Association of accelerometer measured sedentary behavior patterns with cognitive functions in sedentary office workers: Insights from SMART-STEP study

Abstract

BACKGROUND:

High levels of sedentary behavior in workplaces are currently recognized as an independent risk factor for cognitive dysfunction and poor mental health. However, sedentary patterns vary between workdays and non-workdays, which may influence cognitive functions.

OBJECTIVE:

The present study aimed to quantify and compare work and nonwork device-measured sedentary time (ST) and its association with cognitive function in Indian office workers.

METHODS:

In an ongoing randomized controlled trial (SMART-STEP), the baseline data of 136 full-time office workers, including accelerometer-measured sedentary patterns and cognitive functions, were analyzed. The ST was measured using a hip-worn accelerometer (Actigraph wGT3X-BT) for seven days, and executive functions were measured using computer-based tests. Linear regression models were employed to analyze the relationships between ST and executive function measures.

RESULTS:

The median daily ST of Indian office workers was 11.41 hours. The ST was greater on both workdays (11.43 hrs.) and non-workdays (11.14 hrs.) though different (F = 6.76, p = 0.001, η_p² = 0.032). Office workers accumulate more prolonged sitting bouts (+21.36 min) during work days than non-workdays. No associations between device-measured ST and executive functions were observed.

CONCLUSION:

Indian office workers exhibited high ST patterns, especially on workdays. Although lower than workdays, Indian office workers exhibited more ST patterns during non-workdays than did their Western counterparts. Culturally adaptable workplace and leisure time physical activity interventions are needed to address the high ST of Indian office workers.

Keywords

Prevalence sedentary time workplace sitting reaction times cognition Indian office worker

1 Introduction

Sedentary time (ST), defined as the time spent in seated or reclined postures while awake and requiring less energy expenditure (<1.5 metabolic equivalents), is increasingly ubiquitous in modern workspaces [1, 2]. Typical office workers spend 60–79% of their workday in ST, which is now identified as an independent health risk factor [3]. Epidemiological studies have shown that the accumulation of device-measured high volumes of ST is associated with early chronic disease risk, including diabetes, obesity, and cardiovascular and all-cause mortality, independent of the weekly physical activity goal [4, 5]. However, the current epidemiological evidence linking sedentary behavior to non-cardiometabolic health outcomes, such as cognitive dysfunction and mental health issues, remains at infancy [6, 7].

Executive function or cognitive control refers to a set of cognitive processes that enable individuals to plan, pay attention to, organize, and control specific capacities and behaviors [8]. Executive functions (the ability to respond quickly and accurately to appropriate stimuli while avoiding inappropriate stimuli) are perceived to be critical for maintaining work productivity and efficiency [9]. In short, the executive functions are significant drivers of work productivity in office workers. The findings from the observational studies remain equivocal in substantiating the potential link between the high sedentary behavior and cognitive functions including executive functions among office workers [7, 10]. Nevertheless, no studies from low-middle-income countries such as India exists. The association of sedentary behavior patterns and mediating factors with the cognitive functions may aid in designing and implement appropriate interventions to address the target behavior – increased sedentary behavior and reduced physical activity in Indian workspaces. Further examination of gendered patterns in sedentary behavior and physical activity may help explain the potential health inequalities between male and female office workers [11], among whom the female office workers are tend to share substantial portion of the Indian household work and caregiving while Indian men share least household work [12].

A sound understanding of analytic epidemiology, especially the relation between exposure (ST) and outcome (executive functions), could support the optimization of behavioral change interventions and community engagement [13]. Existing observational studies seeking to substantiate the association between ST and executive functions have used subjective measurements of ST, which are practical methods yet prone to biases [14, 15]. Both observational and empirical evidence targeting device-measured ST and exploring its relation to executive functions in older adults are accumulating; however, the relationship between device-measured ST and executive functions in adult office workers remains unclear [6 , 16–18]. Furthermore, the evidence from high-income countries is less applicable to policy-making in low-middle or low-income countries.

Hence, there is a need to explore the average device used to measure ST at workplaces and its association with executive functions in a cohort of office workers employed in administrative jobs whose ST is speculated to be high. We aimed to explore (1) the device-measured sedentary time and patterns in Indian office workers, (2) the difference in ST between workdays and non-workdays, and (3) the association between device-measured ST and executive functions in Indian office workers.

2 Methods

2.1 Design

This study involved analysis of baseline accelerometer data and executive functions of office workers, who participated in a cluster randomized controlled trial (SMART-STEP) [19]. This trial was approved by the Institutional Ethics Committee (IEC:749/2019) and was prospectively registered in the Indian trial registry (CTRI/2020/03/024138). The study was conducted as per the tenets of the Declaration of Helsinki [20] and reported based on Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [21].

2.2 Sample size

To find a low-moderate correlation Ho ρ² = 0.08 between device-measured ST and reaction times in office workers controlling for at least three predictors (age, body mass and education), we required 133 participants at a power of 80% and 95% significance [10]. We estimated the sample size using the test family ‘Fischer’s exact’ – linear multiple regression: random model using G*Power software (version 3.1.9.6, University of Kiel, Germany). The complete datasets of 136 Indian office workers from our cluster randomized controlled trial were analyzed.

2.3 Participants

The office workers of 13 administrative offices of a multifaceted university were recruited for a cluster randomized controlled trial, SMART STEP [19]. The sampling, interventions and randomization of the participants are depicted in earlier studies [19, 22]. The recruited participants participants were aged 30 – 50 full-time office workers who self-reported high sitting time (>6 hours) and were physically inactive (not complying with global recommendations of < 150 minutes of weekly moderate-vigorous physical activity and not meeting weekly dose of > 600 METmin/week as per the International Physical Activity Questionnaire [IPAQ]). Additionally, the participants did not have any chronic illnesses, such as cardiometabolic disorders (which could influence the participant’s ability to engage in sufficient physical activity) or psychiatric or mental health disorders (which could impact cognitive functions). The details of the study protocol can be found elsewhere [19]. All the participants provided written informed consent before participating in the study.

2.4 Procedure

The data analyzed and presented here were baseline variables of an ongoing randomized controlled trial. The full-time day-shift office workers of 13 university administrative offices were approached for potential participation in the study. Baseline variables, demographic (age, sex, education, experience), cardiorespiratory fitness, seven-day objectively measured sedentary patterns (Actigraph wGT3X-BT accelerometers) and executive functions (assessed through the Eriksen flanker test), were measured after voluntary consent was obtained from the participants. The outcomes assessed are described below.

2.5 Outcomes

Anthropometric data: Height was measured using a stadiometer, and weight was measured using a calibrated weighing scale without shoes to the nearest ≈1 cm and ≈0.1 kg, respectively. BMI was estimated using weight in kilograms divided by the square of height in meters.

2.5.1 Device-measured sedentary time

Actigraph wGT3X-BT (Actigraph, USA), an electronic device was used to measure objectively the movement patterns (ST, LIPA, MVPA) of the office workers. The actigraph was initialized at a 30 Hz low-pass filter to reduce the signal:noise ratio using Actilife version 6.7 software. The participants recived the accelerometer on their first day of their familiarization visit and instructed to wear the device for a maximum of seven days and to remove it only during water-based activities and before sleep. The participants wore the accelerometer with a nonelastic strap in the waistline with the accelerometer aligning to the midline of the right lower limb. The participants were instructed to log their on and off times with the accelerometers and sleep times. At the end of the seventh day of accelerometer return, the raw data were downloaded at 60 s epochs.agd files using the Actilife software. To be included for analysis, the participants should wear for atleast 600 minutes (>10 hours) per day and should have valid day wear for atleast four weekdays and one weekend. The software automatically validated the wear time using the Choi algorithm (zero output for at least 60 seconds classified as “nonwear period”) and estimated the ST (<151 counts per minute (CPM)), light intensity (151 to 2689 CPM), and moderate-vigorous intensity (>2690 CPM) using Freedson’s cutoff points [6, 23].

2.5.2 Executive functions

Executive functions were assessed through a computer-based Eriksen flanker task [24]. The participants were presented with five flanker arrows, with the first and last two arrows pointing in the same direction and the center arrow pointing in the same or opposite direction of the flanker arrows on either end. The participants were presented with five arrows representing congruent (←←← ←← ←, →→→ →) and incongruent (←← → ←←, →→ ← →→) stimuli on a laptop with Inquist software (version 6.6.1, Milliseconds, Seattle, WA, United States). With the central arrow as the target and the other alphabets on the sides as flankers, the participants were instructed to press either the ← or → buttons according to the direction of the central arrow displayed on the computer screen. The participants were presented with four practice trials with error feedback in the practice block. They were moved to the test block after securing a 75% pass in the practice block. Participants were presented with 20 actual trials (ten congruent and ten incongruent stimuli) in the test block. Each stimulus lasted 100 ms and was followed by an interval of 100 ms before the next stimulus. The raw and summary files were exported from the software in CSV format. The reaction time (ms) and accuracy (%) to overall stimuli were determined from the extracted CSV files by Inquist software.

2.5.3 Cardiorespiratory fitness

The aerobic capacity was estimated using an incremental submaximal step test called the Chester step test. The step height was chosen to be 20 cm for our sedentary participants. The test was conducted according to the instructions provided by Sykes et al. [25]. The Chester step test typically requires 10 minutes, increasing step cadence as directed by a metronome every two minutes for five beats per two minutes. Initially, the participants stepped up and down at 15 beats/min at the 1st level, 20 beats/min at the 2nd level, 25 beats/min at the 3rd level, and 30 beats/min at the 4th level and completed at 35 beats/min at the 5th level. The heart rate and the rate of perceived exertion (Borg’s RPE scale 6–20) were recorded at the end of each level. At any level, if the heart rate reached 80% of the age-predicted maximal heart rate (220 – age) or the participants could not comply with the cadence, the test was terminated, and the last level completed was considered for analysis. The heart rates at each level for 3–5 levels were plotted in the graphical sheet, and extending the best line of fit to the age-predicted maximal heart rate and cardiorespiratory fitness was estimated in ml/kg/min from the corresponding X-axis (supplementary file S2). The Chester step test has a moderate error (11–19%) in predicting cardiorespiratory fitness and may underestimate the latter by 1.6–1.8 ml/kg/min [26].

2.5.4 Confidentiality

All the interim data were collected by the blinded assessor (postgraduate with specialization in physical activity) of the primary study (cluster randomized controlled trial) and anonymized data entry was done in a password protected computer operated by the same blinded assessor. Only primary author, corresponding author and blinded assessor had the access to the computer. Appropriate consent were obtained from the participants to publish the anonymized data.

2.6 Statistical analysis

The normality of the ST and executive functions (reaction times and accuracy) was assessed using the Shapiro-Wilk test. Missing accelerometer data were imputed using multiple imputations (median prediction values in Microsoft Excel 2019 version). As the variables were not normally distributed, the above variables are expressed as medians±interquartile ranges and were log-transformed for other analyses. The discrete variables (sex and education) are expressed as frequencies. First, the mean daily percentage of ST was estimated from the accelerometers’ wear times, and the minutes and hours of ST were estimated. To account for variations in wear time during wake hours and because wear time affects the measured ST, we normalized the predictor variables to 16 hours of wear time [27]. $\begin{matrix} Normalized movement behavior time \\ \frac{observed movement behaviour time}{observed wear time} \\ \times assumed \max . wear time (16 hours) \end{matrix}$

Second, two-way analysis of variance was used to evaluate the differences in daily ST (hrs./day), total time spent in prolonged sitting (>30 min/bout), number of prolonged sitting bouts and number of sedentary breaks per day between weekday (workday vs nonworkday) and sex (men vs women). Interactions or main effects of total days and sex were analyzed, and the reported effect sizes were small (η_p² > 0.01), medium (η_p² > 0.06), and large (η_p² > 0.14) [28]. Bonferroni correction were used for post hoc comparisons, and Cohen’s d was used to calculate the effect size among the weekday group (total days, workday and non-workdays) and sex group (men and women), with cutoff values of 0.2 (small), 0.5 (medium) and 0.8 (large) [29].

Third, the association between the normalized device-measured ST (hrs.) Moreover, executive functions (reaction times and accuracy) were analyzed with (multivariate) linear regression models. We used devices that measured ST during weekdays as predictors (exposure), reaction times, and accuracy, with overall stimuli as dependent (outcome) variables. In addition, we included age, sex, BMI, education level, experience and cardiorespiratory fitness as confounding factors (random intercepts) for the linear regression models. The above potential confounders were determined using Directed Acyclic Graph (DAGitty software, version 3.0, 2016, Netherlands) and are provided in supplementary file S3 [30]. We constructed two models: Model 1 was unadjusted, and Model 2 was adjusted for the above confounders (age, sex, BMI, education level, experience and cardiorespiratory fitness), which could influence ST and executive functions. Each model is presented with estimates (β), 95% confidence intervals (CIs) and significance (p)<0.05. Furthermore, we used a network analysis model to explore the interaction between sedentary patterns and executive function variables. All the statistical analyses were performed with R-based statistical software (JASP, version0.17.1.)

3 Results

Of 15 administrative office clusters of 228 participants, 60% (n = 136) were eligible for further assessment of accelerometer and executive functions. Few participants (n = 22, 11%) had self-reported optimal ST (<6 hours per day) or moderate-vigorous physical activity (>150 minutes and > 600 METmin/week as per the IPAQ. Of the 136 participants, 124 had valid accelerometer data (≈88%), while one participant’s Eriksen flanker task data were missing (n = 135, 99%). Figure 1 shows the identification, inclusion and analysis of the participants.

Fig. 1

STROBE flowchart showing the identification, inclusion and analysis of the participants. IPAQ = International physical activity questionnaire, MET = metabolic equivalent, MVPA = moderate–vigorous physical activity (minutes), ST = sedentary time (hours), STROBE = Strengthening the Reporting of Observational Studies in Epidemiology.

3.1 Baseline characteristics

Table 1 shows the baseline characteristics of the participants included in the study. Two-thirds of the 136 participants were women with a postgraduate degree and were designated junior executives (entry level with 1–8 years of administrative service). The median age of the participants was 35 years (IQR 29, 42), with the majority being female (n = 89; 65.20%). Most participants were junior executives (n = 78, 57%). The average compliance was found to be 70% with significant difference between weekday (756 min/day, 79%) and weekend (487 min/day, 51%).

Table 1
Characteristics of the participants included in the study

Demographics Male Female Significance

Age (years) 35 (29–43) 36 (30 – 41.75) –

Experience (years) 8 (4–14) 7 (3 – 11) 0.276

Body mass index 25.51 (23.05, 28.12) 24.77 (21.51, 28.15) 0.167

Device measured ST patterns Overall Weekday Weekend Significance

Device-measured activity levels (raw data) Wear time (%) 69.02 (52.19, 76.15) 78.75 (73.33, 88.75) 50.73 (40.94, 65.96) 0.153

Wear time (min/day) 662.5 (501, 731) 756 (704, 852) 487 (393, 633.25) 0.073

Sedentary time (%) 71.26 (64.7, 76.16) 71.45 (65.12, 75.72) 69.62 (63.69, 74.14) 0.342

Sedentary time (hrs/day) 7.57 (5.84, 8.74) 9.16 (8.18, 9.81) 5.87 (4.24, 7.08) 0.176

Normalized sedentary time (hrs/day) 11.41 (10.43, 12.12) 11.43 (10.35, 12.19) 11.14 (10.18, 11.36) 0.243

Sedentary bouts/breaks Mean bout duration (min) 29.11 (22.43, 42.21) 38.45 (24.21, 48.23) 22.66 (16.36, 31.09) 0.092

Total time in sedentary bouts (> 30 min) 328.17 (287.34, 369.36) 334.40 (250.15, 378.95) 296.19 (271.47, 326.34) 0.318

Total no. of breaks from sedentary time 8 (6.37, 9.36) 7 (4–8) 11 (6–14) 0.218

Executive function tests Overall Weekday Weekend Significance

Overall stimuli Reaction times (ms) 562.16 (510.58, 735.24) 527.52 (505.36, 805.08) 566. 83 (505.36, 799.55) 0.124

Accuracy (%) 100 (99, 100) 100 (98, 100) 100 (100, 100) 0.872

Congruent stimuli Reaction times (ms) 521.05 (481.67, 767.57) 517.38 [490.00, 798.38] 538.59 (481.67, 763.67) 0.273

Accuracy (%) 100 (100, 100) 100 (100, 100) 100 (100, 100) 0.993

Incongruent stimuli Reaction times (ms) 553.38 [490.00, 798.38] 528.75 (487.53, 645.13) 584.75 (511.57, 811.75) 0.153

Accuracy (%) 100 (99, 100) 100 (98, 100) 100 (99, 100) 0.991

Demographics	Male	Female	Significance
Age (years)	35 (29–43)	36 (30 – 41.75)	–
Experience (years)	8 (4–14)	7 (3 – 11)	0.276
Body mass index	25.51 (23.05, 28.12)	24.77 (21.51, 28.15)	0.167
Device measured ST patterns	Overall	Weekday	Weekend	Significance
Device-measured activity levels (raw data)	Wear time (%)	69.02 (52.19, 76.15)	78.75 (73.33, 88.75)	50.73 (40.94, 65.96)	0.153
	Wear time (min/day)	662.5 (501, 731)	756 (704, 852)	487 (393, 633.25)	0.073
	Sedentary time (%)	71.26 (64.7, 76.16)	71.45 (65.12, 75.72)	69.62 (63.69, 74.14)	0.342
	Sedentary time (hrs/day)	7.57 (5.84, 8.74)	9.16 (8.18, 9.81)	5.87 (4.24, 7.08)	0.176
Normalized sedentary time (hrs/day)	11.41 (10.43, 12.12)	11.43 (10.35, 12.19)	11.14 (10.18, 11.36)	0.243
Sedentary bouts/breaks	Mean bout duration (min)	29.11 (22.43, 42.21)	38.45 (24.21, 48.23)	22.66 (16.36, 31.09)	0.092
	Total time in sedentary bouts (> 30 min)	328.17 (287.34, 369.36)	334.40 (250.15, 378.95)	296.19 (271.47, 326.34)	0.318
	Total no. of breaks from sedentary time	8 (6.37, 9.36)	7 (4–8)	11 (6–14)	0.218
Executive function tests	Overall	Weekday	Weekend	Significance
Overall stimuli	Reaction times (ms)	562.16 (510.58, 735.24)	527.52 (505.36, 805.08)	566. 83 (505.36, 799.55)	0.124
	Accuracy (%)	100 (99, 100)	100 (98, 100)	100 (100, 100)	0.872
Congruent stimuli	Reaction times (ms)	521.05 (481.67, 767.57)	517.38 [490.00, 798.38]	538.59 (481.67, 763.67)	0.273
	Accuracy (%)	100 (100, 100)	100 (100, 100)	100 (100, 100)	0.993
Incongruent stimuli	Reaction times (ms)	553.38 [490.00, 798.38]	528.75 (487.53, 645.13)	584.75 (511.57, 811.75)	0.153
	Accuracy (%)	100 (99, 100)	100 (98, 100)	100 (99, 100)	0.991

ST = sedentary time. The data presented here is median (interquartile range, 25th and 75th percentile).

3.2 Sedentary patterns in Indian office workers

percentage of total days with daily STs among Indian office workers was 71.45% [25th, 75th percentiles, 65.12, 75.72]. Table 1 shows the device-measured STs during workdays (71.29% [65.49, 75.42]) and non-workdays (69.62% [63.69, 74.14]). With normalized wear times, the above percentage translates to a total daily ST of 11.41 hrs. [10.43, 12.12], a workday ST of 11.43 hrs. [10.35, 12.19] and a nonworkday ST of 11.14 hrs. [10.18, 11.836]. This translates to a daily median sitting time of 659.56 min.

3.3 Differences in ST among workdays, non-workdays and sex

Two-way ANOVA revealed no significant interaction effect between day (overall, weekday and weekend) and sex (male and female) (F_2,1 = 0.005, p = 0.995, η_p² =0.001) on mean daily ST (Supplementary file S4). However, we found a significant main effect for workday and nonworkday differences (F = 6.76, p = 0.001, η_p² = 0.032); however, there was a weak effect. No significant main effect was found for sex differences (F = 1.09, p = 0.297, η_p² = 0.003). Post hoc analysis within the workday and non-workdays revealed that participants showed a lower ST during non-workdays (- 0.51 hrs. [95% CI 0.13, 0.89], d = 0.40, p = 0.005) and (- 0.52 hrs. [95% CI 0.14, 0.91], d = 0.41, p = 0.004) between total days and workdays, respectively. Similarly, we found a significant difference in prolonged sitting bouts between workdays and non-workdays (F = 5.54, p = 0.007, η_p² = 0.017). Post hoc analysis revealed that participants spent less time in prolonged sitting bouts during nonwork days than during work days (–11 min [95% CI –39.97, 17.96], d = 0.22, p = 0.045). Furthermore, we found significant differences in sex over time during prolonged sedentary bouts, with female office workers having more prolonged sitting bouts (21.36 min [95% CI 1.72, 41.01], d = 0.22, p = 0.033) than males. However, we did not find significant differences in the duration of individual sedentary bouts or the number of sedentary breaks between weekdays or between sexes. Figure 2 shows the significant differences in the ST and the total time spent in prolonged sitting (>30 min/bout) between total days, workdays and non-workdays.

Fig. 2

Violin box plots demonstrating the significant differences in sedentary patterns (a) sedentary time and (b) prolonged sedentary bouts (>30 minutes) between workdays and non-workdays. ^*p < 0.05, ^**p < 0.01.

3.4 Associations between sedentary time and cognitive function

Table 2 shows the association between the device-measured ST and the response to the Eriksen flanker task in both adjusted and unadjusted models. However, for every hour of sedentary time measured by the additional device, the reaction times were reduced by ≈20 ms, and the association did not reach statistical significance. The median accuracy for total stimuli remained high (100±0%) for almost all the participants. We did not find any statistically significant associations between the device-measured total day’s ST during workdays or non-workdays and the reaction times or accuracy to Flanker stimuli. Even after adjusting for potential confounders (age, sex, BMI, education, cardiorespiratory fitness), the association between device-measured ST and executive functions remained nonsignificant.

Table 2
Association of device-measured sedentary time with executive functions (reaction times and accuracy). Unadjusted and adjusted models for confounding factors (age, body mass, sex, experience, education and cardiorespiratory fitness) are shown separately

Accelerometer measured the sedentary pattern Overall stimuli Congruent stimuli Incongruent stimuli

Reaction times (ms) Accuracy (%) Reaction times (ms) Accuracy (%) Reaction times (ms) Accuracy (%)

Overall Sedentary time (min/day) Unadjusted model –0.267 (0.059) 0.036 (0.466) –0.094 (0.057) 0.011 (0.825) –0.089 (0.072) 0.064 (0.198)

Adjusted model –0.081 (0.083) 0.035 (0.477) –0.078 (0.095) 0.005 (0.927) –0.082 (0.080) 0.067 (0.185)

Time in prolonged sedentary bout (>30 min) Unadjusted model –0.071 (0.152) 0.041 (0.414) –0.082 (0.098) 0.040 (0.425) –0.057 (0.250) 0.019 (0.696)

Adjusted model –0.064 (0.178) 0.040 (0.433) –0.069 (0.145) 0.040 (0.427) –0.058 (0.219) 0.017 (0.738)

Total sedentary breaks Unadjusted model –0.045 (0.364) 0.014 (0.783) –0.065 (0.193) –0.087 (0.078) –0.022 (0.655) –0.046 (0.364)

Adjusted model –0.030 (0.520) 0.019 (0.703) –0.053 (0.256) –0.083 (0.094) –0.003 (0.941) –0.047 (0.283)

Workday Sedentary time (min/day) Unadjusted model –0.068 (0.430) –0.025 (0.769) –0.057 (0.506) 0.002 (0.998) –0.072 (0.406) –0.038 (0.657)

Adjusted model –0.055 (0.500) –0.011 (0.903) –0.048 (0.562) 0.011 (0.900) –0.056 (0.497) –0.030 (0.740)

Time in prolonged sedentary bout (>30 min) Unadjusted model –0.043 (0.621) 0.013 (0.878) –0.041 (0.233) 0.009 (0.914) –0.046 (0.619) 0.029 (0.736)

Adjusted model –0.011 (0.098) 0.019 (0.830) –0.007 (0.753) 0.011 (0.901) –0.018 (0.128) 0.034 (0.708)

Total sedentary breaks Unadjusted model –0.073 (0.400) 0.039 (0.648) –0.083 (0.336) –0.074 (0.394) –0.058 (0.505) –0.009 (0.916)

Adjusted model –0.017 (0.834) 0.044 (0.619) –0.033 (0.687) –0.077 (0.385) 0.002 (0.982) –0.008 (0.930)

Nonworkday Sedentary time Unadjusted model –0.158 (0.066) 0.106 (0.220) –0.165 (0.054) 0.063 (0.464) –0.154 (0.073) 0.146 (0.090)

Adjusted model –0.148 (0.073) 0.092 (0.288) –0.148 (0.070) 0.044 (0.609) –0.152 (0.067) 0.144 (0.101)

Time in prolonged sedentary bout (>30 min) Unadjusted model –0.133 (0.123) 0.142 (0.099) –0.152 (0.077) 0.105 (0.224) –0.103 (0.233) 0.109 (0.209)

Adjusted model –0.106 (0.215) 0.121 (0.174) –0.113 (0.179) 0.093 (0.296) –0.091 (0.286) 0.092 (0.310)

Total sedentary breaks Unadjusted model –0.074 (0.393) 0.051 (0.552) –0.098 (0.256) –0.073 (0.401) –0.044 (0.608) 0.012 (0.892)

Adjusted model –0.072 (0.385) 0.057 (0.515) –0.097 (0.239) –0.060 (0.488) –0.043 (0.605) 0.006 (0.944)

Accelerometer measured the sedentary pattern	Overall stimuli	Congruent stimuli	Incongruent stimuli
Overall	Sedentary time (min/day)	Unadjusted model	–0.267 (0.059)	0.036 (0.466)	–0.094 (0.057)	0.011 (0.825)	–0.089 (0.072)	0.064 (0.198)
		Adjusted model	–0.081 (0.083)	0.035 (0.477)	–0.078 (0.095)	0.005 (0.927)	–0.082 (0.080)	0.067 (0.185)
	Time in prolonged sedentary bout (>30 min)	Unadjusted model	–0.071 (0.152)	0.041 (0.414)	–0.082 (0.098)	0.040 (0.425)	–0.057 (0.250)	0.019 (0.696)
		Adjusted model	–0.064 (0.178)	0.040 (0.433)	–0.069 (0.145)	0.040 (0.427)	–0.058 (0.219)	0.017 (0.738)
	Total sedentary breaks	Unadjusted model	–0.045 (0.364)	0.014 (0.783)	–0.065 (0.193)	–0.087 (0.078)	–0.022 (0.655)	–0.046 (0.364)
		Adjusted model	–0.030 (0.520)	0.019 (0.703)	–0.053 (0.256)	–0.083 (0.094)	–0.003 (0.941)	–0.047 (0.283)
Workday	Sedentary time (min/day)	Unadjusted model	–0.068 (0.430)	–0.025 (0.769)	–0.057 (0.506)	0.002 (0.998)	–0.072 (0.406)	–0.038 (0.657)
		Adjusted model	–0.055 (0.500)	–0.011 (0.903)	–0.048 (0.562)	0.011 (0.900)	–0.056 (0.497)	–0.030 (0.740)
	Time in prolonged sedentary bout (>30 min)	Unadjusted model	–0.043 (0.621)	0.013 (0.878)	–0.041 (0.233)	0.009 (0.914)	–0.046 (0.619)	0.029 (0.736)
		Adjusted model	–0.011 (0.098)	0.019 (0.830)	–0.007 (0.753)	0.011 (0.901)	–0.018 (0.128)	0.034 (0.708)
	Total sedentary breaks	Unadjusted model	–0.073 (0.400)	0.039 (0.648)	–0.083 (0.336)	–0.074 (0.394)	–0.058 (0.505)	–0.009 (0.916)
		Adjusted model	–0.017 (0.834)	0.044 (0.619)	–0.033 (0.687)	–0.077 (0.385)	0.002 (0.982)	–0.008 (0.930)
Nonworkday	Sedentary time	Unadjusted model	–0.158 (0.066)	0.106 (0.220)	–0.165 (0.054)	0.063 (0.464)	–0.154 (0.073)	0.146 (0.090)
		Adjusted model	–0.148 (0.073)	0.092 (0.288)	–0.148 (0.070)	0.044 (0.609)	–0.152 (0.067)	0.144 (0.101)
	Time in prolonged sedentary bout (>30 min)	Unadjusted model	–0.133 (0.123)	0.142 (0.099)	–0.152 (0.077)	0.105 (0.224)	–0.103 (0.233)	0.109 (0.209)
		Adjusted model	–0.106 (0.215)	0.121 (0.174)	–0.113 (0.179)	0.093 (0.296)	–0.091 (0.286)	0.092 (0.310)
	Total sedentary breaks	Unadjusted model	–0.074 (0.393)	0.051 (0.552)	–0.098 (0.256)	–0.073 (0.401)	–0.044 (0.608)	0.012 (0.892)
		Adjusted model	–0.072 (0.385)	0.057 (0.515)	–0.097 (0.239)	–0.060 (0.488)	–0.043 (0.605)	0.006 (0.944)

Note: The values are expressed as estimates (β) and levels of significance (p < 0.05). Only the predictors (device-measured sedentary time, time in prolonged sedentary bouts and total breaks per day during work and nonwork days) were included in the unadjusted model, while in the adjusted model, the covariates (age, body mass, cardiorespiratory fitness, experience, education and sex) were adjusted.

Further network analysis revealed ten nodes with lower sparsity. There was a lower inverse association between sedentary time, prolonged sitting bouts and reaction times; however, the closeness remained nonsignificant. Figure 3 depicts the network plot demonstrating the interaction between the sedentary pattern and the executive function variables.

Fig. 3

Network plot showing the interaction between sedentary behavior and executive function variables. Green spheres represent executive function variables, and pink spheres represent sedentary pattern variables. The orange lines represent positive associations, while the blue lines represent negative associations. The distance between spheres represents the closeness of the association between sedentary patterns and EFs.

4 Discussion

This cross-sectional analysis explored ST among Indian office workers and investigated its association with executive functions. The mean daily ST was estimated to be 65–76%, with more sedentary patterns observed during workdays than on non-workdays. We did not observe any sex differences in daily ST during work or non-workdays. In addition, our study showed no significant association between device-measured ST and executive functions among Indian office workers.

4.1 Sedentary patterns in Indian office workers

The participants in our study were from the administrative offices of a multifaceted university. Their roles involve diverse tasks such as billing and financial transactions with clients, scheduling academic commitments, and designing and marketing. Majority (85–90%) of their work hours were spent sitting at computers. The average compliance to accelerometer wear was 11 hours (weekdays 12.6 hours and weekend 8.1 hours) which is significantly when compared to literature from high income countries which found a higher compliance (13.9 – 14.1 hours/day) [31, 32]. High education level, lack of awareness, fear of being watched, demanding or challenging task and higher sitting time are perceived to be the major barriers to wear accelerometers [31, 33]. Our study revealed that Indian office workers had a daily ST of 66–75% during workdays, comparable to the sedentary patterns observed among office workers from high-income countries [34, 35]. However, the observed daily ST (69%) was much greater than that on workdays reported in other studies (54–66%) reviewed by Prince et al. (2019) [36]. Additionally, the ST in our Indian office workers was much greater than that in Japanese office workers (57.5%), as observed by Kurita et al. 2019. However, the study administered device-based ST measurements in a heterogeneous working population [37]. Furthermore, the ST during nonwork days observed in our study was 2–3% less than the workday ST (64–75%). The median normalized ST duration of Indian office workers was 660 min/day, substantially greater than that of office workers in high-income countries (593.1 min) [36].

Furthermore, Indian office workers accumulate significantly more prolonged sitting bouts (>30 minutes) than do their Western counterparts. A lack of sports or movement as a cultural and social norm and the absence of organizational or national-level policies might be the reasons for the accumulation of high levels of sedentary patterns in the workplaces of low-middle income countries [23, 38]. The results of the present study cannot necessarily be generalized, as participants were recruited based on self-reported ST and moderate-vigorous physical activity. We found that only a few office workers were excluded based on high moderate-vigorous physical activity and low ST (n = 22, 11%). The exclusion criterion of having low ST (<6 hours per day) initially proposed was not common among Indian office workers. Hence, this threshold likely did not exclude many who work in office-based occupations [19].

In our study, Indian office workers were relatively less sedentary (–30 min) during non-workdays than during workdays, which agrees with previous Asian studies [34, 37]. However, an existing Spanish study reported that office workers from high-income countries spend more ST outside working hours (non-workdays) [35]. However, the above study by van Dommelen et al. [35] objectively measured sedentary behaviors among a mixed population of blue and white-collar workers from the construction and financial sectors, respectively. Nevertheless, a systematic review by Prince et al. [36] revealed that office workers spent 55–60% of their day sedentary outside working hours while spending substantially more ST during work days (70–80%).

4.2 Differences in weekly sedentary patterns among Indian office workers

Our study failed to find significant sex differences in device-measured ST time during the total days, workdays or non-workdays. The sex differences in device-measured ST in contemporary studies remain equivocal. While Toomingas et al. [39] reported that Swedish women call center workers indulging in 11% more ST than their male counterparts, Johansson et al. [11] reported that 55% of men and 34% of women employed at Swedish government transport offices spent 75% of their work time sedentary. However, the devices used for the quantification of ST (portable inclinometer [39] and accelerometer, Actigraph – GT3X [11]) and the nature of the work (call center [39] and transport administrative office [11]) differed among both Swedish studies. While previous studies [35, 37] have shown that men and women office workers spend 70% and 67.4% of their workdays, respectively, in the ST, we found similar statistics, i.e., men and women office workers spend 68.5% and 69.19%, respectively, of their workdays in the ST [35].

4.3 Association of sedentary behavior with cognitive functions

Linear regression analyses revealed no significant association between the ST and EFs, which is consistent with recent observational studies exploring a similar association [10, 40]. The findings from systematic reviews and meta-analyses that explored the association between ST and cognitive functions remain equivocal [7 , 15]. We agree that high ST patterns, including median time in prolonged sitting bouts alone, may not be an important predictor of executive functions in the workplace when considering other contributing factors, such as work nature, cognitively demanding tasks even after controlling for age, sex, BMI, education and cardiorespiratory fitness [10]. All our young participants were already proficient in computer-based tasks; hence, they had high executive functions at baseline, and their nonsignificant association with daily ST was not surprising [10, 40]. Further Flanker task chosen to measure executive functions may not truly assess “task irrelevant stimuli rather participants tend to choose “task relevant stimuli” [41]. As the task chosen may not be demanding and irrelevant to the administrative work, the association of accelerometer derived ST and PA patterns with executive functions might have insignificant. Recent recommendations are skeptical about using Flanker task for measuring domain-general inhibitory control among participants with higher order thought process i.e., office workers [42]. Although ST was found to be detrimental to executive functions in acute experimental trials [43], pragmatic empirical studies failed to demonstrate a significant association, probably due to potential confounding factors such as work prioritization, cognitive task demands, social relationships, the work environment, and organizational policies regarding movement, sleep and diet behaviors. It is challenging to control these factors in an actual workplace setting [44].

4.4 Strengths and limitations

To the best of our knowledge, our study is the first in India to explore device-measured sedentary time and its potential association with executive functions among Indian office workers. As India provides the world’s second-largest formal labor workforce with 500 million and 12 million new additions yearly, India would be one of the largest countries producing skilled labor because of amended educational policies [45]. According to the organizational consulting firm Korn Ferry, India is expected to have a surplus of 245 million skilled laborers by 2030, even though the majority of advanced and emerging economies are anticipated to face a shortage of skilled personnel during this period, and substantial migration of the skilled Indian workforce is expected to occur by 2030 [46]. As skilled labor and computerized jobs increase in the Indian labor market, an increase in ST and resulting chronic disease risk becomes inevitable. This study may raise an alarm and urge Indian public experts and firms hiring Indian office workers to design and implement cost-effective, culturally adaptable strategies to reduce ST in workplaces that offer desk-based jobs.

Few limitations of our study that are worth mentioning are: (1) The primary limitation is that the data analyzed belongs to the participants who were recruited for the cluster randomized controlled trial (selection bias was inevitable). Though this might have introduced a chance of selection bias, the inclusion criteria (the participants should have high ST and low moderate-vigorous physical activity) which is common for all the desk-based office workers across globe makes the results still generalizable. As only a few participants (n = 22, 11%) were excluded, we feel our data are still important because of the high ST in Indian office settings; (2) the participants included in the present study belong to a randomised controlled trial and belong to a single university. The narrow and smaller sample size may limit the generalization to other workplaces of a densely populated country; (3) the potential confounders of cognitive functions, namely, controllable factors (sleep and diet) and noncontrollable factors (work prioritization, task load, work nature and schedule, organizational policies on breaks and movement, infrastructure), did not account for deriving device-measured ST or its association with executive functions; and (4) only one domain (executive functions) of the array of cognitive functions was assessed in our cohort of office workers. It is still unclear whether the device-measured ST may be related to cognitive domains (attention, memory, problem-solving) other than executive functions.

4.5 Implications for occupational health practice

Contemporary evidence emerging from high-income countries indicates the adverse health effects of high ST in contemporary workspaces [47]. Anecdotal evidence indicates that high ST negatively affects cognitive functions, eventually leading to poor work productivity in workspaces [15, 48]. Our findings remain unconvincing in proving the adverse association of workplace ST with cognitive functions among sedentary office workers. The above finding is critically important for occupational health experts when framing occupational wellness programs, who should consider ST a mediating factor for cognitive function rather than primary risk. Other determinants, such as workload, task priority, sleep and mood, should be considered when designing workplace wellness programs [49].

Further studies assessing other components of cognitive functions, such as memory problem solving, are warranted. The study revealed a high amount of sitting time for Indian office workers on both weekdays (working hours) and weekends (nonworking hours), unlike their counterparts from high-income countries. Occupational health policies are needed to minimize sitting time and mitigate early cardiometabolic risk [50].

4.6 Conclusions

Indian office workers exhibit high sedentary time (≈70% of their daily wake hours), and most of the time (≈70%) is accrued in prolonged sedentary bouts (>30 minutes). The present study revealed no associations between sedentary time and its patterns and cognitive functions. Culturally relevant/applicable interventions should be developed and implemented to mitigate health risks associated with high sedentary time in Indian office-based workers.

Abbreviations used in the study

BMI

Body mass index

CPM

Count per minute

MET

Metabolic equivalent

Physical activity

RPE

Rate of perceived exertion

Sedentary behavior

Sedentary time

Ethical approval

This trial was approved by the Kasturba Medical College & Kasturba Hospital Institutional Ethics Committee (IEC:749/2019) and prospectively registered in Clinical Trial Registry of India (CTRI/2020/03/024138). All the participants of the study provided written informed consent for the study. The study was conducted as per the tenets of Declaration of Helsinki.

Footnotes

Acknowledgments

The authors express their sincere gratitude to the desk-based office workers who willingly volunteered for the rigorous seven days of accelerometer and cognitive measurements, despite their demanding schedules. Special thanks are extended to Manipal Academy of Higher Education for providing a conducive research environment that facilitated the smooth execution of this study.

Conflicts of interest

The authors declare no conflicts of interest associated with this research study. There are no financial, personal, or professional relationships that could potentially bias or influence the outcomes, interpretations, or conclusions presented in this manuscript.

Funding

The lead author BC received partial funding support (innovation grant) from the Indian Association of Physiotherapists. It is important to note that the funder played no role in the design, conduct, or findings of the study, ensuring the independence and integrity of the research process. The authors gratefully acknowledge the support provided by the Indian Association of the grant. However, the funding support did not cover open access publishing of this research.

Use of AI tools declaration

The authors declare that AI tools were not used for any form of the manuscript presented here.

Authors contribution

CRR, AA, and AP conceptualized the idea for the study. BC and CRR conducted data analysis and drafted the initial manuscript. AP and AA contributed extensively by providing critical insights, conducting additional data analysis, and editing the language of the manuscript, thereby significantly enhancing its quality. All authors have reviewed and approved the final version of the manuscript.

The supplementary material is available in the electronic version of this article: .

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