Replacing Sedentary Time: Meta-analysis of Objective-Assessment Studies

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REVIEW ARTICLE Replacing Sedentary Time: Meta-analysis of Objective-Assessment Studies Q1 X XTaggedPJes us del Pozo-Cruz, PhD, 1 Antonio García-Hermoso, PhD, 2 Rosa M. Alfonso-Rosa, PhD, 1,3 Francisco Alvarez-Barbosa, PhD, 1,4 Neville Owen, PhD, 5,6 Sebastien Chastin, PhD, 7,8 Borja del Pozo-Cruz, PhD 9,10 Context: The aim was to summarize estimates of the potential benefits for cardiometabolic risk markers and all-cause mortality of replacing time spent in sedentary behaviors with light-intensity physical activity or with moderate to vigorous physical activity, from studies using device-based measurement. Evidence acquisition: Four databases covering the period up to December 2016 were searched and analyzed (February 2017). Data were extracted by two independent reviewers. For the metaanalyses, the estimated regression coefficients (b) and 95% CIs were analyzed for BMI, waist circumference, and high-density lipoprotein cholesterol. Pooled relative rate and 95% CIs were calculated for fasting glucose, fasting insulin, and homeostatic model assessment-insulin resistance values. Hazard ratios were extracted from studies of all-cause mortality risk. Evidence synthesis: Ten studies (with 17,390 participants) met the inclusion criteria. Reallocation of 30 minutes of sedentary time to light-intensity physical activity was associated with reductions in waist circumference, fasting insulin, and all-cause mortality risk; and with an increase in high-density lipoprotein cholesterol. Reallocating 30 minutes of sedentary time to moderate to vigorous physical activity was associated with reductions in BMI, waist circumference, fasting glucose, fasting insulin, and all-cause mortality (not pooled) and with an increase in high-density lipoprotein cholesterol. Conclusions: Replacing sedentary time with either light-intensity physical activity or moderate to vigorous physical activity may be beneficial, but when sedentary time is replaced with moderate to vigorous physical activity, the predicted impacts are stronger and apparent for a broader range of risk markers. These findings point to potential benefits of replacing sedentary time with light-intensity physical activity, which may benefit those less able to tolerate or accommodate higher-intensity activities, including many older adults. Am J Prev Med 2018; & ( & ): &&&  &&& . © 2018 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved. TAGGEDH1CONTEXTTAGGEDEND The adverse health consequences of sedentary behavior (time spent sitting) have more recently been identified in relation to cardiovascular disease risk 1,2 and all-cause mortality, 3 controlling for the influence of moderate to vigorous physical activity 4 (MVPA); these adverse health consequences of sedentary behavior have been shown for a range of health outcomes. 5 Some countries have now expanded the scope of their PA guidelines, with joint recommendations on increasing PA and reducing sedentary time. 6,7 TaggedPTime spent standing or being physically active is time that is not spent sedentary. Thus, the effect of spending From the 1 Department of Physical Education and Sports, University of Seville, Seville, Spain; 2 Laboratorio de Ciencias de la Actividad Física, el Deporte y la Salud, Facultad de Ciencias M edicas, Universidad de Santiago de Chile, Santiago de Chile, Chile; 3 Area of Human Motricity and Sport Performance, University of Seville, Seville, Spain; 4 Department of Physical Activity and Sport, Cardenal Espinola CEU, Seville, Spain; 5 Swinburne University of Technology, Melbourne, Australia; 6 The Baker Heart and Diabetes Institute, Melbourne, Australia; 7 Institute for Applied Health Research, School of Health and Life Science, Glasgow Caledonian University, Glasgow, Scotland, United Kingdom; 8 Department of Sports and Movement Sciences, Ghent University, Ghent, Belgium; 9 Institute for Positive Psychology and Education, Faculty of Health Sciences, Australian Catholic University, Sydney, Australia; and 10 Department of Exercise Sciences, University of Auckland, Auckland, New Zealand Address correspondence to: Borja del Pozo-Cruz, PhD, Institute for Positive Psychology and Education, Faculty of Health Sciences, Australian Catholic University, P.O. Box 968, North Sydney, NSW 2059, Australia. E-mail: [email protected]. 0749-3797/$36.00 https://doi.org/10.1016/j.amepre.2018.04.042 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 ARTICLE IN PRESS © 2018 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved. Am J Prev Med 2018; & ( & ): &&&  &&& 1 Name is “Jesús” and “del Pozo-Cruz” is the surname Name is “Borja” and “del Pozo-Cruz” is the surname TaggedPmore or less time being either physically active or sedentary will impact outcomes in a direct way, but those outcomes will also depend at least in part on the other activities being displaced. A recent meta-analysis pointed out that although reducing total sedentary time is feasible, in most intervention studies it has not been clear to which component of time use the sedentary time has been reallocated. 8 Although there are published studies identifying potential for cardiometabolic health benefits and mortality risk reductions of replacing sedentary time with light-intensity PA (LIPA) or MVPA, 914 there are no meta-analysis findings available to synthesize what is known about the potential impacts of such substitutions. TaggedPA systematic review and meta-analysis is necessary to synthesize the findings of studies that have used an isotemporal substitution approach (this approach assumes that activity time in a day is finite and that performing one activity involves substitution for another; and depending on the intensity of activity that is replaced, the estimated effects on health might be different 15 )to estimate the potential cardiometabolic and all-cause mortality outcomes of reallocating objectively assessed time spent in sedentary behaviors to LIPA or to MVPA. TAGGEDH1EVIDENCE ACQUISITIONTAGGEDEND TaggedPThis meta-analysis was undertaken in accordance with the Guidelines for Meta-Analyses and Systematic Reviews of Observational Studies (MOOSE) 16 and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). 17 The protocol was registered with the National Institute for Health Research International prospective register of systematic reviews (PROSPERO) under the registration number CRD42016037585. Data Sources TaggedPElectronic database searchers of Ovid Medline (through the search engine PubMed); Scopus; SPORTdiscus; and Web of Knowledge conducted in December 2016. The last search was performed on December 30, 2016. No limit on the date of publication was imposed. The following search terms and keywords were used: ([sedentary lifestyle or light physical activity or sedentary behaviour or sedentary time]oraccelerometry or accelerometer) and (isotemporal substitution or sedentary break or displac* sedentary time or replacing or displacing or reallocating or substituting) and (metabolic disease or body composition or cholesterol or mortality or metabolic risk or metabolic biomarker or waist circumference or cardiovascular disease). The complete search strategy is shown in Appendix Table 1 (available online). Also, reference lists were examined to detect studies potentially eligible for inclusion. Eligibility Criteria TaggedPStudies were included in the review if they met the following inclusion criteria: (1) included people aged 18 years; (2) reported objective measure of activity (self-report methods can under- or over-estimate the amount of PA and sedentary behavior (SB) performed, because of biases introduced by recall error, social desirability, and other issues; therefore, only studies conducted Table 1. Pooled Effect Size of Replacing 30 Minutes of Sedentary Time With LIPA and MVPA Replacing sedentary time with LIPA Replacing sedentary time with MVPA Outcomes of interest n Effect size (95% CI) p-value I 2 Egger test (p-value) n Effect size (95% CI) p-value I 2 Egger test (p-value) BMI, b60.091 (0.244, 0.063) 0.247 73.7 0.446 6 1.071 (1.796, 0.346) 0.004** 60.8 0.381 Waist circumference, b 50.567 (0.862, 0.272) <0.001** 37.0 0.057 5 2.955 (3.878, 2.032) <0.001** 83.0 <0.001 HDL-c, b3 0.012 (0.002, 0.023) 0.022* 73.4 0.459 3 0.035 (0.021, 0.050) <0.001** 0.0 0.650 Fasting glucose, RR 4 0.998 (0.996, 1.000) 0.058 0.0 0.505 4 0.992 (0.984, 0.999) 0.024* 0.0 0.447 Fasting insulin, RR 3 0.974 (0.964, 0.984) <0.001** 0.0 0.718 3 0.879 (0.847, 0.911) <0.001** 0.0 0.482 HOMA-IR, RR 2 0.994 (0.879, 1.124) 0.919 81.3 2 0.998 (0.877, 1.136) 0.977 97.1  Note: Boldface indicates statistical significance (*p<0.05; **p<0.01). HDL-c, high-density lipoprotein cholesterol; HOMA-IR, homeostatic model assessmentinsulin resistance; LIPA, light-intensity physical activity; MVPA, moderate to vigorous physical activity; RR, risk ratio. ARTICLE IN PRESS www.ajpmonline.org 2Pozo-Cruz et al / Am J Prev Med 2018; & ( & ): &&&  &&& 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 TaggedPwith objective methods of PA assessment were included); (3) reported, using isotemporal models, on the effects of replacing sedentary behavior with LIPA or MVPA on at least one cardiometabolic or mortality as an outcome of interest; (4) were written in English; and (5) reported primary research findings. TaggedPTwo independent reviewers carried out the screening and review, with a third reviewer sought in case of disagreement. Articles were first screened and selected for eligibility based on title and abstract. The full text was then reviewed after confirming eligibility to be included and data were extracted. Data Collection TaggedPInformationextractedwasasfollows: characteristics of the sample, method of objective measurement of PA, the amount of sedentary time being replaced, outcomes of interest, analytical approach, and main results from each of the studies. Also, data that would assist in findings from the meta-analysis from the different studies (i.e., regression coefficient and 95% CI representing the effect of replacing SB with more active behavior on the outcome of interest) were extracted. To maximize the generalizability of the findings, when data were not available the original authors were contacted. Risk of Bias TaggedPAn assessment of each study’s quality was made using an adjusted format of the NewcastleOttawa quality assessment scale. 18 This scale contains eight items categorized into three domains (selection, comparability, and exposure). A star system is used to enable semiquantitative assessment of study quality, such that the highest-quality studies are awarded a maximum of one star per item with the exception of the comparability domain, which allows allocating two stars. Thus, the score ranges from zero to nine stars (maximum score for cohort and cross-sectional studies was nine and seven, respectively). Data Synthesis and Analytical Approach TaggedPA one-step individual participant data meta-analysis was conducted. All analyses were carried out using Comprehensive Metaanalysis Software, Biostat, version 3. The estimated regression coefficients (b) and 95% CIs were combined and used in the meta-anal- ysis for BMI, body fat, waist circumference (WC), and high-density lipoprotein cholesterol (HDL-c). Assuming linear regression properties, 19 results from original studies reporting estimated band 95% CIs for 10-minute units (n=2) were scaled up to 30 minutes (band 95% CIs X 3) for comparison purposes. Also, the relative rate (RR) and 95% CIs were calculated for fasting glucose, fasting insulin, and homeostatic model assessmentinsulin resistance (HOMA-IR) values. Finally, hazard ratios (HRs) with associated 95% CIs were extracted from studies for risk of all-cause mortality. The random effects model (DerSimonianLaird approach) was used in all cases to summarize the pooled band RR. All-cause mortality studies HRs were not pooled because all three studies utilized overlapping data from the National Health and Nutrition Examination Survey. In that case, the range of values was reported. The likelihood approach with random effects was used to better account for the imprecision in the estimate of between-study variance. 20 When studies presented several statistical risk-adjustment models, only values associated with the statistical models that contained the fewest number of additional covariates were considered, in order to improve comparability across studies. TaggedPThe percentage of total variations across the studies because of heterogeneity (Cochran’s Q-statistic) was estimated using I 2 . Values I 2 of <25%, 25%50%, and >50% were considered as small, medium, and large amounts of heterogeneity respectively. 21 TaggedPSmall-study effects bias was assessed using the extended Egger’s test,andfunnelplotswere usedtographicallyinvestigatepublication biasamongstudies. 22,23 TaggedPFinally, a sensitivity analysis was conducted to assess the robustness of the summary estimates to determine whether or not a particular study accounted for the heterogeneity. A series of analyses were therefore conducted by sequentially omitting one study at each turn. TAGGEDH1EVIDENCE SYNTHESISTAGGEDEND Study Selection TaggedPThe search strategy initially identified 1,118 articles (Figure 1). After initial screening, 39 full articles were retrieved. Of these, 29 were rejected (ten had not used an isotemporal substitution model, four only addressed sedentary behavior, eight examined correlates of behavior, three had no accelerometer-based measures, one was a duplicated publication in different data sets, and three had no study outcomes). Finally, only ten studies were included in the systematic review and meta-analysis. Study Characteristics TaggedPThe 10 studies included a total of 17,390 participants. Sample sizes ranged from 279 to 5,377 individuals. Gender was evenly distributed (men, 50.31%) and the mean age was 55.8 years. TaggedPSeven of the studies were cross-sectional, observational investigations, and three were prospective survival analyses. The characteristics of the studies are summarized in Appendix Table 2 (available online). All used accelerometers to assess SB and PA. Definitions of SB, MVPA, and LIPA were based on previously validated counts per minute (cpm) cut-off points registered with an accelerometer. The SB cut offs that were employed varied across the studies, with <100 cpm when only data from the vertical axis of the accelerometer were used or <200 cpm when data from the vector axis of the accelerometer were used. Similarly, the LIPA cut-off criterion used was either 1001,951 cpm (vertical-axis data) or 2002,689 cpm. The MVPA cut offs that were >1,951 cpm when only data from the vertical axis were used and >2,690 cpm when data from the vector axis of the accelerometer were used. In one study 9 where data from the vertical axis of the accelerometer were used, the cut offs for LIPA and MVPA were 100 to 2,019 cpm and 2,020 cpm respectively. Epoch duration was similar across the studies (i.e., 1-minute). There was one study that utilized an epoch of 1 second 10 and employed a proportional to <100 cpm SB/60-second epoch cut off. ARTICLE IN PRESS & 2018 Pozo-Cruz et al / Am J Prev Med 2018; & ( & ): &&&  &&& 3 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 TaggedPAnother study 19 employed an epoch of 15 seconds and a proportional to <100 cpm SB/15-second epoch cut off. Age ranged from 20 to 89 years. Sample size and sampling strategy varied from small convenience samples to large representative samples. Isotemporal substitution modeling was reported for BMI (n=6); WC (n=5); HDL- c(n=3); fasting glucose (n=4); insulin (n=3); HOMA-IR (n=2); and all-cause mortality (n=3), with time blocks exchanged being 30 minutes in duration. Risk of Bias TaggedPAll of the nine cross-sectional studies and all three of the longitudinal studies were deemed to be of high quality, with a NewcastleOttawa score 6 (Appendix Table 3, available online). Effects of Reallocating Sedentary Time to Light- Intensity Physical Activity and to Moderate to Vigorous Physical Activity TaggedPTable 1 and Appendix Figure 4 (available online) show the estimated regression coefficients (b) and RRs, and TaggedP associated 95% CI of replacing 30 minutes of sedentary time with LIPA and MVPA on the selected outcomes. TaggedPReallocation of 30 minutes of sedentary time to LIPA was predicted to be associated with reductions in WC (0.57 cm/30 minutes, b=0.57, 95% CI= 0.86, 0.27, p<0.001) and fasting insulin (2.4%/30 minutes, RR=0.97, 95% CI=0.96, 0.98, p<0.001); and with an increase in HDL-c (0.012 mmol/L/30 minutes, b=0.012, 95% CI=0.002, 0.023, p=0.022). Estimates were not significant for BMI, fasting insulin, and HOMA-IR. TaggedPReallocating 30 minutes of sedentary time to MVPA was predicted to be associated with reductions in BMI (1/30 minutes, b=1.07, 95% CI= 1.80, 0.35, p=0.004); WC (2.9 cm/30 minutes, b=2.95, 95% CI= 3.88, 2.03, p=0.005); fasting glucose (0.01%/30 minutes, RR=0.99, 95% CI=0.98, 0.99, p=0.023); and fasting insulin (1.12%/30 minutes, RR=0.88, 95% CI=0.85, 0.91, p<0.001); and with an increase in HDL-c (0.03 mmol/L/30 minutes, b=0.03, 95% CI=0.02, 0.05, p<0.001). Estimates were not significant for HOMA-IR. Figure 1. PRISMA flow diagram of the study.Q2 X X ARTICLE IN PRESS www.ajpmonline.org 4Pozo-Cruz et al / Am J Prev Med 2018; & ( & ): &&&  &&& 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 OK TaggedPResults from the all-cause mortality studies show that replacing 30 minutes of sedentary time with LIPA (with HRs ranging from 0.80 to 0.87) or MVPA (with HRs ranging from 0.19 to 0.51) is estimated to be associated with a lower risk of all-cause mortality. Publication Bias and Sensitivity Analysis TaggedPBoth funnel plot asymmetry and Egger test show no significant publication bias (Table 1 and Appendix Figure 5, available online). TaggedPThe sensitivity analysis conducted showed that one particular study accounted for the majority of the heterogeneity 10 showing a nonsignificant effect of replacing SB with MVPA on BMI after removing the aforementioned study from the pooled analysis. TAGGEDH1DISCUSSIONTAGGEDEND TaggedPThe main findings of this meta-analysis show that reallocating sedentary time to LIPA or MVPA may result in reductions in WC and fasting insulin, and increased HDL-c. Equal time-exchange of SB with MVPA may lead to additional reductions of fasting glucose, all-cause of mortality, and increased HDL-c levels. These findings suggest potential benefits of replacing SB with LIPA. This might provide alternative intervention strategies as it may be more feasible and less challenging than more strenuous activity (which is also more difficult than lighter activity to be fit into daily life routines, especially in particular domains, such as work or education, where SB is particularly prevalent), for enhancing cardiovascular health among the general adult population. 8 Also, LIPA could be a feasible strategy to increase the total volume of PA among those considered already active and therefore could bring additional cardiovascular benefits. 24 TaggedPThere is evidence from experimental trials that breaking up sedentary time can beneficially impact cardiometabolic risk markers. 25,26 Consistent with what was found in this meta-analysis, such experimental studies have demonstrated that regular brief activity breaks during otherwise sedentary periods translate into cardiometabolic risk improvement in adults 27,28 and may have the potential of preventing mortality from, at least, cardiovascular disease. 29,30 There is evidence that interruption of sitting with short, frequent bouts of at least LIPA improves postprandial glycemia. 31,32 However, the current evidence on effectiveness of interventions targeting exclusively SB to influence biological dimensions of health risk is limited. 33 TaggedPEvidence from experimental studies suggests that SB detrimentally alters metabolic function and can be associated with chronic inflammation. 34 Observational studies and RCTs have shown that increased PA improves TaggedP insulin sensitivity 35 and lowers chronic inflammation. 36 Statistically significant associations with cardiometabolic biomarkers identified in this meta-analysis (i.e., HDL-c, fasting glucose, and fasting insulin) appeared to be different, depending on the intensity of the type of PA with which sedentary time was replaced. The current findings suggest that replacing SB with MVPA may have greater beneficial effects as compared with doing so with lowerintensity activities. Also, there is other evidence to suggest benefit when time in sedentary bouts was reallocated to long PA bouts: substituting 120 minutes of sedentary time with equal LIPA may have about the same theoretic beneficial effect on HOMA-IR as would substituting 40 minutes of SB for an equal duration of MVPA. 37 TaggedPAlthough mechanisms underpinning the findings of this meta-analysis are not well known, some supporting evidence could be found in animal models as described by Hamilton and colleagues. 38 Even modest local muscle contractions seem to maintain lipoprotein lipase activity, which could contribute to the detected associations. Of particular interest is that total volume of PA activity appears to have stronger associations with cardiometabolic biomarkers than MVPA. 39,40 In addition, energy expenditure associated with spontaneous PA, the Nonexercise Activity Thermogenesis has been associated with human obesity markers. 41 The findings presented here need to be considered in light of the potential limitations of isotemporal substitution method. 42 Therefore, confirming these observations in experimental designs is of interest for public health authorities, in particular, identifying for how long these reallocations of time need to be sustained to achieve beneficial outcomes. Findings from a recent meta-analysis 8 suggest that it is possible to reduce sedentary time by 30 minutes per day in the short and medium term with potentially clinically relevant benefits (as shown in this meta-analysis). However, the same meta-analysis states that it is still unclear whether such behavioral change is feasible and sustainable over the long term, because of the lack of studies. TaggedPConsistently with previous findings, 43,44 reallocating 30 minutes of sedentary time to LIPA or MVPA predicted estimated reductions in WC (approximately 0.57 cm and 2.95 cm for LIPA and MVPA respectively). Similarly, replacing 30 minutes of sedentary time with MVPA was associated with reductions in BMI. However, the estimations for the reallocation of 30 minutes of sedentary time to LIPA on BMI were not significant. There seems to be evidence that the relationship between PA and WC is consistent across all intensities whereas the relationship between PA and BMI is intensity-dependent (i.e., only higher intensities seem to affect BMI). 43 Although the former could provide a plausible Pozo-Cruz et al / Am J Prev Med 2018; & ( & ): &&&  &&& 5 ARTICLE IN PRESS & 2018 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538