Maternal and paternal support for physical activity and healthy eating in preschool children : a cross-sectional study

Background: Parental support is a key influence on children’s health behaviours; however, no previous investigation has simultaneously explored the influence of mothers’ and fathers’ social support on eating and physical activity in preschool-aged children. This study evaluated the singular and combined effects of maternal and paternal support for physical activity (PA) and fruit and vegetable consumption (FV) on preschoolers’ PA and FV. Methods: A random sample comprising 173 parent–child dyads completed validated scales assessing maternal and paternal instrumental support and child PA and FV behaviour. Pearson correlations, controlling for child age, parental age, and parental education, were used to evaluate relationships between maternal and paternal support and child PA and FV. K-means cluster analysis was used to identify families with distinct patterns of maternal and paternal support for PA and FV, and one-way ANOVA examined the impact of cluster membership on child PA and FV. Results: Maternal and paternal support for PA were positively associated with child PA (r = 0.37 and r = 0.36, respectively; P < 0.001). Maternal but not paternal support for FV was positively associated with child FV (r = 0.35;P < 0.001). Five clusters characterised groups of families with distinct configurations of maternal and paternal supportfor PA and FV: 1) above average maternal and paternal support for PA and FV, 2) below average maternal and paternal support for PA and FV, 3) above average maternal and paternal support for PA but below average maternal and paternal support for FV, 4) above average maternal and paternal support for FV but below average maternal and paternal support for PA, and 5) above average maternal support but below average paternal support for PA and FV. Children from families with above average maternal and paternal support for both health behaviours had higher PA and FV levels than children from families with above average support for just one health behaviour, or below average support for both behaviours. Conclusions: The level and consistency of instrumental support from mothers and fathers for PA and FV may be an important target for obesity prevention in preschool-aged children

10,000 Steps Rockhampton : establishing a multi-strategy physical activity promotion project in a community

Issues addressed: To describe the process of developing an innovative, multi-strategy community-based physical activity (PA) intervention project. Method: Project development utilised key informant discussions, a nominal group process and researcher and community discussions to identify the target community and to develop the proposed intervention and evaluation strategies. Results: Five strategies with a central co-ordinating theme of '10,000 steps a day' were identified as being 'best buys' for the promotion of PA in the selected community. They were: 1) a local media campaign; 2) promotion of PA through the general practice setting and other health services; 3) improving social support for PA among disadvantaged groups; 4) policy and environmental approaches; and 5) establishment of a fund to support small, community-led PA promotion initiatives. Conclusion: The development of multi-strategy, community-based health promotion projects based on evidence-based 'best buys' but with promotion of community ownership, can be a complex process. To our knowledge the concurrent trialing of several interventions with an innovative core component focusing on pedometers and the '10,000 steps' PA recommendations has not previously been attempted in a community-based PA intervention

Comparison of surveys used to measure physical activity

Objective: To compare the level of agreement in results obtained from four physical activity (PA) measurement instruments that are in use in Australia and around the world. Methods: 1,280 randomly selected participants answered two sets of PA questions by telephone, 428 answered the Active Australia (AA) and National Health Surveys, 427 answered the AA and CDC Behavioural Risk Factor Surveillance System surveys (BRFSS), and 425 answered the AA survey and the short international Physical Activity Questionaaire (IPAQ. Results: Among the three pairs of survey items, the difference in mean total PA time was lowest when the AA and NHS items were asked (difference=24) (SE:17) minutes, compared with 144 (SE:21) mins for AA/BRFSS and 406 (SE:27) mins for AA/IPAQ). Correspondingly, prevalence estimates for 'sufficiently active' were similar for AA and NHS (56% and 55% respectively), but about 10% higher when BRFSS data were used, and about 25% higher when the IPAQ items were used, compared with the estimates from the AA survey. Conclusions: The findings clearly demonstrate that there are large differences reported in PA times and hence in prevalence estimates of 'sufficient activity' from these four measures. Implications: It is important to consistently use the same survey for population monitoring purposes. As the AA survey has now been used three times in national surveys, its continued use for population surveys is recommended so that trend data over a longer period of time can be established

Test-retest reliability of four physical activity measures used in population surveys

Accurate monitoring of prevalence and trends in population levels of physical activity (PA) is a fundamental public health need. Test-retest reliability (repeatability) was assessed in population samples for four self-report PA measures: the Active Australia survey (AAN=356), the short Physical Activity Questionnaire (IPAQ, N=104), the physical activity items in the Behavioral Risk Factor Surveillance System (BRFSS, N=127) and in the Australian National health Survey (NHS, N=122). Percent agreement and Kappa statistics were used to assess reliability of classification of activity status as 'active', 'insufficiently active' or 'sedentary'. Interclass correlations (ICCs) were used to assess agreement on minutes of activity reported for each item of each survey and for total minutes. Percent agreement scores for activity status were very good on all four instruments, ranging from 80% for the NHS to 79% for the IPAQ. Corresponding Kappa statistics ranged from 0.40 (NHS) to 0.52 (AA). For individual items, ICCs were highest for walking (0.45 to 0.78) and vigorous activity (0.22 to 0.64) and lowest for moderate questions (0.16 to 0.44). All four measures provide acceptable levels of test-retest reliability for assessing activity status and sedentariness, and moderate reliability for assessing total minutes of activity

Chronic disease risks and use of a smartphone application during a physical activity and dietary intervention in Australian truck drivers

Objective: This study examined chronic disease risks and the use of a smartphone activitytracking application during an intervention in Australian truck drivers (April-October 2014). Methods: Forty-four men (mean age=47.5 [SD 9.8] years) completed baseline health measures, and were subsequently offered access to a free wrist-worn activity tracker and smartphone application (Jawbone UP) to monitor step counts and dietary choices during a 20-week intervention. Chronic disease risks were evaluated against guidelines; weekly step countand dietary logs registered by drivers in the application were analysed to evaluate use of the Jawbone UP. Results: Chronic disease risks were high (e.g. 97% high waist circumference [≥94 cm]). Eighteen drivers (41%) did not start the intervention; smartphone technical barriers were the main reason for drop out. Across 20-weeks, drivers who used the Jawbone UP logged step counts for an average of 6 [SD 1] days/week; mean step counts remained consistent across the intervention (weeks 1-4=8,743[SD 2,867] steps/day; weeks 17-20=8,994[SD 3,478] steps/day).The median number of dietary logs significantly decreased from start (17 [IQR 38] logs/weeks) to end of the intervention (0 [IQR 23] logs/week; p<0.01); the median proportion of healthy diet choices relative to total diet choices logged increased across the intervention (weeks1-4=38[IQR 21]%; weeks 17-20=58[IQR 18]%). Conclusions: Step counts were more successfully monitored than dietary choices in those drivers who used the Jawbone UP. Implications: Smartphone technology facilitated active living and healthy dietary choices, but also prohibited intervention engagement in a number of these high-risk Australian truck drivers

Increasing physical activity using an just-in-time adaptive digital assistant supported by machine learning: A novel approach for hyper-personalised mHealth interventions

Objective: Physical inactivity is a leading modifiable cause of death and disease worldwide. Population-based interventions to increase physical activity are needed. Existing automated expert systems (e.g., computer-tailored interventions) have significant limitations that result in low long-term effectiveness. Therefore, innovative approaches are needed. This special communication aims to describe and discuss a novel mHealth intervention approach that proactively offers participants with hyper-personalised intervention content adjusted in real-time. Methods: Using machine learning approaches, we propose a novel physical activity intervention approach that can learn and adapt in real-time to achieve high levels of personalisation and user engagement, underpinned by a likeable digital assistant. It will consist of three major components: (1) conversations: to increase user's knowledge on a wide range of activity-related topics underpinned by Natural Language Processing; (2) nudge engine: to provide users with hyper-personalised cues to action underpinned by reinforcement learning (i.e., contextual bandit) and integrating real-time data from activity tracking, GPS, GIS, weather, and user provided data; (3) Q&A: to facilitate users asking any physical activity related questions underpinned by generative AI (e.g., ChatGPT, Bard) for content generation. Results: The detailed concept of the proposed physical activity intervention platform demonstrates the practical application of a just-in-time adaptive intervention applying various machine learning techniques to deliver a hyper-personalised physical activity intervention in an engaging way. Compared to traditional interventions, the novel platform is expected to show potential for increased user engagement and long-term effectiveness due to: (1) using new variables to personalise content (e.g., GPS, weather), (2) providing behavioural support at the right time in real-time, (3) implementing an engaging digital assistant and (4) improving the relevance of content through applying machine learning algorithms. Conclusion: The use of machine learning is on the rise in every aspect of today's society, however few attempts have been undertaken to harness its potential to achieve health behaviour change. By sharing our intervention concept, we contribute to the ongoing dialogue on creating effective methods for promoting health and well-being in the informatics research community. Future research should focus on refining these techniques and evaluating their effectiveness in controlled and real-world circumstances

PREDICT-CP: study protocol of implementation of comprehensive surveillance to predict outcomes for school-aged children with cerebral palsy

Objectives Cerebral palsy (CP) remains the world’s most common childhood physical disability with total annual costs of care and lost well-being of $A3.87b. The PREDICT-CP (NHMRC 1077257 Partnership Project: Comprehensive surveillance to PREDICT outcomes for school age children with CP) study will investigate the influence of brain structure, body composition, dietary intake, oropharyngeal function, habitual physical activity, musculoskeletal development (hip status, bone health) and muscle performance on motor attainment, cognition, executive function, communication, participation, quality of life and related health resource use costs. The PREDICTCP cohort provides further follow-up at 8–12 years of two overlapping preschool-age cohorts examined from 1.5 to 5 years (NHMRC 465128 motor and brain development; NHMRC 569605 growth, nutrition and physical activity). Methods and analyses This population-based cohort study undertakes state-wide surveillance of 245 children with CP born in Queensland (birth years 2006–2009). Children will be classified for Gross Motor Function Classification System; Manual Ability Classification System, Communication Function Classification System and Eating and Drinking Ability Classification System. Outcomes include gross motor function, musculoskeletal development (hip displacement, spasticity, muscle contracture), upper limb function, communication difficulties, oropharyngeal dysphagia, dietary intake and body composition, participation, parent-reported and child-reported quality of life and medical and allied health resource use. These detailed phenotypical data will be compared with brain macrostructure and microstructure using 3 Tesla MRI (3T MRI). Relationships between brain lesion severity and outcomes will be analysed using multilevel mixed-effects models. Ethics and dissemination The PREDICT-CP protocol is a prospectively registered and ethically accepted study protocol. The study combines data at 1.5–5 then 8–12 years of direct clinical assessment to enable prediction of outcomes and healthcare needs essential for tailoring interventions (eg, rehabilitation, orthopaedic surgery and nutritional supplements) and the projected healthcare utilisation