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Muscle Inactivity and Activity Patterns after Sedentary Time-Targeted Randomized Controlled Trial

Pesola, Arto J.1; Laukkanen, Arto1,2; Haakana, Piia1; Havu, Marko1; Sääkslahti, Arja2; Sipilä, Sarianna3; Finni, Taija1

Author Information
Medicine & Science in Sports & Exercise: November 2014 - Volume 46 - Issue 11 - p 2122-2131
doi: 10.1249/MSS.0000000000000335


Physical activity patterns illustrating typical daily life of modern people include a large proportion of sitting with relatively idle muscles, whereas the other dominant part consists of nonexercise physical activity, and only a fraction of the day can be categorized as more intense exercise (18). The sedentary part of this pattern has been recognized as an independent predictor of adverse health outcomes (11) even in people doing regular moderate-to-vigorous physical activity (25). The underlying cause of the independence of exercise and inactivity may be the different metabolic pathways that have been identified to convey the effects of inactivity compared with those of exercise (5). Similarly, muscle activities required for standing and walking slowly have been found to improve postprandial glucose and insulin responses (12) and to prevent the effects of complete inactivity (5,35,40), which emphasizes the importance of nonexercise activities. The driving concept behind these findings is the so-called inactivity physiology paradigm, which states that “the brief, yet frequent, muscular contraction throughout the day may be necessary to short-circuit unhealthy molecular signals causing metabolic diseases” (18). Therefore, understanding and improving this typical physical activity pattern of modern people requires quantification of the whole physical activity continuum with careful evaluation of the balance between the two most dominant components: nonexercise physical activity and sedentary time, which in this article is defined as a lack of any muscular activity of the locomotor muscles.

In addition to different metabolic pathways, exercise and inactivity have been shown to be independent factors of behavior (6,14,27), which highlights the need for promotion of reduced sitting in addition to traditional exercise guidelines. Despite a promising hypothesis, there is paucity of randomized controlled trials (RCT) that have assessed sedentary time instead of physical activity as a primary outcome. In addition, workplace interventions promoting increased physical activity through mixed behavioral approaches have been shown to be ineffective at decreasing self-reported sitting time (7). Because of the potential health benefits of replacing sitting with light-intensity activities, promotion of this small change as a primary intervention goal could be an accessible, viable, and effective method for busy and sedentary target groups including people in sedentary occupations (38) and parents of young children (31).

To gain further insight into interventions targeting sedentary time, the effect of behavioral change needs to be studied across the whole physical activity spectrum with objective measures. Given that the key mechanism proposed for the associations of sedentary time with health is lack of muscular activity, it is important to measure the changes in this outcome. By using novel wearable textile electrodes, it is possible to measure muscle activity from the main locomotor muscles with similar or even better repeatability compared with that in traditional bipolar electrodes (15) across the whole physical activity spectrum (36).

The purpose of this study was to examine whether tailored counseling designed to reduce and break up sedentary time decreases muscle inactivity and increases muscle activity as measured by EMG from the quadriceps and hamstring muscles, which are some of the main locomotor muscles. We hypothesized that this specific counseling would decrease muscle inactivity time without increasing moderate-to-vigorous muscle activity.


As a part of a year-long RCT “A Family Based Tailored Counseling to Increase Nonexercise Physical Activity in Adults with a Sedentary Job and Physical Activity in Their Young Children” (InPact project, (16)), this study investigated the short-term (within 2 wk of the counseling) main outcomes of the RCT. The study was approved by the ethics committee of the Central Hospital, District of Central Finland, on March 25, 2011 (Dnro 6U/2011), and the participants signed an informed consent before measurements.

Recruitment and study sample

Sampling was performed in the Jyväskylä region located in central Finland, with a population of 133,000. Jyväskylä has a surface area of 1171 km2 with a relatively small city center, is near lakes and forests, and has numerous opportunities for active commuting with an extensive network of bike paths and sidewalks in the city region. Homogeneous regions around the city were identified in terms of socioeconomic status and environmental possibilities for outdoor physical activities, and cluster randomization was done within these regions. These seven regions included eight schools and 20 kindergartens (2–5 schools or kindergartens per region). The recruitment was done in three phases, where recruitment forms asking profession, percentage of sitting time at work, health status, and contact information were delivered to parents via kindergartens and primary schools in spring 2011, autumn 2011, and spring 2012. In total, 1055 recruitment forms were delivered including information about the study, inclusion and exclusion criteria, and an incentive to get diverse information about personal health, diet and physical activity, and motor skills of their children. Inclusion criteria were as follows: healthy men and women with children 3–8 yr old, parental occupation where they self-reportedly sat more than 50% of their work time, and children in all-day day care in kindergarten or in the first grade of primary school. Exclusion criteria were as follows: body mass index >35 kg·m−2, self-reported chronic diseases, families with a pregnant mother at baseline, children with disorders that delay motor development, and concurrent participation in another study. No monetary incentive was offered to the participants.

We received 300 responses. People fulfilling the criteria were contacted by phone and invited to an information lecture, where the procedures were explained in detail. If people were unable to attend the lecture, details of the project were explained on the phone. Finally, 133 participants were measured at baseline. Figure 1 summarizes the recruitment and randomization process.

Randomization and recruitment procedure for EMG analysis in the present InPact project.

Of the 133 participants, 48 were selected for the EMG analysis on the basis of the following criteria: 1) measured days were self-reportedly typical and identical in terms of occupational tasks, workday duration, and leisure time activities (31 excluded), 2) both days included artifact-free EMG signal from the same muscles recorded with the same electrodes (34 excluded), 3) length of measurement >9 h (10 excluded), and 4) diaries were returned properly filled (three excluded). In addition, seven participants dropped out before the second measurement day. The final study sample included 24 participants in the intervention group and 24 in the control group.

Study protocol

Muscle activity from the quadriceps and hamstring muscles was recorded during a structured laboratory test protocol and during daily measurements from two workdays before and after the counseling intervention. The participants were asked to select two measurement days that were as similar and typical as possible in terms of working schedule and duties. On the first day, the participants’ height and weight were measured after arriving at the laboratory in the morning. Subsequently, EMG shorts (Myontec Ltd., Kuopio, Finland) were put on. To measure the minimum level of EMG, the laboratory test protocol began by asking the participants to sit in front of a table while informing them about the diaries and questionnaires to be filled in. After the sitting period, a treadmill (OJK-1; Telineyhtymä, Kotka, Finland) protocol including walking at 5, 6, and 7 km·h−1 and running at 10 km·h−1 with 1-min steps was performed. Next, muscle activity was measured while standing still, while standing on each leg individually, and while walking up and down the stairs twice. Standing in different positions was measured for 15 s per task, and participants were asked to stand still as they usually do, except that the weight was first supported by both legs and then by one leg at a time. For the stair walking, the participants were instructed to step on every step and to use their normal pace. EMG amplitudes (percentage of EMG during maximal voluntary isometric contractions (MVC) (%EMGMVC)) from these tests are presented in Figure 2. Participants then performed bilateral MVC in a knee extension/flexion machine (David 220; David Health Solutions, Helsinki, Finland) with a 140° knee angle in both flexion and extension. After thorough familiarization and warm-up, at least three 3- to 5-s maximal efforts with strong verbal encouragement were performed with 1-min rest periods between trials. If torque improved by more than 5% in the last trial, more trials were performed.

Mean and SD of %EMGMVC measured during various laboratory tests. The horizontal lines show thresholds for intensity classification, which was done individually for each participant.

After the laboratory experiments, the participants left for work and were expected to continue normal living while wearing the shorts and keeping a diary of commuting, working, and leisure time. Any abnormal tasks and behaviors (e.g., abnormal working tasks, working time, or leisure activities) were to be reported to include only structurally similar days for analysis. After baseline measurements, the intervention group received tailored counseling. The postintervention measurements were performed within 2 wk of the counseling session.

Description of intervention

The intervention was designed on the basis of previous knowledge of effective interventions (9) and theory of planned behavior (1). Briefly, the intervention consisted of a common 30-min lecture for a maximum of six participants at a time followed by face-to-face discussions with the researchers (16). The lecture included research-based information about health hazards of prolonged sitting and encouragement to incorporate even the smallest physical activities into everyday routines to overcome these health problems. In the face-to-face discussions, the participants were first asked to describe their normal daily routines during commuting, working hours, and leisure time. Regarding leisure time, routines of the entire family were discussed because they are relevant to the individual’s behavior. During the discussion, participants were encouraged to think of feasible ways to reduce long sitting periods, to increase nonexercise physical activity, and to increase family-based activities from their personal premises, accompanied by ideas from the researcher. The participants set small-step goals, which were written into a contract signed by the researcher and the participant. An example from the contract of one participant is as follows:

My goals to decrease sitting time and to increase nonexercise physical activity during working time are:

  • - I stand up from my chair every half an hour;
  • - When answering the phone, I stand up from the chair;
  • - Instead of calling, I walk to my colleague’s room;
  • - I take the stairs instead of the elevator; and
  • - I walk for lunch and once a week choose a restaurant that is farther away.

Mine and my family’s goals to decrease sitting time and to increase physical activity during leisure time are:

  • - At least once a day, we go out as a family in order toreplace family sitting activity;
  • - We cycle to work whenever the weather permits us to do so;
  • - Instead of taking the car, we walk or bicycle to the grocery shop more often as a family;
  • - We organize family dancing sessions; and
  • - We will work hard with snow removal, using child labour together with us :) :).

The goals related to occupational tasks and leisure time activities were printed for participants to place them in a visible location at home and at work. In addition, participants were given a material about simple break exercises, local outdoor activities, and simple games suitable for the whole family. The materials were gathered to a project web page (, from which the participants were encouraged to find the relevant information.

Assessment of outcomes

The primary study outcomes were EMG-derived muscle inactivity time, duration of the five longest inactivity periods, and light muscle activity time assessed during working time, commute, and leisure time. The domains were separated on the basis of diaries. Because differences in EMG wear time may affect the time spent at different physical activity intensities (20), an equal recording time was analyzed for both measurements on the basis of the shorter measurement.

EMG recordings

EMG was measured with shorts made of knitted fabric similar to elastic clothes used for sport activities or functional underwear, with the exception of the capability to measure EMG from the skin surface of the quadriceps and hamstring muscles (Myontec Ltd., Kuopio and Suunto Ltd., Vantaa, Finland). Bipolar electrode pairs are located on the distal part of the quadriceps and hamstrings, and the reference electrodes are located longitudinally along the left and right lateral sides (over the tractus iliotibialis). The EMG signal was stored in a 50-g electronic module attached to the waist. In this study, eight pairs of shorts (four different sizes) were used. Electrode paste (Redux Electrolyte Crème; Parker, Inc.) was used on the electrode surfaces to improve and stabilize conductivity between the skin and electrodes. After every measurement day, the shorts were washed after detaching the electronics module. The EMG shorts have been tested for validity, repeatability, and feasibility in our laboratory, and detailed descriptions of the recording devices and analysis software have been reported previously (15,36).

Signal processing and categorizing

The individual channels from the right and left quadriceps and hamstring muscles were normalized to EMG amplitude measured during bilateral MVC. The repetition with the highest force level was chosen, from which the most consistent 1-s mean EMG amplitude was used for each channel. To reflect the overall inactivity or activity of thigh muscles, the normalized channels from the quadriceps and hamstring muscles were averaged. The threshold between inactivity and light activity was set individually at 90% of the mean %EMGMVC measured while standing still for 15 s in the laboratory (Fig. 2). This approach enabled determination of inactivity periods in the main locomotor muscles. The thresholds between light and moderate and moderate and vigorous muscle activity intensities were defined individually as a 1-min mean EMG value when walking at 5 km·h−1 and running at 10 km·h−1, respectively. Because some of the participants reported being unfamiliar with walking and running on a treadmill and MVC increased on the second laboratory test (P < 0.001), the values from the second measurement were used for both days to minimize the effect of learning on the thresholds. Adequate repeatability of the EMG–force relation (0.74 ≤ ICC ≤ 0.93) (see table, Supplemental Digital Content 1, supplemental Table 4—Repeatability of MVC) ensured the consistency of EMG signals between days. Detailed descriptions of signal processing, artifact removal, and MATLAB analysis procedures are presented in Supplemental Digital Contents 1,, and 2, (additional details on EMG analysis procedures and EMG channel averaging and baseline correction, respectively).

Statistical analyses

The initial sample size calculations for the entire intervention have been reported previously (16), and for this particular sample of EMG study, the calculated post hoc statistical powers and effect sizes (eta squared, η2) for the outcomes are reported. Effect sizes can be interpreted as follows: small, >0.01; medium, >0.06; and large, >0.14. Statistical analyses were performed with PASW Statistics version 18.0 (SPSS Inc., Chicago, Ill). Data are presented as mean ± SD. The Shapiro–Wilk test was used to evaluate whether the data were normally distributed. Where data were not normally distributed, log transformation was used, and normality was retested. Differences between the groups at baseline were tested with independent samples t-test, the Mann–Whitney test, or chi-square test. The effect of the intervention on EMG variables was assessed using repeated-measures ANOVA, with measurement time and baseline values of variables as covariates. Not normally distributed variables (total and leisure time average EMG and leisure time vigorous muscle activity time) were tested with the Mann–Whitney test by comparing within-group changes (postvalues − prevalues) between the groups; after which, within-group changes were tested with the Wilcoxon test. The differences between percentages of inactivity and activity time before and after the intervention were calculated as the arithmetic difference (percentage of measurement time after − percentage of measurement time before), yielding a percentage point (pp). Significance level was set at P < 0.05.



The study groups were comparable in terms of anthropometry, profession, weekly work time, and self-reported sitting at work (Table 1). There were no significant differences between the participants in the EMG study as compared with the remaining InPact study sample (females EMG study: 58%, age = 38.0 ± 5.5 yr, BMI = 24.6 ± 3.7 kg·m−2, managerial employees = 54%, work time per week = 37.6 ± 5.6 h, self-reported sitting at work = 82.7% ± 13.4%; females InPact study: 54%, age = 37.9 ± 5.3 yr, BMI = 24.4 ± 3.8 kg·m−2, managerial employees = 41%, work time per week = 38.0 ± 14.7 h, self-reported sitting at work = 85.8% ± 12.5%). As compared with the recruitment region’s mean, a higher proportion of InPact study participants had university education (35% vs 71%, respectively).

Basic characteristics of the subjects.

Recording time

The total recording time was 11.8 ± 1.1 h on both days. The duration of work time increased from 5.9 ± 1.2 to 6.7 ± 1.0 h (P < 0.001), whereas the duration of leisure time decreased from 5.0 ± 1.3 to 4.2 ± 1.2 h (P < 0.001), with no differences between the groups. The commuting time was 0.9 ± 0.4 h on both days.

Baseline observations

At baseline, there were no differences between the groups in any of the muscle inactivity variables. Detailed group, gender, and domain-specific baseline values are presented at Supplemental Digital Content 1,, supplemental Table 1. Both groups were inactive for an average of 69.1% ± 11.1% of the whole day, and the sum of the five longest inactivity periods was, on average, 36.7 ± 16.0 min. During working hours, an average 78.6% ± 10.8% of signals fell below the inactivity threshold and the duration of the five longest inactivity periods averaged 31.7 ± 16.0 min. During commuting and leisure time, muscle inactivity times were, on average, 44.3% ± 21.6% and 61.6% ± 15.6% of measurement time, and the durations of the five longest inactivity periods were, on average, 7.8 ± 6.8 and 25.6 ± 12.0 min, respectively.

At baseline, light muscle activity covered 21.9% ± 10.0% of the whole day, with values of 16.2% ± 9.3% during work, 32.8% ± 14.0% during commuting, and 27.5% ± 13.6% during leisure time. Less than 8% (7.4% ± 3.2%) of the whole day involved moderate muscle activity, consisting of 4.5% ± 2.7%, 19.1% ± 12.8%, and 8.7% ± 4.5% during work, commuting, and leisure time, respectively. On average, only 1.5% ± 2.6% of the whole day included vigorous muscle activity. The lowest value, 0.7% ± 1.0%, was measured during work time, whereas during commuting and leisure time, the vigorous muscle activity times were 3.9% ± 5.4% and 2.2% ± 5.1%, respectively. The only difference between the groups at baseline was the greater amount of moderate muscle activity during commuting time among the controls (24.1% ± 14.5%) compared with that in the participants in the intervention group (14.1% ± 8.4%, P < 0.05).

At baseline, %EMGMVC was, on average, 2.4% ± 1.6% of EMGMVC during the whole day, with a value of only 1.5% ± 0.8% of EMGMVC measured during working hours. During commuting and leisure time, the %EMGMVC were 4.7% ± 2.9% and 3.0% ± 3.1% of EMGMVC, respectively. Both groups had, on average, 23.4 ± 14.9 muscle activity bursts per minute during the whole day. During work time, commuting, and leisure time, the number of bursts per minute was 19.4 ± 14.3, 31.3 ± 25.1, and 27.2 ± 17.7, respectively. At baseline, there were no differences between the groups in either %EMGMVC or the number of bursts per minute.

Intervention effects

Table 2 and Figures 3 and 4 summarize the effects of intervention on EMG inactivity and EMG activity variables in the intervention group compared with those in the control group. During the whole day, muscle inactivity time (P < 0.05, power = 0.54, η2 = 0.09) and the sum of the five longest muscle inactivity periods (P < 0.05, power = 0.61, η2 = 0.11) decreased with concomitant increases in light muscle activity time (P < 0.05, power = 0.63, η2 = 0.11) and the number of bursts per minute (P < 0.05, power = 0.61, η2 = 0.11) in the intervention group compared with those in the controls. Despite the significant group–time interaction, the number of bursts per minute did not change significantly within the intervention group (Table 2). During work time, a decrease in muscle inactivity time (P < 0.05, power = 0.63, η2 = 0.11) was accompanied by an increase in light muscle activity time (P < 0.01, power = 0.77, η2 = 0.15) and average %EMGMVC (P < 0.05, power = 0.52, η2 = 0.09) in the intervention group compared with those in the controls. Compared with those in the control group, muscle inactivity time (P < 0.05, power = 0.70, η2 = 0.13) and the sum of the five longest muscle inactivity periods (P < 0.01, power = 0.83, η2 = 0.17) decreased and light muscle activity time (P < 0.05, power = 0.64, η2 = 0.11) increased in the intervention group during leisure time. Compared with that in the intervention group, the sum of the five longest muscle inactivity periods decreased during commuting in the control group (P < 0.01, power = 0.83, η2 = 0.17).

Changes of EMG inactivity and activity variables in intervention and control groups in different domains.
EMG-derived muscle inactivity time in intervention and control groups across different domains in pre- and postmeasurements. Cont, control group; int, intervention group. *Denotes significance at P < 0.05 and **P < 0.01.
EMG-derived muscle inactivity and activity variables in the intervention and control groups during the whole day in pre- and postmeasurements. Units for each variable are presented after the variable names. *Denotes significance at P < 0.05.


In this intervention, a one-time lecture and face-to-face tailored counseling aimed at reducing and breaking up sitting time and increasing nonexercise physical activity time led to decreases in muscle inactivity time and long inactivity periods with concomitant increases in light muscle activity time. The effects were achieved partly during work time and more profoundly during leisure time. However, given the minimal use of muscle MVC capacity (1.5% of EMGMVC during working hours), these changes resulted in a significant increase in average %EMGMVC during working hours of office workers. In other activity variables, there were no significant group–time changes during the whole day, suggesting that this specific counseling changed muscle inactivity and activity patterns, as hypothesized. Reallocation of muscle inactivity to ambulatory activity of the observed magnitude (approximately 30 min) have been shown to decrease metabolic risk factors in short-term interventions (12,30).

According to a previous review (7), strategies aimed at promoting physical activity are often not able to reduce self-reported sitting time despite increasing physical activity in various workplace interventions. However, behavioral interventions targeted specifically at reducing sedentary behavior in overweight office workers (23) and in the elderly (17) showed similar results to those of the present study, i.e., reductions of sedentary time with simultaneous increases in light-intensity physical activity time. In addition, a lecture and a specific prompt program for office workers of normal weight (13), a television lockout system for overweight and obese individuals (26), and the implementation of sit–stand workstations (2,19) were able to reduce sedentary behavior by changing the physical environment, resulting mostly in more substantial changes as compared with those in behavioral intervention alone. These results illustrate that to reduce sedentary time, the specific physical and social contexts that modify participation in sedentary activities must be modified, and these factors are likely different from factors related to physical activity (28).

In the present study, both the intervention instructions and data analysis were classified into commute, work time, and leisure time to emphasize the effect of the intervention within these domains. The changes during leisure time were approximately twofold bigger as compared with those in the work time, whereas no intervention effects were observed during commuting. The specific contexts affecting sedentary behavior are likely various and present throughout the day. Specifically, a potential to decrease sedentariness through behavioral intervention is different between these domains. Even though workplace settings include challenges for behavioral interventions in terms of structured time use, social norms, and environment, among others, the present behavioral intervention was effective in participants from various professional backgrounds. We also tested the potential confounding effect of occupational status by using it as a covariate in the statistical tests, but the results remained largely unchanged, suggesting that the intervention was independent of professional background within our study population. On average, the magnitude of the change induced by this simple intervention is rather modest and may benefit from environmental support and a multilevel approach at the workplace. In addition, given the high education level of the study participants, these results may not be fully generalizable. However, because sedentary work seems to be most prevalent in highly educated people (8), there might be a need for sedentary time-targeted intervention within this particular group.

Leisure time, on the other hand, offers a more flexible environment for behavioral changes, as evidenced by a twofold bigger decrease in sedentary time as compared with that in the work time. In particular, the family based approach, which incorporates educational and parental aspects in addition to individual priorities, may have exposed the motivation toward nonexercise activity through the desire for activities that are important for children. About 40% of Finnish families have children, 50% of whom have children under the age of 6 (34). The findings of this study show the potential of family based intervention in a population representing busy stage of life and low daily physical activity level (31). To increase the effectiveness of future interventions targeting sedentary time, workplace settings might benefit from environmental support and commuting time may require a more powerful and wide-ranging intervention (28).

In addition to different domains, it is also important to consider changes in behavior across the entire physical activity spectrum. For example, an increase in high-intensity physical activity may occur independently of inactivity (10,14) or may even be paralleled by a decrease in light-intensity physical activity (32), changing the interpretation of findings. From the perspective of sedentary time, laboratory studies have revealed different metabolic pathways that are activated by physical inactivity and by reallocation of inactivity to light or to more intense activities (4,18,24). Because of these differences, it is important to consider not only the change but also the reallocation of sedentary time. In the present study, the intervention achieved the stated goals because the only significant group–time interactions during the whole day were seen in muscle inactivity time, sum of the five longest muscle inactivity periods, and light muscle activity time, which were the primary outcome variables. The main intervention message of reducing prolonged sedentary time and increasing nonexercise physical activity was thus well transferred to the muscle level.

The beneficial effects of reduced sedentary time have been suggested by cross-sectional and prospective studies, but evidence from long-term interventions is lacking. However, short-term experimental studies have induced a positive change on postprandial glucose and insulin responses with regular activity breaks of 1 min 40 s to 2 min, totaling approximately 30-min reallocation of sitting to ambulatory activity a day (12,30), a change of similar magnitude as that seen in this study. In the long term, a 2-h reduction in objectively measured sedentary time was associated with a favorable change in cardiometabolic biomarkers, reflecting 7% lower risk of cardiovascular events (21,39). When adjusted to similar wear time, 21% of the participants in the present study achieved a change of this magnitude. Although the results of this study show potential in terms of clinical significance, more research is needed to confirm the required minimum reduction in sedentary time yielding clinically significant end point in the long term.

The limitations of the present study include 1-d measurement periods and a systematic increase in the working time between the measurement days. This is likely due to longer duration of laboratory measurements on the first day, whereas on the second day, the participants had fewer questionnaires to fill in and instruction time was shorter. By having a control group and selecting only self-reportedly typical workdays in the analysis, the effect of between-day variability on the results was minimized. On the other hand, many participants were excluded on the basis of this criterion. These “atypical” days included, for example, organized exercise evenings at workplace, giving visitors a grand tour of the workplace, or staying at home because kids were sick. Because of device availability and study schedule, we were not able to replicate these measurements, resulting in reduced sample and limited power in some variables. During commuting, the control group showed a decrease in the longest inactivity periods compared with that in the intervention group. This may be explained by their more active commuting habits at baseline in combination with participation in a study entitled “Daily Activity” that included an informed consent, which potentially provided a cognitive intervention to the participants. On the other hand, there were no differences in the change in total muscle inactivity or activity parameters during commuting between the groups.

The main strength of this study was the use of EMG, which shows both the duration and intensity of muscle activity with high precision (36,37). Classifying the EMG signal, and accelerometer counts (22), merely by threshold values makes it impossible to determine whether the participants were actually sitting, standing, or moving. However, because the inactivity threshold was set individually to be between the values of sitting and standing, it is likely that the inactivity time presented in this study reflects the actual sitting time accompanied with complete inactivity periods from the quadriceps and hamstring muscles. Concerning associations between physical activity and health, the underlying enzymatic processes related to insulin resistance and substrate use are initiated by muscle activity, not physical impact, measured by accelerometer counts or the posture itself. The definition of sedentary behavior has gained wide attention, but consensus is yet to be reached (3,27,29,33). With these considerations in mind, the present study focused on complete inactivity and activity periods measured directly from the locomotor muscles, which we believe is the most insightful method for the measurement of physical inactivity and activity.

Only a small fraction (2.4%) of the muscle’s maximal voluntary strength capacity is used in normal daily life, and the main locomotor muscles are inactive almost 70% of the day. Tailored counseling was effective in decreasing muscle inactivity time by 33 min (4.5 pp), with concomitant increases in light muscle activity by 21 min (2.8 pp). This resulted in 13% increase in work time average %EMGMVC without increases in high-intensity EMG. These results reveal the potential of behavioral interventions targeting decreased sedentary time, rather than merely increased physical activity time, to decrease muscle inactivity time.

We are grateful to Marko Tanskanen, M.Sc., for his assistance in data analysis, to Olli Tikkanen, M.Sc., for his contribution to EMG analysis methods, and to Neil Cronin, Ph.D., for revising the language. Myontec Ltd. and Suunto Ltd. are acknowledged for their technical support.

This study was funded by the Finnish Ministry of Education and Culture (DNRO42/627/2010) and the Juho Vainio Foundation.

The authors have no financial conflict of interest to declare.

The results of the present study do not constitute endorsement by the American College of Sports Medicine.


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