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Journal Watch—September 2016

| September 1, 2016

A quick look at the noteworthy articles in bariatric and metabolic research

THIS MONTH’S TOPIC:

Sedentary Behavior and Obesity

A systematic review of correlates of sedentary behaviour in adults aged 18-65 years: a socio-ecological approach.
O’Donoghue G, Perchoux C, Mensah K, Lakerveld J, van der Ploeg H, Bernaards C, Chastin SF, Simon C, O’Gorman D, Nazare JA; DEDIPAC Consortium. BMC Public Health. 2016 Feb 17;16:163.

Synopsis: The aim of this review was to identify individual, social, environmental, and policy-related determinants or correlates of sedentary behaviours among adults aged 18-65 years.

PubMed, Embase, CINAHL, PsycINFO and Web of Science were searched for articles published between January 2000 and September 2015. The search strategy was based on four key elements and their synonyms: (a) sedentary behaviour (b) correlates (c) types of sedentary behaviours (d) types of correlates. Articles were included if information relating to sedentary behaviour in adults (18-65 years) was reported. Studies on samples selected by disease were excluded. The full protocol is available from PROSPERO (PROSPERO 2014:CRD42014009823).

Seventy-four original studies were identified out of 4041: 71 observational, two qualitative and one experimental study. Sedentary behaviour was primarily measured as self-reported screen leisure time and total sitting time. In 15 studies, objectively measured total sedentary time was reported: accelerometry (n = 14) and heart rate (n = 1). Individual level factors such as age, physical activity levels, body mass index, socio-economic status and mood were all significantly correlated with sedentariness. A trend towards increased amounts of leisure screen time was identified in those married or cohabiting while having children resulted in less total sitting time. Several environmental correlates were identified including proximity of green space, neighbourhood walkability and safety and weather.

Results provide further evidence relating to several already recognised individual level factors and preliminary evidence relating to social and environmental factors that should be further investigated. Most studies relied upon cross-sectional design limiting causal inference and the heterogeneity of the sedentary measures prevented direct comparison of findings. Future research necessitates longitudinal study designs, exploration of policy-related factors, further exploration of environmental factors, analysis of inter-relationships between identified factors and better classification of sedentary behaviour domains.
PMID: 26887323

B-MOBILE–a smartphone-based intervention to reduce sedentary time in overweight/obese individuals: a within-subjects experimental trial.
Bond DS, Thomas JG, Raynor HA, Moon J, Sieling J, Trautvetter J, Leblond T, Wing RR. PLoS One. 2014 Jun 25;9(6):e100821.

Synopsis: In this article, the authors tested a smartphone-based intervention to monitor and decrease excessive sedentary time (SED) in individuals with overweight/obesity, and compared three approaches to prompting physical activity (PA) breaks and delivering feedback on SED.
Participants [N=30; Age= 47.5(13.5) years; 83% female; Body Mass Index (BMI)=36.2(7.5) kg/m2] wore the SenseWear Mini Armband (SWA) to objectively measure SED for seven days at baseline. Participants were then presented with three smartphone-based PA break conditions in counterbalanced order: (1) 3-min break after 30 SED min; (2) 6-min break after 60 SED min; and (3) 12-min break after 120 SED min. Participants followed each condition for 7 days and wore the SWA throughout.

All PA break conditions yielded significant decreases in SED and increases in light (LPA) and moderate-to-vigorous PA (MVPA) (p<0.005). Average % SED at baseline (72.2%) decreased by 5.9%, 5.6%, and 3.3% [i.e. by mean (95% CI) -47.2(-66.3, -28.2), -44.5(-65.2, -23.8), and -26.2(-40.7, -11.6) min/d] in the 3-, 6-, and 12-min conditions, respectively. Conversely, % LPA increased from 22.8% to 26.7%, 26.7%, and 24.7% [i.e. by 31.0(15.8, 46.2), 31.0(13.6, 48.4), and 15.3(3.9, 26.8) min/d], and % MVPA increased from 5.0% to 7.0%, 6.7%, and 6.3% (i.e. by 16.2(8.5, 24.0), 13.5(6.3, 20.6), and 10.8(4.2, 17.5) min/d] in the 3-, 6-, and 12-min conditions, respectively.

Planned pairwise comparisons revealed the 3-min condition was superior to the 12-min condition in decreasing SED and increasing LPA (p<0.05).
They found that the smartphone-based intervention significantly reduced SED. They concluded that prompting frequent short activity breaks may be the most effective way to decrease SED and increase PA in individuals with overweight/obesity and that future investigations should determine whether these SED reductions can be maintained long-term.
PMID: 24964010

Associations of total amount and patterns of sedentary behaviour with type 2 diabetes and the metabolic syndrome: The Maastricht Study.
van der Berg JD, Stehouwer CD, Bosma H, van der Velde JH, Willems PJ, Savelberg HH, Schram MT, Sep SJ, van der Kallen CJ, Henry RM, Dagnelie PC, Schaper NC, Koster A. Diabetologia. 2016 Apr;59(4):709-18. Epub 2016 Feb 2.

Synopsis: This study investigated cross-sectional associations of total amount and patterns of sedentary behaviour with glucose metabolism status and the metabolic syndrome.

The authors  included 2,497 participants (mean age 60.0±8.1 years, 52% men) from The Maastricht Study who were asked to wear an activPAL accelerometer 24 h/day for 8 consecutive days. We calculated the daily amount of sedentary time, daily number of sedentary breaks and prolonged sedentary bouts (≥30 min), and the average duration of the sedentary bouts. To determine glucose metabolism status, participants underwent an oral glucose tolerance test. Associations of sedentary behaviour variables with glucose metabolism status and the metabolic syndrome were examined using multinomial logistic regression analyses.

Overall, 1,395 (55.9%) participants had normal glucose metabolism, 388 (15.5%) had impaired glucose metabolism and 714 (28.6%) had type 2 diabetes. The odds ratio per additional hour of sedentary time was 1.22 (95% CI 1.13, 1.32) for type 2 diabetes and 1.39 (1.27, 1.53) for the metabolic syndrome. No significant or only weak associations were seen for the number of sedentary breaks, number of prolonged sedentary bouts or average bout duration with either glucose metabolism status or the metabolic syndrome.

The authors concluded that an extra hour of sedentary time was associated with a 22% increased odds for type 2 diabetes and a 39% increased odds for the metabolic syndrome. The pattern in which sedentary time was accumulated was weakly associated with the presence of the metabolic syndrome. They found that these results suggest that sedentary behaviour may play a significant role in the development and prevention of type 2 diabetes, although longitudinal studies are needed to confirm their findings.
PMID: 26831300

What is the effect on obesity indicators from replacing prolonged sedentary time with brief sedentary bouts, standing and different types of physical activity during working days? A cross-sectional accelerometer-based study among blue-collar workers
Gupta N, Heiden M, Aadahl M, Korshøj M, Jørgensen MB, Holtermann A. PLoS One. 2016 May 17;11(5):e0154935.

Synopsis: The aim of the study was to investigate if (a) substituting total sedentary time or long sedentary bouts with standing or various types of physical activity and (b) substituting long sedentary bouts with brief sedentary bouts; is associated with obesity indicators using a cross sectional isotemporal substitution approach among blue-collar workers.

A total of 692 workers from transportation, manufacturing and cleaning sectors wore an Actigraph GT3X+ accelerometer on the thigh for 1-4 working days. The sedentary (sit and lie), standing, walking, and moderate to vigorous physical activity (MVPA) time on working days was computed using validated Acti4 software. The total sedentary time and uninterrupted sedentary time spent in brief (≤5 mins), moderate (>5 and ≤30 mins), and long (>30mins) bouts, were determined for the whole day and during work and non-work time separately. The obesity indicators, BMI (kg/m2), waist circumference (cm) and fat percentage were objectively measured.

Isotemporal substitution modelling was utilized to determine the linear association with obesity indicators of replacing 30 min of total sedentary time or long sedentary bouts with standing, walking or MVPA and separately replacing 30 min of long sedentary bouts with brief sedentary bouts.

Workers [mean (standard deviation, SD); age = 45.1 (9.9) years, BMI = 27.5 (4.9) kg/m2, %BF = 29.6 (9.5), waist circumference = 94.4 (13.0) cm] sat for 2.4 hours (~32% of the measured time, SD = 1.8 hours) across the day during work period and 5.5 hours (~62% of the measured time, SD = 1.5 hours) during non-work period. Most of the sedentary time was accrued in moderate bouts [work = 1.40 (SD = 1.09) hours] during work and in long bouts during non-work [2.7 (SD = 1.4) hours], while least in long sedentary bouts during work [work = 0.5 (SD = 0.9)] and in brief sedentary bouts [0.5 hours (SD = 0.3)] during non-work. Significant associations with all obesity indicators were found when 30 min of total sedentary time or long sedentary bouts were replaced with standing time (~1-2% lower) or MVPA (~4-9% lower) during whole day, work, and non-work periods. The exception was that a statistically significant association was not observed with any obesity indicator when replacing total sedentary time or long sedentary bouts with standing time during the work period. Significant beneficial associations were found when replacing the long sedentary bouts with brief sedentary bouts (~3-5% lower) during all domains.

The authors concluded that replacing total sedentary time and long sedentary bouts, respectively, not only with MVPA but also standing time appears to be beneficially associated with obesity indicators among blue-collar workers. Additionally, replacing long sedentary bouts with brief sedentary bouts was also beneficially associated with obesity indicators. Studies using prospective design are needed to confirm the findings.
PMID: 27187777

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