Replacing sedentary time with physical activity and sleep: Associations with cardiometabolic health markers in adults

Farrahi, Vahid; Rostami, Mehrdad; Nauha, Laura; Korpisaari, Maija; Niemelä, Maisa; Jämsä, Timo; Korpelainen, Raija; Oussalah, Mourad

doi:10.1111/sms.14323

Cited by 3 publications

(1 citation statement)

References 44 publications

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“…The present study extends knowledge by demonstrating the potential for improving sitting, health and wellbeing outcomes over a longer time period. Reductions in daily sitting and waist circumference observed in the intervention group could be clinically relevant in light of evidence that replacing 30 min/day of sitting with light-intensity physical activity is associated with improvements in insulin sensitivity and cardiometabolic biomarkers [ 69 , 70 ]. There is also a significantly higher risk of cardiovascular disease for each 1 cm increase in waist circumference [ 71 ].…”

Section: Discussionmentioning

confidence: 99%

The Regulate your Sitting Time (RESIT) intervention for reducing sitting time in individuals with type 2 diabetes: findings from a randomised-controlled feasibility trial

Brierley,

Chater,

Edwardson

et al. 2024

Diabetol Metab Syndr

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Background Reducing and breaking up sitting is recommended for optimal management of Type 2 diabetes mellitus (T2DM). Yet, there is limited evidence of interventions targeting these outcomes in individuals with this condition. The primary aim of this study was to assess the feasibility and acceptability of delivering and evaluating a tailored online intervention to reduce and break up sitting in adults with T2DM. Methods A mixed-methods two-arm randomised controlled feasibility trial was conducted in ambulatory adults with T2DM who were randomised 1:1 to the REgulate your SItting Time (RESIT) intervention or usual care control group. The intervention included online education, self-monitoring and prompt tools (wearable devices, smartphone apps, computer apps) and health coaching. Feasibility outcomes were recruitment, attrition, data completion rates and intervention acceptability. Measurements of device-assessed sitting (intended primary outcome for definitive trial), standing and stepping, and physical function, psychosocial health and wellbeing were taken at baseline, 3 months and 6 months. Individual semi-structured interviews were conducted at six-months (post intervention) to explore acceptability, feasibility and experiences of the trial and intervention using the Framework Method. Results Seventy participants aged 55 ± 11 years were recruited. Recruitment rate (proportion of eligible participants enrolled into the study) was 67% and participant retention rate at 6 months was 93% (n = 5 withdrawals). Data completion rates for daily sitting were 100% at baseline and ranged from 83 to 91% at 3 months and 6 months. Descriptive analysis demonstrated potential for the intervention to reduce device-measured sitting, which was 30.9 ± 87.2 and 22.2 ± 82.5 min/day lower in the intervention group at 3 and 6 months, respectively, compared with baseline. In the control group, sitting was 4.4 ± 99.5 and 23.7 ± 85.2 min/day lower at 3 and 6 months, respectively. Qualitative analysis identified three themes: reasons for participating in the trial, acceptability of study procedures, and the delivery and experience of taking part in the RESIT intervention. Overall, the measurement visits and intervention were acceptable to participants. Conclusions This study demonstrated the feasibility and acceptability of the RESIT intervention and evaluation methods, supporting a future definitive trial. If RESIT is found to be clinically effective, this could lead to changes in diabetes healthcare with a focus on reducing sitting. Trial registration The trial was registered with ISRCTN (number ISRCTN14832389).

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Section: Discussionmentioning

confidence: 99%

The Regulate your Sitting Time (RESIT) intervention for reducing sitting time in individuals with type 2 diabetes: findings from a randomised-controlled feasibility trial

Brierley,

Chater,

Edwardson

et al. 2024

Diabetol Metab Syndr

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Deep learning of movement behavior profiles and their association with markers of cardiometabolic health

Farrahi,

Collings,

Oussalah

2024

BMC Med Inform Decis Mak

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Background Traditionally, existing studies assessing the health associations of accelerometer-measured movement behaviors have been performed with few averaged values, mainly representing the duration of physical activities and sedentary behaviors. Such averaged values cannot naturally capture the complex interplay between the duration, timing, and patterns of accumulation of movement behaviors, that altogether may be codependently related to health outcomes in adults. In this study, we introduce a novel approach to visually represent recorded movement behaviors as images using original accelerometer outputs. Subsequently, we utilize these images for cluster analysis employing deep convolutional autoencoders. Methods Our method involves converting minute-by-minute accelerometer outputs (activity counts) into a 2D image format, capturing the entire spectrum of movement behaviors performed by each participant. By utilizing convolutional autoencoders, we enable the learning of these image-based representations. Subsequently, we apply the K-means algorithm to cluster these learned representations. We used data from 1812 adult (20–65 years) participants in the National Health and Nutrition Examination Survey (NHANES, 2003–2006 cycles) study who worn a hip-worn accelerometer for 7 seven consecutive days and provided valid accelerometer data. Results Deep convolutional autoencoders were able to learn the image representation, encompassing the entire spectrum of movement behaviors. The images were encoded into 32 latent variables, and cluster analysis based on these learned representations for the movement behavior images resulted in the identification of four distinct movement behavior profiles characterized by varying levels, timing, and patterns of accumulation of movement behaviors. After adjusting for potential covariates, the movement behavior profile characterized as “Early-morning movers” and the profile characterized as “Highest activity” both had lower levels of insulin (P < 0.01 for both), triglycerides (P < 0.05 and P < 0.01, respectively), HOMA-IR (P < 0.01 for both), and plasma glucose (P < 0.05 and P < 0.1, respectively) compared to the “Lowest activity” profile. No significant differences were observed for the “Least sedentary movers” profile compared to the “Lowest activity” profile. Conclusions Deep learning of movement behavior profiles revealed that, in addition to duration and patterns of movement behaviors, the timing of physical activity may also be crucial for gaining additional health benefits.

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Isotemporal Substitution Effects of Daily Time Use on Cardiorespiratory Fitness of Children in the OptiChild Study: A Mediation Analysis with Diet Quality

Wang,

Zhang,

Wang

et al. 2024

Nutrients

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(1) Background: Although daily time-use is associated with diet quality and cardiorespiratory fitness (CRF) in children, their interdependence remains unexplored. This study first examined the associations between reallocating daily movement time and diet quality and CRF, and second the mediating role of diet quality in the relationship between daily time-use and CRF. (2) Methods: This study included 1131 Chinese children (aged 8 to 10 years; median [interquartile range]: 8.5 [8.3, 8.8]) at baseline (September 2022) and 1268 children at the 9-month follow-up (June 2023) from the OptiChild study. Daily durations of moderate-to-vigorous physical activity (MVPA), sleep, and sedentary behavior (e.g., screen time) were self-reported or proxy-reported by parents. Diet quality was assessed via the Diet Quality Questionnaire (DQQ), which uses a 24 h dietary recall and is categorized according to the Global Dietary Recommendations (GDR) score and Food Group Diversity Score (FGDS). The CRF was measured using VO2max after the 20 m shuttle run test. Longitudinal associations between daily time-use, diet quality, and CRF were calculated using isotemporal substitution models. Mediation analyses were used to determine whether diet quality mediated the associations between daily time-use and CRF. (3) Results: Reallocation of 30 min from screen time to MVPA resulted in significant improvements in the GDR score (β baseline = 0.11, p = 0.024; β follow-up = 0.26, p < 0.001), FGDS (β baseline = 0.11, p = 0.006; β follow-up = 0.19, p < 0.001), and CRF (β baseline = 0.40, p < 0.001; β follow-up = 0.26, p = 0.001). Diet quality partially mediated the associations between MVPA, screen time, and CRF. Substituting 30 min of screen time for MVPA led to diet quality mediating a proportion of the association with CRF (GDR score: 11.4%, FGDS: 6.6%). (4) Conclusions: These findings underscore the importance of optimizing daily time-use of MVPA and screen time and improving diet quality to promote physical fitness in school-aged children.

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Replacing sedentary time with physical activity and sleep: Associations with cardiometabolic health markers in adults

Cited by 3 publications

References 44 publications

The Regulate your Sitting Time (RESIT) intervention for reducing sitting time in individuals with type 2 diabetes: findings from a randomised-controlled feasibility trial

The Regulate your Sitting Time (RESIT) intervention for reducing sitting time in individuals with type 2 diabetes: findings from a randomised-controlled feasibility trial

Deep learning of movement behavior profiles and their association with markers of cardiometabolic health

Isotemporal Substitution Effects of Daily Time Use on Cardiorespiratory Fitness of Children in the OptiChild Study: A Mediation Analysis with Diet Quality

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