Autistic and Non-Autistic Experiences of Decreased Sound Tolerance and Their Association with Mental Health and Quality of Life
Bibliographic record
Abstract
BackgroundDecreased sound tolerance (DST) is an increased sensitivity to sound at levels that would not bother most people. DST is highly prevalent in autistic adults; however, the extent to which DST differs across autistic and non-autistic adults is uncertain. This study explores multiple domains of DST symptoms and the severity of DST symptoms, as well as the behavioural reactions and coping strategies adopted to manage DST in both autistic and non-autistic adults. Lastly relationships between DST, autism characteristics, mental health, and quality of life were explored.MethodsThis study used online surveys to investigate the characteristics of DST in 77 autistic and 128 non-autistic adults who self-report DST, as well as the relationship between DST and autistic characteristics, mental health, and quality of life.ResultsThe results of this study indicated that misophonia, an aversion to specific sounds, was more severe in autistic adults. Similarly, hyperacusis, a reduced tolerance to everyday sounds at volumes that would not be distressing to most people, was also more severe in the autistic sample. Across the entire sample, misophonia symptoms were associated with more autistic traits and higher anxiety, while hyperacusis symptom severity was associated with more autistic traits, higher anxiety and depression symptoms, and poorer quality of life.ConclusionAlthough misophonia and hyperacusis appear to be more severe in autistic samples, these forms of DST may be related in similar ways to mental health and quality of life of both autistic and non-autistic adults. Future work should focus on differentiating the subtypes of DST in order to facilitate the development of treatments that specifically target the symptoms of each subtype (i.e. misophonia, and hyperacusis) rather than treating DST as a homogeneous problem.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".