Prevalence and management of musculoskeletal pain in rural communities
Notice bibliographique
Résumé
The general aim of this thesis was to investigate the prevalence and management of chronic musculoskeletal conditions, such as low back pain and knee pain, in rural and remote communities. This thesis included one systematic review with a meta-analysis of observational studies, reported in Chapter Two, that compared the prevalence of physical activity, physical inactivity, and sedentary behaviour in a population of rural and urban Australian adults. Twenty-eight studies reporting data on a total of 515,532 people were included and found that the prevalence of physical inactivity was higher in rural populations (prevalence difference 4%; 95% confidence interval [CI] = 0.4% to 8%), but the prevalence of physical activity was similar in both rural and urban populations (prevalence difference 1%; 95% CI = -3% to 5%). The implications of the study call to action to reduce the urban-rural inequality of different factors that are associated with physical inactivity, such as the promotion of current guidelines focused on leisure physical activity, and adequate infrastructure for the safe practice of sports and exercise. \n \nThe systematic review reported in Chapter Three includes a meta-analysis of observational studies reporting on the worldwide prevalence of musculoskeletal conditions, such as back, knee, hip, and shoulder pain in rural compared to urban populations. The review included 42 studies from 24 countries with a total sample of 489,439 people, and the results indicated that hip (mean odds ratio [OR]=1.62; 95% CI=1.23 to 2.15), shoulder (OR=1.42; 95% CI=1.06 to 1.90), and overall musculoskeletal pain ([OR]=1.26, 95% [CI]= 1.08 to 1.47; n=302,911) were more prevalent in rural compared to urban populations. Similarly, although not statistically significant, back (OR=1.18, 95% CI=0.97 to 1.43; n=225,950), and knee pain (OR=1.13; 95% CI=0.83 to 1.52), but not neck pain (OR= 0.89; 95% CI=0.60 to 1.32), were more prevalent in rural compared to urban populations. Interestingly, the meta-analysis showed that adults in rural areas were less likely to seek treatment for musculoskeletal conditions than their urban counterparts (OR= 0.76; 95% CI=0.55 to 1.03). \n \nLastly, a randomised controlled trial that included 156 participants was conducted and reported in Chapters Four and Five of this thesis. The trial aimed to assess the effectiveness of a physiotherapist delivered real-time eHealth intervention including a physical activity plan and a personalised resistance training program, compared with usual care on physical function in adults with chronic non-specific low back pain or knee osteoarthritis in rural Australia. The primary outcome of physical function was assessed with the Patient-Specific Functional Scale, which ranges from 0 to 30, with higher values indicating better levels of function. The secondary outcome of disability was assessed using the Roland-Morris Disability Questionnaire in participants with low back pain, which ranges from 0 to 24 with lower scores indicating lower disability, or with the Western Ontario and McMaster Osteoarthritis Index in participants with knee osteoarthritis, which ranges from 0 to 68 (function section) with lower scores indicating lower disability. Health-related quality of life was measured using the Assessment of Quality of Life 8D instrument, which ranges from 0 to 100, with higher scores indicating better quality of life. \n \nThe findings reported in Chapter Five showed that the eHealth intervention provided greater clinically significant benefits in physical function at three months (mean between-group difference 3.63; 95% CI= 1.31 to 5.94), and at six months (mean between-group difference: 3.59; 95%CI= 1.14 to 6.05) compared to usual care. Disability (mean between-group difference 7.26; 95%CI= 2.14 to 12.38), and quality of life (mean between-group difference 4.51; 95%CI= 0.01 to 9.01) were statistically significantly higher in the eHealth intervention group at the three-month follow-up. No other between-group differences were found for the remaining outcomes or follow-ups. Results of this study showed that an eHealth intervention is effective to improve physical function, and can potentially complement and enhance face-to-face consultations, for people suffering from musculoskeletal pain in rural communities. These findings are important to inform rural and primary healthcare policy and clinical practice in Australia.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».