Evaluation of an intervention to provide brief support and personalized feedback on food shopping to reduce saturated fat intake (PC-SHOP): A randomized controlled trial.

BACKGROUND: Guidelines recommend reducing saturated fat (SFA) intake to decrease cardiovascular disease (CVD) risk, but there is limited evidence on scalable and effective approaches to change dietary intake, given the large proportion of the population exceeding SFA recommendations. We aimed to develop a system to provide monthly personalized feedback and healthier swaps based on nutritional analysis of loyalty card data from the largest United Kingdom grocery store together with brief advice and support from a healthcare professional (HCP) in the primary care practice. Following a hybrid effectiveness-feasibility design, we tested the effects of the intervention on SFA intake and low-density lipoprotein (LDL) cholesterol as well as the feasibility and acceptability of providing nutritional advice using loyalty card data. METHODS AND
FINDINGS: The Primary Care Shopping Intervention for Cardiovascular Disease Prevention (PC-SHOP) study is a parallel randomized controlled trial with a 3 month follow-up conducted between 21 March 2018 to 16 January2019. Adults ≥18 years with LDL cholesterol >3 mmol/L (n = 113) were recruited from general practitioner (GP) practices in Oxfordshire and randomly allocated to "Brief Support" (BS, n = 48), "Brief Support + Shopping Feedback" (SF, n = 48) or "Control" (n = 17). BS consisted of a 10-minute consultation with an HCP to motivate participants to reduce their SFA intake. Shopping feedback comprised a personalized report on the SFA content of grocery purchases and suggestions for lower SFA swaps. The primary outcome was the between-group difference in change in SFA intake (% total energy intake) at 3 months adjusted for baseline SFA and GP practice using intention-to-treat analysis. Secondary outcomes included %SFA in purchases, LDL cholesterol, and feasibility outcomes. The trial was powered to detect an absolute reduction in SFA of 3% (SD3). Neither participants nor the study team were blinded to group allocation. A total of 106 (94%) participants completed the study: 68% women, 95% white ethnicity, average age 62.4 years (SD 10.8), body mass index (BMI) 27.1 kg/m2 (SD 4.7). There were small decreases in SFA intake at 3 months: control = -0.1% (95% CI -1.8 to 1.7), BS = -0.7% (95% CI -1.8 to 0.3), SF = -0.9% (95% CI -2.0 to 0.2); but no evidence of a significant effect of either intervention compared with control (difference adjusted for GP practice and baseline: BS versus control = -0.33% [95% CI -2.11 to 1.44], p = 0.709; SF versus control = -0.11% [95% CI -1.92 to 1.69], p = 0.901). There were similar trends in %SFA based on supermarket purchases: control = -0.5% (95% CI -2.3 to 1.2), BS = -1.3% (95% CI -2.3 to -0.3), SF = -1.5% (95% CI -2.5 to -0.5) from baseline to follow-up, but these were not significantly different: BS versus control p = 0.379; SF versus control p = 0.411. There were small reductions in LDL from baseline to follow-up (control = -0.14 mmol/L [95% CI -0.48, 0.19), BS: -0.39 mmol/L [95% CI -0.59, -0.19], SF: -0.14 mmol/L [95% CI -0.34, 0.07]), but these were not significantly different: BS versus control p = 0.338; SF versus control p = 0.790. Limitations of this study include the small sample of participants recruited, which limits the power to detect smaller differences, and the low response rate (3%), which may limit the generalisability of these findings.
CONCLUSIONS: In this study, we have shown it is feasible to deliver brief advice in primary care to encourage reductions in SFA intake and to provide personalized advice to encourage healthier choices using supermarket loyalty card data. There was no evidence of large reductions in SFA, but we are unable to exclude more modest benefits. The feasibility, acceptability, and scalability of these interventions suggest they have potential to encourage small changes in diet, which could be beneficial at the population level. TRIAL REGISTRATION: ISRCTN14279335.

RCT Entities:   Population Interventions Outcomes

Introduction

A diet high in saturated fatty acids (SFA) elevates low-density lipoprotein cholesterol (LDL-C) and increases the risk of cardiovascular disease (CVD) [1-3]. SFA intake in the UK population (13.5% total energy) and in other higher income countries remains higher than the dietary recommendation of <10% of the total energy intake, and it is estimated that >70% of the UK population exceeds this level [4, 5]. Systematic reviews and recent trials have shown that reductions in dietary SFA intake can lower LDL-C, but this has only been achieved with specialist staff and high-intensity behavioural support as well as with the provision of appropriate food substitutes in place of higher SFA products [6-9]. There is evidence that brief behavioral interventions delivered in routine primary care can help people engage in weight loss programs with the potential to achieve population-level impact [10]. However, there is limited evidence of brief interventions that are effective for improving the nutritional quality of diet and sufficiently scalable for routine delivery in primary care settings by generalist staff or at the population level.

Food purchased to prepare and eat at home comprises the majority of food consumed in most countries, and interventions targeting the nutritional quality of food purchases could improve diet quality, especially among those motivated to change [11]. Many foods high in SFA, such as meat, dairy, ready meals, cakes, and biscuits, are bought to consume at home [12]. However, previous evidence suggests that information alone on the nutrient content of foods rarely leads to sustained dietary change, and other support may be needed to change dietary behaviours [13]. Interventions providing tailored dietary advice, personalized feedback, and encouraging self-monitoring appear promising [13-17]. Interventions delivered in grocery stores can also support individual behavior change. For example, in-store education increases fruit and vegetable purchases [18], while recommending healthier swaps at the point of purchase, including lower SFA alternatives, has been shown to improve the nutritional quality of food selection or purchasing [19-21].

Building on what is known, but with a specific focus on scalability and sustainability, we developed a behavioral intervention to reduce SFA consumption using data collected through a supermarket loyalty card. Loyalty cards recording food purchases are operated by many major retailers and offer the opportunity to provide personalized dietary interventions at scale. We tested the intervention among patients in primary care with raised LDL-C who received additional motivation to change and brief advice from a healthcare professional (HCP) in a short counselling session. Following a hybrid effectiveness-feasibility design [22], the aim of this study was to seek preliminary evidence of the effectiveness and feasibility of these interventions to reduce SFA intake and lower CVD risk.

Methods

Trial design

The Primary Care Shopping Intervention for Cardiovascular Disease Prevention Study (PC-SHOP) was a randomized, 3-arm parallel controlled open-label trial with blinded assessment of the primary outcome. Participants were individually randomized to either the control group or one of 2 active interventions for 3 months after giving written informed consent to participate in the study.

This study was reviewed and approved by the National Health Service Health Research Authority (HRA) Research Ethics Committee (Ref: 17/SC/0168). The trial protocol includes further information regarding the trial design and interventions [23]. A protocol and statistical analysis plan were prospectively registered at ISRCTN14279335 in September 2017 and March 2019, respectively, with prospective changes introduced to reflect delays in the planned recruitment and follow-up dates as well as the addition of a qualitative substudy and a few exploratory outcomes (S1 Appendix).

Trial setting and recruitment

Four primary care practices in Oxfordshire, UK, near the participating supermarket were selected so it was more likely that potential participants shopped at this store. General practices sent invite letters to patients who were aged ≥18 years and with a recorded LDL cholesterol >3.5 mmol/L or total cholesterol >5.5 mmol/L in the previous 5 years. Eligible participants were adults aged ≥18 years; with a baseline LDL ≥3 mmol/L on retesting; willing to make changes to their diet in order to reduce CVD risk; who had responsibility for the majority of the household food/grocery shopping (e.g., complete at least half of their household shopping); who usually shop at the collaborating grocery store (at least every 2 weeks in store and/or online); with a loyalty card registered exclusively under their name before recruitment; with access and ability to use a computer with internet connection; and willing and able to give informed consent for participation in the study. During screening, we excluded people with a self-reported recent incident of CVD, recent or planned changes to lipid medications, or those taking part in other relevant research. Interested and potentially eligible people were invited to a baseline appointment with the study team at their general practice to discuss and agree to participate and check eligibility again. People who who failed to complete the 2 baseline dietary assessments were excluded (Fig 1, CONSORT diagram; S2 Appendix, List of inclusion and exclusion criteria).

Fig 1

CONSORT flowchart.

CONSORT, Consolidated Standards of Reporting Trials; LDL-C, low-density lipoprotein cholesterol.

Randomisation and blinding

Participants were individually randomized in a 1:3:3 ratio, to either the control group or one of 2 active interventions. A computer-generated randomization sequence was generated by an independent statistician using fixed block sizes of 7 with random order. After enrollment, participants’ allocation to each trial arm was revealed to the researchers using an online program (RedCap, https://www.project-redcap.org/) to ensure full concealment. The researchers needed to know allocation in order to generate personalized shopping feedback reports for participants randomized to the SF group.

The brief advice component of the interventions meant that it was impossible to fully blind HCPs, but they remained blind to which of the 2 active intervention groups the participant was allocated. Likewise, participants could not be blind to allocation, and there were insufficient research staff to blind them to allocation at follow-up when some of the clinical measures (secondary outcomes) were taken. The primary outcome was saturated fat intake collected through a web-based questionnaire, which individuals completed without involving the study team and was analyzed blind by an independent statistician.

Interventions

Details of the active interventions and their theoretical framework can be found in the protocol [23]. In brief, HCPs received 1 hour of training plus materials from the study team to deliver the brief intervention. Participants allocated to the “Brief Support” (BS) or “Brief Support plus Shopping Feedback” (SF) intervention attended a single 10-minute appointment with a HCP (e.g., nurse or healthcare assistant) at their usual GP practice, which was intended to boost motivation and capability to enact dietary change. They informed participants about the effect of SFA on blood cholesterol and CVD risk and provided tips to reduce SFA, guided by the British Heart Foundation “Cut the Saturated Fat” chart [24, 25] and the National Health System (NHS) Choices website (provided in printed form).

Participants allocated to the SF intervention also received a report on the saturated fat in their household grocery store food purchases during the intervention period. The study team received data from the participating supermarket each week and generated and sent participants a personalized shopping report on purchases in the preceding period using supermarket loyalty card data, at baseline and at the end of the first, second, and third month of the intervention period. The shopping reports provided information on the mean weekly saturated fat content of their food shopping, identified 5 major contributors to SFA intake over the previous period, as well as suggestions for one-for-one swaps to foods containing less SFA but with similar functional characteristics. Participants were encouraged to use the monthly reports to guide their shopping list and to monitor their progress in reducing SFA. Details of the approach and algorithm to generate personalized shopping advice can be found elsewhere [23].

Participants in the control group received no intervention. They were informed of the results of their blood tests by post and invited to a further check after 3 months (end of follow-up period).

Box 1. Intervention components.

Brief advice session with a nurse or healthcare assistant at the GP practice.

Single 10-minute appointment.

Written materials: British Heart Foundation “Cut the Saturated Fat” leaflet and NHS Choices information together with blood results.

Personalized shopping report.

One report per month from baseline to follow-up (up to 4 reports in total, print form and email) containing information on weekly amount of saturated fat in shopping, top 5 food contributors to saturated fat plus lower saturated fat swaps to foods with similar functional characteristics.

Procedures

Participants were asked to complete a computerized 24-hour dietary recall prior to the baseline visit and a second one at the baseline visit. Participants provided their supermarket loyalty card number and consent to access information on all food purchases from 6 months before baseline until the end of the study. Trained researchers measured weight, height, blood pressure, and collected demographic information and relevant medical history and medications. A fasting capillary blood sample was collected and analyzed using a point of care device (Alere Cholestech LDX) to determine total cholesterol, high-density lipoprotein cholesterol (HDL-C), LDL-C, and triglycerides. After randomization, participants in BS and SF groups met with a HCP for the brief advice session.

A single follow-up visit took place 3 months later, repeating the baseline measures (except height) and including an additional questionnaire assessing the acceptability of the intervention.

Dietary intake and shopping data

Individual self-reported dietary intake was assessed using the validated questionnaire, the Oxford Biobank WebQ, which collects information on the quantities of all foods and beverages consumed over the previous day [26]. The quantity (g) of each food and beverage consumed was calculated by multiplying the average portion size [27] by the number of portions consumed. Nutrient intakes including total energy and SFA in grams were automatically calculated from a UK-specific food composition table [28] linked to the WebQ. We calculated the percentage of energy intake (%EI) from each macronutrient as well as the average daily intakes across the 2 days that were recorded at baseline and again at follow-up. We also calculated the energy contribution (%EI) of those food groups that are important sources of SFA: cakes, biscuits and desserts, meat (including red and processed meat), poultry, cheese, yogurt, spreads, and salty snacks.

Shopping data on all recorded food purchases from 6 months before baseline and during the study were also used to evaluate the effect of the intervention given the high correlations found with dietary intake measurements [29]. Shopping data were linked to a database of approximately 20,000 food and drink products, which included information on nutrient composition, price, and volume per unit (Brand View Limited). We calculated the total basket (kcal and g) and SFA, total fat, sugars, salt, and fiber as well as the energy contribution from food groups aligned to the WebQ food groups, plus ready-to-eat meals. We calculated averages before and after baseline to obtain the percentage of energy from SFA and percentage from total basket energy (%TE) from grocery store purchases, as well as percentage of other macronutrients, and the percentage contribution of food groups before and after the intervention.

Outcomes

The primary outcome was the change in reported saturated fat intake (%SFA) measured on 2 days at baseline and again at follow-up. The full list of prespecified secondary and nonefficacy outcomes included changes from baseline to follow-up in SFA (kcal), total energy intake (kcal), total fat (kcal, %EI), total sugars (kcal, %EI), fiber (g/100 kcal), and key food groups (%EI). Other exploratory outcomes from the dietary measurements were changes in total monounsaturated fatty acids (MUFA) and polyunsaturated fatty acids (PUFA) intakes between baseline and follow-up. Prespecified secondary outcomes from food purchases included changes from baseline to follow-up in SFA (%TE; kcal per £), total fat and sugars (%TE; kcal per £), salt (g/100 g), cost (£/week and £/Kg), energy density (kcal/g), and key food groups (%TE) expressed as percentages to be comparable between the diet and the shopping data. We also assessed changes in LDL-C, HDL-C, total cholesterol, and triglycerides.

For the process evaluation, we assessed the feasibility of recruitment and follow-up by measuring the number of participants who accepted the invitation and consented to take part as well as the number of participants that completed follow-up. We measured acceptability of the intervention through nonvalidated questionnaires completed at baseline and follow-up (rated on a scale 1–5 from least to most helpful, S3 Appendix). The fidelity of the intervention delivery by the HCP was measured through the analysis of audio-taped sessions.

Statistical analysis

We powered the study to detect a difference in %SFA reduction of 3% (3% SD) between each intervention group and control, which was considered plausible based on previous interventions [8, 9] and likely to be beneficial at the individual level to reduce LDL cholesterol [3]. We calculated a total sample of 112 (16 control, 48 BS, and 48 SF group) using intention-to-treat analysis with 90% power, 10% attrition, and statistical adjustment for multiple comparisons (using 2-sided α = 0.01 in the primary outcome analysis). Using a 1:3:3 allocation ratio, we calculated a sample of 48 participants in each active intervention arms to have 90% power to detect a further difference in %SFA intake between BS and SF interventions of 2% (3% standard deviation). All the other tests were performed at a 5% two-sided significance level, Stata 14 was used for the analyses.

After the statistical analysis plan was approved by the trial management group and an independent statistician, the database was locked, and the statistical analysis plan was implemented without changes to the primary or secondary outcome analyses. The results of the trial were computed as comparative summary statistics by group (difference in means between baseline and follow-up, 95% CIs), and the primary comparisons between groups were analyzed with a linear regression model with adjustment for GP practice and baseline values. Prior to analysis, assumptions of linear regression (normality of residuals, homogeneity of variances, and outliers) were tested and met. Information on the acceptability of the intervention was collected by questionnaire and summarized by presenting the frequencies of each response.

We performed prespecified sensitivity analyses to assess the impact of missing data on the primary outcome, including using baseline observation carried forward, as well as excluding people with only one dietary recall or excluding specific days with reported dietary intakes reflecting implausible habitual intake (<500 kcal/day or >3,500 kcal/day [30]). We also excluded people who received their intervention session with the HCP >45 days from baseline, as well as those affected by changes in relevant medications (e.g., statins) during the study. We performed a post hoc analysis adjusting for body mass index (BMI) to account for imbalance in the baseline measures. We also assessed whether the effect of the interventions differed by sociodemographic status in prespecified exploratory subgroup analyses.

Results

Baseline characteristics

Participants were recruited from 4 GP practices in Oxfordshire between 21 March 2018 and 10 October 2018. A final sample of 113 were randomized to either “No intervention” (n = 17), “BS” (n = 48) or “SF” (n = 48). Follow-up was completed on 16 January 2019, and 106 (94%) participants completed the study after an average of 115 days (SD 26, minimum 77, maximum 208) (Fig 1).

Participants were mostly women (68%), of white ethnicity (95%), with average age of 62.4 years (SD 10.8), and BMI 27.1 kg/m2 (SD 4.7) (Table 1). Fourteen percent of participants reported a family history of CVD; 24% had hypertension, and 2% were taking lipid-lowering medication.

Table 1

Baseline characteristics of participants assigned to interventions or control.

	Total(n = 113)		Control(n = 17)		Brief Support (n = 48)		Brief Support plus Shopping Feedback (n = 48)
	Mean/n	SD/%	Mean/n	SD/%	Mean/n	SD/%	Mean/n	SD/%
Age, years, mean (SD)	62.4	10.8	62.9	11.2	64.7	9.2	59.9	11.7
Gender, female	77	68.1	11	64.7	34	70.8	32	66.7
BMI, kg/m2, mean (SD)	27.1	4.7	26.0	5.1	26.8	4.0	27.8	5.2
BMI categories
Normal weight (18.5–24.9)	38	33.6	7	41.2	14	29.2	17	35.4
Overweight (25–29.9)	53	46.9	8	47.1	27	56.3	18	37.5
Obesity (≥30)	22	19.5	2	11.8	7	14.6	13	27.1
Blood pressure, mean (SD)
Systolic, mmHg	131.5	17.5	130.5	11.6	130.0	19.6	133.3	17.2
Diastolic, mmHg	79.2	9.8	77.4	8.1	77.2	10.8	81.8	8.9
Smoking
Current	6	5.3	1	5.9	2	4.2	3	6.3
Ex-smoker	37	32.7	5	29.4	17	35.4	15	31.3
Never	67	59.3	10	58.8	28	58.3	29	60.4
Missing	3	2.7	1	5.9	1	2.1	1	2.1
Alcohol intake
Never	7	6.2	1	5.9	1	2.1	5	10.4
Sometimes	46	40.7	7	41.2	21	43.8	18	37.5
Every week	57	50.4	8	47.1	24	50.0	25	52.1
Missing	3	2.7	1	5.9	2	4.2	0	0.0
Ethnic group
White	107	94.7	15	88.2	45	93.8	47	97.9
Black/Asian	3	2.7	0	0.0	2	4.2	1	2.1
Mixed/Other	1	0.9	1	5.9	0	0.0	0	0.0
Missing	2	1.8	1	5.9	1	2.1	0	0.0
Education
No formal qualifications	16	14.2	1	5.9	9	18.8	6	12.5
Secondary education	49	43.4	6	35.3	21	43.8	22	45.8
Higher education	46	40.7	9	52.9	17	35.4	20	41.7
Missing	2	1.8	1	5.9	1	2.1	0	0.0
Household size, median (IQR)	2.4	1.2	2.4	1.2	2.3	1.2	2.5	1.3
Adults, mean (SD)	2.1	0.9	2.1	0.8	2.0	0.7	2.2	1.0
Children, mean (SD)	0.3	0.8	0.2	0.6	0.3	0.8	0.4	0.8
Weekly grocery shopping, e.g., spending >£25/trip
>Once a week	32	28.3	3	17.7	9	18.8	20	41.7
Once a week	64	56.6	9	52.9	31	64.6	24	50.0
Once a fortnight	6	5.3	2	11.8	2	4.2	2	4.2
Once a month	6	5.3	1	5.9	4	8.3	1	2.1
<Once a month	3	2.7	1	5.9	1	2.1	1	2.1
Missing	2	1.8	1	5.9	1	2.1	0	0.0
Relevant health history
Family history	16	14.2	4	23.5	6	12.5	6	12.5
CVD	4	3.5	2	11.8	1	2.1	1	2.1
High blood pressure	27	23.9	4	23.5	14	29.2	9	18.8
Diabetes	0	0.0	0	0.0	0	0.0	0	0.0
AF	4	3.5	2	11.8	1	2.1	1	2.1
CKD	1	0.9	0	0.0	1	2.1	0	0.0
Relevant medications
Statins	2	1.8	0	0.0	1	2.1	1	2.1

AF, atrial fibrillation; BMI, body mass index; CKD, chronic kidney disease; CVD, cardiovascular disease; IQR, interquartile range.

Changes in reported dietary intake

Change in mean saturated fat intake (%EI) at 3 months (primary outcome) was −0.1% (95% CI −1.8 to 1.7) in the control group, −0.7% (95% CI −1.8 to 0.3) in BS, and −0.9% (95% CI −2.0 to 0.2) in SF (Fig 2, Tables A-B in S4 Appendix). There was no statistically significant difference in either intervention group compared with control (BS versus control = −0.33% [95% CI −2.11, 1.44] p = 0.709; SF versus control = −0.11% [95% CI −1.92, 1.69] p = 0.901; adjusted for GP practice and baseline value) or between intervention groups (S1 Table B).

Fig 2

Mean (± standard error) changes in saturated fat (% energy intake) from A. dietary recalls (primary outcome) and B. purchases (secondary outcome).

SFA, saturated fatty acids.

There were no statistically significant differences between the intervention and control groups in total fat, total daily EI, sugars, dietary fiber (secondary outcomes), or PUFA and MUFA intakes (exploratory outcomes) (Tables A-B in S4 Appendix). Mean total fat intakes (kcal) and daily EIs (kcal) were reduced from baseline to follow-up among the intervention groups: BS = −109.8 kcal (95% CI −214 to −5.6); SF = −79.4 kcal (95% CI −186 to 27.2); and BS = −176.9 kcal (95% CI −393.1 to 39.2); SF = −106.2 kcal (95% CI −327.2 to 114.9), respectively, although these changes were not significant between intervention groups compared with control (Tables A-B in S1 Appendix).

Changes in the intakes of key food groups (%EI) were not statistically significantly different between intervention and control, but there were nonsignificant reductions from baseline to follow-up among the intervention groups (Fig 3, Tables A-B in S4 Appendix): cakes, biscuits, and desserts (BS = −2.6% [95% CI –5.7 to 0.4], SF = 0.5% [95% CI –2.7 to 3.6]); meat (BS = −2.3% [95% CI −4.5 to −0.2], SF = 0.0% [95% CI –2.2 to 2.2]); higher fat cheese (BS = −0.9% [95% CI −1.9 to 0.2]; SF = −1.3% [95% CI −2.3 to −0.2]), higher fat yogurt (BS = 0.2% [95% CI –0.4 to 0.8], SF = −0.8% [95% CI −1.4 to −0.2]), and higher fat spread (BS = −0.8% [95% CI −1.8 to 0.1]; SF = −1.3% [95% CI −2.2 to −0.3]).

Fig 3

Mean (95% CI) changes in key food groups from A. dietary recalls (%EI) and B. loyalty card data (%TE).

EI, energy intake; TE, total energy.

Changes in food purchasing

Changes in SFA (%TE) from food purchases at 3 months (secondary outcome) were −0.5% (95% CI −2.3 to 1.2) in the control group, −1.3% (95% CI −2.3 to −0.3) in BS, and −1.5% (95% CI −2.5 to −0.5) in SF (Fig 2, Tables C-D in S4 Appendix), but there were no statistically significant differences between the groups. The adjusted difference in %SFA in the total basket was −0.8% (95% CI −2.5 to 0.9) p = 0.379 between BS versus control; and −0.7% (95% CI −2.4 to 1.0) p = 0.411 between SF versus control. There were no statistically significant differences between groups in total fat, energy density of the basket, sugars, dietary fiber, salt, or basket cost (Tables C-D in S4 Appendix).

Purchases of key food groups (%TE) were not statistically significant between the intervention arms versus control, but there were nonsignificant reductions from baseline to follow-up among the intervention groups, which usually paralleled those observed from the dietary questionnaire data: cakes, biscuits, and desserts (BS = −2.7% [95% CI −6.1 to 0.7]; SF = −3.0% [95% CI −6.4 to 0.4]); meat (BS = −2.1% [95% CI −4.4 to 0.2]; SF = −0.2% [95% CI −2.4 to 2.1]); higher fat cheese (BS = −0.4% [95% CI −1.3 to 0.5]; SF = −0.5% [95% CI −1.4 to 0.5]); and higher fat spread (BS = −0.2% [95% CI −1.4 to 1.1]; SF = −0.7% [95% CI −2.0 to 0.5]) (Fig 3, Tables C-D in S4 Appendix).

Changes in clinical outcomes

Changes in blood lipids were not statistically significantly different between intervention and control. In the BS group, there was a nonsignificant reduction from baseline to follow-up in LDL (−0.39 mmol/L [95% CI −0.59 to −0.19]), and in the SF group (−0.14 mmol/L [95% CI −0.34 to 0.07]) (Table 2, Table E in S4 Appendix). Changes in body weight and blood pressure were not significantly different between groups either, but there were small nonsignificant reductions in weight from baseline to follow-up in both intervention groups (BS = −1.1% [95% CI −1.9 to −0.3]; SF = −0.8% [95% CI −1.6 to 0.0]).

Table 2

Changes in clinical outcomes: Secondary and exploratory outcomes by group allocation.

	Change from baseline within group						Between-group differences adjusted for baseline and practice*
	Control(n = 16)		BS(n = 45)		BS plusSF(n = 44)		BS versus control			BS+SF versus control			BS+SF versus BS
Secondary Outcomes	Mean	95% CI	Mean	95% CI	Mean	95% CI	Mean	95% CI	p-value	Mean	95% CI	p-value	Mean	95% CI	p-value
LDL cholesterol (mmol/L)	−0.14	(−0.48, 0.19)	−0.39	(−0.59, −0.19)	−0.14	(−0.34, 0.07)	−0.15	(−0.47, 0.16)	0.338	0.04	(−0.28, 0.36)	0.790	0.2	(−0.03, 0.43)	0.095
HDL cholesterol (mmol/L)	0.08	(−0.03, 0.19)	−0.01	(−0.08, 0.05)	−0.02	(−0.09, 0.04)	−0.11	(−0.24, 0.01)	0.081	−0.13	(−0.26, 0.00)	0.047	−0.02	(−0.11, 0.07)	0.713
Total cholesterol (mmol/L)	−0.13	(−0.46, 0.21)	−0.46	(−0.66, −0.25)	−0.14	(−0.35, 0.06)	−0.3	(−0.62, 0.02)	0.069	−0.02	(−0.34, 0.30)	0.910	0.28	(0.04, 0.51)	0.02
Triglycerides (mmol/L)	−0.14	(−0.54, 0.26)	−0.14	(−0.38, 0.11)	−0.01	(−0.25, 0.24)	0.04	(−0.37, 0.46)	0.836	0.27	(−0.15, 0.69)	0.211	0.22	(−0.09, 0.53)	0.154
Non−HDL cholesterol (mmol/L)	−0.21	(−0.56, 0.14)	−0.44	(−0.65, −0.24)	−0.12	(−0.33, 0.09)	−0.16	(−0.51, 0.19)	0.362	0.15	(−0.20, 0.50)	0.393	0.31	(0.06, 0.56)	0.016
Total cholesterol/HDL ratio	−0.15	(−0.59, 0.29)	−0.34	(−0.60, −0.07)	0.02	(−0.24, 0.29)	−0.13	(−0.64, 0.37)	0.601	0.25	(−0.26, 0.76)	0.335	0.38	(0.01, 0.75)	0.042
Nonefficacy Outcomes
Systolic blood pressure (mmHg)	0.5	(−6.3, 7.4)	1.3	(−2.8, 5.4)	1.2	(−3.0, 5.3)	0.58	(−7.01, 8.18)	0.879	1.44	(−6.23, 9.11)	0.710	0.86	(−4.68, 6.40)	0.759
Diastolic blood pressure (mmHg)	1.2	(−2.7, 5.2)	−0.3	(−2.7, 2.0)	−0.5	(−2.9, 1.9)	−1.42	(−5.80, 2.97)	0.523	−0.01	(−4.50, 4.48)	0.997	1.41	(−1.87, 4.69)	0.397
Weight (kg)	−0.2	(−1.5, 1.1)	−1.1	(−1.9, −0.3)	−0.8	(−1.6, 0.0)	−1.00	(−2.52, 0.53)	0.197	−0.57	(−2.11, 0.98)	0.468	0.43	(−0.69, 1.55)	0.449

* Estimates from linear regression models adjusting for GP practice and baseline values, all p-values > 0.05.

BS, brief support; GP, general practioner; HDL, high-density lipoprotein; LDL, low-density lipoprotein; SF, brief support plus shopping feedback.

Exploratory subgroup analyses

There was no evidence of a significant interaction between trial arm and education status (secondary education only versus more advanced education) on %SFA intake, %SFA purchases, or LDL-C (Fig 4, Table F in S4 Appendix). Among those allocated to BS group but not SF group, there was a greater reduction in SFA consumption and purchase in participants with lower levels of education, but that was not statistically significant to control (Fig 4).

Fig 4

Mean (95% CI) changes in saturated fat from A. dietary recalls (%EI) and loyalty card data (%TE) and B. LDL cholesterol (mmol/L) by group and education status.

BS, brief support; EI, energy intake; LDL, low-density lipoprotein; SF, brief support plus shopping feedback; SFA, saturated fatty acids; TE, total energy.

Sensitivity analyses

None of the sensitivity analyses changed the overall pattern of results but in most cases, resulted in slightly larger absolute differences between the intervention groups and control group (Table G in S4 Appendix).

Process evaluation: Feasibility, intervention fidelity, and acceptability

Of 4,766 people invited to take part in the study, 181 (4%) responded to the invitation, from which 143 participants (3%) fulfilled some of the eligibility criteria (e.g., were customers of the participating supermarket) and consented and agreed to be screened for full eligibility. Of those, 30 (21%) were ineligible, mostly because LDL cholesterol was <3 mmol/L at the time of recruitment, giving 2.4% of the original sample of invited participants who were then randomized. Of these, 94% were followed up after 3 months.

There was an average of 37 days (SD 22) between the baseline visit with researchers and the brief advice appointment with the HCP. The BS sessions delivered by the HCP were audio-recorded with an average duration of 10 minutes (range 3 minutes to 30 minutes). Out of 19 possible behavioral elements included in the training, an average of 10 (range 3–19) were evident in the recordings. Two of the elements that were missed the most were asking participants if they had already identified a specific food to change and the take-home message in which the HCP provided an overall overview of the main points discussed.

The SF group received a mean of 3.6 shopping reports. Each shopping report included 5 potential swaps. Approximately 41% of lower SFA swaps offered were dairy products (e.g., cheese, yogurt, milk/cream, spread/butter); 21% cakes, biscuits, and desserts; 17% meat (e.g. beef, pork, processed meat); 7% ready meals (e.g., pizza, pies); and 13% from other categories (e.g., salty snacks, oils, bread, cereal). One in 4 suggested swaps appeared in the subsequent purchase record during the intervention period. These “accepted swaps” came from cheese, yogurt, milk/cream or spread/butter (58%); cakes, biscuits, and desserts (15%); meat (13%); salty snacks (2%); and ready meals (2%) (Table H in S4 Appendix).

Overall, participants reported that the HCP intervention session and self-help materials were helpful, with mean scores of 3.9 among BS and 3.7 among SF (on a scale 1–5). Participants in the SF group considered the shopping report was moderately helpful with a score of 3.5, and 43% reported using the shopping report “almost always” or “most of the time” when shopping. A higher proportion of participants in the intervention groups reported being “very/somewhat confident” that they knew the major sources of SFA in their diets after the intervention. A higher proportion of participants in all groups were trying to reduce SFA in their diets at follow-up than at baseline (Table H in S4 Appendix).

Discussion

Among people identified as having modestly raised LDL-C, there was no strong evidence that a brief behaviorally informed appointment with a HCP with or without additional personalized shopping feedback and swap suggestions for the saturated fat content of food purchases reduced SFA consumption, SFA food purchases, or LDL-C relative to a control group. However, the reductions in SFA and LDL cholesterol in the intervention groups were not significant but larger than those in the control with no formal intervention. The intervention was feasible, acceptable, and potentially scalable.

This pragmatic study did not detect a reduction in SFA or LDL cholesterol of a magnitude to imply that this intervention is an effective alternative to lipid-lowering therapy for individuals with raised LDL. Explanatory trials, which provided specific foods to replace higher SFA products, together with intensive tailored counseling, have shown greater reductions in SFA (approximately 4%–5%) with small reductions in LDL cholesterol of approximately 0.2 mmol/L [8] and other CVD risk factors over 12–14 weeks of intervention [8, 9]. However, our data are consistent with a previous pilot trial of dietary counseling for people with dyslipidaemia in primary care that showed a similar small reduction in LDL (−0.32 mmol/L, p < 0.05 in the active intervention arm at 6 months compared with baseline) [31].

Although there is limited clinical benefit to individuals of very small reductions in SFA, there could be important population-level health gains. Systematic reviews of trials of interventions to reduce SFA intake compared with usual diets showed interventions reduced LDL by −0.19 mmol/L (−0.33, −0.05) yet decreased the incidence of nonfatal CVD events by 17% [3], and modeling suggests that just replacing 1% of EI from SFA with PUFA reduces CVD events by 8% [32]. In testing this novel intervention, we opted for a small “hybrid” trial examining the feasibility of the shopping feedback and looking for evidence of effectiveness as a clinical intervention, recognizing that it would be underpowered to detect important population effects [22]. The process measures suggest further research on the shopping feedback delivered at a larger scale is now justified.

A strength of this study is the randomized controlled trial (RCT) design and high follow-up rate (94%), which reduces the effect of confounding and provides high internal validity. The average monthly spend across groups and over time was largely unchanged, suggesting that the intervention did not disrupt usual shopping patterns. Our primary outcome was based on a dietary recall method, which is subject to misreporting, and included only 2 days of reporting at baseline and follow-up, but potential bias is reduced by the comparison with a control group. Total EI data measures are consistent with the observed weight changes, and there was good agreement in the measured change in SFA and key food groups in each group between the dietary recall and the household food purchase data. A previous study also showed strong correlations approximately 0.8 between total energy and fat between dietary recalls and food purchases, which suggest that the use of purchasing data is feasible, inexpensive, and accurate for nutritional research in populations in which most food comes from supermarkets [29].

The principal limitation of the study is the sample size, which lacked power to detect smaller effect sizes. The participants randomized were a very small proportion of those eligible and may have been biased to those with higher levels of concern about their diet. It is possible that participants in the control group could have been prompted to reduce their SFA upon receiving their blood test results and the notice of a future check 3 months later, which reduced the chance of detecting a difference between groups. Only 2 participants were taking lipid-lowering medications, and this may also imply a motivated group already taking action, which could limit the opportunity for further change. Our population included a higher proportion of middle/older age participants, reflecting the population with dyslipidaemia [33]. Women were over-represented, but this probably reflects the requirement to be the main shopper in the household to participate in the study. The recruitment of participants was also limited to people whose weekly shopping was mostly from the participating supermarket. However, the collaborating supermarket we worked with accounts for 27% of the UK grocery market, with customers across the socioeconomic spectrum. A future trial recruiting a larger sample of participants would require a larger sample of GP practices in order to recruit at a satisfactory rate if similar inclusion criteria are used. The limited fidelity in the HCP-led intervention needs to be acknowledged, because only half of the intended behavioral intervention components were delivered on average. In an explanatory trial, this would be problematic, but in this pragmatic trial, lower fidelity is not inherently a limitation. However, it suggests that further attention needs to be given to the training for HCPs in any future trial. Likewise, it would be ideal to develop a system to ensure that participants randomized to the intervention arms were able to see the HCP sooner than occurred here to capitalize on initial enthusiasm. A sensitivity analysis, which excluded people who received their intervention after 45 days from recruitment, revealed slightly stronger though still nonsignificant differences between intervention groups and control.

The shopping feedback used a novel computerized algorithm to process information from loyalty cards and provide tailored dietary advice in real-time. By drawing on the power of data already collected by grocery stores, we could deliver personalized advice, which would otherwise require specialist practitioners, usually dieticians, considerable time to analyze and would strain healthcare resources. There was no difference observed between the groups receiving brief advice only and those also receiving personalized shopping reports. For this highly selected population, it is possible that brief advice is enough to change food purchasing and consumption, and we do not know the effect of tailored shopping advice alone. Only 3 studies have previously observed absolute reductions in SFA intake with interventions focused on food purchasing. A study of tailored education (e.g., frequently mailed tailored shopping lists, recipes, and healthier alternatives) based on previous purchases showed a reduction of −1.4% in SFA in all food purchases at 6 months, but this was not significantly greater than the control group [34]. The amount of SFA in purchases also decreased by 0.7 g SFA/week in a pilot study providing web-based advice based on the traffic-light labeling system to improve the quality of ready meals (e.g., pizza) [35]. Only one study, performed in an online supermarket, which offered lower SFA swaps on food products (excluding meat), achieved a significant difference between the intervention and control group of −0.66% SFA [19].

Importantly saturated fat intake and %SFA in total purchases declined in both intervention groups for the food categories that contribute the most to SFA intakes in the UK, and the changes were congruent with the emphasis of the advice provided. The written information provided in the brief advice group focused on meat as well as dairy intake and purchases and intake of these foods, especially meat, declined in the BS group. In contrast, most swaps offered in the shopping reports and accepted in the SF group were for dairy products. The slightly larger reductions in LDL cholesterol in the BS compared with the SF group are consistent with recent epidemiological evidence on the effects of individual fatty acids found in different concentrations in meat and dairy products [36].

In qualitative questionnaires, participants highly valued the shopping reports and reported them to be useful for self-monitoring. Given the relative ease and low cost of providing this information at scale, it warrants further investigation as a public health intervention for people seeking to reduce the SFA content of the diet. A similar approach could potentially be adopted for other nutrients of concern, e.g., salt or free sugars, by retail partners who may wish to offer this type of support to their customers. Only a few studies have used information on household food purchases from loyalty cards, usually to monitor the effectiveness of interventions or to provide personalized dietary advice [34, 35, 37–39], but none have developed a scalable system as we used here to provide this information in real-time. At present, access to this data requires complex legal agreements with the retailers to address the privacy concerns of consumers and commercial concerns of retailers [40]. However, in an era of open data, greater awareness by the “data creators” (i.e., their customers) that the “data holders” (i.e., companies) are required to share this information on request might persuade companies to set up routine systems to use these data in ways that add value to the consumer experience and also promote healthier purchasing.

In conclusion, our trial showed nonsignificant reductions in SFA intake, the SFA content of household food purchases, and LDL-C. The small changes we observed are consistent with other individual-level behavioral interventions to reduce SFA, but the study was underpowered to detect all but large improvements. With further enhancement of the intervention based on participants’ feedback, the good evidence of feasibility and acceptability of using grocery loyalty card data offers the potential to develop an automated, low-cost, and scalable intervention that could reach and benefit large numbers of people to deliver population-level impacts, which may improve public health.

Study protocol.

(DOCX)

Click here for additional data file.

List of inclusion and exclusion criteria.

(DOCX)

Click here for additional data file.

Qualitative questionnaire.

(DOCX)

Click here for additional data file.

Supplementary tables.

(DOCX)

Click here for additional data file.

CONSORT checklist.

CONSORT, Consolidated Standards of Reporting Trials.

(DOC)

Click here for additional data file.

31 Jan 2020

Dear Dr. Piernas,

Thank you very much for submitting your manuscript "Brief support and personalised feedback on food shopping to encourage saturated fat reduction: results from the PC-SHOP randomised controlled trial" (PMEDICINE-D-19-03921) for consideration at PLOS Medicine.

Your paper was evaluated by a senior editor, discussed with an academic editor with relevant expertise, and sent to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

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In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

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Introduction

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Methods

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Line 96 - please clarify whether informed consent was written or oral

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Results

Please provide p values in addition to 95% CIs throughout your results section.

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Line 261 - Please indicate which factors are adjusted for

Line 275 - please revise ‘somewhat greater reductions’ to clarify that reductions were non-significant

Line 306 - please revise ‘slightly larger reductions’ to clarify that reductions were non-significant

Line 356 - please replace % with ‘percentage’, ‘proportion’ or similar

Discussion

Line 369 - please revise ‘somewhat greater reductions’ to clarify that reductions were non-significant

Line 450 - please clarify which groups are being compared when discussing these results

Line 482 - please avoid using the word ‘novel’, in order to avoid assertions of primacy

Figure 1 - please define SFA and SEM in the figure legend

Figure 2 - please define EI and TE in the figure legend

Figure 3 - please define EI, TE, SFA, BS and SF, LDL in the figure legend

Supplementary Tables - please define all abbreviations used in the table legend

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References

Please ensure that all references are appropriately formatted and capitalised e.g. refs 7, 25, 27, 28, 29, 31, 33

Comments from the reviewers:

Reviewer #1: Thank you for the opportunity to review your manuscript. This study presents the findings of a 3-arm RCT that tests whether there are changes in saturated fat intake (% total energy) when patients with high LDL cholesterol are exposed to usual care, brief support, and brief support with enhanced feedback on shopping purchases. My role is as a statistical reviewer and I have focused on the design and analysis. The protocol and statistical analysis plan are included, the supplementary material attached to this publication is well-organised and I found it helpful when conducting my review. I have previously collaborated with colleagues working in general practice, primary prevention, and food databases and I appreciate the thoughtfulness and potential of this work.

P6, Line 114. A 1:3:3 allocation ratio is used, but there doesn't appear to be a justification for this in the protocol or manuscript. Was there a specific reason that the usual practice control group had a smaller allocation?

P6, Line 116. The protocol (p21) states that randomly varying block sizes were used, but then specifies that all the blocks were of size 7. Can you confirm whether they were randomly sized, or used a fixed block size with random order?

P10, L213. 'Statistically significant' is used throughout the reporting of results, but there are not details as to what was statistically significant in the methods (e.g. alpha < 0.05, 95% CI overlaps null effect?). If 'statistically significant' is to be used, then this should be formally defined, if based on a p-value then this should be presented.

P10, L214 Where was the standard deviation for % of SFA for the sample size calculations taken from?

P10, L219. In the power calculation it says that a multiciplicy adjustment is included, but there are no details of what the selected multiplicity adjustment was that was incorporated into the sample size calculation, and whether a multiplicity adjustment was applied to the final analysis (and what it was).

P13, Table 1. Just confirming the results for BMI by category vs. average BMI. There is very little difference between groups in average BMI, but when reported as % per categories there appears to be some variation between groups.

P21, L226. The primary analysis not only includes an adjustment for baseline value, but also for practice. Just to clarify, was the practice included as a fixed effect or as a random effect in the main regression model?

P22, L318. The most powerful test of differences between subgroups showed no important differences between groups, but the last sentence gives the impression that there was a useful difference found - I think the wording should be adjusted to reflect this. Also, if a p-value is used as evidence for or against an interaction, it should be presented.

P24, L326. The results of the sensitivity analyses should be included in the supplementary materials.

Consort checklist: Have the page numbers reported in the checklist shifted when it was formatted for submission? I couldn't find some of the items (e.g. eligibility and trial design should be on p4 and p3) in the manuscript in the pages they are reported.

Reviewer #2:

Thank you for the opportunity to read this interesting paper reporting a well-conducted 3-arm hybrid trial examining changes in SFA intake after a behavioural intervention. The retention rate is excellent and the finding that no significant between-group differences in changes emerged over this timescale in this particular sample size is an important one to inform future work. I have some suggestions to further clarify aspects of the manuscript, which the authors may like to consider.

1. Typo in short title - 'amd' should be 'and'

Abstract

2. Would the authors consider mentioning that that RCT was a hybrid trial in the Abstract? Also, in the abstract it may help the reader to mention the retention rate / note the final N and to note that the primary outcome came from self-reported 24hr recalls of intake.

3. The values for adjusted between group differences do not appear to be reported in the Abstract, although this is stated as the primary outcome in the Abstract itself. Unless I have misunderstood this, these adjusted values should be reported as well as - or instead of - the absolute, unadjusted % SFA intake changes currently reported.

Method

4. Were outcome assessors of the variables that could not be reported via online portal blinded to allocation - for example, body weight? I assume that this is what is referred to when talking about blinding at follow up, but this could benefit from clarification.

5. I found myself searching for some of the information contained in supplementary material in order to understand the basics of the RCT. A key example of this was participant exclusion/inclusion based on sufficient use (or not) of the specific supermarket chain. Is there scope to include more details about inclusion/exclusion in the main body of the Method?

6. Could data for the numbers of participants who did not respond to the invitation / who were screened ineligible prior to consent be added to the CONSORT diagram at the first stage - this is not essential but provides additional interesting information that is presently presented in the text?

7. Please consider whether it would be appropriate to provide brief extra behavioural details of the intervention delivered (e.g. 'box 1' as shown in Lombard et al, 2016; https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001941). I realise that details of the intervention are provided in the protocol paper, but a very brief summary would be immensely helpful to behaviourally-oriented readers.

8. Please could the rationale for presenting both 24 hr recall and supermarket data be more clearly stated?

Results

9. Please consider whether figure and table notes need to define acronyms like SFA and EI.

10. As it took on average 37 days from baseline to intervention delivery, would the authors also consider reporting the mean number of days between receiving the intervention and completing follow-up assessment? Did the 3 month period start from the day of the baseline assessment or the day of the intervention appointment? How did this month-long average delay affect the number of shopping feedback components delivered to participants in this group - did all participants receive all 4 sets of feedback (as is suggested by an average of 3.6 in the SF group)?

11. Line 274: "Intakes of key food groups (%EI) were not statistically significant between intervention and control" - should 'significant' read 'different'; the sentence does not quite scan?

12. Were any particular behavioural components omitted from delivery more frequently than others? It would be interesting to know a little more about the outcome of the fidelity analyses but not essential.

Discussion

13. "However, the reductions in SFA and LDL-cholesterol in the intervention groups were somewhat greater than those in the control with no formal intervention" - can this claim be modified to remind the reader that such changes (and, later in the Discussion, the change in SFA in purchases) did not reach statistical significance?

14. Might the relatively brief duration of the intervention period be a study limitation?

15. Given that the trial also assessed the feasibility of the recruitment process, what changes would the team consider to this process given the relatively low response rate of 3% of potentially eligible participants?

Abbreviations

16. Might EI and TE be profitably added to the list of abbreviations?

References

17. Is there an issue with reference 8 (Office of national statistics?) Similarly references 20 and 21. References 33 and 34 seem to be duplicates.

Reviewer #3: Thank you for the opportunity to review this paper.

This is a small randomized trial (n=113) to 3 groups a Brief Support a Brief support +Shopping feedback and a control group that reports feasibility but no effectiveness. This is an interesting intervention, potentially scalable and a novel idea which needs evaluation, however this is underpowered and can only really establish the feasibility of implementing this strategy and not the effectiveness or otherwise of this strategy. Important partnerships have been established to enable this and likely work done to develop the intervention. Overall this is well written, but I think there could be on balance more emphasis on the feasibility/ implementation aspects and less on the effectiveness.

Methods:

Why did the investigators only target patients with high LDL in the last 5 years? An Absolute risk approach could have been taken, or it could have been justified to target a larger population group as a saturated fat intervention is likely to be relevant to a wider group of patients. Why were patients with CVD excluded? A dietary intervention on-top of there usual treatment would seem reasonable. And the primary outcome was self-reported saturated fat and not LDL cholesterol.

Why was a 1:3:3 ratio used?

Research staff not being blinded to allocation could have been an issue, but the primary outcome was assessed through an electronic questionnaire without involving the study team.

It would be good to describe here or in discussion about the development of the partnership with the UK grocery store in more detail and the design of the shopping report, who was involved in the design and was it tested with consumers prior to the trial. I think this might add more depth to this work.

Results:

There seems to be some imbalances in some risk factors (e.g. normal weight), this is likely to have been chance given the small sample size of groups, however it would be helpful to see what the distribution of absolute cardiovascular risk was across participants.

Discussion:

This is generally well discussed. There is some repetition and could probably be trimmed if word count/ length an issue. The focus of conclusions is effectiveness but probably the focus should be feasibility and implementation evaluation. It would be good to emphasize and discuss the learnings from the intervention implementation.

The issue of not all the behavioural components being delivered is probably a challenge of implementation and it may be good to discuss how this may be addressed in the future, is it just the training? Or is the HCP step needed? Or are there other ways of delivering this? Or does the content of delivery matter so much as the contact with the HCP?

In addition:

I would usually think good to include the inclusion/ exclusion criteria and consort diagram in main paper as opposed to appendix.

I think a figure to show an example of the shopping report would also be helpful to visualize what this comprised.

I think having a consort diagram and figure to show the shopping report would be more helpful than existing figures. E.g. perhaps Figure 1 could be put in appendix (this repeats data in text/tables doesn't it), figure 2 could be kept and figure 3 could be put in appendix.

Any attachments provided with reviews can be seen via the following link:

[LINK]

20 Feb 2020

Submitted filename: PMEDICINE responses to review 20 02 2020 final.docx

Click here for additional data file.

19 Aug 2020

Dear Dr. Piernas,

Thank you very much for re-submitting your manuscript "Evaluation of an intervention to provide brief support and personalised feedback on food shopping to reduce saturated fat intake (PC-SHOP): a randomised controlled trial" (PMEDICINE-D-19-03921R1) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

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We look forward to receiving the revised manuscript by Aug 26 2020 11:59PM.

Sincerely,

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PLOS Medicine

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Requests from Editors:

Abstract

GP – perhaps not commonly used outside the UK so I would recommend introducing on first view

Please provide brief participant demographics-include age range, sex etc in the abstract

Please provide p-values as needed. Al p-values should be exact numbers, unless p<0.001

Data statement: We understand the data restrictions due to the ethics and contractual obligations. However, please note we do not permit authors to act as gatekeepers to the primary underlying data. Please provide an institutional or ethics committee contact instead. Regarding the Brand View dataset, please provide details of the dataset and contact details of someone at BrandView who can be approached by interested parties to gain access to the data.

Author summary

Please add bullet points

Nurse or healthcare professional seems a bit redundant. Could you perhaps just say healthcare professional?

I suggest rephrasing “we followed them for approximately 3 months” as this could mean any number of things

Methods

All references to questionnaires used in the study should be accompanied by a citation to the reference, either as published or as provided in SI files

Line 277- suggest revising “results were averaged…”

CONSORT flowchart-please specify reasons for ineligibility

Results

Line 375 and line 397 onwards- please provide all p-values to accompany 95% CI

In all instances where you mention “significance” or “non-significance” please provide 95% CI and p values.

I suggest toning this down “Participants in the control group appeared to have been prompted to reduce their SFA upon receiving their blood test results and the notice of a future check three months later, which reduced the chance of detecting a difference between groups”

CONSORT checklist- please use paragraphs and sections instead of page numbers as these are likely to change

Overall- please avoid the use of the phrase “somewhat greater” and use significant or non-significant instead.

Comments from Reviewers:

Reviewer #1: Thanks for the revisions to the manuscript and the details given in the response to my comments (and the other reviewers). The comments and the revisions to the manuscripts have resolved all my original queries.

One comment that may be covered during further editorial process was that the figures have some issues - in my PDF some of the axis label text is obscured by background stippling and the text isn't rendered properly making it difficult to read. This could be something induced by the MS submission system.

For Figure 3 I'd recommend representing the % change as a dot or square rather than a bar.

Reviewer #2: I'd like to thank the authors very much for their response to my comments and those of the editor and other reviewers - the manuscript is much clearer. The response to comment number 15 in my review regarding engagement from colleagues in primary care in the recruitment process is particularly interesting and illuminating, and very much highlights the need for this sort of study!

I have just one other suggestion for the revised manuscript.

Box describing intervention: Could the description of the shopping report be clarified further please? It currently appears as if the 'report' in point 1 is separate from the information listed in point 2, whereas my understanding is that the report consisted of the information under point 2. Apologies if I have misunderstood. In addition, also in this box, is there any possibility that the number of reports could be specified alongside the statement that they were provided monthly from baseline onwards?

Any attachments provided with reviews can be seen via the following link:

[LINK]

16 Sep 2020

Submitted filename: Second review queries_PCSHOP 24 08 2020.docx

Click here for additional data file.

18 Sep 2020

Dear Dr. Piernas,

On behalf of my colleagues and the academic editor, Dr. Barry M. Popkin, I am delighted to inform you that your manuscript entitled "Evaluation of an intervention to provide brief support and personalised feedback on food shopping to reduce saturated fat intake (PC-SHOP): a randomised controlled trial" (PMEDICINE-D-19-03921R2) has been accepted for publication in PLOS Medicine.

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Thank you again for submitting the manuscript to PLOS Medicine. We look forward to publishing it.

Best wishes,

Adya Misra, PhD

Senior Editor

PLOS Medicine

plosmedicine.org