Free-Living Muscle Activity in Type 2 Diabetes: Sitting, Standing and Walking

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Dunstan, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5918242/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract AIMS: Using thigh-worn accelerometers and wearable electromyographic (EMG) shorts, we investigated muscle activity during sitting, standing and walking in adults with type 2 diabetes. METHODS: Isometric maximal voluntary contraction measures for quadriceps, hamstring, and gluteal muscle groups normalized the EMG signal to individual maximum capacity. Participants concurrently wore accelerometers and EMG shorts for 3.2 days, and average EMG amplitude (aEMG) was assessed from quadriceps, hamstring, and gluteal muscle groups within accelerometer-derived sitting, standing, walking times. RESULTS: Muscle groups examined used only 2.7–4.4% of their maximum voluntary capacity (%EMG MVC ) and were inactive for 75-80% of the measurement time. Sitting time was significantly correlated with muscle inactivity across all three muscle groups, but inversely so for hamstring aEMG (r = -0.51). Standing (r = 0.51) and walking (r = 0.48) were correlated with daily aEMG only in hamstrings. Relative to sitting, standing aEMG was 1.3–5.6 times higher and walking aEMG was 3.1–15.2 times higher, indicating varied inter-individual responsiveness. CONCLUSIONS: Reducing daily sitting, especially in favor of walking, may benefit hamstring and gluteal muscle engagement and help to prevent high levels of muscle inactivity in type 2 diabetes. Individual variability in EMG responses highlights the potential to personalize recommendations on sitting, standing and walking. Physiology Sports Medicine and Kinesiology Electromyography muscle contractile activity diabetes activity behaviours sedentary behaviour Figures Figure 1 Figure 2 Figure 3 HIGHLIGHTS Using accelerometers and electromyographic shorts, we analyzed quadriceps, hamstring, and gluteal muscle activity during sitting, standing, and walking in adults with type 2 diabetes. Average electromyographic (EMG) activity ranged from 1.8–3.3% of maximal capacity during sitting, 3.4–19.8% during standing, and 7.5–34.1% during walking. Standing increased EMG activity 1.3–5.6 times compared to sitting, while walking increased it 3.1–15.2 times, highlighting inter-individual variability. Reducing sitting time, especially by walking, may benefit type 2 diabetes management, and personalized recommendations should consider individual EMG responses. 1. INTRODUCTION Interrupting and replacing sitting with physical activity is important for multiple diabetes-relevant metabolic health indicators, including insulin ( 1 , 2 ), and glucose control ( 1 , 3 , 4 ). Muscle contractile activity is one of the key mechanisms of physical activity benefit ( 5 – 7 ). Even minimal skeletal muscle contractions, such as standing or briefly interrupting sedentary time, can increase blood flow and the concentration of glucose transporter 4 (GLUT4) at the muscle cell surface, likely contributing to the observed improvements in glycaemic control ( 4 , 8 , 9 ). Older adults can have substantial variability in muscle activity responses to daily sitting and movement behaviours ( 10 ), with periods of muscle inactivity negatively associated with health markers ( 7 ). Additionally, the quadriceps, hamstrings, and gluteal muscle groups are utilized differently across daily activities, leading to distinct activity patterns and associations with health outcomes. Consequently, how individuals engage these muscles in their daily routines may influence their overall health ( 6 , 11 ). Advances in wearable electromyographic (EMG) technology now allow for continuous, full-day quantification of these patterns of muscle activity. Optimizing muscle activity patterns during daily living may be a novel approach to enhance glycemic control. To date, free-living muscle activity has not been quantified in those with type 2 diabetes. We examined EMG-assessed free-living muscle activity in type 2 diabetes. Daily behaviours (sitting, standing, walking) are described in terms of quadriceps, hamstring, and gluteal muscle group activity. We also describe correlations between the three daily behaviours and the EMG signals, and individual EMG responses. 2. METHODS Data were from the free-living measurement component of a four-armed randomized cross-over trial (the OPTIMUS study) conducted in a single-center laboratory in Mikkeli, Finland. Each participant completed a 2–3-hour baseline assessment, and four approximately 7-hour laboratory study visits where they were instructed to sit for one prolonged period or interrupt prolonged sitting with intermittent physical activity or standing. After this period, data were recorded in their free-living setting with electromyography (EMG) and thigh-worn accelerometer devices. Only free-living data subsequent to the laboratory experiment day is reported in this paper. The OPTIMUS study was approved by the Human Research Ethics Committee of the Hospital District of Northern Savo (475/13.02.00/2021) and was registered with ISRCTN trial registry NO45350 ( https://www.isrctn.com/ ). All participants provided written informed consent. 2.1 Study recruitment and screening Participants were recruited during August 2023 to May 2024 through nearby health clinics and via social media. They were eligible if they were aged between 35–65 years, had a body mass index (BMI) between 25–50 kg/m 2 , medically diagnosed with type 2 diabetes for at least three months, on a stable treatment regimen for > 3 months, inactive (self-reported 7 hours per day for > 3 months). Exclusion criteria were pregnancy, current smoker, using insulin medication, major illness/physical problems (acute or chronic) that may limit participation, unable to communicate in Finnish, and unable to provide written informed consent. 2.2 Study design and protocol Eligible participants attended a baseline assessment at the laboratory which involved recording demographic information and assessing anthropometrics: weight, height, waist, and hip circumference using standard procedures. Participants were then instructed to wear a pair of tight-fitting EMG shorts (Myontec Ltd., Kuopio, Finland) and the Fibion® tri-axial accelerometer (Fibion, Jyväskylä, Finland). Isometric maximal voluntary contractions (MVC) for each muscle group were measured to normalize the EMG signal to maximum capacity (%EMG MVC ). Quadriceps MVC was measured using isometric knee extension and hamstrings MVC using isometric knee flexion with the knee joint positioned at 60° flexion (0°=full extension). Gluteal muscles MVC was determined using isometric hip extension with the hip joint positioned at 20° abduction and knee joint at 20° flexion. Two warm-up contractions (verbally instructed to be 50% effort level from the maximum) were performed for each movement (both legs at same time). These were followed by three 5-second maximal contractions, with a 1-minute break between efforts. The maximal contraction with the highest EMG amplitude (one second average) was used for signal normalization. Participants were instructed to continue to wear EMG-shorts and the accelerometer after the laboratory visit and until they went to bed on the following day. Electrode paste was used to optimize the skin–electrode contact, and participants were instructed to re-apply electrode paste after any temporary removal of the EMG shorts. On a second visit to the laboratory, participants had their body composition assessed after having fasted for 10 hours. Body composition was assessed using the Inbody 750 bioelectrical impedance device (InBody, Seoul, Korea). On subsequent visits, they were provided with EMG shorts and accelerometers again and instructed to wear them during their normal life, also when sleeping, until next evening before went to bed. 2.3 EMG measurement and data synthesis Textile EMG shorts measured daily muscle activity from the quadriceps, hamstring, and gluteal muscles in the participant’s free-living setting. Reference bipolar electrodes of the shorts were placed longitudinally on the lateral sides of the left and right on the covering membranes of the iliotibial tract. The electrodes were situated on the distal regions of the quadriceps and hamstring muscles and on the middle of the gluteal muscles. The EMG signal was stored in a 50-g electronic module attached to the waist. The signal was recorded with a sampling frequency of 1000 Hz, band-pass filtered at 40 Hz − 200 Hz (-3dB), digitalized with a 24-bit A/D converter and a gain of 0, averaged with non-overlapping windows of 40 ms (to 25 Hz) and saved in the module. The data were downloaded and visualized with Muscle Monitor software (Myontec Ltd, Kuopio, Finland). Next, the individual channels from the right and left quadriceps, hamstring and gluteal muscles were normalized to the respective maximum voluntary contractions (%EMG MVC ). The signal was further smoothed with a 200 ms moving average algorithm as per previous methods ( 12 , 13 ). An automated artefact removal procedure detected presence of artefacts using the following rules within five-minute rolling windows: 1) if baseline was > 5%EMG MVC , 2) median value > 50%EMG MVC , or 3) maximum value > 300% EMG MVC . If a channel included more than 30 minutes of time as artefact, the whole channel was removed from the analyses. The artefact segments were overlaid on the signal from each channel and visually inspected, confirming that the algorithm successfully removed artefacts without affecting the physiological signal. Corrected baseline drift and artefacts removal led 138/1146 channels removed. Removal of individual channels has minimal influence on extracted EMG outcomes ( 13 ). 2.4 Sitting, standing, walking The Fibion® tri-axial accelerometer (Fibion, Jyväskylä, Finland, 12.5 Hz, ± 4 g) was worn concurrently with EMG shorts to assess muscle activity during sitting, standing, and walking. The accelerometer was affixed vertically at the centerline and horizontally at the upper third level on the anterior side of the thigh, secured in a waterproof covering with medical adhesive tape on to the EMG shorts. Participants filled out electronic sleep diaries each morning indicating the time when they went to sleep (from previous night) and when they woke up. Sleeptime was excluded from the analysis. Accelerometer data were categorized into sitting, standing, and walking minutes. For synchronized analyses, only minute intervals that were comprised completely of sitting, or > = 50 s of standing or walking were included. 2.5 Data processing, synchronization, and statistical analysis Analyses were conducted using R (R version 4.2.2; R Foundation for Statistical Computing, Vienna, Austria). EMG and accelerometer signals were synchronized by clock time (both devices were initiated from the same study computer) and visually scanned to ensure continuous interpretation of %EMG MVC matched with the accelerometers continuous (minute interval) determination of METs (Fig. 1 .). Initially, there were 95 days of free-living observations; 10 days were excluded due to recording durations of less than 10 hours. Following visual inspection, 8 days were removed for poor synchronization, and 19 days were excluded due to the absence of EMG or accelerometry data, leaving a final sample of 58 days of synchronized EMG and accelerometer observations. With the synchronized EMG and accelerometer data, average EMG amplitude (aEMG, %EMG MVC ) was first analyzed across right and left hamstring, quadriceps, and gluteal groups and after that the values were averaged and estimated by day and within the accelerometer-derived behaviours. Total EMG inactivity duration was also analyzed as summed duration of EMG bouts when the signal amplitude was below the inactivity threshold of 3 µV and presented as a proportion of the day and proportion of behaviour length. This inactivity threshold has been reported to provide the best responsiveness to detect changes in total EMG inactivity duration and pattern of accumulation (Pesola et al. 2022). INSERT FIG.1 HERE The relationship of daily accelerometer-derived behaviour minute totals with daily quadriceps, hamstring, and gluteal aEMG and inactivity durations were analyzed with Pearson correlation. To understand inter-individual responses to hypothetical substitutions of sitting time with standing or walking, each participant’s standing aEMG relative to their sitting aEMG was regressed against their walking aEMG relative to their sitting aEMG. The fit of this regression model was assessed with the adjusted R-squared estimate. For all analyses statistical significance was set at p < 0.05, two-tailed. 3. RESULTS 3.1 Participant characteristics Sample characteristics of the overweight or obese adults (n = 19; 7 male) who participated in the study are shown in Table 1 . On average, participants had been diagnosed with type 2 diabetes 10.8 (± 8.0) years previously. Muscle activity relative to maximum during free-living observation was low, with slight variation by muscle group observed. All muscle groups measured were inactive, on average, over 75% of the waking day. Participants spent 91.1% of their days sitting, 23.6% standing, and 14.6% walking relative to the actual device wear time. Table 1 Characteristics of the free-living living study. n = 19 Sex, male 7 (36.8) Age, y 61.8 (8.5) Anthropometrics BMI, kg/m 2 a 28.9 (27.4, 32.8) Body weight, kg 86.3 (11.2) Fat, % 35.4 (9.5) Waist circumference, cm 104.7 (9.7) Waist-hip ratio 0.97 (0.05) Diabetes status Hba1c, % a 6.7 (6.1,7.4) Hba1c, mmol/l a 50 (43,57) Years diagnosed with T2D, years 10.8 (8.0) Muscle activity and inactivity Quadriceps aEMG, %EMG MVC 2.7 (0.9) Hamstrings aEMG, %EMG MVC 3.5 (2.3) Gluteals aEMG, %EMG MVC 4.4 (1.5) Quadriceps inactivity, % 78.7 (6.1) Hamstrings inactivity, % 75.6 (11.1) Gluteals inactivity, % 80.4 (7.8) Accelerometer-derived behaviours Sitting time, % 91.1 (11.4) Standing time, % 23.6 (8.1) Walking time, % 14.6 (4.1) Concurrent device wear time, h a 12.3 (11.9, 13.2) Days of wear, n a 3.2 (2.0, 4.0) Estimates are presented as number and proportion or mean (SD) unless indicated otherwise a denotes estimates presented as median and interquartile range. Concurrent device wear time and days of wear indicate the time and number of days where both accelerometer and EMG were worn at the same time and synchronized. INSERT TABLE1 HERE 3.2 EMG amplitudes during daily sitting, standing and walking Figure 2 shows free-living muscle activity in sitting, standing and walking for all muscle groups and between the quadriceps (Q), hamstring (H), and gluteal muscle (G) groups. On average, free-living aEMG in sitting was 2.8%EMG MVC (95%CI: 2.4, 3.1), in standing was 7.1%EMG MVC (95%CI: 5.5, 8.7) and in walking was 20.8%EMG MVC (95%CI: 17.3, 24.4). Across different activities, aEMG varied among muscle groups, with 2.5% EMG MVC (95%CI: 2.0, 3.1) in the quadriceps and 2.5% EMG MVC (95%CI: 2.0, 3.0) in the hamstrings, and 3.3% EMG MVC (95%CI: 2.8, 3.7) in the gluteal muscles during sitting; 4.7% EMG MVC (95%CI: 3.9, 5.5) (Q), 8.7% EMG MVC (95%CI: 5.6, 11.9) (H), and 7.8% EMG MVC (95%CI: 5.9, 9.7) (G), respectively, during standing; and 21.9% EMG MVC (95%CI: 16.9, 26.9) (Q), 23.5% EMG MVC (95%CI: 18.1, 28.8) (H), and 17.1% EMG MVC (95%CI: 13.8, 20.3) (G) during walking. aEMG in all muscle groups were at least two times higher during walking than standing with the biggest difference seen in the quadriceps muscle group (4.7–21.9%). Inter-individual activity by muscle groups within these behaviours are shown in Supplementary Digital Content (SDC) 1. INSERT FIG.2 HERE 3.3 Correlation of sitting, standing and walking with overall free-living EMG outcomes Table 2 shows correlations of synchronized accelerometer-derived behaviours and quadriceps, hamstring, and gluteal muscle activity and inactivity. Sitting was inversely correlated with muscle activity, with the strongest correlations in hamstrings muscle group, and positively associated with EMG inactivity across all muscle groups. Standing was positively correlated with muscle activity, again with the strongest correlations in the hamstrings muscle group, and inversely correlated with EMG inactivity, with the strongest correlations in quadriceps and hamstrings. Walking was positively associated with muscle activity with the strongest correlations in hamstring aEMG and inversely correlated with EMG inactivity across all muscles. Table 2 Correlation of accelerometry derived behaviours and quadriceps, hamstring, and gluteal muscle activity (aEMG). Sitting Standing Walking Quadriceps aEMG Hamstring aEMG Gluteal aEMG Quadriceps EMG inactivity Hamstring EMG inactivity Sitting time — Standing time -0.95* — Walking time -0.85* 0.67* — Quadriceps aEMG -0.02 0.01 0.14 — Hamstring aEMG -0.51* 0.51* 0.47* 0.33 — Gluteal aEMG -0.07 0.13 0.06 0.38 0.66* — Quadriceps EMG inactivity 0.54* -0.45* -0.58* -0.02 -0.28 0.13 — Hamstring EMG inactivity 0.71* -0.68* -0.63* -0.04 -0.77* -0.27 0.77* — Gluteal EMG inactivity 0.55* -0.48* -0.58* -0.01 -0.74* -0.45* 0.63* 0.82* All values correspond to daily behaviour time and daily muscle activity and inactivity. * = values p < 0.05. Muscle groups also exhibited inter-correlations: quadriceps aEMG showed weak correlations with both hamstrings and gluteal aEMG compared to the stronger correlations between hamstring and gluteal aEMG. Quadriceps aEMG did not have strong correlations with EMG inactivity in any muscle group, while both hamstring and gluteal aEMG were negatively correlated with their respective inactivity measures. INSERT TABLE2 HERE 3.4 Differences in EMG relative to sitting and standing and sitting and walking Standing aEMG relative to sitting aEMG was associated with walking aEMG relative to sitting aEMG (β aEMG [CI%95] = 4.6 [1.34, 7.86], p-value = 0.03, Fig. 3 ). That is, the higher standing aEMG was associated with a higher walking aEMG. Some individuals had lower standing aEMG than sitting aEMG. There was variation in this relationship (adjusted r 2 value = 0.13); indicating that some individuals responded relatively greater to standing than to walking (Fig. 3 region D) as compared to others, and some individuals had a lower response to standing but a relatively higher response to walking as compared to sitting muscle activity (Fig. 3 region A). Finally, there were some individuals who had a low muscle activity response relative to sitting for both standing and walking (Fig. 2 region C). INSERT FIG.3 HERE 4. DISCUSSION This is the first study to quantify muscle activity during free-living sitting, standing, and walking in type 2 diabetes, where muscle activity is a key factor to glycaemic control and thus disease management. Muscle activity during free-living behaviours was low, ranging from 2.7–4.4% of participants’ maximal voluntary contraction capacity, with muscle groups being inactive for 75–80% of measurement time. Thigh muscle aEMG varied from 1.8–3.3% during sitting, 3.4–19.8% while standing, and 7.5–34.1% when walking. Hamstring aEMG had the strongest correlation with these behaviours. This variation meant that among individuals, standing aEMG ranged 1.3–5.6 times higher than sitting, and walking resulted in 3.1–15.2 times higher than sitting aEMG. This suggests that sedentary behaviour interventions, which are recommended in diabetes management guidelines, may have varying success in increasing muscle activity depending on the individual and on whether sitting is interrupted by standing or walking. Previous studies examining sedentary behavior and physical activity in individuals with type 2 diabetes have primarily relied on accelerometers ( 14 , 15 ), consistently reporting higher sitting times compared to healthy referents. However, free-living EMG activity has not been quantified in individuals with type 2 diabetes, making direct comparisons to previous research challenging. Studies investigating free-living muscle activity in healthy older adults (aged 71 ± 2.9 years) without type 2 diabetes found higher daily aEMG (4–5.9%) and a lower prevalence of inactivity (50–70%) ( 10 ) compared to the present study. These findings suggest that type 2 diabetes may contribute to reduced overall muscle activity, highlighting the need for further investigation. Notably, both our study cohort and the cohort of healthy older adults ( 10 ) had high level of inter-individual variation in their muscle activity – a finding that may be influenced by several factors. Type 2 diabetes is associated with decreased muscle mass, strength, and walking speed as compared to normoglycemic controls in older adults ( 16 , 17 ). An increase in neuropathy, glycated hemoglobin HbA1c, and duration of diabetes has been linked to a reduction in strength ( 18 , 19 ). In people with type 2 diabetes, loss of muscle strength is most pronounced in the thighs, particularly the quadriceps femoris, which appears especially susceptible to age-related muscle mass loss ( 20 , 21 ). These declines in thigh muscle mass and strength significantly affect daily functional capacity in individuals with type 2 diabetes ( 22 , 23 ), EMG measures can potentially add insights on the relative muscle intensity occurring within activity types or intensities, with their effects on health warranting further investigation. We found walking to be positively associated with muscle activity, with the strongest correlations in the hamstring muscles. Notably, even though the strongest correlations and the highest muscle activity (aEMG) in walking were seen in the hamstrings, the greatest difference in aEMG from standing to walking were seen in the quadriceps, highlighting the importance of walking to fully engage lower limb muscles. Increased muscle activity in these muscle groups has been linked to improved glycemic control ( 6 , 7 ). Different types of muscle activity and different muscle groups can have distinct effects on metabolism because of their differences in fiber type profiles and functions and general activity in daily living. Gluteal, hamstring and quadriceps muscle fiber type compositions are all described as oxidative, glycolytic, or mixed ( 24 , 25 ), but the quadriceps muscles predominantly consist of phasic fibers that are activated during more intense physical activity ( 26 , 27 ). A recent study found that insulin sensitivity in hamstrings, rather than quadriceps, increased after reducing sitting time 0.5 hours ( 28 ), which was speculated to be a result of low-intensity physical activity mainly for holding postural control. However, when more dynamic movement was investigated, the glycemic response was primarily attributed to higher muscle activity by quadriceps and gluteal muscles ( 6 ). Therefore, although all the muscle groups that we examined share also similar compositional features, they may demonstrate differing metabolic tendencies during free-living activities, and it may be that more dynamic or higher intensity physical activities are required to sufficiently activate quadriceps and gluteal muscles in free-living contexts. Ensuring that the behaviours that maximize muscle activity across muscle groups could be beneficial to glucose response, however, further research is needed. Experimental studies in type 2 diabetes have shown that acutely replacing sitting time with light physical activity can lead to improvements in glucose incremental AUC (iAUC), triacylglycerol levels, insulin, and insulin sensitivity ( 29 ). Reducing sitting time through a combination of light physical activity and standing time has also been demonstrated to have positive effects on insulin sensitivity in postmenopausal women ( 30 ). However, sedentary behaviour interventions outside of the laboratory setting have resulted only in modest average improvements in body anthropometry, glucose metabolism, lipid metabolism, and blood pressure ( 5 , 31 ). These trials primarily replace sitting time with standing (typically via sit-stand workstations), which may partly explain the limited associations observed with glycemic outcomes ( 31 ). Overall, evidence from sedentary behaviour research suggests that in addition to replacing sitting only with standing, individuals may need to incorporate more movement and ambulatory activities to achieve greater benefits for glucose metabolism ( 32 , 33 ). Our findings align with this evidence, offering a mechanistic explanation for why increasing standing time alone may not suffice for all individuals to improve glycemic control. 4.1 Implications for the management of type 2 diabetes This study shows that there was considerable variation in how those with type 2 diabetes responded to standing and walking, suggesting that some individuals may naturally activate their muscles more during these activities, while others might require more activity volume or specific interventions to achieve similar muscle activity. Interventions with standing have shown only modest effectiveness in improving health outcomes in persons with overweight or obesity ( 34 – 36 ). However, previous EMG studies have shown that muscle activity is higher in overweight than normal weight individuals when standing, suggesting that muscles of those who are overweight contract with greater intensity ( 37 ). We found that some participants had standing muscle activity that was of a similar level to their walking muscle activity. Personalized intervention approaches, which utilize understanding of an individual's unique "myoprint"—their characteristic EMG activity patterns during daily behaviours—may enhance intervention efficacy and guide selection of behaviours. For example, by identifying these daily EMG patterns, it would be possible to infer whether an individual exhibits high muscle activity during standing, which would then suggest that strategies that promote regular bouts of standing may be of particular benefit. The main strength of this study was the new insights of muscle activity in free-living daily behaviours in people with type 2 diabetes. Synchronization of accelerometer and wearable EMG data was a key factor in improving our understanding of the variability of muscle activity during different behaviours. Accelerometers are unable to measure the direct contraction of muscles, and there is a lack of consensus of normalizing their data to relative capacity ( 38 ), which is possible with EMG measures. Our study has several limitations. The EMG measurements used represent only the superficial muscle groups located below the electrodes and are not representative of other lower- or upper-extremity muscles that are also important for upright and ambulatory movement during daily behaviours. However, as thigh muscles play a significant role in sitting, standing, and walking, and thigh muscle atrophy is particularly prevalent in people with type 2 diabetes ( 20 , 21 ), this makes the use of EMG shorts potentially quite relevant. Our synchronized data do not show exactly when the to sit-to-stand transitions occur. Such transitions, and the timing of these transitions, may have distinct health benefits ( 17 ). Examining sit-to-stand transitions would be relevant given those with type 2 diabetes might take longer and in a greater muscular effort to complete these kinds of movements compared to healthy adults ( 17 ). The measurement duration (average 3.2 days per participant) was relatively short compared to accelerometer wear periods ( 39 ). However, previous studies have reported adequate test-retest repeatability for shorter EMG measurement durations ( 13 , 40 ), and it should be noted that our findings were mostly driven by synchronized data: examining EMG within sitting, standing, and walking events, rather than examining activity totals to quantify and determine a ‘typical’ day. In future, extending the wear period (as possible) will further our understanding of day-to-day muscle activity variability. In conclusion, we have shown that daily muscle activity is low in those with type 2 diabetes, ranging from 2.7–4.4% of their maximal capacity, and that their quadriceps, hamstring, and gluteal muscle groups were inactive for 75–80% of free-living time. We observed that walking can activate lower extremity muscles more comprehensively than standing, and that walking may be critical for maximizing whole-of-day free-living muscle activity. Inter-individual response to sitting, standing, and walking varies widely by individual, highlighting the potential to personalize behaviour interventions to maximize muscle activity in type 2 diabetes. Declarations Funding This project was supported by Academy of Finland, grant ID 332731 (AJP, CB and SL). D unstan was supported by the National Health and Medical Research Council Fellowships Scheme and the Victorian Operational Infrastructure Support Program. Healy was supported by an Australian Medical Research Future Fund Fellowship Scheme. No potential conflict of interest was reported by the authors. CRediT authorship contribution statement Suvi Lamberg : Writing – original draft, Conceptualization, Investigation, Formal analysis, Methodology, Visualization, Project administration. Christian Brakenridge : Writing – original draft, Conceptualization, Formal analysis, Methodology, Visualization, Supervision. Ying Gao : Writing – review & editing. David Dunstan : Writing – review & editing, Conceptualization. Taija Finni : Writing – review & editing, Conceptualization, Methodology. Genevieve Healy : Writing – review & editing, Conceptualization. Neville Owen : Writing – original draft, Conceptualization, Methodology. Arto Pesola : Writing – original draft, Conceptualization, Formal analysis, Methodology, Visualization, Project administration, Funding acquisition, Supervision. Declaration of competing interest The authors would like to thank the participants of the study for their contributions. 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Diabetic Medicine [Internet]. 2013 Sep 1 [cited 2025 Jan 28];30(9):1063–6. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/dme.12199 Hamer M, Bostock S, Hackett R, Steptoe A. Objectively assessed sedentary time and type 2 diabetes mellitus: A case-control study. Diabetologia [Internet]. 2013 Dec 1 [cited 2025 Jan 28];56(12):2761–2. Available from: https://link.springer.com/article/10.1007/s00125-013-3051-5 Volpato S, Bianchi L, Lauretani F, Lauretani F, Bandinelli S, Guralnik JM, et al. Role of muscle mass and muscle quality in the association between diabetes and gait speed. Diabetes Care. 2012 Aug;35(8):1672–9. Leenders M, Verdijk LB, van der Hoeven L, Adam JJ, van Kranenburg J, Nilwik R, et al. Patients with type 2 diabetes show a greater decline in muscle mass, muscle strength, and functional capacity with aging. J Am Med Dir Assoc [Internet]. 2013 [cited 2025 Jan 14];14(8):585–92. Available from: https://pubmed.ncbi.nlm.nih.gov/23537893/ Andersen H, Nielsen S, Mogensen CE, Jakobsen J. Muscle strength in type 2 diabetes. Diabetes. 2004 Jun;53(6):1543–8. Andersen H, Poulsen PL, Mogensen CE, Jakobsen J. Isokinetic muscle strength in long-term IDDM patients in relation to diabetic complications. Diabetes [Internet]. 1996 [cited 2025 Jan 15];45(4):440–5. Available from: https://pubmed.ncbi.nlm.nih.gov/8603765/ Seok WP, Goodpaster BH, Jung SL, Kuller LH, Boudreau R, De Rekeneire N, et al. Excessive loss of skeletal muscle mass in older adults with type 2 diabetes. Diabetes Care [Internet]. 2009 Nov [cited 2025 Jan 14];32(11):1993–7. Available from: https://pubmed.ncbi.nlm.nih.gov/19549734/ Seok WP, Goodpaster BH, Strotmeyer ES, Kuller LH, Broudeau R, Kammerer C, et al. Accelerated loss of skeletal muscle strength in older adults with type 2 diabetes: the health, aging, and body composition study. Diabetes Care [Internet]. 2007 May [cited 2025 Jan 15];30(6):1507–12. Available from: https://pubmed.ncbi.nlm.nih.gov/17363749/ Fridolfsson J, Arvidsson D, Ekblom-Bak E, Ekblom Ö, Bergström G, Börjesson M. Accelerometer-measured absolute versus relative physical activity intensity: cross-sectional associations with cardiometabolic health in midlife. BMC Public Health [Internet]. 2023 Dec 1 [cited 2025 Jan 14];23(1). Available from: https://pubmed.ncbi.nlm.nih.gov/37996871/ Kujala UM, Pietilä J, Myllymäki T, Mutikainen S, Föhr T, Korhonen I, et al. Physical Activity: Absolute Intensity versus Relative-to-Fitness-Level Volumes. Med Sci Sports Exerc [Internet]. 2017 Mar 1 [cited 2025 Jan 15];49(3):474–81. Available from: https://pubmed.ncbi.nlm.nih.gov/27875497/ Johnson MA, Polgar J, Weightman D, Appleton D. Data on the distribution of fibre types in thirty-six human muscles: An autopsy study. J Neurol Sci. 1973 Jan 1;18(1):111–29. Dahmane R, Srdjan AE, Ae D, Smerdu V, Dahmane R, Smerdu V, et al. Adaptive potential of human biceps femoris muscle demonstrated by histochemical, immunohistochemical and mechanomygraphical methods. Med Bio Eng Comput [Internet]. 2007 [cited 2024 Feb 6];45:323–4. Available from: http://dx.doi.org/10.1007/s11517-006-0114-5 Eskelinen JJ, Heinonen I, Löyttyniemi E, Saunavaara V, Kirjavainen A, Virtanen KA, et al. Muscle-specific glucose and free fatty acid uptake after sprint interval and moderate-intensity training in healthy middle-aged men. J Appl Physiol [Internet]. 2015;118:1172–80. Available from: http://www.jappl.org Sjöros TJ, Heiskanen MA, Motiani KK, Löyttyniemi E, Eskelinen JJ, Virtanen KA, et al. Increased insulin-stimulated glucose uptake in both leg and arm muscles after sprint interval and moderate-intensity training in subjects with type 2 diabetes or prediabetes. Scand J Med Sci Sports [Internet]. 2018 Jan 1 [cited 2024 Feb 9];28(1):77–87. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/sms.12875 Sjöros T, Laine S, Garthwaite T, Vähä-Ypyä H, Koivumäki M, Eskola O, et al. The effects of a 6-month intervention aimed to reduce sedentary time on skeletal muscle insulin sensitivity: a randomized controlled trial. Am J Physiol Endocrinol Metab. 2023 Aug 1;325(2):E152–62. Duvivier BMFM, Schaper NC, Hesselink MKC, van Kan L, Stienen N, Winkens B, et al. Breaking sitting with light activities vs structured exercise: a randomised crossover study demonstrating benefits for glycaemic control and insulin sensitivity in type 2 diabetes. Diabetologia. 2017 Mar 1;60(3):490–8. Remie CME, Janssens GE, Bilet L, Van Weeghel M, Duvivier BMFM, De Wit VHW, et al. Sitting less elicits metabolic responses similar to exercise and enhances insulin sensitivity in postmenopausal women. Available from: https://doi.org/10.1007/s00125-021-05558-5 Hadgraft NT, Winkler E, Climie RE, Grace MS, Romero L, Owen N, et al. Effects of sedentary behaviour interventions on biomarkers of cardiometabolic risk in adults: Systematic review with meta-analyses. Vol. 55, British Journal of Sports Medicine. BMJ Publishing Group; 2021. p. 144–54. Yates T, Edwardson CL, Henson J, Zaccardi F, Khunti K, Davies MJ. Prospectively Reallocating Sedentary Time: Associations with Cardiometabolic Health. Med Sci Sports Exerc. 2020 Apr 1;52(4):844–50. Brakenridge CJ, Koster A, de Galan BE, Carver A, Dumuid D, Dzakpasu FQS, et al. Associations of 24 h time-use compositions of sitting, standing, physical activity and sleeping with optimal cardiometabolic risk and glycaemic control: The Maastricht Study. Diabetologia. 2024 Jul 1;67(7):1356–67. Henson J, Davies MJ, Bodicoat DH, Edwardson CL, Gill JMR, Stensel DJ, et al. Breaking up prolonged sitting with standing or walking attenuates the postprandial metabolic response in postmenopausal women: A randomized acute study. Diabetes Care [Internet]. 2016 [cited 2021 Aug 16];39:130–8. Available from: http://care.diabetesjournals.org/lookup/suppl/doi:10.2337/dc15-1240/-/DC1. Loh R, Stamatakis E, Folkerts D, Allgrove JE, Moir HJ. Effects of interrupting prolonged sitting with physical activity breaks on blood glucose, insulin and triacylglycerol measures: A systematic review and meta-analysis. Vol. 50, Sports Medicine. Springer; 2020. p. 295–330. Bailey DP, Locke CD. Breaking up prolonged sitting with light-intensity walking improves postprandial glycemia, but breaking up sitting with standing does not. J Sci Med Sport. 2015 May 1;18(3):294–8. Pesola AJ, Laukkanen A, Tikkanen O, Finni T. Heterogeneity of muscle activity during sedentary behavior. Applied Physiology, Nutrition and Metabolism [Internet]. 2016 Jul 19 [cited 2022 Jan 5];41(11):1155–62. Available from: https://cdnsciencepub.com/doi/abs/10.1139/apnm-2016-0170 Pesola AJ, Rantalainen T, Gao Y, Finni T. Aerobic capacity determines habitual walking acceleration, not electromyography-indicated relative effort. J Meas Phys Behav [Internet]. 2022 Jan 22 [cited 2022 Mar 1];1(aop):1–10. Available from: https://journals.humankinetics.com/view/journals/jmpb/aop/article-10.1123-jmpb.2021-0018/article-10.1123-jmpb.2021-0018.xml Edwardson CL, Winkler EAH, Bodicoat DH, Yates T, Davies MJ, Dunstan DW, et al. Considerations when using the activPAL monitor in field-based research with adult populations. J Sport Health Sci [Internet]. 2017 Jun 1 [cited 2025 Jan 15];6(2):162–78. Available from: https://pubmed.ncbi.nlm.nih.gov/30356601/ Pesola AJ, Laukkanen A, Haakana P, Havu M, Sääkslahti A, Sipilä S, et al. Muscle inactivity and activity patterns after sedentary time-targeted randomized controlled trial. Med Sci Sports Exerc. 2014 Nov 10;46(11):2122–31. Additional Declarations The authors declare no competing interests. Supplementary Files SDC1.png Supplementary Digital Content Supp lementary digital content 1. Inter-individual activity (aEMG) by muscle groups within behaviours. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5918242","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":408156062,"identity":"048944e7-8d31-4bcd-9fa2-1e62bb70d0cf","order_by":0,"name":"Suvi Lamberg","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYJCCAwwFDAwGINYHIJZgYHxwgLAWA4gWxhlgLcwGBLUwwLQw80C14FUsPyPHEGiLXbQ5e+/j17ZtdtGSDcyMeG0xuJEDdIZBcu7OnuNm1jlnknNnMzAz4NcikZYA1MKcu+FGGptxTgVz7jwG/gN4tcjPAGupz91w/xmbsQWQMY+QLQw3koFmGhwG2sLG/Jih4jARDjvz+MCBBIPjQL+ksTH2nDmeO7OZgBb59sTmDx8qqnO3sx9j/vCzrTp3xvFm5g94HQYCCRCKTQJMMRNUjwBEGD4KRsEoGAUjEgAAWqxMlpsIxu4AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-3452-9001","institution":"South- Eastern Finland University of Applied Sciences","correspondingAuthor":true,"prefix":"","firstName":"Suvi","middleName":"","lastName":"Lamberg","suffix":""},{"id":408156063,"identity":"9b0c5b04-cf7c-4fb4-9591-6088447ee2ac","order_by":1,"name":"Christian Brakenridge","email":"","orcid":"https://orcid.org/0000-0001-6022-7539","institution":"Swinburne University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Christian","middleName":"","lastName":"Brakenridge","suffix":""},{"id":408156064,"identity":"4561c56f-6b25-4520-8304-ecdcd7e6324c","order_by":2,"name":"Ying Gao","email":"","orcid":"https://orcid.org/0000-0003-1440-0681","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Gao","suffix":""},{"id":408156065,"identity":"29bdf93b-6009-41cc-9322-081efed0d8b5","order_by":3,"name":"David W. Dunstan","email":"","orcid":"https://orcid.org/0000-0003-2629-9568","institution":"Deakin University","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"W.","lastName":"Dunstan","suffix":""},{"id":408156066,"identity":"56c33094-9601-4587-97bf-09897825812c","order_by":4,"name":"Taija Finni","email":"","orcid":"https://orcid.org/0000-0002-7697-2813","institution":"University of Jyväskylä","correspondingAuthor":false,"prefix":"","firstName":"Taija","middleName":"","lastName":"Finni","suffix":""},{"id":408156067,"identity":"fea2af13-b62a-412b-a385-8a06d7d3eae7","order_by":5,"name":"Genevieve N. Healy","email":"","orcid":"https://orcid.org/0000-0001-7093-7892","institution":"The University of Queensland","correspondingAuthor":false,"prefix":"","firstName":"Genevieve","middleName":"N.","lastName":"Healy","suffix":""},{"id":408156068,"identity":"0b661712-6f98-4f67-9fad-501e913eb1c8","order_by":6,"name":"Neville Owen","email":"","orcid":"https://orcid.org/0000-0003-2784-4820","institution":"Swinburne University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Neville","middleName":"","lastName":"Owen","suffix":""},{"id":408156069,"identity":"9eddd9df-c417-4073-8c01-5320c72ae662","order_by":7,"name":"Arto J. Pesola","email":"","orcid":"https://orcid.org/0000-0002-2984-9847","institution":"South- Eastern Finland University of Applied Sciences","correspondingAuthor":false,"prefix":"","firstName":"Arto","middleName":"J.","lastName":"Pesola","suffix":""}],"badges":[],"createdAt":"2025-01-28 11:20:06","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":true,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5918242/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5918242/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75001172,"identity":"f8246305-a7ca-40d4-979c-a51b0a3bc68d","added_by":"auto","created_at":"2025-01-29 09:46:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":221022,"visible":true,"origin":"","legend":"\u003cp\u003eSynchronized EMG and accelerometer signals. EMG signal %EMG\u003csub\u003eMVC\u003c/sub\u003e matched with the accelerometers’ continuous (minute interval) determination of METs multiplied by 10 for visual clarify.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5918242/v1/4d88072506ad9b2f1f4e940a.png"},{"id":75001171,"identity":"9221749f-8fa5-40de-9594-f24bf7cc84d8","added_by":"auto","created_at":"2025-01-29 09:46:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":164119,"visible":true,"origin":"","legend":"\u003cp\u003eFree-living muscle activity (aEMG; %EMG\u003csub\u003eMVC\u003c/sub\u003e) in sitting, standing, and walking between quadriceps, hamstring, and gluteal muscle groups.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5918242/v1/8c2c779c1c171dc6ea245a8a.png"},{"id":75001175,"identity":"431bb191-fab5-4f24-b303-9ee307c6ded6","added_by":"auto","created_at":"2025-01-29 09:46:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":140913,"visible":true,"origin":"","legend":"\u003cp\u003eWalking muscle activity (aEMG) relative to sitting compared to standing muscle activity relative to sitting.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5918242/v1/52ae4f846a11993a188d8936.png"},{"id":75002603,"identity":"7cdacfa7-b29a-43dc-8bec-5781b17a0050","added_by":"auto","created_at":"2025-01-29 10:02:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1329501,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5918242/v1/0c6888fe-bd74-491a-85ba-b19f11a8b603.pdf"},{"id":75002247,"identity":"aa6b4821-a008-40a3-b445-9ed6a0ba9aff","added_by":"auto","created_at":"2025-01-29 09:54:44","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":75381,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Digital Content\u003c/p\u003e\n\u003cp\u003eSupp lementary digital content 1. Inter-individual activity (aEMG) by muscle groups within behaviours.\u003c/p\u003e","description":"","filename":"SDC1.png","url":"https://assets-eu.researchsquare.com/files/rs-5918242/v1/cc3ee9cb72d6a6d4b64c0d96.png"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eFree-Living Muscle Activity in Type 2 Diabetes: Sitting, Standing and Walking\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"HIGHLIGHTS","content":"\u003cp\u003eUsing accelerometers and electromyographic shorts, we analyzed quadriceps, hamstring, and gluteal muscle activity during sitting, standing, and walking in adults with type 2 diabetes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAverage electromyographic (EMG) activity ranged from 1.8–3.3% of maximal capacity during sitting, 3.4–19.8% during standing, and 7.5–34.1% during walking.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStanding increased EMG activity 1.3–5.6 times compared to sitting, while walking increased it 3.1–15.2 times, highlighting inter-individual variability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eReducing sitting time, especially by walking, may benefit type 2 diabetes management, and personalized recommendations should consider individual EMG responses.\u003c/p\u003e"},{"header":"1. INTRODUCTION","content":"\u003cp\u003eInterrupting and replacing sitting with physical activity is important for multiple diabetes-relevant metabolic health indicators, including insulin (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), and glucose control (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Muscle contractile activity is one of the key mechanisms of physical activity benefit (\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Even minimal skeletal muscle contractions, such as standing or briefly interrupting sedentary time, can increase blood flow and the concentration of glucose transporter 4 (GLUT4) at the muscle cell surface, likely contributing to the observed improvements in glycaemic control (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOlder adults can have substantial variability in muscle activity responses to daily sitting and movement behaviours (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), with periods of muscle inactivity negatively associated with health markers (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Additionally, the quadriceps, hamstrings, and gluteal muscle groups are utilized differently across daily activities, leading to distinct activity patterns and associations with health outcomes. Consequently, how individuals engage these muscles in their daily routines may influence their overall health (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Advances in wearable electromyographic (EMG) technology now allow for continuous, full-day quantification of these patterns of muscle activity. Optimizing muscle activity patterns during daily living may be a novel approach to enhance glycemic control. To date, free-living muscle activity has not been quantified in those with type 2 diabetes.\u003c/p\u003e \u003cp\u003eWe examined EMG-assessed free-living muscle activity in type 2 diabetes. Daily behaviours (sitting, standing, walking) are described in terms of quadriceps, hamstring, and gluteal muscle group activity. We also describe correlations between the three daily behaviours and the EMG signals, and individual EMG responses.\u003c/p\u003e"},{"header":"2. METHODS","content":"\u003cp\u003eData were from the free-living measurement component of a four-armed randomized cross-over trial (the OPTIMUS study) conducted in a single-center laboratory in Mikkeli, Finland. Each participant completed a 2\u0026ndash;3-hour baseline assessment, and four approximately 7-hour laboratory study visits where they were instructed to sit for one prolonged period or interrupt prolonged sitting with intermittent physical activity or standing. After this period, data were recorded in their free-living setting with electromyography (EMG) and thigh-worn accelerometer devices. Only free-living data subsequent to the laboratory experiment day is reported in this paper. The OPTIMUS study was approved by the Human Research Ethics Committee of the Hospital District of Northern Savo (475/13.02.00/2021) and was registered with ISRCTN trial registry NO45350 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.isrctn.com/\u003c/span\u003e\u003cspan address=\"https://www.isrctn.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). All participants provided written informed consent.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study recruitment and screening\u003c/h2\u003e \u003cp\u003eParticipants were recruited during August 2023 to May 2024 through nearby health clinics and via social media. They were eligible if they were aged between 35\u0026ndash;65 years, had a body mass index (BMI) between 25\u0026ndash;50 kg/m\u003csup\u003e2\u003c/sup\u003e, medically diagnosed with type 2 diabetes for at least three months, on a stable treatment regimen for \u0026gt;\u0026thinsp;3 months, inactive (self-reported\u0026thinsp;\u0026lt;\u0026thinsp;150 minutes per week of moderate-to-vigorous physical activity), and sedentary (self-reported sitting for \u0026gt;\u0026thinsp;7 hours per day for \u0026gt;\u0026thinsp;3 months). Exclusion criteria were pregnancy, current smoker, using insulin medication, major illness/physical problems (acute or chronic) that may limit participation, unable to communicate in Finnish, and unable to provide written informed consent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study design and protocol\u003c/h2\u003e \u003cp\u003eEligible participants attended a baseline assessment at the laboratory which involved recording demographic information and assessing anthropometrics: weight, height, waist, and hip circumference using standard procedures. Participants were then instructed to wear a pair of tight-fitting EMG shorts (Myontec Ltd., Kuopio, Finland) and the Fibion\u0026reg; tri-axial accelerometer (Fibion, Jyv\u0026auml;skyl\u0026auml;, Finland). Isometric maximal voluntary contractions (MVC) for each muscle group were measured to normalize the EMG signal to maximum capacity (%EMG\u003csub\u003eMVC\u003c/sub\u003e). Quadriceps MVC was measured using isometric knee extension and hamstrings MVC using isometric knee flexion with the knee joint positioned at 60\u0026deg; flexion (0\u0026deg;=full extension). Gluteal muscles MVC was determined using isometric hip extension with the hip joint positioned at 20\u0026deg; abduction and knee joint at 20\u0026deg; flexion. Two warm-up contractions (verbally instructed to be 50% effort level from the maximum) were performed for each movement (both legs at same time). These were followed by three 5-second maximal contractions, with a 1-minute break between efforts. The maximal contraction with the highest EMG amplitude (one second average) was used for signal normalization. Participants were instructed to continue to wear EMG-shorts and the accelerometer after the laboratory visit and until they went to bed on the following day. Electrode paste was used to optimize the skin\u0026ndash;electrode contact, and participants were instructed to re-apply electrode paste after any temporary removal of the EMG shorts.\u003c/p\u003e \u003cp\u003eOn a second visit to the laboratory, participants had their body composition assessed after having fasted for 10 hours. Body composition was assessed using the Inbody 750 bioelectrical impedance device (InBody, Seoul, Korea). On subsequent visits, they were provided with EMG shorts and accelerometers again and instructed to wear them during their normal life, also when sleeping, until next evening before went to bed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 EMG measurement and data synthesis\u003c/h2\u003e \u003cp\u003eTextile EMG shorts measured daily muscle activity from the quadriceps, hamstring, and gluteal muscles in the participant\u0026rsquo;s free-living setting. Reference bipolar electrodes of the shorts were placed longitudinally on the lateral sides of the left and right on the covering membranes of the iliotibial tract. The electrodes were situated on the distal regions of the quadriceps and hamstring muscles and on the middle of the gluteal muscles.\u003c/p\u003e \u003cp\u003eThe EMG signal was stored in a 50-g electronic module attached to the waist. The signal was recorded with a sampling frequency of 1000 Hz, band-pass filtered at 40 Hz \u0026minus;\u0026thinsp;200 Hz (-3dB), digitalized with a 24-bit A/D converter and a gain of 0, averaged with non-overlapping windows of 40 ms (to 25 Hz) and saved in the module. The data were downloaded and visualized with Muscle Monitor software (Myontec Ltd, Kuopio, Finland). Next, the individual channels from the right and left quadriceps, hamstring and gluteal muscles were normalized to the respective maximum voluntary contractions (%EMG\u003csub\u003eMVC\u003c/sub\u003e). The signal was further smoothed with a 200 ms moving average algorithm as per previous methods (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAn automated artefact removal procedure detected presence of artefacts using the following rules within five-minute rolling windows: 1) if baseline was \u0026gt;\u0026thinsp;5%EMG\u003csub\u003eMVC\u003c/sub\u003e, 2) median value\u0026thinsp;\u0026gt;\u0026thinsp;50%EMG\u003csub\u003eMVC\u003c/sub\u003e, or 3) maximum value\u0026thinsp;\u0026gt;\u0026thinsp;300% EMG\u003csub\u003eMVC\u003c/sub\u003e. If a channel included more than 30 minutes of time as artefact, the whole channel was removed from the analyses. The artefact segments were overlaid on the signal from each channel and visually inspected, confirming that the algorithm successfully removed artefacts without affecting the physiological signal. Corrected baseline drift and artefacts removal led 138/1146 channels removed. Removal of individual channels has minimal influence on extracted EMG outcomes (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Sitting, standing, walking\u003c/h2\u003e \u003cp\u003eThe Fibion\u0026reg; tri-axial accelerometer (Fibion, Jyv\u0026auml;skyl\u0026auml;, Finland, 12.5 Hz, \u0026plusmn; 4 g) was worn concurrently with EMG shorts to assess muscle activity during sitting, standing, and walking. The accelerometer was affixed vertically at the centerline and horizontally at the upper third level on the anterior side of the thigh, secured in a waterproof covering with medical adhesive tape on to the EMG shorts. Participants filled out electronic sleep diaries each morning indicating the time when they went to sleep (from previous night) and when they woke up. Sleeptime was excluded from the analysis. Accelerometer data were categorized into sitting, standing, and walking minutes. For synchronized analyses, only minute intervals that were comprised completely of sitting, or \u0026gt;\u0026thinsp;=\u0026thinsp;50 s of standing or walking were included.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Data processing, synchronization, and statistical analysis\u003c/h2\u003e \u003cp\u003eAnalyses were conducted using R (R version 4.2.2; R Foundation for Statistical Computing, Vienna, Austria). EMG and accelerometer signals were synchronized by clock time (both devices were initiated from the same study computer) and visually scanned to ensure continuous interpretation of %EMG \u003csub\u003eMVC\u003c/sub\u003e matched with the accelerometers continuous (minute interval) determination of METs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.). Initially, there were 95 days of free-living observations; 10 days were excluded due to recording durations of less than 10 hours. Following visual inspection, 8 days were removed for poor synchronization, and 19 days were excluded due to the absence of EMG or accelerometry data, leaving a final sample of 58 days of synchronized EMG and accelerometer observations. With the synchronized EMG and accelerometer data, average EMG amplitude (aEMG, %EMG\u003csub\u003eMVC\u003c/sub\u003e) was first analyzed across right and left hamstring, quadriceps, and gluteal groups and after that the values were averaged and estimated by day and within the accelerometer-derived behaviours. Total EMG inactivity duration was also analyzed as summed duration of EMG bouts when the signal amplitude was below the inactivity threshold of 3 \u0026micro;V and presented as a proportion of the day and proportion of behaviour length. This inactivity threshold has been reported to provide the best responsiveness to detect changes in total EMG inactivity duration and pattern of accumulation (Pesola et al. 2022).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eINSERT FIG.1 HERE\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe relationship of daily accelerometer-derived behaviour minute totals with daily quadriceps, hamstring, and gluteal aEMG and inactivity durations were analyzed with Pearson correlation. To understand inter-individual responses to hypothetical substitutions of sitting time with standing or walking, each participant\u0026rsquo;s standing aEMG relative to their sitting aEMG was regressed against their walking aEMG relative to their sitting aEMG. The fit of this regression model was assessed with the adjusted R-squared estimate. For all analyses statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, two-tailed.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Participant characteristics\u003c/h2\u003e\n \u003cp\u003eSample characteristics of the overweight or obese adults (n\u0026thinsp;=\u0026thinsp;19; 7 male) who participated in the study are shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. On average, participants had been diagnosed with type 2 diabetes 10.8 (\u0026plusmn;\u0026thinsp;8.0) years previously. Muscle activity relative to maximum during free-living observation was low, with slight variation by muscle group observed. All muscle groups measured were inactive, on average, over 75% of the waking day. Participants spent 91.1% of their days sitting, 23.6% standing, and 14.6% walking relative to the actual device wear time.\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of the free-living living study.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;19\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex, \u003cem\u003emale\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.8 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnthropometrics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI, \u003cem\u003ekg/m\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.9 (27.4, 32.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBody weight, kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.3 (11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFat, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.4 (9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWaist circumference, cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104.7 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWaist-hip ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97 (0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHba1c, \u003cem\u003e%\u003c/em\u003e \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.7 (6.1,7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHba1c, mmol/l \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50 (43,57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYears diagnosed with T2D, \u003cem\u003eyears\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.8 (8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMuscle activity and inactivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQuadriceps aEMG, \u003cem\u003e%EMG\u003c/em\u003e\u003csub\u003e\u003cem\u003eMVC\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.7 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHamstrings aEMG, \u003cem\u003e%EMG\u003c/em\u003e\u003csub\u003e\u003cem\u003eMVC\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.5 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGluteals aEMG, \u003cem\u003e%EMG\u003c/em\u003e\u003csub\u003e\u003cem\u003eMVC\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.4 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQuadriceps inactivity, \u003cem\u003e%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.7 (6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHamstrings inactivity, \u003cem\u003e%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.6 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGluteals inactivity, \u003cem\u003e%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.4 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccelerometer-derived behaviours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSitting time, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.1 (11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStanding time, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.6 (8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWalking time, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.6 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConcurrent device wear time, \u003cem\u003eh\u003c/em\u003e \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.3 (11.9, 13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDays of wear, \u003cem\u003en\u003c/em\u003e \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.2 (2.0, 4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eEstimates are presented as number and proportion or mean (SD) unless indicated otherwise\u003c/p\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e denotes estimates presented as median and interquartile range.\u003c/p\u003e\n \u003cp\u003eConcurrent device wear time and days of wear indicate the time and number of days where both accelerometer and EMG were worn at the same time and synchronized.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eINSERT TABLE1 HERE\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 EMG amplitudes during daily sitting, standing and walking\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows free-living muscle activity in sitting, standing and walking for all muscle groups and between the quadriceps (Q), hamstring (H), and gluteal muscle (G) groups. On average, free-living aEMG in sitting was 2.8%EMG\u003csub\u003eMVC\u003c/sub\u003e (95%CI: 2.4, 3.1), in standing was 7.1%EMG\u003csub\u003eMVC\u003c/sub\u003e (95%CI: 5.5, 8.7) and in walking was 20.8%EMG\u003csub\u003eMVC\u003c/sub\u003e (95%CI: 17.3, 24.4). Across different activities, aEMG varied among muscle groups, with 2.5% EMG\u003csub\u003eMVC\u003c/sub\u003e (95%CI: 2.0, 3.1) in the quadriceps and 2.5% EMG\u003csub\u003eMVC\u003c/sub\u003e (95%CI: 2.0, 3.0) in the hamstrings, and 3.3% EMG\u003csub\u003eMVC\u003c/sub\u003e (95%CI: 2.8, 3.7) in the gluteal muscles during sitting; 4.7% EMG\u003csub\u003eMVC\u003c/sub\u003e (95%CI: 3.9, 5.5) (Q), 8.7% EMG\u003csub\u003eMVC\u003c/sub\u003e (95%CI: 5.6, 11.9) (H), and 7.8% EMG\u003csub\u003eMVC\u003c/sub\u003e (95%CI: 5.9, 9.7) (G), respectively, during standing; and 21.9% EMG\u003csub\u003eMVC\u003c/sub\u003e (95%CI: 16.9, 26.9) (Q), 23.5% EMG\u003csub\u003eMVC\u003c/sub\u003e (95%CI: 18.1, 28.8) (H), and 17.1% EMG\u003csub\u003eMVC\u003c/sub\u003e (95%CI: 13.8, 20.3) (G) during walking. aEMG in all muscle groups were at least two times higher during walking than standing with the biggest difference seen in the quadriceps muscle group (4.7\u0026ndash;21.9%). Inter-individual activity by muscle groups within these behaviours are shown in Supplementary Digital Content (SDC) 1.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eINSERT FIG.2 HERE\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Correlation of sitting, standing and walking with overall free-living EMG outcomes\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows correlations of synchronized accelerometer-derived behaviours and quadriceps, hamstring, and gluteal muscle activity and inactivity. Sitting was inversely correlated with muscle activity, with the strongest correlations in hamstrings muscle group, and positively associated with EMG inactivity across all muscle groups. Standing was positively correlated with muscle activity, again with the strongest correlations in the hamstrings muscle group, and inversely correlated with EMG inactivity, with the strongest correlations in quadriceps and hamstrings. Walking was positively associated with muscle activity with the strongest correlations in hamstring aEMG and inversely correlated with EMG inactivity across all muscles.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelation of accelerometry derived behaviours and quadriceps, hamstring, and gluteal muscle activity (aEMG).\u0026nbsp;\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSitting\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStanding\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWalking\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQuadriceps aEMG\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHamstring\u003c/p\u003e\n \u003cp\u003eaEMG\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGluteal\u003c/p\u003e\n \u003cp\u003eaEMG\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQuadriceps\u003c/p\u003e\n \u003cp\u003eEMG inactivity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHamstring\u003c/p\u003e\n \u003cp\u003eEMG inactivity\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSitting time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStanding time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.95*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWalking time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.85*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQuadriceps aEMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHamstring aEMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.51*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGluteal aEMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQuadriceps EMG inactivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.45*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.58*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHamstring EMG inactivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.71*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.68*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.63*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.77*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGluteal EMG inactivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.48*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.58*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.74*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.45*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003eAll values correspond to daily behaviour time and daily muscle activity and inactivity.\u003c/p\u003e\n \u003cp\u003e* = values p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eMuscle groups also exhibited inter-correlations: quadriceps aEMG showed weak correlations with both hamstrings and gluteal aEMG compared to the stronger correlations between hamstring and gluteal aEMG. Quadriceps aEMG did not have strong correlations with EMG inactivity in any muscle group, while both hamstring and gluteal aEMG were negatively correlated with their respective inactivity measures.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eINSERT TABLE2 HERE\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Differences in EMG relative to sitting and standing and sitting and walking\u003c/h2\u003e\n \u003cp\u003eStanding aEMG relative to sitting aEMG was associated with walking aEMG relative to sitting aEMG (\u0026beta; aEMG [CI%95]\u0026thinsp;=\u0026thinsp;4.6 [1.34, 7.86], p-value\u0026thinsp;=\u0026thinsp;0.03, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). That is, the higher standing aEMG was associated with a higher walking aEMG. Some individuals had lower standing aEMG than sitting aEMG. There was variation in this relationship (adjusted r\u003csup\u003e2\u003c/sup\u003e value\u0026thinsp;=\u0026thinsp;0.13); indicating that some individuals responded relatively greater to standing than to walking (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e region D) as compared to others, and some individuals had a lower response to standing but a relatively higher response to walking as compared to sitting muscle activity (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e region A). Finally, there were some individuals who had a low muscle activity response relative to sitting for both standing and walking (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e region C).\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eINSERT FIG.3 HERE\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eThis is the first study to quantify muscle activity during free-living sitting, standing, and walking in type 2 diabetes, where muscle activity is a key factor to glycaemic control and thus disease management. Muscle activity during free-living behaviours was low, ranging from 2.7\u0026ndash;4.4% of participants\u0026rsquo; maximal voluntary contraction capacity, with muscle groups being inactive for 75\u0026ndash;80% of measurement time. Thigh muscle aEMG varied from 1.8\u0026ndash;3.3% during sitting, 3.4\u0026ndash;19.8% while standing, and 7.5\u0026ndash;34.1% when walking. Hamstring aEMG had the strongest correlation with these behaviours. This variation meant that among individuals, standing aEMG ranged 1.3\u0026ndash;5.6 times higher than sitting, and walking resulted in 3.1\u0026ndash;15.2 times higher than sitting aEMG. This suggests that sedentary behaviour interventions, which are recommended in diabetes management guidelines, may have varying success in increasing muscle activity depending on the individual and on whether sitting is interrupted by standing or walking.\u003c/p\u003e \u003cp\u003ePrevious studies examining sedentary behavior and physical activity in individuals with type 2 diabetes have primarily relied on accelerometers (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), consistently reporting higher sitting times compared to healthy referents. However, free-living EMG activity has not been quantified in individuals with type 2 diabetes, making direct comparisons to previous research challenging. Studies investigating free-living muscle activity in healthy older adults (aged 71\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9 years) without type 2 diabetes found higher daily aEMG (4\u0026ndash;5.9%) and a lower prevalence of inactivity (50\u0026ndash;70%) (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) compared to the present study. These findings suggest that type 2 diabetes may contribute to reduced overall muscle activity, highlighting the need for further investigation.\u003c/p\u003e \u003cp\u003eNotably, both our study cohort and the cohort of healthy older adults (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) had high level of inter-individual variation in their muscle activity \u0026ndash; a finding that may be influenced by several factors. Type 2 diabetes is associated with decreased muscle mass, strength, and walking speed as compared to normoglycemic controls in older adults (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). An increase in neuropathy, glycated hemoglobin HbA1c, and duration of diabetes has been linked to a reduction in strength (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). In people with type 2 diabetes, loss of muscle strength is most pronounced in the thighs, particularly the quadriceps femoris, which appears especially susceptible to age-related muscle mass loss (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). These declines in thigh muscle mass and strength significantly affect daily functional capacity in individuals with type 2 diabetes (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), EMG measures can potentially add insights on the relative muscle intensity occurring within activity types or intensities, with their effects on health warranting further investigation.\u003c/p\u003e \u003cp\u003eWe found walking to be positively associated with muscle activity, with the strongest correlations in the hamstring muscles. Notably, even though the strongest correlations and the highest muscle activity (aEMG) in walking were seen in the hamstrings, the greatest difference in aEMG from standing to walking were seen in the quadriceps, highlighting the importance of walking to fully engage lower limb muscles. Increased muscle activity in these muscle groups has been linked to improved glycemic control (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Different types of muscle activity and different muscle groups can have distinct effects on metabolism because of their differences in fiber type profiles and functions and general activity in daily living. Gluteal, hamstring and quadriceps muscle fiber type compositions are all described as oxidative, glycolytic, or mixed (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), but the quadriceps muscles predominantly consist of phasic fibers that are activated during more intense physical activity (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). A recent study found that insulin sensitivity in hamstrings, rather than quadriceps, increased after reducing sitting time 0.5 hours (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), which was speculated to be a result of low-intensity physical activity mainly for holding postural control. However, when more dynamic movement was investigated, the glycemic response was primarily attributed to higher muscle activity by quadriceps and gluteal muscles (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Therefore, although all the muscle groups that we examined share also similar compositional features, they may demonstrate differing metabolic tendencies during free-living activities, and it may be that more dynamic or higher intensity physical activities are required to sufficiently activate quadriceps and gluteal muscles in free-living contexts. Ensuring that the behaviours that maximize muscle activity across muscle groups could be beneficial to glucose response, however, further research is needed.\u003c/p\u003e \u003cp\u003eExperimental studies in type 2 diabetes have shown that acutely replacing sitting time with light physical activity can lead to improvements in glucose incremental AUC (iAUC), triacylglycerol levels, insulin, and insulin sensitivity (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Reducing sitting time through a combination of light physical activity and standing time has also been demonstrated to have positive effects on insulin sensitivity in postmenopausal women (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). However, sedentary behaviour interventions outside of the laboratory setting have resulted only in modest average improvements in body anthropometry, glucose metabolism, lipid metabolism, and blood pressure (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). These trials primarily replace sitting time with standing (typically via sit-stand workstations), which may partly explain the limited associations observed with glycemic outcomes (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Overall, evidence from sedentary behaviour research suggests that in addition to replacing sitting only with standing, individuals may need to incorporate more movement and ambulatory activities to achieve greater benefits for glucose metabolism (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Our findings align with this evidence, offering a mechanistic explanation for why increasing standing time alone may not suffice for all individuals to improve glycemic control.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Implications for the management of type 2 diabetes\u003c/h2\u003e \u003cp\u003eThis study shows that there was considerable variation in how those with type 2 diabetes responded to standing and walking, suggesting that some individuals may naturally activate their muscles more during these activities, while others might require more activity volume or specific interventions to achieve similar muscle activity. Interventions with standing have shown only modest effectiveness in improving health outcomes in persons with overweight or obesity (\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). However, previous EMG studies have shown that muscle activity is higher in overweight than normal weight individuals when standing, suggesting that muscles of those who are overweight contract with greater intensity (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). We found that some participants had standing muscle activity that was of a similar level to their walking muscle activity. Personalized intervention approaches, which utilize understanding of an individual's unique \"myoprint\"\u0026mdash;their characteristic EMG activity patterns during daily behaviours\u0026mdash;may enhance intervention efficacy and guide selection of behaviours. For example, by identifying these daily EMG patterns, it would be possible to infer whether an individual exhibits high muscle activity during standing, which would then suggest that strategies that promote regular bouts of standing may be of particular benefit.\u003c/p\u003e \u003cp\u003eThe main strength of this study was the new insights of muscle activity in free-living daily behaviours in people with type 2 diabetes. Synchronization of accelerometer and wearable EMG data was a key factor in improving our understanding of the variability of muscle activity during different behaviours. Accelerometers are unable to measure the direct contraction of muscles, and there is a lack of consensus of normalizing their data to relative capacity (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), which is possible with EMG measures. Our study has several limitations. The EMG measurements used represent only the superficial muscle groups located below the electrodes and are not representative of other lower- or upper-extremity muscles that are also important for upright and ambulatory movement during daily behaviours. However, as thigh muscles play a significant role in sitting, standing, and walking, and thigh muscle atrophy is particularly prevalent in people with type 2 diabetes (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), this makes the use of EMG shorts potentially quite relevant. Our synchronized data do not show exactly when the to sit-to-stand transitions occur. Such transitions, and the timing of these transitions, may have distinct health benefits (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Examining sit-to-stand transitions would be relevant given those with type 2 diabetes might take longer and in a greater muscular effort to complete these kinds of movements compared to healthy adults (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The measurement duration (average 3.2 days per participant) was relatively short compared to accelerometer wear periods (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). However, previous studies have reported adequate test-retest repeatability for shorter EMG measurement durations (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e), and it should be noted that our findings were mostly driven by synchronized data: examining EMG within sitting, standing, and walking events, rather than examining activity totals to quantify and determine a \u0026lsquo;typical\u0026rsquo; day. In future, extending the wear period (as possible) will further our understanding of day-to-day muscle activity variability.\u003c/p\u003e \u003cp\u003eIn conclusion, we have shown that daily muscle activity is low in those with type 2 diabetes, ranging from 2.7\u0026ndash;4.4% of their maximal capacity, and that their quadriceps, hamstring, and gluteal muscle groups were inactive for 75\u0026ndash;80% of free-living time. We observed that walking can activate lower extremity muscles more comprehensively than standing, and that walking may be critical for maximizing whole-of-day free-living muscle activity. Inter-individual response to sitting, standing, and walking varies widely by individual, highlighting the potential to personalize behaviour interventions to maximize muscle activity in type 2 diabetes.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis project was supported by Academy of Finland, grant ID 332731 (AJP, CB and SL).\u0026nbsp;D\u003cu\u003eunstan was\u003c/u\u003e supported by the National Health and Medical Research Council Fellowships Scheme and the Victorian Operational Infrastructure Support Program. Healy was supported by an Australian Medical Research Future Fund Fellowship Scheme. No potential conflict of interest was reported by the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSuvi Lamberg\u003c/strong\u003e: Writing – original draft, Conceptualization, Investigation, Formal analysis, Methodology, Visualization, Project administration. \u003cstrong\u003eChristian Brakenridge\u003c/strong\u003e: Writing – original draft, Conceptualization, Formal analysis, Methodology, Visualization, Supervision. \u003cstrong\u003eYing Gao\u003c/strong\u003e:\u0026nbsp;Writing – review \u0026amp; editing. \u003cstrong\u003eDavid Dunstan\u003c/strong\u003e: Writing – review \u0026amp; editing, Conceptualization. \u003cstrong\u003eTaija Finni\u003c/strong\u003e: Writing – review \u0026amp; editing, Conceptualization, Methodology. \u003cstrong\u003eGenevieve Healy\u003c/strong\u003e: Writing – review \u0026amp; editing, Conceptualization. \u003cstrong\u003eNeville Owen\u003c/strong\u003e: Writing – original draft, Conceptualization, Methodology. \u003cstrong\u003eArto Pesola\u003c/strong\u003e: Writing – original draft, Conceptualization, Formal analysis, Methodology, Visualization, Project administration, Funding acquisition, Supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the participants of the study for their contributions. The findings are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eDempsey PC, Larsen RN, Sethi P, Sacre JW, Straznicky NE, Cohen ND, et al. Benefits for type 2 diabetes of interrupting prolonged sitting with brief bouts of light walking or simple resistance activities. Diabetes Care [Internet]. 2016 Jun 1 [cited 2021 Jun 29];39(6):964\u0026ndash;72. Available from: www.anzctr.org.au.ThisarticlecontainsSupplementaryDataonlineathttp://care.diabetesjournals.org/lookup/suppl/doi:10.2337/dc15-2336/-/DC1.\u003c/li\u003e\n \u003cli\u003eDunstan DW, Kingwell BA, Larsen R, Healy GN, Cerin E, Hamilton MT, et al. Breaking up prolonged sitting reduces postprandial glucose and insulin responses. 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Available from: https://pubmed.ncbi.nlm.nih.gov/17363749/\u003c/li\u003e\n \u003cli\u003eFridolfsson J, Arvidsson D, Ekblom-Bak E, Ekblom \u0026Ouml;, Bergstr\u0026ouml;m G, B\u0026ouml;rjesson M. Accelerometer-measured absolute versus relative physical activity intensity: cross-sectional associations with cardiometabolic health in midlife. BMC Public Health [Internet]. 2023 Dec 1 [cited 2025 Jan 14];23(1). Available from: https://pubmed.ncbi.nlm.nih.gov/37996871/\u003c/li\u003e\n \u003cli\u003eKujala UM, Pietil\u0026auml; J, Myllym\u0026auml;ki T, Mutikainen S, F\u0026ouml;hr T, Korhonen I, et al. Physical Activity: Absolute Intensity versus Relative-to-Fitness-Level Volumes. Med Sci Sports Exerc [Internet]. 2017 Mar 1 [cited 2025 Jan 15];49(3):474\u0026ndash;81. Available from: https://pubmed.ncbi.nlm.nih.gov/27875497/\u003c/li\u003e\n \u003cli\u003eJohnson MA, Polgar J, Weightman D, Appleton D. Data on the distribution of fibre types in thirty-six human muscles: An autopsy study. 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Increased insulin-stimulated glucose uptake in both leg and arm muscles after sprint interval and moderate-intensity training in subjects with type 2 diabetes or prediabetes. Scand J Med Sci Sports [Internet]. 2018 Jan 1 [cited 2024 Feb 9];28(1):77\u0026ndash;87. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/sms.12875\u003c/li\u003e\n \u003cli\u003eSj\u0026ouml;ros T, Laine S, Garthwaite T, V\u0026auml;h\u0026auml;-Ypy\u0026auml; H, Koivum\u0026auml;ki M, Eskola O, et al. The effects of a 6-month intervention aimed to reduce sedentary time on skeletal muscle insulin sensitivity: a randomized controlled trial. Am J Physiol Endocrinol Metab. 2023 Aug 1;325(2):E152\u0026ndash;62.\u003c/li\u003e\n \u003cli\u003eDuvivier BMFM, Schaper NC, Hesselink MKC, van Kan L, Stienen N, Winkens B, et al. Breaking sitting with light activities vs structured exercise: a randomised crossover study demonstrating benefits for glycaemic control and insulin sensitivity in type 2 diabetes. Diabetologia. 2017 Mar 1;60(3):490\u0026ndash;8.\u003c/li\u003e\n \u003cli\u003eRemie CME, Janssens GE, Bilet L, Van Weeghel M, Duvivier BMFM, De Wit VHW, et al. Sitting less elicits metabolic responses similar to exercise and enhances insulin sensitivity in postmenopausal women. Available from: https://doi.org/10.1007/s00125-021-05558-5\u003c/li\u003e\n \u003cli\u003eHadgraft NT, Winkler E, Climie RE, Grace MS, Romero L, Owen N, et al. Effects of sedentary behaviour interventions on biomarkers of cardiometabolic risk in adults: Systematic review with meta-analyses. Vol. 55, British Journal of Sports Medicine. BMJ Publishing Group; 2021. p. 144\u0026ndash;54.\u003c/li\u003e\n \u003cli\u003eYates T, Edwardson CL, Henson J, Zaccardi F, Khunti K, Davies MJ. Prospectively Reallocating Sedentary Time: Associations with Cardiometabolic Health. 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Effects of interrupting prolonged sitting with physical activity breaks on blood glucose, insulin and triacylglycerol measures: A systematic review and meta-analysis. Vol. 50, Sports Medicine. Springer; 2020. p. 295\u0026ndash;330.\u003c/li\u003e\n \u003cli\u003eBailey DP, Locke CD. Breaking up prolonged sitting with light-intensity walking improves postprandial glycemia, but breaking up sitting with standing does not. J Sci Med Sport. 2015 May 1;18(3):294\u0026ndash;8.\u003c/li\u003e\n \u003cli\u003ePesola AJ, Laukkanen A, Tikkanen O, Finni T. Heterogeneity of muscle activity during sedentary behavior. Applied Physiology, Nutrition and Metabolism [Internet]. 2016 Jul 19 [cited 2022 Jan 5];41(11):1155\u0026ndash;62. Available from: https://cdnsciencepub.com/doi/abs/10.1139/apnm-2016-0170\u003c/li\u003e\n \u003cli\u003ePesola AJ, Rantalainen T, Gao Y, Finni T. Aerobic capacity determines habitual walking acceleration, not electromyography-indicated relative effort. J Meas Phys Behav [Internet]. 2022 Jan 22 [cited 2022 Mar 1];1(aop):1\u0026ndash;10. Available from: https://journals.humankinetics.com/view/journals/jmpb/aop/article-10.1123-jmpb.2021-0018/article-10.1123-jmpb.2021-0018.xml\u003c/li\u003e\n \u003cli\u003eEdwardson CL, Winkler EAH, Bodicoat DH, Yates T, Davies MJ, Dunstan DW, et al. Considerations when using the activPAL monitor in field-based research with adult populations. J Sport Health Sci [Internet]. 2017 Jun 1 [cited 2025 Jan 15];6(2):162\u0026ndash;78. Available from: https://pubmed.ncbi.nlm.nih.gov/30356601/\u003c/li\u003e\n \u003cli\u003ePesola AJ, Laukkanen A, Haakana P, Havu M, S\u0026auml;\u0026auml;kslahti A, Sipil\u0026auml; S, et al. Muscle inactivity and activity patterns after sedentary time-targeted randomized controlled trial. Med Sci Sports Exerc. 2014 Nov 10;46(11):2122\u0026ndash;31.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"South-Eastern Finland University of Applied Sciences","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Electromyography, muscle contractile activity, diabetes, activity behaviours, sedentary behaviour","lastPublishedDoi":"10.21203/rs.3.rs-5918242/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5918242/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eAIMS: \u003c/strong\u003eUsing thigh-worn accelerometers and wearable electromyographic (EMG) shorts, we investigated muscle activity during sitting, standing and walking in adults with type 2 diabetes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMETHODS:\u003c/strong\u003e Isometric maximal voluntary contraction measures for quadriceps, hamstring, and gluteal muscle groups normalized the EMG signal to individual maximum capacity.\u003cstrong\u003e \u003c/strong\u003eParticipants concurrently wore accelerometers and EMG shorts for 3.2 days, and average EMG amplitude (aEMG) was assessed from quadriceps, hamstring, and gluteal muscle groups within accelerometer-derived sitting, standing, walking times.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRESULTS: \u003c/strong\u003eMuscle groups examined used only 2.7–4.4% of their maximum voluntary capacity (%EMG\u003csub\u003eMVC\u003c/sub\u003e) and were inactive for 75-80% of the measurement time. Sitting time was significantly correlated with muscle inactivity across all three muscle groups, but inversely so for hamstring aEMG (r = -0.51). Standing (r = 0.51) and walking (r = 0.48) were correlated with daily aEMG only in hamstrings. Relative to sitting, standing aEMG was 1.3–5.6 times higher and walking aEMG was 3.1–15.2 times higher, indicating varied inter-individual responsiveness.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONCLUSIONS: \u003c/strong\u003eReducing daily sitting, especially in favor of walking, may benefit hamstring and gluteal muscle engagement and help to prevent high levels of muscle inactivity in type 2 diabetes. Individual variability in EMG responses highlights the potential to personalize recommendations on sitting, standing and walking.\u003c/p\u003e","manuscriptTitle":"Free-Living Muscle Activity in Type 2 Diabetes: Sitting, Standing and Walking","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-29 09:46:40","doi":"10.21203/rs.3.rs-5918242/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bc6f9df8-fccb-4185-af0b-23be73ea17a6","owner":[],"postedDate":"January 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":43517818,"name":"Physiology"},{"id":43517819,"name":"Sports Medicine and Kinesiology"}],"tags":[],"updatedAt":"2025-01-29T09:46:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-29 09:46:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5918242","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5918242","identity":"rs-5918242","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}