Jacka et al. BMC Medicine (2017) 15:23
DOI 10.1186/s12916-017-0791-y

RESEARCH ARTICLE

Open Access

A randomised controlled trial of dietary
improvement for adults with major
depression (the ‘SMILES’ trial)
Felice N. Jacka1,4,9,10,13*, Adrienne O’Neil1,2,13, Rachelle Opie5,13, Catherine Itsiopoulos5, Sue Cotton3,
Mohammedreza Mohebbi1, David Castle4,11, Sarah Dash1,13, Cathrine Mihalopoulos7, Mary Lou Chatterton7,
Laima Brazionis5,6, Olivia M. Dean1,4,12,13, Allison M. Hodge8 and Michael Berk1,3,12,13

Abstract
Background: The possible therapeutic impact of dietary changes on existing mental illness is largely unknown.
Using a randomised controlled trial design, we aimed to investigate the efficacy of a dietary improvement program
for the treatment of major depressive episodes.
Methods: ‘SMILES’ was a 12-week, parallel-group, single blind, randomised controlled trial of an adjunctive dietaryintervention in the treatment of moderate to severe depression. The intervention consisted of seven individual
nutritional consulting sessions delivered by a clinical dietician. The control condition comprised a social support
protocol to the same visit schedule and length. Depression symptomatology was the primary endpoint, assessedusing the Montgomery–Åsberg Depression Rating Scale (MADRS) at 12 weeks. Secondary outcomes included
remission and change of symptoms, mood and anxiety. Analyses utilised a likelihood-based mixed-effects model
repeated measures (MMRM) approach. The robustness of estimates was investigated through sensitivity analyses.
Results: We assessed 166 individuals for eligibility, of whom 67 were enrolled (diet intervention, n = 33; control,n = 34). Of these, 55 were utilising some form of therapy: 21 were using psychotherapy and pharmacotherapy
combined; 9 were using exclusively psychotherapy; and 25 were using only pharmacotherapy. There were 31 in
the diet support group and 25 in the social support control group who had complete data at 12 weeks. The dietary
support group demonstrated significantly greater improvement between baseline and 12 weeks on the MADRS
than the social support control group, t(60.7) = 4.38, p < 0.001, Cohen’s d = –1.16. Remission, defined as a MADRS
score <10, was achieved for 32.3% (n = 10) and 8.0% (n = 2) of the intervention and control groups, respectively(χ2 (1) = 4.84, p = 0.028); number needed to treat (NNT) based on remission scores was 4.1 (95% CI of NNT 2.3–27.8).
A sensitivity analysis, testing departures from the missing at random (MAR) assumption for dropouts, indicated thatthe impact of the intervention was robust to violations of MAR assumptions.
Conclusions: These results indicate that dietary improvement may provide an efficacious and accessible treatment
strategy for the management of this highly prevalent mental disorder, the benefits of which could extend to the
management of common co-morbidities.
Trial registration: Australia and New Zealand Clinical Trials Register (ANZCTR): ACTRN12612000251820. Registeredon 29 February 2012.
Keywords: Depression, Major depressive disorder, Diet, Nutrition, Randomised controlled trial, Dietetics* Correspondence: f.jacka@deakin.edu.au
1
IMPACT Strategic Research Centre, Deakin University, Geelong, VIC, Australia
4
Department of Psychiatry, University of Melbourne, Melbourne, VIC, Australia
Full list of author information is available at the end of the article© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Jacka et al. BMC Medicine (2017) 15:23

Background
There is now extensive observational evidence acrosscountries and age groups supporting the contention that
diet quality is a possible risk or protective factor for
depression [1–5]. Although there are many versions of a‘healthful diet’ in different countries and cultures, theavailable evidence from observational studies suggests thatdiets higher in plant foods, such as vegetables, fruits,
legumes and whole grains, and lean proteins, including
fish, are associated with a reduced risk for depression,
whilst dietary patterns that include more processed foodand sugary products are associated with an increased riskof depression [1, 6, 7]. Whilst cognisant of the limitationsof observational data, these associations are usually observed to be independent of socioeconomic status, education and other potentially confounding variables and notnecessarily explained by reverse causality (see, e.g. [7–10]).
Recently, a meta-analysis confirmed that adherence to a‘healthful’ dietary pattern, comprising higher intakes of fruitand vegetables, fish and whole grains, was associated with areduced likelihood of depression in adults [1]. Similarly, another meta-analysis reported that higher adherence to a
Mediterranean diet was associated with a 30% reduced riskfor depression, with no evidence for publication bias [11].
The Mediterranean diet is recognised as a healthful dietary
pattern and has been extensively associated with chronic
disease risk reduction [12]. More recently, a systematic review confirmed relationships between unhealthful dietary
patterns, characterised by higher intakes of foods with saturated fat and refined carbohydrates, and processed foodproducts, and poorer mental health in children and adolescents [2]. Several cohort studies also reported associations
between the quality of women’s diets during pregnancy andthe risk for emotional dysregulation in children [13–15],
with new insights into potential mechanisms of action that
include brain plasticity [16], the gut microbiota [17] and inflammatory [18] and oxidative stress [19] pathways.
Although there are data suggesting that some nutritional supplements may be of utility as adjunctive therapies in psychiatric disorders [20], the field of research
focusing on the relationships between overall dietary quality and mental disorders is new and has thus far been
largely limited to animal studies and observational studies
in humans. Thus, whilst the existing observational data
support a causal relationship between diet quality and depression on the basis of the Bradford Hill criteria [3] andare supported by extensive experimental data in animals(see, e.g. [21]), randomised controlled trials are required
to test causal relationships and identify whether or not
dietary change can improve mental health in people withsuch conditions. We conducted a systematic review andidentified a number of interventions with a dietary change
component that had examined mental health-related outcomes [22]. Whilst approximately half of these studies

Page 2 of 13

reported improvements in measures of depression or anxiety following the intervention, at the time of the review
no studies fulfilling quality criteria had been conducted inmental health populations or had been designed to test
the hypothesis that dietary improvement might result in
improvements in mental health. Since then, one study has
been published evaluating the possible impact of a lifestyleprogram, comprising both diet and exercise, on mental
health symptoms in patients with depression and/or anxiety; this study failed to show any differences in symptom
levels between those in the intervention and those in theattention control group [23]. On the other hand, post hoc
analysis of a large-scale intervention trial provides preliminary support for dietary improvement as a strategy forthe primary prevention of depression. Individuals at increased risk for cardiovascular events were randomised toa Mediterranean diet supplemented with either extravirgin olive oil or mixed nuts, or a low-fat control diet[12]. Whilst not statistically powered to assess the effectiveness of the intervention for preventing depression,
there was evidence (albeit non-significant) of a reduced
risk for incident depression for those randomised to a
Mediterranean diet with nuts. This protective effect was
statistically significant in those with type 2 diabetes, whocomprised approximately half the sample [24].
Using a randomised controlled trial (RCT) design, we
thus aimed to investigate the efficacy of a dietary program
for the treatment of major depressive episodes. In thistrial, Supporting the Modification of lifestyle In Lowered
Emotional States (SMILES), we hypothesised that structured dietary support, focusing on improving diet quality
using a modified Mediterranean diet model, would be superior to a social support control condition (befriending)in reducing the severity of depressive symptomatology.

Methods
Study design

This was a 12-week, parallel-group, single blind RCT of a
dietary intervention in the treatment of moderate to severe
depression (for the protocol see [25]). This trial was registered in the Australia and New Zealand Clinical Trials
Register (ANZCTR): (ACTRN12612000251820) prior tocommencing recruitment. Participants were recruited fromtwo sites: Barwon Health in Geelong and St. Vincent’s
Health in Melbourne (Victoria, Australia) over a 3-year
period. Participants were randomised to receive either
dietary support or social support (‘befriending’ [26]). Participants in both groups completed assessments prior to program commencement (baseline), with the primary andsecondary outcomes measured at program completion(12 weeks, primary endpoint). Approval to conduct the
study was received from Human Research Ethics Committees of St. Vincent’s and Barwon Health. Written informed
consent was obtained from all participants after they had

Jacka et al. BMC Medicine (2017) 15:23

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received a complete description of the study. The study’s
protocol was developed in accordance with the Standard
Protocol Items: Recommendations for Interventional Trials(SPIRIT) guidelines. Reporting of findings pertaining toprimary and secondary outcomes was done in accordancewith the Consolidated Standards of Reporting Trials(CONSORT) 2010 guidelines and their extension to nonpharmacologic treatments.

stations were also employed as recruitment strategies. Ethics
committee requirements meant that we needed to be explicit regarding our planned intervention, with the advertisements stating: ‘We are trialling the effect of an educationaland counselling program focusing on diet that may help
improve the symptoms of depression’.

Participants
Inclusion criteria

The dietary intervention comprised personalised dietaryadvice and nutritional counselling support, including motivational interviewing, goal setting and mindful eating, from aclinical dietician in order to support optimal adherence tothe recommended diet. This comprised the ‘ModiMedDiet’,
developed by RO and CI, which was based on the Australian
Dietary guidelines [29] and the Dietary Guidelines for Adults
in Greece [30] and is concordant with our previous dietary
recommendations for the prevention of depression [31].
The primary focus was on increasing diet quality by supporting the consumption of the following 12 key foodgroups (recommended servings in brackets): whole grains(5–8 servings per day); vegetables (6 per day); fruit (3 perday), legumes (3–4 per week); low-fat and unsweetened
dairy foods (2–3 per day); raw and unsalted nuts (1 per day);fish (at least 2 per week); lean red meats (3–4 per week)[32], chicken (2–3 per week); eggs (up to 6 per week); andolive oil (3 tablespoons per day), whilst reducing intake of‘extras’ foods, such as sweets, refined cereals, fried food, fastfood, processed meats and sugary drinks (no more than 3
per week). Red or white wine consumption beyond 2 standard drinks per day and all other alcohol (e.g. spirits, beer)
were included within the ‘extras’ food group. Individuals
were advised to select red wine preferably and only drinkwith meals. The dietary composition of the ModiMedDiet
was as follows: protein 18% of total energy (E); fat 40% of E;
carbohydrates 37% of E; alcohol 2% of E; fibre/other 3% of
E. The diet was designed to be easy to follow, sustainable,
palatable, and satiating. Individuals were advised to consume
the diet ad libitum, as the intervention did not have aweight loss focus. The method for scoring the ModiMedDiet is similar to those used in PREDIMED [33] and the
Framingham Offspring Cohort [34]. It is a criterion-based
diet score that uses pre-defined absolute or normative goalsof consumption for specific food items, independent of theindividual’s characteristics. It was developed based on therecommended intakes of the 11 food group componentsthat comprise the ModiMedDiet (as above), and of the scorehas a theoretical maximum value of 120.
Participants received seven individual dietary support
sessions of approximately 60 minutes each, delivered by an
Accredited Practising Dietician; the first four sessions occurred weekly and the remaining three sessions occurred
every 2 weeks. At the first session, the dietician conducteda diet history to assess usual dietary intake. Participants

Eligibility criteria included participants who were at screening: aged 18 or over and could provide informed consent;
successfully fulfilled the Diagnostic and Statistical Manualof Mental Disorders (4th ed.; DSM-IV-TR) diagnostic criteria for a major depressive episode (MDE); scored 18 orover on the Montgomery–Åsberg Depression Rating Scale(MADRS) [27]; and scored 75 or less, out of a possible
score of 104, on a Dietary Screening Tool (DST) [28]modified for Australian food products. The DST wascompleted to confirm ‘poor’ dietary quality, before enrolment. This screening tool was used to reflect usual daily or
weekly intake of specified foods. Broadly defined, participants had to report a poor (low) intake of dietary fibre, leanproteins and fruit and vegetables, and a high intake ofsweets, processed meats and salty snacks. If participants
were on antidepressant therapy or undergoing psychotherapy, they were required to be on the same treatment for atleast 2 weeks prior to randomization. Participants had tobe readily available for a 12-week period and have theability to eat foods as prescribed, without religious,
medical, socio-cultural or political factors precluding
participation or adherence to the diet.
Exclusion criteria

Participants were ineligible if they had: (1) a concurrent
diagnosis of bipolar I or II disorder; (2) two or more failed
trials of antidepressant therapy for the current MDE; (3)known or suspected clinically unstable systemic medical
disorder; (4) pregnancy; (5) commencement of new psychotherapy or pharmacotherapy within the preceding
2 weeks; (6) severe food allergies, intolerances or aversions; (7) current participation in an intervention targeting
diet or exercise; (8) a primary clinical diagnosis of a personality disorder and/or a current substance use disorder.
Sample recruitment

Community-based recruitment strategies were used to identify study participants, including flyers in medical waiting
rooms, pharmacies and university campuses; newsletters;
and contact with potential referral sources (e.g. general practitioners, private psychiatrists and local psychiatric inpatient
units). Media interviews and advertisements in social media(e.g. Twitter, Facebook), Google, local newspapers and radio

Interventions
Dietary support

Jacka et al. BMC Medicine (2017) 15:23

were provided with supporting written information specifically designed for the intervention to assist with achieving
dietary adherence. In order to provide examples of serving
sizes and exposure to the recommended foods, participants
were also provided with a food hamper, incorporating themain components of the diet, along with recipes and mealplans. Subsequent sessions used motivational interviewing
techniques, and participants were encouraged to set personalised goals.
Social support

The social support control condition comprised a manualised ‘befriending’ protocol [26], using the same visit
schedule and length as the dietary support intervention.
Befriending consists of trained personnel discussing neutral topics of interest to the participant, such as sport,
news or music, or in cases where participants found theconversation difficult, engaging in alternate activities suchas cards or board games, with the intention of keeping theparticipant engaged and positive. This is done without
engaging in techniques specifically used in the major
models of psychotherapy. Research assistants (RAs) in thistrial completed manual-guided training and also participated in role-playing training exercises to ensure consistent delivery of the protocol. Befriending aims to control
for four factors: time; client expectancy; therapeutic alliance; and therapist factors when compared to the intervention group in an RCT and is often used as a control
condition for clinical trials of psychotherapy [26]. Participants in the social support control group were provided
with movie tickets as compensation for their time andparticipation in the study and were offered participation
in a group dietary counselling session at the conclusion of
the trial.
Assessments and outcomes

Once deemed to be eligible, participants completed a 7-day
food diary and the Cancer Council of Victoria foodfrequency questionnaire [35], in the week leading up to
baseline assessment. Participants attended a local pathology
clinic to provide fasting blood samples before undertaking
baseline assessment and randomization.
Baseline and follow-up assessments

Details of baseline and follow-up assessments have beenreported elsewhere [25]. Briefly, primary and secondary
endpoints were as described in the following sections.

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It has been found to be a robust and psychometricallysound measure of depressive symptomatology [27].
Secondary outcomes The Hospital Anxiety and Depression Scale (HADS) [36] was administered as a self-reportquestionnaire. The Profile of Mood States (POMS) wasused to assess mood [37], and the Clinical Global Impression - Improvement (CGI-I) Scale [38] was used to assesschange in symptoms from baseline to endpoint. The
World Health Organization wellbeing scale (WHO-5) [39]and the Generalized Self-Efficacy Scale [40] were used toassess wellbeing and self-efficacy, respectively. Clinicaldata including height, weight and waist circumference
were also collected and the body mass index (BMI) was
calculated. Participants were also asked the following:whether they were a current smoker (yes/no); if they had
an existing medical condition (physical or mental); andthe names and doses of any medications they were taking.
Current levels of physical activity were assessed using
International Physical Activity Questionnaire (IPAQ)
scores, which capture Metabolic Equivalent of Task (MET)
minutes per week. A total MET score was calculated foreach participant as a summary of Walking, Moderate andVigorous MET scores [41]. Dietary quality was assessedusing the ModiMedDiet score, which was based on consumption of the key food groups (i.e. wholegrains, vegetables, fruits, legumes, nuts, fish, lean red meats, chicken,
low fat dairy, eggs, olive oil, extras) and will be presented
in more detail, along with the dietary strategy, in a forthcoming publication. Dietary assessments, using 7-day fooddiaries, were administered at baseline and endpoint to bothgroups to identify dietary changes and adherence to therecommended diet; this was done by assessing change in
the ModiMedDiet score, which is based on the consumption of the key food groups. Biomarkers, including plasma
fatty acids, fasting glucose, total and HDL and LDL cholesterol and triglycerides were also assessed.
Sample size

Our original sample size calculation required 88 people
per group, assuming an attrition of 15%, with 8 predictors. For a one-tailed analysis with type I error or alphaset at the .05 level, the study would have been powered
at 80% to detect a true difference in rating scale score
between the diet and befriending groups if the effect size
was 0.15 or greater on the MADRS.
Randomization

Primary outcome The MADRS was used to assess thechange in depressive symptomatology at baseline and at
the primary endpoint of 12 weeks. The MADRS is aninterviewer-rated instrument, comprising 10 items, eachmeasured on a 6-point scale (scores range from 0–60
with higher scores depicting greater symptom severity).

The randomization sequence was computer generated by anindependent person (OD) using a 2 × 2 block design. The
sequence was saved to a password-protected spreadsheet,and groups were coded A and B. The randomization allocation was managed by the trial dieticians or ‘befrienders’, inorder to ensure that the research assistants responsible forJacka et al. BMC Medicine (2017) 15:23

mental health assessments were blind to participants’ group
allocations, and the randomization schedule and coding ofgroup allocations were not, at any time, accessible to theresearch assistants conducting the assessments, or to thebiostatistician (SC). At the conclusion of the baseline appointment, the dietician/befriender would meet privately
with the participant and inform them of their group allocation in order to maintain blinding of the research assistants.
Blinding

Although full blinding of participants to condition in thisstudy was not possible, several strategies were employed
to reduce the risk of bias. First, participants were provided
with only partial information on the study hypothesis; thesocial support control condition was termed ‘befriending’
and research assistants emphasised the link between social
support and mental health as an outcome of interest; andparticipants in both the intervention and the social
support control group were provided with standardisedcare, with all participants attending appointments in thesame location and with the same format, as well as similar
duration and frequency. All communication between
participants and research staff during the period of intervention (i.e. scheduling concerns, questions regarding
intervention) was done directly between participants andtheir respective ‘clinician’. Participants were clearly
instructed only to contact the dietician/befriender personally and to avoid contact with the research assistant, andvoice messages were checked daily by the dietician/befriender to avoid unintended contact or information on
participants’ allocation. Research assistants did not havedirect contact with participants for the duration of theintervention. Final assessments were organised by the
dietician or befriender, and research assistants remained
blind to condition for the final assessment of outcomes.
Prior to assessment, participants were reminded not to reveal the group to which they had been assigned. Statisticalanalyses were conducted by an external statistician (SC),
who was blind to group allocation prior to analysis.
Data analyses

The analyses were conducted in accordance with theInternational Conference on Harmonization E9 statisticalprinciples. Independent samples t tests and chi-square (χ2)
analyses were used to compare participants who completed and did not complete the 12 weeks of the trial.
Intention-to-treat (ITT) analyses were adopted. The primary efficacy analysis was based on between-group differences in average change from baseline to 12 weeks for the
primary outcome measure (MADRS); these analyses wereconducted using planned comparisons within a restrictedmaximum likelihood (REML)-based mixed-effects model,
repeated measures (MMRM) approach. Within the
MMRM, treatment and assessment occasion and thePage 5 of 13

interaction between treatment group and assessment occasion were included as fixed factors. The MMRM approachis the preferred method of dealing with clinical trial data in
psychiatry [42]. The benefits of these MMRM methods arethat all available participant data are included in the model[42]. By planning to use MMRM, we made the a priori assumption that missing data were missing at random(MAR); however, we tested these assumptions in sensitivity
analyses (as below). The Toeplitiz covariance structure wasused to model the relations between observations on different occasions. Planned comparisons using MMRM werealso conducted to examine group differences in meanchange on the secondary outcome measures from baseline
to 12 weeks. Cohen’s d as a measure of effect size wascalculated based on observed data. Supplementary sensitivity analyses with the MMRM models were conducted, controlling for relevant confounding variables such as gender,
education, physical activity, baseline BMI and baseline
ModiMedDiet score. All tests of treatment effects wereconducted using an alpha level of 0.05 and reporting 95%
confidence intervals. Pearson’s product-moment correlations were calculated to determine whether changes in
MADRS scores correlated to changes in biomarkers.
Analysis of covariance (ANCOVA) was implemented toevaluate interactions between group allocation and change
adherence to ModiMedDiet on MADRS scores at 12 weeks,
adjusting for MADRS at baseline. Whilst acknowledging
the increased potential for type 1 errors, given that reported
comparisons for all primary and secondary outcomes werepre-planned comparisons that were determined a priori
and documented in the trial protocol, we did not make adjustments for multiple comparisons.

Sensitivity analyses

We compared demographic, health measures, current
treatment, diet quality and psychological measures atbaseline between participants with complete follow-up
and those with missing data at follow-up, using the chisquared test for categorical data and t tests for continuous measures. To test departures from missing at random (MAR), a weighted sensitivity analysis using the
Selection Model Approach was applied to the main outcome findings [43, 44]. Briefly, once data had been imputed under MAR (n = 5), parameter estimates fromeach imputed dataset were reweighted to allow for thedata to be missing not at random (MNAR). The chosen
constant values used to add to the imputed missing datato account for MNAR were multiplications of standard
error (i.e. 1.6) for main outcome comparison under
MAR assumptions. To evaluate the robustness of ourfindings, different degrees of departure from the MAR
assuming plausible values ranging from 10*SE to –8*SE
were considered.

Jacka et al. BMC Medicine (2017) 15:23

Results
We assessed 166 individuals for eligibility. Of these, 99
were excluded. We thus randomised 67 individuals with
MDD to the trial (intervention, n = 33; social support
control, n = 34). Figure 1 presents a CONSORT flow
chart. Baseline characteristics of all enrolled participants
are presented in Table 1. The dietary group had significantly lower scores on the dietary screening tool and theModiMedDiet score than the social support control
group at baseline, primarily due to lower intakes of fruitand higher intakes of extras. Otherwise, groups were
well matched on characteristics.
Completer analysis

Fifty-six individuals (83.6%) completed the assessment at
the 12-week endpoint. There were significantly more
completers in the dietary support group (93.9%, n = 31)
than the social support control group (73.5%, n = 25), χ2(1) = 5.08, p = 0.024. Those who did not complete theintervention were significantly more likely to have postsecondary education (81.8%, n = 9) than those whocompleted (45.5%, n = 25), χ2 (1) = 4.85, p = 0.028; thisrelationship was observed for the social support control

Fig. 1 CONSORT flow chart

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group, χ2 (1) = 6.92, p = 0.009 and not in the dietary
support group, χ2 (1) = 0.01, p = 0.965.
Primary outcome: depressive symptomatology

The dietary support group demonstrated significantly
greater improvement in MADRS scores between baseline
and 12 weeks than the social support control group,t(60.7) = 4.38, p < .001 (Fig. 2). The effect size for thisdifference was a Cohen’s d of –1.16 (95% CI –1.73, –0.59)
and represented an estimated average between groupdifference, in terms of change from baseline to 12 weeks,of 7.1 points on the MADRS (SE = 1.6). The MMRM was
rerun, adjusting for variables such as sex, education, physical activity, baseline BMI and baseline ModiMedDiet
score; the significant between-group difference in change
from baseline to 12 weeks remained, t(58.7) = 4.40, p <0.001.
Results from sensitivity analyses accounting for missing
data under the NMAR assumption are presented in Fig. 3.
Two NMAR scenarios were investigated in the sensitivity
analyses: (1) dropouts in the intervention group had worse
MADRS outcome at 12 weeks, and (2) dropouts in the control group had better MADRS outcomes. As Fig. 3 shows,Jacka et al. BMC Medicine (2017) 15:23

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Table 1 Baseline characteristics of all those randomised to the dietary support (DS) and social support (SS) groups
Total(n = 67)DS(n = 33)SS(n = 34)

Demographic
Gender % female% (n)71.6 (48)81.8 (27)61.8 (21)Age

M (SD)

40.3 (13.1)

37.5 (10.7)

43.1 (14.6)

Post-secondary school education% (n)51.5 (34)51.5 (17)51.5 (17)% (n)23.1 (15)25.0 (8)21.2 (7)Household income
Above $80,000 per annum
Health measures
BMI

M (SD)

29.5 (8.0)

30.0 (9.3)

29.0 (6.5)

Current smoker% (n)14.1 (9)10.0 (3)17.6 (6)Comorbid disorder% (n)71.2 (47)75.0 (24)

67.6 (23)

Number of comorbid disorders

M (SD)

1.5 (1.4)

1.6 (1.4)

1.5 (1.4)

Physical activity (IPAQ score)M (SD)

2336 (2585)

2146 (2565)

2509 (2629)

Current treatments
Psychopharmacotherapy% (n)68.7 (46)75.8 (25)61.8 (21)Psychological therapy% (n)44.8 (30)39.4 (13)50.0 (17)Screen of diet quality

M (SD)

51.2 (11.0)

48.5 (10.3)

53.9 (11.3)

ModiMedDiet (0-120)

M (SD)

41.5 (14.3)

36.2 (12.8)

47.3 (13.7)

Diet quality

Psychological measures
MADRS (0-60)

M (SD)

25.4 (4.6)

26.1 (4.9)

24.7 (4.2)

HADS – (D) (0-21)

M (SD)

9.6 (3.7)

10.0 (4.0)

9.2 (3.4)

HADS – (A) (0-21)

M (SD)

11.7 (3.0)

12.1 (3.1)

11.2 (2.8)

BMI body mass index, MADRS Montgomery-Åsberg Depression Rating Scale, HADS Hospital Anxiety and Depression Scale

30

20
MADRS

Estimated mean (±SE) for

25

15

10

5

0
Diet intervention

Social support

Group
Baseline

3 monthFig. 2 MADRS scores for dietary support and social support control groups at baseline and endpoint. Effect size: Cohen’s d = –1.16 (95% CI –1.73, –0.59).
Baseline data n = 67; 12 week data n = 56

Jacka et al. BMC Medicine (2017) 15:23

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Fig. 3 Weighted sensitivity analyses using the Selection Model Approach for MADRS scores, accounting for missing data under the non-missing
at random (NMAR) assumption

findings were insensitive to assumption 1, even when assuming outcomes as large as 10*SE (an increase of 16 in
MADRS score compared to imputation under the MAR assumption). Findings were also robust under assumption 2,
and only a large departure from the MAR assumption (i.e.
8*SE = 12.8 reduction on MADRS) made the observed
intervention effect non-significant.
Secondary outcomes

At 12 weeks, 32.3% (n = 10) of the dietary support groupand 8.0% (n = 2) of the social support control group
achieved remission criteria of a score less than 10 on the
MADRS; this between-group difference was significant,χ2 (1) = 4.84, p = 0.028. Based on these remission data,
the number needed to treat (NNT) is 4.1 (95% CI of
NNT 2.3–27.8).
Concordant with the findings for the MADRS, the dietary support group demonstrated significantly greater improvement from baseline to 12 weeks than the social
support control group on the Hospital Anxiety andDepression Scale (HADS)-depression subscale, t(55.1) =2.20, p = 0.032 (Table 2). Similar findings were obtainedwith the HADS-anxiety subscale, t(59.0) = 2.19, p = 0.033.
These significant differences remained after controllingfor sex, education, physical activity, baseline BMI andbaseline ModiMedDiet scores. Cohen’s d for HADSdepression was –0.632 (95% CI –1.186, –0.078), and for
HADS-anxiety it was –0.594 (95% CI –1.147, –0.042).
On the CGI-I at 12 weeks, the dietary support group
had significantly lower average scores (M = 2.1, SD = 1.3)
than the social support control group (M = 3.0, SD =1.3), t(50) = –2.58, p = 0.013. Based on these figures, thedietary support group on average had ’much improved’scores, whereas the social support control group had’minimally improved’ scores on the CGI-I.
On the POMS total mood disturbance score, as well as
the subscale scores (subscales not reported) there wereno significant differences between the groups. Similarly,
there were no significant differences between groups
with respect to self-efficacy or wellbeing.

At intervention cessation, the dietary support group had
significant improvements in the consumption of thefollowing food groups: whole grain cereals (mean increase
1.21 (SD 1.77) servings/day); fruit (0.46 (0.71) servings/day); dairy (0.52 (0.72) servings/day); olive oil (0.42 (0.49)
servings/day); pulses (1.40 (2.39) servings/week); and fish
(1.12 (2.65) servings/week). With respect to the consumption of unhealthful food items, intake of extras substantially declined (mean decrease 21.76 (SD 16.01) servings/week) in the dietary support group. Conversely, there wereno significant changes observed in the social support
control group for any of the key food groups. These findings were confirmed by analysis of the ModiMedDiet
scores: the dietary support group showed significantly
greater improvement from baseline to 12 weeks on ModiMedDiet scores than controls, t(55.6) = –4.78, p < 0.001;
the differences remained after controlling for sex, education, physical activity, baseline BMI and baseline ModiMedDiet score. Cohen’s d for the ModiMedDiet was 1.36
(95% CI 0.74–1.98). There were no significant differences
between groups with respect to BMI or physical activity.
Data on change in psychopharmacological medications
over the 12 weeks were available for 53 individuals. One
person in each of the dietary support and social support
groups started taking psychopharmacological medications
over the 12 weeks. There were two patients in the social
support group who ceased their medications. There were
too few participants to undertake inferential statistics.
Changes in biomarkers are also detailed in Table 2. The
only significant difference between the two groups was withrespect to change in total polyunsaturated fatty acids; thesocial support group showed a significant drop in polyunsaturates over the 12 weeks, t(54.9) = –2.41, p = 0.019.
Changes in MADRS did not correlate with any of thechanges in biomarkers; all correlations were less than 0.2
and were not significant at the p < .050 level. Finally, change
in dietary quality, measured using 12 week ModiMedDiet
score differences from baseline scores, was associated withchange in depression scores in the intervention group: theinteraction between group allocation and change inJacka et al. BMC Medicine (2017) 15:23

Page 9 of 13

Table 2 Mean (±standard error) estimates derived from mixed model repeated measures (MMRM, unadjusted estimates) comparing
differences between the dietary support (DS) and social support (SS) groups in terms of changes from baseline to primary endpointof 12 weeks
Characteristic

DS

SS

Between-group differencesin change from baseline to 12 weeks

Baseline

12 weeks

Baseline

12 weeks

M (SE)M (SE)M (SE)M (SE)M (SE)

95% CI [LCI, UCI]

ta

df

p

26.1 (1.0)

14.8 (1.1)

24.7 (1.0)

20.5 (1.2)

7.1 (1.6)

3.9, 10.4

4.38

60.7

<.001

Symptoms
MADRS (0-60)
HADS – (D) (0-21)

10.0 (0.6)

5.3 (0.7)

9.2 (0.6)

6.8 (0.7)

2.3 (1.1)

0.2, 4.4

2.20

55.1

.032

HADS – (A) (0-21)

12.1 (0.6)

8.4 (0.6)

11.2 (0.6)

9.5 (0.7)

2.0 (0.9)

0.2, 3.9

2.19

59.0

.033

54.5 (6.0)

30.3 (6.5)

40.9 (5.8)

32.1 (6.7)

15.4 (9.9)-4.6, 35.3

1.56

43.5

.127

24.6 (1.0)

28.1 (1.0)

25.7 (1.0)

26.7 (1.1)-2.4 (1.6)-5.7, 0.9

-1.44 59.0

.156

6.9 (0.8)

12.3 (0.9)

6.7 (0.8)

10.3 (1.0)-1.8 (1.7)-5.2, 1.6

-1.04 62.0

.304

BMI

30.0 (1.4)

29.9 (1.4)

29.0 (1.4)

29.1 (1.4)

0.15 (0.27)-0.4, 0.7

0.56

47.0

.579

Physical activity(IPAQ scores)

2307.7 (664.8) 2251.3 (645.9) 2509.4 (636.3) 2892.2 (636.3) 439.3 (1097.6)-1757.2, 2635

0.40

58.9

.690

36.2 (2.5)

55.1 (2.8)

47.3 (2.6)

45.4 (2.9)-20.7 (4.3)-20.7, -12.1-4.78 55.62 <.001

Glucose (mmol/L)

4.5 (0.5)

4.7 (5.5)

5.4 (0.5)

5.5 (0.6)-0.1 (4.3)-0.9, 0.8

-0.10 48.5

.919

Cholesterol (mmol/L)

4.9 (0.2)

4.8 (0.2)

5.5 (0.2)

5.2 (0.2)-0.2 (0.2)-0.5, 0.2

-1.08 47.7

.287

Triglycerides (mmol/L)

1.3 (0.2)

1.2 (0.2)

1.4 (0.2)

1.5 (0.2)

0.2 (0.2)-0.3, 0.7

0.88

.386

Mood
POMS (-32–200)
Self-efficacy
GSE (10-40)
Wellbeing
WHO-5 (0-25)
Anthropometry

Diet
ModiMedDiet (0-120)
Biomarkers

53.4

HDL cholesterol (mmol/L) -1.5 (0.1)

1.5 (0.1)

1.4 (0.1)

1.3 (0.1)-0.1 (0.1)-0.2, 0.1

-1.11 47.7

.272

LDL cholesterol (mmol/L) 2.9 (0.2)

2.8 (0.1)

3.4 (0.2)

3.2 (0.2)-0.1 (0.2)-0.4, 0.3

-0.48 47.1

.636

Total saturates (% FA)

35.7 (0.5)

36.6 (0.5)

35.6 (0.5)

38.1 (0.6)

1.6 (1.0)-0.5, 3.6

1.54

62.1

.128

Total monounsaturates(% FA)

23.2 (0.4)

22.2 (0.5)

22.8 (0.4)

23.2 (0.6)

1.4 (0.8)-0.3, 3.1

1.69

55.4

.096

Total polyunsaturates(% FA)

41.1 (0.7)

41.3 (0.8)

41.6 (0.7)

38.6 (0.9)-3.1 (1.3)-5.7, 0.5

-2.41 54.9.019

n6 (% PUFA)

14.6 (0.4)

14.5 (0.4)

14.3 (0.4)

12.8 (0.5)-1.4 (0.8)-3.0, 0.3

-1.69 62.1

.096

n3 (% PUFA)

6.9 (0.3)

6.8 (0.3)

6.5 (0.3)

7.2 (0.4)

0.9 (0.6)-0.3, 2.1

1.57

.123

57.8

MADRS Montgomery-Åsberg Depression Rating Scale, HADS Hospital Anxiety and Depression Scale, POMS Profile of Mood States, GSE Generalized Self-Efficacy
scale, WHO-5 WHO (Five) wellbeing index, BMI body mass index, FA fatty acids, PUFA polyunsaturated fatty acidsa
Derived from planned comparison within mixed model repeated measures (MMRM) using all available data (baseline data n = 67, 12 week data n = 56) and basedon unadjusted estimates

ModiMedDiet scores after adjusting for baseline MADRS
scores was statistically significant, F(2) = 9.6, p < 0.001. The
correlation was only significant in the intervention group(p < 0.001); the unstandardised beta coefficient was –0.22(95% CI –0.32, –0.12), indicating a 2.2 score improvementin MADRS with every 10% increase in dietary adherence.

Discussion
These results provide preliminary RCT evidence for
dietary improvement as an efficacious treatment strategy
for treating major depressive episodes. We reportsignificant reductions in depression symptoms as a resultof this intervention, with an overall effect size of –1.16.
These effects appear to be independent of any changes in
BMI, self-efficacy, smoking rates and/or physical activity.
Concordant with our primary outcome, significant improvements were also observed on self-reported depressive and anxiety symptoms and on the Clinical Global
Impressions Improvement scale. Whilst other mood
(POMS) and wellbeing (WHO-5) scores did not differ
between groups, changes were in the expected direction
and were likely affected by lack of statistical power.

Jacka et al. BMC Medicine (2017) 15:23

Critically, substantial improvements on the ModiMedDiet
score were evident in the dietary support group but not in
the social support control group, and these changes correlated with changes in MADRS scores.
The results of this trial suggest that improving one’s dietaccording to current recommendations targeting depression [31] may be a useful and accessible strategy for addressing depression in both the general population and inclinical settings. Whilst there are many data to suggestthat eating a more healthful diet is more expensive than aless healthful diet [45], our detailed modelling of the costsof 20 of the SMILES participants’ baseline diets comparedto the costs of the diet we advocated showed that our
strategy can be affordable [46]. Indeed, we estimated thatparticipants spent an average of AU$138 per week on foodand beverages for personal consumption at baseline,
whilst the costs per person per week for the diet we recommended was AU$112 per week, with both estimations
based on mid-range product costs [46].
A pertinent observation was that improvements in depressive symptoms were independent of weight change.
These findings were expected, as the diet intervention was
ad libitum and did not have a weight loss focus, but
provide further support for the beneficial role of dietary
improvement per se. The extensive observational evidence
linking diet quality to mental health has repeatedly shownthat the observed relationships exist independently ofvarious measures of body composition.
Although dietary changes were not reflected in the
traditional cardiovascular disease biomarkers, the protective
effects of healthful dietary patterns are often independentof these risk factors [47]. There are many other biologicalpathways by which dietary improvement may influence
depressive illness; previous discussions have centered on inflammatory [18] and oxidative stress [19] pathways, as wellas brain plasticity [16] and the new evidence base focusedon the gut microbiota [17]. Each of these pathways is suggested to play a role in depression and is also influenced bydiet quality. Moreover, behavioural changes associated withfood (cooking/shopping/meal patterns) are an expected
outcome of a nutrition intervention, and these changes in
activity may also have had a therapeutic benefit.
Strengths and limitations

There are methodological features of our study thatmust be considered. Firstly, there is the issue of expectation bias due to the fact that we needed to be explicit
in our advertising regarding the nature of the intervention and to the inability to blind the participants to theirintervention group; this may have biased the results andalso resulted in differential dropout rates. Moreover, inregard to our randomisation process, a block size of four,
whilst recommended for small sample sizes to avoid imbalances in allocation, may have been insufficient toPage 10 of 13

support allocation concealment. As discussed above, tomitigate these issues significant effort was made to mask
our hypothesis from the participants, and emphasis wasplaced on the potential benefit of social support to mental health. Clearly, our results must also be considered inlight of the small sample size. Failure to reach ourplanned sample size increases the possibility that oursample was not representative and limited our ability to
conduct subgroup analyses. It may also have inflated theeffect size we observed. However, our original power calculations were based on a very small effect size; arguably, this would not have been clinically significant.
There were differential completion rates in each group:
94% versus 73.5% in the dietary and social support
groups, respectively. This suggests that the mechanisms
underpinning missingness may be different between thetwo groups; however, results from comprehensive sensitivity analyses testing alternatives to the MAR assumption revealed that, whilst under the NMAR assumptions
observed intervention effects moved towards the null,
our findings remained robust against departures from
the MAR assumption. A larger sample size and assessments at more than two time points would haveafforded more sophisticated statistical modelling; thisshould be a key focus of future replication studies.
Importantly, the high completion rates in the intervention group point to the acceptability of the dietary intervention to the participants. The fact that the dietaryintervention group was able to make significant improvements to their diet quality suggests that dietary improvement is achievable for those with clinical depression
despite the fatigue and lack of motivation that are prominent symptoms of this disorder. On the other hand, thechallenges we had with recruiting this clinical population,
likely due to the aforementioned symptoms and the requirement to attend the study centre on several occasions,points to the need to utilise different methods for delivering the intervention that do not require attendance withthe dietician in person, such as telephone or Skype. Finally,
given that we recruited participants on the basis of existing‘poor’ quality diet, this may limit the generalisability of ourfindings to the wider population of individuals with depression. However, evidence suggests that our study sample
was not necessarily a special subgroup; the recent 2014–2015 Australian Health Survey tells us that only 5.6% ofAustralian adults had an adequate intake of vegetables andfruits. In this study, only 15 out of 166 people screened
were excluded on the basis of a pre-existing ‘good’ diet,suggesting that — concordant with the wider population— poor diet is the norm in those with depressive illness.
Implications

Recent updates to clinical guidelines for the treatment ofmood disorders in Australia have, in recognition of theJacka et al. BMC Medicine (2017) 15:23

emerging and established data regarding the importanceof health behaviours (diet, exercise, sleep and smoking)to mood disorders, made explicit recommendations regarding the need to address these behaviours as a firststep in the treatment of patients [48]. The results of thisRCT offer further support for the need to focus on addressing poor diet in clinical practice and provide some
guidance regarding the strategies that may be used tosupport this imperative. They suggest the new possibilityof adding clinical dieticians to multidisciplinary mental
health teams and making dietician support available tothose experiencing depressive symptoms in primary andother care settings. Clearly, successfully improving dietquality in patients will also benefit the physical illnesses
that are so commonly comorbid with depression andwhich are both a cause and consequence of depression.
Upskilling dieticians to best deliver this program to thispatient population may also be required.

Conclusions
In summary, this is the first RCT to explicitly seek to answer the question: If I improve my diet, will my mental
health improve? Whilst emphasising the preliminary nature of this study and the imperative for replication instudies with larger sample sizes, the results of our study
suggest that dietary improvement guided by a clinicaldietician may provide an efficacious treatment strategy
for the management of this highly prevalent mental
disorder. Future work in this new field of nutritional
psychiatry research should focus on replication, ensuring
larger samples and more sophisticated study designs, inorder to confirm effects and afford sensitivity analyses to
identify predictors of treatment response. The scaling up
of interventions and identification of the pathways thatmediate the impact of dietary improvement on depressive illness are also key imperatives. Clinicians should
also consider promoting the benefits of dietary improvement and facilitating access to dietetics support for theirpatients with depression.
Acknowledgements
We would like to acknowledge the contributions of Josephine Pizzinga,
Melanie Ashton, Siobahn Housden, Laura Nicholls, Thea Valkidis, Thaise
Mondin, Meghan Hockey, Olivia Young and Clare Daley — all of whom
contributed their time and efforts to the running of this RCT. Finally, we offer
our profound thanks to the participants, who contributed their time to thisstudy.
Authors’ contributions
FNJ conceived the study and developed the protocol, led the RCT and has
been primarily responsible for drafting the manuscript. AON acted as trial
coordinator in the first year of the RCT, developed and applied for ethicalapprovals, played a primary role in overseeing the RCT and has made asubstantial contribution to the drafting of the manuscript. RO and CI were
responsible for the development of the ModiMedDiet and the associatedscore, and RO played a primary role in delivering the dietary intervention. CI(and AON) developed the bloods protocol for the RCT. Both RO and CI havemade a substantial contribution to the data interpretation and the draftingof the manuscript. As the team statistician, SC played the primary role inPage 11 of 13

data analysis, as well as data interpretation and the drafting of themanuscript. MM undertook sensitivity and adherence analyses at the review
stage and contributed to the final manuscript. SD has made a substantial
contribution to the running of the study, the data collation, cleaning andanalysis and the drafting of the manuscript. CM, MLC, LB, OMD and AMH
have all made an important contribution to the original design of the study
protocol, data interpretation and the drafting of the manuscript. DC and MB
were the consultant psychiatrists on the study and made an important
contribution to the design of the study protocol, the running of the study,
the interpretation of the data and the drafting of the manuscript. All authors
read and approved the final manuscript.
Competing interests
This study was supported by a grant from the National Health and Medical
Research Council of Australia (NHMRC) (#1021347). Woolworths Limited
provided sponsorship in the form of food vouchers for participants. Village
cinemas donated cinema vouchers and Carman’s Fine Foods donated muesli
bars for participants. A grant from Meat and Livestock Australia (2013)
funded biochemistry data collected and analysed as part of the SMILES trial.
These sponsors had no role in the design, analysis or preparation of themanuscript for publication.
Felice N Jacka has received Grant/Research support from the Brain and
Behaviour Research Institute, the National Health and Medical ResearchCouncil (NHMRC), Australian Rotary Health, the Geelong Medical Research
Foundation, the Ian Potter Foundation, Eli Lilly, Meat and Livestock Australia,
Woolworths Limited and The University of Melbourne and has received
speakers honoraria from Sanofi-Synthelabo, Janssen Cilag, Servier, Pfizer,
Health Ed, Network Nutrition, Angelini Farmaceutica, Metagenics and Eli Lilly.
She is supported by an NHMRC Career Development Fellowship (2)(#1108125).
Adrienne O’Neil has received funding from Meat and Livestock Australia andis supported by an NHMRC ECR Fellowship (#1052865).
Catherine Itsiopoulos has received funding from the NHMRC, the Universityof Melbourne, Deakin University, La Trobe University, Meat and Livestock
Board, Australian Society for Enteral and Parenteral Nutrition, Harokopio
University in Athens, Commonwealth Department of Education, Employment
and Workplace relations, Diabetes Australia and SWISSE Wellness P/L. She
has received speaker honoraria from Astra Zeneca, Boehringer Ingelheim andDairy Australia.
Cathrine Mihalopoulos is supported by an NHMRC Early Career Development
Fellowship (#1035887). She has received funding support from the NHMRC,
Cancer Council, Mental Illness Research Fund, Medibank Private HealthResearch Fund, Macquarie University and Beyond Blue.
David Castle has received grant monies for research from Eli Lilly, Janssen
Cilag, Roche, Allergen, Bristol-Myers Squibb, Pfizer, Lundbeck, Astra Zeneca
and Hospira and travel support and honoraria for talks and consultancy
from Eli Lilly, Bristol-Myers Squibb, Astra Zeneca, Lundbeck, Janssen Cilag,
Pfizer, Organon, Sanofi-Aventis, Wyeth, Hospira and Servier. He is a current
Advisory Board Member for Lu AA21004; Lundbeck; Varenicline: Pfizer;
Asenapine: Lundbeck; Aripiprazole LAI: Lundbeck; Lisdexamfetamine: Shire;
Lurasidone: Servier. He has no stocks or shares in any pharmaceuticalcompany.
Olivia Dean has received grant support from the Brain and Behaviour
Foundation, Marion and EH Flack Trust, Simons Autism Foundation,
Australian Rotary Health, Stanley Medical Research Institute, Deakin
University, Eli Lilly, NHMRC, Australasian Society for Bipolar and Depressive
Disorders and Servier. She has also received in-kind support from BioMedica
Nutracuticals, NutritionCare and Bioceuticals.
Michael Berk has received Grant/Research Support from the NIH, Cooperative
Research Centre, Simons Autism Foundation, Cancer Council of Victoria,
Stanley Medical Research Foundation, MBF, NHMRC, Beyond Blue, Rotary
Health, Geelong Medical Research Foundation, Bristol Myers Squibb, Eli Lilly,
Glaxo SmithKline, Meat and Livestock Board, Organon, Novartis, Mayne
Pharma, Servier and Woolworths. He has been a speaker for Astra Zeneca,
Bristol Myers Squibb, Eli Lilly, Glaxo SmithKline, Janssen Cilag, Lundbeck,
Merck, Pfizer, Sanofi Synthelabo, Servier, Solvay and Wyeth, and served as aconsultant to Allergan, Astra Zeneca, Bioadvantex, Bionomics, Collaborative
Medicinal Development, Eli Lilly, Glaxo SmithKline, Janssen Cilag, Lundbeck
Merck, Pfizer and Servier. He is supported by an NHMRC Senior Principal
Research Fellowship (#1059660).
The other authors have no relevant financial disclosures to declare.

Jacka et al. BMC Medicine (2017) 15:23

Author details
1
IMPACT Strategic Research Centre, Deakin University, Geelong, VIC, Australia.
2
School of Population Health, The University of Melbourne, Melbourne, VIC,
Australia. 3Orygen, The National Centre of Excellence in Youth Mental Health,
Parkville, VIC, Australia. 4Department of Psychiatry, University of Melbourne,
Melbourne, VIC, Australia. 5School of Allied Health, La Trobe University,
Melbourne, VIC, Australia. 6Department of Medicine, The University of
Melbourne, Melbourne, VIC, Australia. 7Centre for Population Health Research,
Deakin University, Geelong, VIC, Australia. 8Cancer Epidemiology andIntelligence Division, Cancer Council Victoria, Carlton, VIC, Australia. 9Centre
for Adolescent Health, Murdoch Childrens Research Institute, Melbourne, VIC,
Australia. 10Black Dog Institute, Randwick, NSW, Australia. 11St Vincents
Hospital, Fitzroy, VIC, Australia. 12The Florey Institute of Neuroscience andMental Health, Parkville, VIC, Australia. 13Food & Mood Centre, Deakin
University, IMPACT SRC, School of Medicine, PO Box 281, Geelong 3220,
Victoria, Australia.
Received: 31 August 2016 Accepted: 11 January 2017

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