Springer Science and Business Media LLC

Health and fitness applications (apps) have gained popularity in interventions to improve diet, physical activity and sedentary behaviours but their efficacy is unclear. This systematic review examined the efficacy of interventions that use apps to improve diet, physical activity and sedentary behaviour in children and adults. Systematic literature searches were conducted in five databases to identify papers published between 2006 and 2016. Studies were included if they used a smartphone app in an intervention to improve diet, physical activity and/or sedentary behaviour for prevention. Interventions could be stand-alone interventions using an app only, or multi-component interventions including an app as one of several intervention components. Outcomes measured were changes in the health behaviours and related health outcomes (i.e., fitness, body weight, blood pressure, glucose, cholesterol, quality of life). Study inclusion and methodological quality were independently assessed by two reviewers. Twenty-seven studies were included, most were randomised controlled trials (n = 19; 70%). Twenty-three studies targeted adults (17 showed significant health improvements) and four studies targeted children (two demonstrated significant health improvements). Twenty-one studies targeted physical activity (14 showed significant health improvements), 13 studies targeted diet (seven showed significant health improvements) and five studies targeted sedentary behaviour (two showed significant health improvements). More studies (n = 12; 63%) of those reporting significant effects detected between-group improvements in the health behaviour or related health outcomes, whilst fewer studies (n = 8; 42%) reported significant within-group improvements. A larger proportion of multi-component interventions (8 out of 13; 62%) showed significant between-group improvements compared to stand-alone app interventions (5 out of 14; 36%). Eleven studies reported app usage statistics, and three of them demonstrated that higher app usage was associated with improved health outcomes. This review provided modest evidence that app-based interventions to improve diet, physical activity and sedentary behaviours can be effective. Multi-component interventions appear to be more effective than stand-alone app interventions, however, this remains to be confirmed in controlled trials. Future research is needed on the optimal number and combination of app features, behaviour change techniques, and level of participant contact needed to maximise user engagement and intervention efficacy.

Millions of children attend residential summer camps each year. However, few studies have examined the potential of camps for obesity prevention efforts. Research in the domain of positive youth development has shown that camp programs as short as one week have both short- and long-term positive effects on self-esteem, self-efficacy and other youth outcomes. The objective of the present study was to highlight the potential of resident camps as promising venues for the promotion of healthy eating and physical activity behaviors in the children who attend. Data for this study came from the American Camp Association 2007 Emerging Issues Survey. This survey assessed camp professionals’ perspectives on a diverse array of issues, including the healthy eating and physical activity of children. Data analysis focused on responses from 247 camp professionals whose camps offered resident camp programs. Descriptive and Chi-square statistics were calculated. Ninety-two percent of camp professionals reported that the healthy eating and physical activity of campers was an “important” or “very important” issue for camps. The majority of camps reported offering vegetarian options, healthy snacks and salad bars, and allergen-free options. Additionally, 86% of camp professionals indicated that they had implemented one or more strategies to address concerns related to the unhealthy eating behaviors of children, with top strategies including increasing the availability of fruits and vegetables, increasing the availability of healthy drink options, and improving the nutritional quality of menus. Fewer camp professionals (50%) indicated they had implemented strategies to increase children’s physical activity levels, but many professionals indicated that their camp programs were inherently active and additional strategies to promote physical activity were not necessary. Associations were found between camp affiliation and food options available to campers. The majority of camp professionals believe the healthy eating and physical activity of children are important issues for camps and have implemented strategies to address these issues. An important question for future research is to examine whether these strategies are effective in promoting healthy eating and physical activity behaviors in children, as well as ways that camp programs could be improved.

Objective measurement of physical activity remains an important challenge. For wearable monitors such as accelerometer-based physical activity monitors, more accurate methods are needed to convert activity counts into energy expenditure (EE). The purpose of this study was to examine the accuracy of the refined Crouter 2-Regression Model (C2RM) for estimating EE during the transition from rest to walking and walking to rest. A secondary purpose was to determine the extent of overestimation in minute-by-minute EE between the refined C2RM and the 2006 C2RM. Thirty volunteers (age, 28 ± 7.7 yrs) performed 15 minutes of seated rest, 8 minutes of over-ground walking, and 8 minutes of seated rest. An ActiGraph GT1M accelerometer and Cosmed K4b2 portable metabolic system were worn during all activities. Participants were randomly assigned to start the walking bout at 0, 20, or 40 s into the minute (according to the ActiGraph clock). Acceleration data were analyzed by two methods: 2006 Crouter model and a new refined model. The 2006 Crouter 2-Regression model over-predicted measured kcal kg-1 hr-1 during the first and last transitional minutes of the 20-s and 40-s walking conditions (P < 0.001). It also over-predicted the average EE for a walking bout (4.0 ± 0.5 kcal kg-1 hr-1), compared to both the measured kcal kg-1 hr-1 (3.6 ± 0.7 kcal kg-1 hr-1) and the refined Crouter model (3.5 ± 0.5 kcal kg-1 hr-1) (P < 0.05). The 2006 Crouter 2-regression model over-predicts EE at the beginning and end of walking bouts, due to high variability in accelerometer counts during the transitional minutes. The new refined model eliminates this problem and results in a more accurate prediction of EE during walking.

There is little empirical evidence of the impact of transition from primary to secondary school on obesity-related risk behaviour. The purpose of this study was to examine the effect of a change of school system on physical activity (PA) and sedentary behaviour in pre-early adolescents. Fifteen schools in Victoria, Australia were recruited at random from the bottom two strata of a five level socio-economic scale. In nine schools, students in year 6 primary school transitioned to a different school for year 7 secondary school, while in six schools (combined primary-secondary), students remained in the same school environment from year 6 to year 7. Time 1 (T1) measures were collected from students (N=245) in year 6 (age 11-13). Time 2 (T2) data were collected from 243 (99%) of the original student cohort when in year 7. PA and sedentary behaviour data were collected objectively (via ActiGraph accelerometer) and subjectively (via child self-report recall questionnaire). School environment data were collected via school staff survey. Change of behaviour analyses were conducted longitudinally i) for all students and ii) by change/no change of school. Mixed model regression analysis tested for behavioural interaction effects of changing/not changing school. Sixty-three percent (N=152) changed schools from T1 to T2. Across all students we observed declines in average daily moderate to vigorous physical activity (MVPA) (−4 min) and light PA (−23 min), and increases in average daily sedentary behaviour (16 min), weekday leisure screen time (17 min) and weekday homework screen time (25 min), all P<0.05. Compared to students who remained in the same school environment, students who changed school reported a greater reduction in PA intensity at recess and lunch, less likelihood to cycle to/from school, greater increase in weekday (41 mins) and weekend (45 mins) leisure screen time (P<0.05) and greater encouragement to participate in sport. School staff surveys identified that sport participation encouragement was greater in primary and combined primary-secondary than secondary schools (P<0.05). Transitioning from primary to secondary school negatively impacts on children’s PA and sedentary behaviour, and has further compounding effects on behaviour type by changing school environments.

Active travel (walking or cycling for transport) is associated with favourable health outcomes in adults. However, little is known about the concurrent patterns of health behaviour associated with active travel. We used compositional data analysis to explore differences in how people doing some active travel used their time compared to those doing no active travel, incorporating physical activity, sedentary behaviour and sleep. We analysed cross-sectional data from the 2014/15 United Kingdom Harmonised European Time Use Survey. Participants recorded two diary days of activity, and we randomly selected one day from participants aged 16 years or over. Activities were categorised into six mutually exclusive sets, accounting for the entire 24 h: (1) sleep; (2) leisure moderate to vigorous physical activity (MVPA); (3) leisure sedentary screen time; (4) non-discretionary time (work, study, chores and caring duties); (5) travel and (6) other. This mixture of activities was defined as a time-use composition. A binary variable was created indicating whether participants reported any active travel on their selected diary day. We used compositional multivariate analysis of variance (MANOVA) to test whether mean time-use composition differed between individuals reporting some active travel and those reporting no active travel, adjusted for covariates. We then used adjusted linear regression models and bootstrap confidence intervals to identify which of the six activity sets differed between groups. 6143 participants (mean age 48 years; 53% female) provided a valid diary day. There was a statistically significant difference in time-use composition between those reporting some active travel and those reporting no active travel. Those undertaking active travel reported a relatively greater amount of time in leisure MVPA and travel, and a relatively lower amount of time in leisure sedentary screen time and sleep. Compared to those not undertaking active travel, those who did active travel reported 11 min more in leisure MVPA and 18 min less in screen time per day, and reported lower sleep. From a health perspective, higher MVPA and lower screen time is favourable, but the pattern of sleep is more complex. Overall, active travel was associated with a broadly health-promoting composition of time across multiple behavioural domains, which supports the public health case for active travel.

Extensive research has demonstrated the role of the Home Environment (HE) in shaping children’s energy balance behaviours. Less is known about direct relationships with bodyweight. This review examines associations between the social and physical aspects of three pre-defined Home Environment domains (food, physical activity and media) and adiposity measures in children ≤12 years. Six electronic databases (PubMed, Medline, EBSCO CINAHL, EMBASE, Web of Science, PsycInfo) were systematically searched up to October 2020. Studies reporting at least one physical and/or social aspect of the food, physical activity and/or media domains of the Home Environment in relation to child adiposity outcomes were included (n = 62). Most studies examined one (n = 41) or two domains (n = 16). Only five studies assessed all three domains of the Home Environment. Most consistent relationships were observed for physical aspects of the home media environment; with greater availability of electronic devices associated with higher child adiposity (21/29 studies). Findings were less consistent for the smaller number of studies examining physical aspects of the home food or physical activity environments. 8/15 studies examining physical food environments reported null associations with adiposity. Findings were similarly mixed for physical activity environments; with 4/7 reporting null associations, 2/7 reporting negative associations and 1/7 reporting positive associations between access to physical activity equipment/garden space and adiposity. Fewer studies assessed social aspects (e.g. caregiver modelling or limit setting) of the Home Environment in relation to child adiposity and findings were again mixed; 9/16 media environment, 7/11 food environment and 9/13 physical activity environment studies reported null associations with child adiposity outcomes. The home media environment was most consistently associated with adiposity in childhood. Findings were less consistent for the home food and physical activity environments. Greater agreement on definitions and the measurement of the obesogenic home environment is required in order to clarify the strength and direction of relationships with child adiposity. Robust longitudinal research using comprehensive measures of the holistic home environment is needed to better identify which aspects contribute to excess weight gain in childhood. PROSPERO Systematic review registration number:  CRD42018115139 .

Active commuting may make an important contribution to population health. Accurate measures of these behaviours are required, but it is unknown how self-reported estimates compare to those derived from objective measures. We sought to develop methods for objectively deriving time spent in specific travel behaviours from a combination of locational and activity data, and to assess the convergent validity of two self-reported estimates. In 2010 and 2011, a sub-sample of participants from the Commuting and Health in Cambridge study concurrently completed objective monitoring using combined heart rate and movement sensors and global positioning system devices and reported their past-week commuting in a questionnaire (modes used, and usual time spent walking and cycling per trip) and in a day-by-day diary (all modes and durations). Automated and manual approaches were used to objectively identify total time spent using active and motorised modes. Agreement between self-reported and objectively-derived times was assessed using Lin’s concordance coefficients, Bland-Altman plots and signed-rank tests. Compared to objective assessments, day-by-day diary estimates of time spent using active modes on the commute were overestimated by a mean of 1.1 minutes/trip (95% limits of agreement (LOA): -7.7 to 9.9, p < 0.001). The magnitude of overestimation was slightly larger, but not significant (p = 0.247), when walking or cycling was used alone (mean: 2.4 minutes/trip, 95% LOA: -6.8 to 11.5). Total time spent on the commute was overestimated by a mean of 1.9 minutes/trip (95% LOA: -15.3 to 19.0, p < 0.001). The mean differences between self-reported usual time and objective estimates were -1.1 minutes/trip (95% LOA: -8.7 to 6.4) for cycling and +2.4 minutes/trip (95% LOA: -10.9 to 15.7) for walking. Mean differences between usual and daily estimates of time were <1 minute/trip for both walking and cycling. We developed a novel method of combining objective data to identify time spent using active and motorised modes, and total time spent commuting. Compared to objectively-derived times, self-reported times spent active commuting were slightly overestimated with wide LOA, suggesting that they should be used with caution to infer aggregate weekly quantities of activity on the commute at the individual level.

Unplanned readmission may result in consequences for both the individual and society. The transition of patients from hospital to postdischarge settings often represents a discontinuity of care and is considered crucial in the prevention of avoidable readmissions. In older patients, physical decline and malnutrition are considered risk factors for readmission. The purpose of the study was to determine the effects of nutritional and physical exercise interventions alone or in combination after hospital admission on the risk of hospital readmission among older people. A systematic review and meta-analysis of randomized controlled studies was conducted. The search involved seven databases (Medline, AMED, the Cochrane Library, CINAHL, Embase (Ovid), Food Science Source and Web of Science) and was conducted in November 2018. An update of this search was performed in March 2020. Studies involving older adults (65 years and above) investigating the effect of nutritional and/or physical exercise interventions on hospital readmission were included. A total of 11 randomized controlled studies (five nutritional, five physical exercise and one combined intervention) were included and assessed for quality using the updated Cochrane Risk of Bias Tool. Nutritional interventions resulted in a significant reduction in readmissions (RR 0.84; 95% CI 0.70–1.00, p = 0.049), while physical exercise interventions did not reduce readmissions (RR 1.05; 95% CI 0.84–1.31, p-value = 0.662). This meta-analysis suggests that nutrition support aiming to optimize energy intake according to patients’ needs may reduce the risk of being readmitted to the hospital for people aged 65 years or older.

The potential benefits of active school travel (AST) are widely recognized, yet there is consistent evidence of a systematic decline in the use of active modes of transportation to school since the middle part of the 20th century. This study explored parental accounts of the school travel mode choice decision-making process. Thirty-seven parents of children (17 who walked; 20 who were driven) from four elementary schools in Toronto, Canada participated in semi-structured interviews. The schools varied with respect to walkability of the built environment and socio-economic status. Thematic analysis of interview transcripts identified a two-stage decision-making process. An initial decision concerned the issue of escorting or chauffeuring a child to/from school. This decision appeared to be primarily influenced by concerns about traffic, the child's personal safety, and the child's maturity and cognitive ability regarding navigating his/her way to/from school safely. Following the escort decision, parents considered mode choice, typically selecting what they perceived to be the easiest and most convenient way to travel. The ascription of convenience to the various modes of transportation was influenced by perceptions of travel time and/or distance to/from school. Convenience became a particularly salient theme for parents who found it necessary to complete multi-activity trip chains. The school travel mode choice decision process is complex. Future research and practice should continue to address safety concerns that are typically the focus of active school transport initiatives while addressing more explicitly the behavioural cost of competing mode choices.

Reliable estimates of habitual sleep, physical activity, and sedentary time are essential to investigate the associations between these behaviours and health outcomes. While the number of days needed and hours/day for estimates of physical activity and sedentary time are generally known, the criteria for sleep estimates are more uncertain. The objective of this study was to identify the number of nights needed to obtain reliable estimates of habitual sleep behaviour using the GENEActiv wrist worn accelerometer. The number of days to obtain reliable estimate of physical activity was also examined. Data was used from a two-year longitudinal study. Children wore an accelerometer for up to 8 days 24 h/day across three timepoints. The sample included 2,745 children (51 % girls) between the ages of 7-12-years-old (mean = 9.8 years, SD = 1.1 year) with valid accelerometer data from any timepoint. Reliability estimates were calculated for sleep duration, sleep efficiency, sleep onset, wake time, time in bed, light physical activity, moderate physical activity, moderate-to-vigorous physical activity, vigorous physical activity, and sedentary time. Intraclass correlations and the Spearman Brown prophecy formula were used to determine the nights and days needed for reliable estimates. We found that between 3 and 5 nights were needed to achieve acceptable reliability (ICC = 0.7) in sleep outcomes, while physical activity and sedentary time outcomes required between 3 and 4 days. To obtain reliable estimates, researchers should consider these minimum criteria when designing their studies and prepare strategies to ensure sufficient wear time compliance.