Journal of activity, sedentary and sleep behaviors最新文献

Background: People living with severe mental illness (SMI) face significant health inequalities, including reduced quality of life and life expectancy. Evidence has shown that people living with SMI are highly sedentary, face challenges when seeking to engage in physical activity (PA), and experience sleep difficulties. Motivation, mood and energy have been identified as critical determinants of these behaviours. PA and sleep are traditionally measured in isolation using quantitative approaches, limiting our understanding of the contexts and interactive ways in which these occur, especially for this population. Here, we adopted a flexible and holistic approach, using audio diaries to explore the usability and acceptability of capturing movement behaviours in people living with SMI.

Methods: This study employed a qualitative design. Data were collected with 10 participants self-identifying as living with SMI, who completed 7-days of audio diaries, pre and post diary use interviews. Reflexive thematic analysis was used to analyse participants' movement behaviours and their experiences of using the audio diaries.

Results: Audio diaries were perceived as acceptable to participants and their use for data capture was feasible, with participants experiencing their use as a flexible and empowering method of data capture. Within the exploratory data generated we identified four themes relating to participants' movement behaviours: finding themselves in a "vicious circle" with physical and mental issues impacting movement behaviours; a daily internal fight and dialogue concerning fear of feeling guilty and wasting time; a determination to "not let fatigue win" by pushing through the day; and the mixed effects of understanding the importance of movement behaviours yet finding it challenging to engage.

Conclusion: Audio diaries offered an easy to use and relatively inclusive means of exploring movement behaviours for people living with SMI, especially their context and interrelated nature. Our findings reinforced the well-established link between mental and physical health, and their influence on 24 h movement behaviours, identifying population-specific challenges derived from medication side effects, rigid engagement opportunities, and illness symptoms. Given this, co-production involving individuals with lived experience is crucial for developing tailored recommendations and support to promote sleep and movement among those living with SMI. We emphasized the need for holistic measurement approaches and opportunities that consider the interconnected impact of disrupted sleep and movement.

Background: The World Health Organisation (WHO) has called for more evidence on 24-hour movement behaviours from low- and middle-income countries. We examined the proportion of Ethiopian children aged 3.0-4.9 years who met the WHO guidelines on physical activity, screen time and sleep for children under the age of five, and the associations between meeting the guidelines and socio-demographic factors.

Methods: A cross-sectional study was conducted in Adama and Lume district, Ethiopia. Children were recruited through kindergartens in Adama city and rural villages of Lume district, Ethiopia. Physical activity and sleep were measured using ActiGraph accelerometer. Screen time and restrained sitting were parent-reported. Multivariable logistic regression models tested associations between meeting the individual and combined WHO guidelines and socio-demographic factors.

Results: A total of 430 children participated in the study (mean age 4.2 ± 0.6 years). More than half the children (58.0%) met all the WHO guidelines. A higher proportion met the physical activity (96.1%) and sleep guidelines (91.9%) compared to the screen time guideline (63.5%). Children who lived in rural areas were more likely to meet the screen time (84.6% vs. 38.2%, AOR = 7.31; 95%CI: 3.93, 14.02), sleep (98.6% vs. 83.8%, AOR = 8.60; 95%CI: 3.55, 23.73) and combined (81.3% vs. 30.1%, AOR = 7.41; 95%CI: 4.04, 13.97) guidelines than those who lived in urban areas.

Conclusions: Children from rural Ethiopia were more compliant with the WHO guidelines than their urban counterparts. Strategies to reduce screen time and promote healthy movement behaviours in urban areas are needed. Further studies with a larger representative sample might provide better insight across the regions within the country.

Background: Physical activity (PA) plays a critical role in preventing non-communicable diseases, and maintaining the overall health and well-being of adults. PA surveillance has become a global concern leading to the development of numerous surveys for its measurement. Global Physical Activity Questionnaire (GPAQ) merges the advantages of both the short and long versions of the International Physical Activity Questionnaire (IPAQ) by incorporating various domains (work, transport, and leisure time), while still maintaining a significantly shorter length. This study aimed to cross-culturally adapt the original English version of GPAQ to the Igbo culture and environment.

Methods: The original version of GPAQ (GPAQ) was translated, synthesized, back-translated, and subjected to expert panel review before producing the final Igbo version of the GPAQ (GPAQ-I). The GPAQ and GPAQ-I were administered to 155 community-dwelling adults (36.1% males and 63.9% females) living in Nnewi who were recruited consecutively. The data obtained was analyzed using frequency counts, percentages, mean, standard deviations, Spearman rank order test, and Mann-Whitney U test with p-value set at 0.05.

Results: There was no significant difference in the scores on the English and Igbo versions of the GPAQ (p < 0.001). The correlations between the corresponding domains and total scores on both versions of the GPAQ (rho = 1.0)showed excellent concurrent agreement.

Conclusion: The successfully adapted GPAQ-I demonstrated a strong agreement with the original English version. Further psychometric testing is required to fully establish its internal consistency, reliability, and validity.

Background: Physical activity is a crucial aspect of health benefits in the public society. Although studies on the temporal physical activity patterns might lead to the protocol for efficient intervention/program, a standardized procedure to determine and analyze the temporal physical activity patterns remains to be developed. Here, we attempted to develop a procedure to cluster 24-hour patterns of physical activity as step counts measured with an accelerometer-based wearable sensor.

Methods: The 1 Hz step count data was collected by a hip-worn triaxial accelerometer from 42 healthy participants, comprising 35 males and 7 females, at the Sendai Oroshisho center in 2008. This is a cross sectional study using unsupervised machine learning, specifically the kernel k-means algorithm with the global alignment kernel was applied on a total of 815 days from 42 participants, and 6 activity patterns were identified. Further, the probability of each 24-hour step-counting pattern was calculated for every participant and used for spectral clustering of step-behavioral patterns.

Results: We could identify six 24-hour step-counting patterns and five daily step-behavioral clusters. We could further identify five step-behavioral clusters, all-day dominant (21 participants), all-day + bi-phasic dominant (8 participants), bi-phasic dominant (6 participants), all-day + evening dominant (4 participants), and morning dominant (3 participants). When the amount of physical activity was categorized into tertile groups reflecting highly active, moderately active, and low active, each tertile group consisted of different proportions of six 24-hour step-counting patterns CONCLUSIONS: Our study introduces a novel approach using an unsupervised machine learning method to categorize daily hourly activity, revealing six distinct step counting patterns and five clusters representing daily step behaviors. Our procedure would be reliable for finding and clustering physical activity patterns/behaviors and reveal diversity in the categorization by a traditional tertile procedure using total step amount.

Background: The purpose of this study was to examine the associations of physical activity (PA), recreational screen time, and sleep with indicators of poor oral health in youth.

Methods: Participants were children and adolescents whose parents completed the 2022 National Survey of Children's Health (N = 34,342; 49% female; Mean age = 11.9 +/- 3.5 years). The dependent variables were three binary response items that indicated the presence of toothaches, bleeding gums, and cavities within the previous 12 months. Independent variables were three items indicating the weekly frequency of 60 min of PA, hours of recreational screen time, and hours of weeknight sleep. Relationships between variables were examined using double-selection logistic regression with demographic, dietary, oral hygiene, and dental service covariates selected using the plug-in method of the least absolute shrinkage and selection operator.

Results: Compared to zero days of PA, 4-6 days of PA per week was associated with 30% lower odds of bleeding gums (p = 0.021). Compared to less than 1 h of recreational screen time, 2 h or more hours of recreational screen time were associated with a 1.26 to 1.62 times higher odds of cavities (p < 0.01). Compared to 5 h or less of sleep, 7 to 10 h of sleep was associated with 47-61% lower odds of bleeding gums and 31-47% lower odds of cavities (p < 0.01). Secondary analyses indicated that at least 2 of the movement behavior recommendations had to have been adhered to for positive associations with indicators of oral health to be observed. For toothaches, bleeding gums, and cavities, meeting 2 or 3 recommendations was associated with lower odds of poor oral health; whereas adhering to only one recommendation was not.

Conclusion: Children with higher PA and sleep durations had improved oral health indicators and children with longer periods of screen time exposure had poorer oral health indicators. Our findings recommend adhering to multiple movement behavior recommendations to achieve improvements in oral health indicators.

Background: Canada, Australia, the World Health Organization and other countries have released 24-hour movement guidelines for the early years which integrate physical activity, sedentary behaviour, and sleep, focusing on supporting children to achieve a healthy 24-hour day. The guideline evidence synthesis, however, highlighted the dearth of high-quality evidence, particularly from large-scale studies. The Sleep and Activity Database for the Early Years (SADEY) project aims to assemble a large, pooled database of 24-hour movement behaviours and health indicators in young children (birth to 5.99 years), to advance knowledge in these areas. This paper describes the SADEY design and methods.

Methods: Data sets were identified with > 100 children and device-measured (hip-worn ActiGraph accelerometers) physical activity and sedentary behaviour, parent-reported or device-measured sleep, and at least one health outcome: physical (BMI, waist circumference, blood pressure), social-emotional (Strength and Difficulties Questionnaire), cognitive (Early Years Toolbox), or motor development (Test of Gross Motor Development 2). Led by the University of Wollongong co-ordinating centre, the SADEY project collates the datasets to create a pooled database.

Findings: To date, 13 studies from 7 countries have been included in the database. Ethics clearance and data sharing agreements have been secured for all studies and the SADEY 1.0 database is being assembled including ~ 8,000 participants.

Discussion: SADEY will be used to address questions of global importance to public health policy and practice, for example - Is the mix of movement behaviours across the 24-hour day associated with healthy development?, What is the optimal mix of these behaviours?, and; What factors can be targeted to support young children in achieving the optimal mix of 24-hour movement behaviours? Additionally, SADEY seeks to develop and disseminate protocols, develop capacity on the device-based measurement of movement behaviours, and seeks partnerships with stakeholders that promote knowledge translation on movement behaviours to support healthy development among young children.