SMARTPHONE APPLICATION
FOR SYNCHRONIZED REAL-TIME DIETARY ASSESSMENT AND PHYSICAL ACTIVITY ANALYSES
Sigrid Beer-Borst for the «smartAPP» research group, aR&D in Nutrition and Dietetics
What to expect
Mobile Health services (mHealth)
in Public Health Nutrition practice and research
Android Smartphone application «smartAPP»
− joint data capture on physical activity & food intake − Application test results
Next steps: ongoing and projected R&D activities
mHealth in Public Health Nutrition
Mobile health services (mHealth) are trendy
In CH, every second person 15 years and older has a Smartphone
Smartphones
− allow synchronizing the collection/measure and use of diet and physical activity data as major lifestyle (risk) factors
− offer a wide range of applications with increasing requirement as to data accuracy: life style coaching (open access), dietary counseling, research
− can serve as a motivational tool supporting behavior change
Objective: provide a user friendly (different user groups), easy to
handle, reliable and valid tool (app)
Android «SmartAPP»
Capture joint data on physical activity & food intake
Physical activity analysis
: from acceleration to energy expenditure
Acceleration
Variance of acceleration
MET value, energy expenditure in metabolic equivalents (1 MET = 3.5 ml O2 x kg-1 x min-1 or 1 kcal x kg-1 x h-1)
Android «SmartAPP»
Capture joint data on physical activity & food intake
Android «SmartAPP»
Capture joint data on physical activity & food intake
-
Activity list
Limitations of carrying a Smartphone
Android «SmartAPP»
Capture joint data on physical activity & food intake
−
Food consumption
: semi-quantitative food record
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8
Prototype application test 2010/2011 - Study design
Technical Development
• Android Smartphone App • Real-time data assessment • Computer-based data analyses • Dietary counseling application
(smartERB) system
Pilot test and App refinement
• Feature, Functionality • General usability • Design
5 BUAS dieticians, Sept 2010 5 non Smartphone user
laypersons, Oct 2010 Application test in dietetics (prototype) • Feasibility • Suitability • Plausibility 5 RDs: 33-46y; 21-25 kg/m2 20 Clients: 25-49y; 25-38 kg/m2
partly non Smartphone users
Median energy intakes and expenditures (kcal/d)
Reference (RD, n=5) and client group (n=20) over time
Non parametric ANOVA Model for repeated measurements, F1_LD_F1-Model by Brunner, Domhof and Langer 2002; p=<0.05
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Clients, time effect
Energy intake p=0.086
Energy expenditure p=0.137
Group effect, wk 1 (clients vs. RD) Energy intake p=0.094 Energy expenditure p=0.224 1600 1800 2000 2200 2400 kca l
day 1 day 2 day 3 day 4 day 5 day 6 day 1 day 2 day 3 day 4 day 5 day 6
week 1 week 2
-energy intake clients energy expenditure clients energy intake dieticians
energy expenditure dieticians Fr Sat Sun
Plausibility check – Underreporting (Wk 1)
Goldberg cut-off Technic
(Black AE. Int J Obes 2000; Livingston & Black J Nutr 2003)− Comparison of the mean energy intake at group level with the estimated energy requirement/expenditure at PAL 1.55
EI
rep: BMR < / > PAL 95% lower and upper Conf limits (cut-offs)
− 20 overweight clients EIrep : BMR = 1.21 < [1.548; 1552] − 5 dieticians EIrep : BMR = 1.28 < [1.549; 1551]
Food intake quantification by group
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Clients RDs OR [95%CI]
Predefined units 1437 (61%) 218 (27%)
Exact measures 917 (39%) 594 (73%) 5.88 [2.43, 14.25]
Use of exact measures (ml, g) or predefined units (household measures, portions) by clients vs. RD’s in N (%) (GLMM, p=0.0007)
Completeness of food data entry
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Necessity to enter foods manually (not found in food list) by clients vs. RDs, N (%) (GLMM, p=0.5477)
Clients RDs OR [95%CI]
Complete data 2219 (96%) 785 (97%)
Missing data 100 (4%) 27 (3%) 0.68 [0.20, 2.31]
Necessity to enter foods manually (not found in food list) for clients, wk1 vs. wk 2, N(%) (GLMM, p=0.0003)
Week 1 Week 2 OR [95%CI]
Complete data 2219 (96%) 2226 (98%)
Next steps (1 - ongoing)
Implement a web-/server based system concept
Test «SmartAPP» image feature for supporting portion size estimation
Next steps (2 – projected, 2013+)
«SmartAPP» validation study
Step 1 - Physical activity
a) mechanical validation
− power calculation from phone accelerometer vs. ground reaction forces
b) energy expenditure by spiroergometry
− indirect calorimetry
Step 2 – Food record: against calibrated measure of physical activity
Reprogram for iPhone
Intervention study in dietary counseling / lifestyle coaching
Contacts
Health
Sigrid Beer-Borst, MSc
Lecturer / senior fellow for aR&D
in Nutrition & Dietetics
E [email protected]
URL www.gesundheit.bfh.ch
Engineering and Information
Technology
Dr. Ing. ETH Marcel Jacomet
