Scaling(up(Dietary(Data(for(Decision3Making(in(Africa(and ... · estimation and food...
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Scaling up Dietary Data for DecisionMaking in Africa and Asia: New Technological Fron>ers Jennifer Coates PhD*, Winnie F.L. Bell MS/MPH, Brooke Colaiezzi MS, and Cathleen Cissé MPH Background Acknowledgments Friedman School of NutriDon, TuFs University 150 Harrison Ave., Boston MA 02111 hOp://nutriDon.tuFs.edu Methods Figure 2. Traits of Technologies Reviewed Table 1. Innovative Technologies Identified Discussion and Conclusion This research was conducted as part of the International Dietary Data Expansion (INDDEX) Project, which is implemented by Tufts University's Gerald J. and Dorothy R. Friedman School of Nutrition Science and Policy with funding from the Bill & Melinda Gates Foundation. Dr. Jennifer Coates is the Principal Investigator of the INDDEX Project and an Assistant Professor at Tufts University's Gerald J. and Dorothy R. Friedman School of Nutrition Science and Policy. Contact: [email protected] Interventions and policies that seek to improve human health through diet or influence the effects of food systems on dietary outcomes require high quality data on the food and nutrient intake of individuals. Dietary surveys in low-income countries generally rely on interviewer-administered recalls using paper questionnaires and re-develop food composition databases and other key components with each new survey. This approach is cumbersome, prone to error, and cost- and time-prohibitive, resulting in a dearth of dietary information in many low-income countries. As part of a multi-year effort to scale up research architecture for dietary assessment in low-income countries, this study investigates the suitability of emerging innovative dietary assessment technologies for developing countries in Africa and Asia. Using PubMed and Google pre-defined search terms identified technologies from peer-reviewed and recent gray literature. Android apps were searched through the Google Play store. For each technology, available information was extracted about the a) primary purpose, b) terminal type, c) type of method, d) data input type, e) data processing, f) data output type and content, g) mode of administration, h) evidence of validity, i) resource requirements, j) appropriateness for low-income countries, and k) additional strengths and weaknesses. Devices were assessed on criteria for use in low-income countries, including whether literacy is required for use of the tool, the length of the battery life, connectivity requirements, and evidence of validity. A total of 48 devices, plus 37 unique applications were identified (Figure 1). Six categories of innovative devices were identified: 1) Android applications, 2) computer-based devices, 3) camera-enabled devices, 4) wearable devices, 5) scale-based devices, and 6) handheld devices (Table 1). The Android applications, wearable devices, scale-based devices, and handheld devices are primarily designed for self-management purposes, while the computer-based and camera-enabled devices tend to be for research or for both self-management and research purposes. None of these emergent technologies met all of the criteria for use in large- scale surveys in low-income countries (Figure 2). Many are still under development and further assessment of their scientific validity and feasibility for population studies is required (Figure 3). However some of the user- friendly features of smartphone apps could be integrated into this platform, to enhance the experience of the interviewers, interviewees, and those using the data (Table 2). Interviewer-administered, tablet-based 24-hour dietary recall platforms are in widespread use in industrialized countries, but have not yet penetrated low- income country contexts. A standardized, adaptable assessment and processing platform tailored to low-income contexts would be a feasible and scientifically defensible investment with potentially huge benefits for increasing dietary data availability. Along with the necessary research architecture, certain features of the more emergent technologies could be integrated into such a platform, including computer-assisted personal interviewing (CAPI) features for enhanced respondent interaction -- possibly allowing for concurrent dietary counseling -- and image recognition software to improve the accuracy of portion size estimation and food identification. Figure 1. Summary of Dietary Assessment Devices Table 2. Characteristics of 37 Android Applications CAMERA-ENABLED (n=18) Food photography 24-hour recall: Respondents use a handheld digital camera to take pictures before and after eating. Photos then are used in a 24-HR to enhance recall and portion size estimates. Digital photography method: Respondents use handheld digital camera to capture images of their evening meals before and after eating. Mobile phone food record: Users capture images of food before and after consumption using smartphone camera. Smartplate: A plate containing 3 cameras and weight sensors, which allow it to automatically identify and quantify foods. Connects to cloud-based database for nutrient analysis. Digital photography plus recall: Digital photos of meals consumed in a cafeteria setting are used in combination with dietary recalls to assess energy intake. Remote Food Photography Method: Respondents capture images of food selections and plate waste with cell phone or smartphone camera. Mealsnap: Smartphone app. Users snap a photo of their meal before consumption and the image is sent over a server and processed using Amazon Mechanical Turks. Platemate: Smartphone app. Allows users to take photos of their meals and receive estimates of food intake and composition based on analysis performed by Amazon Mechanical Turks. Nutricam/NUDAM: A mobile phone application for recording dietary intake. Food intake visual and voice recognizer: Using cell phone camera and associated app, respondents take three images of food before and after eating. Diet data recorder system (DDRS): Designed as a replacement for written food records with the plan that coders can transfer DDR data (image capture, voice recording, food volume estimation) into NDSR software. DIMS: DIMS includes a digital camera, weighing scale, infrared thermometer, radio-frequency identification reader, and a user RFID transponder card. Gives an account of the amount and serving temperature of food on the plate. Food recognition technology: Smartphone app. Combines food recognition and volume estimation to generate information about the food item, portion size and nutritional value. Developed by the same lab that created Siri. Vicon Revue: Camera worn around the neck that automatically captures point-of-view images in response to movement, heat and light (every 20–30 s). ebutton: A small wearable computer with a data processor, massive data storage, wireless communication links, and a large array of sensors. The primary objective of this device is a tool that can collect data for evaluation of diet, physical activity, and lifestyle. Image DietDay: Mobile phone is worn around the neck and images are captured every 10 seconds over a 24-hour period. Images are used as memory aids during a 24-hour recall. Digital Photography of Foods Method: Uses a digital video camera to capture food selection and plate waste in a cafeteria. Willpower watch: Wrist-worn camera that automatically takes pictures of food (including food-hand interactions and food-mouth interactions) and measures the types and amounts of foods consumed. WEARABLES (n=6) WearSens: Necklace made of piezoelectric sensors that detects swallows, classifies food (using an algorithm that produces a spectrogram) and provides the user with guidance and feedback on their smartphone. GoBe by Healbe: A wristband that automatically measure nutrient intake by applying an algorithm to calculate the glucose in the cells and relies on an impedance sensor, accelerometer, pressure sensor. UP by Jawbone: Wristband or clip-on device with corresponding app that tracks activity, sleep, and consumption; food and drink consumption can be scanned with barcodes or logged manually. Bite Counter: Watch-like device that tracks wrist motion to count bites and estimate the number of calories consumed; wrist band is fitted with micro-electro-mechanical gyroscope. Automatic Ingestion Monitor: Device includes a jaw motion sensor, a hand gesture sensor, and an accelerometer that wirelessly links to a smartphone to objectively and automatically monitor food intake. Counts of Chews and Swallows Model: Will automatically assess dietary intake using throat microphone, piezoelectric strain sensor, and digital eye camera resulting in a wearable sensor system with automated food photography. SCALE-BASED (n=3) SITU Scale: Scale that calculates the nutritional value of food, it is connected to app and iPad and tracks consumption over time. Smart Diet Scale: Scale that calculates the nutritional value of food items linked with food, grocery and restaurant items; connected to app and smartphone and tracks consumption over time for up to six people. GE Calorie-Counter Technology: Proposes to use 'advanced sensor technology' to estimate the fat, water, and weight of the food to calculate calories. HANDHELD DEVICES (n=2) SCiO: A handheld sensor that uses near-infrared spectroscopy to analyze the composition of foods (and other objects); scans are stored in a 'global database of matter' for research and science. TellSpec: A handheld sensor that uses near-infrared spectroscopy to analyze the composition of foods. Figure 3. Stage of Development of Technologies 37 19 18 6 3 2 Smartphone applications Computer-based Camera-enabled Wearables Scale-based Handheld Entry Method Dietary Data Tracked Barcode & type-in search 38% Calories and macronutrients 57% Type-in search 32% Calories, macro, & micronutrients 24% Barcode only 8% Calories only 11% Photo & type-in search 8% Macronutrients only 8% Barcode, photo, & type-in search 5% Voice only OR 3% Voice, photo, & type-in search 3% Voice, barcode, & type-in search 3% 10 13 1 5 Available for purchase Being developed Private license only Not commercially available 0% 20% 40% 60% 80% 100% Computer (n=19) Camera (n=18) Wearables (n=6) Scale-based (n=3) Handheld (n=2) % devices reviewed with trait Literacy is not required Battery life lasts >8 hrs Wireless/cellular connection is not required Validation study exists
Transcript of Scaling(up(Dietary(Data(for(Decision3Making(in(Africa(and ... · estimation and food...
Scaling up Dietary Data for Decision-‐Making in Africa and Asia: New Technological Fron>ers
Jennifer Coates PhD*, Winnie F.L. Bell MS/MPH, Brooke Colaiezzi MS, and Cathleen Cissé MPH
Background
Acknowledgments
Friedman School of NutriDon, TuFs University 150 Harrison Ave., Boston MA 02111
hOp://nutriDon.tuFs.edu
Methods
Figure 2. Traits of Technologies Reviewed
Table 1. Innovative Technologies Identified
Discussion and Conclusion
This research was conducted as part of the International Dietary Data Expansion (INDDEX) Project, which is implemented by Tufts University's Gerald J. and Dorothy R. Friedman School of Nutrition Science and Policy with funding from the Bill & Melinda Gates Foundation. !!Dr. Jennifer Coates is the Principal Investigator of the INDDEX Project and an Assistant Professor at Tufts University's Gerald J. and Dorothy R. Friedman School of Nutrition Science and Policy. Contact: [email protected] !
Interventions and policies that seek to improve human health through diet or influence the effects of food systems on dietary outcomes require high quality data on the food and nutrient intake of individuals. Dietary surveys in low-income countries generally rely on interviewer-administered recalls using paper questionnaires and re-develop food composition databases and other key components with each new survey. This approach is cumbersome, prone to error, and cost- and time-prohibitive, resulting in a dearth of dietary information in many low-income countries. As part of a multi-year effort to scale up research architecture for dietary assessment in low-income countries, this study investigates the suitability of emerging innovative dietary assessment technologies for developing countries in Africa and Asia. !
Using PubMed and Google pre-defined search terms identified technologies from peer-reviewed and recent gray literature. Android apps were searched through the Google Play store. For each technology, available information was extracted about the a) primary purpose, b) terminal type, c) type of method, d) data input type, e) data processing, f) data output type and content, g) mode of administration, h) evidence of validity, i) resource requirements, j) appropriateness for low-income countries, and k) additional strengths and weaknesses. Devices were assessed on criteria for use in low-income countries, including whether literacy is required for use of the tool, the length of the battery life, connectivity requirements, and evidence of validity.!
A total of 48 devices, plus 37 unique applications were identified (Figure 1). Six categories of innovative devices were identified: 1) Android applications, 2) computer-based devices, 3) camera-enabled devices, 4) wearable devices, 5) scale-based devices, and 6) handheld devices (Table 1). The Android applications, wearable devices, scale-based devices, and handheld devices are primarily designed for self-management purposes, while the computer-based and camera-enabled devices tend to be for research or for both self-management and research purposes.!!None of these emergent technologies met all of the criteria for use in large-scale surveys in low-income countries (Figure 2). Many are still under development and further assessment of their scientific validity and feasibility for population studies is required (Figure 3). However some of the user-friendly features of smartphone apps could be integrated into this platform, to enhance the experience of the interviewers, interviewees, and those using the data (Table 2). !!Interviewer-administered, tablet-based 24-hour dietary recall platforms are in widespread use in industrialized countries, but have not yet penetrated low-income country contexts. A standardized, adaptable assessment and processing platform tailored to low-income contexts would be a feasible and scientifically defensible investment with potentially huge benefits for increasing dietary data availability. !!Along with the necessary research architecture, certain features of the more emergent technologies could be integrated into such a platform, including computer-assisted personal interviewing (CAPI) features for enhanced respondent interaction -- possibly allowing for concurrent dietary counseling -- and image recognition software to improve the accuracy of portion size estimation and food identification. !
Figure 1. Summary of Dietary Assessment Devices
Table 2. Characteristics of 37 Android Applications CAMERA-ENABLED (n=18)!
Food photography 24-hour recall: Respondents use a handheld digital camera to take pictures before and after eating. Photos then are used in a 24-HR to enhance recall and portion size estimates.!
Digital photography method: Respondents use handheld digital camera to capture images of their evening meals before and after eating. !
Mobile phone food record: Users capture images of food before and after consumption using smartphone camera. !Smartplate: A plate containing 3 cameras and weight sensors, which allow it to automatically identify and quantify foods. Connects to cloud-based database for nutrient analysis. !
Digital photography plus recall: Digital photos of meals consumed in a cafeteria setting are used in combination with dietary recalls to assess energy intake.!
Remote Food Photography Method: Respondents capture images of food selections and plate waste with cell phone or smartphone camera.!Mealsnap: Smartphone app. Users snap a photo of their meal before consumption and the image is sent over a server and processed using Amazon Mechanical Turks.!Platemate: Smartphone app. Allows users to take photos of their meals and receive estimates of food intake and composition based on analysis performed by Amazon Mechanical Turks.!
Nutricam/NUDAM: A mobile phone application for recording dietary intake.!
Food intake visual and voice recognizer: Using cell phone camera and associated app, respondents take three images of food before and after eating.!Diet data recorder system (DDRS): Designed as a replacement for written food records with the plan that coders can transfer DDR data (image capture, voice recording, food volume estimation) into NDSR software.!DIMS: DIMS includes a digital camera, weighing scale, infrared thermometer, radio-frequency identification reader, and a user RFID transponder card. Gives an account of the amount and serving temperature of food on the plate.!
Food recognition technology: Smartphone app. Combines food recognition and volume estimation to generate information about the food item, portion size and nutritional value. Developed by the same lab that created Siri.!
Vicon Revue: Camera worn around the neck that automatically captures point-of-view images in response to movement, heat and light (every 20–30 s). !
ebutton: A small wearable computer with a data processor, massive data storage, wireless communication links, and a large array of sensors. The primary objective of this device is a tool that can collect data for evaluation of diet, physical activity, and lifestyle. !
Image DietDay: Mobile phone is worn around the neck and images are captured every 10 seconds over a 24-hour period. Images are used as memory aids during a 24-hour recall.!
Digital Photography of Foods Method: Uses a digital video camera to capture food selection and plate waste in a cafeteria.!
Willpower watch: Wrist-worn camera that automatically takes pictures of food (including food-hand interactions and food-mouth interactions) and measures the types and amounts of foods consumed. !
WEARABLES (n=6)!WearSens: Necklace made of piezoelectric sensors that detects swallows, classifies food (using an algorithm that produces a spectrogram) and provides the user with guidance and feedback on their smartphone.!
GoBe by Healbe: A wristband that automatically measure nutrient intake by applying an algorithm to calculate the glucose in the cells and relies on an impedance sensor, accelerometer, pressure sensor.!
UP by Jawbone: Wristband or clip-on device with corresponding app that tracks activity, sleep, and consumption; food and drink consumption can be scanned with barcodes or logged manually.!
Bite Counter: Watch-like device that tracks wrist motion to count bites and estimate the number of calories consumed; wrist band is fitted with micro-electro-mechanical gyroscope.!
Automatic Ingestion Monitor: Device includes a jaw motion sensor, a hand gesture sensor, and an accelerometer that wirelessly links to a smartphone to objectively and automatically monitor food intake.!
Counts of Chews and Swallows Model: Will automatically assess dietary intake using throat microphone, piezoelectric strain sensor, and digital eye camera resulting in a wearable sensor system with automated food photography.!
SCALE-BASED (n=3)!SITU Scale: Scale that calculates the nutritional value of food, it is connected to app and iPad and tracks consumption over time. !
Smart Diet Scale: Scale that calculates the nutritional value of food items linked with food, grocery and restaurant items; connected to app and smartphone and tracks consumption over time for up to six people.!
GE Calorie-Counter Technology: Proposes to use 'advanced sensor technology' to estimate the fat, water, and weight of the food to calculate calories.!
HANDHELD DEVICES (n=2)!
SCiO: A handheld sensor that uses near-infrared spectroscopy to analyze the composition of foods (and other objects); scans are stored in a 'global database of matter' for research and science. !
TellSpec: A handheld sensor that uses near-infrared spectroscopy to analyze the composition of foods.!
Figure 3. Stage of Development of Technologies
37!
19!
18!
6!3! 2!
Smartphone applications!
Computer-based!
Camera-enabled!
Wearables !
Scale-based !
Handheld !
Entry Method! ! Dietary Data Tracked! !Barcode & type-in search! 38%! Calories and macronutrients! 57%!Type-in search! 32%! Calories, macro, & micronutrients! 24%!Barcode only! 8%! Calories only! 11%!Photo & type-in search! 8%! Macronutrients only! 8%!Barcode, photo, & type-in search! 5%! ! !Voice only OR ! 3%! ! !Voice, photo, & type-in search! 3%! ! !Voice, barcode, & type-in search! 3%! ! !
10!
13!
1!5!
Available for purchase
Being developed
Private license only
Not commercially available
0%!
20%!
40%!
60%!
80%!
100%!
Computer (n=19)!
Camera (n=18)!
Wearables (n=6)!
Scale-based (n=3)!
Handheld (n=2)!
% d
evic
es r
evie
wed
with
trai
t! Literacy is not required!
Battery life lasts >8 hrs!
Wireless/cellular connection is not required!
Validation study exists!
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