The impacts of an mHealth intervention targeting parents on health service usage and out-of-pocket costs in the first 9 months of life: The Growing healthy app.

Mobile health (mHealth) interventions provide a low-cost, scalable approach to supporting parents with infant feeding advice with the potential to reduce health care visits and associated costs for infant feeding support. This Australian study examined the impact of the Growing healthy (GH) app on health service utilisation and out-of-pocket costs for families in the first 9 months of their infants life. A quasi-experimental study with a comparison group was conducted in 2015-2016 with an mHealth intervention group (GH app, n = 301) and a nonrandomized usual care group (n = 344). The GH app aimed to support parents of young infants with healthy infant feeding behaviours from birth to 9 months of age. App-generated notifications directed parents to age-and feeding-specific content within the app. Both groups completed surveys at baseline when infants were less than 3 months old (T1), at 6 months (T2) and 9 months (T3) of age. At T3, participants reported health services used and any out-of-pocket costs for advice on infant feeding, growth or activity. App users had lower odds (odds ratio: 0.38 95% confidence interval: 0.25, 0.59) of using one or more services and had lower number of visits to a general practitioner (1.0 vs. 1.5 visits, p = 0.003) and paediatrician (0.3 vs. 0.4 visits, p = 0.049) compared to the usual care group. There was no difference in out-of-pocket costs between groups. Provision of an evidenced-based infant feeding app may provide substantial savings to the health system and potentially to parents through fewer primary health care and paediatrician visits.

Perinatal support for breastfeeding using mHealth: A mixed methods feasibility study of the My Baby Now app.

Despite the well-known benefits of breastfeeding, breastfeeding rates remain suboptimal, particularly for women with lower socioeconomic position. Although popular, breastfeeding apps are often poor quality; their impact on breastfeeding knowledge, attitudes, confidence and intentions is unknown. A mixed method pre-post feasibility study was conducted to: 1) explore the feasibility of the My Baby Now app in providing perinatal breastfeeding support; 2) examine the impact on breastfeeding knowledge, attitudes, confidence and intentions; 3) to examine any differences in acceptability and impact of the app according to maternal education. The My Baby Now app was offered to pregnant women 20-30 weeks gestation. Breastfeeding knowledge and intentions were collected at baseline (T1) and 36-38 weeks gestation (T2); attitudes and confidence were collected at baseline, T2 and T3 (8-12 weeks post-partum). App engagement was measured via app analytics. Qualitative interviews were conducted with a purposeful sample following T3. Of 266 participants recruited, 169 (64%) completed T2 and 157 (59%) completed T3. Mothers without university education rated the app to be higher quality, more useful and impactful than mothers with university education. From T1-T2, breastfeeding knowledge (59.6% vs. 66.5%, p < 0.001) and exclusive breastfeeding intentions (76.6% vs. 80.9%, p < 0.001) increased. Breastfeeding attitudes and confidence scores also increased significantly across T1-T2 and T1-T3. App engagement during pregnancy predicted changes in breastfeeding attitudes from T1-T2 among participants without university education. App engagement did not predict changes in breastfeeding knowledge, confidence or intentions. Future randomised controlled studies should examine the effectiveness of mHealth interventions on breastfeeding outcomes.

Pregnancy nutrition knowledge of antenatal care providers: An evaluation of an online training module.

BACKGROUND: Nutrition education for clinicians providing pregnancy care has the potential to enhance pregnant women's diet quality, leading to healthier outcomes for mother and baby. Following a study demonstrating nutrition knowledge gaps, an online training module was developed, implemented, and evaluated. METHODS: Antenatal care providers completed a pregnancy nutrition knowledge questionnaire to assess knowledge levels at baseline (n = 97) and nine months after the introduction of online training (n = 64). FINDINGS: Knowledge scores at baseline and post-training implementation were not significantly different. Sub-group post-training knowledge scores between clinicians who completed the training module was higher than for those that did not complete the training module. User experience of the training module was positive and they felt it was useful in enhancing nutrition knowledge. Just under half of clinicians felt more confident in providing nutrition advice as part of their pregnancy care. Clinicians reported that time constraints prevented them from completing the nutrition training and/or providing nutrition education. Despite the positive experience of completing the online training module, a majority of clinicians surveyed indicated they preferred face-to-face training. CONCLUSION: Further research exploring how nutrition is prioritised in antenatal appointments and how knowledge levels influence nutrition education practices is needed.

Parental work hours and household income as determinants of unhealthy food and beverage intake in young Australian children.

OBJECTIVE: This study examined parental work hours and household income as determinants of discretionary (energy dense, nutrient poor) food and beverage intake in young children, including differences by eating occasion. DESIGN: Secondary analysis of cross-sectional data. Three hierarchical regression models were conducted with percent energy from discretionary food and beverages across the day, at main meals, and at snack times being the outcomes. Dietary intake was assessed by 1x24-hour recall and 1-2x24-hour food record(s). Both maternal/paternal work hours were included, and total household income. Covariates included household, parent and child factors. SETTING: Data from the NOURISH/SAIDI studies were collected between 2008-13. PARTICIPANTS: Participants included 526 mother-child dyads (median(IQR) child age 1.99(1.96,2.03) years). Forty-one percent of mothers did not work while 57% of fathers worked 35-40 hours/week. Most (85%) households had an income of ≥$50k AUD/year. RESULTS: Household income was consistently inversely associated with discretionary energy intake (ß= -0.12 to -0.15). Maternal part-time employment (21-35 hours/week) predicted child consumption of discretionary energy at main meals (ß=0.10, p=0.04). Paternal unemployment predicted a lower proportion of discretionary energy at snacks (ß= -0.09, p=0.047). CONCLUSIONS: This work suggests that household income should be addressed as a key opportunity-related barrier to healthy food provision in families of young children. Strategies to reduce the time burden of healthy main meal provision may be required in families where mothers juggle longer part-time working hours with caregiving and domestic duties. The need to consider the role of fathers and other parents/caregivers in shaping children's intake was also highlighted.

Commercially Available Apps to Support Healthy Family Meals: User Testing of App Utility, Acceptability, and Engagement.

BACKGROUND: Parents juggling caregiving and paid employment encounter a range of barriers in providing healthy food to their families. Mobile apps have the potential to help parents in planning, purchasing, and preparing healthy family food. The utility and acceptability of apps for supporting parents are unknown. User perspectives of existing technology, such as commercially available apps, can guide the development of evidence-based apps in the future. OBJECTIVE: This study aims to determine the feasibility of existing commercially available apps for supporting the healthy food provision practices of working parents. METHODS: Working parents (N=133) were recruited via the web and completed a 10-item Capability, Opportunity, Motivation, and Behavior (COM-B) self-evaluation survey assessing their needs in relation to the provision of healthy family meals. A total of 5 apps were selected for testing, including a meal planning app, recipe app, recipe manager app, family organizer app, and barcode scanning app. Survey items were mapped to app features, with a subsample of parents (67/133, 50.4%) allocated 2 apps each to trial simultaneously over 4 weeks. A semistructured interview exploring app utility and acceptability and a web-based survey, including the System Usability Scale and the user version of the Mobile App Rating Scale, followed app testing. The interview data were analyzed using a theoretical thematic approach. RESULTS: Survey participants (N=133; mean age 34 years, SD 4 years) were mainly mothers (130/133, 97.7%) and partnered (122/133, 91.7%). Participants identified a need for healthy recipes (109/133, 82% agreed or strongly agreed) and time for food provision processes (107/133, 80.5%). Engagement quality was the lowest rated domain of the user version of the Mobile App Rating Scale across all 5 apps (mean score per app ranging from 3.0 to 3.7 out of a maximum of 5). The family organizer, requiring a high level of user input, was rated the lowest for usability (median 48, IQR 34-73). In the interviews, participants weighed the benefits of the apps (ie, time saving) against the effort involved in using them in determining their acceptability. Organization was a subtheme emerging from interviews, associated with the use of meal planners and shopping lists. Meal planners and shopping lists were used in time, while behavior was occurring. CONCLUSIONS: Meal planning apps and features promoting organization present feasible, time-saving solutions to support healthy food provision practices. Attention must be paid to enhancing app automation and integration, as well as recipe and nutrition content, to ensure that apps do not add to the time burden of food provision and are supportive of healthy food provision behavior in time.

Consumer Engagement in Mobile Application (App) Interventions Focused on Supporting Infant Feeding Practices for Early Prevention of Childhood Obesity.

Background and Aims: There has been increasing interest in using mobile applications ("apps") for innovative health service delivery and public health interventions. This paper describes two independent studies investigating mothers' or pregnant women's perceptions of, interest in and experiences with technological devices, apps and websites about infant feeding practices. Methods: Study 1 was a cross-sectional survey conducted with 107 pregnant women in their third trimester in late 2016 and early 2017. Multiple logistic regression analyses were conducted to examine factors associated with their app usage. The second was a qualitative study of 29 mothers of infants aged <1 year conducted in 2014. Thematic network analysis was used to explore the themes from the transcribed interviews. Results: Study 1 found that the use of apps was common among the pregnant women, with 100% having previously downloaded an app on their phone either free or paid. About 60% had used an app for health purposes. The majority reported that they were likely to use an app promoting healthy infant feeding practices, including 30% extremely likely and 53% very likely. Women with university or other tertiary level of education were more likely to use an app for promoting healthy infant feeding practices than those with other levels of education (adjusted odds ratio 3.22, 95% confidence interval 1.28-8.13). The qualitative interviews found that all the mothers were interested in a mobile program to support them with infant feeding practices. Participants felt they would benefit from individualized messages although did not want them to be sent too frequently. Further, participants also expressed the importance of having non-judgmental information and they were interested in receiving information using different modes such as videos, SMS or an app. Conclusions: Both studies suggest that using apps for promoting healthy infant feeding practices is acceptable from the perspective of mothers. There is great potential for health promotion practitioners to be engaged in app development for the purpose of promoting health in early years and health promotion in general.

Mobile Apps to Support Healthy Family Food Provision: Systematic Assessment of Popular, Commercially Available Apps.

BACKGROUND: Modern families are facing conflicting demands on their time and resources, which may be at the detriment of child and family diet quality. Innovative nutrition interventions providing parents with behavioral support for the provision of healthy food could alleviate this issue. Mobile apps have the potential to deliver such interventions by providing practical behavioral support remotely, interactively, and in context. OBJECTIVE: This review aimed to identify and assess popular, commercially available food- and nutrition-related mobile apps that offer support for the provision of healthy family food by (1) describing app scope and characteristics, (2) assessing app quality, and (3) conducting a behavioral analysis of app content and features. METHODS: Searches in the Google Play Store and Apple App Store between August 2017 and November 2017 identified apps addressing the food provision process. Apps were included if they were applicable to parents or families, written in English, and with a user rating of ≥4 stars. Weight loss and diet monitoring apps and subscription apps with no free versions were excluded. App quality was assessed using the Mobile App Rating Scale (4 domains: engagement, functionality, aesthetics, and information). App content and features were extracted and behavior change techniques (BCTs) identified. RESULTS: Of the 2881 apps screened, 1.77% (51/2881) were included for assessment, comprising 23 recipe and recipe manager apps, 12 meal planning apps, 10 shopping list apps, 4 family organizers, and 2 food choice apps. Half (n=26) of the apps functioned primarily through user data input. Food choice and family organizer apps scored highest for app quality (mean 3.5 [SD 0.6] out of 5), whereas most apps scored well for functionality and poorly for engagement. Common app features with the potential to support healthy food provision included meal planners (n=26), shopping lists (n=44), and the ability to share app content (n=48). Behavioral support features mapped to relatively few BCTs (mean 3.9 [SD 1.9] per app), with Adding objects to the environment present in all apps, and 65% (33/51) including Instruction on how to perform the behavior. CONCLUSIONS: Recipe and recipe manager apps, meal planning apps, and family organizers with integrated meal planning and shopping lists scored well for functionality and incorporated behavioral support features that could be used to address barriers to healthy food provision, although features were focused on planning behaviors. Future apps should combine a range of features such as meal planners, shopping lists, simple recipes, reminders and prompts, and food ordering to reduce the burden of the food provision pathway and incorporate a range of BCTs to maximize behavior change potential. Researchers and developers should consider features and content that improve the engagement quality of such apps.

Impact of the Growing Healthy mHealth Program on Maternal Feeding Practices, Infant Food Preferences, and Satiety Responsiveness: Quasi-Experimental Study.

BACKGROUND: Infancy is an important life stage for obesity prevention efforts. Parents' infant feeding practices influence the development of infants' food preferences and eating behaviors and subsequently diet and weight. Mobile health (mHealth) may provide a feasible medium through which to deliver programs to promote healthy infant feeding as it allows low cost and easy access to tailored content. OBJECTIVE: The objective of this study was to describe the effects of an mHealth intervention on parental feeding practices, infant food preferences, and infant satiety responsiveness. METHODS: A quasi-experimental study was conducted with an mHealth intervention group (Growing Healthy) and a nonrandomized comparison group ("Baby's First Food"). The intervention group received access to a free app with age-appropriate push notifications, a website, and an online forum that provided them with evidence-based advice on infant feeding for healthy growth from birth until 9 months of age. Behavior change techniques were selected using the Behaviour Change Wheel framework. Participants in both groups completed three Web-based surveys, first when their infants were less than 3 months old (baseline, T1), then at 6 months (time 2, T2), and 9 months of age (time 3, T3). Surveys included questions on infant feeding practices and beliefs (Infant Feeding Questionnaire, IFQ), satiety responsiveness (Baby Eating Behaviour Questionnaire), and infant's food exposure and liking. Multivariate linear regression models, estimated using maximum likelihood with bootstrapped standard errors, were fitted to compare continuous outcomes between the intervention groups, with adjustment for relevant covariates. Multivariate logistic regression adjusting for the same covariates was performed for categorical outcomes. RESULTS: A total of 645 parents (Growing Healthy: n=301, Baby's First Food: n=344) met the eligibility criteria and were included in the study, reducing to a sample size of 546 (Growing Healthy: n=234, Baby's First Food: n=312) at T2 and a sample size of 518 (Growing Healthy: n=225, Baby's First Food: n=293) at T3. There were approximately equal numbers of boy and girl infants, and infants were aged less than 3 months at baseline (Growing Healthy: mean 7.0, SD 3.7 weeks; Baby's First Food: mean 7.9, SD 3.8 weeks), with Growing Healthy infants being slightly younger than Baby's First Food infants (P=.001). All but one (IFQ subscale "concerns about infant overeating or becoming overweight" at T2) of the measured outcomes did not differ between Growing Healthy and Baby's First Food. CONCLUSIONS: Although mHealth can be effective in promoting some health behaviors and offers many advantages in health promotion, the results of this study suggest that design and delivery characteristics needed to maximize the impact of mHealth interventions on infant feeding are uncertain. The sensitivity of available measurement tools and differences in baseline characteristics of participants may have also affected the results.

Key Lessons and Impact of the Growing Healthy mHealth Program on Milk Feeding, Timing of Introduction of Solids, and Infant Growth: Quasi-Experimental Study.

BACKGROUND: The first year of life is an important window to initiate healthy infant feeding practices to promote healthy growth. Interventions delivered by mobile phone (mHealth) provide a novel approach for reaching parents; however, little is known about the effectiveness of mHealth for child obesity prevention. OBJECTIVE: The objective of this study was to determine the feasibility and effectiveness of an mHealth obesity prevention intervention in terms of reach, acceptability, and impact on key infant feeding outcomes. METHODS: A quasi-experimental study was conducted with an mHealth intervention group (Growing healthy) and a nonrandomized comparison group (Baby's First Food). The intervention group received access to a free app and website containing information on infant feeding, sleep and settling, and general support for parents with infants aged 0 to 9 months. App-generated notifications directed parents to age-and feeding-specific content within the app. Both groups completed Web-based surveys when infants were less than 3 months old (T1), at 6 months of age (T2), and 9 months of age (T3). Survival analysis was used to examine the duration of any breastfeeding and formula introduction, and cox proportional hazard regression was performed to examine the hazard ratio for ceasing breast feeding between the two groups. Multivariate logistic regression with adjustment for a range of child and parental factors was used to compare the exclusive breastfeeding, formula feeding behaviors, and timing of solid introduction between the 2 groups. Mixed effect polynomial regression models were performed to examine the group differences in growth trajectory from birth to T3. RESULTS: A total of 909 parents initiated the enrollment process, and a final sample of 645 parents (Growing healthy=301, Baby's First Food=344) met the eligibility criteria. Most mothers were Australian born and just under half had completed a university education. Retention of participants was high (80.3%, 518/645) in both groups. Most parents (226/260, 86.9%) downloaded and used the app; however, usage declined over time. There was a high level of satisfaction with the program, with 86.1% (143/166) reporting that they trusted the information in the app and 84.6% (170/201) claiming that they would recommend it to a friend. However, some technical problems were encountered with just over a quarter of parents reporting that the app failed to work at times. There were no significant differences between groups in any of the target behaviors. Growth trajectories also did not differ between the 2 groups. CONCLUSIONS: An mHealth intervention using a smartphone app to promote healthy infant feeding behaviors is a feasible and acceptable mode for delivering obesity prevention intervention to parents; however, app usage declined over time. Learnings from this study will be used to further enhance the program so as to improve its potential for changing infant feeding behaviors.

Factors Influencing Engagement and Behavioral Determinants of Infant Feeding in an mHealth Program: Qualitative Evaluation of the Growing Healthy Program.

BACKGROUND: Infant feeding practices, including breastfeeding and optimal formula feeding practices, can play a role in the prevention of childhood obesity. The ubiquity of smartphone ownership among women of childbearing age provides important opportunities for the delivery of low-cost, broad reach parenting interventions delivered by mobile phone (mHealth or mobile health interventions). Little is known about how parents engage with mHealth programs targeting infant feeding and how such programs might influence infant feeding practices. OBJECTIVE: The objectives of this study were to explore participant views on (1) factors influencing engagement with the Growing healthy program, an mHealth program targeting healthy infant feeding practices from birth to 9 months of age, and (2) the ways in which the program influenced behavioral determinants of capability, opportunity, and motivation for breastfeeding and optimal formula feeding behaviors. METHODS: Semistructured, telephone interviews were conducted with a purposeful sample (n=24) of mothers participating in the Growing healthy program. Interviews explored participants' views about engagement with the program and its features, and the ways the program influenced determinants of infant feeding behaviors related to breastfeeding and optimal formula feeding. The interview schedule was informed by the Capability, Opportunity, Motivation, and Behavior (COM-B) model. RESULTS: Participants reported that engagement fluctuated depending on need and the degree to which the program was perceived to fit with existing parenting beliefs and values. Participants identified that the credibility of the program source, the user friendly interface, and tailoring of content and push notifications to baby's age and key transition points promoted engagement, whereas technical glitches were reported to reduce engagement. Participants discussed that the program increased confidence in feeding decisions. For breastfeeding mothers, this was achieved by helping them to overcome doubts about breast milk supply, whereas mothers using formula reported feeling more confident to feed to hunger and satiety cues rather than encouraging infants to finish the bottle. Participants discussed that the program provided around-the-clock, readily accessible, nonjudgmental information and support on infant feeding and helped to reinforce information received by health professionals or encouraged them to seek additional help if needed. Participants reflected that their plans for feeding were typically made before joining the program, limiting the potential for the program to influence this aspect of motivation. Rather, the program provided emotional reassurance to continue with current feeding plans. CONCLUSIONS: Our findings suggest that engagement with the program was influenced by an interplay between the program features and needs of the user. Participants reported that the program enhanced confidence in feeding decisions by providing a 24/7 accessible, expert, nonjudgmental support for infant feeding that complemented health professional advice. It is likely that interventions need to commence during pregnancy to maximize the impact on breastfeeding intentions and plans.

A Comparison of Recruitment Methods for an mHealth Intervention Targeting Mothers: Lessons from the Growing Healthy Program.

BACKGROUND: Mobile health (mHealth) programs hold great promise for increasing the reach of public health interventions. However, mHealth is a relatively new field of research, presenting unique challenges for researchers. A key challenge is understanding the relative effectiveness and cost of various methods of recruitment to mHealth programs. OBJECTIVE: The objectives of this study were to (1) compare the effectiveness of various methods of recruitment to an mHealth intervention targeting healthy infant feeding practices, and (2) explore factors influencing practitioner referral to the intervention. METHODS: The Growing healthy study used a quasi-experimental design with an mHealth intervention group and a concurrent nonrandomized comparison group. Eligibility criteria included: expectant parents (>30 weeks of gestation) or parents with an infant <3 months old, ability to read and understand English, own a mobile phone, ≥18 years old, and living in Australia. Recruitment to the mHealth program consisted of: (1) practitioner-led recruitment through Maternal and Child Health nurses, midwives, and nurses in general practice; (2) face-to-face recruitment by researchers; and (3) online recruitment. Participants' baseline surveys provided information regarding how participants heard about the study, and their sociodemographic details. Costs per participant recruited were calculated by taking into account direct advertising costs and researcher time/travel costs. Practitioner feedback relating to the recruitment process was obtained through a follow-up survey and qualitative interviews. RESULTS: A total of 300 participants were recruited to the mHealth intervention. The cost per participant recruited was lowest for online recruitment (AUD $14) and highest for practice nurse recruitment (AUD $586). Just over half of the intervention group (50.3%, 151/300) were recruited online over a 22-week period compared to practitioner recruitment (29.3%, 88/300 over 46 weeks) and face-to-face recruitment by researchers (7.3%, 22/300 over 18 weeks). No significant differences were observed in participant sociodemographic characteristics between recruitment methods, with the exception that practitioner/face-to-face recruitment resulted in a higher proportion of first-time parents (68% versus 48%, P=.002). Less than half of the practitioners surveyed reported referring to the program often or most of the time. Key barriers to practitioner referral included lack of time, difficulty remembering to refer, staff changes, lack of parental engagement, and practitioner difficulty in accessing the app. CONCLUSIONS: Online recruitment using parenting-related Facebook pages was the most cost effective and timely method of recruitment to an mHealth intervention targeting parents of young infants. Consideration needs to be given to addressing practitioner barriers to referral, to further explore if this can be a viable method of recruitment.

Factors influencing participation in a vascular disease prevention lifestyle program among participants in a cluster randomized trial.

BACKGROUND: Previous research suggests that lifestyle intervention for the prevention of diabetes and cardiovascular disease (CVD) are effective, however little is known about factors affecting participation in such programs. This study aims to explore factors influencing levels of participation in a lifestyle modification program conducted as part of a cluster randomized controlled trial of CVD prevention in primary care. METHODS: This concurrent mixed methods study used data from the intervention arm of a cluster RCT which recruited 30 practices through two rural and three urban primary care organizations. Practices were randomly allocated to intervention (n = 16) and control (n = 14) groups. In each practice up to 160 eligible patients aged between 40 and 64 years old, were invited to participate. Intervention practice staff were trained in lifestyle assessment and counseling and referred high risk patients to a lifestyle modification program (LMP) consisting of two individual and six group sessions over a nine month period. Data included a patient survey, clinical audit, practice survey on capacity for preventive care, referral and attendance records at the LMP and qualitative interviews with Intervention Officers facilitating the LMP. Multi-level logistic regression modelling was used to examine independent predictors of attendance at the LMP, supplemented with qualitative data from interviews with Intervention Officers facilitating the program. RESULTS: A total of 197 individuals were referred to the LMP (63% of those eligible). Over a third of patients (36.5%) referred to the LMP did not attend any sessions, with 59.4% attending at least half of the planned sessions. The only independent predictors of attendance at the program were employment status - not working (OR: 2.39 95% CI 1.15-4.94) and having high psychological distress (OR: 2.17 95% CI: 1.10-4.30). Qualitative data revealed that physical access to the program was a barrier, while GP/practice endorsement of the program and flexibility in program delivery facilitated attendance. CONCLUSION: Barriers to attendance at a LMP for CVD prevention related mainly to external factors including work commitments and poor physical access to the programs rather than an individuals' health risk profile or readiness to change. Improving physical access and offering flexibility in program delivery may enhance future attendance. Finally, associations between psychological distress and attendance rates warrant further investigation. TRIAL REGISTRATION: ACTRN12607000423415.

The impact of a brief lifestyle intervention delivered by generalist community nurses (CN SNAP trial).

BACKGROUND: The risk factors for chronic disease, smoking, poor nutrition, hazardous alcohol consumption, physical inactivity and weight (SNAPW) are common in primary health care (PHC) affording opportunity for preventive interventions. Community nurses are an important component of PHC in Australia. However there has been little research evaluating the effectiveness of lifestyle interventions in routine community nursing practice. This study aimed to address this gap in our knowledge. METHODS: The study was a quasi-experimental trial involving four generalist community nursing (CN) services in New South Wales, Australia. Two services were randomly allocated to an 'early intervention' and two to a 'late intervention' group. Nurses in the early intervention group received training and support in identifying risk factors and offering brief lifestyle intervention for clients. Those in the late intervention group provided usual care for the first 6 months and then received training. Clients aged 30-80 years who were referred to the services between September 2009 and September 2010 were recruited prior to being seen by the nurse and baseline self-reported data collected. Data on their SNAPW risk factors, readiness to change these behaviours and advice and referral received about their risk factors in the previous 3 months were collected at baseline, 3 and 6 months. Analysis compared changes using univariate and multilevel regression techniques. RESULTS: 804 participants were recruited from 2361 (34.1%) eligible clients. The proportion of clients who recalled receiving dietary or physical activity advice increased between baseline and 3 months in the early intervention group (from 12.9 to 23.3% and 12.3 to 19.1% respectively) as did the proportion who recalled being referred for dietary or physical activity interventions (from 9.5 to 15.6% and 5.8 to 21.0% respectively). There was no change in the late intervention group. There a shift towards greater readiness to change in those who were physically inactive in the early but not the comparison group. Clients in both groups reported being more physically active and eating more fruit and vegetables but there were no significant differences between groups at 6 months. CONCLUSION: The study demonstrated that although the intervention was associated with increases in advice and referral for diet or physical activity and readiness for change in physical activity, this did not translate into significant changes in lifestyle behaviours or weight. This suggests a need to facilitate referral to more intensive long-term interventions for clients with risk factors identified by primary health care nurses. TRIAL REGISTRATION: ACTRN12609001081202.

The impact of a team-based intervention on the lifestyle risk factor management practices of community nurses: outcomes of the community nursing SNAP trial.

BACKGROUND: Lifestyle risk factors like smoking, nutrition, alcohol consumption, and physical inactivity (SNAP) are the main behavioural risk factors for chronic disease. Primary health care is an appropriate setting to address these risk factors in individuals. Generalist community health nurses (GCHNs) are uniquely placed to provide lifestyle interventions as they see clients in their homes over a period of time. The aim of the paper is to examine the impact of a service-level intervention on the risk factor management practices of GCHNs. METHODS: The trial used a quasi-experimental design involving four generalist community nursing services in NSW, Australia. The services were randomly allocated to either an intervention group or control group. Nurses in the intervention group were provided with training and support in the provision of brief lifestyle assessments and interventions. The control group provided usual care. A sample of 129 GCHNs completed surveys at baseline, 6 and 12 months to examine changes in their practices and levels of confidence related to the management of SNAP risk factors. Six semi-structured interviews and four focus groups were conducted among the intervention group to explore the feasibility of incorporating the intervention into everyday practice. RESULTS: Nurses in the intervention group became more confident in assessment and intervention over the three time points compared to their control group peers. Nurses in the intervention group reported assessing physical activity, weight and nutrition more frequently, as well as providing more brief interventions for physical activity, weight management and smoking cessation. There was little change in referral rates except for an improvement in weight management related referrals. Nurses' perception of the importance of 'client and system-related' barriers to risk factor management diminished over time. CONCLUSIONS: This study shows that the intervention was associated with positive changes in self-reported lifestyle risk factor management practices of GCHNs. Barriers to referral remained. The service model needs to be adapted to sustain these changes and enhance referral. TRIAL REGISTRATION: ACTRN12609001081202.

A cluster randomised controlled trial of vascular risk factor management in general practice.

OBJECTIVE: To evaluate the impact of a lifestyle intervention in Australian general practice to reduce the risk of vascular disease. DESIGN, SETTING AND PARTICIPANTS: Stratified cluster randomised controlled trial among 30 general practices in New South Wales from July 2008 to January 2010. Patients aged 40-64 years were invited to participate. The subgroup who were 40-55 years of age were included only if they had either hypertension or dyslipidaemia. INTERVENTION: A general practice-based health-check with brief lifestyle counselling and referral of high-risk patients to a program consisting of one to two individual visits with an exercise physiologist or dietitian, and six group sessions. MAIN OUTCOME MEASURES: Outcomes at baseline, 6 and 12 months included the behavioural and physiological risk factors for vascular disease - self-reported diet and physical activity, and measured weight, body mass index, waist circumference, blood lipid and blood sugar levels, and blood pressure. RESULTS: Of the 3128 patients who were invited, 958 patients (30.6%) responded and 814 were eligible to participate. Of these, 699 commenced the study, and 655 remained in the study at 12 months. Physical activity levels increased to a greater extent in the intervention group than the control group at 6 and 12 months (P = 0.005). There were no other changes in behavioural or physiological outcomes or in estimated absolute risk of cardiovascular disease at 12 months. Of the 384 enrolled in the intervention group, 117 patients (30.5%) attended the minimum number of group program sessions and lost more weight (mean weight loss, 1.06 kg) than those who did not attend the minimum number of sessions (mean weight gain, 0.73 kg). CONCLUSION: While patients who received counselling by their general practitioner increased self-reported physical activity, only those who attended the group sessions sustained an improvement in weight. However, more research is needed to determine whether group programs offer significant benefits over individual counselling in general practice. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12607000423415.

Factors influencing participant enrolment in a diabetes prevention program in general practice: lessons from the Sydney diabetes prevention program.

BACKGROUND: The effectiveness of lifestyle interventions in reducing diabetes incidence has been well established. Little is known, however, about factors influencing the reach of diabetes prevention programs. This study examines the predictors of enrolment in the Sydney Diabetes Prevention Program (SDPP), a community-based diabetes prevention program conducted in general practice, New South Wales, Australia from 2008-2011. METHODS: SDPP was an effectiveness trial. Participating general practitioners (GPs) from three Divisions of General Practice invited individuals aged 50-65 years without known diabetes to complete the Australian Type 2 Diabetes Risk Assessment tool. Individuals at high risk of diabetes were invited to participate in a lifestyle modification program. A multivariate model using generalized estimating equations to control for clustering of enrolment outcomes by GPs was used to examine independent predictors of enrolment in the program. Predictors included age, gender, indigenous status, region of birth, socio-economic status, family history of diabetes, history of high glucose, use of anti-hypertensive medication, smoking status, fruit and vegetable intake, physical activity level and waist measurement. RESULTS: Of the 1821 eligible people identified as high risk, one third chose not to enrol in the lifestyle program. In multivariant analysis, physically inactive individuals (OR: 1.48, P = 0.004) and those with a family history of diabetes (OR: 1.67, P = 0.000) and history of high blood glucose levels (OR: 1.48, P = 0.001) were significantly more likely to enrol in the program. However, high risk individuals who smoked (OR: 0.52, P = 0.000), were born in a country with high diabetes risk (OR: 0.52, P = 0.000), were taking blood pressure lowering medications (OR: 0.80, P = 0.040) and consumed little fruit and vegetables (OR: 0.76, P = 0.047) were significantly less likely to take up the program. CONCLUSIONS: Targeted strategies are likely to be needed to engage groups such as smokers and high risk ethnic groups. Further research is required to better understand factors influencing enrolment in diabetes prevention programs in the primary health care setting, both at the GP and individual level.

Predictors of primary care referrals to a vascular disease prevention lifestyle program among participants in a cluster randomised trial.

BACKGROUND: Cardiovascular disease accounts for a large burden of disease, but is amenable to prevention through lifestyle modification. This paper examines patient and practice predictors of referral to a lifestyle modification program (LMP) offered as part of a cluster randomised controlled trial (RCT) of prevention of vascular disease in primary care. METHODS: Data from the intervention arm of a cluster RCT which recruited 36 practices through two rural and three urban primary care organisations were used. In each practice, 160 eligible high risk patients were invited to participate. Practices were randomly allocated to intervention or control groups. Intervention practice staff were trained in screening, motivational interviewing and counselling and encouraged to refer high risk patients to a LMP involving individual and group sessions. Data include patient surveys; clinical audit; practice survey on capacity for preventive care; referral records from the LMP. Predictors of referral were examined using multi-level logistic regression modelling after adjustment for confounding factors. RESULTS: Of 301 eligible patients, 190 (63.1%) were referred to the LMP. Independent predictors of referral were baseline BMI ≥ 25 (OR 2.87 95%CI:1.10, 7.47), physical inactivity (OR 2.90 95%CI:1.36,6.14), contemplation/preparation/action stage of change for physical activity (OR 2.75 95%CI:1.07, 7.03), rural location (OR 12.50 95%CI:1.43, 109.7) and smaller practice size (1-3 GPs) (OR 16.05 95%CI:2.74, 94.24). CONCLUSIONS: Providing a well-structured evidence-based lifestyle intervention, free of charge to patients, with coordination and support for referral processes resulted in over 60% of participating high risk patients being referred for disease prevention. Contrary to expectations, referrals were more frequent from rural and smaller practices suggesting that these practices may be more ready to engage with these programs. TRIAL REGISTRATION: ACTRN12607000423415.

Diabetes prevention research: a systematic review of external validity in lifestyle interventions.

CONTEXT: Type 2 diabetes is a major contributor to disease burden globally. A number of systematic reviews support the efficacy of lifestyle interventions in preventing Type 2 diabetes in adults; however, relatively little attention has been paid to the generalizability of study findings. This study systematically reviews the reporting of external validity components and generalizability of diabetes prevention studies. EVIDENCE ACQUISITION: Lifestyle intervention studies for the prevention of Type 2 diabetes in adults with at least 6 months' follow-up, published between 1990 and 2011, were identified through searches of major electronic databases. External validity reporting was rated using an assessment tool, and all analysis was undertaken in 2011. EVIDENCE SYNTHESIS: A total of 31 primary studies (n=95 papers) met the selection criteria. All studies lacked full reporting on external validity elements. Description of the study sample, intervention, delivery agents, and participant attrition rates were reported by most studies. However, few studies reported on the representativeness of individuals and settings, methods for recruiting settings and delivery agents, costs, and how interventions could be institutionalized into routine service delivery. It is uncertain to what extent the findings of diabetes prevention studies apply to men, socioeconomically disadvantaged individuals, those living in rural and remote communities, and to low- and middle-income countries. CONCLUSIONS: Reporting of external validity components in diabetes prevention studies needs to be enhanced to improve the evidence base for the translation and dissemination of these programs into policy and practice.

How feasible are lifestyle modification programs for disease prevention in general practice?

Vascular disease is a leading cause of death and disability. While it is preventable, little is known about the feasibility or acceptability of implementing interventions to prevent vascular disease in Australian primary health care. We conducted a cluster randomised controlled trial assessing prevention of vascular disease in patients aged 40-65 by providing a lifestyle modification program in general practice. Interviews with 13 general practices in the intervention arm of this trial examined their views on implementing the lifestyle modification program in general practice settings. Qualitative study, involving thematic analysis of semi-structured interviews with 11 general practitioners, four practice nurses and five allied health providers between October 2009 and April 2010. Providing brief lifestyle intervention fitted well with routine health-check consultations; however, acceptance and referral to the program was dependent on the level of facilitation provided by program coordinators. Respondents reported that patients engaged with the advice and strategies provided in the program, which helped them make lifestyle changes. Practice nurse involvement was important to sustaining implementation in general practice, while the lack of referral services for people at risk of developing vascular disease threatens maintenance of lifestyle changes as few respondents thought patients would continue lifestyle changes without long-term follow up. Lifestyle modification programs to prevent vascular disease are feasible in general practice but must be provided in a flexible format, such as being offered out of hours to facilitate uptake, with ongoing support and follow up to assist maintenance. The newly formed Medicare Locals may have an important role in facilitating lifestyle modification programs for this target group.

Is there scope for community health nurses to address lifestyle risk factors? the community nursing SNAP trial.

BACKGROUND: This paper examines the opportunity and need for lifestyle interventions for patients attending generalist community nursing services in Australia. This will help determine the scope for risk factor management within community health care by generalist community nurses (GCNs). METHODS: This was a quasi-experimental study conducted in four generalist community nursing services in NSW, Australia. Prior to service contacts, clients were offered a computer-assisted telephone interview to collect baseline data on socio-demographics, health conditions, smoking status, physical activity levels, alcohol consumption, height and weight, fruit and vegetable intake and 'readiness-to-change' for lifestyle risk factors. RESULTS: 804 clients participated (a response rate of 34.1%). Participants had higher rates of obesity (40.5% vs 32.1%) and higher prevalence of multiple risk factors (40.4% vs 29.5%) than in the general population. Few with a SNAPW (Smoking-Nutrition-Alcohol-Physical-Activity-Weight) risk factor had received advice or referral in the previous 3 months. The proportion of clients identified as at risk and who were open to change (i.e. contemplative, in preparation or in action phase) were 65.0% for obese/overweight; 73.8% for smokers; 48.2% for individuals with high alcohol intake; 83.5% for the physically inactive and 59.0% for those with poor nutrition. CONCLUSIONS: There was high prevalence of lifestyle risk factors. Although most were ready to change, few clients recalled having received any recent lifestyle advice. This suggests that there is considerable scope for intervention by GCNs. The results of this trial will shed light on how best to implement the lifestyle risk factor management in routine practice.