Measurement of Daily Actions Associated With Mental Health Using the Things You Do Questionnaire–15-Item: Questionnaire Development and Validation Study
Bibliographic record
Abstract
BACKGROUND: A large number of modifiable and measurable daily actions are thought to impact mental health. The "Things You Do" refers to 5 types of daily actions that have been associated with mental health: healthy thinking, meaningful activities, goals and plans, healthy habits, and social connections. Previous studies have reported the psychometric properties of the Things You Do Questionnaire (TYDQ)-21-item (TYDQ21). The 21-item version, however, has an uneven distribution of items across the 5 aforementioned factors and may be lengthy to administer on a regular basis. OBJECTIVE: This study aimed to develop and evaluate a brief version of the TYDQ. To accomplish this, we identified the top 10 and 15 items on the TYDQ21 and then evaluated the performance of the 10-item and 15-item versions of the TYDQ in community and treatment-seeking samples. METHODS: Using confirmatory factor analysis, the top 2 or 3 items were used to develop the 10-item and 15-item versions, respectively. Model fit, reliability, and validity were examined for both versions in 2 samples: a survey of community adults (n=6070) and adults who completed an assessment at a digital psychology service (n=14,878). Treatment responsivity was examined in a subgroup of participants (n=448). RESULTS: Parallel analysis supported the 5-factor structure of the TYDQ. The brief (10-item and 15-item) versions were associated with better model fit than the 21-item version, as revealed by its comparative fit index, root-mean-square error of approximation, and Tucker-Lewis index. Configural, metric, and scalar invariance were supported. The 15-item version explained more variance in the 21-item scores than the 10-item version. Internal consistency was appropriate (eg, the 15-item version had a Cronbach α of >0.90 in both samples) and there were no marked differences between how the brief versions correlated with validated measures of depression or anxiety symptoms. The measure was responsive to treatment. CONCLUSIONS: The 15-item version is appropriate for use as a brief measure of daily actions associated with mental health while balancing brevity and clinical utility. Further research is encouraged to replicate our psychometric evaluation in other settings (eg, face-to-face services). TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12613000407796; https://tinyurl.com/2s67a6ps.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".