Feasibility, Acceptability, and Preliminary Outcomes of a Cognitive Behavioral Therapy–Based Mobile Mental Well-being Program (Noom Mood): Single-Arm Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: The prevalence of anxiety, depression, and general distress has risen in recent years. Mobile mental health programs have been found to provide support to nonclinical populations and may overcome some of the barriers associated with traditional in-person treatment; however, researchers have voiced concerns that many publicly available mobile mental health programs lack evidence-based theoretical foundations, peer-reviewed research, and sufficient engagement from the public. OBJECTIVE: This study aimed to evaluate the feasibility, acceptability, and preliminary outcomes of Noom Mood, a commercial mobile cognitive behavioral therapy- and mindfulness-based program. METHODS: In this single-arm prospective cohort study, individuals who joined Noom Mood between August and October 2021 completed surveys at baseline and 4-week follow-up. Per-protocol analyses included those who completed both surveys (n=113), and intention-to-treat analyses included all participants (N=185). RESULTS: A majority of the sample reported that the program is easy to use, they felt confident recommending the program to a friend, and they perceived the program to be effective at improving stress and anxiety. There were significant improvements in anxiety symptoms, perceived stress, depressive feelings, emotion regulation, and optimism in both the per-protocol and intention-to-treat analyses (all P<.001). Participants reported benefiting most from learning skills (eg, breathing and cognitive reframing techniques), interacting with the program features, and gaining awareness of their emotions and thought patterns. Participants also made a number of suggestions to improve product functionality and usability. CONCLUSIONS: Results suggest that Noom Mood is feasible and acceptable to participants, with promising preliminary outcomes. Future studies should build on these results to evaluate the effects of Noom Mood using more rigorous designs.
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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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".