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Abstract Objective: To characterise the nature of digital food and beverage advertising in Singapore. Setting: Food and beverage advertisements within twenty clicks on the top twelve non-food websites and all posts on the Facebook and Instagram pages of fifteen major food companies in Singapore were sampled from 1 January to 30 June 2018. Design: Advertised foods were classified as being core (healthier), non-core or mixed dishes (e.g. burger) using the WHO nutrient profile model and national guidelines. Marketing techniques were assessed using published coding frameworks. Participants: NA Results: Advertisements (n 117) on the twelve non-food websites were largely presented as editorial content. Food companies posted twice weekly on average on social media sites (n 1261), with eatery chains posting most frequently and generating the largest amount of likes and shares. Key marketing techniques emphasised non-health attributes, for example, hedonic or convenience attributes (85 % of advertisements). Only a minority of foods and beverages advertised were core foods (non-food website: 16·2 %; social media: 13·5 %). Conclusions: Top food and beverage companies in Singapore actively use social media as a platform for promotion with a complex array of marketing techniques. A vast majority of these posts were unhealthy highlighting an urgent need to consider regulating digital food and beverage advertising in Singapore.

Abstract Background Dependency on screen viewing (SV) has reached unprecedented levels, and mental health issues are becoming a major public health concern. However, the associations between SV, including variations in purposes, devices, and timing, and mental health remain unclear. This study aims to provide insights into these associations among university students. Methods This analysis used baseline data from a longitudinal cohort study among first-year university students matriculating in the 2021–2022 academic year. Self-reported data on sociodemographics, health behaviors and mental health outcomes alongside anthropometric measurements were collected. Unadjusted and adjusted logistic regression analyses were conducted. Results The average age of the 997 valid students was 20.2 years, with 59.6% being female and 41.4% male. Students spent 14.3 h daily on SV, with females reporting higher SV than males. Daily SV was predominant for study purposes (7.6 h/day). Computer usage was the highest (7.0 h/day), while TV usage was the lowest (1.7 h/day). Poor mental wellbeing was reported by 33.6% of students, while 13.9% experienced psychological distress. Compared to students with low total SV, those with high levels were more likely to have poor mental wellbeing [OR (95% CI): 1.40 (0.99, 1.98)] and psychological distress [1.56 (1.00, 2.44)]. High levels of recreational and study related SV were significantly associated with poor mental wellbeing [1.81 (1.27, 2.56)] and psychological distress [1.75 (1.11, 2.83)], respectively. Those with high levels of computer time were more likely to have poor mental wellbeing [1.44 (1.01, 2.06)], and high weekend day SV was associated with greater odds of psychological distress [2.16 (1.17, 4.06)]. Conclusions SV among university students was high, as was the high prevalence of poor mental wellbeing and psychological distress. Greater SV was associated with poor mental wellbeing and psychological distress. Differences according to purpose of SV were noted. Although recreational SV was associated with poor mental wellbeing, study related SV was associated with psychological distress. Variations across different devices and timing were also noted. This highlights the need for further longitudinal research to understand the impact of SV on mental health and to guide interventions for promoting mental health of university students globally.

BackgroundIntegration of mobile health data collection methods into cohort studies enables the collection of intensive longitudinal information, which gives deeper insights into individuals’ health and lifestyle behavioral patterns over time, as compared to traditional cohort methods with less frequent data collection. These findings can then fill the gaps that remain in understanding how various lifestyle behaviors interact as students graduate from university and seek employment (student-to-work life transition), where the inability to adapt quickly to a changing environment greatly affects the mental well-being of young adults. ObjectiveThis paper aims to provide an overview of the study methodology and baseline characteristics of participants in Health@NUS, a longitudinal study leveraging mobile health to examine the trajectories of health behaviors, physical health, and well-being, and their diverse determinants, for young adults during the student-to-work life transition. MethodsUniversity students were recruited between August 2020 and June 2022 in Singapore. Participants would complete biometric assessments and questionnaires at 3 time points (baseline, 12-, and 24-month follow-up visits) and use a Fitbit smartwatch and smartphone app to continuously collect physical activity, sedentary behavior, sleep, and dietary data over the 2 years. Additionally, up to 12 two-week-long bursts of app-based ecological momentary surveys capturing lifestyle behaviors and well-being would be sent out among the 3 time points. ResultsInterested participants (n=1556) were screened for eligibility, and 776 participants were enrolled in the study between August 2020 and June 2022. Participants were mostly female (441/776, 56.8%), of Chinese ethnicity (741/776, 92%), undergraduate students (759/776, 97.8%), and had a mean BMI of 21.9 (SD 3.3) kg/m2, and a mean age of 22.7 (SD 1.7) years. A substantial proportion were overweight (202/776, 26.1%) or obese (42/776, 5.4%), had indicated poor mental well-being (World Health Organization-5 Well-Being Index ≤50; 291/776, 37.7%), or were at higher risk for psychological distress (Kessler Psychological Distress Scale ≥13; 109/776, 14.1%). ConclusionsThe findings from this study will provide detailed insights into the determinants and trajectories of health behaviors, health, and well-being during the student-to-work life transition experienced by young adults. Trial RegistrationClinicalTrials.gov NCT05154227; https://clinicaltrials.gov/study/NCT05154227 International Registered Report Identifier (IRRID)DERR1-10.2196/56749

BackgroundEcological momentary assessments (EMAs) are short, repeated surveys designed to collect information on experiences in real-time, real-life contexts. Embedding periodic bursts of EMAs within cohort studies enables the study of experiences on multiple timescales and could greatly enhance the accuracy of self-reported information. However, the burden on participants may be high and should be minimized to optimize EMA response rates. ObjectiveWe aimed to evaluate the effects of study design features on EMA response rates. MethodsEmbedded within an ongoing cohort study (Health@NUS), 3 bursts of EMAs were implemented over a 7-month period (April to October 2021). The response rate (percentage of completed EMA surveys from all sent EMA surveys; 30-42 individual EMA surveys sent/burst) for each burst was examined. Following a low response rate in burst 1, changes were made to the subsequent implementation strategy (SMS text message announcements instead of emails). In addition, 2 consecutive randomized controlled trials were conducted to evaluate the efficacy of 4 different reward structures (with fixed and bonus components) and 2 different schedule lengths (7 or 14 d) on changes to the EMA response rate. Analyses were conducted from 2021 to 2022 using ANOVA and analysis of covariance to examine group differences and mixed models to assess changes across all 3 bursts. ResultsParticipants (N=384) were university students (n=232, 60.4% female; mean age 23, SD 1.3 y) in Singapore. Changing the reward structure did not significantly change the response rate (F3,380=1.75; P=.16). Changing the schedule length did significantly change the response rate (F1,382=6.23; P=.01); the response rate was higher for the longer schedule (14 d; mean 48.34%, SD 33.17%) than the shorter schedule (7 d; mean 38.52%, SD 33.44%). The average response rate was higher in burst 2 and burst 3 (mean 50.56, SD 33.61 and mean 48.34, SD 33.17, respectively) than in burst 1 (mean 25.78, SD 30.12), and the difference was statistically significant (F2,766=93.83; P

Background Modifiable risk factors for non-communicable diseases, including eating an unhealthy diet and being physically inactive, are influenced by complex and dynamic interactions between people and their social and physical environment. Therefore, understanding patterns and determinants of these risk factors as they occur in real life is essential to enable the design of precision public health interventions. Objective This paper describes the protocol for the Continuous Observations of Behavioural Risk Factors in Asia study (COBRA). The study uses real-time data capture methods to gain a comprehensive understanding of eating and movement behaviours, including how these differ by socio-demographic characteristics and are shaped by people's interaction with their social and physical environment. Methods COBRA is an observational study in free-living conditions. We will recruit 1500 adults aged 21–69 years from a large prospective cohort study. Real-time data capture methods will be used for nine consecutive days: an ecological momentary assessment app with a global positioning system enabled to collect location data, accelerometers to measure movement, and wearable sensors to monitor blood glucose levels. Participants receive six EMA surveys per day between 8 a.m. and 9.30 p.m. to capture information on behavioural risk factors including eating behaviours and diet composition movement behaviours (physical activity, sedentary behaviour, sleep), and related contextual factors. The second wave of ecological momentary assessment surveys with a global positioning system enabled will be sent 6 months later. Data will be analysed using generalised linear models to examine associations between behavioural risk factors and contextual determinants. Discussion Findings from this study will advance our understanding of dietary and movement behaviours as they occur in real-life and inform the development of personalised interventions to prevent chronic diseases.