DE-PASS best evidence statement (BESt): determinants of adolescents' device-based physical activity and sedentary behaviour in settings: a systematic review and meta-analysis.

BACKGROUND: Although physical activity (PA) is associated with significant health benefits, only a small percentage of adolescents meet recommended PA levels. This systematic review with meta-analysis explored the modifiable determinants of adolescents' device-based PA and/or sedentary behaviour (SB), evaluated in previous interventions and examined the associations between PA/SB and these determinants in settings. METHODS: A search was conducted on five electronic databases, including papers published from January 2010 to July 2023. Randomized Controlled Trials (RCTs) or Controlled Trials (CTs) measuring adolescents' device-based PA/SB and their modifiable determinants at least at two time points: pre- and post-intervention were considered eligible. PA/SB and determinants were the main outcomes. Modifiable determinants were classified after data extraction adopting the social-ecological perspective. Robust Bayesian meta-analyses (RoBMA) were performed per each study setting. Outcomes identified in only one study were presented narratively. The risk of bias for each study and the certainty of the evidence for each meta-analysis were evaluated. The publication bias was also checked. PROSPERO ID: CRD42021282874. RESULTS: Fourteen RCTs (eight in school, three in school and family, and one in the family setting) and one CT (in the school setting) were included. Fifty-four modifiable determinants were identified and were combined into 33 broader determinants (21 individual-psychological, four individual-behavioural, seven interpersonal, and one institutional). RoBMAs revealed none or negligible pooled intervention effects on PA/SB or determinants in all settings. The certainty of the evidence of the impact of interventions on outcomes ranged from very low to low. Narratively, intervention effects in favour of the experimental group were detected in school setting for the determinants: knowledge of the environment for practicing PA, d = 1.84, 95%CI (1.48, 2.20), behaviour change techniques, d = 0.90, 95%CI (0.09, 1.70), choice provided, d = 0.70, 95%CI (0.36, 1.03), but no corresponding effects on PA or SB were found. CONCLUSIONS: Weak to minimal evidence regarding the associations between the identified modifiable determinants and adolescents' device-based PA/SB in settings were found, probably due to intervention ineffectiveness. Well-designed and well-implemented multicomponent interventions should further explore the variety of modifiable determinants of adolescents' PA/SB, including policy and environmental variables.

Incidence of sick leave and disability pension in adults with musculoskeletal pain and co-occurring long-term conditions: data from the Norwegian HUNT study and national registries.

BACKGROUND: Musculoskeletal pain is one of the leading causes of work productivity loss. Long-term conditions (LTCs) commonly occur alongside musculoskeletal pain. However, the incidence of sick leave and disability pension according to LTC status in people with musculoskeletal pain has not been previously described. METHODS: Working-age participants (20-65 years) with persistent musculoskeletal pain who participated in the HUNT3 Study (1995-97) were included. Twenty-five LTCs were classified into 8 LTC groups according to the International Classification of Diseases version 11. Data on sickness and disability benefits were obtained from the National Insurance Database and linked to the HUNT3 data using participants' personal identification number. Age-adjusted incidence rates (IRs) (per 10,000 person-years) and hazard ratios (HRs) of sick leave during 5-year follow-up and disability pension during ~ 25-year follow-up were estimated with 95% confidence intervals (CIs) and presented according to LTC status. RESULTS: Overall, 11,080 participants with musculoskeletal pain were included. Of those, 32% reported one LTC and 45% reported ≥ 2 LTCs. During the follow up period, 1,312 participants (12%) received disability pension due to musculoskeletal conditions. The IR of sick leave and disability pension due to musculoskeletal conditions increased with number of LTCs. Specifically, the IR of sick leave was 720 (95% CI 672 to 768) in participants without any LTCs and 968 (95% CI 927 to 1,009) if they had ≥ 2 LTCs. The IRs of disability pension were 87 (95% CI 75 to 98) and 167 (95% CI 154 to 179) among those with no LTCs and ≥ 2 LTCs, respectively. The incidence of sick leave and disability pension due to musculoskeletal conditions was largely similar across LTCs, although the incidence of disability pension was somewhat higher among people with sleep disorders (IR: 223, 95% CI 194 to 252). CONCLUSIONS: Among people with persistent musculoskeletal pain, the incidence of prematurely leaving the work force due to musculoskeletal conditions was twice as high for those with multiple LTCs compared to those without any LTCs. This was largely irrespective of the type of LTC, indicating that the number of LTCs are an important feature when evaluating work participation among people with musculoskeletal pain.

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.

Physical activity and associations with health-related quality of life in adults born small for gestational age at term: a prospective cohort study.

BACKGROUND: Adults born small for gestational age (SGA) have increased risk of adverse health outcomes. Physical activity (PA) is a key determinant of health and health-related quality of life (HRQoL). We aimed to investigate if being born SGA at term is associated with lower objectively measured and self-reported PA during adulthood. We also examined if objectively measured and self-reported PA were associated with HRQoL. METHODS: As part of the 32-year follow-up in the NTNU Low Birth Weight in a Lifetime Perspective study, SGA and non-SGA control participants wore two tri-axial accelerometers for seven days (37 SGA, 43 control), and completed the International Physical Activity Questionnaire (IPAQ) (42 SGA, 49 control) and the Short Form 36 Health Survey (SF-36) (55 SGA, 67 control). Group differences in objectively measured daily metabolic equivalent of task (MET) minutes spent sedentary (lying, sitting), on feet (standing, walking, running, cycling), on the move (walking, running, cycling) and running/cycling, and group differences in self-reported daily MET minutes spent walking and in moderate and vigorous PA were examined using linear regression. Associations with SF-36 were explored in a general linear model. RESULTS: Mean (SD) daily MET minutes on the move were 218 (127) in the SGA group and 227 (113) in the control group. There were no group differences in objectively measured and self-reported PA or associations with HRQoL. In the SGA group, one MET minute higher objectively measured time on the move was associated with 4.0 (95% CI: 0.6-6.5, p = 0.009) points higher SF-36 physical component summary. CONCLUSION: We found no differences in objectively measured and self-reported PA or associations with HRQoL between term-born SGA and non-SGA control participants in adulthood.

Health-related quality of life in young adults born small for gestational age: a prospective cohort study.

BACKGROUND: Individuals born small for gestational age (SGA) have an increased risk of several adverse health outcomes, but their health-related quality of life (HRQoL) across young adulthood has yet to be studied. The main aim of this study was to investigate if being born SGA at term is associated with poor HRQoL at 32 years of age. A second aim was to explore longitudinal changes in HRQoL from age 20 to 32 years. METHODS: In the prospective NTNU Low Birth Weight in a Lifetime Perspective study, 56 participants born SGA and 68 non-SGA control participants completed the Short Form 36 Health Survey (SF-36) at age 32 years to assess HRQoL. The SF-36 was also administrated at age 20 and 28 years. Longitudinal changes in the eight SF-36 domains and the two component summaries from 20 to 32 years were analyzed by linear mixed models. In total, 82 adults born SGA and 98 controls participated at least once and were included in the longitudinal analyses. RESULTS: At age 32 years the participants born SGA scored 14.8 (95% CI 4.7 to 25.3) points lower in the SF-36 role-physical domain compared with the control group, i.e. more problems with work or other daily activities due to physical health problems. The longitudinal analyses showed significant group differences from 20 to 32 years in the role-emotional domain, and in the physical and mental component summaries. Among participants born SGA, the physical component summary decreased from age 20 to 28 years (-3.2, 95% CI -5.0 to -1.8), while the mental component summary (6.0, 95% CI 2.9 to 8.6) and role-emotional domain score (19.3, 95% CI 9.9 to 30.3) increased, but there were no further changes from 28 to 32 years. There were no longitudinal changes in the control group from 20 to 32 years. CONCLUSION: Overall, individuals born SGA at term reported similar HRQoL at age 32 years compared with non-SGA controls. Self-perceived mental health improved during young adulthood among individuals born SGA, while self-perceived physical health deteriorated. The latter findings warrant further investigation.

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.

Using Intervention Mapping to Develop a Decision Support System-Based Smartphone App (selfBACK) to Support Self-management of Nonspecific Low Back Pain: Development and Usability Study.

BACKGROUND: International guidelines consistently endorse the promotion of self-management for people with low back pain (LBP); however, implementation of these guidelines remains a challenge. Digital health interventions, such as those that can be provided by smartphone apps, have been proposed as a promising mode of supporting self-management in people with chronic conditions, including LBP. However, the evidence base for digital health interventions to support self-management of LBP is weak, and detailed descriptions and documentation of the interventions are lacking. Structured intervention mapping (IM) constitutes a 6-step process that can be used to guide the development of complex interventions. OBJECTIVE: The aim of this paper is to describe the IM process for designing and creating an app-based intervention designed to support self-management of nonspecific LBP to reduce pain-related disability. METHODS: The first 5 steps of the IM process were systematically applied. The core processes included literature reviews, brainstorming and group discussions, and the inclusion of stakeholders and representatives from the target population. Over a period of >2 years, the intervention content and the technical features of delivery were created, tested, and revised through user tests, feasibility studies, and a pilot study. RESULTS: A behavioral outcome was identified as a proxy for reaching the overall program goal, that is, increased use of evidence-based self-management strategies. Physical exercises, education, and physical activity were the main components of the self-management intervention and were designed and produced to be delivered via a smartphone app. All intervention content was theoretically underpinned by the behavior change theory and the normalization process theory. CONCLUSIONS: We describe a detailed example of the application of the IM approach for the development of a theory-driven, complex, and digital intervention designed to support self-management of LBP. This description provides transparency in the developmental process of the intervention and can be a possible blueprint for designing and creating future digital health interventions for self-management.

Exploratory application of machine learning methods on patient reported data in the development of supervised models for predicting outcomes.

BACKGROUND: Patient-reported outcome measurements (PROMs) are commonly used in clinical practice to support clinical decision making. However, few studies have investigated machine learning methods for predicting PROMs outcomes and thereby support clinical decision making. OBJECTIVE: This study investigates to what extent different machine learning methods, applied to two different PROMs datasets, can predict outcomes among patients with non-specific neck and/or low back pain. METHODS: Using two datasets consisting of PROMs from (1) care-seeking low back pain patients in primary care who participated in a randomized controlled trial, and (2) patients with neck and/or low back pain referred to multidisciplinary biopsychosocial rehabilitation, we present data science methods for data prepossessing and evaluate selected regression and classification methods for predicting patient outcomes. RESULTS: The results show that there is a potential for machine learning to predict and classify PROMs. The prediction models based on baseline measurements perform well, and the number of predictors can be reduced, which is an advantage for implementation in decision support scenarios. The classification task shows that the dataset does not contain all necessary predictors for the care type classification. Overall, the work presents generalizable machine learning pipelines that can be adapted to other PROMs datasets. CONCLUSION: This study demonstrates the potential of PROMs in predicting short-term patient outcomes. Our results indicate that machine learning methods can be used to exploit the predictive value of PROMs and thereby support clinical decision making, given that the PROMs hold enough predictive power.

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 .

HARTH: A Human Activity Recognition Dataset for Machine Learning.

Existing accelerometer-based human activity recognition (HAR) benchmark datasets that were recorded during free living suffer from non-fixed sensor placement, the usage of only one sensor, and unreliable annotations. We make two contributions in this work. First, we present the publicly available Human Activity Recognition Trondheim dataset (HARTH). Twenty-two participants were recorded for 90 to 120 min during their regular working hours using two three-axial accelerometers, attached to the thigh and lower back, and a chest-mounted camera. Experts annotated the data independently using the camera's video signal and achieved high inter-rater agreement (Fleiss' Kappa =0.96). They labeled twelve activities. The second contribution of this paper is the training of seven different baseline machine learning models for HAR on our dataset. We used a support vector machine, k-nearest neighbor, random forest, extreme gradient boost, convolutional neural network, bidirectional long short-term memory, and convolutional neural network with multi-resolution blocks. The support vector machine achieved the best results with an F1-score of 0.81 (standard deviation: ±0.18), recall of 0.85±0.13, and precision of 0.79±0.22 in a leave-one-subject-out cross-validation. Our highly professional recordings and annotations provide a promising benchmark dataset for researchers to develop innovative machine learning approaches for precise HAR in free living.

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.

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.

Parental chronic pain in relation to chronic pain in their adult offspring: family-linkage within the HUNT Study, Norway.

BACKGROUND: Little is known about the association between parental chronic musculoskeletal pain (CMP) and occurrence of CMP in the adult offspring. The main objective of this study was to assess the parent-offspring association of CMP, and also to examine possible modifying effects of age and sex. METHODS: The study includes 11 248 parent-offspring trios from the Norwegian HUNT Study with information on parental CMP obtained in 1995-97 and offspring CMP obtained in 2006-08. Logistic regression was used to calculate adjusted odds ratios (ORs) for offspring CMP associated with parental CMP. RESULTS: Maternal and paternal CMP was associated with 20-40% increased odds of CMP in sons and daughters. Both sons and daughters had an OR of 1.6 (95% CI 1.4 to 1.9) when both parents reported CMP, compared to when none of the parents had CMP. Restricting the analyses to parental CMP that was associated with limited work ability and leisure time activity did not change the strength of the association. Further, analyses stratified by parental age ± 65 years showed no clear difference in the estimated associations, and there was no evidence of interaction for parental sex (P ≥ 0.39) or offspring age ± 40 years (P ≥ 0.26). CONCLUSIONS: This large family-linkage study show that maternal and paternal CMP are positively associated with CMP in the adult offspring, irrespective of parental and offspring age, and that the associations are strongest when both parents have CMP. Although the high prevalence of CMP in both parents and offspring suggests that not all cases are clinically relevant, the results suggest that chronic pain has a heritable component.

Sleep problems, exercise and obesity and risk of chronic musculoskeletal pain: the Norwegian HUNT study.

BACKGROUND: The objective was to investigate the association between self-reported sleep problems and risk of chronic pain in the low back and neck/shoulders, and whether physical exercise and body mass index (BMI) alter this association. METHODS: The study comprised data on 26 896 women and men in the Nord-Trøndelag Health Study (Norway) without chronic pain or physical impairment at baseline in 1984-86. Occurrence of chronic pain was assessed at follow-up in 1995-97. A generalized linear model was used to calculate adjusted risk ratios. RESULTS: Sleep problems were dose-dependently associated with risk of pain in the low back and neck/shoulders in both women and men (P < 0.001 both genders). Women and men who reported sleep problems 'sometimes' and 'often/always' had a higher risk of chronic pain of 23-32% and 51-66%, respectively, than those who reported sleep problems 'never'. Combined analyses showed that persons with sleep problems 'sometimes' and who exercised ≥1 hour per week had lower risk of chronic pain in the low back (P < 0.04) and neck/shoulders (P < 0.001) than inactive persons with a similar level of sleep problems (P < 0.04). Likewise, persons with BMI <25 kg/cm(2) and sleep problems 'sometimes' had lower risk of chronic pain in the low back (P < 0.001) and neck/shoulders (P < 0.001) than persons with BMI ≥25 kg/cm(2) and a similar level of sleep problems. CONCLUSION: Sleep problems are associated with an increased risk of chronic pain in the low back and neck/shoulders. Regular exercise and maintenance of normal body weight may reduce the adverse effect of mild sleep problems on risk of chronic 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.