Explore the vast range of titles available.

Background Sedentary behaviour (SB; time spent sitting) is associated with musculoskeletal pain (MSP) conditions; however, no prior systematic review has examined these associations according to SB domains. We synthesised evidence on occupational and non-occupational SB and MSP conditions. Methods Guided by a PRISMA protocol, eight databases (MEDLINE, CINAHL, PsycINFO, Web of Science, Scopus, Cochrane Library, SPORTDiscus, and AMED) and three grey literature sources (Google Scholar, WorldChat, and Trove) were searched (January 1, 2000, to March 17, 2021) for original quantitative studies of adults ≥ 18 years. Clinical-condition studies were excluded. Studies’ risk of bias was assessed using the QualSyst checklist. For meta-analyses, random effect inverse-variance pooled effect size was estimated; otherwise, best-evidence synthesis was used for narrative review. Results Of 178 potentially-eligible studies, 79 were included [24 general population; 55 occupational (incuding15 experimental/intervention)]; 56 studies were of high quality, with scores > 0.75. Data for 26 were meta-synthesised. For cross-sectional studies of non-occupational SB, meta-analysis showed full-day SB to be associated with low back pain [LBP – OR = 1.19(1.03 – 1.38)]. Narrative synthesis found full-day SB associations with knee pain, arthritis, and general MSP, but the evidence was insufficient on associations with neck/shoulder pain, hip pain, and upper extremities pain. Evidence of prospective associations of full-day SB with MSP conditions was insufficient. Also, there was insufficient evidence on both cross-sectional and prospective associations between leisure-time SB and MSP conditions. For occupational SB, cross-sectional studies meta-analysed indicated associations of self-reported workplace sitting with LBP [OR = 1.47(1.12 – 1.92)] and neck/shoulder pain [OR = 1.73(1.46 – 2.03)], but not with extremities pain [OR = 1.17(0.65 – 2.11)]. Best-evidence synthesis identified inconsistent findings on cross-sectional association and a probable negative prospective association of device-measured workplace sitting with LBP-intensity in tradespeople. There was cross-sectional evidence on the association of computer time with neck/shoulder pain, but insufficient evidence for LBP and general MSP. Experimental/intervention evidence indicated reduced LBP, neck/shoulder pain, and general MSP with reducing workplace sitting. Conclusions We found cross-sectional associations of occupational and non-occupational SB with MSP conditions, with occupational SB associations being occupation dependent, however, reverse causality bias cannot be ruled out. While prospective evidence was inconclusive, reducing workplace sitting was associated with reduced MSP conditions. Future studies should emphasise prospective analyses and examining potential interactions with chronic diseases. Protocol registration PROSPERO ID # CRD42020166412 (Amended to limit the scope)

Abstract Background While there is evidence that physical activity, sedentary behaviour (SB) and sleep may all be associated with modified levels of inflammatory markers in adolescents and children, associations with one movement behaviour have not always been adjusted for other movement behaviours, and few studies have considered all movement behaviours in the 24-hour day as an exposure. Purpose The aim of the study was to explore how longitudinal reallocations of time between moderate-to-vigorous physical activity (MVPA), light physical activity (LPA), SB and sleep are associated with changes in inflammatory markers in children and adolescents. Methods A total of 296 children/adolescents participated in a prospective cohort study with a 3-year follow-up. MVPA, LPA and SB were assessed by accelerometers. Sleep duration was assessed using the Health Behavior in School-aged Children questionnaire. Longitudinal compositional regression models were used to explore how reallocations of time between movement behaviours are associated with changes in inflammatory markers. Results Reallocations of time from SB to sleep were associated with increases in C3 levels (difference for 60 min/d reallocation [d 60] = 5.29 mg/dl; 95% confidence interval [CI] = 0.28, 10.29) and TNF-α (d 60 = 1.81 mg/dl; 95% CI = 0.79, 15.41) levels. Reallocations from LPA to sleep were also associated with increases in C3 levels (d 60 = 8.10 mg/dl; 95% CI = 0.79, 15.41). Reallocations from LPA to any of the remaining time-use components were associated with increases in C4 levels (d 60 ranging from 2.54 to 3.63 mg/dl; p < 0.05), while any reallocation of time away from MVPA was associated with unfavourable changes in leptin (d 60 ranging from 3088.44 to 3448.07 pg/ml; p < 0.05). Conclusions Reallocations of time between 24-h movement behaviours are prospectively associated with some inflammatory markers. Reallocating time away from LPA appears to be most consistently unfavourably associated with inflammatory markers. Given that higher levels of inflammation during childhood and adolescence are associated with an increased risk of chronic diseases in adulthood, children and adolescents should be encouraged to maintain or increase the level of LPA to preserve a healthy immune system.

Background While there is evidence that physical activity, sedentary behaviour (SB) and sleep may all be associated with modified levels of inflammatory markers in adolescents and children, associations with one movement behaviour have not always been adjusted for other movement behaviours, and few studies have considered all movement behaviours in the 24-hour day as an exposure. Purpose The aim of the study was to explore how longitudinal reallocations of time between moderate-to-vigorous physical activity (MVPA), light physical activity (LPA), SB and sleep are associated with changes in inflammatory markers in children and adolescents. Methods A total of 296 children/adolescents participated in a prospective cohort study with a 3-year follow-up. MVPA, LPA and SB were assessed by accelerometers. Sleep duration was assessed using the Health Behavior in School-aged Children questionnaire. Longitudinal compositional regression models were used to explore how reallocations of time between movement behaviours are associated with changes in inflammatory markers. Results Reallocations of time from SB to sleep were associated with increases in C3 levels (difference for 60 min/d reallocation [ d 60 ] = 5.29 mg/dl; 95% confidence interval [CI] = 0.28, 10.29) and TNF-α ( d 60  = 1.81 mg/dl; 95% CI = 0.79, 15.41) levels. Reallocations from LPA to sleep were also associated with increases in C3 levels ( d 60  = 8.10 mg/dl; 95% CI = 0.79, 15.41). Reallocations from LPA to any of the remaining time-use components were associated with increases in C4 levels ( d 60 ranging from 2.54 to 3.63 mg/dl; p  < 0.05), while any reallocation of time away from MVPA was associated with unfavourable changes in leptin ( d 60 ranging from 3088.44 to 3448.07 pg/ml; p  < 0.05). Conclusions Reallocations of time between 24-h movement behaviours are prospectively associated with some inflammatory markers. Reallocating time away from LPA appears to be most consistently unfavourably associated with inflammatory markers. Given that higher levels of inflammation during childhood and adolescence are associated with an increased risk of chronic diseases in adulthood, children and adolescents should be encouraged to maintain or increase the level of LPA to preserve a healthy immune system.

Objectives To identify the modifiable determinants targeted in interventions involving older adults, and to determine which of these interventions effectively increased physical activity (PA) and/or reduced sedentary behaviour (SB). Additionally, to explore whether the effects of these interventions vary based on the implementation setting. Methods A search of randomized controlled trials (RCTs) and controlled trials (CTs) was performed in Medline, APA PsycArticles, SPORTDiscus, and Web of Science. Risk of bias assessment was performed with Cochrane’s tool. Modifiable determinants were narratively synthesized, and random-effects models were performed to meta-analyse studies reporting device-measured physical activity or sedentary behaviour. Moderator analyses were performed to investigate the role of implementation setting. Standardized between-group mean difference (SMD) with 95% confidence interval (CI) was used to indicate effect sizes. Results From 31,727 individual records, 52 eligible studies published between 2012–2022 were identified, 30 and 22 studies from community and health care settings, respectively. Determinants within the category physical health and wellbeing ( n  = 23) were most frequently reported while only one study reported determinants within a social or cultural context. Eighteen studies were included in the meta-analysis. Interventions targeting physical health and wellbeing revealed an increase in steps (SMD = 0.46; 95%CI: 0.15 to 0.77) and minutes of moderate-to-vigorous intensity physical activity (SMD = 0.41; 95%CI: 0.19 to 0.64) among intervention participants compared to controls, whereas interventions targeting psychological or behavioural determinants showed no between-group differences in steps (SMD = 0.10; 95%CI: -0.12 to 0.32) and moderate-to-vigorous intensity physical activity (SMD = 0.26; 95%CI: -0.24 to -0.75). Interventions targeting physical health and wellbeing showed significant heterogeneity ( p  < 0.0001; I 2  = 73.10%). Subgroup analyses showed a significant effect on device-measured physical activity for the eight community-based interventions (SMD = 0.42; 95%CI: 0.07 to 0.77), while no significant effect was found for the eight studies performed in healthcare settings (SMD = 0.26; 95%CI; -0.10 to 0.62). Conclusion Interventions targeting physical health and wellbeing may increase PA in older adults, with community-based studies appearing more effective than studies in healthcare settings. The significant heterogeneity of study findings indicates that further research is needed to fully understand the influence of PA and SB determinants across settings, particularly those related to psychological, behavioural, social, and cultural factors. Systematic review registration PROSPERO: CRD42022287606.

Background Interventions that provide pregnant women with opportunities to access and participate in physical activity have been shown to be beneficial to their health. Much of this evidence however has been based on self-reported physical activity data, which may be prone to inflated effects due to recall bias and social desirability bias. No previous synthesis of randomised controlled trials has assessed the effectiveness of these interventions using only device measured data, to assess their health benefits more accurately in pregnant women. This systematic review and meta-analysis aimed to address this evidence gap. Data sources Cochrane Central Register of Controlled Trials, Medline, SportDiscus, APA PsycINFO, Embase and Web of Science databases were queried from inception up to December 2, 2021. An updated search of PubMed was conducted on May 16, 2022. Study eligibility criteria Randomised controlled trials that recruited pregnant women, participating in any physical activity intervention (excluding interventions aimed entirely at body conditioning), compared with standard antenatal care (comparators), using device-measured total physical activity as an outcome were eligible for inclusion. Methods 3144 titles and abstracts were screened for eligibility, and 18 met the inclusion criteria. Data were analysed using random effect models, (standardised mean difference and mean difference), using data from baseline to last available follow-up (primary end point), and until between 24 to 30 weeks gestation. Gestational weight gain was also assessed at these timepoints in the included trials. Results No significant differences between the groups were found for total physical activity at last available follow-up or 24 to 30 weeks gestation (95% CI 0.03 to 0.27, p  = 0.10: 95% CI -0.05 to 0.33, p  = 0.15) respectively. On average, pregnant women randomised to a physical activity intervention completed 435 and 449 more steps per day than comparators at last available follow-up and at 24 to 30 weeks gestation (95% CI -0.5-870.6, p  = 0.05: 95% CI 5.5-892.7, p  = 0.05) respectively. Intervention participants also gained 0.69 kg less (95% CI -1.30 to -0.08, p  = 0.03) weight than comparators. Conclusion Based on device-measured data, interventions to promote physical activity during pregnancy have small but important effects on increasing physical activity and managing excessive gestational weight gain.

Purpose Low levels of physical activity during pregnancy go along with increased risks for numerous health complications. We investigated whether an integrated lifestyle intervention leads to higher levels of physical activity and reduces the rate of excessive gestational weight gain (EGWG). Methods We conducted a randomized-controlled trial on 97 pregnant women, randomly assigned to receive an additional telehealth lifestyle intervention (experimental group, EG; n  = 49) or conventional antenatal care (control group, CG; n  = 48). The core lifestyle intervention comprised regular video calls, providing integrated personal support and motivation to physical activity. The primary outcome was change in physical activity measured in steps per day. An additional exploratory outcome was the proportion of participants with EGWG. Results The mean step count during the third trimester was 6483 steps/day (EG) and 5957 steps/day (CG), respectively ( p  = 0.078). Repeated-measures ANOVA revealed a significant interaction effect ( p  = 0.045) reflecting an overall increase of 497 steps per day in the EG vs. a decrease of 300 steps per day in the CG. The proportion of participants who met the IOM recommendation for total weight gain during pregnancy was significantly higher in the EG ( p  = 0.048) and the ratio of women that gained excessively was higher in the CG ( p  = 0.026). Conclusions We assume that the personalized online intervention supports women in increasing or at least maintaining their level of physical activity during the course of pregnancy. Additionally, it reduces the rate of excessive weight gain.

Objective Forward bending of the back is common in many jobs and a risk factor for sickness absence. However, this knowledge is based on self-reported forward bending that is generally imprecise. Thus, we aimed to investigate the dose-response relation between device-measured forward bending at work and prospective register-based risk of long-term sickness absence (LTSA). Methods At baseline, 944 workers (93% from blue-collar jobs) wore accelerometers on their upper back and thigh over 1-6 workdays to measure worktime with forward bending (>30° and >60°) and body positions. The first event of LTSA (>6 consecutive weeks) over a 4-year follow-up were retrieved from a national register. Compositional Cox proportional hazard analyses were used to model the association between worktime with forward bending of the back in an upright body position and LTSA adjusted for age, sex, body mass index (BMI), occupational lifting/ carrying, type of work, and, in an additional step, for leisure time physical activity (PA) on workdays. Results During a mean worktime of 457 minutes/day, the workers on average spent 40 and 10 minutes on forward bending >30° and >60° in the upright position, respectively. Five more minutes forward bending >30° and >60° at work were associated with a 4% [95% confidence interval (CI) 1.01-1.07] and 8% (95% CI 1.01-1.16) higher LTSA risk, respectively. Adjustment for leisure-time PA did not influence the results. Conclusion We found a dose-response association between device-measured forward bending of the back and prospective LTSA risk. This knowledge can be integrated into available feasible methods to measure forward bending of the back for improved workplace risk assessment and prevention.

Background Physical activity (PA) is imperative for healthy ageing and is a modifiable lifestyle factor. Accurate, clinically meaningful estimates of daily PA among older adults can inform targeted interventions to maintain function and independence. This study describes daily PA in older adults across levels of care as a first step contributing to the limited evidence on potential associations between PA and the use of care services. Methods This study used data from the Trondheim 70 + cohort in the population-based Norwegian HUNT Study. In total, 1042 participants aged 70 years or older with valid activity data were included. PA was assessed using two accelerometers over 7 consecutive days and was classified into PA (walking, standing, running, and cycling) and sedentary behavior (sitting and lying). Data on received care services were retrieved from municipal registers and participants were classified into four levels of care: 1) independently living (81.9%), 2) independently living with low-level home care services (6.5%), 3) recipients of home care services (6.0%), and 4) nursing home residents (5.7%). Time spent in the activity types and duration of bouts are presented across levels of care. Results Participants mean age was 77.5 years (range: 70.1–105.4, 55% female) and PA was lower with higher age. Across levels of care, significant group differences were found in the total time spent in PA, particularly in walking and standing. Daily PA, duration of active bouts, and number of daily walking bouts were lower for participants receiving higher levels of care. Standing was the dominant type of PA and walking appeared predominantly in short bouts at all care levels. Conclusions This is the first population-based study using device-measured PA to describe daily PA across levels of care. The results showed that low-intensity activities constitute the primary component of everyday PA, advocating for placing greater emphasis on the significant role these activities play in maintaining daily PA at older age. Furthermore, the study demonstrated that activity types and bout durations are related to the ability to live independently among older adults. Overall, these findings can contribute to better target interventions to maintain function and independence in older adults.

Cardiovascular disease accounts for 17.9 million deaths globally each year. Many research study data sets have been collected to answer questions regarding the relationship between cardiometabolic health and accelerometer-measured physical activity. This scoping review aimed to map the available data sets that have collected accelerometer-measured physical activity and cardiometabolic health markers. These data were then used to inform the development of a publicly available resource, the Global Physical Activity Data set (GPAD) catalogue. This review aimed to systematically identify data sets that have measured physical activity using accelerometers and cardiometabolic health markers using either an observational or interventional study design. Databases, trial registries, and gray literature (inception until February 2021; updated search from February 2021 to September 2022) were systematically searched to identify studies that analyzed data sets of physical activity and cardiometabolic health outcomes. To be eligible for inclusion, data sets must have measured physical activity using an accelerometric device in adults aged ≥18 years; a sample size >400 participants (unless recruited participants in a low- and middle-income country where a sample size threshold was reduced to 100); used an observational, longitudinal, or trial-based study design; and collected at least 1 cardiometabolic health marker (unless only body mass was measured). Two reviewers screened the search results to identify eligible studies, and from these, the unique names of each data set were recorded, and characteristics about each data set were extracted from several sources. A total of 17,391 study reports were identified, and after screening, 319 were eligible, with 122 unique data sets in these study reports meeting the review inclusion criteria. Data sets were found in 49 countries across 5 continents, with the most developed in Europe (n=53) and the least in Africa and Oceania (n=4 and n=3, respectively). The most common accelerometric brand and device wear location was Actigraph and the waist, respectively. Height and body mass were the most frequently measured cardiometabolic health markers in the data sets (119/122, 97.5% data sets), followed by blood pressure (82/122, 67.2% data sets). The number of participants in the included data sets ranged from 103,712 to 120. Once the review processes had been completed, the GPAD catalogue was developed to house all the identified data sets. This review identified and mapped the contents of data sets from around the world that have collected potentially harmonizable accelerometer-measured physical activity and cardiometabolic health markers. The GPAD catalogue is a web-based open-source resource developed from the results of this review, which aims to facilitate the harmonization of data sets to produce evidence that will reduce the burden of disease from physical inactivity.

Background Pediatric healthcare professionals facilitate children to enhance and maintain a physically active lifestyle. Activity monitors (AM) can help pediatric healthcare professionals assess physical activity in everyday life. However, validation research of activity monitors has often been conducted in laboratories and insight into physical activity of children in their own everyday environment is lacking. Our goal was to study the criterion validity of a prototype AM (AM-p) model in a natural setting. Methods Cross-sectional community-based study with ambulatory children (2-19 years) with and without developmental disability. Children wore the AM-p on the ankle and were filmed (gold standard) while performing an activity protocol in a natural setting. We labelled all videos per 5-second epoch with individual activity labels. Raw AM-p data were synchronized with activity labels. Using machine learning techniques, activity labels were subdivided in three pre-defined categories. Accuracy, recall, precision, and F1 score were calculated per category. Results We analyzed data of 93 children, of which 28 had a developmental disability. Mean age was 11 years (SD 4.5) with 55% girls. The AM-p model differentiated between ‘stationary’, ‘cycling’ and ‘locomotion’ activities with an accuracy of 82%, recall of 78%, precision of 75%, and F1 score of 75%, respectively. Children older than 13 years with typical development can be assessed more accurately than younger children (2-12 years) with and without developmental disabilities. Conclusion The single ankle-worn AM-p model can differentiate between three activity categories in children with and without developmental disabilities with good accuracy (82%). Because the AM-p can be used for a heterogenous group of ambulatory children with and without developmental disabilities, it may support the clinical assessment for pediatric healthcare professionals in the future.