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BackgroundPoor diet and physical inactivity are leading modifiable causes of death and disease. Advances in artificial intelligence technology present tantalizing opportunities for creating virtual health coaches capable of providing personalized support at scale. ObjectiveThis proof of concept study aimed to test the feasibility (recruitment and retention) and preliminary efficacy of physical activity and Mediterranean-style dietary intervention (MedLiPal) delivered via artificially intelligent virtual health coach. MethodsThis 12-week single-arm pre-post study took place in Adelaide, Australia, from March to August 2019. Participants were inactive community-dwelling adults aged 45 to 75 years, recruited through news stories, social media posts, and flyers. The program included access to an artificially intelligent chatbot, Paola, who guided participants through a computer-based individualized introductory session, weekly check-ins, and goal setting, and was available 24/7 to answer questions. Participants used a Garmin Vivofit4 tracker to monitor daily steps, a website with educational materials and recipes, and a printed diet and activity log sheet. Primary outcomes included feasibility (based on recruitment and retention) and preliminary efficacy for changing physical activity and diet. Secondary outcomes were body composition (based on height, weight, and waist circumference) and blood pressure. ResultsOver 4 weeks, 99 potential participants registered expressions of interest, with 81 of those screened meeting eligibility criteria. Participants completed a mean of 109.8 (95% CI 1.9-217.7) more minutes of physical activity at week 12 compared with baseline. Mediterranean diet scores increased from a mean of 3.8 out of 14 at baseline, to 9.6 at 12 weeks (mean improvement 5.7 points, 95% CI 4.2-7.3). After 12 weeks, participants lost an average 1.3 kg (95% CI –0.1 to –2.5 kg) and 2.1 cm from their waist circumference (95% CI –3.5 to –0.7 cm). There were no significant changes in blood pressure. Feasibility was excellent in terms of recruitment, retention (90% at 12 weeks), and safety (no adverse events). ConclusionsAn artificially intelligent virtual assistant-led lifestyle-modification intervention was feasible and achieved measurable improvements in physical activity, diet, and body composition at 12 weeks. Future research examining artificially intelligent interventions at scale, and for other health purposes, is warranted.

BackgroundWearable trackers for monitoring physical activity (PA) and total sleep time (TST) are increasingly popular. These devices are used not only by consumers to monitor their behavior but also by researchers to track the behavior of large samples and by health professionals to implement interventions aimed at health promotion and to remotely monitor patients. However, high costs and accuracy concerns may be barriers to widespread adoption. ObjectiveThis study aimed to investigate the concurrent validity of 6 low-cost activity trackers for measuring steps, moderate-to-vigorous physical activity (MVPA), and TST: Geonaut On Coach, iWown i5 Plus, MyKronoz ZeFit4, Nokia GO, VeryFit 2.0, and Xiaomi MiBand 2. MethodsA free-living protocol was used in which 20 adults engaged in their usual daily activities and sleep. For 3 days and 3 nights, they simultaneously wore a low-cost tracker and a high-cost tracker (Fitbit Charge HR) on the nondominant wrist. Participants wore an ActiGraph GT3X+ accelerometer on the hip at daytime and a BodyMedia SenseWear device on the nondominant upper arm at nighttime. Validity was assessed by comparing each tracker with the ActiGraph GT3X+ and BodyMedia SenseWear using mean absolute percentage error scores, correlations, and Bland-Altman plots in IBM SPSS 24.0. ResultsLarge variations were shown between trackers. Low-cost trackers showed moderate-to-strong correlations (Spearman r=0.53-0.91) and low-to-good agreement (intraclass correlation coefficient [ICC]=0.51-0.90) for measuring steps. Weak-to-moderate correlations (Spearman r=0.24-0.56) and low agreement (ICC=0.18-0.56) were shown for measuring MVPA. For measuring TST, the low-cost trackers showed weak-to-strong correlations (Spearman r=0.04-0.73) and low agreement (ICC=0.05-0.52). The Bland-Altman plot revealed a variation between overcounting and undercounting for measuring steps, MVPA, and TST, depending on the used low-cost tracker. None of the trackers, including Fitbit (a high-cost tracker), showed high validity to measure MVPA. ConclusionsThis study was the first to examine the concurrent validity of low-cost trackers. Validity was strongest for the measurement of steps; there was evidence of validity for measurement of sleep in some trackers, and validity for measurement of MVPA time was weak throughout all devices. Validity ranged between devices, with Xiaomi having the highest validity for measurement of steps and VeryFit performing relatively strong across both sleep and steps domains. Low-cost trackers hold promise for monitoring and measurement of movement and sleep behaviors, both for consumers and researchers.

BackgroundTo date, many online health behavior programs developed by researchers have not been translated at scale. To inform translational efforts, health researchers must work with marketing experts to design cost-effective marketing campaigns. It is important to understand the characteristics of end users of a given health promotion program and identify key market segments. ObjectiveThis study aimed to describe the characteristics of the adopters of Active Team, a gamified online social networking physical activity app, and identify potential market segments to inform future research translation efforts. MethodsParticipants (N=545) were Australian adults aged 18 to 65 years who responded to general advertisements to join a randomized controlled trial (RCT) evaluating the Active Team app. At baseline they provided demographic (age, sex, education, marital status, body mass index, location of residence, and country of birth), behavioral (sleep, assessed by the Pittsburgh Quality Sleep Index) and physical activity (assessed by the Active Australia Survey), psychographic information (health and well-being, assessed by the PERMA [Positive Emotion, Engagement, Relationships, Meaning, Achievement] Profile; depression, anxiety and stress, assessed by the Depression, Anxiety, and Stress Scale [DASS-21]; and quality of life, assessed by the 12-Item Short Form Health Survey [SF-12]). Descriptive analyses and a k-medoids cluster analysis were performed using the software R 3.3.0 (The R Foundation) to identify key characteristics of the sample. ResultsCluster analyses revealed four clusters: (1) younger inactive women with poor well-being (218/545), characterized by a higher score on the DASS-21, low mental component summary score on the SF-12, and relatively young age; (2) older, active women (153/545), characterized by a lower score on DASS-21, a higher overall score on the SF-12, and relatively older age; (3) young, active but stressed men (58/545) with a higher score on DASS-21 and higher activity levels; and (4) older, low active and obese men (30/545), characterized by a high body mass index and lower activity levels. ConclusionsUnderstanding the characteristics of population segments attracted to a health promotion program will guide the development of cost-effective research translation campaigns. Trial RegistrationAustralian New Zealand Clinical Trial Registry ACTRN12617000113358; https://www.anzctr.org .au/Trial/Registration/TrialReview.aspx?id=371463 International Registered Report Identifier (IRRID)RR2-10.1186/s12889-017-4882-7

BackgroundThe disproportionate burden of noncommunicable disease among Indigenous Australians living in remote Indigenous communities (RICs) is a complex and persistent problem. Smartphones are increasingly being used by young Indigenous adults and therefore represent a promising method to engage them in programs seeking to improve nutritional intake. ObjectiveThis study aimed to consult RIC members to inform the content of a smartphone app that can be used to monitor and reduce sugar-sweetened beverage intake in RICs. MethodsThe study was conducted in two phases. The formative phase involved a simulated grocery selection activity with think aloud (“think aloud shop”), a semistructured interview, a questionnaire outlining current smartphone and app use, and a paper prototyping activity. A preliminary end-user testing phase involved a think aloud prototype test and a semistructured interview regarding user satisfaction. Convenience sampling was used to recruit 20 18- to 35-year-old smartphone users for each phase from two RICs in the Northern Territory, Australia. Thematic analysis of transcribed audio recordings was used to identify determinants of food choice from the think aloud shop; themes related to the Theory of Planned Behavior (TPB) from the eating behaviors interview; and usability, comprehension, and satisfaction with the app from the preliminary end-user testing. ResultsSmartphone use in RICs is currently different to that found in urban environments; in particular, extremely low use of Facebook, restricted variety of phone types, and limited Internet access. Findings regarding promoting app engagement indicate that utilizing an opt-in approach to social features such as leader boards and team challenges is essential. The inclusion of games was also shown to be important for satisfaction, as were the use of audio features, contextually embedded dissemination, and streamlined app design for comprehension in this target group. ConclusionsThis research provides critical insights and concrete recommendations for the development of lifestyle improvement apps targeted toward disadvantaged young adults in nonurban settings, specifically RICs. It serves as a framework for future app development projects using a consultative user-centered design approach, supporting calls for the increased use of this strategy in app development.

BackgroundOnline social networks offer considerable potential for delivery of socially influential health behavior change interventions. ObjectiveTo determine the efficacy, engagement, and feasibility of an online social networking physical activity intervention with pedometers delivered via Facebook app. MethodsA total of 110 adults with a mean age of 35.6 years (SD 12.4) were recruited online in teams of 3 to 8 friends. Teams were randomly allocated to receive access to a 50-day online social networking physical activity intervention which included self-monitoring, social elements, and pedometers (“Active Team” Facebook app; n=51 individuals, 12 teams) or a wait-listed control condition (n=59 individuals, 13 teams). Assessments were undertaken online at baseline, 8 weeks, and 20 weeks. The primary outcome measure was self-reported weekly moderate-to-vigorous physical activity (MVPA). Secondary outcomes were weekly walking, vigorous physical activity time, moderate physical activity time, overall quality of life, and mental health quality of life. Analyses were undertaken using random-effects mixed modeling, accounting for potential clustering at the team level. Usage statistics were reported descriptively to determine engagement and feasibility. ResultsAt the 8-week follow-up, the intervention participants had significantly increased their total weekly MVPA by 135 minutes relative to the control group (P=.03), due primarily to increases in walking time (155 min/week increase relative to controls, P

BackgroundThe dramatic growth of Web 2.0 technologies and online social networks offers immense potential for the delivery of health behavior change campaigns. However, it is currently unclear how online social networks may best be harnessed to achieve health behavior change. ObjectiveThe intent of the study was to systematically review the current level of evidence regarding the effectiveness of online social network health behavior interventions. MethodsEight databases (Scopus, CINAHL, Medline, ProQuest, EMBASE, PsycINFO, Cochrane, Web of Science and Communication & Mass Media Complete) were searched from 2000 to present using a comprehensive search strategy. Study eligibility criteria were based on the PICOS format, where “population” included child or adult populations, including healthy and disease populations; “intervention” involved behavior change interventions targeting key modifiable health behaviors (tobacco and alcohol consumption, dietary intake, physical activity, and sedentary behavior) delivered either wholly or in part using online social networks; “comparator” was either a control group or within subject in the case of pre-post study designs; “outcomes” included health behavior change and closely related variables (such as theorized mediators of health behavior change, eg, self-efficacy); and “study design” included experimental studies reported in full-length peer-reviewed sources. Reports of intervention effectiveness were summarized and effect sizes (Cohen’s d and 95% confidence intervals) were calculated wherever possible. Attrition (percentage of people who completed the study), engagement (actual usage), and fidelity (actual usage/intended usage) with the social networking component of the interventions were scrutinized. ResultsA total of 2040 studies were identified from the database searches following removal of duplicates, of which 10 met inclusion criteria. The studies involved a total of 113,988 participants (ranging from n=10 to n=107,907). Interventions included commercial online health social network websites (n=2), research health social network websites (n=3), and multi-component interventions delivered in part via pre-existing popular online social network websites (Facebook n=4 and Twitter n=1). Nine of the 10 included studies reported significant improvements in some aspect of health behavior change or outcomes related to behavior change. Effect sizes for behavior change ranged widely from −0.05 (95% CI 0.45-0.35) to 0.84 (95% CI 0.49-1.19), but in general were small in magnitude and statistically non-significant. Participant attrition ranged from 0-84%. Engagement and fidelity were relatively low, with most studies achieving 5-15% fidelity (with one exception, which achieved 105% fidelity). ConclusionsTo date there is very modest evidence that interventions incorporating online social networks may be effective; however, this field of research is in its infancy. Further research is needed to determine how to maximize retention and engagement, whether behavior change can be sustained in the longer term, and to determine how to exploit online social networks to achieve mass dissemination. Specific recommendations for future research are provided.

Background: Healthy lifestyles depend on forming crucial habits through the process of habit formation, emphasising the need to establish positive habits and break negative ones for lasting behaviour changes. This systematic review aims to explore the time required for developing health-related habits. Methods: Six databases (Scopus, PsychINFO, CINAHL, EMBASE, Medline and PubMed) were searched to identify experimental intervention studies assessing self-report habit or automaticity questionnaires (e.g., the self-report habit index (SRHI) or the self-report behavioural automaticity index (SRBAI)), or the duration to reach automaticity in health-related behaviours. Habit formation determinants were also evaluated. Meta-analysis was performed to assess the change in the SRHI or SRBAI habit scores between pre- and post-intervention, and the study quality was assessed using the PEDro scale. Results: A total of 20 studies involving 2601 participants (mean age range: 21.5–73.5 years) were included. Most studies had a high risk of bias rating (n = 11). Health behaviours included physical activity (n = 8), drinking water (n = 2), vitamin consumption (n = 1), flossing (n = 3), healthy diet (n = 8), microwaving a dishcloth (for foodborne disease reduction, n = 2) and sedentary behaviour reduction (n = 1). Four studies reported the median or mean times to reach habit formation, ranging from 59–66 days (median) and 106–154 days (means), with substantial individual variability (4–335 days). The meta-analysis showed significant improvements in habit scores pre- to post-intervention across different habits (standardised mean difference: 0.69, 95% CI: 0.49–0.88). Frequency, timing, type of habit, individual choice, affective judgements, behavioural regulation and preparatory habits significantly influence habit strength, with morning practices and self-selected habits generally exhibiting greater strength. Conclusions: Emerging evidence on health-related habit formation indicates that while habits can start forming within about two months, the time required varies significantly across individuals. A limitation of this meta-analysis is the relatively small number of studies included, with flossing and diet having the most evidence among the behaviours examined. Despite this, improvements in habit strength post-intervention are evident across various behaviours, suggesting that targeted interventions can be effective. Future research should aim to expand the evidence base with well-designed studies to better understand and enhance the process of establishing beneficial health habits. [ABSTRACT FROM AUTHOR]

Artificially intelligent physical activity digital assistants that use the full spectrum of machine learning capabilities have not yet been developed and examined. This study aimed to explore potential users' perceptions and expectations of using such a digital assistant. Six 90‐min online focus group meetings (n = 45 adults) were conducted. Meetings were recorded, transcribed and thematically analysed. Participants embraced the idea of a 'digital assistant' providing physical activity support. Participants indicated they would like to receive notifications from the digital assistant, but did not agree on the number, timing, tone and content of notifications. Likewise, they indicated that the digital assistant's personality and appearance should be customisable. Participants understood the need to provide information to the digital assistant to allow for personalisation, but varied greatly in the extent of information that they were willing to provide. Privacy issues aside, participants embraced the idea of using artificial intelligence or machine learning in return for a more functional and personal digital assistant. In sum, participants were ready for an artificially intelligent physical activity digital assistant but emphasised a need to personalise or customise nearly every feature of the application. This poses challenges in terms of cost and complexity of developing the application. [ABSTRACT FROM AUTHOR]

Background: Around 40% of Australian children do not participate in sport. Cost is a major barrier to participation, particularly for children from low socioeconomic backgrounds. This study aimed to evaluate the uptake of a population-level children's sports subsidy scheme, including sociodemographic differences in uptake. Methods: A state-wide cross-sectional analysis comparing sports voucher claimants (primary school-aged children with a valid Medicare or Australian visa number) from the 2019 financial year with population census data from South Australia. Chi-square was used to examine whether the percentage of eligible children who claimed a voucher differed based on age, sex, socioeconomic status (SES), and geographical remoteness. Subgroup analyses were conducted for the lowest 2 socioeconomic disadvantage deciles, split by gender. Scatterplots were used to compare sports between high and low SES children. Results: A total of 74,668 children claimed sports vouchers (45.5% of eligible children). Children who were relatively younger, female, from low socioeconomic backgrounds, and from major cities were least likely to claim the voucher. The 5 most common sports were Australian rules football (30.2%), netball (13.6%), soccer (13.1%), gymnastics (10.4%), and basketball (5.7%), with the popular sports similar for high and low SES children. Conclusions: Future work is needed to understand how Sports Voucher, and sport participation rates have changed over time, and to improve voucher uptake among girls, city dwellers, and low SES children. [ABSTRACT FROM AUTHOR]

Poor diet is a major risk factor for non-communicable disease. The aims of this study were to describe temporal patterns and seasonal changes in diet across the year in Australian adults. A total of 375 adults from a prospective cohort study conducted between 1 December 2019 and 31 December 2021 in Adelaide, Australia, were asked to complete the Dietary Questionnaire for Epidemiological Studies at eight timepoints over a year. Average intakes over the previous month of total energy, macronutrients, healthy food groups, and discretionary foods and beverages were derived. Temporal patterns in diet were analysed descriptively. Multilevel linear regression modelling was used to assess seasonal differences in diet. Of the 375 participants recruited, 358 provided sufficient data for analysis. Intake of total energy, all macronutrients, and most discretionary foods and beverages peaked in December. Total energy intake was higher in summer than in autumn, winter, and spring. Fruit intake was higher in summer than in winter. Consumption of alcoholic beverages was higher in summer than in autumn, winter, and spring. Consumption of non-alcoholic beverages was higher in summer than in autumn and winter. This study identified temporal differences in dietary intake among Australian adults. Seasonal effects appear to be driven largely by increases in consumption of foods and beverages over the December (summer) holiday period. These findings can inform the design and timing of dietary interventions. [ABSTRACT FROM AUTHOR]

Poor youth mental health is an area of global concern. Summer holiday programs may provide environments that support mental health when the structures and supports of school are not available. The aim of this review was to determine the effectiveness of summer holiday programs in improving the mental health, social–emotional well-being, and cognitive (non-academic) outcomes of children and adolescents. Studies of summer holiday programs for school-aged children (5–18 years) were included if they measured any mental, socio-emotional or cognitive (non-academic) outcome. Studies were excluded if they were published prior to 2000, targeted clinical populations or lasted less than five days. Six databases were searched (April 2023). Risk of bias was assessed using the PEDro tool. Study outcomes were grouped according to three main constructs: mental health (psychological well-being, anxiety, depression, distress, and self-perception including self-esteem, self-worth, self-concept, confidence, and competence); social–emotional well-being (behavior and social skills, e.g., communication, bullying, conflict resolution, empathy, and social skills); and cognitive function (memory, selective attention, and executive function). A fourth "other" group captured substance use, personality traits, character skills, and values. Effect sizes were calculated as the standardized mean difference between pre- and post-intervention scores. The synthesis involved a random-effects meta-analysis (presented in forest plots), where possible, with the remaining outcomes narratively synthesized. Twenty-six studies (n = 6812 participants) were included. The results of the meta-analysis suggested that summer programs showed a statistically non-significant trend toward reducing symptoms of anxiety and depression (k = 2 studies, SMD = −0.17, 95% CI −2.94, 2.60), psychological distress (k = 2 studies, SMD −0.46, 95% CI –1.71, 0.79), and no effect on self-esteem (k = 6 studies, SMD = 0.02, 95% CI −0.02, 0.06) or self-worth (k = 3 studies, SMD = 0.05, 95% CI 0.00, 0.11). Narrative syntheses indicated a pattern toward improvements in general mental health, self-perception, social–emotional outcomes, and cognition. Studies were generally small, with a high risk of bias. Summer holiday programs for children and adolescents show trends toward improving mental, social, emotional, and cognitive outcomes. Programs targeting disadvantaged children showed stronger patterns of improvement related to mental health and self-perception than programs targeting the general population. While effect sizes are small to negligible, they consistently indicate improvements. Summer programs present a promising avenue to promote mental health in children; however, further rigorously designed, clearly reported control-group studies are required to more fully understand their effects. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study was to determine whether there was an association between dietary patterns and children's academic performance. Methods: This cross-sectional study involved 315 children aged 9-11 years from 26 schools in Australia. Academic performance was measured in 4 domains (reading, writing, numeracy, and language--subdomains: spelling, grammar, and punctuation) using the National Assessment Program in Literacy and Numeracy (NAPLAN). A self-reported child questionnaire collected dietary intake data. "Core" (healthy) and "noncore" (unhealthy) dietary patterns were derived using principal components analysis. Results: The noncore pattern was associated with lower NAPLAN scores across all academic domains (mean: -12.6, 95% CI: -18.7 to -6.4, r[superscript 2] = 0.073, p < 0.001) except writing, while the core foods pattern was not associated with NAPLAN scores across all domains. When the noncore model was adjusted for sociodemographic covariates (child body mass index, ethnicity, sex, parental education, household income, marital status, mother's employment hours, and number of siblings), the association was attenuated but remained statistically significant (NAPLAN summary score: -8.5, 95% CI -15.0 to -1.9, r[superscript 2] = 0.123, p = 0.011). Conclusions: Academic performance was deleteriously associated with a nutrient-poor, energy-dense diet, yet not associated with a nutritious diet.