Perspectives on Remote Monitoring via Smartphones and Wearables Among Individuals With Lived Experience or at Risk of Eating Disorders (“This Could Go Very, Very Wrong”): Qualitative Interview Study (Preprint)
Bibliographic record
Abstract
Background: Remote measurement technology (RMT) is increasingly used in health research to collect real-world data relevant to clinical states (eg, sleep, activity, and stress). Concerns exist about the impact of remote tracking via personal devices and wearables on individuals with or at risk of eating disorders (EDs) by promoting a focus on exercise, diet, and appearance. There is a lack of research applying RMT to EDs. Objective: This study aimed to explore how smartphone- and wearable-based RMTs influence eating-, exercise-, and weight-related experiences among individuals with a history of or at risk of EDs and to identify perceived benefits, harms, and recommendations for their use in this population. Methods: In total, 14 semistructured interviews were conducted with former participants of Remote Assessment of Disease and Relapse: Major Depressive Disorder, a 2-year digital health study tracking depression outcomes via RMTs. Participants were included in this follow-up if they had disclosed a history of a comorbid ED or were within the at-risk age range (18-30 years) for EDs during Remote Assessment of Disease and Relapse: Major Depressive Disorder and displayed subclinical ED symptoms (Eating Disorder Diagnostic Scale). Interviews explored the impact of app engagement and wearables (Fitbits) on food, activity, and weight-related behaviors and attitudes. Template analysis was adopted to capture themes guided by the focus on ED-relevant domains. Results: In total, 6 themes captured participants' experiences with RMTs across clinical status and presentation. Participants broadly appreciated the convenience and reflective potential, while some described emotional strain linked to constant self-tracking. Health data impacted participants' eating and exercise habits through a dynamic process from awareness to cognition to action, fostering healthy routines or obsessive patterns, depending on emotional state, ED presentation, and recovery stage. Self-tracking appeared to mirror illness stage, supporting ED recovery among those with greater distance from illness, but risking reinforcement of compulsive patterns among those with residual or emerging symptoms. Participants' recommendations for future studies in EDs stressed balancing autonomy with safeguards for vulnerable individuals. Conclusions: These exploratory findings, drawn from individuals with lived ED experience and young people at subclinical risk, suggest that RMT use was shaped by recovery stage and contextual factors, rather than being inherently beneficial or harmful. While findings should not be interpreted as evidence of RMT safety or acceptability in ED cohorts broadly, they raise important questions about ethical RMT design, including the selection of wearables, access to data, and researcher communication with participants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".