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Background Physical inactivity and sedentary behaviour have detrimental consequences to the individual and the economy. Our study examined the prevalence of perceived barriers to physical activity in Singapore’s adult population and their associations with physical activity and sedentary behaviour. Methods This cross-sectional analysis utilised data from a nationwide survey in Singapore. Participants ( n  = 2867) were recruited from February 2019 to March 2020. The independent variables were internal (e.g. fatigue, age) and external (e.g. weather, cost) perceived barriers to physical activity. The outcomes were domain-specific physical activity (work, transport and leisure) and sedentary behaviour, all of which were assessed using the Global Physical Activity Questionnaire. The associations were examined using zero-inflated negative binomial regressions for physical activity and linear regression for sedentary behaviour. Results The median (Interquartile range) for work-related, transport-related and leisure-related physical activity were 0 (0 – 1440), 600 (160 – 1120) and 360 (0 – 1080) MET (metabolic equivalent)-minutes per week. The median sedentary behaviour (IQR) was 360 (240 – 540) minutes per day. The top three barriers were lack of time (65.3%), fatigue (64.7%) and pollution (56.1%). After adjustment, the level of transport-related physical activity was lower for respondents who cited lacking pavement or parks as a barrier, but higher for those who indicated cost and safety concerns. Respondents who reported pollution as a barrier were more likely to engage in transport-related physical activity. The level of leisure-related physical activity was lower for respondents indicating weather, lack of time and age as barriers, but higher for those reporting safety concerns. The odds of engaging in leisure-related physical activity was lower for those citing age, cost and fatigue as barriers, but higher for those indicating the weather. Sedentary behaviour was positively associated with work and limited accessibility to exercise facilities, but negatively with safety concerns. Conclusion Individuals can be motivated to overcome internal barriers (fatigue, lack of time, cost and age) through social support and emphasis on exercise benefits. External barriers (weather and lack of pavements or parks) can be reduced by raising awareness of existing infrastructure. Sedentary behaviour can be improved by implementing workplace measures, such as reducing the time spent sitting.

Objective The study aimed to examine the prevalence and sociodemographic correlates of physical activity and sedentary behaviour in the general population of the multi-ethnic nation of Singapore as part of the Knowledge, Practice and Attitudes towards Diabetes study, a cross-sectional and population-based survey. It also examined the relationship between physical activity, sedentary behaviour, and health-related quality of life (HRQoL). Methods Physical activity and sedentary behaviour were assessed via the Global Physical Activity Questionnaire (GPAQ), while physical and mental HRQoL was assessed via the Short Form Health Survey (SF-12v2). Survey weights were employed to account for complex survey design. Multivariable logistic regression models were utilized to examine sociodemographic correlates of physical activity (insufficient vs. sufficient physical activity) and sedentary behaviour (< 7 h/day vs ≥7 h/day). Descriptive statistics were calculated to examine the percentage of time spent in different domains of physical activity. Multivariable linear regressions were conducted to examine the association between physical activity and sedentary behaviour with physical and mental HRQoL. Results Two thousand eight hundred sixty seven participants recruited from February 2019 to March 2020 (prior to COVID-19 lockdown and related restrictions in Singapore) were included in the analyses. 83.3% of respondents had sufficient physical activity. Age (65 years and above) and income (SGD 2000 to 3999) were associated with a higher likelihood of insufficient physical activity. In contrast, those of Malay ethnicity and having one chronic physical condition were associated with a lower likelihood of insufficient physical activity. 47.7% reported that they had sedentary behaviour of ≥7 h/day. Older age and a primary school education were related to a lower likelihood of sedentary behaviour, while being single, having higher income, obesity, and multimorbidity were associated with higher sedentary behaviour. Insufficient physical activity was significantly associated with lower physical HRQoL but was not significantly associated with mental HRQoL. Sedentary behaviour was not significantly associated with mental or physical HRQoL. Conclusion About 17% of the population did not meet the minimum requirements for physical activity, while around half of the population spent a considerable time being sedentary. As insufficient physical activity was associated with poorer physical HRQoL, policymakers should promote moderate physical activity and encouraging the breaking up of prolonged sedentary periods within the middle- and high-income groups, especially at the workplace. Increased leisure-time exercise should be encouraged for those in the lower- income group.

Accelerometers are commonly used to assess physical activity. Consumer activity trackers have become increasingly popular today, such as the Fitbit. This study aimed to compare the average number of steps per day using the wrist-worn Fitbit Flex and waist-worn ActiGraph (wGT3X-BT) in free-living conditions. 104 adult participants (n = 35 males; n = 69 females) were asked to wear a Fitbit Flex and an ActiGraph concurrently for 7 days. Daily step counts were used to classify inactive (<10,000 steps) and active (≥10,000 steps) days, which is one of the commonly used physical activity guidelines to maintain health. Proportion of agreement between physical activity categorizations from ActiGraph and Fitbit Flex was assessed. Statistical analyses included Spearman's rho, intraclass correlation (ICC), median absolute percentage error (MAPE), Kappa statistics, and Bland-Altman plots. Analyses were performed among all participants, by each step-defined daily physical activity category and gender. The median average steps/day recorded by Fitbit Flex and ActiGraph were 10193 and 8812, respectively. Strong positive correlations and agreement were found for all participants, both genders, as well as daily physical activity categories (Spearman's rho: 0.76-0.91; ICC: 0.73-0.87). The MAPE was: 15.5% (95% confidence interval [CI]: 5.8-28.1%) for overall steps, 16.9% (6.8-30.3%) vs. 15.1% (4.5-27.3%) in males and females, and 20.4% (8.7-35.9%) vs. 9.6% (1.0-18.4%) during inactive days and active days. Bland-Altman plot indicated a median overestimation of 1300 steps/day by the Fitbit Flex in all participants. Fitbit Flex and ActiGraph respectively classified 51.5% and 37.5% of the days as active (Kappa: 0.66). There were high correlations and agreement in steps between Fitbit Flex and ActiGraph. However, findings suggested discrepancies in steps between devices. This imposed a challenge that needs to be considered when using Fibit Flex in research and health promotion programs.

Advancements in technology offer new opportunities for the prevention and management of type 2 diabetes. Venture capital companies have been investing in digital diabetes companies that offer digital behavior change interventions (DBCIs). However, little is known about the scientific evidence underpinning such interventions or the degree to which these interventions leverage novel technology-driven automated developments such as conversational agents (CAs) or just-in-time adaptive intervention (JITAI) approaches. Our objectives were to identify the top-funded companies offering DBCIs for type 2 diabetes management and prevention, review the level of scientific evidence underpinning the DBCIs, identify which DBCIs are recognized as evidence-based programs by quality assurance authorities, and examine the degree to which these DBCIs include novel automated approaches such as CAs and JITAI mechanisms. A systematic search was conducted using 2 venture capital databases (Crunchbase Pro and Pitchbook) to identify the top-funded companies offering interventions for type 2 diabetes prevention and management. Scientific publications relating to the identified DBCIs were identified via PubMed, Google Scholar, and the DBCIs' websites, and data regarding intervention effectiveness were extracted. The Diabetes Prevention Recognition Program (DPRP) of the Center for Disease Control and Prevention in the United States was used to identify the recognition status. The DBCIs' publications, websites, and mobile apps were reviewed with regard to the intervention characteristics. The 16 top-funded companies offering DBCIs for type 2 diabetes received a total funding of US $2.4 billion as of June 15, 2021. Only 4 out of the 50 identified publications associated with these DBCIs were fully powered randomized controlled trials (RCTs). Further, 1 of those 4 RCTs showed a significant difference in glycated hemoglobin A (HbA ) outcomes between the intervention and control groups. However, all the studies reported HbA improvements ranging from 0.2% to 1.9% over the course of 12 months. In addition, 6 interventions were fully recognized by the DPRP to deliver evidence-based programs, and 2 interventions had a pending recognition status. Health professionals were included in the majority of DBCIs (13/16, 81%,), whereas only 10% (1/10) of accessible apps involved a CA as part of the intervention delivery. Self-reports represented most of the data sources (74/119, 62%) that could be used to tailor JITAIs. Our findings suggest that the level of funding received by companies offering DBCIs for type 2 diabetes prevention and management does not coincide with the level of evidence on the intervention effectiveness. There is considerable variation in the level of evidence underpinning the different DBCIs and an overall need for more rigorous effectiveness trials and transparent reporting by quality assurance authorities. Currently, very few DBCIs use automated approaches such as CAs and JITAIs, limiting the scalability and reach of these solutions.

Daily step count is a readily accessible physical activity measure inversely related to many important health outcomes. However, its day-to-day variability is not clear, especially when measured by recent mobile devices. This study investigates number of measurement days required to reliably estimate the weekly and monthly levels of daily step count in adults using wrist-worn fitness trackers and smartphones. Data were from a 5-month physical activity program in Singapore. The 5-month period was divided into 22 weekly and 5 monthly time windows. For each time window, we leveraged data sampling procedures and estimated the minimum number of measurement days needed to achieve reliable mean daily step count with intraclass correlation coefficients (ICC) above 80%. The ICCs were derived using linear mixed effect models. We examined both simple random and random consecutive measurement days and conducted subgroup analysis by participant characteristics and tracking devices. Analysis of weekly and monthly step count included 212,048 and 112,865 adults, respectively. Fewer simple random measurement days are needed than random consecutive days for weekly time windows (mean 2.5, SD 0.5 vs mean 2.7, SD 0.5; p-value = 0.025). Similarly, monthly time windows require fewer measurements of simple random days than random consecutive days (mean 3.4, SD 0.5 vs mean 4.4, SD 0.5; p-value = 0.025). Younger participants and those tracking steps via smartphones consistently required more days. Being obese was associated with more measurement days for weekly time windows. In sum, to obtain reliable daily step count level, we recommend at least 3 measurement days for weekly and 5 days for monthly time window in adults. Fewer days could be considered for adults age 60+ years, while more days are required when tracking daily step via smartphones.

ObjectivesCurrent evidence is unclear due to methodological limitations. We bridge critical knowledge gaps by quantifying the longitudinal changes in movement behaviours and their correlates from early childhood through adolescence.DesignLongitudinal observational cohort study.SettingGeneral healthy child and adolescent sample in Singapore.ParticipantsGrowing Up in Singapore Towards healthy Outcomes study participants.Primary and secondary outcome measuresWe used wrist-worn accelerometry and proxy-reported data to examine movement behaviours (sleep, inactivity, light physical activity (PA; LPA) and moderate-to-vigorous PA (MVPA) and screen-viewing) at ages 5.5, 8, 10 and 12 years and the sociodemographic and maternal lifestyle-related correlates using linear regression models with generalised estimating equations.ResultsAmong 837 children, sleep, LPA and MVPA declined by 3% (from 9.1 to 8.8 hours/day), 24% (from 5.8 to 4.4 hours/day) and 44% (from 71.3 to 40.1 min/day), respectively, while inactivity and screen viewing increased by 26% (from 8.0 to 10.1 hours/day) and 155% (from 1.8 to 4.6 hours/day), respectively, from ages 5.5 to 12 years. The greatest annual increase in inactivity (0.6 hour/annum) and screen-viewing (0.8 hour/annum) and decrease in LPA (0.3 hour/annum) and MVPA (10.4 min/annum) occurred from ages 8 to 10 years. Girls of Malay ethnicity and lower socioeconomic status, and whose mothers had less favourable movement behaviours, had significantly less sleep, higher inactivity and screen-viewing and/or lower PA. Maternal PA levels and/or sitting time were associated with children’s sleep, inactivity and MVPA up to age 8 years, while maternal sitting and screen-viewing behaviours were associated with children’s screen-viewing at all ages.ConclusionsUsing contemporaneous datasets relevant to the present day, we confirmed that children become less physically active and have longer screen-viewing as they transition into adolescence and highlighted characteristics to be prioritised in future interventions.

Physical inactivity and sedentary behavior are significant risk factors for various non-communicable diseases. Bangkok, Thailand's capital, is one of the fastest-growing metropolitans in Southeast Asia. Few studies have investigated the epidemiology of physical activity and sedentary behavior among Bangkok residents. This study aims to investigate the prevalence of combined physical activity and sedentary behavior patterns among Bangkok residents and examine relationships between participants' characteristics and the combined movement patterns. We analyzed data from the nationally representative 2021 Health Behavior Survey conducted by the Thailand National Statistical Office. The Global Physical Activity Questionnaire was used to assess physical activity and sedentary behavior. 'Sufficiently active' was defined as meeting the World Health Organization's guidelines for aerobic physical activity ([greater than or equal to]150 minutes of moderate-to-vigorous physical activity per week). 'Low sedentary time' was defined as sitting for [less than or equal to]7 hours per day. Participants were categorized into one of four movement patterns: highly active/low sedentary, highly active/highly sedentary, low active/low sedentary, and low active/highly sedentary. Multinomial logistic regression was used to identify the factors associated with each group of four movement patterns. Among the 3,137 individuals included in the study, the majority were categorized as highly active/highly sedentary (64.8%), followed by highly active/low sedentary (17.9%) and low active/highly sedentary (14.3%). Only a few (3.0%) of participants were categorized as being low active/low sedentary. Compared to males, female participants had a significantly higher likelihood of belonging to the highly active/low sedentary (AOR = 1.69, 95%CI: 1.25, 2.28) or highly active/highly sedentary (AOR = 1.51, 95%CI: 1.19, 1.93) group, rather than the low active/high sedentary group. Compared to unemployed/retired participants, those in labor-intensive occupations had a significantly higher likelihood of being in the highly active/low sedentary group (AOR = 1.89, 95%CI: 1.22, 2.94). Compared to participants with no chronic physical conditions, participants who reported multimorbidity had a significantly lower likelihood of being in the highly active/low sedentary group (AOR = 0.60, 95%CI: 0.37, 0.98). This study provides valuable insights into the patterns of physical activity and sedentary behavior among residents of Bangkok using up-to-date data. The majority belonged to the highly active/highly sedentary group, followed by the highly active/low sedentary group. Correlates such as sex, occupation, and chronic conditions were associated with these patterns. Targeted interventions in recreational activities, workplaces, and urban areas, including screen time control measures, movement breaks and improved built environments, are crucial in reducing sedentary behavior and promoting physical activity.

Introduction University years represent a crucial period in a student’s life, with mounting pressures on mental health and the formation of lifestyle habits that may endure into adulthood. This study examined the associations between movement and dietary behaviours and mental health among university students. It further explored potential sex-specific differences in these associations. Methods This cross-sectional study used data (2020–2022) from the Health@NUS prospective cohort study. Six behaviours—moderate-to-vigorous physical activity (MVPA), sedentary behaviour, sleep duration, and intake of fruit, vegetables, and unhealthy food—were assessed and categorised as either healthy or unhealthy based on established guidelines. Mental health was measured using the 6-item Kessler Psychological Distress Scale and the 5-item World Health Organisation Well-Being Index. Multivariable linear regression was used to analyse the associations between the behaviours and mental health, and to evaluate effect modification by sex. Results Among 773 students (mean age 22.7 years, 56.8% female), 23.3% engaged in 4–6 healthy behaviours; 14.1% and 37.7% reported high distress and poor well-being, respectively. Compared to students engaging in 0–1 healthy behaviours, those engaging in 2 or more healthy behaviours reported less distress and greater well-being. Students who engaged in 4–6 behaviours reported the lowest distress (females: -1.51, 95% CI -2.75, -0.27; males: -1.72, 95% CI -3.06, -0.39) and the best well-being (females: 10.66, 95% CI 6.04, 15.23; males: 9.98, 95% CI 6.04, 15.23). For individual behaviours, adequate sleep and less intake of unhealthy foods were associated with both less distress and better mental well-being, whereas sufficient MVPA and less sedentary behaviour were associated with better well-being. Additionally, among female students, less sedentary behaviour and unhealthy food intake were associated with better well-being. Conclusions The more healthy movement and dietary behaviours that students engaged in, the better their reported mental health outcomes. The type of behaviour and sex also appear to play a role. These findings highlight the need for interventions addressing both lifestyle behaviours and mental health in university students, potentially using sex-tailored strategies. Clinical trial number NCT05154227 (registered on 16 September 2021; https://www.clinicaltrials.gov/)

Background Changing lifestyle patterns over the last decades have seen growing numbers of people in Asia affected by non-communicable diseases and common mental health disorders, including diabetes, cancer, and/or depression. Interventions targeting healthy lifestyle behaviours through mobile technologies, including new approaches such as chatbots, may be an effective, low-cost approach to prevent these conditions. To ensure uptake and engagement with mobile health interventions, however, it is essential to understand the end-users’ perspectives on using such interventions. The aim of this study was to explore perceptions, barriers, and facilitators to the use of mobile health interventions for lifestyle behaviour change in Singapore. Methods Six virtual focus group discussions were conducted with a total of 34 participants (mean ± SD; aged 45 ± 3.6 years; 64.7% females). Focus group recordings were transcribed verbatim and analysed using an inductive thematic analysis approach, followed by deductive mapping according to perceptions, barriers, facilitators, mixed factors, or strategies. Results Five themes were identified: (i) holistic wellbeing is central to healthy living (i.e., the importance of both physical and mental health); (ii) encouraging uptake of a mobile health intervention is influenced by factors such as incentives and government backing; (iii) trying out a mobile health intervention is one thing, sticking to it long term is another and there are key factors, such as personalisation and ease of use that influence sustained engagement with mobile health interventions; (iv) perceptions of chatbots as a tool to support healthy lifestyle behaviour are influenced by previous negative experiences with chatbots, which might hamper uptake; and (v) sharing health-related data is OK, but with conditions such as clarity on who will have access to the data, how it will be stored, and for what purpose it will be used. Conclusions Findings highlight several factors that are relevant for the development and implementation of mobile health interventions in Singapore and other Asian countries. Recommendations include: (i) targeting holistic wellbeing, (ii) tailoring content to address environment-specific barriers, (iii) partnering with government and/or local (non-profit) institutions in the development and/or promotion of mobile health interventions, (iv) managing expectations regarding the use of incentives, and (iv) identifying potential alternatives or complementary approaches to the use of chatbots, particularly for mental health.

Background Sedentary behaviours (SB) can be characterized by low energy expenditure in a reclining position (e.g., sitting) often associated with work and transport. Prolonged SB is associated with increased risk for chronic conditions, and due to technological advances, the working population is in office settings with high occupational exposure to SB. This study aims to assess SB among office workers, as well as barriers and strategies towards reducing SB in the work setting. Methods Using a mixed-methods approach guided by the socio-ecological framework, non-academic office workers from a professional school in a large public university were recruited. Of 180 eligible office workers, 40 enrolled and completed all assessments. Self-reported and objectively measured SB and activity levels were captured. Focus group discussion (FGD) were conducted to further understand perceptions, barriers, and strategies to reducing workplace SB. Environmental factors were systematically evaluated by trained research staff using an adapted version of the Checklist for Health Promotion Environments at Worksites (CHEW). Thematic analysis of FGD was conducted and descriptive analysis of quantitative data was performed. Results The sample was mostly Chinese ( n  = 33, 80 %) with a total of 24 (60 %) female participants. Most participants worked five days a week for about 9.5(0.5) hrs/day. Accelerometer data show that participants spend the majority of their days in sedentary activities both on workdays (76.9 %) and non-workdays (69.5 %). Self-report data confirm these findings with median sitting time of 420(180) minutes at work. From qualitative analyses, major barriers to reducing SB emerged, including the following themes: workplace social and cultural norms, personal factors, job scope, and physical building/office infrastructure. CHEW results confirm a lack of support from the physical infrastructure and information environment to reducing SB. Conclusions There is high SB among office workers in this sample. We identified multiple levels of influence for prolonged occupational SB, with a particular emphasis on workplace norms and infrastructure as important barriers to reducing SB and increasing PA. A larger, representative sample of the Singaporean population is needed to confirm our findings but it seems that any intervention aimed at reducing SB in the workplace should target individual, environmental, and organizational levels.