The Impact of Externally Worn Diabetes Technology on Sexual Behavior and Activity, Body Image, and Anxiety in Type 1 Diabetes
Bibliographic record
Abstract
Background: We aimed to explore the impact of externally worn diabetes technologies on sexual behavior and activity, body image, and anxiety in adopters and nonadopters of these devices. Methods: People with type 1 diabetes aged 16-60 years living in Western Australia were invited to complete an online survey. Results: Of the 289 respondents (mean age 34.3 years), 45% used continuous subcutaneous insulin infusion (CSII) and 35% used continuous glucose monitoring (CGM). Approximately half of CSII users stated that the pump interferes with sex. Of these, 75% disconnect their pump during sexual activity to avoid this issue. Comfort during sex influenced the location of the CSII insertion site in 22% of respondents, with the abdomen being preferred. One in four non-CSII users cited sex-related concerns as a factor for not adopting the technology. CGM interfered with sexual activity in 20% of users, but did not commonly affect CGM placement (only 18%). Sexual activity was reported as a factor for not adopting the technology in 10% of non-CGM users. No differences in body dissatisfaction ( P = .514) or anxiety ( P = .304) between CSII and non-CSII users were observed. No differences in sexual activity and behavior between technology users and nontechnology users were observed. Conclusion: Wearable technologies impact upon sexual activity and this influences the decision to adopt the technology. Despite this, technology users are similar in terms of sexual behavior, anxiety, and body image compared to nontechnology users. Where appropriate, these data can be used to identify potential concerns, address strategies to mitigate them, and inform people with diabetes when considering adopting external technologies.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".