Article Text

Original research
Preventing chronic disease in overweight and obese patients with low health literacy using eHealth and teamwork in primary healthcare (HeLP-GP): a cluster randomised controlled trial
  1. Sharon M Parker1,
  2. Margo Barr1,
  3. Nigel Stocks2,
  4. Elizabeth Denney-Wilson3,
  5. Nicholas Zwar4,
  6. Jon Karnon5,
  7. Alamgir Kabir1,
  8. Don Nutbeam6,
  9. Jackie Roseleur5,
  10. Siaw-Teng Liaw7,
  11. Carmel McNamara8,
  12. Oliver Frank2,
  13. An Tran1,
  14. Richard Osborne9,
  15. Annie Y S Lau10,
  16. Mark Harris1
  1. 1Centre for Primary Health Care and Equity, University of New South Wales, Sydney, New South Wales, Australia
  2. 2Discipline of General Practice, Adelaide Medical School, The University of Adelaide, Adelaide, South Australia, Australia
  3. 3Sydney Nursing School, University of Sydney, Sydney, New South Wales, Australia
  4. 4Health Sciences & Medicine, Bond University, Gold Coast, Queensland, Australia
  5. 5Flinders Medical Research Institute, Flinders University, Adelaide, South Australia, Australia
  6. 6Public Health, The University of Sydney, Sydney, New South Wales, Australia
  7. 7School of Population Health, University of New South Wales, Sydney, New South Wales, Australia
  8. 8College of Nursing and Health Sciences, Flinders University, Adelaide, New South Wales, Australia
  9. 9Centre for Global Health and Equity, Swinburne University of Technology, Burwood, Victoria, Australia
  10. 10Australian Institute of Health Innovation, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, New South Wales, Australia
  1. Correspondence to Mark Harris; m.f.harris{at}unsw.edu.au

Abstract

Objectives To evaluate a multifaceted intervention on diet, physical activity and health literacy of overweight and obese patients attending primary care.

Design A pragmatic two-arm cluster randomised controlled trial.

Setting Urban general practices in lower socioeconomic areas in Sydney and Adelaide.

Participants We aimed to recruit 800 patients in each arm. Baseline assessment was completed by 215 patients (120 intervention and 95 control).

Intervention A practice nurse-led preventive health check, a mobile application and telephone coaching.

Primary and secondary outcome measures Primary outcomes were measured at baseline, 6 and 12 months, and included patient health and eHealth literacy, weight, waist circumference and blood pressure. Secondary outcomes included changes in diet and physical activity, preventive advice and referral, blood lipids, quality of life and costs. Univariate and multivariate analyses of difference-in-differences (DiD) estimates for each outcome were conducted.

Results At 6 months, the intervention group, compared with the control group, demonstrated a greater increase in Health Literacy Questionnaire domain 8 score (ability to find good health information; mean DiD 0.22; 95% CI 0.01 to 0.44). There were similar differences for domain 9 score (understanding health information well enough to know what to do) among patients below the median at baseline. Differences were reduced and non-statistically significant at 12 months. There was a small improvement in diet scores at 6 months (DiD 0.78 (0.10 to 1.47); p=0.026) but not at 12 months. There were no differences in eHealth literacy, physical activity scores, body mass index, weight, waist circumference or blood pressure.

Conclusions Targeted recruitment and engagement were challenging in this population. While the intervention was associated with some improvements in health literacy and diet, substantial differences in other outcomes were not observed. More intensive interventions and using codesign strategies to engage the practices earlier may produce a different result. Codesign may also be valuable when targeting lower socioeconomic populations.

Trial registration number Australian New Zealand Clinical Trials Registry (ACTRN 12617001508369) (http://www.ANZCTR.org.au/ACTRN12617001508369.aspx).

Trial protocol The protocol for this trial has been published (open access; https://bmjopen.bmj.com/content/8/6/e023239).

  • PRIMARY CARE
  • PREVENTIVE MEDICINE
  • Health informatics

Data availability statement

Individual participant data that underlie the results reported in this article will be available within 12 months of publication after deidentification (text, tables, figures and appendices) to investigators whose proposed use of the data has been approved by our Ethics Committee.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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STRENGTHS AND LIMITATIONS OF THIS STUDY

  • The cluster randomised design allowed testing of the nurse-led intervention among patients without contamination.

  • Recruitment of practices and patients did not meet our planned sample size.

  • We noted variable uptake of the intervention components among patients reflecting real-world general practice.

  • The measures used to assess health literacy, diet and physical activity had some limitations.

  • The study was conducted in only two urban areas of Australia and the findings may not therefore be generalised to other communities, such as rural areas.

Introduction

Obesity is a complex health issue and is influenced by biological, environmental, social and psychological factors.1 Overweight and obesity account for 8.4% of the burden of disease being a risk factor for 11 types of cancer, 3 cardiovascular conditions, chronic kidney disease, diabetes, dementia, gallbladder disease, fatty liver, gout, back pain and osteoarthritis.2 In 2017/2018, 67% of the Australian population were overweight (body mass index (BMI) 25–29 kg/m2; 35.6%) or obese (BMI 30+ kg/m2; 31.3%) with those who were more socially disadvantaged being more likely to be overweight or obese.3 Within Australia, rates of overweight and obesity peak for men at age 55–64 years (83.6%) and for women at 65–74 years (73.3%).4

Current Australian guidelines recommend that people who are overweight and obese attending general practice undergo routine measurements (BMI and waist circumference) and are engaged in discussions about lifestyle risk factors and positive messaging to improve health and well-being.5 Behavioural interventions in primary care have been demonstrated to achieve a 5%–7% improvement in weight, blood pressure (BP) or lipids for patients, potentially preventing or delaying the onset of type 2 diabetes and cardiovascular disease.6 A recent systematic review and meta-analysis supports weight loss programmes delivered by primary care practitioners as they provide effective weight loss and reduction in waist circumference.7 Multicomponent intensive behavioural interventions (delivered by various clinicians and provided through group, individual, technology or print-based methods) have been recommended for patients with a BMI of 30 or higher.8 Health coaching provided by a trained professional has become a popular tool to address weight through behaviour change strategies9 and high-intensity behavioural counselling (12 or more sessions per year delivered in person, by phone or electronically) is accepted to produce clinically meaningful weight loss.10

The Track Study11 which combined tailored weight-related behaviour change goals for patients as a basis for self-monitoring with 18 coaching calls over 12 months found intervention patients significantly more likely to lose ≥5% of their baseline weight at 6 months and 12 months. A recent retrospective analysis of 25 000 people receiving blended care behaviour change interventions (a combination of digital care and coaching)12 supports the use of these interventions for weight loss but highlights the need for more understanding as to which elements would be best delivered by health coaches and which can be delegated to a digital device.

Patients generally accept their general practitioners’ (GPs) role in management of overweight and obesity13; however, lower socioeconomic groups tend to be less likely to take up weight management programmes.14 15 Low functional health literacy (ie, health-related reading and numeracy) is more common in socioeconomically disadvantaged populations and is associated with an increased likelihood of overweight and obesity.16 17 It is also a potential barrier to the uptake and effectiveness of a range of preventive interventions that mediate change in lifestyle behaviours.18 19 Patients with low health literacy are less likely to engage in health-promoting behaviours20–22 and attend or complete programmes to which they have been referred.23 24 Interventions with multiple components to improve health literacy for behavioural risk factors have been shown to be more effective at improving nutritional health literacy in primary care than those with single components.6 Other barriers to delivering weight loss management have also been identified, including low confidence levels of clinicians in obesity management,25 stigmatisation of patients26 and lost opportunities by providers to initiate earlier, effective weight loss conversations.27

Objectives

The HeLP-GP trial aimed to evaluate a multifaceted intervention provided to overweight and obese patients attending primary care. The primary hypothesis was that the intervention would lead to improved health literacy, eHealth literacy, physiological risk factors, lifestyle behaviours and quality of life.

Methods

Trial design

A pragmatic, two-arm, unblinded cluster randomised controlled trial. This design was chosen to provide protection against contamination within sites (general practices) as practice staff were providing the intervention. Primary and secondary outcomes were assessed at the patient level.

Participants and setting

The trial was conducted in general practices located in metropolitan and urban fringe areas of south-western and western Sydney in New South Wales and Adelaide in South Australia. Practice eligibility included:

  • Geographical location in Local Government Areas with a Socio-Economic Index for Area Index of Relative Socio-economic Disadvantage28 equal to or below the eighth decile.

  • Using clinical software compatible with the trial data extraction and recruitment tool, Doctors Control Panel (DCP),29 and an active internet connection.

  • Participation by at least one practice nurse (PN) and one GP from the practice.

  • Participation of reception staff to distribute trial materials to eligible trial participants as they present for appointments.

Patient eligibility included:

  • Aged 40–74 years.

  • BMI ≥28 recorded within the previous 12 months (the cut-off point for BMI was chosen to target people at higher risk and to capture people from Asian backgrounds who have a lower equivalent BMI).

  • BP and total serum cholesterol recorded within the previous 12 months.

  • Speaking English and/or Arabic, Vietnamese or Chinese (languages representing common migrant groups in the catchment areas—there were very few patients who spoke other languages but not English).

  • Access to a smartphone or tablet device and internet connection.

Patients were excluded if they:

  • Had a diagnosis of diabetes requiring insulin or a current prescription for insulin, a diagnosis of cardiovascular disease (angina, myocardial infarction, heart failure, heart valve disease (rheumatic or non-rheumatic)), stroke (cerebrovascular accident).

  • Had experienced weight loss of >5% in the past 3 months, were taking medication for weight loss (orlistat or phentermine) or had undergone weight loss surgery.

  • Had cognitive impairment (including serious mental illness).

  • Had a physical impairment which would prohibit engaging in moderate-level physical activity.

Practice recruitment

Between March 2018 and October 2018, general practices within the specified geographical locations were approached by partner Primary Health Networks (PHNs), which are regional organisations providing quality improvement and education to general practices. Invitations to express interest were distributed through mail, email, newsletters, GP educational events, websites, Facebook groups for health professionals, discussion groups and research networks. A face-to-face meeting was held between responding practices, a PHN representative and a member of the research team to discuss participation in detail and confirm eligibility.

Randomisation

Randomisation of practices was performed by an epidemiologist (MB) who was not involved in the data collection or intervention using the SAS30 v.9.4 statistical package. Practices were characterised by size (fewer than five GPs, or five or more GPs) and by state into four strata, and intervention and control lists of random numbers (six-digit) were generated for each stratum. The resultant intervention and control strata lists were combined and sorted. Four batches were created. Allocation of intervention or control was then sequentially allocated from the lists based on the date of entry of the practice into the study by an independent researcher. Batching was undertaken to ensure similar numbers of control and intervention practices at any point in time. Practices were informed in writing as to what allocation they had received.

Recruitment of patients

From October 2018 to September 2019, patients of participating practices were flagged at the point of presentation using DCP. The software was programmed with clinical inclusion/exclusion criteria to identify potential participants as they presented. Once flagged, patient information was automatically printed and attached to trial information and consent forms by the reception staff. It was not the responsibility of GPs to gain consent, but patients could discuss the trial with their GP or PN. As DCP was only able to determine eligibility based on the information within the practice’s clinical software, eligibility was also checked by a member of the practice. Patients could return their consent forms by leaving them in a secure collection point at the practice or returning them in a reply-paid envelope to the study centre (University of New South Wales, Sydney).

The HeLP-GP intervention

The intervention was a multicomponent intervention, which has been previously described and piloted.31 32 It aimed to increase the knowledge of patients relating to diet and physical activity and their individual skills to address weight management behaviours. It comprised:

  1. A PN-led health check designed to support Australian Guidelines for the management of overweight and obesity5 33 and based on the 5As (Assess, Advise, Agree, Assist and Arrange).34 35 Review was conducted by the PN at 6 weeks and the GP at 12 weeks.

  2. A lifestyle app (mysnapp) modified from healthy.me, a personally controlled health management platform designed to help patients and consumers to manage their health.36 The components of mysnapp were informed by research into behaviour change through mobile and electronic platforms that suggest that goal setting and self-monitoring, and additional methods to interact with patients, particularly text messaging, can be more effective than advice alone.37–40 Mysnapp allowed patients to set and revise physical activity and diet-based goals and to view graphs of their progress over the previous 6 weeks. A free-text diary allowed patients to document individualised content. A range of video and written resources related to diet and physical activity, linked to the app, were available for the patient to view. Text messages reminded patients to attend the follow-up with the PN and GP and once registered, each patient received one nutrition and one physical activity message each week for 6 weeks.32

  3. Health coaching via the ‘Get Healthy’ telephone coaching programme (https://www.gethealthynsw.com.au/) provided free, confidential telephone-based health coaching to support patients to reach personalised lifestyle goals relating to healthy eating, increasing physical activity, alcohol reduction and achieving and maintaining a healthy weight. Coaching was available in multiple languages with the assistance of an interpreter service.

At the health check, patients could choose to take up mysnapp, Get Healthy or both. Control practices provided ‘usual care’ (the clinical practice routinely offered to patients by the GP and PN of the practice).

Training and implementation of the intervention

Training was completed by all participating PNs. Training comprised three online modules covering physical assessment (weight, height, BP, waist circumference and BMI), delivery of relevant lifestyle advice and promotion of individual goal setting. The ‘teach-back’ method41 (asking the patient to repeat in their own words what they have understood) was encouraged to ensure they had understood and were confident with the content of the health check. PNs assisted patients to download and set up mysnapp including setting goals during the health check and were encouraged to review the patient’s use of the app and the progress of health coaching at the 6-week follow-up. Written and video resources were developed for PNs and patients on the installation and use of the app. PNs referred patients to Get Healthy using a trial-specific online referral form.

Patients could claim Medicare benefits (usually without out-of-pocket payments) for GP visits as part of the intervention (Medicare is Australia’s national universal health insurance scheme). Patients did not pay for the PN visits. The PN health checks were reimbursed directly to the practice by the study at a rate of $A40 per patient for the health check and $A20 per patient for follow-up.

Patient and public involvement

Patients and members of the public were not involved in the design of this study. Consumer volunteers with the Adelaide PHN did pilot the lifestyle app (mysnapp) and provide input to its final design.

Data collection and trial outcomes

The methods are described in the protocol paper.32 Table 1 provides a summary of the data collected to assess trial outcomes, the collection method and the time points of collection. A proposed 18-month follow-up of patients was abandoned due to the need to extend the period for patient recruitment and lower than expected numbers of patients being recruited to the trial. Surveys administered over the telephone were used to collect demographic and other patient data.

Table 1

Patient-level outcomes

Primary outcomes

We used two domains of the Health Literacy Questionnaire (HLQ) (domain 8: ability to find good health information (five items) and domain 9: understand health information well enough to know what to do (five items)).42 The individual domains of the HLQ can be selected to identify specific health literacy strengths and challenges or to test a hypothesis.43 44 Domains 8 and 9 have a 5-point response option scale (cannot do or always difficult, usually difficult, sometimes difficult, usually easy or always easy). The scores for these domains are averages for the domain (with a range between 1 and 5). The electronic Health Literacy Scale (eHEALS) was used to assess digital health literacy.45

DCP was used to extract clinical patient data related to biomedical risk factors (BMI, systolic and diastolic BP, and waist circumference). We used the measurements recorded by the GP at the nearest time point to follow-up (baseline and 12-month follow-up interviews).

Secondary outcomes

Patient self-report was used to determine lifestyle behaviours including a diet score (portions of fruit (between 0 and a maximum of 2 per day) plus portions of vegetable intake (between 0 and a maximum of 5 per day) with a range between 0 and 7 based on the sum of fruit and vegetable scores), the number of 30-minute sessions of physical activity (moderate/vigorous) per week and changes in diet and physical activity. Questions to assess these behaviours were adapted from previous research.46 47 The scores for diet were between 1 and 7.

Patient self-report was used to determine advice received and referral for diet, physical activity and weight loss. Patient questions also assessed quality of life (using the EQ-5D-5L standardised to UK reference population with no imputation of missing values).48 49 Total cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL) and triglyceride (TG) values were extracted by the DCP from the GP medical record at baseline and 12-month follow-up.

Sample size calculation

The original sample size calculation of 400 in each arm was based on the primary hypothesis that the intervention would lead to improved health and eHealth literacy, diet, physical activity, weight and BP. This was based on assumption of hypothesised effect sizes described in the trial protocol.32 Sample size estimates were based on a two-sided test of significance at α=0.05. 1−β=0.8 and 20% loss to follow-up. For HLQ, the anticipated mean difference was 0.4 for both domains 8 and 9 (based on domain 8 mean 3.7 (SD=0.9) and domain 9 mean 3.9 (SD 0.8). For BMI and systolic BP, the effect sizes were 0.2, respectively (based on means of 30 (SD 6) and 131 (SD 15), respectively).

Analysis

Statistical analyses were conducted on the intention-to-treat (ITT) population for both primary and secondary outcomes. The ITT population was defined as all those recruited at baseline regardless of what intervention they received and what follow-up data were available.

Summary participant baseline characteristics and primary outcomes at baseline were compared between control and intervention groups using either Χ2 test, t-test or Mann-Whitney test. Means and SDs were reported for continuous outcomes and the number and percentage were reported for dichotomous outcomes at baseline, 6-month and 12-month follow-up.

To measure the effect of the intervention on the outcomes of interest (primary or secondary), we used difference-in-differences (DiD) estimate as some of the outcomes at baseline were significantly different.50 We used generalised estimating equation (GEE) with Gaussian family and identity link function to estimate DiD accounting for the cluster (general practice)-level correlation.51 We put an interaction term for intervention group and a dummy variable for before/after the follow-up measurement (6-month or 12-month follow-up) in the GEE model and the coefficient of the interaction term was considered as a DiD estimate.52 Separate models were used for estimating DiD at 6-month and 12-month follow-up. The DiD estimates were adjusted for the potential confounders which were substantially different between control and intervention groups at baseline. To adjust for possible ceiling effects, we did stratified analysis for the health literacy scores by above or below the median score at baseline. We set 5% as a level of statistical significance. We used the R V4.0.3 programming language and environment for the statistical analysis.53

Economic evaluation

The extracted cost data informed a cost consequence analysis, undertaken from the Australian healthcare system perspective. We categorised costs as follows: (1) services provided or requested by GPs (excluding consultations by specialists), (2) services provided or requested by GPs or specialists (excluding services related to surgical procedures), and (3) pharmaceutical costs. The number of times participants visited a GP was also analysed. Costs and number of GP visits were calculated for the 12 months preceding and the 12 months following the enrolment date for each participant, from which unadjusted DiD estimates were derived for each of the cost categories, as well as aggregate costs and GP visits. Bootstrapping (using 1000 resamples) was used to represent the uncertainty around the DiD estimates.

Results

We used the Consolidated Standards of Reporting Trials extension for cluster trials statement to guide reporting (online supplemental file 1) and summarise the flow of participants (figure 1) through the HeLP-GP trial.54

Figure 1

CONSORT flow diagram. CONSORT, Consolidated Standards of Reporting Trials; EOI, Expression of Interest; GPs, general practitioners; ITT, intention-to-treat; NSW, New South Wales; PN, practice nurse; Pt, Participant; SA, South Australia.

Baseline

We recruited 215 participants to the study (120 to the intervention group and 95 to the control group) through 22 practices (clusters). Baseline characteristics of the intervention group were similar to the control group except that the proportion of men was higher (66.3% vs 50.0%). Participants in both groups were predominantly aged between 46 and 65 years, with over one-third having been born overseas (mostly from Europe or Asia) but only one-third of those born overseas had arrived in Australia in the past 10 years and one in six of all participants spoke a language other than English. A total of 39.5% had school qualifications only and 59% were employed. The median BMI was 33.3 kg/m2. The intervention outcome measures at baseline were all similar to the control group except for health literacy which was lower (mean 4.0 vs 4.3 for domain 8, and 4.1 vs 4.3 for domain 9) (table 2).

Table 2

Baseline characteristics and outcomes by intervention and control

Intervention uptake

There was variable uptake of the intervention components by the 120 participants in the intervention group. Eighty-five attended the nurse health check and 73 also received either mysnapp, Get Healthy or both. Thirty-eight took up both mysnapp and Get Healthy coaching. Of the 62 who adopted mysnapp, 60 participants set goals on 132 occasions to increase vegetables, 131 to increase fruits, 97 less takeaway, 117 smaller portions, 73 less soft drink, 129 to increase physical activity time. Of the 49 who adopted Get Healthy telephone coaching, 31 set weight-related goals.

Change between baseline and 12 months

Primary outcomes

For health literacy, at 6 months, there was a greater increase in the intervention group for the HLQ domain 8 ability to find good health information score (DiD 0.22; 95% CI 0.01 to 0.44; table 3). This difference was not sustained at 12 months. There was no difference in the HLQ domain 9 understanding health information or for eHealth literacy both at 6 and 12 months. For the group that was below the median at baseline, there was also an increase in the intervention group for the HLQ domain 8 and eHealth literacy score at 6 months, and in HLQ domain 9 score at both 6 and 12 months.

Table 3

Effect of intervention on health literacy score at 6 and 12 months of follow up—intention-to-treat analysis

There was no statistically significant effect of the intervention on BMI or BP at 12 months (table 4). The intervention group’s mean BMI decreased but mean waist circumference at 12 months increased (DiD 7.08, 95% CI 2.26 to 11.90).

Table 4

Effect of intervention on anthropometry and blood pressure at 12 months of follow up—intention-to-treat analysis

Secondary outcomes

There was a greater increase in diet score in the intervention group at 6 months (DiD 0.98; 95% CI 0.50 to 1.47) due to an increase in fruit intake (DiD 0.50; 95% CI 0.20 to 0.80); however, this was not sustained at 12 months. There was no statistically significant effect of the intervention on physical activity score at 6 months (table 5).

Table 5

Effect of intervention on physical activity and diet score at 6 and 12 months of follow up—intention-to-treat analysis

HDL fell in both groups by 7% (control) and 8% (intervention). However, total cholesterol, LDL and TGs all fell in the intervention group (table 6). There were no statistically significant effects of the intervention on lipids (total cholesterol, LDL, HDL or TG) or quality of life (EQ-5D-5L) at 12 months. Quality of life did not change in control or the intervention group (table 6).

Table 6

Effect of intervention on the secondary outcomes intention-to-treat analysis (who had two different measurements at baseline and 12 months)

At 6 months, the control group self-reported a decrease in the frequency of receiving advice on physical activity, whereas the level stayed the same in intervention group (DiD 16.3%, 95% CI 1.4% to 31.1%). Similarly, the frequency of weight loss counselling or referral for physical activity fell in the control group but both increased in the intervention group (weight loss counselling DiD 27.8%, 95% CI 8.8% to 46.8%; physical activity referral DiD 13.3%, 95% CI 2.32% to 24.2%). There were no statistically significant differences between the groups in frequency of receiving information on healthy eating or being referred for healthy eating or weight loss (table 7).

Table 7

Effect of intervention on the secondary outcomes (from survey data)—intention-to-treat analysis

Economic analysis

The intervention costs included fixed (development of the mysnapp app and the online training modules) and variable (practice facilitation visits, PN health check payments and telephone coaching sessions) costs. Across the 120 patients in the intervention group, the per patient fixed and variable costs were $787 and $558, respectively, generating a total intervention cost per patient of $1345.

The baseline characteristics and outcome measurements of participants in the cohort providing consent to access their cost data (n=65; 33 in the intervention group and 32 in the control group) and full cohort (n=215) were similar (see online supplemental table 1). Two participants were excluded, one due to having only 6 months of cost data available after the enrolment date, and one due to extremely high pharmaceutical costs in the 12 months prior to enrolment for the treatment of age-related macular degeneration, a condition unrelated to the focus of the intervention.

Online supplemental table 1C presents the mean crude cost DiD between the 12 months prior and after recruitment to the trial. Excluding the outlier participant with high pharmaceutical costs, mean costs were higher in the intervention group in all cost categories, but there were no statistically significant differences between the intervention and control groups for the alternative cost categories (GP costs, GP and specialist costs and Pharmaceutical Benefit Scheme (PBS) costs) nor for the aggregated cost. Including the participant with outlier PBS costs, mean costs are lower in the intervention group for comparisons including PBS cost data, but the CIs remain very wide (online supplemental table 1D).

There were no adverse events or harms reported during the trial.

Discussion

In this trial of an intervention involving a PN health check, a mobile app and phone coaching in primary healthcare, we found positive effects on some outcomes (health literacy and diet at 6 months) but not on physical activity, weight or other outcomes. The primary hypothesis was that the intervention would lead to improved health literacy, health behaviours and positive changes in weight and other physiological measures. There were some differences between intervention and control groups at baseline but minimal differences in the outcomes and these were unlikely to have had a major influence on the findings. Health literacy improved in the intervention group at 6 months, although there was no further change by 12 months. Additionally, eHealth literacy improved only among those whose baseline health literacy was below the median. Although similar proportions of participants in both groups set goals for diet and physical activity, patients in the intervention group were more likely to report an improved diet score (due to a greater increase in fruit intake) compared with the control group. There was no difference in the physical activity score between the intervention and control groups. A lack of change in physical activity outcomes may reflect a need for group rather than individual approaches to physical activity promotion for people from migrant or low socioeconomic backgrounds.55 The intervention was tailored to patients’ needs and motivation but was not codesigned or specifically tailored to differences in individual cultural and religious beliefs and practices, which may mediate changes in physical activity.56

Although there were small changes in health literacy and diet, the intervention was not associated with differences in clinical endpoints such as BMI, BP, lipids or in quality of life after adjustment for age, gender and state. This may be because we did not recruit our required sample size or because the intervention lacked sufficient intensity and duration, as has been observed in other studies.10 The lack of change in physical activity, especially at 12 months, may also have contributed, and changes in BP and lipids may have been confounded by treatment with medications since most patients’ BP and lipids were within recommended guideline levels at baseline. Further research is thus required to evaluate digital interventions which allow tailoring to patients’ differing health literacy and culture and actively supported in their use over a longer period.

Only two-thirds of the patients in the intervention group received the full intervention (ie, received the health check with mysnapp and/or Get Healthy coaching components). This was influenced by patient choice through discussion with their clinicians reflecting the real-world setting of Australian general practice. This variable engagement with the different components of the intervention may have reduced its overall effectiveness. However, patients in the intervention group were more likely to recall being offered information or referral for physical activity or weight loss counselling than their counterparts in the control group.

In the cost analyses, low recruitment made the study insufficiently powered to draw meaningful conclusions. There was no evidence of difference in numbers of GP visits, Medicare Benefits Schedule or PBS costs between the groups over the period of the study. Despite some positive changes in some behavioural endpoints (health literacy and diet), there were no changes in clinical endpoints such as weight or other physiological measures, or in quality of life at 12 months. Trials of weight loss in primary care often show little or no change.57 However, previous studies involving the use of apps and behavioural counselling by healthcare providers have proven successful even in low socioeconomic groups where goals were individually tailored to the patient’s level of health literacy and the interventions were of moderate to high intensity.11 This suggests that the intervention in the current study may have been more effective if it was more tailored to the patient’s individual health literacy needs.

There were several limitations to our study. Like other studies, this study failed to achieve its planned sample size due to major challenges recruiting practices and patients despite considerable effort and an extension to the time frame of the study.58 Post-hoc power calculations, based on our results, showed that with a sample of 100 in each arm, we would be able to detect a mean difference in diet score of 0.6–0.7 (servings per day) and a mean difference in the Health Literacy Scale scores of 0.2–0.3. Both these differences are less than in previous studies and may not be clinically meaningful.32 59 For all the other measures, the differences that were able to be detected were larger than expected from moderate-intensity interventions (mean physical activity score difference of 1.5, mean BMI difference of 5.5 kg/m2, mean BP change of 15 mm Hg, mean cholesterol difference of 0.8).10 Our recruitment challenges suggest the need for greater efforts to increase the perceived benefits (such as improved access to quality care) and decrease barriers (especially time) associated with participation in studies such as this in the future.

There were five primary outcomes (including two HLQ domains, eHEALS, weight and BP). Furthermore, the health literacy measures were assessed at both 6 and 12 months increasing the likelihood of a type 1 error (ie, finding a significant difference). The study was conducted in only two urban areas of Australia and the findings may not therefore be generalised to other communities such as rural areas. Lastly, the measures of health literacy, diet and physical activity had some limitations, and may have not been sensitive enough to capture all changes due to the intervention.

Assessments of patient socioeconomic variables and health literacy indicate that the study fell short in recruiting its target population of people with low socioeconomic status and low health literacy. At baseline, levels of health literacy were higher than anticipated and were in fact comparable with overweight or obese patients in the general population who were part of the national health literacy survey.60 Our figures for ‘born overseas’ are higher than the Australian average but ‘language spoken at home’ and ‘employment status’ are similar to the Australian average.61 It is therefore possible that the requirements for written consent and engagement with the research study may have tended to discourage those with lower English language literacy, as has been found in some research.62 Furthermore, uptake by the participants in our study in the various components of the intervention varied. Previous research has identified that socioeconomic factors have impacts on intervention/trial uptake, intervention adherence and trial attrition.63 Future research could consider using codesign principles to help better engage specific population groups, as well as GPs and PNs working with these groups, in the research design and development of the intervention.64

Conclusion

This trial of a multifaceted intervention designed to support better preventive care for overweight and obese patients from low socioeconomic areas in the real-world environment of Australian general practice showed some short-term improvement in health literacy and diet but did not show any change in weight or other physiological variables. It was insufficiently powered for cost analysis. While there was evidence that the intervention was implemented as planned, there was variable uptake of its components, and it may therefore have been of insufficient intensity to achieve sustained change in weight and other primary outcomes. However, any preventive intervention in primary care needs to be sustainable and tailored to its capacity.

Data availability statement

Individual participant data that underlie the results reported in this article will be available within 12 months of publication after deidentification (text, tables, figures and appendices) to investigators whose proposed use of the data has been approved by our Ethics Committee.

Ethics statements

Patient consent for publication

Ethics approval

This trial was approved by the University of New South Wales Human Research Ethics Committee (HC17474). The University of Adelaide Human Research Ethics Committee ratified this approval. Written consent was obtained from all participating practices to conduct the trial in the practice and access practice data; individual consent was obtained from all participating GPs and PNs. Patients provided written consent to participate in the trial and additional written consent was obtained for the researchers to access individual health service usage data (Medicare Benefits Schedule (MBS)) and pharmaceutical use (Pharmaceutical Benefit Scheme (PBS)) according to protocols governing access to these data through Services Australia.42 All practices received $A1000 payment to cover the administrative costs of participation. To compensate them for their time, patients from both groups who completed the baseline and 6-month follow-up received $A30 shopping voucher and then an additional $A30 voucher if they completed the 12-month follow-up.

References

Supplementary materials

  • Supplementary Data

    This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

Footnotes

  • Twitter @denneywilson, @ElAstee, @richardosborne4

  • Contributors MH was the chief investigator, is guarantor, led the development of and implementation of the study, interpreted the data and drafted the manuscript. SMP developed the trial processes, coordinated the trial across sites, contributed to the development of the data collection tools, collected and analysed data and drafted the manuscript. MB cleaned the data, designed the analysis plan, conducted the analysis, interpreted the data and contributed to the draft of the manuscript. NS led the development and implementation of the study in SA, was instrumental in designing the DCP module for the trial, interpreted the data and contributed to the manuscript. ED-W contributed to the design of the study, developed the training modules, and contributed to the interpretation of the data and the manuscript. NZ contributed to the trial design, trial implementation and interpretation of the data for the manuscript. JK contributed to the trial design, designed the economic analysis and interpreted these data for the manuscript. AK cleaned the DCP data, designed the analysis plan, conducted the analysis, developed the data tables and contributed to the draft of the manuscript. DN contributed to the trial design, trial implementation and interpretation of the data for the manuscript, particularly the health literacy content. JR conducted the economic analysis and interpreted the data for inclusion in the manuscript. S-TL contributed to the trial design, trial implementation and interpretation of the data for the manuscript. CM liaised with SA practices to collect patient and practice data, collated the data and contributed to the interpretation of the data for the manuscript. OF was instrumental in designing the DCP module for the trial and troubleshooting data collection using DCP, interpreted the outcome data and contributed to the manuscript. AT liaised with NSW practices to collect patient and practice data, collated and cleaned the data, and contributed to the management and interpretation of the data. RO contributed to the trial design, particularly the tools to collect patient data and interpretation of data particularly the health literacy outcome data. He contributed to the draft manuscript. AYSL designed and developed the mysnapp app and the data collected via the application and interpretation and plan for analysing these data. All authors approved the final version for publication and agree to be accountable for the integrity of the content, and responsible for any issues that arise from publication of the trial data.

  • Funding This work was supported by the National Health and Medical Research Council (NHMRC) of Australia (project grant APP1125681, 2017).

  • Competing interests None declared.

  • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.