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Record W4403406936 · doi:10.2196/47251

Intervention to Improve Well-Being, Nutrition, and Physical Activity in Adults: Experimental Study

2024· article· en· W4403406936 on OpenAlexvenueno aff
Morghane Aubert, Céline Clavel, Christine Le Scanff, Jean‐Claude Martin

Bibliographic record

VenueJMIR Formative Research · 2024
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsMindfulnessSession (web analytics)MeditationIntervention (counseling)Psychological interventionPsychologyApplied psychologyPsychotherapistClinical psychologyComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Mindfulness improves well-being, improves emotional regulation, reduces impulses to eat, and is linked to increased physical activity. Mindfulness interventions usually focus on 1 aspect but do not offer an approach to holistically improving lifestyle. OBJECTIVE: This study aims to address this gap by designing and evaluating a holistic mindfulness intervention. METHODS: Committing to a 12-week intervention with 2-hour sessions without knowing whether you will enjoy it can be a hindrance for someone completely unfamiliar with mindfulness. For this reason, we decided to design a mindfulness intervention with short sessions over a reduced number of weeks. The aim is to enable novices to discover different aspects of mindfulness while at the same time offering a satisfactory practice for people who are already practicing mindfulness. We designed and evaluated a web-based mindfulness intervention in 5 sessions of 5 to 10 minutes each on well-being, diet, and physical activity to support a healthier lifestyle. The first 2 sessions focus on formal mindfulness meditation to enable novices to discover mindfulness and its main principles. Then there are 2 sessions about food. The first session about food aims to develop a sense of satisfaction with the food we eat and to focus our attention on new sensations. The second session about food aims to develop the ability to resist the lure of unhealthy foods. Finally, there is a session on physical activity. The aim is to develop a particular awareness of the body during movement, to increase satisfaction with physical activity, and to develop regular exercise. RESULTS: In total, 32 participants completed the intervention. After the intervention, we observed decreases in negative affect, anxiety, and emotional distress, and an increase in dispositional mindfulness. There was no effect on reported healthy eating habits and physical activity habits. Few participants repeated the exercises as recommended. The majority of our participants were new to mindfulness. The majority of our participants reported being satisfied with the different sessions. A few minor difficulties were mentioned, mainly related to the environment in which the participants carried out the sessions. Only 1 session was less satisfactory for one-third of the participants. The session on resistance to unhealthy foods was formulated too strictly and the idea of banning certain foods was a hindrance for one-third of the participants. A reformulation is needed. CONCLUSIONS: The mindfulness exercises were well accepted and promoted a state of mindfulness. It would be interesting to provide easier technical access to the exercises via a mobile app so that they can be repeated easily.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.042
GPT teacher head0.474
Teacher spread0.432 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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