Interplay of Support, Comparison, and Surveillance in Social Media Weight Management Interventions: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: There has been a significant increase in the trend of using social media as a platform to deliver weight management interventions. This illustrates a need to develop a holistic understanding of doctor-patient communication and peer-to-peer communication in social media interventions and to determine their influences on weight management for people with overweight or obesity. Such studies will highlight how social media can be more effectively integrated into weight management programs to enhance individuals' short-term and long-term weight management behaviors. OBJECTIVE: The aim of this study was to examine patients' experiences with doctor-patient communication and peer interactions in a social media-based (WeChat) weight management program, and to describe the interplay of three social influence factors-social support, social comparison, and surveillance-in their weight control practices. The program, designed and implemented by the research team located in a tertiary referral hospital in a southeastern province in China, included both diet and physical activity components that targeted people with overweight or obesity. METHODS: We conducted in-depth interviews with 32 program participants of different ages (mean 35.6, SD 7.7 years), gender (18 women), duration of program membership (mean 1.4 years), and weight loss outcomes (54% weight loss to 9% weight gain). All interview data were audio-recorded, transcribed, and translated using the translation-backtranslation technique. Nvivo software was used to facilitate the coding process. RESULTS: Results of thematic analysis indicated the distinct functions of professionally led support and peer support. Professional support was presented in the form of knowledge infusion, efficacy enhancement, and provision of timely feedback. Peer support fostered empathy and sense of belonging, and had a mutually reinforcing relationship with peer comparison and peer-based surveillance. Peer comparison enhanced motivation and positive competition. However, it also reinforced negative group norms, and resulted in downturns in reference standards and collective inactivity. Social media surveillance prompted participants' reactions to the gaze from medical professionals and peers that could be encouraging or inhibiting. Surveillance enhanced vigilance with weight control norms; however, its influence weakened when participants chose to fake weight data and turn off notifications. Findings from this study illustrated the interrelated and fluctuating influences of support, comparison, and surveillance. CONCLUSIONS: The interactive traits of social media eased the practices of social support and social comparison, and created new forms of surveillance. This study contributes to an in-depth understanding of social media influences on individuals' weight control behaviors. Practical implications of the study concern improved strategies for maintaining the positive dynamics of social media interactions and preventing negative resistance to surveillance technology. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR1900025861; http://www.chictr.org.cn/showprojen.aspx?proj=42497.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".