The impact of an online, lifestyle intervention programme on the lives of patients with a rheumatic and musculoskeletal disease: a pilot study
Bibliographic record
Abstract
OBJECTIVES: To evaluate the short- and long-term effects of an online, interactive, multifactorial lifestyle intervention programme (Leef! Met Reuma) on health risk and all ICHOM-recommended patient-reported outcome measures (PROMs) in patients with an inflammatory arthritis (IA), osteoarthritis (OA) or fibromyalgia (FM). METHODS: Patients with an IA, OA or FM could register for the lifestyle intervention programme. The programme consists of a 3-month intensive part followed by a 21-month aftercare period and focuses on four pillars, namely nutrition, exercise, relaxation and sleep. Health risk and PROMs are collected 3-monthly during the first 6 months and 6-monthly during the next 18 months. Health risk includes self-reported weight, waist circumference and BMI. Following PROMs were included: pain, morning stiffness severity, fatigue, Health Assessment Questionnaire, quality of life, perceived stress, sleep disturbance and impact on life. Descriptive statistics were used to assess the change in health risk and PROMs during the intensive part of the programme and aftercare period. RESULTS: Of the 264 patients studied, 88, 105 and 71 were diagnosed with IA, OA and FM, respectively. Health risk significantly improved in all three diagnosis groups during the intensive part of the programme. The mean BMI reduction was -1.36 (0.26), -1.22 (0.23) and -1.48 (0.33), whereafter it stabilized in the aftercare period. All PROMs showed a similar trend. CONCLUSION: An online, interactive lifestyle intervention programme has a positive long-term effect, even after 2 years of follow-up, on health risk and all PRO domains in patients with an IA, OA and FM.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 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".