PhD, RN; Mary O Whipple
email: whipp042@umn.edu

PhD Ana J Pinto

MD, MPH Layla A Abushamat

PhD Audrey Bergouignan

BS Kristina Chapman

MD Amy G Huebschmann

PhD Kevin S Masters

MD Kristen J Nadeau

PhD Rebecca L Scalzo

MD, PhD Irene E Schauer

MS Deirdre Rafferty

MD Jane E B Reusch

PhD Judith G Regensteiner

Sex Differences in Physical Activity Among Individuals with Type 2 Diabetes Across the Lifespan: A Systematic Review and Meta-Analysis

Keywords: Systematic searches (CRD42021254246) were conducted using Ovid Medline, EMBASE, Web of Science, CINAHL, AMED, PsychINFO, and SPORTDiscus

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Conclusions: Sex differences in meeting PA guidelines were not observed among adolescents, but were apparent among adults and older adults with T2DM. Females consistently engaged in less MVPA than males across the lifespan.

Diabetes affects more than 537 million adults ages 20-79 years worldwide [START_REF]IDF Diabetes Atlas[END_REF].

More than 90% of adults with diabetes have type 2 diabetes mellitus (T2DM) [START_REF]IDF Diabetes Atlas[END_REF] and the prevalence of T2DM has continued to increase since the 1990s (2). Sex differences exist in T2DM incidence. Females have higher rates of T2DM in youth and males have a higher incidence in midlife, though rates are equal between sexes in older age (3; 4). While a diagnosis of T2DM alone carries significant disease burden, it is also associated with a greatly increased risk of cardiovascular disease (CVD) morbidity and mortality [START_REF] Church | Exercise Capacity and Body Composition as Predictors of Mortality Among Men With Diabetes[END_REF]. In addition, being diagnosed with T2DM at a younger age (<40 years) further increases the risk of CVD and early mortality [START_REF] Sattar | Age at Diagnosis of Type 2 Diabetes Mellitus and Associations With Cardiovascular and Mortality Risks[END_REF]. Cardiorespiratory fitness is a prognostic factor for cardiovascular mortality, and adults and adolescents with T2DM have lower cardiorespiratory fitness compared to individuals without T2DM, even in the absence of clinically significant CVD [START_REF] Regensteiner | Effects of non-insulindependent diabetes on oxygen consumption during treadmill exercise[END_REF]. There are notable sex differences in CVD burden and cardiorespiratory fitness; females with T2DM have lower cardiorespiratory fitness than males with T2DM [START_REF] Regensteiner | Effects of non-insulindependent diabetes on oxygen consumption during treadmill exercise[END_REF] and bear a disproportionate burden of CVD in the context of T2DM (3; 8). Physical activity (PA) is a cornerstone of treatment of T2DM. PA has positive effects on blood glucose control and diabetes complications [START_REF] Colberg | Physical Activity/Exercise and Diabetes: A Position Statement of the American Diabetes Association[END_REF] and meeting PA guidelines [START_REF] Piercy | The Physical Activity Guidelines for Americans[END_REF] has been associated with 40% risk reduction in CVD mortality [START_REF]Physical Activity Guidelines for Americans[END_REF]. It has been suggested that the relationship between low PA and increased risk of coronary heart disease may be stronger in females than in males [START_REF] Sattelmair | Dose response between physical activity and risk of coronary heart disease: A meta-analysis[END_REF]. Population studies consistently demonstrate that among adults, females are less active than males and females tend to engage in less MVPA [START_REF] Scholes | Education-related disparities in reported physical activity during leisure-time, active transportation, and work among US adults: repeated crosssectional analysis from the National Health and Nutrition Examination Surveys, 2007 to 2016[END_REF]. Whether these differences are also present among individuals with T2DM has received less attention.

The shift of the 2018 Physical Activity Guidelines for Americans [START_REF] Piercy | The Physical Activity Guidelines for Americans[END_REF] away from "bouts" to "all activity counts" highlights the need to not only understand participation in MVPA but also involvement in other types of PA (i.e., light PA [LPA]). Additionally, barriers to PA, which are likely influenced by both biology (sex) and psychological and/or sociocultural factors (gender), among other factors, may be different between men and women as well as individuals with and without T2DM [START_REF] Miller | Sex differences in cardiovascular disease risk and exercise in type 2 diabetes[END_REF]. For the purposes of this paper, the terms "female" and "male" will be used in the context of sex differences in participation in PA, while the terms "girls/women" and "boys/men" will be used when describing barriers/facilitators to PA to acknowledge the potential role of psychological and/or sociocultural factors in these outcomes. No previous systematic review and meta-analysis has summarized evidence of participation in PA by sex (and associated barriers) among individuals with T2DM across the lifespan. Therefore, the aim of the present systematic review and meta-analysis was to summarize the evidence related to sex differences in participation in PA among individuals with T2DM across the lifespan, with additional subgroup analyses according to 1) type of PA assessment and 2) study quality. Additionally, we sought to examine the evidence related to gender differences in barriers and facilitators to PA among individuals with T2DM.

Methods

We followed the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [START_REF] Page | The PRISMA 2020 statement: an updated guideline for reporting systematic reviews[END_REF] and the review and meta-analysis protocol was registered on PROSPERO (CRD42021254246). The inclusion/exclusion criteria, search strategy, process used to evaluate evidence quality, and meta-analysis methods are detailed below.

Population and condition

Studies of male and female individuals with T2DM of any age were included. Age categories were defined using the mean age of the study sample (i.e., adolescents 10-18 years, adults 18-64 years, and older adults ≥65 years). Studies that included individuals with other conditions were included, provided the data for participants with T2DM were reported separately. Additionally, due to changes in the terminology used to describe T2DM [START_REF]Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus[END_REF], we also included articles where diabetes type was not specified provided there was reasonable evidence to suggest that all, or a significant majority of the participants had T2DM (e.g., elevated BMI, waist/hip ratio, lipids; presence of comorbidities such as obesity, hypertension, coronary artery disease, insulin use).

Studies that included only individuals with type 1 diabetes, prediabetes, or gestational diabetes or studies that did not report the PA data for these groups separately, were excluded. Additionally, studies that focused on PA as a risk factor for the development of T2DM or baseline data from studies in which inactive or sedentary individuals were preferentially selected (e.g., baseline data from a randomized controlled trial designed specifically for inactive patients with T2DM), were also excluded.

Outcomes

Studies that reported percentage or number of participants meeting PA guidelines (≥150 minutes of moderate to vigorous PA [MVPA] per week or its equivalent in metabolic equivalent of task [MET]ꞏminutes per week [i.e., ≥ 600 METꞏminutes per week] among adults and older adults [START_REF] Kanaley | Exercise/Physical Activity in Individuals with Type 2 Diabetes: A Consensus Statement from the American College of Sports Medicine[END_REF], ≥60 minutes of MVPA per day among children and adolescents [START_REF] Kanaley | Exercise/Physical Activity in Individuals with Type 2 Diabetes: A Consensus Statement from the American College of Sports Medicine[END_REF]) or assessed PA or sedentary behaviors (SB, waking behavior performed in a sitting or lying position with low energy expenditure) by any method (e.g., questionnaire, pedometer, accelerometer, observation, etc.) and reported or examined data by sex or gender were included. PA could be reported in any format, including time in activity (e.g., minutes, hours), percent time in activity, frequency of activity, proportion of people engaging in any activity, steps, number of flights of stairs, and METꞏminutes or METꞏhours per week (amount of energy expended during PA).

Additionally, studies that assessed barriers and/or facilitators to PA and reported those data by gender were also included. included a combination of subject heading and keyword searches to identify articles that reported findings related to PA, sex differences, and T2DM. The search was optimized with the assistance of a research librarian from the University of Colorado Strauss Health Sciences Library. Additional limits (when available) were placed on the searches to restrict the output to articles in English and human subjects. No limits were placed on publication date, thus the search included articles published from database inception to November 11, 2021. The search strategies used for each database are included in Supplemental File S1. In addition to the database searches, the reference lists of the selected articles were also reviewed for additional eligible articles.

Search strategy

A

Study selection

Review and selection of potential manuscripts was facilitated by the web-based software platform Covidence (covidence.org, Melbourne, Australia). Identified records were imported into Covidence, and duplicates were removed. Titles and abstract of all identified records were screened in duplicate by independent reviewers (MOW, AJP, LAA, or KC). Manuscripts deemed to be potentially relevant were retrieved for full-text review and screened in duplicate by independent reviewers (MOW, AJP, or LAA).

Discrepancies in inclusion or exclusion were resolved via discussion.

Data extraction

The following data were extracted from each individual manuscript: country, study aim and design, participant characteristics (target population, sample size, sex distribution, age, body mass index [BMI], race distribution, diabetes duration, age at diagnosis, fasting glucose and glycated hemoglobin [HbA1c], diabetes medications, and diabetes complications and other comorbidities), key components of outcome assessment (method of assessment, frequency and duration of assessment, intensity, pattern and setting/context of each assessed behavior, and unit of measurement), outcomes related to PA level and/or barriers and facilitators to PA, primary findings for each outcome, and stated conclusions. Data were extracted by two independent reviewers (MOW, AJP, or LAA), with discrepancies resolved via discussion.

Study quality and risk of bias

Study quality and the risk of bias of the qualified cross-sectional studies were assessed using the Appraisal tool for Cross-Sectional Studies (AXIS) [START_REF] Downes | Development of a critical appraisal tool to assess the quality of cross-sectional studies (AXIS)[END_REF]. The AXIS tool is comprised of 20 questions covering quality of reporting (7 questions), study design quality (7 questions), and possible introduction of biases (6 questions). Each question was answered with "yes", "no" or "don't know". For this critical appraisal, responses indicating high quality were scored as "1" while those indicating poor or unknown quality were scored as "0". The publication score was considered high quality if ≥70% (≥14 out of 20), fair quality if between 60-69.9% (12-13 out of 20), and low quality if <60% (≤11 out of 20) [START_REF] Rovito | Assessing Health-Related Quality of Life Among Survivors of Testicular Cancer: A Systematic Review[END_REF]. Methodological quality and risk of bias assessment were independently conducted by two researchers (MOW, AJP, or LAA), with discrepancies resolved via discussion to achieve consensus.

Statistical analysis

To estimate sex differences in meeting PA guidelines (primary outcome) among individuals with T2DM across age groups, odds ratios (OR) and 95% confidence intervals (CI) were calculated using the number of events (individuals meeting PA guidelines) and the total number of observations for each sex. To estimate sex differences in participation in SB, LPA and MVPA (secondary outcomes), standardized mean differences (SMD, Cohen's d effect size) and 95% CI were calculated using the sample size, mean and standard deviation (SD) for each sex. Pooled effect estimates for each outcome and age group (adolescents, adults, and older adults) were calculated using the inverse variance method and a random effects model, due to the small number of studies included in some analyses and the methodological diversity of included studies, with between-study variance being estimated through a restricted maximum likelihood (REML) model. An OR higher than 1 indicated that males were more likely than females to meet PA guidelines and a positive SMD indicated that males had higher participation in SB, LPA or MVPA. Subsequently, a sub-group analysis was performed to investigate the potential influence of 1) type of PA level assessment (subjective and objective methods) and 2) study quality as assessed by AXIS tool (low, fair, and high quality).

A sensitivity analysis was carried out to identify the presence of highly influential studies by removing one study at a time (leave-one-out sensitivity analysis), and then examining the resulting effect on sex differences in participation in PA. The most and least benefit were reported, and studies were considered as influential if their removal resulted in a change of the OR and SMD significance or magnitude. Publication bias was evaluated by visual inspection of the Begg's funnel plot when at least 10 trials were included in the meta-analysis, as with fewer studies the power of the tests is too low to distinguish chance from real asymmetry [START_REF] Higgins | Cochrane Handbook for Systematic Reviews of Interventions[END_REF]. Heterogeneity was measured using Higgin's I 2 test and was interpreted by the following thresholds: 0-40%, might not be important; 30-60%, may represent moderate heterogeneity; 50-90%, may represent substantial heterogeneity; and 75-100%, represents considerable heterogeneity.

Significance was set at p<0.050 (two tailed). Data are presented as OR or SMD (95% CI). All statistical analyses were performed using the R 'meta' package (21) in RStudio © (RStudio Inc., Boston, MA, EUA) for Windows, version 1.4.1717.

Results

A total of 9591 articles were identified from the included databases and an additional 14 articles were identified via other sources. Of these, 1984 were duplicate records. Among the 7621 articles screened, 7527 were excluded as irrelevant based on abstract review and 1 was unable to be retrieved. The remaining 80 articles were assessed for eligibility and 53 articles representing 48 unique studies met criteria for inclusion in this review. Of these included articles, 50 reported subjective and objective PA (46 unique studies) and three articles (22; 45; 60) only discussed barriers/facilitators to PA. Seven articles reported both PA and barriers/facilitators to PA (22; 25; 33; 40; 44; 48; 71). All articles except for one had a cross-sectional design [START_REF] Mccarthy | Predictors of physical inactivity in men and women with type 2 diabetes from the detection of ischemia in asymptomatic diabetics (DIDA) study[END_REF].

The PRISMA flowchart is provided in Supplemental Figure S1. [START_REF] Hays | Correlates of physical activity in a sample of older adults with type 2 diabetes[END_REF]. Among women, lack of social support, motivation, and embarrassment were the most frequently reported barriers (22; 25; 33). Conversely, lack of time was more frequently reported among men (22; 25; 33; 45; 48). Only three studies assessed facilitators, but there were no differences reported by gender (25; 45; 60).

Study quality

The AXIS quality score for each study is reported in Supplemental Table S3.

Overall study quality was fair (mean [SD] 62.7% [12.2], range: 25-90%). Fourteen studies were considered low quality, 19 were considered fair quality, and 20 were considered high quality. Criteria related to the quality of reporting and study design were most frequently met (5.0 [1.5]/7, range: 1-7; and 4.9 [0.9]/7, range: 2-6; respectively). In contrast, questions related to the risk of bias were poorly rated (2.6 [1.1]/6, range: 1-5).

Study inclusion in meta-analysis

Of the 53 articles included in the narrative synthesis, only 21 articles reported the necessary data for inclusion in the quantitative synthesis (Supplemental Figure S1). Among these, 15 articles were included in the meta-analysis on the sex differences in meeting PA guidelines among individuals with T2DM across the lifespan (23; 25; 28; 30; 32-34; 36; 38; 52; 55; 56; 64; 65; 69). Five, three and 13 articles, respectively, were included in the meta-analysis on the sex differences in participation in SB (

Sex differences among adolescents

There was no sex difference in meeting PA guidelines among adolescents (OR 3).

Sex differences among adults

Among adults, males were more likely than females to meet PA guidelines (OR [95% CI], 1.65 [1.36, 2.01], p<0.001, Figure 1 Panel B) and performed more MVPA than females (SMD [95%CI], 0.29 [0.17, 0.42], p<0.001). No sex differences were observed in time spent in SB (SMD [95% CI], 0.06 [-0.30, 0.42], p=0.751) and LPA (SMD [95% CI], -0.13 [-0.59, 0.32], p=0.653) (Table 3).

Sex differences among older adults

Males were more likely than females to meet PA guidelines (OR [95% CI], 1.63 [1.27, 2.09], p<0.001, Figure 1 Panel B) and performed more MVPA than females (SMD [95% CI], 0.23 [0.12, 0.33], p<0.001) among older adults (Table 3). Meta-analyses were not performed for SB and LPA due to the lack of studies assessing these outcomes.

Subgroup analysis

Subgroup analysis by type of PA assessment is presented in Supplemental Subgroup analysis by study quality is presented in Supplemental Figure S3. 65)). Sensitivity analysis was not performed for adolescents as only two studies were included in the meta-analysis.

Males

Similarly, none of the included studies unduly affected the results for participation in SB, LPA and MVPA (Table 3).

Publication bias and heterogeneity

Visual inspection of the funnel plot for sex differences in meeting PA guidelines across all age groups (Panel A), in meeting PA guidelines among adults (Panel B), and in participation in MVPA across all age groups (Panel C) revealed asymmetry (Supplemental Figure S4). Funnel plots were not used for other population groups and outcomes due to the small number of studies (all <10 studies).

Heterogeneity was not important for sex differences in meeting PA guidelines among adolescents and older adults, for participation in SB and LPA among adolescents, and in MVPA among older adults (Figure 1 Panel B, and Table 2). However, substantial heterogeneity was observed for sex differences in participation in MVPA among adolescents (Table 2) and for meeting PA guidelines and participation in LPA and MVPA among adults (Figure 1 Panel B, and Table 2). Finally, there was considerable heterogeneity for sex differences in participation in SB among adults (Table 2). The high heterogeneity is probably due to the small number of included studies and sample and methodological heterogeneity. Sources of sample and methodological heterogeneity may include, but are not limited to, participant characteristics (city/country of residency, culture, clinical parameters, etc.), methods used to assess PA level, and the use of different cut-points, thresholds, and units of measurement to report PA outcomes.

Discussion

Sex differences were not observed in meeting PA guidelines among adolescents with T2DM, but males had significantly greater odds of meeting PA guidelines than females among adults and older adults with T2DM. Despite the inconsistent sex differences in meeting PA guidelines across the lifespan, females with T2DM consistently engaged in less MVPA than males among adolescents, adults, and older adults-thus demonstrating important sex differences across the lifespan in MVPA. The lack of agreement in these outcomes may be due, in part, to a lack of data on meeting PA guidelines among children and adolescents with T2DM. The scarcity of studies including LPA and SB as outcomes limit our conclusions for those measures.

Globally, 1 in 4 adults do not meet PA guidelines [START_REF]Physical Activity[END_REF] based on self-reported data and previous research suggests that individuals with T2DM are, on average, less active than the general population [START_REF] Brawner | Prevalence of Physical Activity Is Lower among Individuals with Chronic Disease[END_REF]. Our results are consistent with previous studies (76), as only 36.2% (males: 40.9%; females: 31.5%) of individuals with T2DM across all age groups in the included articles met PA guidelines. These findings highlight the need of PA promotion for individuals with T2DM across the lifespan irrespective of sex (9; 10; 17; 77). In this context, assessing participation in PA, and identifying facilitators and overcoming barriers to PA may support the development of personalized, optimal PA prescriptions and increase engagement in PA among individuals with T2DM.

With respect to sex differences in participation in PA, there were no differences in meeting PA guidelines between males and females among adolescents with T2DM, which is contrary to findings from apparently healthy adolescents [START_REF] Guthold | Global trends in insufficient physical activity among adolescents: a pooled analysis of 298 population-based surveys with 1•6 million participants[END_REF]. Nonetheless, our findings suggest that adolescent females with T2DM engaged in less MVPA than males and this difference persisted across the lifespan. Importantly, sex differences in meeting PA guidelines become evident among adults and older adults with T2DM, which corroborate evidence from the general population demonstrating that adult females are less likely to meet PA guidelines than adult males [START_REF] Scholes | Education-related disparities in reported physical activity during leisure-time, active transportation, and work among US adults: repeated crosssectional analysis from the National Health and Nutrition Examination Surveys, 2007 to 2016[END_REF]. Given that females with T2DM have lower cardiorespiratory fitness than males with T2DM (7) and bear a disproportionate burden of CVD in the context of T2DM (3; 8), promoting participation in PA for girls/women with T2DM across the lifespan is instrumental to enhance health, improve glycemic control and reduce CVD burden.

Only nine of the identified articles included assessment of barriers to PA, of which only three also included facilitators to PA. We observed some gender differences in barriers to PA, with lack of social support, motivation, and embarrassment more frequently reported by women and lack of time more frequently reported by men. These barriers are consistent with existing data on barriers to PA among adults with obesity [START_REF] Baillot | Physical activity motives, barriers, and preferences in people with obesity: A systematic review[END_REF]. Of note, none of the studies included in the present review assessed barriers to PA among children and adolescents with T2DM. As differences in barriers to PA have been noted among healthy boys and girls [START_REF] Rosselli | Gender differences in barriers to physical activity among adolescents[END_REF], work is needed to understand barriers and facilitators to PA among children and adolescents with T2DM to optimize the design of PA interventions for this population. This is the first systematic review and meta-analysis to characterize participation in PA by sex and barriers/facilitators to PA by gender among individuals with T2DM across the lifespan. It is notable that many studies limited their evaluation of PA to MVPA, rather than also including assessment of LPA and SB. Increased understanding of the impact of LPA on health outcomes is important, because increasing LPA is frequently targeted as part of interventions designed to reduce SB (replace/interrupting SB with LPA). This focus is particularly notable in the context of the updated PA guidelines [START_REF] Piercy | The Physical Activity Guidelines for Americans[END_REF], where recommendations have shifted away from the requirement that PA occur in "bouts" of ≥10 minutes to be meaningful to an "all activity counts" approach. This shift highlights the need to not only understand participation in MVPA but also involvement in other types of PA as important for health. For example, research has demonstrated that time spent in LPA is beneficially associated with 2-hour blood glucose, with the opposite true for SB, independent of participation in MVPA [START_REF] Healy | Objectively measured light-intensity physical activity is independently associated with 2-h plasma glucose[END_REF].

Additionally, breaking up prolonged SB with brief activity bouts also shows promise in improving blood glucose control among individuals with and without T2DM [START_REF] Dempsey | Benefits for Type 2 Diabetes of Interrupting Prolonged Sitting With Brief Bouts of Light Walking or Simple Resistance Activities[END_REF][START_REF] Saunders | The Acute Metabolic and Vascular Impact of Interrupting Prolonged Sitting: A Systematic Review and Meta-Analysis[END_REF][START_REF] Homer | Frequency of Interruptions to Sitting Time: Benefits for Postprandial Metabolism in Type 2 Diabetes[END_REF] Additional research is needed to understand the role of targeting increasing LPA and reducing SB in combination with other lifestyle interventions in the long-term management of T2DM.

Although the risk of T2DM increases with age [START_REF] Kirkman | on D, Older A. Diabetes in older adults: a consensus report[END_REF], T2DM is becoming a major concern among children and young adults, with an estimated prevalence of 0.67 per 1000 youths aged 10 to 19 years [START_REF] Lawrence | Trends in Prevalence of Type 1 and Type 2 Diabetes in Children and Adolescents in the US, 2001-2017[END_REF]. Despite this increasing prevalence, only 6 of the eligible articles included individuals with T2DM <18 years old (39; 41; 53; 55; 68; 69). As the population of children and young adults with T2DM increases, studies that include this younger age group are needed in order to understand PA levels in this population, particularly because individuals diagnosed at a young age are more likely to experience T2DM complications and have an increased risk of CVD morbidity and mortality [START_REF] Today | Long-Term Complications in Youth-Onset Type 2 Diabetes[END_REF].

Additionally, T2DM in children and adolescents presents a challenge that is distinct from T2DM in adults, as the development of complications is accelerated among children and adolescents and there are fewer approved treatment options available [START_REF] Nadeau | Youth-Onset Type 2 Diabetes Consensus Report: Current Status, Challenges, and Priorities[END_REF].

Understanding the unique pathophysiology of T2DM in children and adolescents, including the role of PA in delaying the onset of complications, is essential.

In addition to the lack of data available in younger age groups, the variability of methods used to classify participants as active/inactive is a limitation. More than 20% (n=10) (41; 42; 47; 49; 51; 54; 59; 61; 63; 73) of the studies that sought to classify activity levels did so using methods that were inconsistent with national or international guidelines for PA. In some cases, this is the product of relatively low levels of PA among individuals with T2DM, with any regular PA being considered 'active'. Using published guidelines (e.g., ADA [START_REF] Colberg | Physical Activity/Exercise and Diabetes: A Position Statement of the American Diabetes Association[END_REF], ACSM [START_REF] Kanaley | Exercise/Physical Activity in Individuals with Type 2 Diabetes: A Consensus Statement from the American College of Sports Medicine[END_REF], Physical Activity Guidelines for Americans [START_REF] Piercy | The Physical Activity Guidelines for Americans[END_REF], or WHO guidelines on PA and SB ( 77)) to guide these classifications is critical to enable comparisons across populations.

We also identified other limitations with respect to the scientific rigor of the included articles and available evidence in the literature. First, most of the included articles did not report PA data by age groups. Consequently, our meta-analysis was based on mean age provided by the authors and participation in PA in younger participants might have not been summarized well in most of the studies that were conducted in an adult population. There were inconsistencies in the use of sex/gender terms, which precluded analysis of both sex and gender differences in participation in PA. There were also inconsistencies in the reporting of data on diabetes status (e.g., self-report vs. diagnosed diabetes, lack of laboratory data to confirm status, lack of ruling out type 1 diabetes mellitus). There was high variability in outcome reporting for the same measure (e.g., IPAQ data were summarized by meeting PA guidelines, time spent in each intensity with/without time spent in SB, by total MET•minute, by MET•minute in each intensity, etc.), which precluded the inclusion of some articles in our analysis. Reporting all possible outcomes from PA assessment would facilitate future meta-analysis. Finally, it is important to note that "sex differences" is a relatively unused subject heading. We used a complex search strategy which we optimized with the help of a research librarian but potentially may have missed some relevant articles.

Gaps and areas for future research

Articles included in this review provided initial evidence about sex differences in participation in PA among individuals with T2DM. However, the literature on the topic is still incipient and extensive further research needs to be conducted in this area. First, there is limited evidence on sex/gender differences in engagement in SB and LPA.

Additionally, researchers should focus on stratification of data by sex/gender among individuals with T2DM, with specific attention to the appropriate use of the terms sex and gender, as well as assessment of diabetes status. It is also critical to disaggregate data by age (i.e., analyze data in smaller age groupings, such as 5-year increments) to facilitate understanding of age-related differences. Researchers should also consider the inclusion of racially and ethnically diverse samples. These data will support the advancement of personalized PA prescriptions and the delivery of optimal health interventions.

Conclusions

In summary, sex differences in meeting PA guidelines were not observed among adolescents with T2DM, but were apparent among adults and older adults with T2DM. Despite these inconsistencies in sex differences of meeting PA guidelines across the lifespan, females with T2DM consistently engaged in less MVPA than males across the lifespan. Additional studies are needed that include evaluation of sex differences in PA guidelines, SB, LPA, and MVPA across all age groups of people with T2DM, as well as studies on the role of gender and its sociocultural implications. Overall, the field needs to continue to move forward so that we attain a greater understanding of how differences in sex and gender influence participation in PA. This will potentially lead to an improved ability to increase PA in females, as well as an enhanced capability to develop individualized PA interventions for both sexes.

[

  95% CI], 0.70 [0.31, 1.59], p=0.399, Figure 1 Panel B), adolescent males performed more MVPA than adolescent females (SMD [95% CI], 0.42 [0.20, 0.64], p<0.001). In contrast, males performed less LPA than females (SMD [95% CI], -0.21 [-0.35, -0.07], p=0.003). No sex differences were observed in time spent in SB among adolescents (SMD [95% CI], 0.08 [-0.07, 0.22], p=0.292) (Table

Figure S2 .

 S2 Figure S2. Males are significantly more likely than females to meet PA guidelines based

  systematic search was conducted of MEDLINE (via Ovid MEDLINE® and Epub

	Ahead of Print, In-Process & Other Non-Indexed Citations, Daily and Versions®, 1946
	to present), Embase (via Elsevier, Embase.com, 1947 to present), Web of Science Core
	Collection (via Clarivate Analytics, including Science Citation Index Expanded and
	Social Sciences Citation Index, 1974 to present), CINAHL (Cumulative Index to Nursing
	and Allied Health Literature via EBSCOhost, 1981 to present), AMED (Allied and
	Complementary Medicine via Ovid, 1985 to present), SPORTDiscus (via EBSCOhost,
	1800 to present), and PsychINFO (via Ovid, 1806 to present). The search strategy

Table 1

 1 summarizes the characteristics of the included articles that assessed PA. Panel A represents the age and population distribution by sex of studies (n=21) that measured meeting PA guidelines (by either subjective or objective measures). Supplemental Table1presents detailed characteristics of the included articles that assessed PA.Of the included articles, nine evaluated barriers and facilitators to PA by gender(22; 25; 33; 40; 44; 45; 48; 60; 71) (Table2and Supplemental TableS2). Three articles did not explicitly define the type of diabetes of the sample(22; 45; 71). Seven articles assessed barriers and facilitators via questionnaire(22; 33; 40; 44; 45; 48; 71); two articles used semi-structured interviews or focus groups(25; 60). Barriers and facilitators data were only available among adults, with a mean sample age ranging from 48 years among males and 52 years among females (71) to 67 years among both sexes

	Studies included a total of 65,344 individuals age >9 years from 22 countries. Seven
	articles focused exclusively on adolescents (39; 41; 53; 55; 62; 68; 69). Four articles did
	not include sex distribution (27; 47; 54; 74); however, out of 45,157 individuals with
	reported sex, 49.3% were female. Eleven articles did not explicitly define the type of
	diabetes of the sample (23; 27; 28; 36; 56; 59; 67; 71-74). Thirty-eight articles reported
	subjective measures only (all questionnaires) (23-25; 27-31; 33-42; 44; 46-48; 50-52;
	54; 58; 59; 61; 63-65; 67; 69; 71-74), 5 objective measures only (2 pedometers (49; 70),
	3 accelerometers (32; 56; 57)), and 7 included both subjective (6 questionnaires (43;
	53; 55; 62; 66; 68), 1 pictograms (26)) and objective measures (5 accelerometers (26;
	43; 53; 66; 68) , 2 pedometer (55; 62)). The most common questionnaire was the
	International Physical Activity Questionnaire (IPAQ) (n=9) (28; 34; 36; 38; 42; 50-52;
	67). Figure 1

  were significantly more likely than females to meet PA guidelines irrespective of study quality (OR [95% CI], low: 1.37 [1.21, 1.56], p<0.001; fair: 1.36 [1.02, 1.82],

	p=0.039; high: 1.74 [1.40, 2.16], p<0.001) (test for subgroup differences: p=0.163). No
	sex differences were observed in time spent in SB in fair quality (SMD [95% CI], 0.16 [-
	0.02, 0.34], p=0.075) and high quality (SMD [95% CI], -0.06 [-0.74, 0.63], p=0.876)
	studies (test for subgroup differences: p=0.548). For LPA, males significantly performed
	less LPA than females in fair quality (SMD [95% CI], -0.21 [-0.35, -0.07], p=0.003), but
	not in high quality studies (SMD [95% CI], -0.13 [-0.59, 0.32], p=0.653) (test for
	subgroup differences: p=0.754). Males engaged in significantly more MVPA than
	females irrespective of study quality (SMD [95% CI], low: 0.36 [0.12, 0.60], p=0.003;
	fair: 0.31 [0.18, 0.44], p<0.001; high: 0.32 [0.16, 0.48], p<0.001) (test for subgroup
	differences: p=0.933).
	Sensitivity analysis
	Leave-one-out sensitivity analysis indicated that none of the included studies
	unduly influenced the results for meeting PA guidelines in adults (OR [95% CI], most
	benefit: 1.72 [1.43, 2.05] omitting Falconer et al., 2017 (34); least benefit: 1.54 [1.31,
	1.83] omitting Alghafri et al., 2018 (23)) and older adults (OR [95% CI], most benefit:
	1.74 [1.26, 2.41] omitting Plotnikoff et al., 2006 [age 60-69 y] (65); least benefit: 1.57
	[1.16, 2.12] omitting Plotnikoff et al., 2006 [age >70 y] (

Table 3 .

 3 Meta-analysis of SB, LPA, and MVPA by age group. Meta-analysis and sensitivity analysis were not performed for SB and LPA in older adults due to the low number of studies. et al., 2013 (53) (age 15-18 y, objective); b omitting Kriska et al., 2013 (53) (age 10-14 y, subjective); c omitting Alghafri et al., 2018 (23); d omitting Falconer et al., 2017 (34); e omitting Kriska et al., 2013 (53) (age 10-14 y, objective); f omitting Kriska et al., 2013 (53) (age 15-18 y, subjective); g omitting Loprinzi et al., 2013 (56) (age 18-64 y); h omitting Do et al., 2019 (32); I omitting Qian et al., 2021 (66); j omitting McCarthy et al., 2014 (5-year follow-up); k omitting Loprinzi et al., 2013 (56) (age >65 y)Abbreviations: CI, confidence interval; LPA, light physical activity; MVPA, moderate to vigorous physical activity; NA, not applicable; PA, physical activity; SB, sedentary behavior; SMD, standard mean difference (Cohen's d effect size). Sex differences in meeting PA guidelines among individuals with T2DM across age groups. Panel A) Bubble graph of meeting PA guidelines by age and sex. Panel B) Forest plot for inverse variance, random effects meta-analysis of studies on sex differences in meeting PA guidelines in individuals with T2DM across age groups, expressed as OR between prevalence of meeting PA guidelines in males and females.

				Main Findings			Leave-One-Out Sensitivity Analysis
	Outcome	Number of	Number of	Pooled effect	P value	I 2 , p value	Most benefit				Least benefit
		studies	participants	(SMD [95% CI])			SMD [95% CI]				SMD [95% CI]
	SB
	Adolescents	4	825	0.08 [-0.07, 0.22]	0.292	0%, p=0.68	0.11 [-0.04, 0.26]	a	0.04 [-0.14, 0.21]	b
	Adults	4	7532	0.06 [-0.30, 0.42]	0.751	94%, p<0.01	0.22 [-0.05, 0.50]	c	-0.07 [-0.46, 0.32]	d
	Older Adults	NA	NA	NA	NA	NA	NA				NA
	LPA
	Adolescents	4	825	-0.21 [-0.35, -0.07]	0.003	0%, p=0.95	-0.23 [-0.38, -0.07]	e	-0.19 [-0.37, -0.02] f
	Adults	3	863	-0.06 [-0.30, 0.19]	0.653	61%, p=0.08	-0.15 [-0.56, 0.26]	g	0.05 [-0.09, 0.20]	h
	Older Adults	NA	NA	NA	NA	NA	NA				NA
	MVPA
	Adolescents	7	1030	0.42 [0.20, 0.64]	<0.001	57%, p=0.03	0.48 [0.26, 0.71]	f	0.30 [0.17, 0.44]	a
	Adults	9	13569	0.29 [0.17, 0.42]	<0.001	90%, p<0.01	0.33 [0.20, 0.45]	d	0.25 [0.14, 0.36] i
	Older Adults	4	1363	0.23 [0.12, 0.33]	<0.001	0%, p=0.97	0.26 [0.06, 0.46]	j	0.22 [0.09, 0.34]

k Note. a omitting Kriska

Acknowledgements

Funding Source

This study was supported by the National Institutes of Health (T32AG000279 [MOW], L30NR019425 [MOW], R01DK123334 [AB], P50CA244688 [AGH], P30DK048520 [KSM], P30DK116073 [JEBR], UL1TR001082 [JEBR], R01AG066562 [JEBR], R01DK124344 [JEBR, JGR], K12HD057022 [JGR]), the Ludeman Family Center for Women's Health Research (MOW, AJP, AGH, KJN, RLS, JEBR, JGR), the American Diabetes Association (1-21-CMF-003 [LAA]), and the Department of Veterans Affairs (BX004533 [RLS], BX002046 [JEBR], CX001532 [JEBR]).

Author Contributions

MOW and JGR conceived of idea; MOW, AJP, LAA, AB, AGH, KSM, JEBR, and JGR refined the topic to be studied; MOW performed the literature searches, MOW, AJP, LAA, and KC screened articles for eligibility; MOW, AJP, and LAA extracted relevant data; AJP performed the analysis; MOW, AJP, and LAA drafted the manuscript.

All authors discussed the results, critically revised the manuscript, and approved the final version to be published.

The authors would like to acknowledge Strauss Research Librarian Christi Piper, MLIS, AHIP for her valuable help in optimizing the search strategy.

COI statements

The authors declare that there is no conflict of interest.

Guarantor's statement

MOW, AJP, and LAA are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Note. *Studies that included assessment of both levels of PA and barriers/facilitators to PA. † Refers to studies in which diabetes type was not specified.

Abbreviations: NR, not reported; NS, non-significant; PA, physical activity; T2DM, type 2 diabetes mellitus.


Carmen Ortega-Santos

Ana J Pinto

PhD Mary O Whipple

PhD Zhaoxing Pan

PhD Kristen E Boyle

PhD Edward L Melanson

PhD Kevin Masters

MD Daniel H Bessesen

Audrey

PhD Audrey Bergouignan
email: audrey.bergouignan@cuasnchutz.edu

Breaking up sedentary time to improve glucose control in a population at risk for developing type 2 diabetes (BURST2D study): a randomized controlled trial

Keywords: NTC05041491 Sedentary behavior, pre-diabetes, obesity, physical activity, active breaks, glucose kinetics, stable isotope tracers, skeletal muscle, insulin sensitivity, substrates use

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Approximately 88 million American adults have pre-diabetes. [START_REF]National Diabetes Statistics Report: Estimates of Diabetes and its Burden in the United States[END_REF] Almost 1 in 5 will progress to type 2 diabetes (T2D) per year. [START_REF]National Diabetes Statistics Report: Estimates of Diabetes and its Burden in the United States[END_REF] At this rate, it is estimated that 642 million people will have T2D by 2040. [START_REF] Collaboration | Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4.4 million participants[END_REF] There is a need to consider what strategies might be safe, acceptable, cost-effective, and clinically useful population-wide to improve glucose metabolism and reduce the risk for T2D. Meta-analyses suggest there is a 112% relative risk of T2D in those with high levels of sedentary behaviors (SB; any waking behavior characterized by a low energy expenditure [≤1.5 MET] while sitting, reclining or lying down), [START_REF] Tremblay | Sedentary Behavior Research Network (SBRN) -Terminology Consensus Project process and outcome[END_REF] independent of time spent in physical activity (PA). [START_REF] Wilmot | Sedentary time in adults and the association with diabetes, cardiovascular disease and death: systematic review and meta-analysis[END_REF] Importantly, the elevated relative risk of T2D in those with high SB is independent of time spent in moderate-to-vigorous PA (MVPA; >3 METs) [START_REF] Wilmot | Sedentary time in adults and the association with diabetes, cardiovascular disease and death: systematic review and meta-analysis[END_REF] . This highlights the importance of reducing SB in addition to promoting participation in MVPA.

Reducing total sedentary time with frequent, short active bouts seems to lower metabolic risk even in adults who regularly exercise. [START_REF] Miyashita | Interrupting Sitting Time with Regular Walks Attenuates Postprandial Triglycerides[END_REF][START_REF] Miyashita | Postprandial lipaemia: effects of sitting, standing and walking in healthy normolipidaemic humans[END_REF][START_REF] Mccarthy | Fitness Moderates Glycemic Responses to Sitting and Light Activity Breaks[END_REF] For anyone with T2D, inclusion of taking more frequent breaks to SB is likely to bestow even greater glycemic benefits. [START_REF] Healy | Breaks in sedentary time: beneficial associations with metabolic risk[END_REF][START_REF] Dempsey | Sitting Less and Moving More: Improved Glycaemic Control for Type 2 Diabetes Prevention and Management[END_REF] Significant improvements in postprandial glycemia and insulinemia have been observed following frequent, short (≤5 min) light- [START_REF] Miyashita | Interrupting Sitting Time with Regular Walks Attenuates Postprandial Triglycerides[END_REF][START_REF] Nygaard | Slow postmeal walking reduces postprandial glycemia in middle-aged women[END_REF][START_REF] Dunstan | Breaking up prolonged sitting reduces postprandial glucose and insulin responses[END_REF][START_REF] Larsen | Breaking up of prolonged sitting over three days sustains, but does not enhance, lowering of postprandial plasma glucose and insulin in overweight and obese adults[END_REF] or moderate-intensity [START_REF] Dunstan | Breaking up prolonged sitting reduces postprandial glucose and insulin responses[END_REF] walking breaks to sitting, with MVPA being the most potent health-enhancing behavior. [START_REF] Matthews | Accelerometer-measured dose-response for physical activity, sedentary time, and mortality in US adults[END_REF][START_REF] Buman | Reallocating time to sleep, sedentary behaviors, or active behaviors: associations with cardiovascular disease risk biomarkers, NHANES 2005-2006[END_REF] These acute experimental studies showed that regular brief interruptions to SB can improve glucose control in healthy adults but to a greater extent in those with impaired glucose regulation. [START_REF] Dempsey | Prolonged uninterrupted sitting elevates postprandial hyperglycaemia proportional to degree of insulin resistance[END_REF] However, it is still unclear whether benefits related to glucose control are similar or more pronounced following frequent breaks compared to a calorie-matched continuous PA bout.

Recent data suggest that frequent 5-min bouts of PA spread throughout the day may be better for glycemic control than performing a single 45-min continuous bouts of PA, even when interventions are matched for total active time and total energy expenditure (TEE). [START_REF] Jong | Effect of frequent interruptions of sedentary time on nutrient metabolism in sedentary overweight male and female adults[END_REF][START_REF] Loh | Effects of Interrupting Prolonged Sitting with Physical Activity Breaks on Blood Glucose, Insulin and Triacylglycerol Measures: A Systematic Review and Meta-analysis[END_REF] We found in sedentary adults with overweight/obesity that 4 days of frequent hourly 5-min brisk walking interruptions in prolonged sitting (45 min/d, BREAK) leads to greater reliance upon carbohydrates as fuel both after a meal and over 24h as compared to a sedentary control condition. In contrast, a single 45-min isoenergetic continuous bout of brisk walking (ONE) increased 24h fat oxidation despite a similar increase in TEE, as measured by whole room calorimetry. [START_REF] Jong | Effect of frequent interruptions of sedentary time on nutrient metabolism in sedentary overweight male and female adults[END_REF] The greater use of carbohydrates during BREAK intervention may explain the reduced postprandial glycemia observed following active breaks. [START_REF] Miyashita | Interrupting Sitting Time with Regular Walks Attenuates Postprandial Triglycerides[END_REF][START_REF] Nygaard | Slow postmeal walking reduces postprandial glycemia in middle-aged women[END_REF][START_REF] Dunstan | Breaking up prolonged sitting reduces postprandial glucose and insulin responses[END_REF][START_REF] Larsen | Breaking up of prolonged sitting over three days sustains, but does not enhance, lowering of postprandial plasma glucose and insulin in overweight and obese adults[END_REF] The mechanistic underpinnings of the beneficial effects of breaking up SB are likely to be multifactorial, and involve peripheral organs that play a key role in the regulation of intermediary metabolism. Skeletal muscle is the largest glucose consuming organ in the body, accounting for more than 80% of the insulin-stimulated glucose disposal and is quantitatively the predominant tissue during exercise. [START_REF] Egan | Exercise metabolism and the molecular regulation of skeletal muscle adaptation[END_REF] In adults with overweight/obesity favorable changes in skeletal muscle gene and protein expression were observed acute (5-h) [START_REF] Bergouignan | Frequent interruptions of sedentary time modulates contraction-and insulin-stimulated glucose uptake pathways in muscle: Ancillary analysis from randomized clinical trials[END_REF] and short-term (3-4 days) [START_REF] Bergouignan | Frequent interruptions of sedentary time modulates contraction-and insulin-stimulated glucose uptake pathways in muscle: Ancillary analysis from randomized clinical trials[END_REF][START_REF] Jong | Short-Term Adaptations in Skeletal Muscle Mitochondrial Oxidative Capacity and Metabolic Pathways to Breaking up Sedentary Behaviors in Overweight or Obese Adults[END_REF] exposure to breaking up SB with short bouts of PA that likely contribute to improved cellular glucose uptake [START_REF] Bergouignan | Frequent interruptions of sedentary time modulates contraction-and insulin-stimulated glucose uptake pathways in muscle: Ancillary analysis from randomized clinical trials[END_REF] , insulin signaling cascade [START_REF] Bergouignan | Frequent interruptions of sedentary time modulates contraction-and insulin-stimulated glucose uptake pathways in muscle: Ancillary analysis from randomized clinical trials[END_REF] and oxidative capacity. [START_REF] Jong | Short-Term Adaptations in Skeletal Muscle Mitochondrial Oxidative Capacity and Metabolic Pathways to Breaking up Sedentary Behaviors in Overweight or Obese Adults[END_REF] These changes include some which align with, and others which are distinct from, the known effects of continuous acute PA. [START_REF] Bergouignan | Frequent interruptions of sedentary time modulates contraction-and insulin-stimulated glucose uptake pathways in muscle: Ancillary analysis from randomized clinical trials[END_REF][START_REF] Latouche | Effects of breaking up prolonged sitting on skeletal muscle gene expression[END_REF][START_REF] Jong | Short-Term Adaptations in Skeletal Muscle Mitochondrial Oxidative Capacity and Metabolic Pathways to Breaking up Sedentary Behaviors in Overweight or Obese Adults[END_REF] The 2016 Position Statement on PA and diabetes from the American Diabetes Association, [START_REF] Colberg | Physical Activity/Exercise and Diabetes: A Position Statement of the American Diabetes Association[END_REF] the 2018 U.S. PA guidelines [START_REF] Piercy | The Physical Activity Guidelines for Americans[END_REF] and the American College of Sports Medicine PA recommendations for T2D [START_REF] Kanaley | Exercise/Physical Activity in Individuals with Type 2 Diabetes: A Consensus Statement from the American College of Sports Medicine[END_REF] include recommendations to reduce and interrupt prolonged sitting. Most studies on the effects of reducing and interrupting prolonged SB on glycemic control have mostly been acute (<1-4d) and conducted in controlled laboratory settings or derived from population-level evidence. Current gaps in knowledge include: (1) establishing whether the acute metabolic benefits of interrupting SB are sustained or mitigated over time; (2) establishing whether frequently interrupting prolonged sedentary periods is more beneficial than or like performing a time-matched single continuous bout of PA for glucose homeostasis; and (3) better understanding the underlying mechanisms.

Objectives & Hypotheses

Our objective is to compare the acute and chronic effects of frequent PA breaks vs a single continuous bout of PA on glucose control and potential mechanisms in adults at risk for T2D. Our aims are to compare the acute and 1-month effect of breaking up SB (BREAK) vs a time-matched continuous PA bout (ONE) on (1) daily glycemia and insulin sensitivity, (2) glucose fluxes, and (3) skeletal muscle insulin-dependent andindependent oxidative and non-oxidative glucose disposal pathways. Our hypothesis is that both acute and one-month exposure to BREAK will lower postprandial glycemia and insulinemia in response to standard meals, lower daily glycemic variability and improve insulin sensitivity to a greater extent than ONE. These changes will be associated with greater increases in both endogenous and exogenous carbohydrate uptake and oxidation following BREAK than ONE. Finally, both BREAK and ONE will enhance the amount and phosphorylation of proteins involved in insulin-dependent glucose uptake and non-oxidative glucose pathways in skeletal muscle, but insulin-independent pathway and glucose transport signaling proteins and phosphorylation will be greater after BREAK than ONE. We will also pursue two exploratory aims. We will compare the 3-month effects of BREAK vs ONE on daily patterns of SB and PA, glucose control, insulin sensitivity, lipid profile, body composition, waist circumference, appetite, vigor, fatigue, mood, and sleep. We will further assess the changes in knowledge and attitude towards SB and PA, the barriers, challenges, and facilitators following one month and three months of BREAK or ONE.

Trial design

BURST2D is a single-center, parallel-group, randomized controlled trial. Eligible participants will be randomly assigned into one of the two arms: BREAK or ONE.

Primary and secondary outcome measures will take place at baseline and after one month of intervention. Exploratory outcome measures will take place after one month and three months of interventions. Overall study design is illustrated in Figure 1.

Methods: Participants, interventions, and outcomes

Study setting

The study will be conducted at the University of Colorado Denver Anschutz Medical Campus (CU-AMC) and all study visits (except Visit A) will occur at the University of Colorado Hospital Clinical and Translational Research Center (CTRC). The current protocol is approved by the Colorado Multiple Institutional Review Board (COMIRB) of the CU-AMC (identifier 20-1900). This study is registered at ClinicalTrials.gov (identifier NTC05041491).

Eligibility criteria

The trial inclusion and exclusion criteria are described in Table 1. More than 6 hours/day of sitting time and less than 150 min/week of MVPA, as self-reported by the volunteers using the IPAQ, or less than 6500 of steps per day as measured by a pedometer over 5 days, including at least 1 weekend day.

Being positive for human immunodeficiency virus or hepatitis B or C.

Passing medical and physical screening, and analysis of blood and urine pregnancy screening test.

Taking medications affecting weight, triglycerides, energy intake/energy expenditure, or sleep in the last 3 months.

Low-moderate caffeine use (<3 cups/day).

Having abnormal blood chemistry and/or hematology as deemed significant by the study physician.

Agree to refrain from alcohol for 72 h before and during the inpatient CTRC visits.

Being a smoker or having been a smoker in the 3 months prior to their screening visit.

Agree to maintain their habitual PA and not start any new structured exercise and or PA.

Having donated over 400 mL of blood within 3 months (90 days) of screening for the study.

Agree to eat control diets for 3 days before and during the CTRC visits.

Working night shifts or traveling across more than 2 time zones within 1 month of and throughout the study.

Agree to refrain from taking any over the counter (including nonsteroidal anti-inflammatory drugs) or prescribed medication (apart from oral contraceptives) for 3 days prior to the inpatient CTRC visits.

Not completing the trial days of BREAK and ONE during the screening period to assess the willingness and ability of the participant to perform each of the interventions.

Agree to wear a Fitbit activity monitor and upload data on the website daily for the whole duration of the study.

Not being fully vaccinated against COVID-19 based on CDC definition.

Agree to follow the PA interventions and to be randomly assigned to one of the two arms of the study.

Agree to complete all the study procedures.

Abbreviations

Recruitment

The recruitment strategies will include direct mailing, e-mail newsletters, newspapers advertisement, study pamphlets, social media websites, university e-newsletters, and participation in health fairs and walk-in clinics. If needed, other recruitment strategies may be explored.

Consent

A detailed description of the screening visits is provided in Figure 1. Participants will complete a pre-screening questionnaire. If they meet study eligibility criteria, the informed consent and risk-benefits of the study will be discussed via a private Zoom meeting (Visit A). A fasting blood sample will be drawn for measurement of a comprehensive metabolic panel, lipid profiles, complete blood count, and fasting glucose (Visit B). Eligible participants will be invited to attend a second screening visit that will consist in a history and physical examination (H&P) and a 75g standard oral glucose tolerance test (OGTT). Plasma HbA1c will be measured in fasting state and blood glucose at 0, 30, 60, 90 and 120min post glucose load. [START_REF] Ma | Muscle and liver insulin resistance indexes derived from the oral glucose tolerance test[END_REF] Participants will be asked to wear a pedometer on their waist and maintain their habitual daily steps for 5 days including at least one weekend day. They will also be asked to perform Questionnaire. Eligible participants will be invited to participate in the study.

Interventions description

Interventions

Participants will be randomly assigned using a randomization table (1:1 ratio) for each sex to one of the two following 3-month interventions:

1. BREAK: Participants will be asked to break up their prolonged sitting in daily living with 5-min of brisk walking performed every hour for nine consecutive hours, 5 d/wk. The timing and days the intervention will be performed will be of their choosing. Pace will be determined at the familiarization visit (Visit F).

Familiarization visit

Participants will perform a sub-maximal exercise test on a treadmill to determine a walking pace corresponding to moderate intensity. The walking test will start at a pace of 2.0 mph and the pace will increase by 0.3 mph every minute. At each level, participants will report their rate of perceived exertion (RPE) on a Borg scale from 6

(very light) to 20 (maximal exertion). The aim will be to identify the speed associated with a RPE of 13 (somewhat hard). The walking test will stop when the participant rates the speed of the treadmill with RPE of 15 (hard).

Volunteers will be asked to perform a moderate-intensity bout during the inpatient study visits while in the whole-room calorimeter. For safety reasons and following hospital policies, a stepping equivalent (beats per min [bpm]) to the treadmill will be performed inside the chamber. A stepping test will therefore be performed during Visit F to identify the stepping pace equivalent to the walking speed. The stepping test will be conducted following the same protocol of the walking test, excepted by starting at a pace of 55-70 bpm and the pace will increase by 5 bpm every minute. In both tests heart rate and breath-to-breath gas exchange will be measured by using a heart rate monitor placed on the chest (Polar, Polar Electro, US) and indirect calorimetry (ParvoMedics TrueOne 2400, Salt Lake City), respectively. Rate of oxygen consumption (kcal/min) and heart rate will be used to match walking speed and stepping pace.

Strategies to improve adherence to interventions

Participants will perform one week of habituation, to incorporate to their routine and troubleshoot any eventual problems that may raise from starting the new intervention. To verify compliance with PA interventions, each subject will be equipped with a FitBit Inspire 2 (San Francisco, CA, US) and a personal online account. The study team will follow-up with participants on a weekly basis with phone or Zoom call, check Fitbit online accounts and send regular motivational text messages to ensure adherence to the intervention protocol throughout the study.

Monetary compensation incentives according to the number of visits and compliance to intervention will be used to ensure adherence to their new lifestyle and study procedures. At the end of the study, we will provide to each subject an individual subject report containing results from study procedures.

Compliance

Outcomes

Daily pattern of time spent sedentary and physically active

Before and after 1-and 3-month of intervention, participants will be fitted with two accelerometers for 10 days to assess daily patterns of SB/PA (activPAL;

PALTechnologies: Glasgow, Scotland; ActiGraph GT3X+, LLC, Fort Walton Beach, FL). They will be asked to fill out a dairy log to record sleeping and waking times and any period during which they removed the devices for >15 min. SB/PA data will be analysed as previously published. 26

Daily glycemia

Over the same 10 days, participants will be equipped with a continuous glucose monitor (CGM, FreeStyle LibrePro, Abbott) to assess mean glucose levels and daily glucose oscillations before and after one and three months of intervention.

Body weight, body composition and waist circumference

Before, and after 1-and 3-month of intervention, body weight will be measured in undergarments using a standard calibrated scale. At baseline and after three months of intervention, total, lean, and fat mass will be measured by DXA (Hologic Delphi W, Bedford, MA, software version 11.2) as previously described. [START_REF] Villalon | A losing battle: weight regain does not restore weight loss-induced bone loss in postmenopausal women[END_REF] Waist circumference will be measured at the smallest circumference between the lowest margin of the ribs and the upper margin of the iliac crest.

24h substrates use, glucose kinetics and whole-body insulin sensitivity

Run-in period prior to the inpatient visits: To reduce inter-individual variability and the impact of unprescribed PA and diet, a 3-day run-in period will precede the inpatient visits. At baseline, participants will be instructed to maintain their sedentary lifestyle before the inpatient visit. In post intervention, they will be asked to perform their prescribed PA until the day prior to the inpatient visit. During this period, participants will be provided with a personalized calorie-matched standardized diet. To estimate the daily energy content of the diet, we will calculate the average kcal from: 1) freeliving energy requirements as 24h energy intake (EI): Resting Metabolic Rate (RMR)

x an activity factor [START_REF] Jong | Effect of frequent interruptions of sedentary time on nutrient metabolism in sedentary overweight male and female adults[END_REF][START_REF] Bergouignan | Twenty-Four Hour Total and Dietary Fat Oxidation in Lean, Obese and Reduced-Obese Adults with and without a Bout of Exercise[END_REF] and 2) estimated RMR: ((23.9 x FFM in kg) + 372) x an activity factor. RMR will be measured on Visit E in fasting state by indirect calorimetry (ParvoMedics TrueOne 2400, Salt Lake City), as done previously. [START_REF] Melanson | When energy balance is maintained, exercise does not induce negative fat balance in lean sedentary, obese sedentary, or lean endurance-trained individuals[END_REF] The macronutrient composition of the diet will be: 55% CHO, 30% fat, and 15% protein (percent daily EI: 30% breakfast, 35% lunch, 35% dinner and the remaining energy of the total calories will be a snack). Participants will be instructed to consume only the meals prepared and provided by the study, and nothing else but water. They will be asked to bring any leftover.

Inpatient visits:

The protocol of the inpatient study visits (Visits G and I) is summarized in Figure 2. Participants will be asked to report to the CTRC at 0630h.

They will be asked to void. An IV will be placed on one arm for glucose tracer infusion and another one on the hand of the other arm for serial blood draws. A baseline blood sample will be taken for metabolic substrates, hormones, and isotopic background. A primed (13.5 mol/kg) infusion (0.35 mol/kg/min) of [6,6-2 H 2 ] glucose (99%, Cambridge Isotope Laboratories -CIL, MA) will begin at 0700-0730h

and continue until 1835h. The infusion rate will be increased during the active period to account for an expected increase in glucose uptake [START_REF] Ahlborg | Substrate turnover during prolonged exercise in man. Splanchnic and leg metabolism of glucose, free fatty acids, and amino acids[END_REF] and reduced following meal consumption to account for changes in endogenous glucose production. Participants will enter a whole-room calorimeter at 0800h for measurement of 24h energy expenditure and nutrient oxidation. [START_REF] Melanson | A new approach for flow-through respirometry measurements in humans[END_REF] At 0930h, an OGTT will be performed by giving participants 72g of glucose + 3 g of [U- [START_REF] Matthews | Accelerometer-measured dose-response for physical activity, sedentary time, and mortality in US adults[END_REF] C]-glucose (99%, Cambridge Isotope Laboratories -CIL, MA). Blood glucose and insulin will be measured. Matsuda index, [START_REF] Ma | Muscle and liver insulin resistance indexes derived from the oral glucose tolerance test[END_REF] HOMA-IR and HOMA-B 32 will be calculated as surrogates of insulin sensitivity, insulin resistance, and beta-cell function. Participants will be asked to remain sedentary except to void; this will be the resting period of the test. At 1335h, they will receive a standard lunch (55% CHO, 30% fat and 15% protein, 35% of estimated energy needs) mixed with 3g of glucose tracer ([U- [START_REF] Matthews | Accelerometer-measured dose-response for physical activity, sedentary time, and mortality in US adults[END_REF] C]-glucose (99%, Cambridge Isotope Laboratories -CIL, MA). Then, participants in the BREAK group will complete 9 bouts of 5-min moderate intensity stepping every 30 minutes from 1410h to 1815h and participants in the ONE group will perform a 45-min moderate intensity stepping from 1410h to 1455h. For everyone, the stepping pace will correspond to the pace identified during the familiarization visit and will be imposed by a metronome. Finally, participants will be offered a standardized dinner at 1900h

and a snack at 2100h. Bedtime will be at 2200h. Participants will be awakened at 0630h and exit the room at 0700h. Vastus lateralis skeletal muscle biopsy will be collected from participants by the Bergstrom technique [START_REF] Bergstrom | Percutaneous needle biopsy of skeletal muscle in physiological and clinical research[END_REF] in fasting state upon exiting the chamber and immediately frozen in liquid N 2 and stored for protein analyses.

During the inpatient visit, serial blood sampling (Figure 2) will be performed for measurements of metabolites, hormones, and glucose kinetics. Participants will collect their own breath at 15-60 min intervals sampling for 13 CO 2 by blowing through a straw into two 15ml Vacutainer. Finally, urine will be collected from 0730h to 0730h the following day. Of note, only compliant participants will be asked to complete visit I.

Using collected data and samples, the following analysis will be done:

 Total substrate oxidation: Energy expenditure and substrate oxidation will be calculated from O 2 consumption, RER, and urinary nitrogen excretion as previously published [START_REF] Melanson | A new approach for flow-through respirometry measurements in humans[END_REF] .

 Metabolites and hormones: Blood samples will be used for the analysis of insulin, FFA, and triglycerides, all data will be analyzed by the CTRC core lab using standard methods.

 Fasting and postprandial glycemia and insulinemia: Areas under the curve (AUC) post OGTT (from 0930h to 1330h), post lunch (from 1335h to 1835h) and for the whole study visit period (from 0800h to day+1 0700h) will be calculated.

 Exogenous glucose oxidation: Breath CO 2 will be sampled directly from the Vacutainer with a syringe, and 13 CO 2 / 12 CO 2 measured with isotope-ratio mass spectrometer (IRMS, Delta V, Thermo Electron, Bremen, Germany).

The results will be calculated as previously published. [START_REF] Jong | Effect of frequent interruptions of sedentary time on nutrient metabolism in sedentary overweight male and female adults[END_REF]  Isotopic glucose measurement: The absolute concentration of both [START_REF] Matthews | Accelerometer-measured dose-response for physical activity, sedentary time, and mortality in US adults[END_REF] C]glucose will be calculated by multiplying the respective molar percent enrichment by the concentration of total glucose, as done previously. [START_REF] Rudwill | Metabolic Inflexibility Is an Early Marker of Bed-Rest-Induced Glucose Intolerance Even When Fat Mass Is Stable[END_REF] Glucose from the meals will be obtained by acid hydrolysis and be quantified by GC/MS after derivatization in glucose pentaacetate. This glucose derivative will then be analyzed by GC/C/IRMS for [START_REF] Matthews | Accelerometer-measured dose-response for physical activity, sedentary time, and mortality in US adults[END_REF] C enrichment.

 Calculations: Steele's equation for non-steady-state will be used to compute

RaT and RaE, as well as the rates of disappearance (RdT and RdE) from the percentage of [6,6-2 H 2 ]glucose [START_REF] Debodo | On the Hormonal Regulation of Carbohydrate Metabolism; Studies with C14 Glucose[END_REF] and of [START_REF] Matthews | Accelerometer-measured dose-response for physical activity, sedentary time, and mortality in US adults[END_REF] C-glucose in plasma glucose. [START_REF] Tissot | Use of a new gas chromatograph isotope ratio mass spectrometer to trace exogenous 13C labelled glucose at a very low level of enrichment in man[END_REF] Endogenous glucose production will be computed as RaT-RaE. Nonoxidative glucose disposal will be calculated by subtracting total carbohydrate oxidation from (RdT + RdE). Plasma glucose utilization will be assumed to be equivalent to RdT as has been confirmed previously. [START_REF] Jeukendrup | Carbohydrate ingestion can completely suppress endogenous glucose production during exercise[END_REF] Muscle glycogen utilization during the active period will be calculated as total carbohydrate utilization during exercise minus plasma glucose utilization during PA.

Expression of proteins involved in the regulation of insulin action and glucose

Sleeping time

Enter the chamber Dinner 35% total EI, 55% CHO metabolism Before and after 1-month of intervention muscle samples will be used to measure insulin signaling proteins associated with insulin sensitivity and glucose uptake using Simple Western Jess: Serine phosphorylation of insulin receptor substrate (IRS)1 (Ser312), total protein content of IRS1, phosphorylation of protein kinase B (Akt) (Aktser473/total), and phosphorylation of glycogen synthase kinase (GSK)3ß (GSK3ß Ser9/total). We will also measure phosphorylation of ACC (ACCS79/ total) as an index of changes in AMPK and of changes in contraction-mediated glucose uptake regulation membrane will be assessed by changes in phosphorylation of TBC1D4 (AS160/ total) that is regulated by both insulin-dependent and insulinindependent pathways. Content proteins involved in mitochondrial activities/density (OXPHOS, COX4) and central regulator of energy fuel (PPARg, PGC1a) will also be measured.

Attitudes and knowledge towards PA/SB, self-perceived mood, appetite, and sleep

Before, after 1-and 3-month of intervention, participants will be asked to complete questionnaires in REDCap (Research Electronic Data Capture), a secure, HIPAAcompliant web-based application designed for data collection for research studies.

During the three days of run-in diet, participants will be asked to fill in visual analog scales to rate their hunger and appetite before and after each meal. Vigor, fatigue, and mood will be assessed using the Profile of Mood States. [START_REF] Jong | Breaking up Sedentary Time in Overweight/Obese Adults on Work Days and Non-Work Days: Results from a Feasibility Study[END_REF][START_REF] Bergouignan | Effect of frequent interruptions of prolonged sitting on self-perceived levels of energy, mood, food cravings and cognitive function[END_REF] Sleep duration and quality will be assessed using the Pittsburgh Sleep Quality Index Questionnaire, and daytime sleepiness will be assessed using the Epworth sleepiness questionnaire [START_REF] Johns | A new method for measuring daytime sleepiness: the Epworth sleepiness scale[END_REF] and the Karolinska sleepiness questionnaire. [START_REF] Akerstedt | Subjective and objective sleepiness in the active individual[END_REF] Participants will be asked to complete seven questionnaires related to SB/PA. The Hu Self-Efficacy in Walking Questionnaire will assess self-confidence of the ability to walk increasing periods at a moderately fast pace. [START_REF] Hu | Influence of self-efficacy on the functional relationship between ratings of perceived exertion and exercise intensity[END_REF] The Behavioral Regulation in Walking Questionnaire will assess motivation for walking. [START_REF] Niven | Using self-determination theory to understand motivation for walking: Instrument development and model testing using Bayesian structural equation modelling[END_REF] The Low-Level Physical Activity Recall Questionnaire will measure time spent in work activities, household/yard activities, and recreational/leisure activities in the past week. [START_REF] Regensteiner | Exercise training improves functional status in patients with peripheral arterial disease[END_REF] Participants will also complete a questionnaire designed by KM, MOW and AB to assess confidence/self-efficacy to reduce SB (Confidence to Reduce Sedentary Behavior Questionnaire). The Social Environmental Support Survey will be used to understand social and environmental support for engaging in healthy behaviors. [START_REF] King | Self-efficacy, problem solving, and social-environmental support are associated with diabetes self-management behaviors[END_REF] The Interpersonal Support Evaluation List-12 assesses perceived social support. [START_REF] Cohens | Measuring the functional components of social support[END_REF] Finally, the Meaning in Life questionnaire has two subscales that assess participants' sense of presence of life meaning/purpose and search for meaning/purpose. [START_REF] Rush | Brief report: Meaning in life is mediated by self-efficacy in the prediction of physical activity[END_REF] Participants will participate in a semi-structured interview to assess knowledge and attitudes towards SB/PA and potential moderators.

Qualitative Data Analysis

Atlas.ti (Scientific Software Development GmbH, Berlin, Germany) will be used to facilitate qualitative analysis. All interviews will be transcribed verbatim. Content

analysis will be used to analyze the data. [START_REF] Hsieh | Three approaches to qualitative content analysis[END_REF] We will read the interviews transcripts individually and carefully to identify categories that appear to represent key concepts or ideas. Once all interviews have been read at least three times, we will begin to identify patterns or similarities across the interviews. We will then return to the text of the interviews and documents and use Atlas.ti to code representative sections of text. This will allow for identification of the frequency of specific codes, and identification of exemplars that will be used in reporting study findings. Each decision made throughout data analysis, along with the rationale, will be recorded. The categories, coding, and themes identified will be reviewed with the study investigators to determine if there is agreement. Finally, we will compare themes between BREAK and ONE.

Sample size

Effect sizes for aims 1 and 2 were estimated based on our preliminary study with similar design, in sedentary adults with overweight/obesity (13 ONE and 11 BREAK participants who reached >80% compliance). The effect sizes (ES) were 0.87 and 0.83 common SD of the individual change score for postprandial glycemia and 24h exogenous carbohydrate oxidation, respectively. We conservatively elected to power the study on detecting a smaller but clinically meaningful effect size of 0.80. Twentysix participants per treatment arm is required to ensure 80% power at 5% significance for Linear Mixed effect Model (LMM) analysis. An attrition rate of 21% (n=7) of participants per arm will be assumed; this is more than twice higher than the rate observed in our pilot study. Thirty-three participants (50% male) per arm (total n=66) will be enrolled.

Changes in the total amount of TBC1D4 protein will be the primary outcome for aim 3. ES was estimated based on published data from a cross-over study [START_REF] Bergouignan | Frequent interruptions of sedentary time modulates contraction-and insulin-stimulated glucose uptake pathways in muscle: Ancillary analysis from randomized clinical trials[END_REF] during which adults with overweight/obesity were asked to either perform moderateintensity active breaks for three days or remain sedentary for three days; a skeletal muscle biopsy was collected at the end of the study. ES was 1.46 common SD of the individual change score for total TBC1D4. Eleven participants per treatment arm (total n=22) is required to ensure 80% power at 5% significance for LMM analysis.

With 19 participants per treatment arm (total n=38), we will reach a power of 95% at 5% significance with LMM analysis.

Statistical Analysis

Participant demographics, anthropometric data, and clinical data will be summarized using descriptive statistics by study group. The primary analysis will be conducted on h blood glucose and insulin sensitivity, or others, they will be adjusted in the LMM model as appropriate. A p<0.05 will be considered significant for the main effects and a p<0.10 will be considered significant for the interaction. No adjustment for multiple comparisons will be applied. electronic driver. All the hard copies will be stored in a locked cabinet in a locked office where only study team members have access. The data collected in the study will be double-entered and/or double checked. Data records will be maintained for at least 7 years after the publication of results and most destroy after.

Oversight and Monitoring

The inpatient CTRC staff will be responsible for monitoring for problems and reporting them to the PI immediately. The corresponding amendments to the protocol will be submitted to the COMIRB and other appropriate parties involved in the study. We will not implement any modifications to the study until the corresponding ethical party approves these unless the amendments prevent an immediate hazard to participants. In that case, we will proceed to protect participants and immediately report to the corresponding parties. Data and Safety Monitoring Board composed by CU-AMC physicians and researchers will oversee the clinical trial.

Dissemination Plan

Results from this study will be disseminate via publications in peer-reviewed journals, as well as in abstracts and poster presentations at national and international conferences.

Discussion

This study will evaluate the acute and chronic effects of implementing breaks in SB on glycemic control in participants at risk for T2D and begin to inform interventions to reduce the risk of developing T2D.

The strengths of this study include the randomized controlled study design; the use of objective measures of daily SB and PA; the comprehensive clinical, physiological, and metabolic assessments using robust techniques (e.g., double-tracer approach); and the attempt of revealing new mechanisms underlying the potential differential effects of BREAK and ONE. However, the present project has some potential pitfalls that we identified, including a potential high withdrawal rate; difficulty to increase daily life PA; and recruiting participants representing the U.S. population. To circumvent these potential issues and to maximize retention and adherence 1) we will ask participants to complete only one arm of the study, either the BREAK or ONE; 2) we will work with participants to ensure they understand all study procedures and aim of the study, as the more engaged the participants are the ones that are more motivated to complete the study; 3) we will follow up with participants regularly and reschedule any missed study appointments; 4) we have proposed a week of habituation to the interventions, frequent contacts, flexibility in the timing of performing the bouts of PA, and incentives to help participants comply with the instructed PA; 5) we will also gather information on the barriers, challenges and facilitators to the intervention for planning future longer term interventions. Finally, we will do our best to have as many males as females and we will also target more diverse communities.

The importance of understanding the physiological and molecular mechanisms underlying the effect of breaking up SB on glucose control is significant given the current epidemic of T2D and the prevalence of cardiometabolic diseases related to the clinical problem of diabetes. [START_REF] Al-Salameh | Cardiovascular Disease in Type 2 Diabetes: A Review of Sex-Related Differences in Predisposition and Prevention[END_REF] Physical inactivity and SB are major risk factors for the development of T2D, and obesity-related comorbidities in the general population. [START_REF] Wilmot | Sedentary time in adults and the association with diabetes, cardiovascular disease and death: systematic review and meta-analysis[END_REF][START_REF] Smith | Physical activity and incident type 2 diabetes mellitus: a systematic review and dose-response meta-analysis of prospective cohort studies[END_REF] It represents the fourth cause for mortality, with an estimated three million preventable deaths annually across the world (~7%). [START_REF] Katzmarzyk | Physical inactivity and non-communicable disease burden in low-income, middle-income and high-income countries[END_REF] PA is a cornerstone in T2D prevention and management. In addition to aerobic and resistance exercises, the PA guidelines now recommend that adults (with or without T2D) reduce and interrupt long periods of sitting. [START_REF] Colberg | Physical Activity/Exercise and Diabetes: A Position Statement of the American Diabetes Association[END_REF][START_REF] Piercy | The Physical Activity Guidelines for Americans[END_REF][START_REF] Kanaley | Exercise/Physical Activity in Individuals with Type 2 Diabetes: A Consensus Statement from the American College of Sports Medicine[END_REF] An important gap is that these consensus-based guidelines lack mechanistic support. In this context, the proposed study has strong scientific, clinical, public health and economic relevance.

Figure 1 .

 1 Figure 1. Study timeline.

  the ONE intervention on one day and the BREAK intervention on another day to make sure they understand what the interventions are about. Their habitual SB/PA will be assessed by filling out the short version of the International Physical Activity

2 .

 2 ONE: Participants will be asked to perform 45 consecutive minutes of brisk walking, 5 d/wk.

Figure 2 .

 2 Figure 2. Overview of the whole-room calorimeter inpatient visits (visits G and I) protocol.

  participants who will have successfully complied with the interventions and therefore completed the post-intervention inpatient study visit (Visit I). The between-treatment difference in the primary outcomes is the change from baseline to the end of the study (1-month and/or 3-month). It will be used to assess the effect of BREAK intervention as compared to ONE treatment. A LMM with appropriate covariance will be used to model baseline and end of treatment (EoT) outcome. This LMM consists of treatment (ONE or BREAK), time (Pre vs. Post [1-month and/or 3-month]) and their interaction term as fixed effects, time as repeated factor, and participants as random factor. Between-group difference in post-intervention change in outcome will be assessed using contrast under the LMM model. If there is imbalance between two study arms with respect to potential confounding variables such as sex, age, body composition, adherence rate, time spent active and/or sedentary, baseline HAb1c, 2-

8 Study Timeline (Weeks)

		0			2	4		6	10	12	14	16	18	19
										INTERVENTIONS
		A B	C	D	E	F	G	H	I	J	K
			Screening		Pre-intervention	Post-1-month intervention	Post-3-month intervention
		measurements	measurements		measurements	measurements
			A	Pre-screening zoom interview (30 min)
											Enrollment in the study
			B	Screening visit (1 hr)
											Randomization
			C	Screening visit 2: H&P and OGGT (3.5 hr)			Check compliance (80% of prescribed PA)
			D	7-d measurement of SB/PA
	E	H J	E-H-J: 1-d measurement of SB/PA + questionnaires + semi-directed interview to assess attitude and knowledge towards SB/PA (1 hr) E-J: Body composition (15 min) E: RMR (45 min)	Habituation week Interventions: either BREAK or ONE
			F	Familiarization visit to determine pace for moderate-intensity brisk-walking (45 min)
		G	I	Inpatient study visits (24 hours)
			K	OGTT (3hrs)

Table 1 . Inclusion and Exclusion Criteria Inclusion Criteria Exclusion Criteria

 1

	Male and Female

  A participant will be considered compliant if more than 80% of the instructed PA is achieved based on the FitBit data. This will correspond to ≥ 7 PA bouts/d, ≥ 36 min/d of structured PA, ≥ 4 d/wk in the BREAK arm and to ≥ 36 min/day of structured PA, ≥ 4 d/wk in the ONE arm.

Acknowledgements

We want to thank all the CTRC staff, participants, COMIRB, and NIH reviewers for their support and continuous help in the success of this project. We want to acknowledge collaborators and other people who had help throughout the process with their feedback developing the protocol: Danielle Ostendorf, Victoria Catenacci, Bryan Bergman, Jane Reusch, Michael Rudolph, Corey Rynders, Kristen Bing, and Liza Wayland.

Funding

This study is funded by NIDDK (project R01DK123334). The funding agency does not have any role in the study design, data collection, analysis, interpretation, and dissemination.

Data collection and management

All participants will be de-identified and data will be collected, managed, and stored using REDcap. Other digital data will be stored on a CU-AMC owned encrypted

Authors' contributions

AB is the principal investigator; she assumes overall responsibility for the study design, leads the proposal and data collection, analysis, and interpretation. DHB is the study physician; he is responsible for the screening visits and muscle biopsies, and assists with subject recruitment, data collection, analysis, and dissemination.

ELM is responsible for the supervision of the room calorimeter use and data collection and analysis; he contributes to the dissemination of study findings. KM oversees the weekly small group meetings to optimize adherence to the protocol; he assists with the questionnaire analysis and dissemination of results. ZP is the study statistician; he assists with the statistical analysis and interpretation, and dissemination of results. KB, a study collaborator; she assists with the molecular biology analysis. MOW, she is a collaborator; she conducts indirect interviews, and assist with qualitative analysis and interpretation; she contributes to the dissemination of study findings. AJP and COS are postdoctoral fellows who assist with participants recruitment and data collection, analysis, and dissemination. AB, AJP, and COS drafted the manuscript with critical revision from DHB, ELM, KM, ZP, KB, and MOW. All authors have seen and approved the final version of the manuscript.