| 1. |
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| 2. |
- Kattge, Jens, et al.
(författare)
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TRY plant trait database - enhanced coverage and open access
- 2020
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Ingår i: Global Change Biology. - : Wiley-Blackwell. - 1354-1013 .- 1365-2486. ; 26:1, s. 119-188
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Tidskriftsartikel (refereegranskat)abstract
- Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.
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| 3. |
- Falster, Daniel, et al.
(författare)
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AusTraits, a curated plant trait database for the Australian flora
- 2021
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Ingår i: Scientific Data. - : Nature Portfolio. - 2052-4463. ; 8:1
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Tidskriftsartikel (refereegranskat)abstract
- We introduce the AusTraits database - a compilation of values of plant traits for taxa in the Australian flora (hereafter AusTraits). AusTraits synthesises data on 448 traits across 28,640 taxa from field campaigns, published literature, taxonomic monographs, and individual taxon descriptions. Traits vary in scope from physiological measures of performance (e.g. photosynthetic gas exchange, water-use efficiency) to morphological attributes (e.g. leaf area, seed mass, plant height) which link to aspects of ecological variation. AusTraits contains curated and harmonised individual- and species-level measurements coupled to, where available, contextual information on site properties and experimental conditions. This article provides information on version 3.0.2 of AusTraits which contains data for 997,808 trait-by-taxon combinations. We envision AusTraits as an ongoing collaborative initiative for easily archiving and sharing trait data, which also provides a template for other national or regional initiatives globally to fill persistent gaps in trait knowledge.
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| 4. |
- Tremblay, Mark S, et al.
(författare)
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Sedentary Behavior Research Network (SBRN) - Terminology Consensus Project process and outcome.
- 2017
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Ingår i: The international journal of behavioral nutrition and physical activity. - : Springer Science and Business Media LLC. - 1479-5868. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- The prominence of sedentary behavior research in health science has grown rapidly. With this growth there is increasing urgency for clear, common and accepted terminology and definitions. Such standardization is difficult to achieve, especially across multi-disciplinary researchers, practitioners, and industries. The Sedentary Behavior Research Network (SBRN) undertook a Terminology Consensus Project to address this need.First, a literature review was completed to identify key terms in sedentary behavior research. These key terms were then reviewed and modified by a Steering Committee formed by SBRN. Next, SBRN members were invited to contribute to this project and interested participants reviewed and provided feedback on the proposed list of terms and draft definitions through an online survey. Finally, a conceptual model and consensus definitions (including caveats and examples for all age groups and functional abilities) were finalized based on the feedback received from the 87 SBRN member participants who responded to the original invitation and survey.Consensus definitions for the terms physical inactivity, stationary behavior, sedentary behavior, standing, screen time, non-screen-based sedentary time, sitting, reclining, lying, sedentary behavior pattern, as well as how the terms bouts, breaks, and interruptions should be used in this context are provided.It is hoped that the definitions resulting from this comprehensive, transparent, and broad-based participatory process will result in standardized terminology that is widely supported and adopted, thereby advancing future research, interventions, policies, and practices related to sedentary behaviors.
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| 5. |
- Blodgett, Joanna M., et al.
(författare)
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Device-measured physical activity and cardiometabolic health : the Prospective Physical Activity, Sitting, and Sleep (ProPASS) consortium
- 2023
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Ingår i: European Heart Journal. - 0195-668X .- 1522-9645.
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Tidskriftsartikel (refereegranskat)abstract
- Background and Aims: Physical inactivity, sedentary behaviour (SB), and inadequate sleep are key behavioural risk factors of cardiometabolic diseases. Each behaviour is mainly considered in isolation, despite clear behavioural and biological interdependencies. The aim of this study was to investigate associations of five-part movement compositions with adiposity and cardiometabolic biomarkers.Methods: Cross-sectional data from six studies (n = 15 253 participants; five countries) from the Prospective Physical Activity, Sitting and Sleep consortium were analysed. Device-measured time spent in sleep, SB, standing, light-intensity physical activity (LIPA), and moderate-vigorous physical activity (MVPA) made up the composition. Outcomes included body mass index (BMI), waist circumference, HDL cholesterol, total:HDL cholesterol ratio, triglycerides, and glycated haemoglobin (HbA1c). Compositional linear regression examined associations between compositions and outcomes, including modelling time reallocation between behaviours.Results: The average daily composition of the sample (age: 53.7 ± 9.7 years; 54.7% female) was 7.7 h sleeping, 10.4 h sedentary, 3.1 h standing, 1.5 h LIPA, and 1.3 h MVPA. A greater MVPA proportion and smaller SB proportion were associated with better outcomes. Reallocating time from SB, standing, LIPA, or sleep into MVPA resulted in better scores across all outcomes. For example, replacing 30 min of SB, sleep, standing, or LIPA with MVPA was associated with −0.63 (95% confidence interval −0.48, −0.79), −0.43 (−0.25, −0.59), −0.40 (−0.25, −0.56), and −0.15 (0.05, −0.34) kg/m2 lower BMI, respectively. Greater relative standing time was beneficial, whereas sleep had a detrimental association when replacing LIPA/MVPA and positive association when replacing SB. The minimal displacement of any behaviour into MVPA for improved cardiometabolic health ranged from 3.8 (HbA1c) to 12.7 (triglycerides) min/day.Conclusions: Compositional data analyses revealed a distinct hierarchy of behaviours. Moderate-vigorous physical activity demonstrated the strongest, most time-efficient protective associations with cardiometabolic outcomes. Theoretical benefits from reallocating SB into sleep, standing, or LIPA required substantial changes in daily activity.
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| 6. |
- Brooke, Hannah L, et al.
(författare)
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A systematic literature review with meta-analyses of within- and between-day differences in objectively measured physical activity in school-aged children.
- 2014
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Ingår i: Sports Medicine. - : Springer Science and Business Media LLC. - 0112-1642 .- 1179-2035. ; 44:10, s. 1427-38
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Targeting specific time periods of the day or week may enhance physical activity (PA) interventions in youth. The most prudent time segments to target are currently unclear.OBJECTIVES: To systematically review the literature describing differences in young people's objectively measured PA on weekdays vs. weekends, in school vs. out of school, weekends vs. out of school and lesson time vs. break time.METHODS: Electronic databases were searched for English-language, cross-sectional studies of school-aged children (4-18 years) reporting time-segment-specific accelerometer-measured PA from 01/1990 to 01/2013. We meta-analysed standardised mean differences (SMD) between time segments for mean accelerometer counts per minute (TPA) and minutes in moderate-to-vigorous PA (MVPA). SMD is reported in units of standard deviation; 0.2, 0.5 and 0.8 represent small, moderate and large effects. Heterogeneity was explored using meta-regression (potential effect modifiers: age, sex and study setting).RESULTS: Of the 54 included studies, 37 were eligible for meta-analyses. Children were more active on weekdays than weekends [pooled SMD (95 % CI) TPA 0.14 (0.08; 0.20), MVPA 0.42 (0.35; 0.49)]. On school days, TPA was lower in school than out of school; however, marginally more MVPA was accumulated in school [TPA -0.24 (-0.40; -0.08), MVPA 0.17 (-0.03; 0.38)]. TPA was slightly lower on weekends than out of school on school days, but a greater absolute volume of MVPA was performed on weekends [TPA -0.10 (-0.19; -0.01), MVPA 1.02 (0.82; 1.23)]. Heterogeneity between studies was high (I (2) 73.3-96.3 %), with 20.3-53.1 % of variance between studies attributable to potential moderating factors.CONCLUSIONS: School-aged children are more active on weekdays than weekend days. The outcome measure influences the conclusions for other comparisons. Findings support the tailoring of intervention strategies to specific time periods.
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| 7. |
- Brooke, Hannah L, et al.
(författare)
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Changes in time-segment specific physical activity between ages 10 and 14 years : A longitudinal observational study.
- 2016
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Ingår i: Journal of Science and Medicine in Sport. - : Elsevier BV. - 1440-2440 .- 1878-1861. ; 19:1, s. 29-34
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVES: Describe (1) time-segment specific changes in physical activity (PA) into adolescence, (2) differences in change in PA between specific time-segments (weekdays-weekends, in-school-out-of-school, out-of-school-weekends, lesson-time-lunch-time), and (3) associations of change in time-segment specific with overall PA.DESIGN: Longitudinal observational study (4-year follow-up).METHODS: Children from the SPEEDY study (n=769, 42% boys) had PA measured by accelerometer for at least three days at ages 10.2±0.3, 11.2±0.3 and 14.3±0.3years. Changes in moderate-to-vigorous PA (ΔMVPA, minutes ≥2000counts/minute [cpm]) and total PA (ΔTPA, average cpm) during weekdays, weekends, in-school, out-of-school, lesson-times and lunch-times, were tested using three level (age, individual, school) mixed-effects linear regression. Differences in ΔMVPA/ΔTPA between time-segments were tested using time-segment×age interaction terms. Associations of four-year time-segment specific ΔMVPA/ΔTPA with four-year overall ΔMVPA/ΔTPA were tested using two level (time-segment specific ΔMVPA/ΔTPA, school) mixed-effects linear regression.RESULTS: MVPA and TPA declined in all time-segments, except lesson-time MVPA. Annual ΔMVPA and, for boys only, ΔTPA was greater on weekends than weekdays (beta±SE for interaction term: boys, -3.53±0.83min, -29.64±7.64cpm; girls, -2.20±0.64min) and out-of-school (boys, -4.36±0.79min, -19.36±8.46cpm; girls, -2.44±0.63min). ΔMVPA and ΔTPA during lunch-time was greater than during lesson-time (boys, -0.96±0.20min, -36.43±6.55cpm; girls, -0.90±0.13min, -38.72±4.40cpm). ΔTPA was greater out-of-school than in-school (boys, -19.89±6.71cpm; girls, -18.46±6.51cpm). For all time-segments, four-year ΔMVPA/ΔTPA was positively associated with four-year overall ΔMVPA/ΔTPA (all p<0.042), except for girl's in-school and lunch-time TPA.CONCLUSIONS: Interventions focused on PA maintenance could target all time-segments, but weekends and out-of-school may be particularly advantageous due to the relatively large declines observed.
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| 8. |
- Brooke, Hannah L, et al.
(författare)
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Frequency and duration of physical activity bouts in school-aged children : A comparison within and between days.
- 2016
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Ingår i: Preventive medicine reports. - : Elsevier BV. - 2211-3355. ; 4, s. 585-590
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Tidskriftsartikel (refereegranskat)abstract
- Understanding how physical activity (PA) patterns vary within and between days may guide PA promotion in young people. We aimed to 1) describe and compare the frequency (bouts/day) and duration (min/bout) of bouts of moderate-to-vigorous intensity PA (MVPA) on weekdays vs. weekends and in-school vs. out-of-school, and 2) assess associations of bout frequency and duration in these time-segments with overall PA. We used cross-sectional accelerometer data from 2737 children (aged 6-19 years) in the United States National Health and Nutrition Examination Survey (NHANES) 2003-2006. A bout was defined as MVPA (≥ 2000 counts per minute [cpm]) lasting ≥ 3 min. Adjusted Wald tests were used to assess differences in bout characteristics between time-segments. Linear regression was used to examine the association of time-segment specific bout characteristics with daily minutes of MVPA and PA volume (average cpm). Bout frequency was higher on weekdays than weekends (median [IQR] 4.3 [2.2-7.2] vs. 3.0 [1.0-6.5] bouts/day, p < 0.001); however, bout duration did not differ (4.7 [4.0-5.7] vs. 4.5 [3.7-5.8] min/bout, p = 0.33). More bouts were accumulated out-of-school compared with in-school (2.2 [1.0-4.0] vs. 1.8 [0.8-3.2] bouts/day, p < 0.001), but bout duration was similar (4.7 [3.8-5.8] vs. 4.5 [3.8-5.7] min/bout, p = 0.158). For all time-segments, the frequency and duration of bouts of MVPA were independently and positively associated with overall MVPA and PA volume. In conclusion, the characteristics of children's PA vary within and between days; accounting for this in intervention design may improve future interventions. However, increasing bout frequency or duration in any time-segment may be beneficial for overall PA.
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| 9. |
- Stamatakis, Emmanuel, et al.
(författare)
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A Physical Behaviour Partnership From Heaven : The Prospective Physical Activity, Sitting, and Sleep Consortium and the International Society for the Measurement of Physical Behaviour
- 2022
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Ingår i: Journal for the Measurement of Physical Behaviour. - : Human Kinetics. - 2575-6605 .- 2575-6613. ; , s. 1-3
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- The advancement of science demands people and organizations work together toward worthy goals, a need that is especially pronounced in nascent fields like physical behavior. Partnerships offer an ideal vehicle for long-term collaboration to build research capacity, develop new scientific endeavors, and nurture talent. In this commentary, we discuss the newly formed partnership between two key players of the field of physical behavior, the Prospective Physical Activity, Sitting, and Sleep consortium (ProPASS) and the International Society for the Measurement of Physical Behaviour (ISMPB).
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| 10. |
- Stamatakis, Emmanuel, et al.
(författare)
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Emerging collaborative research platforms for the next generation of physical activity, sleep and exercise medicine guidelines : the Prospective Physical Activity, Sitting, and Sleep consortium (ProPASS)
- 2020
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Ingår i: British Journal of Sports Medicine. - : BMJ. - 0306-3674 .- 1473-0480. ; 54:8, s. 435-437
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Galileo Galilei’s quote ‘measure what is measurable, and make measurable what is not so’ has particular relevance to health behaviours, such as physical activity (PA), sitting and sleep, whose measurement during free living is notoriously difficult. To date, much of what we know about how these behaviours affect our health is based on self-report by questionnaires which have limited validity, are prone to bias and inquire about selective aspects of these behaviours. Although self-reported evidence has made great contributions to shaping public health and exercise medicine policy and guidelines until now,1 the ongoing advancements of accelerometry-based measurement and evidence synthesis methods are set to change the landscape. The aim of this editorial is to outline new directions in PA and sleep-related epidemiology that open new horizons for guideline development and improvement; and to describe a new research collaboration platform: the Prospective Physical Activity, Sitting, and Sleep consortium (ProPASS).
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