UCSI Research Excellence & Innovative Grant (REIG-FAS-2021-040), UCSI University, Kuala Lumpur, Malaysia.

A pre-emptive digital strategy to promote nutrition-conscious purchasing and enhance household dietary quality

By Asst Prof Dr Vaidehi Ulaganathan

Faculty of Applied Sciences, UCSI University

Design and development of

app

INTRODUCTION

Ongoing efforts in nutrition education and counseling,

INTRODUCTION

INTRODUCTION

MODERATE

TO HIGH

INTRODUCTION

MODERATE

TO HIGH

KNOWLEDGE

POOR NUTRITIONAL PRACTICES

POOR NUTRITIONAL BAHVIOUR

LOW

SELF

EFFICACY

LACK OF

GOAL

SETTING

INTRODUCTION

MODERATE

TO HIGH

KNOWLEDGE

POOR NUTRITIONAL PRACTICES

POOR NUTRITIONAL BAHVIOUR

LOW

SELF

EFFICACY

LACK OF

GOAL

SETTING

It’s not just about what people KNOW, but how they ACT

PROBLEM STATEMENT

“There are already so many health and nutrition apps out there — calorie counters, meal trackers, barcode scanners...

what more do we need?”

PROBLEM STATEMENT

Most existing health tech focuses on post-consumption:

• Calorie tracking
• Meal logging
• Fitness wearables

PROBLEM STATEMENT

Most existing health tech focuses on post-consumption:

• Calorie tracking
• Meal logging
• Fitness wearables

at the point of purchase.�What consumers really need is guidance right when they’re deciding:

• Should I pick this high-sugar cereal or a better fiber-rich option?
• Is this snack really as healthy as it claims on the front label?
• What does this ingredient list really mean for me and my family?

app

NutriDIY-trolley (Nutritious Dietary Inventory grocerY trolley) app

Are There Similar Apps Out There?

Are There Similar Apps Out There?

A preventive, behavior-change tool designed with:

• Local data: over 5,000 audited Malaysian grocery items
• Smart scoring: based on Nutrient Rich Food Index (NRF) & Dietary Inflammatory Index (DII)
• Color-coded nudge system: helps users act fast, no label reading needed
• Healthier swaps: if you scan an unhealthy product, it suggests a better one
• Guidance at the shelf: before the food reaches your home

​

It’s about empowering the consumer where decisions actually happen

in the store aisle, not after the meal.

Methodology

1

2

3

4

5

6

1

2

Sample size

Using the Daniel (1999) Formula:

n= z²  P (1  -  P)

               d²

​

d= Precision level (0.05)

(1.96)2 (0.204) (1-0.204)/ (0.05)2 = 249.52

Approximately 250 sample are required.

With a dropout rate of 20% = 50 + 250 

 Total number or participants, n = 300

Study Instruments

1

2

Data Analysis

1

2

SPSS Version�21.0

Descriptive�Statistics

Frequency and�Percentage

Logistic�Regression

Significance Level

<0.05

17

18

19

Nutrient rich food score (NRF)= daily values of 9 nutrients (protein, fiber, vitamin A, vitamin C, vitamin E, calcium, iron, magnesium, and potassium) - daily values of 3 nutrients (saturated fat, added sugar, and sodium)

If NRF>= 0 Healthy

If NRF<0 Unhealthy

User-facing components

User Interface (UI) & Experience Design (UX): Making the app intuitive, appealing, and easy to navigate

Core Functional Features

What the app does to support healthy food purchase

External Integration & User Engagement

Making the app dynamic, social, and informative

Subscription Model & Pricing Strategy

How users access the app and what they pay for

21

App Selection:

• NutriDIY-trolley (study app)
• MyNutriCart (diet-based grocery planner)
• SaltSwitch (barcode scanner for low-sodium choices)

​

Evaluation Domains:

1. App Design & Usability (System Usability Scale – SUS)
2. Functionality & Features (e.g., barcode scanning, list-making, personalization)
3. Content Quality (evidence-based info, cultural relevance, label clarity)
4. Behavior Change Techniques (goal setting, feedback, nudges)

MyNutriCart

SaltSwitch

22

MyNutriCart

SaltSwitch

Data Collection:

• 2 independent reviewers
• Minimum 7-day use per app
• Evaluation via checklists, SUS scores, and observations

Data Analysis:

• Quantitative: Usability scores & feature checklists
• Qualitative: User experience themes & feature comparison
• Gap Analysis: NutriDIY vs. benchmark apps

23

KL main grocery

chains

Sample size

Using the Daniel (1999) Formula:

n= z²  P (1  -  P)

               d²

​

d= Precision level (0.05)

(1.96)2 (0.204) (1-0.204)/ (0.05)2 = 249.52

Approximately 250 sample are required.

With a dropout rate of 20% = 50 + 250 

 Total number or participants, n = 300

10% for pilot study= 30

24

1. Feasibility�Assess practicality of using the app in real-world settings.�Measured by:

• Success rate of app download and installation
• User completion of key tasks (e.g., adding items to trolley)
• Frequency of technical issues or bugs reported

​

2. Usability�Evaluate ease of use and interface efficiency (via SUS).�Measured by:

• System Usability Scale (SUS) score
• User-reported ease of navigation
• Learnability on first use

3. Satisfaction�Assess user enjoyment and perceived usefulness.�Measured by:

• Overall satisfaction rating (e.g., 5-point Likert scale)
• Perceived usefulness in making healthier food choices
• Visual appeal of design and layout

4. Acceptability�Evaluate willingness to adopt the app in daily life.�Measured by:

• Likelihood of continued use (e.g., "Would you use this again?")
• Willingness to recommend to others
• Perceived fit with shopping habits and lifestyle.

25

Results and Discussions

Sociodemographic characteristics of participants and usage of health app

This reflects trends seen in prior studies where younger, more educated females are more likely to engage with mobile health (mHealth) apps (Ernsting et al., 2017; Krebs & Duncan, 2015).

Krebs and Duncan (2015) found significant association between education and use of health apps

26

Results and Discussions

study by Byambasuren et al. 2019 conducted on 1014 participants in Australia, indicated that the biggest challenge for app usage is lack of knowledge and trustworthy source, and to overcome this challenge, participants suggested a list of safe and effective apps from valid sources and educating on health apps in the form of online video training or vebinar.

27

Results and Discussions

visual aids improve user comprehension and behavior change in health apps (McKay et al., 2018).

This mirrors Bhuyan et al. (2016), who showed experience with health apps enhances perceived usefulness, likely due to increased familiarity and exposure to app benefits.

28

Results and Discussions

privacy concerns is major barrier to mHealth adoption (Huckvale et al., 2015).

These findings align with Boulos et al. (2014) on the importance of usability and affordability, and Higgins (2016), who emphasized educational and behavior-tracking features.

29

Results and Discussions

30

Results and Discussions

Background of study participants

31

Results and Discussions

SUB-THEME 1: USER-RELATED FACTORS

Motivation

Knowledge

32

Results and Discussions

SUB-THEME 2: APP-RELATED FACTORS

33

Results and Discussions

Review existing guideline and standard in the field of mobile health-nutrition related apps.

34

Results and Discussions

Review existing guideline and standard in the field of mobile health-nutrition related apps.

35

Results and Discussions

Review existing guideline and standard in the field of mobile health-nutrition related apps.

36

Results and Discussions

To develop guide and evaluation criteria for mobile health nutrition apps.

37

Results and Discussions

Differences in Usability guidelines in relation to comprehension and perceived importance

There are significant differences between users' perceptions of the value and knowledge of multilingualism.

​

Supported by:

• Zhou et al., it shown that successful multilingual implementation can increase user satisfaction and engagement.

​

• The ability to create multilingual apps will help developers make health-nutrition related apps more inclusive and user-friendly, which will eventually improve user adherence and health outcomes.

Results and Discussions

Differences in Security & Safety guidelines in relation to comprehension and perceived importance

The significant p-value indicates a potential area for increased instructional effort, whether it takes the form of user-friendly explanations or security measure demonstrations.

​

Supported by:

• IBM's "Cost of a Data Breach Report" highlights that user awareness and comprehension of cybersecurity measures are an important factor to prevent data breaches.

​

• The report explained that organization with comprehensive user education programs experience less security occurrences (IBM, 2020).

​

• This significant association implies that users may not have a full grasp of the technical details and security precautions associated with cybersecurity. While they understand its importance, the complexity of cybersecurity measures might not be effectively communicated.

​

Differences in Security & Safety guidelines in relation to comprehension and perceived importance

There are significant association between this guidelines

​

• According to Deloitte's "Third-Party Risk Management Survey", it emphasised the importance of enhancing transparency and education about third-party data protection policy (www.deloitte.com, n.d.).

​

• The challenges of guaranteeing third-party compliance with laws such as PDPA explains the significant difference in SEC 7.

​

• Users may understand the importance but lack detailed knowledge of the processes involved.

• Providing clear guidelines and regular updates on third-party compliance can help bridge this gap

Differences in Privacy guidelines in relation to comprehension and perceived importance

Total mean scores for comprehension ( 9.09±1.10) and perceived importance (9.22±0.95) across all guidelines with a t-value of -1.483 and a p-value of 0.143.

​

The results indicate that there is no significant association between all privacy guidelines

​

• It suggests that all participants generally understand the privacy guidelines well and consider them important.
• A study discusses how privacy concern is a common denominator, resulting in high importance score across many privacy-related aspects (Acquisti, Brandimarte and Loewenstein, 2015).

Differences in Appropriateness & Suitability

guidelines in relation to comprehension and perceived importance

There is no significant association between all guidelines

​

All guidelines have a high mean score for both comprehension and perceived importance, it reflects that stakeholders are well-informed and place high importance on the appropriateness and suitability aspects of the apps

Differences in Transparency & Content guidelines in relation to comprehension and perceived importance

There is no significant association between all guidelines

​

Supported by:

One study investigates the relationship between trust in scientific research and financial conflicts of interest (FCOI) disclosure. It found that transparent disclosure of funding sources and potential conflicts of interest significantly enhances trust among stakeholders, including app users and the general public (Sacco et al., 2014).

Differences in Technical Support & Update guidelines in relation to comprehension and perceived importance

There are no significant association for all guidelines

(p-value >0.05)

​

This implies that users who understand all guidelines and consider them important and vice versa.

​

Supported by:

This study discovered that apps encourage higher levels of user trust and engagement when there is no discernible difference in their perceived importance and understanding of their features. If users recognize and understand the app's features equally, they are more likely to use it regularly (Kuerbis et al., 2017).

Differences in Technology guidelines in relation to comprehension and perceived importance

There are no significant association for all guidelines

​

Supported by:

According to AVV et al., keeping apps performing well and satisfying users depends on making effective use of resources like CPU and memory (AVV et al., 2024).

​

Association between Comprehension among all category’s criterion

In the analysis table showed various strong and significant correlation among all seven categories of guidelines.

​

• According to Wang et al., (2024) research, user engagement on social media platforms is greatly impacted by privacy concerns. This underscores the need for strong security measures to alleviate concerns about privacy and guarantee user safety.

​

• Similarly, Yin, Zhu and Hu (2021) conducted a comprehensive survey on privacy-preserving data sharing in cloud computing, demonstrated the critical role that strong security measures play in safeguarding user privacy and retaining confidence in cloud services.

​

Association between Perceived Importance among all category’s criterion

In the analysis table showed various strong and significant correlation among all seven categories of guidelines.

​

The perceived importance of privacy and appropriateness and suitability are most strongly correlated. This suggests that people who put a high value on privacy also place a high value on the technology's appropriateness and suitability.

​

According to Kumar and Singh (2020), e-commerce users are more likely to be satisfied with a platform's functionality and user-friendliness than with fundamental security precautions, which they take for granted.

Results and Discussions

Association between Comprehension among all category’s criterion after controlled variables

Even controlling for gender, the result showed that there is still a strong positive correlation between all guidelines.

This is supported by Zapata et al., (2015) who report that user-centric designs frequently result in higher perceived security in health apps.

​

Transparency and content were highly correlated with privacy after controlling gender (r=0.855, p<0.001), implying that clear and transparent information support user’s privacy perceptions.

​

This statement is supported by Esmaeilzadeh, (2019), the findings of this study underscore the importance of privacy statement completeness and transparency in influencing consumer perceptions and confidence in online businesses.

Association between Perceived Importance among all category’s criterion

Even controlling for gender, the result showed that there is still a strong positive correlation between all guidelines.

​

Supported by:

The study emphasised that usability and advanced features are crucial quality criteria, and that the integration of technology has a substantial impact on users' opinions and satisfaction with mobile applications (Nitze and Schmietendorf, 2015).

​

Another study investigated user perceptions of mobile app security. It was discovered that the incorporation of advanced safety features not only increases user trust but also leads to a satisfying user experience, supporting the notion that advanced technology is essential for users' perceptions of app quality (Elsantil, 2020).

​

Results and Discussions

50

Results and Discussions

In total: 5740 food products

42 food categories from 7 markets

​

​

​

Instantly reads grocery barcodes and analyzes nutritional content per 100g.

• Empowers consumers at point-of-decision: the supermarket shelf

​

• helping them make informed choices before purchase, not after consumption.

1

Calculates two validated nutritional metric

• Nutrient Rich Food Index (NRF): Scores food based on essential nutrient density vs. limiting nutrients (e.g., sugar, saturated fats).

​

• Dietary Inflammatory Index (DII): Rates food based on its potential to promote or reduce inflammation

2

Offers a dual-layered evaluation of food quality:

nutritional density and inflammatory potential

leading to holistic dietary improvements that address chronic disease risk

Not requiring the user to read complex nutrition labels.

Visually categorizes foods (e.g., Green = Eat often, Yellow = Eat moderately, Red = Limit)

3

Applies behavioral economics (nudging) to drive healthier choices through intuitive, non-intrusive guidance.

​

Great for low-literacy or time-poor consumers.

Automatically recommends nutritionally superior alternatives if an unhealthy item is scanned.

4

Facilitates incremental behavior change by promoting realistic substitutions rather than elimination.

​

This supports gradual dietary improvements with less resistance or decision fatigue.

Tracks nutritional quality of items added to a digital cart in real time, showing cumulative

5

Enables proactive grocery planning, user can adjust their cart before checkout

​

​

Fostering intentional, balanced food purchasing that aligns with health goals.

Tracks nutritional quality of items added to a digital cart in real time, showing cumulative

5

Enables proactive grocery planning, user can adjust their cart before checkout

​

​

Fostering intentional, balanced food purchasing that aligns with health goals.

comprehensive, culturally relevant food item database curated from Malaysian supermarkets

6

Improves cultural accuracy in dietary guidance, enabling more realistic and applicable nutrition decisions.

​

It also builds user trust and relevance in the system, increasing long-term engagement.

• Prioritized based on Nutrition research priorities in Malaysia (‘NUTRITION RESEARCH PRIORITIES IN MALAYSIA’, pp. 117–117).

Research Priority Area 3: Life Course Approach to Food Intake and Dietary Practices

C 2.5 Evaluation on the delivery platform/ mode of nutrition education (digital based approach).

​

• NutriDIY-trolley mobile app ties closely with Sustainable Development Goals (SDGs) (SDG, 2019).

SDG 3: Good Health and Well-being

Significance of the study

CALL FOR COLLABORATORS

Collaborate, Integrate, Enhance

STRENGTHENING REGIONAL NETWORKS FOR NUTRITION ACTION

SUBSCRIBE

for future updates!

Collaborators:

To all our team members and collaborators,

heartfelt acknowledgment

Asst Prof Dr Vaidehi U. (PhD)

Head of Department of Food Science and Nutrition,

Lecturer in Clinical Nutrition and Nutrigenomic

Faculty of Applied Sciences,

UCSI University

vaidehi@ucsiuniversity.edu.my

Assoc Prof Dr Baskaran.G (PhD)

Research Manager, Praxis, Industry and Community Engagement (PICE), CERVIE

Lecturer in Precision Medicine and Metabolic Biochemistry

Dept. of Biotechnology, Faculty of Applied Sciences,

UCSI University

baskaran@ucsiuniversity.edu.my

ASST PROF DR VAIDEHI ULAGANATHAN, PhD

​

Head of Department of Food Science and Nutrition, Faculty of Applied Sciences, UCSI University, Kuala Lumpur, Malaysia

Registered Nutritionist (Clinical Nutrition): MAHPC(NUTR)00520

MCRCR Certification Code: 210522_00033�Health Coach

Certified Trainer, Human Resource Development Corporation (HRDCorp) ID 30171

Women Excellence 2021 in the “4th International Conference on Food and Nutrition”,Singapore

Global Nutrition Early Career Scholar Award for NUTRITION 2021 LIVE ONLINE. by Mondelēz International, USA

Excellent Educator Award 2025 in Excellent Educator Award 2025 at the Telugu Women Empowerment & Leadership Conference

​

​

Research gate: https://www.researchgate.net/profile/Vaidehi_Ulaganathan

LinkedIn: https://www.linkedin.com/in/vaidehi-ulaganathan-29227b28/