Background:The multidimensional array of clinical features and prognostic factors makes it difficult to optimize management within the heterogeneity of patients with common musculoskeletal pain. This study aimed to identify phenotypes across prognostic factors and musculoskeletal complaints. Concurrent and external validity were assessed against an established instrument and a new sample, respectively, and treatment outcome was described. Methods:We conducted a longitudinal observational study of 435 patients (aged 18-67 years) seeking treatment for nonspecific complaints in the neck, shoulder, low back or multisite/complex pain in primary health care physiotherapy in Norway.Latent class analysis was used to identify phenotypes based on 11 common prognostic factors within four biopsychosocial domains; pain, beliefs and thoughts, psychological and activity and lifestyle. Results: Five distinct phenotypes were identified. Phenotype 1 (n = 77, 17.7%) and 2 (n = 142, 32.6%) were characterized by the lowest scores across all biopsychosocial domains. Phenotype 2 showed somewhat higher levels of symptoms across the biopsychosocial domains. Phenotype 3 (n = 89, 20.5%) and 4 (n = 78, 17.9%) were more affected across all domains, but phenotype 3 and 4 had opposite patterns in the psychological and pain domains. Phenotype 5 (n = 49, 11.3%) were characterized by worse symptoms across all domains, indicating a complex phenotype. The identified phenotypes had good external and concurrent validity, also differentiating for the phenotypes in function and health-related quality of life outcome at 3-month follow-up. Conclusion: The phenotypes may inform the development of targeted interventions aimed at improving the treatment efficiency in patients with common musculoskeletal disorders. Significance: This observational prospective study identified five distinct and clinically meaningful phenotypes based on biopsychosocial prognostic factors across common musculoskeletal pain. These phenotypes were independent of primary pain location, showed good external validity, and clear variation in treatment outcome. The findings are particularly valuable as they describe the heterogeneity of patients with musculoskeletal 922 |

BackgroundWe investigated the influence of sleeplessness and number of insomnia symptoms on the probability of recovery from chronic low back pain (LBP), and the possible interplay between sleeplessness and co-occurring musculoskeletal pain on this association.MethodsThe study comprised data on 3712 women and 2488 men in the Norwegian HUNT study who reported chronic LBP at baseline in 1995–1997. A modified Poisson regression model was used to calculate adjusted risk ratios (RRs) for the probability of recovery from chronic LBP at follow-up in 2006–2008, associated with sleep problems and co-occurring musculoskeletal pain at baseline.ResultsCompared with persons without sleeplessness, persons who often/always experienced sleeplessness had a lower probability of recovery from chronic LBP (RR 0.65, 95% CI 0.57 to 0.74 in women and RR 0.81, 95% CI 0.69 to 0.95 in men). Although there was no clear evidence of statistical interaction between sleeplessness and co-occurring musculoskeletal pain, women and men who often/always experienced sleeplessness and had ≥5 additional chronic pain sites had RRs of recovery of 0.40 (95% CI 0.33 to 0.48) and 0.59 (95% CI 0.45 to 0.78), respectively, compared with persons without sleeplessness and 1–2 chronic pain sites.ConclusionThese findings suggest that preventing or reducing sleep problems among people with chronic LBP may have the potential of improving the long-term prognosis of this condition, also among those with several additional pain sites.

BackgroundIn order to establish normative values for body positions and movements during sleep, the objective of this study was to explore the distribution of sleep positions and extent of nocturnal body moments and the association with sex, age, body-mass index (BMI), smoking, alcohol consumption, and insomnia symptoms.Materials and methodsThis cross-sectional study comprised data on a working population (363 men and 301 women) who participated in the Danish Physical Activity Cohort with Objective Measurements (DPHACTO). Measures of body position and movements were obtained from actigraph accelerometers on the thigh, upper back, and upper arm. Linear regression was used to estimate adjusted mean differences in movements among categories of demographic and lifestyle characteristics.ResultsDuring their time in bed, participants spent 54.1% (SD 18.1%) in the side position, 37.5% (SD 18.2%) in the back position, and 7.3% (SD 12.3%) in the front position. Increasing age and BMI were associated with increased time in the side position and a proportional reduction in time in the back position. There were on average 1.6 (SD 0.7) position shifts per hour. Compared to males, females had fewer position shifts (−0.37, 95% CI –0.48 to −0.24) and fewer arm, thigh, and upper-back movements. Participants aged 20–34 years had more arm, thigh, and upper-back movements compared to participants ≥35 years. Obese participants had fewer shifts in body position (−0.15, 95% CI −0.27 to 0), but more arm, thigh, and upper-back movements compared to normal-weight participants. Smokers had fewer shifts in body position than nonsmokers (−0.27, 95% CI −0.4 to −0.13).ConclusionThe predominant sleep position in adults is on the side. This preference increases with age and BMI. The extent of nocturnal body movements is associated with sex, age, BMI, and smoking.

Objectives This study aimed to investigate (i) the associations between occupational physical activity (OPA) and leisure-time physical activity (LTPA) with insomnia symptoms and non-restorative sleep and (ii) the joint associations between OPA and LTPA with insomnia symptoms and non-restorative sleep, respectively. Methods Data were drawn from a cross-sectional study including 650 workers in the Danish PHysical ACTivity cohort with Objective measurements (DPhacto). OPA and LTPA were measured with accelerometers on the thigh and upper back for up to six consecutive days and subsequently divided into quartiles of "very low", "low", "medium" and "high" activity. We used logistic regression to calculate odds ratios (OR) with 95% confidence intervals (CI) for insomnia symptoms and non-restorative sleep associated with OPA and LTPA. Results A 10% increase in OPA was associated with a higher prevalence of insomnia symptoms (OR 1.39, 95% CI 1.03-1.89) but not with the prevalence of non-restorative sleep (OR 0.93, 95% CI 0.71-1.21). On the other hand, a 10% increase in LTPA was associated with a lower prevalence of non-restorative sleep (OR 0.51, 95% CI 0.28-0.92). Although no significant additive interaction was found, analyses of the joint association of OPA and LTPA showed that people with high OPA and low LTPA had an OR of 2.07 (95% CI 1.01-4.24) for insomnia symptoms, compared to those with low OPA and high LTPA, whereas people with high levels of both OPA and LTPA had an OR of 1.47 (95% CI 0.73-2.96). Conclusions While LTPA was associated with lower prevalence of sleep problems, OPA was associated with higher prevalence of insomnia symptoms. A combination of high OPA and low LTPA were more strongly associated with insomnia symptoms compared to a combination of low OPA and high LTPA.

Insomnia and short/long sleep duration increase the risk of AMI, but their interaction with each other or with chronotype is not well known. We investigated the prospective joint associations of any two of these sleep traits on risk of AMI. We included 302 456 and 31 091 participants without past AMI episodes from UK Biobank (UKBB; 2006–10) and the Trøndelag Health Study (HUNT2; 1995–97), respectively. A total of 6 833 and 2 540 incident AMIs were identified during an average 11.7 and 21.0 years follow-up, in UKBB and HUNT2, respectively. Compared to those who reported normal sleep duration (7–8 h) without insomnia symptoms, the Cox proportional hazard ratios (HRs) for incident AMI in UKBB among participants who reported normal, short and long sleep duration with insomnia symptoms were 1.07 (95% CI 0.99, 1.15), 1.16 (95% CI 1.07, 1.25) and 1.40 (95% CI 1.21, 1.63), respectively. The corresponding HRs in HUNT2 were 1.09 (95% CI 0.95, 1.25), 1.17 (95% CI 0.87, 1.58) and 1.02 (95% CI 0.85, 1.23). The HRs for incident AMI in UKBB among evening chronotypes were 1.19 (95% CI 1.10, 1.29) for those who had insomnia symptoms, 1.18 (95% CI 1.08, 1.29) for those with short sleep duration, and 1.21 (95% CI 1.07, 1.37) for those with long sleep duration, compared to morning chronotypes without another sleep symptom. The relative excess risk for incident AMI in UKBB due to interaction between insomnia symptoms and long sleep duration was 0.25 (95% CI 0.01, 0.48). Insomnia symptoms with long sleep duration may contribute more than just an additive effect of these sleep traits on the risk of AMI.