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Trial registered on ANZCTR


Registration number
ACTRN12622001502729
Ethics application status
Approved
Date submitted
24/11/2022
Date registered
1/12/2022
Date last updated
1/12/2022
Date data sharing statement initially provided
1/12/2022
Date results information initially provided
1/12/2022
Type of registration
Retrospectively registered

Titles & IDs
Public title
Investigating the effect of smartphone-based cognitive training completed at home on sustained changes in appetite and food cravings: A randomised controlled trial
Scientific title
A randomised controlled investigation of smartphone-based response inhibition training to elicit sustained changes in food reward, preference, and craving in adults with food cue responsiveness or overweight/obesity
Secondary ID [1] 308475 0
Nil known
Universal Trial Number (UTN)
Trial acronym
Linked study record

Health condition
Health condition(s) or problem(s) studied:
Overweight 328284 0
Obesity 328353 0
Condition category
Condition code
Diet and Nutrition 325330 325330 0 0
Obesity
Public Health 325331 325331 0 0
Health promotion/education

Intervention/exposure
Study type
Interventional
Description of intervention(s) / exposure
A smartphone-based gamified cognitive training app (i.e., 'FoodTrainer' - University of Exeter) was the only intervention arm. The game has an embedded behavioural modification technique called 'Response Inhibition Training' where the user is expected to withhold an approach to any energy-dense, processed foods (associated with a visual signal similar to a red stop light) and approach/select all healthier alternatives (associated with a 'green' light) as they appeared on the screen.

Participants met with the primary researcher individually by Zoom for approximately 30-45 minutes (dependent on questions posed by the participant) prior to study commencement to discuss the trial, receive their condition assignment, and pose any questions or concerns. A condition-specific information sheet was also provided to each participant that outlined the experimental procedure step-by-step so that the study could be completed autonomously thereafter. The information sheet was specifically designed for this study and only the steps relating to completing the training varied based on group allocation. Participants were able to download the intervention or control apps on their Apple or Android devices at no cost.

Participants were informed that they would be randomly assigned to complete one of two apps promoting healthy dietary habits, thus were ostensibly not aware of condition allocation. They were further instructed to complete the intervention 6 times over a training week (i.e., once daily over a 6 day period). Each daily session was recommended to span about 12 minutes. If participants completed a higher or lower frequency of training, they were still included in analyses. All training sessions were recommended to take place in a quiet, familiar environment, particularly at their place of residence if possible. The training was not personalised at any time over the study period.

Data on intervention adherence was collected via the app and sent to an online server. Strategies to improve or maintain fidelity to the study procedure were offered during the online consultation, which included setting daily alarms or calendar reminders during the study period, for example.
Intervention code [1] 324917 0
Behaviour
Intervention code [2] 324918 0
Prevention
Comparator / control treatment
The control group completed a smartphone-based game that was freely available called 'Fruit Ninja' (Halfbrick Studios). The duration and frequency of the activity was exactly the same as the intervention condition. Although the gameplay was similar, this game did not include images of energy-dense, processed foods. Instead, users were expected to withhold responses to neutral (i.e., non-food) cues. As in the intervention app, users had to quickly respond to the appearance of 'healthy' food cues on the screen (i.e., Fruit). As the 'response inhibition' is generally considered to be the active mechanism of behaviour change, this control was considered a placebo, but it can also be argued to be an active control.
Control group
Active

Outcomes
Primary outcome [1] 333193 0
Change in explicit liking of food (cues) based on energy-density as measured by the Leeds Food Preference Questionnaire (Finlayson et al., 2007). This was measured by 100mm visual analogue scale.
Timepoint [1] 333193 0
Week0 (Baseline), Week1 (7 days post commencement of intervention, primary timepoint), Week2 (7 days post termination of intervention)
Primary outcome [2] 333196 0
Change in implicit wanting for food (cues) based on their energy density as measured by the Leeds Food Preference Questionnaire. This was measured during a forced choice task where their response time to choices was also assessed along with the frequency of choice for particular food categories.
Timepoint [2] 333196 0
Week0 (Baseline), Week1 (7 days post commencement of intervention, primary timepoint), Week2 (7 days post termination of intervention)
Primary outcome [3] 333197 0
Change in reward-based eating drive, or hedonic appetite and eating, as assessed by the Reward-based Eating Drive scale (Mason et al., 2017).
Timepoint [3] 333197 0
Week0 (Baseline), Week1 (7 days post commencement of intervention, primary timepoint), Week2 (7 days post termination of intervention)
Secondary outcome [1] 416113 0
Change in explicit wanting for food (cues) based on their energy-density as measured by the Leeds Food Preference Questionnaire. This was measured using 100mm visual analogue scale. This is a primary outcome.
Timepoint [1] 416113 0
Week0 (Baseline), Week1 (7 days post commencement of intervention, primary timepoint), Week2 (7 days post termination of intervention)
Secondary outcome [2] 416116 0
Change in frequency of choice for foods based on their energy-density alone was included and derived from the forced choice task of the Leeds Food Preference Questionnaire. This is a primary outcome.
Timepoint [2] 416116 0
Week0 (Baseline), Week1 (7 days post commencement of intervention, primary timepoint), Week2 (7 days post termination of intervention)
Secondary outcome [3] 416320 0
Changes in experience and control of cravings for energy-dense, processed foods as evaluated by the Control of Eating Questionnaire. This was a primary outcome.
Timepoint [3] 416320 0
Week0 (Baseline), Week1 (7 days post commencement of intervention), Week2 (7 days post termination of intervention)
Secondary outcome [4] 416321 0
Change in appetite sensations as measured by 100mm visual analogue scale. Specifically, participants were assessed how hungry they are, how full they are, and their overall desire to eat at each time point. This was a secondary outcome.
Timepoint [4] 416321 0
Week0 (Baseline), Week1 (7 days post commencement of intervention), Week2 (7 days post termination of intervention)
Secondary outcome [5] 416322 0
Weight was measured by self-report to the nearest .1kg. Participants were asked to use a working weighing scale at each assessment timepoint (not estimate) prior to commencement,
Timepoint [5] 416322 0
Week0 (Baseline), Week1 (7 days post commencement of intervention), Week2 (7 days post termination of intervention)
Secondary outcome [6] 416323 0
Waist circumference was assessed by self-report. Participants were instructed to use a tape measure (with assistance if needed) at each assessment time point. They were provided with instructions on how to measure their waist correctly.
Timepoint [6] 416323 0
Week0 (Baseline), Week1 (7 days post commencement of intervention), Week2 (7 days post termination of intervention)

Eligibility
Key inclusion criteria
Respondents were included if they were aged between 17 and 70 years; resided in Australia; had access to a smartphone and computer with internet access; had access to a weighing scale and a tape measure (to measure anthropometrics); and could be considered overweight/obese as assessed by their BMI (> 25) OR highly food cue responsive as assessed by the Adult Eating Behaviour Questionnaire (Hunot et al., 2016).
Minimum age
17 Years
Maximum age
70 Years
Sex
Both males and females
Can healthy volunteers participate?
No
Key exclusion criteria
Participants were excluded if they were ever diagnosed with an eating disorder; were currently part of a weight loss programme or actively trying to lose weight; were currently taking any prescription or recreational drugs that affect appetite; or currently smoking 5 or more cigarettes per day; or experienced any significant weight changes within the last 3 months (i.e., greater than 10% of their body weight).

Study design
Purpose of the study
Prevention
Allocation to intervention
Randomised controlled trial
Procedure for enrolling a subject and allocating the treatment (allocation concealment procedures)
The researcher was unaware of the group each participant would be allocated to when their eligibility was determined. Randomisation was conducted centrally via computer.
Methods used to generate the sequence in which subjects will be randomised (sequence generation)
The randomisation sequence was generated by Sealed Envelope using computer-generated sequence generation. The method used by Sealed Envelope is permuted block randomisation. Randomisation was stratified by gender (Male versus Female).
Masking / blinding
Blinded (masking used)
Who is / are masked / blinded?
The people receiving the treatment/s


Intervention assignment
Parallel
Other design features
Phase
Not Applicable
Type of endpoint/s
Efficacy
Statistical methods / analysis
An a priori sample size calculation using G*Power 3.1.9.4 (Faul et al., 2007) indicated that 66 participants would be minimally sufficient to detect a small to medium effect of the intervention on food ratings (f = .20; equivalent to d = .40). This calculation was based on a mixed-factorial interaction (condition x time) with 3 dependent means, a power of 95%, an alpha level of 5%, a correlation of r = .70 among repeated measures. The proposed effect size was based on a free-living, quasi-experimental investigation that found the smartphone-based training to have a diminished effect when compared to computer-based training (Lawrence et al., 2018). This has been recently corroborated by Aulbach and colleagues (2021), albeit in an observational study.

Changes in primary outcomes were tested statistically using 2 x 3 mixed-factorial ANOVAs with app condition (2: intervention versus control) as the between-subjects factor and time (3: baseline versus post-training versus 1-week follow-up) as the within-subjects factor. Significant time by condition interactions were explored through Bonferroni-adjusted t-tests. Where the assumption of sphericity was violated, the Greenhouse-Geiser correction was used. Where there were significant differences between groups detected during randomisation checks, these variables were included as covariates in the models. All analyses followed a complete case and intention-to-treat approach.

Recruitment
Recruitment status
Completed
Date of first participant enrolment
Anticipated
Actual
28/10/2020
Date of last participant enrolment
Anticipated
Actual
30/07/2021
Date of last data collection
Anticipated
Actual
30/09/2021
Sample size
Target
132
Accrual to date
Final
84
Recruitment in Australia
Recruitment state(s)
ACT,NSW,NT,QLD,SA,TAS,WA,VIC

Funding & Sponsors
Funding source category [1] 312717 0
University
Name [1] 312717 0
Queensland University of Technology
Country [1] 312717 0
Australia
Primary sponsor type
University
Name
Queensland University of Technology
Address
60 Musk Avenue, Kelvin Grove, Queensland, 4059
Country
Australia
Secondary sponsor category [1] 314338 0
None
Name [1] 314338 0
Address [1] 314338 0
Country [1] 314338 0

Ethics approval
Ethics application status
Approved
Ethics committee name [1] 312018 0
QUT University Human Ethics Research Committee
Ethics committee address [1] 312018 0
60 Musk Avenue, Kelvin Grove, Queensland 4059
Ethics committee country [1] 312018 0
Australia
Date submitted for ethics approval [1] 312018 0
11/09/2020
Approval date [1] 312018 0
28/10/2020
Ethics approval number [1] 312018 0
2000000761

Summary
Brief summary
Cognitive Bias Modification has elicited some intriguing results in studies of substance addiction or appetite pathology. Training in an experimental setting has been shown to reduce alcohol consumption in binge drinkers, smoking frequency, and decrease preference and intake of palatable foods in people with overweight/obesity. Moreover, they do not require some of the onerous demands of time, energy, and commitment required by traditional obesity prevention-oriented interventions.

Response inhibition training, a form of Cognitive Bias Modification, has seen some success in reducing snack food preference and intake, and even weight, although significant results were only seen in short-term, laboratory-based studies. However, the results vary conspicuously based on methodology and training parameters. Additional testing in natural settings was considered beneficial to discern the clinical and real world utility of this type of intervention. The inclusion of a follow up measurement provided the opportunity to detect how long potential changes in appetite control are maintained after training.

The primary aim of this study was to investigate whether response inhibition training delivered by smartphone can produce sustained changes in preferences and cravings for energy-dense foods, as well as reward-driven eating in a free-living setting. It was expected that the response inhibition training intervention would lead to a significant reduction in all primary outcomes relative to the control group from baseline to post-training. Although it was expected that all primary outcomes would increase over the 1-week follow-up period, we posited that they would remain significantly different from baseline.
Trial website
Trial related presentations / publications
Public notes

Contacts
Principal investigator
Name 123190 0
Dr Halim Moore
Address 123190 0
School of Exercise and Nutrition Sciences
Queensland University of Technology
60 Musk Avenue, Kelvin Grove, Queensland 4059
Country 123190 0
Australia
Phone 123190 0
+61 466058800
Fax 123190 0
Email 123190 0
[email protected]
Contact person for public queries
Name 123191 0
Halim Moore
Address 123191 0
School of Exercise and Nutrition Sciences
Queensland University of Technology
60 Musk Avenue, Kelvin Grove, Queensland 4059
Country 123191 0
Australia
Phone 123191 0
+61 466058800
Fax 123191 0
Email 123191 0
[email protected]
Contact person for scientific queries
Name 123192 0
Halim Moore
Address 123192 0
School of Exercise and Nutrition Sciences
Queensland University of Technology
60 Musk Avenue, Kelvin Grove, Queensland 4059
Country 123192 0
Australia
Phone 123192 0
+61 466058800
Fax 123192 0
Email 123192 0
[email protected]

Data sharing statement
Will individual participant data (IPD) for this trial be available (including data dictionaries)?
Yes
What data in particular will be shared?
All of the individual participant data collected during the trial, after de-identification, in a centralised database.
When will data be available (start and end dates)?
The database will be available immediately upon publication with no end date.
Available to whom?
Data will be available to all researchers with sound purpose and intention upon request to the primary sponsor.
Available for what types of analyses?
Data may be available for any type of analyses as long as the rationale and methods are sound.
How or where can data be obtained?
The data will be deposited on QUT's internal research data repository ('Research Data Finder') upon publication. The website can be found at: https://researchdatafinder.qut.edu.au/


What supporting documents are/will be available?

No Supporting Document Provided



Results publications and other study-related documents

Documents added manually
No documents have been uploaded by study researchers.

Documents added automatically
SourceTitleYear of PublicationDOI
EmbaseCan smartphone-based response inhibition training elicit sustained changes in appetite, preference, and cravings for energy-dense foods? A free-living randomized controlled trial.2024https://dx.doi.org/10.1111/bjhp.12693
N.B. These documents automatically identified may not have been verified by the study sponsor.