Smartphone apps for tracking food consumption and recommendations: Evaluating artificial intelligence-based functionalities, features and quality of current apps

Samad, Sabiha; Ahmed, Firoz; Naher, Samsun; Kabir, Muhammad Ashad; Das, Anik; Amin, Sumaiya; Islam, Sheikh Mohammed Shariful

doi:10.1016/j.iswa.2022.200103

Cited by 18 publications

(8 citation statements)

References 95 publications

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“…Future dietary interventions should consider other innovative strategies to maintain the beneficial effects beyond the intervention period. For example, a smartphone application may be a convenient tool for real-time tracking of food consumption (i.e., self-monitoring) [ 59 ] that survivors, families, and caregivers can continue to use after interventions. Current food-tracking applications recognize food items from a food image and provide individualized food recommendations to users based on their nutritional needs due to the integration of artificial intelligence and computer vision technologies, which greatly increases their convenience and efficacy [ 59 ].…”

Section: Discussionmentioning

confidence: 99%

“…For example, a smartphone application may be a convenient tool for real-time tracking of food consumption (i.e., self-monitoring) [ 59 ] that survivors, families, and caregivers can continue to use after interventions. Current food-tracking applications recognize food items from a food image and provide individualized food recommendations to users based on their nutritional needs due to the integration of artificial intelligence and computer vision technologies, which greatly increases their convenience and efficacy [ 59 ]. With these features, the food-tracking applications have the potential to provide survivors, families, and caregivers with real-time guidance on dietary adjustments beyond the intervention period.…”

Section: Discussionmentioning

confidence: 99%

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A Systematic Review of Dietary Interventions for Cancer Survivors and Their Families or Caregivers

Xu,

Hoover,

Woodard

et al. 2023

Nutrients

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Family or caregiver engagement has the potential to support healthy dietary changes among cancer survivors. However, little is known about these family- or caregiver-involved dietary interventions and their effects. This systematic review aimed to identify the behavior change techniques (BCTs) used in dietary interventions for cancer survivors and their families or caregivers and to synthesize intervention effects on dietary and health outcomes. Following the PRISMA guidelines, we conducted systematic searches in three databases and identified 12 trials (16 peer-reviewed manuscripts) for inclusion in this review. Data were extracted from these manuscripts and the BCT taxonomy was used to identify the BCTs. A total of 38 BCTs were identified from 12 trials, 13 of which were used in at least half of the 12 trials. Ten studies reported significant intervention effects on health outcomes (e.g., adiposity) and six suggested significant improvements in dietary behaviors (e.g., fruit and vegetable intake). Overall, this review found that family- or caregiver-involved interventions for cancer survivors significantly improved dietary and health outcomes. Future research should identify BCTs particularly for dietary changes and develop effective dyadic strategies to facilitate diet-related interactions between survivors and their families or caregivers to enhance their engagement in healthy diets.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A Systematic Review of Dietary Interventions for Cancer Survivors and Their Families or Caregivers

Xu,

Hoover,

Woodard

et al. 2023

Nutrients

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“…Food quality scoring systems that aim to capture elements of the food matrix, including degree of processing, relative ratios of nutrients, and missing bioactives, are being developed [ [29] , [30] , [31] ]. Commercial efforts are starting to provide complementary solutions to nutritional guidance such as smart phone applications to personalize and prioritize the whole food selection as well as next-generation products that reincorporate bioactive ingredients into functional foods [ [32] , [33] , [34] , [35] , [36] , [37] ].…”

Section: St-century Nutrition – Bioactives and Filling The Food Gapsmentioning

confidence: 99%

Perspective: Nutrition’s Next Chapter – Bioactive Gaps and the Microbiome–Mitochondria Axis

Damman¹

2023

Advances in Nutrition

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“…YOLO is a real-time detection smart neural network. YOLO has a simple architecture, namely a convolutional neural network [ 27 ]. This neural network uses only standard layer types: convolution with 3 × 3 kernels and max-pooling with 2 × 2 kernels [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

Health to Eat: A Smart Plate with Food Recognition, Classification, and Weight Measurement for Type-2 Diabetic Mellitus Patients’ Nutrition Control

Salaki

Shin

Lee

et al. 2023

Sensors

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The management of type 2 diabetes mellitus (T2DM) is generally not only focused on pharmacological therapy. Medical nutrition therapy is often forgotten by patients for several reasons, such as difficulty determining the right nutritional pattern for themselves, regulating their daily nutritional patterns, or even not heeding nutritional diet recommendations given by doctors. Management of nutritional therapy is one of the important efforts that can be made by diabetic patients to prevent an increase in the complexity of the disease. Setting a diet with proper nutrition will help patients manage a healthy diet. The development of Smart Plate Health to Eat is a technological innovation that helps patients and users know the type of food, weight, and nutrients contained in certain foods. This study involved 50 types of food with a total of 30,800 foods using the YOLOv5s algorithm, where the identification, measurement of weight, and nutrition of food were investigated using a Chenbo load cell weight sensor (1 kg), an HX711 weight weighing A/D module pressure sensor, and an IMX219-160 camera module (waveshare). The results of this study showed good identification accuracy in the analysis of four types of food: rice (58%), braised quail eggs in soy sauce (60%), spicy beef soup (62%), and dried radish (31%), with accuracy for weight and nutrition (100%).

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Smartphone apps for tracking food consumption and recommendations: Evaluating artificial intelligence-based functionalities, features and quality of current apps

Cited by 18 publications

References 95 publications

A Systematic Review of Dietary Interventions for Cancer Survivors and Their Families or Caregivers

A Systematic Review of Dietary Interventions for Cancer Survivors and Their Families or Caregivers

Perspective: Nutrition’s Next Chapter – Bioactive Gaps and the Microbiome–Mitochondria Axis

Health to Eat: A Smart Plate with Food Recognition, Classification, and Weight Measurement for Type-2 Diabetic Mellitus Patients’ Nutrition Control

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