Objective Examine the effectiveness of specific modes of exercise training in non-specific chronic low back pain (NSCLBP). Design Network meta-analysis (NMA). Data sources MEDLINE, CINAHL, SPORTDiscus, EMBASE, CENTRAL. Eligibility criteria Exercise training randomised controlled/clinical trials in adults with NSCLBP. Results Among 9543 records, 89 studies (patients=5578) were eligible for qualitative synthesis and 70 (pain), 63 (physical function), 16 (mental health) and 4 (trunk muscle strength) for NMA. The NMA consistency model revealed that the following exercise training modalities had the highest probability (surface under the cumulative ranking (SUCRA)) of being best when compared with true control: Pilates for pain (SUCRA=100%; pooled standardised mean difference (95% CI): −1.86 (–2.54 to –1.19)), resistance (SUCRA=80%; −1.14 (–1.71 to –0.56)) and stabilisation/motor control (SUCRA=80%; −1.13 (–1.53 to –0.74)) for physical function and resistance (SUCRA=80%; −1.26 (–2.10 to –0.41)) and aerobic (SUCRA=80%; −1.18 (–2.20 to –0.15)) for mental health. True control was most likely (SUCRA≤10%) to be the worst treatment for all outcomes, followed by therapist hands-off control for pain (SUCRA=10%; 0.09 (–0.71 to 0.89)) and physical function (SUCRA=20%; −0.31 (–0.94 to 0.32)) and therapist hands-on control for mental health (SUCRA=20%; −0.31 (–1.31 to 0.70)). Stretching and McKenzie exercise effect sizes did not differ to true control for pain or function (p>0.095; SUCRA<40%). NMA was not possible for trunk muscle endurance or analgesic medication. The quality of the synthesised evidence was low according to Grading of Recommendations Assessment, Development and Evaluation criteria. Summary/conclusion There is low quality evidence that Pilates, stabilisation/motor control, resistance training and aerobic exercise training are the most effective treatments, pending outcome of interest, for adults with NSCLBP. Exercise training may also be more effective than therapist hands-on treatment. Heterogeneity among studies and the fact that there are few studies with low risk of bias are both limitations.

Although it is generally accepted that certain practice conditions can place large demands on working memory (WM) when performing and learning a motor skill, the influence that WM capacity has on the acquisition of motor skills remains unsubstantiated. This study examined the role of WM capacity in a motor skill practice context that promoted WM involvement through the provision of explicit instructions. A cohort of 90 children aged 8 to 10 years were assessed on measures of WM capacity and attention. Children who scored in the lowest and highest thirds on the WM tasks were allocated to lower WM capacity (n = 24) and higher WM capacity (n = 24) groups, respectively. The motor task required children to practice basketball shooting for 240 trials in blocks of 20 shots, with pre- and post-tests occurring before and after the intervention. A retention test was also administered one week after the post-test. Prior to every practice block, children were provided with five explicit instructions that were specific to the step-by-step processes of shooting a basketball. Results revealed that the higher WM capacity group displayed consistent improvements from pre- to post-test and through to the retention test, while the opposite effect occurred in the lower WM capacity group. This implies that the explicit instructions had a negative influence on learning by the lower WM capacity children. The results are discussed in relation to strategy selection for dealing with instructions and the role of attention control.

Total volumes of physical activity and sedentary behaviour have been associated with cardio-metabolic risk profiles; however, little research has examined whether patterns of activity (e.g., prolonged bouts, frequency of breaks in sitting) impact cardio-metabolic risk. The aim of this review was to synthesise the evidence concerning associations between activity patterns and cardio-metabolic risk factors in children and adolescents aged 5–19 years. A systematic search of seven databases was completed in October 2017. Included studies were required to report associations between objectively-measured activity patterns and cardio-metabolic risk factors in children and/or adolescents, and be published between 1980 and 2017. At least two researchers independently screened each study, extracted data, and undertook risk of bias assessments. From the 15,947 articles identified, 29 were included in this review. Twenty-four studies were observational (cross-sectional and/or longitudinal); five were experimental. Ten studies examined physical activity patterns, whilst 19 studies examined sedentary patterns. Only one study examined both physical activity and sedentary time patterns. Considerable variation in definitions of activity patterns made it impossible to identify which activity patterns were most beneficial to children’s and adolescents’ cardio-metabolic health. However, potential insights and current research gaps were identified. A consensus on how to define activity patterns is needed in order to determine which activity patterns are associated with children’s and adolescents’ cardio-metabolic risk. This will inform future research on the impact of activity patterns on children’s and adolescents’ short- and longer-term health.

The study aimed to describe youth time-use compositions, focusing on time spent in shorter and longer bouts of sedentary behavior and physical activity (PA), and to examine associations of these time-use compositions with cardiometabolic biomarkers. Accelerometer and cardiometabolic biomarker data from 2 Australian studies involving youths 7-13 years old were pooled (complete cases with accelerometry and adiposity marker data, n = 782). A 9-component time-use composition was formed using compositional data analysis: time in shorter and longer bouts of sedentary behavior; time in shorter and longer bouts of light-, moderate-, or vigorous-intensity PA; and "other time" (i.e., non-wear/sleep). Shorter and longer bouts of sedentary time were defined as <5 min and ≥5 min, respectively. Shorter bouts of light-, moderate-, and vigorous-intensity PA were defined as <1 min; longer bouts were defined as ≥1 min. Regression models examined associations between overall time-use composition and cardiometabolic biomarkers. Then, associations were derived between ratios of longer activity patterns relative to shorter activity patterns, and of each intensity level relative to the other intensity levels and "other time", and cardiometabolic biomarkers. Confounder-adjusted models showed that the overall time-use composition was associated with adiposity, blood pressure, lipids, and the summary score. Specifically, more time in longer bouts of light-intensity PA relative to shorter bouts of light-intensity PA was significantly associated with greater body mass index z-score (zBMI) (β = 1.79; SE = 0.68) and waist circumference (β = 18.35, SE = 4.78). When each activity intensity was considered relative to all higher intensities and "other time", more time in light- and vigorous-intensity PA, and less time in sedentary behavior and moderate-intensity PA, were associated with lower waist circumference. Accumulating PA, particularly light-intensity PA, in frequent short bursts may be more beneficial for limiting adiposity compared to accumulating the same amount of PA at these intensities in longer bouts.

Background: Despite significant interest in assessing activity patterns in different populations, there has been no consensus concerning the definition and operationalisation of this term. This has limited the comparability, interpretability, and synthesis of study findings to date. The aim of this study was to establish a consensus regarding the way in which activity patterns and activity pattern components are defined and reported.Methods: The activity patterns literature was searched to identify experts to be invited to participate and to develop a proposed definition of activity patterns and activity pattern components. A three-round modified Delphi survey was conducted online (November 2021 to May 2022). In Round 1, participants were asked to rate their agreement with a proposed activity patterns definition, which also included six activity pattern components (e.g., activity intensity, activity bout, transitions), six examples of activity patterns (e.g., frequency of postural transitions in discrete time periods) and eight items for reporting activity patterns in future research (n = 21 items). Open-ended questions enabled participants to provide further comments and suggestions for additional items. Consensus was defined a priori as ≥ 80% participants rating their agreement with an item. In Round 2, participants were asked to rate their agreement with 25 items (13 original items, eight amended, and four new). In Round 3, participants rated their agreement with 10 items (five original items, four amended, and one new).Results: Twenty experts in activity patterns research participated in Round 1, with response rates of 80% and 60% in Rounds 2 and 3, respectively. The proposed activity pattern definition, all activity pattern components definitions, four of the six activity pattern examples, and 10 items in the activity patterns reporting framework achieved consensus. The removal of one activity component item between Rounds 1 and 2 achieved consensus.Conclusion: This modified Delphi study achieved consensus for defining and reporting activity patterns for the first time. This consensus definition enables standardisation of activity patterns terminology, which is important given the significant interest in quantifying how individuals accumulate their physical activity and sedentary behaviour across the lifespan to inform the development of future public health guidelines and interventions efforts.