Characteristics of Users of a Digital Hypnotherapy Intervention for Hot Flashes: Retrospective Study
Bibliographic record
Abstract
BACKGROUND: Hot flashes are associated with a lower quality of life and sleep disturbances. Given the many consequences of hot flashes, it is important to find treatments to reduce them. Hypnotherapy, the use of hypnosis for a medical disorder or concern, has been shown in clinical trials to be effective in reducing hot flashes, but it is not routinely used in clinical practice. One solution to close this implementation gap is to administer hypnotherapy for hot flashes via a smartphone app. Evia is a smartphone app that delivers hypnotherapy for hot flashes. Evia has made hypnotherapy more widely accessible for women who are experiencing hot flashes; however, the app has yet to undergo empirical testing. Additionally, research on user characteristics is lacking. OBJECTIVE: This study aims to (1) determine the average age, stage of menopause, and length of menopause symptoms for users of the Evia app; (2) determine the characteristics of hot flashes and night sweats for users of the Evia app; (3) determine the self-reported sleep quality of users of the Evia app; (4) determine the self-reported mental health of users of the Evia app; and (5) determine the relationship between hot flash frequency and anxiety and depression for users of the Evia app. METHODS: This study analyzed data collected from participants who have downloaded the Evia app. Data were collected at 1 time point from a self-report questionnaire that assessed the demographic and clinical characteristics of users. The questionnaire was given to users when they downloaded the Evia app. Users of the Evia app fill out a questionnaire upon enrolling in the program and prior to beginning the intervention. This included 9764 users. RESULTS: Results showed that the mean age of users was 49.31 years. A total of 41.6% (1942/4665) of users reported experiencing 5 or more hot flashes per day, while 51.2% (1473/2877) of users reported having difficulty falling asleep each night and 47.7% (1253/2626) of users reported their sleep quality to be terrible. In addition, 38.4% (1104/2877) of users reported that they often feel anxious or depressed. There was a small, significant, and negative correlation between hot flash frequency and self-report frequency of anxiety and depression (r=-0.09). CONCLUSIONS: This study showed that the average age of app users is in line with the median age of natural menopause. A large percentage of users reported experiencing 5 or more hot flashes per day, reported difficulties with sleep, and reported experiencing depression and anxiety. These findings are in line with previous studies that assessed hot flash frequency and the consequences of hot flashes. This was the first study to report on the characteristics of users of the Evia app. Results will be used to optimize the hypnotherapy program delivered via the Evia app.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".