):S24-S33 [CrossRef] [Medline]
    38. Eggleston EM, Weitzman ER. Innovative uses of electronic health records and social media for public health surveillance. Curr Diab Rep. 2014 Mar;14(3):468 [CrossRef] [Medline]
    39. Online privacy and safety. European Commission: shaping Europe's digital future. URL: digital-strategy.ec.europa.eu/en/policies/online-privacy [accessed 2023-06-14]
    40. Children’s Online Privacy Protection Rule ("COPPA"). Federal Trade Commission. URL: www.ftc.gov/legal-library/browse/rules/childrens-online-privacy-protection-rule-coppa [accessed 2023-07-19]
    41. Chunara R, Wisk LE, Weitzman ER. Denominator issues for personally generated data in population health monitoring. Am J Prev Med. 2017 Apr;52(4):549-553 [CrossRef] [Medline]
    42. Padrez KA, Ungar L, Schwartz HA, et al. Linking social media and medical record data: a study of adults presenting to an academic, urban emergency department. BMJ Qual Saf. 2016 Jun;25(6):414-423 [CrossRef] [Medline]
    43. Peat G, Rodriguez A, Smith J. Social media use in adolescents and young adults with serious illnesses: an integrative review. BMJ Support Palliat Care. 2019 Sep;9(3):235-244 [CrossRef] [Medline]
    44. Park E, Kwon M. Health-related internet use by children and adolescents: systematic review. J Med Internet Res. 2018 Apr 3;20(4):e120 [CrossRef] [Medline]
    45. Childhood Arthritis and Rheumatology Research Alliance. CARRA. URL: carragroup.org/ [accessed 2023-10-23]
    46. Fuhlbrigge RC, Schanberg LE, Kimura Y. CARRA: the Childhood Arthritis and Rheumatology Research Alliance. Rheum Dis Clin North Am. 2021 Nov;47(4):531-543 [CrossRef] [Medline]
    47. Harris PA, Taylor R, Minor BL, et al. The REDCap consortium: building an international community of software platform partners. J Biomed Inform. 2019 Jul;95:103208 [CrossRef] [Medline]
    48. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap) – a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009 Apr;42(2):377-381 [CrossRef] [Medline]
    49. Nikiphorou E, Radner H, Chatzidionysiou K, et al. Patient global assessment in measuring disease activity in rheumatoid arthritis: a review of the literature. Arthritis Res Ther. 2016 Oct 28;18(1):251 [CrossRef] [Medline]
    50. Reeve BB, Hays RD, Bjorner JB, et al. Psychometric evaluation and calibration of health-related quality of life item banks: plans for the Patient-Reported Outcomes Measurement Information System (PROMIS). Med Care. 2007 May;45(5 Suppl 1):S22-S31 [CrossRef] [Medline]
    51. Broderick JE, DeWitt EM, Rothrock N, Crane PK, Forrest CB. Advances in patient-reported outcomes: the NIH PROMIS(®) measures. EGEMS (Wash DC). 2013 Aug 2;1(1):1015 [CrossRef] [Medline]
    52. Carle AC, Bevans KB, Tucker CA, Forrest CB. Using nationally representative percentiles to interpret PROMIS pediatric measures. Qual Life Res. 2021 Apr;30(4):997-1004 [CrossRef] [Medline]
    53. Thissen D, Liu Y, Magnus B, et al. Estimating minimally important difference (MID) in PROMIS pediatric measures using the scale-judgment method. Qual Life Res. 2016 Jan;25(1):13-23 [CrossRef] [Medline]
    54. SAS: analytics, artificial intelligence and data management. SAS Institute. URL: www.sas.com/en_us/home.html [accessed 2023-07-23]
    55. Chau MM, Burgermaster M, Mamykina L. The use of social media in nutrition interventions for adolescents and young adults-a systematic review. Int J Med Inform. 2018 Dec;120:77-91 [CrossRef] [Medline]
    56. Wicks P, Thorley EM, Simacek K, Curran C, Emmas C. Scaling PatientsLikeMe via a “generalized platform” for members with chronic illness: web-based survey study of benefits arising. J Med Internet Res. 2018 May 7;20(5):e175 [CrossRef] [Medline]
    57. Rothman M, Gnanaskathy A, Wicks P, Papadopoulos EJ. Can we use social media to support content validity of patient-reported outcome instruments in medical product development? Value Health. 2015 Jan;18(1):1-4 [CrossRef] [Medline]
    58. Weitzman ER, Adida B, Kelemen S, Mandl KD. Sharing data for public health research by members of an international online diabetes social network. PLoS One. 2011 Apr 27;6(4):e19256 [CrossRef] [Medline]
    59. Weitzman ER, Kelemen S, Kaci L, Mandl KD. Willingness to share personal health record data for care improvement and public health: a survey of experienced personal health record users. BMC Med Inform Decis Mak. 2012 May 22;12:39 [CrossRef] [Medline]
    60. Li P, Xu L, Tang T, Wu X, Huang C. Users’ willingness to share health information in a social question-and-answer community: cross-sectional survey in China. JMIR Med Inform. 2021 Mar 30;9(3):e26265 [CrossRef] [Medline]
    61. Grande D, Mitra N, Iyengar R, et al. Consumer willingness to share personal digital information for health-related uses. JAMA Netw Open. 2022 Jan 4;5(1):e2144787 [CrossRef] [Medline]
    62. Weitzman ER, Cole E, Kaci L, Mandl KD. Social but safe? quality and safety of diabetes-related online social networks. J Am Med Inform Assoc. 2011 May 1;18(3):292-297 [CrossRef] [Medline]
    63. Lenhart A. Teens, social media & technology overview 2015. Pew Research Center. 2015 Apr 9. URL: www.pewresearch.org/internet/2015/04/09/teens-social-media-technology-2015/ [accessed 2023-07-23]
    64. Weitzman ER, Kaci L, Quinn M, Mandl KD. Helping high-risk youth move through high-risk periods: personally controlled health records for improving social and health care transitions. J Diabetes Sci Technol. 2011 Jan 1;5(1):47-54 [CrossRef] [Medline]
    65. Weitzman ER, Magane KM, Wisk LE. How returning aggregate research results impacts interest in research engagement and planned actions relevant to health care decision making: cohort study. J Med Internet Res. 2018 Dec 21;20(12):e10647 [CrossRef] [Medline]
    66. Goldberg EM, Rosen RK, Dizon DS, et al. Using social media for clinical research: recommendations and examples from the Brown-Lifespan Center for Digital Health. J Med Internet Res. 2022 Jun 13;24(6):e35804 [CrossRef] [Medline]
    67. Houtrow A, Harris D, Molinero A, Levin-Decanini T, Robichaud C. Children with disabilities in the United States and the COVID-19 pandemic. J Pediatr Rehabil Med. 2020;13(3):415-424 [CrossRef] [Medline]
    68. Knight A, Vickery M, Faust L, et al. Gaps in mental health care for youth with rheumatologic conditions: a mixed methods study of perspectives from behavioral health providers. Arthritis Care Res (Hoboken). 2019 May;71(5):591-601 [CrossRef] [Medline]

    ‎
    AOR: adjusted odds ratio
    CARRA: Childhood Arthritis and Rheumatology Research Alliance
    JIA: Juvenile Idiopathic Arthritis
    PGA: Physician Global Assessment
    PRO: Patient-Reported Outcome
    PROMIS: Patient-Reported Outcomes Measurement Information System
    REDCap: Research Electronic Data Capture
    SLE: systemic lupus erythematosus

    Edited by Sherif Badawy; submitted 30.03.23; peer-reviewed by Katie Allen, Misol Kwon; final revised version received 27.07.23; accepted 15.08.23; published 06.12.23

    Copyright

    © Elissa R Weitzman, Machiko Minegishi, Rachele Cox, Lauren E Wisk. Originally published in JMIR Pediatrics and Parenting (https://pediatrics.jmir.org), 6.12.2023.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Pediatrics and Parenting, is properly cited. The complete bibliographic information, a link to the original publication on https://pediatrics.jmir.org, as well as this copyright and license information must be included.