Intergenerational and genetic influences on physical activity: family data from the HUNT study, Norway.

OBJECTIVES: The objectives of this study are to examine the association of physical activity in parents with physical activity in their adult offspring and explore if the offspring's genetic liability (ie, polygenic risk score) to physical activity influences this association. METHODS: The Trøndelag Health Study cohort is a population-based longitudinal study with data collected in 1984-1986, 1995-1997, 2006-2008 and 2017-2019. We calculated the odds ratio for being physically active and mean difference in physical activity levels according to parental physical activity (device-measured and self-reported) and own polygenic risk score. RESULTS: Compared with offspring with mothers in the lowest third of metabolic equivalent of task (MET)-min/day accumulated by vigorous physical activities, offspring with mothers in the upper third had an OR of 1.93 (95% CI 1.65 to 2.27) for accumulating ≥900 MET-min/week of vigorous physical activity. The OR for the corresponding father-offspring association was 1.78 (95% CI 1.48 to 2.14). Compared with offspring of parents not accumulating ≥900 MET-min/week, we found an OR of 1.89 (95% CI 1.45 to 2.44) for offspring to meet the same threshold if both parents accumulated ≥900 MET-min/week. Offspring with higher polygenic risk score to bephysically active and having physically active parents did more weekly physical activity, but we found no strong evidence of multiplicative synergistic effects between these two factors (all p values ≥0.01). CONCLUSION: Both parental physical activity and offspring's polygenic risk score were positively associated with physical activity levels in the adult offspring, but there was no evidence of effect modification between these factors. A family-based approach to promote physical activity may be effective from a public health perspective.

Investigating the causal interplay between sleep traits and risk of acute myocardial infarction: a Mendelian randomization study.

BACKGROUND: Few studies have investigated the joint effects of sleep traits on the risk of acute myocardial infarction (AMI). No previous study has used factorial Mendelian randomization (MR) which may reduce confounding, reverse causation, and measurement error. Thus, it is prudent to study joint effects using robust methods to propose sleep-targeted interventions which lower the risk of AMI. METHODS: The causal interplay between combinations of two sleep traits (including insomnia symptoms, sleep duration, or chronotype) on the risk of AMI was investigated using factorial MR. Genetic risk scores for each sleep trait were dichotomized at their median in UK Biobank (UKBB) and the second survey of the Trøndelag Health Study (HUNT2). A combination of two sleep traits constituting 4 groups were analyzed to estimate the risk of AMI in each group using a 2×2 factorial MR design. RESULTS: In UKBB, participants with high genetic risk for both insomnia symptoms and short sleep had the highest risk of AMI (hazard ratio (HR) 1.10; 95% confidence interval (CI) 1.03, 1.18), although there was no evidence of interaction (relative excess risk due to interaction (RERI) 0.03; 95% CI -0.07, 0.12). These estimates were less precise in HUNT2 (HR 1.02; 95% CI 0.93, 1.13), possibly due to weak instruments and/or small sample size. Participants with high genetic risk for both a morning chronotype and insomnia symptoms (HR 1.09; 95% CI 1.03, 1.17) and a morning chronotype and short sleep (HR 1.11; 95% CI 1.04, 1.19) had the highest risk of AMI in UKBB, although there was no evidence of interaction (RERI 0.03; 95% CI -0.06, 0.12; and RERI 0.05; 95% CI -0.05, 0.14, respectively). Chronotype was not available in HUNT2. CONCLUSIONS: This study reveals no interaction effects between sleep traits on the risk of AMI, but all combinations of sleep traits increased the risk of AMI except those with long sleep. This indicates that the main effects of sleep traits on AMI are likely to be independent of each other.

Can the bias of self-reported sitting time be corrected? A statistical model validation study based on data from 23 993 adults in the Norwegian HUNT study.

BACKGROUND: Despite apparent shortcomings such as measurement error and low precision, self-reported sedentary time is still widely used in surveillance and research. The aim of this study was threefold; (i) to examine the agreement between self-reported and device-measured sitting time in a general adult population; (ii), to examine to what extent demographics, lifestyle factors, long-term health conditions, physical work demands, and educational level is associated with measurement bias; and (iii), to explore whether correcting for factors associated with bias improves the prediction of device-measured sitting time based on self-reported sitting time. METHODS: A statistical validation model study based on data from 23 993 adults in the Trøndelag Health Study (HUNT4), Norway. Participants reported usual sitting time on weekdays using a single-item questionnaire and wore two AX3 tri-axial accelerometers on the thigh and low back for an average of 3.8 (standard deviation [SD] 0.7, range 1-5) weekdays to determine their sitting time. Statistical validation was performed by iteratively adding all possible combinations of factors associated with bias between self-reported and device-measured sitting time in a multivariate linear regression. We randomly selected 2/3 of the data (n = 15 995) for model development and used the remaining 1/3 (n = 7 998) to evaluate the model. RESULTS: Mean (SD) self-reported and device-measured sitting time were 6.8 (2.9) h/day and 8.6 (2.2) h/day, respectively, corresponding to a mean difference of 1.8 (3.1) h/day. Limits of agreement ranged from - 8.0 h/day to 4.4 h/day. The discrepancy between the measurements was characterized by a proportional bias with participants device-measured to sit less overestimating their sitting time and participants device-measured to sit more underestimating their sitting time. The crude explained variance of device-measured sitting time based on self-reported sitting time was 10%. This improved to 24% when adding age, body mass index and physical work demands to the model. Adding sex, lifestyle factors, educational level, and long-term health conditions to the model did not improve the explained variance. CONCLUSIONS: Self-reported sitting time had low validity and including a range of factors associated with bias in self-reported sitting time only marginally improved the prediction of device-measured sitting time.

Self-reported insomnia symptoms, sleep duration, chronotype and the risk of acute myocardial infarction (AMI): a prospective study in the UK Biobank and the HUNT Study.

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.

The interplay between multisite pain and insomnia on the risk of anxiety and depression: the HUNT study.

BACKGROUND: Chronic musculoskeletal pain and insomnia frequently co-occur and are known independent risk factors for anxiety and depression. However, the interplay between these two conditions on the risk of anxiety and depression has not been explored. METHODS: A population-based prospective study of 18,301 adults in the Norwegian HUNT Study without anxiety or depression at baseline (2006-2008). We calculated adjusted risk ratios (RRs) with 95% confidence intervals (CIs) for anxiety and/or depression at follow-up (2017-2019), associated with i) number of chronic pain sites, and ii) chronic pain and insomnia symptoms jointly. RESULTS: At follow-up, 2155 (11.8%) participants reported anxiety and/or depression. The number of pain sites was positively associated with risk of anxiety and/or depression (Ptrend, < 0.001). Compared to people without chronic pain and insomnia symptoms, people with ≥5 pain sites and no insomnia symptoms had a RR of 1.52 (95% CI: 1.28 to 1.81) for anxiety and/or depression, those with no chronic pain but with insomnia had a RR of 1.78 (95% CI: 1.33 to 2.38), whereas the RR among people with both ≥5 pain sites and insomnia was 2.42 (95% CI: 1.85 to 3.16). We observed no synergistic effect above additivity for the combination of ≥5 pain sites and insomnia on risk of anxiety and/or depression. CONCLUSIONS: This study shows that people with multisite chronic pain who also suffer from insomnia are at a particularly high risk for anxiety and/or depression, suggesting that insomnia symptoms are important contributors to the association between multisite pain and common mental health problems.

Long-term changes in self-reported sleep quality and risk of chronic musculoskeletal pain: The HUNT Study.

We examined the association between long-term (~10 years) changes in self-reported sleep quality and risk of any chronic musculoskeletal pain and chronic widespread pain. The study comprised data on 6,033 people who participated in three consecutive surveys in the Norwegian HUNT Study (1995-1997, 2006-2008 and 2017-2019) and who were without chronic musculoskeletal pain at the first two surveys. We used a modified Poisson regression model to calculate adjusted risk ratios for chronic pain at follow-up (2017-2019) associated with categories of poor and good sleep quality reported in 1995-1997 and 2006-2008. Compared with people who reported good sleep at both surveys (crude absolute risk: 32.4%), the risk ratios of any chronic pain were 1.20 (95% confidence interval: 1.02-1.41) for those who changed from poor to good sleep; 1.25 (95% confidence interval: 1.12-1.39) for those who changed from good to poor sleep; and 1.41 (95% confidence interval: 1.21-1.63) for those who reported long-term poor sleep. The corresponding risk ratios for chronic widespread pain were 1.35 (95% confidence interval: 0.82-2.23), 1.55 (95% confidence interval: 1.14-2.12) and 2.09 (95% confidence interval: 1.38-3.17), respectively. In conclusion, these findings indicate that people with long-term poor sleep quality have a markedly higher risk of chronic musculoskeletal pain and chronic widespread pain, compared with people who remain good sleep quality.

Genetic variants related to physical activity or sedentary behaviour: a systematic review.

BACKGROUND: Research shows that part of the variation in physical activity and sedentary behaviour may be explained by genetic factors. Identifying genetic variants associated with physical activity and sedentary behaviour can improve causal inference in physical activity research. The aim of this systematic review was to provide an updated overview of the evidence of genetic variants associated with physical activity or sedentary behaviour. METHODS: We performed systematic literature searches in PubMed and Embase for studies published from 1990 to April 2020 using keywords relating to "physical activity", "exercise", "sedentariness" and "genetics". Physical activity phenotypes were either based on self-report (e.g., questionnaires, diaries) or objective measures (e.g., accelerometry, pedometer). We considered original studies aiming to i) identify new genetic variants associated with physical activity or sedentary behaviour (i.e., genome wide association studies [GWAS]), or ii) assess the association between known genetic variants and physical activity or sedentary behaviour (i.e., candidate gene studies). Study selection, data extraction, and critical appraisal were carried out by independent researchers, and risk of bias and methodological quality was assessed for all included studies. RESULTS: Fifty-four out of 5420 identified records met the inclusion criteria. Six of the included studies were GWAS, whereas 48 used a candidate gene approach. Only one GWAS and three candidate gene studies were considered high-quality. The six GWAS discovered up to 10 single nucleotide polymorphisms (SNPs) associated with physical activity or sedentariness that reached genome-wide significance. In total, the candidate gene studies reported 30 different genes that were associated (p < 0.05) with physical activity or sedentary behaviour. SNPs in or close to nine candidate genes were associated with physical activity or sedentary behaviour in more than one study. CONCLUSION: GWAS have reported up to 10 loci associated with physical activity or sedentary behaviour. Candidate gene studies have pointed to some interesting genetic variants, but few have been replicated. Our review highlights the need for high-quality GWAS in large population-based samples, and with objectively assessed phenotypes, in order to establish robust genetic instruments for physical activity and sedentary behaviour. Furthermore, consistent replications in GWAS are needed to improve credibility of genetic variants. TRIAL REGISTRATION: Prospero CRD42019119456 .

Work-Related Mental Fatigue, Physical Activity and Risk of Insomnia Symptoms: Longitudinal Data from the Norwegian HUNT Study.

OBJECTIVE/BACKGROUND: To examine the prospective association between work-related mental fatigue and risk of insomnia symptoms, and if leisure time physical activity modifies this association. PARTICIPANTS: A total of 8,464 women and 7,480 men who participated in two consecutive surveys of the Norwegian HUNT study. METHODS: The study comprises longitudinal data on persons who were vocationally active and without insomnia symptoms at baseline in 1995-1997. We used a modified Poisson regression model to calculate adjusted risk ratios (RRs) with a 95% confidence interval (CI) for insomnia symptoms at follow-up in 2006-2008 associated with work-related mental fatigue and leisure time physical activity at baseline. RESULTS: Women and men who always experienced mental fatigue after a workday had RRs of insomnia symptoms of 2.55 (95% CI 1.91-3.40) and 2.61 (95% CI 1.80-3.78), respectively, compared to workers who never or seldom had this experience. There was no strong modifying effect of leisure time physical activity on this association, but workers who always experienced mental fatigue had a RR of insomnia symptoms of 3.17 (95% CI 2.28-4.40) if they reported low physical activity and a RR of 2.52 (95% 1.89-3.39) if they reported high physical activity. CONCLUSION: This study shows that work-related mental fatigue, caused by high cognitive workload, is a strong risk factor for insomnia symptoms. There was no clear modifying effect of leisure time physical activity but workers who experienced excessive work-related fatigue accompanied by low physical activity had the highest risk of insomnia symptoms.

It is never too late to start: adherence to physical activity recommendations for 11-22 years and risk of all-cause and cardiovascular disease mortality. The HUNT Study.

OBJECTIVES: To examine associations between long-term (11-22 years) adherence to physical activity recommendations and mortality from all causes and from cardiovascular disease. DESIGN: Prospective population-based study with repeated assessments of self-reported physical activity (1984-86, 1995-97 and 2006-08) and follow-up until the end of 2013. SETTING: County of Nord-Trøndelag, Norway. PARTICIPANTS: Men and women aged ≥20 years; 32 811 who participated in 1984-86 and 1995-97; 22 058 in 1984-86 and 2006-08; 31 948 in 1995-97 and 2006-09 and 19 349 in all three examinations (1984-1986, 1995-95 and 2006-08). MAIN OUTCOME MEASURES: All-cause mortality and cardiovascular disease mortality from the national Cause of Death Registry. RESULTS: Compared with the reference category comprising individuals who adhered to the physical activity recommendations (≥150 min of moderate intensity or ≥60 min of vigorous intensity physical activity per week) over time, individuals who remained inactive (reporting no or very little physical activity) from 1984-86 to 1995-97 had HRs (95% CI) of 1.56 (1.40 to 1.73) for all-cause mortality and 1.94 (1.62 to 2.32) for cardiovascular disease mortality. Individuals who were inactive in 1984-86 and then adhered to recommendations in 2006-08 had HRs of 1.07 (0.85 to 1.35) for all-cause mortality and 1.31 (0.87 to 1.98) for cardiovascular disease mortality. In a subsample of individuals who participated at all three time points, those who were inactive or physically active below the recommended level across three decades (1984-86, 1995-97 and 2006-2008) had an HR of 1.57 (1.22 to 2.03) for all-cause mortality and 1.72 (1.08 to 2.73) for cardiovascular disease mortality. CONCLUSION: Individuals who remained, or became, physically inactive had substantially greater risk of all-cause and cardiovascular disease mortality compared with those who met the physical activity recommendations throughout the lifespan.

Long-term changes in body weight and physical activity in relation to all-cause and cardiovascular mortality: the HUNT study.

BACKGROUND: Most previous studies have relied on single measurements of body weight and physical activity and have not considered the interplay between long-term changes in body weight and physical activity in relation to mortality. The aim of the current study was therefore to examine the joint effect of changes in body weight and leisure-time physical activity over a period of ~ 10 years on all-cause and cardiovascular mortality. METHODS: The study population comprised 34,257 individuals who participated in the first (1984-86) and second (1995-97) waves of the HUNT Study, and were followed up through the Norwegian Cause of Death Registry until December 31st, 2013. We used Cox regression to estimate hazard ratios (HR) with 95% confidence intervals (CI) of death associated with changes in body weight and leisure-time physical activity. RESULTS: Compared to the reference group with stable weight who were long-term physically active, people who gained ≥5% of their weight had a HR for all-cause mortality of 1.54 (95% CI: 1.28-1.85) if they were long-term physically inactive; a HR of 1.23 (1.09-1.40) if they became physically active, and a HR of 1.00 (95% CI 0.94-1.06) if they were long-term physically active. The corresponding HRs for cardiovascular mortality were 1.57 (95% CI 1.17-2.12), 1.28 (95% CI 1.04-1.58) and 1.06 (95% CI 0.96-1.16), respectively. Long-term physical inactivity was associated with increased all-cause (HR 1.29; 95% CI 1.08-1.53) and cardiovascular (HR 1.37; 95% CI 1.05-1.79) mortality among those who were weight stable. CONCLUSIONS: The risk of all-cause and cardiovascular mortality is particularly evident among people who gain weight while remaining inactive during a ~ 10 year period. However, participants who remained physically active had the lowest risk of premature mortality, regardless of maintenance or increase in weight. These findings suggest that there is an interplay between long-term changes in body weight and physical activity that should receive particular attention in the prevention of premature mortality.

The joint association of musculoskeletal pain and domains of physical activity with sleep problems: cross-sectional data from the DPhacto study, Denmark.

PURPOSE: To investigate if occupational physical activity (OPA) and leisure-time physical activity (LTPA) influence the association between musculoskeletal pain and sleep problems. METHODS: Cross-sectional study includes 678 workers in the Danish PHysical ACTivity cohort with Objective measurements (DPhacto). Musculoskeletal pain was assessed by questionnaires, while OPA and LTPA were measured with accelerometers for up to 6 consecutive days. We used logistic regression to calculate odds ratios (ORs) with 95% confidence intervals (CIs) for self-reported insomnia symptoms and non-restorative sleep. RESULTS: Analyses of the joint association of musculoskeletal pain and OPA showed that workers with high pain and high OPA had ORs of 5.80 (95% CI 2.64-12.67) for insomnia symptoms and 2.50 (95% CI 1.37-4.57) for non-restorative sleep, compared to those with low pain and low OPA, whereas workers with high pain and low OPA had ORs of 4.67 (95% CI 2.17-10.07) for insomnia symptoms, and 2.67 (95% CI 1.46-4.89) for non-restorative sleep, respectively. Furthermore, workers with high pain and high LTPA had ORs of 4.23 (95% CI 2.16-8.32) for insomnia symptoms and 1.95 (95% CI 1.09-3.48) for non-restorative sleep, compared to those with low pain and low LTPA, whereas workers with high pain and low LTPA had ORs of 3.34 (95% CI 1.66-6.70) for insomnia symptoms and 2.14 (95% CI 1.21-3.80) for non-restorative sleep, respectively. CONCLUSIONS: Workers with high musculoskeletal pain who also conducted high levels of OPA or LTPA reported higher prevalence of insomnia symptoms.

Physical work exposure, chronic musculoskeletal pain and risk of insomnia: longitudinal data from the HUNT study, Norway.

OBJECTIVES: To prospectively investigate (i) the association of physical work demands and work-related physical fatigue with risk of insomnia symptoms and (ii) if these associations are influenced by chronic musculoskeletal pain. METHODS: Prospective study on a working population of 8563 women and 7598 men participating in the Nord-Trøndelag Health Study (Norway) who reported no insomnia at baseline in 1995-1997. Occurrence of insomnia symptoms was assessed at follow-up in 2006-2008. A Poisson regression model was used to calculate adjusted risk ratios (RRs) for insomnia symptoms with 95% CI. RESULTS: Compared with workers without work-related physical fatigue, women and men who reported that they were always fatigued had RRs of insomnia of 2.34 (95% CI 1.72 to 3.18) and 2.47 (95% CI 1.59 to 3.83), respectively. Overall, physical work demands was not associated with risk of insomnia, although men who reported heavy physical work had an RR of 0.67 (95% CI 0.47 to 0.97) compared with men with mostly sedentary work. Compared with the reference group of workers without work-related physical fatigue and no chronic pain, analyses of joint effects showed that women with excessive work-related fatigue had an RR of 4.20 (95% CI 2.95 to 5.98) if they reported chronic pain and an RR of 1.67 (95% CI 0.87 to 3.18) if they did not. Corresponding RRs in men were 3.55 (95% CI 2.11 to 5.98) and 2.13 (95% CI 1.07 to 4.25). CONCLUSION: These findings suggest that there is an interplay between work-related physical fatigue and musculoskeletal pain that should receive particular attention in the prevention of insomnia in working populations.

The joint association of insomnia disorder and lifestyle on the risk of activity-limiting spinal pain: the HUNT Study.

OBJECTIVES: To investigate whether the combination of multiple healthy lifestyle factors modify the well-established association between insomnia disorder and risk of activity-limiting spinal pain. METHODS: We conducted a prospective study of 10,228 individuals who participated in two surveys over ∼11 years and were free of chronic pain in the neck, upper back, and lower back at baseline. Adjusted risk ratios (RRs) were calculated for the risk of activity-limiting chronic spinal pain (i.e., pain that impairs daily activities at work or leisure time) at follow-up associated with the joint association of insomnia disorder and the combination of five lifestyle factors (body mass index, leisure time physical activity, alcohol consumption, diet, and smoking) at baseline. RESULTS: Our data indicate an additive interaction between insomnia disorder and lifestyle on risk of activity-limiting spinal pain, i.e., compared with participants without insomnia disorder and the best lifestyle score, participants with insomnia disorder and the worst lifestyle score had a RR of activity-limiting spinal pain of 3.57 (95 % CI: 2.65-4.80); participants with insomnia disorder and the best lifestyle score had a RR of 1.56 (95 % CI: 0.97-2.50); and those without insomnia disorder and the worst lifestyle score had a RR of 1.32 (95 % CI: 1.12-1.55). CONCLUSIONS: Poor lifestyle behaviour amplifies the adverse effect of insomnia disorder on the risk of activity-limiting chronic spinal pain.

A Machine Learning Model for Predicting Sleep and Wakefulness Based on Accelerometry, Skin Temperature and Contextual Information.

Purpose: Body-worn accelerometers are commonly used to estimate sleep duration in population-based studies. However, since accelerometry-based sleep/wake-scoring relies on detecting body movements, the prediction of sleep duration remains a challenge. The aim was to develop and evaluate the performance of a machine learning (ML) model to predict accelerometry-based sleep duration and to explore if this prediction can be improved by adding skin temperature data, circadian rhythm based on the estimated midpoint of sleep, and cyclic time features to the model. Patients and Methods: Twenty-nine adults (17 females), mean (SD) age 40.2 (15.0) years (range 17-70) participated in the study. Overnight polysomnography (PSG) was recorded in a sleep laboratory or at home along with body movement by two accelerometers with an embedded skin temperature sensor (AX3, Axivity, UK) positioned at the low back and thigh. The PSG scoring of sleep/wake was used as ground truth for training the ML model. Results: Based on pure accelerometer data input to the ML model, the specificity and sensitivity for predicting sleep/wake was 0.52 (SD 0.24) and 0.95 (SD 0.03), respectively. Adding skin temperature data and contextual information to the ML model improved the specificity to 0.72 (SD 0.20), while sensitivity remained unchanged at 0.95 (SD 0.05). Correspondingly, sleep overestimation was reduced from 54 min (228 min, limits of agreement range [LoAR]) to 19 min (154 min LoAR). Conclusion: An ML model can predict sleep/wake periods with excellent sensitivity and moderate specificity based on a dual-accelerometer set-up when adding skin temperature data and contextual information to the model.

Inpatient multimodal rehabilitation and the role of pain intensity and mental distress on return-to-work: causal mediation analyses of a randomized controlled trial.

OBJECTIVE: Studies suggest that symptom reduction is not necessary for improved return-to-work after occupational rehabilitation programmes. This secondary analysis of a randomized controlled trial examined whether pain intensity and mental distress mediate the effect of an inpatient programme on sustainable return-to-work. METHODS: The randomized controlled trial compared inpatient multimodal occupational rehabilitation (n = 82) with outpatient acceptance and commitment therapy (n = 79) in patients sick-listed due to musculoskeletal and mental health complaints. Pain and mental distress were measured at the end of each programme, and patients were followed up on sick-leave for 12 months. Cox regression with an inverse odds weighted approach was used to assess causal mediation. RESULTS: The total effect on return-to-work was in favour of the inpatient programme compared with the control (hazard ratio (HR) 1.96; 95% confidence interval (95% CI) 1.15-3.35). There was no evidence of mediation by pain intensity (indirect effect HR, 0.98; 95% CI, 0.61-1.57, direct effect HR, 2.00; 95% CI, 1.02-3.90), but mental distress had a weak suppression effect (indirect effect HR, 0.89; 95% CI, 0.59-1.36, direct effect HR, 2.19; 95% CI, 1.13-4.26). CONCLUSION: These data suggest that symptom reduction is not necessary for sustainable return-to-work after an inpatient multimodal occupational rehabilitation intervention.

Effectiveness of digital Cognitive-Behavioural Therapy for Insomnia in patients with musculoskeletal complaints and insomnia in primary care physiotherapy: study protocol for a randomised controlled trial.

INTRODUCTION: Insomnia is prevalent among patients visiting physiotherapists due to musculoskeletal complaints and associated with poorer pain prognosis. Cognitive-Behavioural Therapy for Insomnia (CBT-I) may be effective for improving sleep quality and pain-related outcomes in these patients, but its availability and utility are limited in daily physiotherapy practice. The aim of this randomised controlled trial (RCT) is to evaluate the effectiveness of digital CBT-I in addition to usual treatment in patients with chronic musculoskeletal complaints and insomnia, compared with usual treatment only. METHODS AND ANALYSIS: In this RCT, eligible and consenting participants will be randomised (1:1 ratio) to one of two interventions: (1) digital CBT-I adjunct to physiotherapy treatment or (2) usual physiotherapy treatment. Patients with musculoskeletal complaints and insomnia visiting a physiotherapist in Norway will be invited to participate in the study. We aim to include 188 participants to detect a difference in the primary outcome. Outcome variables will be assessed at baseline (prior to randomisation) and at 6-week, 3-month, 6-month and 12-month follow-up. The primary outcome is between-group difference in insomnia severity, assessed with the Insomnia Severity Index (0-28 points) at 3 months. Secondary outcomes include between-group differences in pain intensity, physical function, work ability, health-related quality of life, mental distress, and self-reported use of sleep and pain medication. Exploratory analyses will identify patient characteristics influencing the effect of the digital intervention. ETHICS AND DISSEMINATION: This trial is approved by the Regional Committee for Medical and Health Research Ethics in Central Norway (Ref. 2023/533381). The results of the trial will be published in peer-review journals and disseminated at national and international conferences. TRIAL REGISTRATION NUMBER: ISRCTN91221906.