Promoting unsupervised walking in women with fibromyalgia: a randomized controlled trial.
Maria-Angeles Pastor-MiraSofia Lopez-RoigFermín Martínez-ZaragozaAna LledóLilian VelascoEva LeónCarmen Écija GallardoCecilia PeñacobaPublished in: Psychology, health & medicine (2020)
The objective of this study is to test the efficacy of a group motivational plus implementation intentions intervention in promoting adherence to an unsupervised walking program recommended for fibromyalgia, compared to an implementation intentions condition and to an active control condition. A triple-blind, randomized, longitudinal study with measures at baseline, short (seven weeks post-intervention), mid (12 weeks) and long-term (36 weeks) is performed. Data are analyzed using multilevel longitudinal growth curve two-level modelling. Participants are 157 women with fibromyalgia. In the short-term, adherence to the minimum and to the standard walking program (primary outcome measures) is explained by time (both p <.001), motivational plus implementation intentions intervention (both p <.001) and by their interaction (both p <.001). Regarding the secondary outcomes, only physical function is explained by time (p <.001), motivational plus implementation intentions intervention (p <.05) and by their interaction (p <.05). Motivational plus implementation intentions intervention achieve the promotion of walking as an exercise in the short-term; furthermore, physical function of the women in this condition is better than in the other two intervention groups, which is a relevant outcome from a rehabilitation point of view. However, more studies are needed to maintain the exercise at mid and long-term.
Keyphrases
- randomized controlled trial
- quality improvement
- primary care
- healthcare
- machine learning
- physical activity
- high intensity
- lower limb
- double blind
- clinical trial
- type diabetes
- polycystic ovary syndrome
- resistance training
- big data
- gestational age
- deep learning
- artificial intelligence
- phase ii
- preterm birth
- data analysis