The interplay between lying, sitting, standing, moving, and walking on obesity risk in older adults: A compositional and isotemporal substitution analysis

doi:10.21203/rs.3.rs-4906385/v1

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The interplay between lying, sitting, standing, moving, and walking on obesity risk in older adults: A compositional and isotemporal substitution analysis

https://doi.org/10.21203/rs.3.rs-4906385/v1

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Introduction: Obesity in older adults is linked to various chronic conditions and decreased quality of life. Traditional physical activity guidelines often overlook the specific postures and movements that older adults engage in daily. This study aims to explore the compositional associations between posture-specific behaviours and obesity risk in younger (M = 67.35 ± 2.03 years) and older (M = 75.73 ± 4.17 years) seniors and investigate the differences in BMI associated with replacing time spent in lying, sitting and standing with moving or walking.

Methods: The study involved 309 older adults aged 65 and above from Czech Republic. Participants' movement behaviours, including lying, sitting, standing, moving, and walking, were measured using accelerometers. The data were analysed using compositional data analysis (CoDA) and isotemporal substitution models to assess the impact of reallocating time between different activities on body mass index (BMI).

Results: The younger group engaged in more overall movement (193.84 min/day vs. 172.41 min/day) and walking (92.15 min/day vs. 76.62 min/day) than the older group. Significant BMI increases were observed when 30 minutes were reallocated from movement to lying, sitting, or standing (up to +3.306 kg/m²), while reallocating the same amount of time from lying, sitting, or standing to movement led to BMI reductions (up to -2.537 kg/m²). In the older group, reallocating time from slow walking to lying or sitting led to BMI increases (up to +1.855 kg/m²), while increasing time spent slow walking at the expense of lying or sitting reduced BMI (up to -0.949 kg/m²).

Conclusions: Promoting movement and walking, particularly slow walking, is crucial in managing obesity risk in older adults. The findings emphasize the importance of reducing sedentary time and encouraging low-intensity physical activity tailored to the capabilities of seniors, especially those aged 70+, to effectively mitigate obesity risk.

backward pivot coordinates

slow walking

standing

sitting

body mass index.

Obesity is a pervasive public health issue that affects individuals of all ages, including older adults [1]. In older adults, obesity is associated with a higher risk of chronic diseases [2], multimorbidity, functional decline [3], increased risk and severity of falls [4], and diminished quality of life [5]. Effectively prevent and manage obesity in older adults, it is crucial to adopt a physically active lifestyle that helps maintain muscle mass and increase energy expenditure.

During the past decade, a more comprehensive approach for describing a physical active lifestyle has been advocated, addressing various movement behaviours throughout the day, including physical activity, sedentary time, and sleep [6]. These behaviours are naturally interrelated and optimizing the balance between them in 24-hour cycles is essential for health and well-being [7].

Prior articles focused on 24-hour behaviour primarily from the perspective of intensity. This entailed distinguishing between Sedentary Behaviour (SB), Light Physical Activity (LPA), and Moderate-to-Vigorous Physical Activity (MVPA), and sleep. However, these intensity-specific classifications fail to accurately account for the postures in which these behaviours are performed. For instance, assessing sedentary behaviour based solely on intensity might overlook postures like sitting, reclining, or even quiet standing. Yet, specific postures significantly influence energy expenditure, muscle function, and metabolic responses. In contrast to the traditional intensity-based approach, posture-specific analyses offer a more adaptable strategy, catering to the unique abilities and limitations of older populations. This makes such an approach especially valuable when designing interventions for seniors, as it addresses the challenge of incorporating physical activity within their capabilities.

Moreover, focusing on posture-specific behaviour also helps reduce the bias inherent in the intensity-based approach. Accelerometers, commonly used in intensity-based studies, do not distinguish between sitting and standing still, leading to measurement bias for SB and LPA [8]. This bias can skew results in favour of the relative contribution of MVPA over other movement behaviours. By employing posture-specific measurements, future studies can provide a more accurate and unbiased representation of all movement behaviours, improving the overall quality of evidence [9].

As the global population continues to age, with the number of individuals aged 60 and over projected to double by 2050 [10], this approach becomes increasingly justified. Older individuals often have varying degrees of mobility and different physical limitations compared to younger or more active populations [11]. Additionally in older populations, it is crucial to monitor walking, alongside other activities as it is a fundamental activity that significantly impacts overall health, mobility, and independence in seniors [12]. By focusing on posture and ensuring that walking is adequately incorporated and monitored, healthcare providers can tailor physical activity recommendations and interventions that are both safe and effective for older adults.

This paper seeks to delve into the interplay among lying down, sitting, standing, moving, and walking. Specifically, we will analyse posture-specific behaviours and their connection to obesity risk in younger and older groups of elderly. Through three compositions with 4, 5, and 6 components, each progressively detailing specific activities, we can comprehensively assess variations in daily activities and their health implications. The first composition looks at lying, sitting, standing, and any form of movement. The second composition further breaks down movement into non-walking movement and walking. The third composition then differentiates walking into slow and fast walking. This approach allows for a nuanced understanding of how each specific activity impacts BMI and obesity risk.

Through compositional and isotemporal substitution analyses, we intend to provide insightful perspectives for crafting targeted interventions tailored to the distinct needs of seniors. Therefore, the aims of this study are to:

A) describe posture-specific behaviours (lying, sitting, standing, moving and slow and fast walking) in younger and older seniors;

B) examine the compositional associations between the 24h posture-specific behaviours and BMI; and

C) investigate the differences in BMI associated with replacing time spent in lying, sitting and standing with moving or walking.

Design and participants

The research protocol received ethical clearance from the Institutional Research Ethics Committee at the Faculty of Physical Culture, Palacký University Olomouc, under protocol number 10/2021. Participants were engaged through telephonic communication, wherein they were comprehensively briefed about all facets of the study protocol. A pivotal aspect of the study's participation procedure involved procuring written informed consent from each participant. The cohort under investigation comprised senior citizens, aged 65 years and above, and the data collection transpired during the period of enforced social distancing measures instated by the Czech Republic's government in response/the COVID-19 pandemic. A total of 382 older adults were recruited within the timeframe of November 2021/December 2023. Regrettably, 73 participants did not adhere to the requisite criteria for a valid measurement, thereby resulting in a final sample of 309 older adults hailing from the Moravian region of the Czech Republic.

Measurements

Quantification of Movement Behaviours

In quantifying movement behaviour, three accelerometers of distinct types were employed. The initial accelerometer, the ActiGraph wGT3X-BT (manufactured by ActiGraph Ltd., Pensacola, Florida, US), was affixed to the participants' right hip and functioned at a data collection frequency of 30 Hz, with a dynamic range spanning ± 8 g. Initialization of the ActiGraph device was executed, and data retrieval was facilitated utilizing ActiLife version 6.13.4 software. The unprocessed data was archived in the raw .gt3x format. This particular accelerometer furnished data pertaining to both the acceleration and movement of the torso.

Two additional accelerometers, the Axivity AX3 devices (developed by Axivity Ltd., Newcastle, UK), were secured to the right thigh and non-dominant wrist. These devices operated at a data sampling frequency of 25 Hz and boasted a dynamic range spanning ± 8 g. Setup and data acquisition for the Axivity devices were accomplished via the utilization of OmGui open-source software (OmGui Version 1.0.0.37, Open Movement, Newcastle, UK), and the stored data was saved in the. cwa format. These accelerometers facilitated the collection of data pertaining to the movement of the lower and upper limbs.

The software employed, Acti4 (developed by The National Research Center for the Working Environment in Copenhagen, Denmark, and BAuA in Berlin, Germany), facilitated the concurrent processing of data from all employed accelerometers. This software facilitated the derivation of comprehensive descriptions concerning the nature and positioning of physical activities. The initial step entailed the conversion of raw data to the .acti4 format, which was then subjected/analytical scrutiny based on accelerometer inclinations and accelerations. Definitions of posture-specific movement behaviours are in Table 1.

Table 1

Definition of movement behaviour types [13]
Type:	Definition:
Lie	Length of periods lying. Lying is detected if the thigh inclination is above 45°and also the hip inclination is above 65°.
Sit	Length of sitting periods. Sitting is detected if the inclination of the thigh is above 45 and lying is not detected.
Stand	Length of periods standing still. Still standing is detected if the inclination of the thigh is less than 45 and no movement of the thigh is detected.
Moving	Length of periods moving. This is an activity used if none of the non-moving activities lying, sitting, or standing is detected. It will normally correspond to every move, including walking, running, cycling, or sports activities.
Move	Length of periods moving. This is an activity used if none of the non-moving activities lying, sitting, or standing is detected. It will normally correspond to every move except walking.
Walk	Length of periods walking. Walking is detected if the standard deviation on the longitudinal axis of the thigh is between 0.1G and 0.72G.
Slow walk	Length of periods with walking speed less than 100 steps pr. minute.
Fast walk	Length of periods with walking above (or equal) 100 steps pr. minute (only the activity walk is used for this calculation, periods with run is not included).

Obesity indicator

Body Mass Index (BMI) was calculated from self-reported body weight in kilograms divided by self-reported body height in meters squared. For sample description, BMI was categorized as ‘normal’ weight (< 25 kg/m2), overweight (25–29.9 kg/m²) and obesity (≥ 30 kg/m²).

Statistical analysis

Statistical analysis was performed using the R Statistical Software, version 4.4.0 (The R Foundation for Statistical Computing) using the robCompositions [14] package, and the IBM Statistical Package for the Social Sciences software, version 23 (SPSS Inc., an IBM Company, Armonk, NY, USA). To examine the association between BMI and the posture-specific 24-hour movement-behaviour compositions, the approach of compositional data analysis (CoDA) [15] was employed. Movement compositions were mapped into real Euclidean space using specific isometric log-ratio coordinates known as backward pivot coordinates (BPC) [16], which enable the use of simpler pairwise logratios. We conducted multivariate robust compositional regression analyses with posture-specific movement behaviours as explanatory variables and BMI as a dependent variable. The construction of BPCs and interpretation of regression analysis can be found in Nesrstová et al. (2023). The regression models were applied for two age groups: “young old” (65–69.99 years) and “old old” (70 + years). They were adjusted for sex, age, season and periods (Covid and after Covid) categories of data collection and applied for three different 24-hour compositions with 4, 5, and 6 components, each progressively detailing specific activities:

Composition 1: This composition includes all basic activities, encompassing lying, sitting, standing, and general movement.

Composition 2: This composition covers the basic activities of lying, sitting, and standing, but excludes walking from the movement category. Instead, it focuses specifically on non-walking movement activities, distinguishing between non-walking movement and walking.

Composition 3: In this composition, the basic activities (lying, sitting, standing) are considered along with more detailed distinctions within movement activities. It differentiates between slow walking, brisk walking, and movement without walking, providing a more granular analysis of how varying intensities of movement impact BMI.

To determine how reallocations of time affect changes in BMI, a compositional isotemporal substitution analysis based on BPCs was used. One-for-one reallocations were utilized to evaluate the impact on BMI – the time was reallocated between two components of the time-use composition (e.g., from SIT to LIE and vice versa). Notably, the reallocation was conducted in a multiplicative manner, rather than the additive approach introduced by [18], meaning for reallocation directly a ratio between two components was considered and we focused on increasing the numerator part and decreasing the denominator part by constant. This is better in line with relative nature of compositional data where (log-)ratios form the elemental information. The significance level for the predicted changes was set at p < 0.05.

Sample characteristics

The main characteristics for younger (years M = 67.35 ± 2.03) and older (years M = 75.73 ± 4.17) group are shown in Table 2. There are comparable body weights, body heights and BMI across the age groups. Looking at the 24hour composition, the "Young old" group generally engages in more physical activity, particularly in general movement and walking, compared/the "Old old" group. This reduction in physical activity with age is significant in overall movement and particularly in fast walking.

Table 2

Sample characteristics
	Young old		Old old
	M	SD	M	SD	p
Age [years]	67,35	2,03	75,73	4,17	< 0,001
Body weight [kg]	77,67	14,84	77,36	13,62	0,852
Body height [cm]	167,84	9,06	166,73	7,74	0,25
Body mass index [kg/m²]	27,52	4,48	27,8	4,43	0,576
BMI CATEGORIES ((N (%))
Normal weight	43 (29,2)		46 (28,4)
Overweight	68 (46,3)		71 (43,8)
Obese	36 (24,5)		45 (27,8)
Movement behaviour (24 hour)
Lie [min/day]	616,77	100,88	620,10	104,15	0,776
Sit [min/day]	423,50	107,82	439,04	124,62	0,245
Stand [min/day]	205,88	62,39	208,45	71,28	0,738
Moving [min/day]	193,84	63,75	172,41	58,55	0,002
• Move [min/day]	101,69	36,38	95,79	35,03	0,149
• Walk [min/day]	92,15	37,13	76,62	34,77	< 0,001
o Slow Walk [min/day]	25,68	13,51	25,24	15,85	0,809
o Fast Walk [min/day]	66,47	30,11	51,38	28,45	< 0,001

M – arithmetic mean, SD – standard deviation, BMI – body mass index, p < 0.05.

Association between BMI and posture-specific movement-behaviour compositions

Table 3 summarizes the regression results for the younger (young old) and older (old old) groups across all three compositions. In this table, there are shown the associations for pairwise log-ratios, which include all possible combinations within the given composition. The regression results for such as pairwise log-ratio SIT/LIE are interpreted such that an increase in sitting at the expense of an equivalent decrease in lying, leads to a positive or negative change in BMI.

In young old group, we found significant associations in logratio MOVING/LIE (-7.849, p < 0.001) and MOVING/SIT (-7.166, p < 0.001), indicating strong negative impacts on BMI. Moreover, significant negative associations with BMI were found in logratio STAND/SIT (Estimate: -2.920, p-value: 0.021) and surprisingly with logratio MOVING/STAND (Estimate: -4.246, p-value: 0.028). In second composition distinguishing between non-walking and walking movement, significant negative associations with BMI were seen in logratios such as WALK/LIE (-3.061, p = 0.038), MOVE/LIE (-8.017, p = 0.002), MOVE/SIT (Estimate: -7.273, p-value: <0.001), WALK/SIT (Estimate: -2.317, p-value: 0.025) and MOVE/STAND (Estimate: -4.987, p-value: 0.032). Positive effects are observed in logratio WALK/MOVE (4.957, p = 0.007), indicating a beneficial impact of increment of walking at the expense of moving on BMI. Based on third composition showing detailed differentiation including walking intensity, both logratios SLOW WALK/LIE (-2.847, p = 0.030) and FAST WALK/SIT (Estimate: -2.459, p-value: 0.041) are negatively associated with BMI. Surprisingly, positive associations with BMI were found in logratios SLOW WALK/MOVE (Estimate: 5.224, p-value: 0.008) and FAST WALK/MOVE (Estimate: 4.850, p-value: 0.004).

In older group, the significant negative association with BMI was found in the logratio MOVING/SITTING (-4.862, p = 0.001) in first composition. Significant negative association with BMI was confirmed in second composition in WALK/SIT (-3.383, p = 0.008) and MOVE/SIT (Estimate: -2.892, p-value: 0.093) logratios. In composition three, the increase of SLOW WALK at the expense of SIT shows a significant negative association with BMI (-2.676, p = 0.026) and similar negative association is noted for logratio FAST WALK/SIT (-2.761, p = 0.027).

Table 3

Association between BMI and posture-specific movement behaviour compositions
	Young old			Old old
	Estimate	Std. Error	p-value	Estimate	Std. Error	p-value
Composition 1
SIT/LIE	-0,683	2,131	0,749	1,222	2,181	0,576
STAND/LIE	-3,603	2,212	0,106	-1,935	2,016	0,339
MOVING/LIE	-7,849	2,082	< 0,001	-3,640	1,860	0,052
STAND/SIT	-2,920	1,249	0,021	-3,157	1,812	0,083
MOVING/SIT	-7,166	1,280	< 0,001	-4,862	1,476	0,001
MOVING/STAND	-4,246	1,908	0,028	-1,705	2,241	0,448
Composition 2
SIT/LIE	-0,744	2,137	0,728	1,611	2,163	0,458
STAND/LIE	-3,030	2,146	0,160	-1,940	2,085	0,354
MOVE/LIE	-8,017	2,560	0,002	-1,281	2,045	0,532
WALK/LIE	-3,061	1,458	0,038	-1,772	1,499	0,239
STAND/SIT	-2,286	1,403	0,105	-3,551	1,931	0,068
MOVE/SIT	-7,273	1,476	< 0,001	-2,892	1,709	0,093
WALK/SIT	-2,317	1,021	0,025	-3,383	1,257	0,008
MOVE/STAND	-4,987	2,303	0,032	0,659	2,369	0,781
WALK/STAND	-0,031	1,217	0,980	0,168	1,735	0,923
WALK/MOVE	4,957	1,812	0,007	-0,491	1,818	0,787
Composition 3
SIT/LIE	-0,761	2,140	0,723	1,443	2,220	0,517
STAND/LIE	-3,077	2,280	0,179	-2,040	2,124	0,338
MOVE/LIE	-8,070	2,523	0,002	-1,430	2,050	0,486
SLOW WALK/LIE	-2,847	1,297	0,030	-1,233	1,543	0,426
FAST WALK/LIE	-3,220	2,226	0,150	-1,318	1,462	0,369
STAND/SIT	-2,316	1,397	0,100	-3,483	2,006	0,085
MOVE/SIT	-7,309	1,449	< 0,001	-2,873	1,748	0,102
SLOW WALK/SIT	-2,086	1,329	0,119	-2,676	1,190	0,026
FAST WALK/SIT	-2,459	1,193	0,041	-2,761	1,233	0,027
MOVE/STAND	-4,993	2,238	0,027	0,610	2,347	0,795
SLOW WALK/STAND	0,231	1,650	0,889	0,807	1,604	0,616
FAST WALK/STAND	-0,143	1,371	0,917	0,722	1,819	0,692
SLOW WALK/MOVE	5,224	1,933	0,008	0,197	1,572	0,900
FAST WALK/MOVE	4,850	1,646	0,004	0,112	1,727	0,948
FAST WALK/SLOW WALK	-0,374	1,920	0,846	-0,085	1,091	0,938

The significance level for the predicted changes was set at p < 0.05. In this table, there are shown the associations for pairwise log-ratios, which include all possible combinations within the given composition. The regression results for such as pairwise log-ratio SIT/LIE are interpreted such that an increase in sitting at the expense of an equivalent decrease in lying, leads to a positive or negative change in BMI.

Reallocations across various movement behaviours with the effects on BMI

Figures 1 and 2 show the results of the analysis of 10, 20, and 30-minute reallocations across various movement behaviours with the effects on BMI in both younger and older groups, respectively. The underlying values from which the figures were obtained can be found in the Appendix 1, 2 and 3. In younger group, significant increases in BMI were found in increasing LIE, SIT and STAND at the expense of MOVE, particularly at 30 minutes, with values reaching 3.286, 3.306 and 3.051 kg/m², respectively. Conversely, significant decreases in BMI were found when MOVE was increased at the expense of LIE, SIT and STAND, with the largest reductions observed at 30 minutes (-2.510, -2.537 and − 2.263 kg/m², respectively). Furthermore, significant increases were found for transitions from MOVE to SLOW WALK and FAST WALK, particularly in 30 minutes, with values 3.264 and 2.89 kg/m², respectively. On the other hand, 20 minutes transition from SLOW WALK to MOVE led to -2.055 kg/m² reduction of BMI and 30 minutes transition from FAST WALK to MOVE decrease BMI of -2.004 kg/m².

The older group exhibited similar trends but with generally smaller changes in BMI. For example, increment of LIE and SIT at the expense of SLOW WALK led to a notable increase in BMI, particularly at 20 minutes (1.743, 1.855 kg/m², respectively), while the increase of SLOW WALK at the expense of LIE and SIT resulted in BMI reductions, particularly in 30 minutes (-0.770 and − 0.949 kg/m², respectively). Consistent with the younger group, transitions from more active to less active states (e.g., from MOVE to SIT, from FAST WALK to LIE) consistently led to increased BMI, while transitions from sedentary to more active states (e.g., from SIT to MOVE, from SIT to FAST WALK) resulted in BMI reductions. In opposite to younger group, there were found significant decreases of BMI in 20 minutes transitions from SLOW WALK to STAND and SLOW WALK to MOVE (-1.51, -1.483, respectively).

This study examined the impact of different compositions of 24-hour movement and non-movement behaviours on BMI across two age groups: the younger (young old) and older (old old) older adults. The findings suggested that an increase in any kind of moving or walking, regardless of intensity, at the expense of low-energy activities such as lying down, sitting, and standing, led to a significant reduction in BMI in older adults. Our results are consistent with previous studies that examined the association between intensity-based movement behaviour composition and adiposity measures such as body mass index, waist circumference, waist-to-hip ratio, and body fat [19–21]. These studies indicated that obesity-related outcomes improved as the relative contribution of MVPA to the 24-hour movement behaviour composition increased. Conversely, reallocating sedentary time to physical activity may be associated with reduced body mass index, body fat percentage, and waist circumference in all age groups, with the magnitude of associations being greater for higher intensities of physical activity.

The study reveals several critical differences between the younger (65-69.99 years) and older (70 + years) group in terms of movement patterns and their effects on BMI. Notably, an increase in any kind of movement or walking, regardless of intensity, at the expense of low-energy activities such as lying down, sitting, and standing led to a significant reduction in BMI in the younger group. In contrast, for the older group, significant results were fewer and primarily involved transitions such as moving to sitting and walking (both fast and slow) to sitting. This suggests that older adults may be particularly sensitive to changes in sedentary behaviour and walking, which aligns with their physical capabilities. This clearly indicates that while younger individuals experience a wider range of transitions across 24 hours, older individuals' 24-hour patterns are more concentrated in specific activities like sitting and walking. This highlights the importance of focusing on reducing sedentary behaviour and promoting walking as key areas for intervention in old older adults.

Moreover, the younger group exhibited stronger BMI responses to changes in movement patterns, suggesting a higher metabolic responsiveness compared to the older group. The observed differences between the younger and older groups can be partly attributed to age-related metabolic changes. As individuals age, basal metabolic rate tends/decline, primarily due to a reduction in lean muscle mass [22]. This decrease in metabolic rate, coupled with changes in hormone levels and an increased propensity for fat accumulation, particularly visceral fat, likely contributes to the less pronounced BMI changes in response to movement observed in the older group [23].

In the younger group, we found interesting results related to standing within a 24-hour period. Based on Composition 1, time spent standing at the expense of sitting was associated with a decrease in BMI. Additionally, an increase in time spent moving at the expense of standing was also associated with a decrease in BMI. This suggests that even minor increases in energy expenditure (for instance, standing instead of sitting) or activity intensity can lead to increased calorie expenditure and potential weight loss, highlighting the importance of maintaining consistent activity levels. A positive association between BMI and standing time was confirmed in other studies in older adult populations, corresponding to our younger group [24].

In older group, transitions from slow walk to lie, sit or stand led to the highest increases of BMI and underscore the importance of specifically slow walking for this 70 + aged group. Therefore, strategies should focus on encouraging sustained physical activity and preventing reductions in activity intensity, even from moderate activities like slow walking.

The study revealed some unexpected results, particularly in the younger group, concerning the transitions between different posture-specific 24-hour behaviours and their effects on BMI. The unexpected finding that increasing time spent in slow or fast walking at the expense of non-walking movement is associated with an increase in BMI suggests that not all types of movement are equally beneficial. This result is counterintuitive as it implies that some activities categorized under "moving" may be more vigorous than walking, leading to increased calorie expenditure and potential weight reduction. This emphasizes the need for precise categorization and communication of activity type and intensity in health recommendations, as not all physical activities contribute equally to energy expenditure and effective weight management.

Strengths and limitations

This study offers several key strengths. The first one is the detailed analysis of specific types of posture-specific movements, including different walking intensities, adds valuable and new insights. Secondly, the use of multiple accelerometer-based assessments provided precise, objective data on 24-hour posture-specific behaviours, surpassing the reliability of self-reported data. Thirdly, by including both younger (young old) and older (old old) groups, the study effectively highlights age-related differences in metabolic responses to physical activity. Additional strength is related to statistical approach. The backwards pivot coordinates approach used in this paper provides an orthonormal alternative to the classic additive log-ratio coordinates, making the interpretation of results straightforward for all practitioners. Due to orthonormality of bpc, the results are consistent and reliable.

Despite its strengths, the study has several limitations. The cross-sectional design limits the ability to establish causal relationships between physical activity and BMI changes, as it captures only a snapshot in time rather than long-term effects. Moreover, the categorization of activities used in our compositions might not fully capture the complexity and variability of real-life movement behaviours. Another limitation is the reliance on self-reported BMI, which may introduce bias due to inaccuracies in self-reporting or misreporting of height and weight. The study's sample size, though adequate, may limit the generalizability of the findings to broader populations. Future research with larger, more diverse samples and longitudinal designs would help to confirm these findings and further explore the nuances of physical activity's impact on BMI.

The study found that increasing any form of movement or walking, regardless of intensity, significantly reduces BMI in older adults, with a stronger effect in younger older adults. Older adults aged 70 + years are particularly sensitive to changes in sedentary behaviour and walking, highlighting the importance of reducing sedentary time and promoting even slow walking. The findings emphasize the need for precise categorization and communication of activity type and intensity in health recommendations, as not all physical activities contribute equally to energy expenditure and weight management.

Ethics approval and consent to participate: All participants were informed that their participation was voluntary and that they could withdraw from the study at any time. They provided their written informed consent before participating in measurements. All methods were carried out in accordance with relevant guidelines and regulations. The study was approved by the Institutional Research Ethics Committee, Faculty of Physical Culture, Palacký University Olomouc (Ref. No. 10/2021).

Consent for publication: Not applicable.

Availability of data and materials: The datasets generated during and/or analyzed during the current study are available in the Figshare repository, https:// 10.6084/m9.figshare.26562718.

Competing interests: The authors declare that they have no competing interests.

Funding: The study was supported by a research grant from the Czech Science Foundation No. 22-02392S. These funding sources had no role in the design of the study and did not have any role in collection, analysis, and interpretation of the data or in writing the manuscript.

Authors' contributions: JP conceived the idea for this study, contributed to acquisition, analysis and interpretation of data, contributed to drafting the manuscript and is guarantor for the study. PJ analyzed data, contributed to the interpretation of data and critically revised the manuscript. KH helped conceptualize the study idea, contributed to methodology of data analyses, and critically revised the manuscript. JV was mainly involved in data collection and data procession, contributed to drafting the manuscript and critically revised the manuscript.

Acknowledgements: We are thankful to the research personnel at the Institute of Active lifestyle for their help with the data collection. We would also like to thank all the participants for their involvement in the study.

Chooi YC, Ding C, Magkos F. The epidemiology of obesity. Metabolism. 2019;92:6–10.
Keramat SA, Alam K, Rana RH, Chowdhury R, Farjana F, Hashmi R, et al. Obesity and the risk of developing chronic diseases in middle-aged and older adults: Findings from an Australian longitudinal population survey, 2009–2017. PLoS One. 2021;16:e0260158.
Lynch DH, Petersen CL, Fanous MM, Spangler HB, Kahkoska AR, Jimenez D, et al. The relationship between multimorbidity, obesity and functional impairment in older adults. J Am Geriatr Soc. 2022;70:1442–9.
Neri SGR, Oliveira JS, Dario AB, Lima RM, Tiedemann A. Does Obesity Increase the Risk and Severity of Falls in People Aged 60 Years and Older? A Systematic Review and Meta-analysis of Observational Studies. The Journals of Gerontology: Series A. 2020;75:952–60.
Corica F, Bianchi G, Corsonello A, Mazzella N, Lattanzio F, Marchesini G. Obesity in the Context of Aging: Quality of Life Considerations. Pharmacoeconomics. 2015;33:655–72.
Pedišić Ž, Dumuid D, Olds TS. Integrating sleep, sedentary behaviour, and physical activity research in the emerging field of time-use epidemiology: definitions, concepts, statistical methods, theoretical framework, and future directions. Kinesiology. 2017;49:252–69.
Dumuid D, Pedišić Ž, Palarea-Albaladejo J, Martín-Fernández JA, Hron K, Olds T. Compositional data analysis in time-use epidemiology: What, why, how. Int J Environ Res Public Health. 2020;17:Article 2220.
Healy GN, Clark BK, Winkler EAH, Gardiner PA, Brown WJ, Matthews CE. Measurement of Adults’ Sedentary Time in Population-Based Studies. Am J Prev Med. 2011;41:216–27.
Janssen I, Clarke AE, Carson V, Chaput JP, Giangregorio LM, Kho ME, et al. A systematic review of compositional data analysis studies examining associations between sleep, sedentary behaviour, and physical activity with health outcomes in adults. Applied physiology, nutrition, and metabolism. 2020;45:S248–57.
World Health Organization. Ageing and health. 2022. https://www.who.int/news-room/fact-sheets/detail/ageing-and-health. Accessed 7 Aug 2024.
Tieland M, Trouwborst I, Clark BC. Skeletal muscle performance and ageing. J Cachexia Sarcopenia Muscle. 2018;9:3–19.
Billot M, Calvani R, Urtamo A, Sánchez-Sánchez JL, Ciccolari-Micaldi C, Chang M, et al. Preserving Mobility in Older Adults with Physical Frailty and Sarcopenia: Opportunities, Challenges, and Recommendations for Physical Activity Interventions. Clin Interv Aging. 2020;Volume 15:1675–90.
Skotte J, Korshøj M, Kristiansen J, Hanisch C, Holtermann A. Detection of physical activity types using triaxial accelerometers. J Phys Act Health. 2014;11:76–84.
Templ M, Hron K, Filzmoser P. robCompositions: An R‐package for Robust Statistical Analysis of Compositional Data. In: Compositional Data Analysis. Wiley; 2011. p. 341–55.
Aitchison J. The Statistical Analysis of Compositional Data. J R Stat Soc Series B Stat Methodol. 1982;44:139–60.
Hron K, Coenders G, Filzmoser P, Palarea-Albaladejo J, Faměra M, Matys Grygar T. Analysing Pairwise Logratios Revisited. Math Geosci. 2021;53:1643–66.
Nesrstová V, Jašková P, Pavlů I, Hron K, Palarea-Albaladejo J, Gába A, et al. Simple enough, but not simpler: reconsidering additive logratio coordinates in compositional analysis. SORT-Statistics and Operations Research Transactions. 2023;47:269–94.
Dumuid D, Pedišić Ž, Stanford TE, Martín-Fernández JA, Hron K, Maher CA, et al. The compositional isotemporal substitution model: A method for estimating changes in a health outcome for reallocation of time between sleep, physical activity and sedentary behaviour. Stat Methods Med Res. 2019;28:846–57.
Janssen I, Clarke AE, Carson V, Chaput J-P, Giangregorio LM, Kho ME, et al. A systematic review of compositional data analysis studies examining associations between sleep, sedentary behaviour, and physical activity with health outcomes in adults. Applied Physiology, Nutrition, and Metabolism. 2020;45 10 (Suppl. 2):S248–57.
Grgic J, Dumuid D, Bengoechea EG, Shrestha N, Bauman A, Olds T, et al. Health outcomes associated with reallocations of time between sleep, sedentary behaviour, and physical activity: a systematic scoping review of isotemporal substitution studies. International Journal of Behavioral Nutrition and Physical Activity. 2018;15:69.
Miatke A, Olds T, Maher C, Fraysse F, Mellow ML, Smith AE, et al. The association between reallocations of time and health using compositional data analysis: a systematic scoping review with an interactive data exploration interface. International Journal of Behavioral Nutrition and Physical Activity. 2023;20:127.
Palmer AK, Jensen MD. Metabolic changes in aging humans: current evidence and therapeutic strategies. Journal of Clinical Investigation. 2022;132.
Bowden Davies KA, Pickles S, Sprung VS, Kemp GJ, Alam U, Moore DR, et al. Reduced physical activity in young and older adults: metabolic and musculoskeletal implications. Ther Adv Endocrinol Metab. 2019;10:204201881988882.
Powell C, Browne LD, Carson BP, Dowd KP, Perry IJ, Kearney PM, et al. Use of Compositional Data Analysis to Show Estimated Changes in Cardiometabolic Health by Reallocating Time to Light-Intensity Physical Activity in Older Adults. Sports Medicine. 2020;50:205–17.

No competing interests reported.

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The interplay between lying, sitting, standing, moving, and walking on obesity risk in older adults: A compositional and isotemporal substitution analysis

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Abstract

Figures

Introduction

Methods

Design and participants

Measurements

Quantification of Movement Behaviours

Obesity indicator

Statistical analysis

Results

Sample characteristics

Association between BMI and posture-specific movement-behaviour compositions

Reallocations across various movement behaviours with the effects on BMI

Discussion

Strengths and limitations

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1