smartphone application · 2012. 7. 18. · smartphone application . for synchronized real-time...
TRANSCRIPT
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 Funding: Bern University of Applied Sciences, Federal Office of Public Health
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
2
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)
3
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)
4
Android «SmartAPP» Capture joint data on physical activity & food intake
5
Android «SmartAPP» Capture joint data on physical activity & food intake
- Activity list Limitations of carrying a Smartphone
Specific individual activities can be added manually
Android «SmartAPP» Capture joint data on physical activity & food intake
− Food consumption: semi-quantitative food record
7 Other foods can be added manually
8
The «SmartAPP» coaching system
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
9
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
10 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
kcal
day 1 day 2 day 3 day 4 day 5 day 6 day 1 day 2 day 3 day 4 day 5 day 6week 1 week 2
----
energy intake clientsenergy expenditure clientsenergy intake dieticiansenergy expenditure dieticians Fr Sat Sun
Fr/Sat 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
EIrep : 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
12
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
13
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%) Missing data 100 (4%) 57 (2%) 0.56 [0.41, 0.77]
Next steps (1 - ongoing)
Implement a web-/server based system concept
Test «SmartAPP» image feature for supporting portion size estimation
14
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
15
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 Head of Institute for Human Centered Engineering E [email protected] URL www.huce.ti.bfh.ch
16