Tracking Personal Health-Environment Interaction with Novel Mobile Sensing Devices

被引:5
|
作者
Deng, Yue [1 ,2 ]
Liu, Nai-Yuan [1 ,2 ]
Tsow, Francis [2 ]
Xian, Xiaojun [2 ]
Krajmalnik-Brown, Rosa [3 ,4 ]
Tao, Nongjian [2 ]
Forzani, Erica [1 ,2 ]
机构
[1] Arizona State Univ, Sch Engn Matter Transport & Energy, Tempe, AZ 85287 USA
[2] Arizona State Univ, Biodesign Inst, Ctr Bioelect & Biosensors, Tempe, AZ 85287 USA
[3] Arizona State Univ, Biodesign Inst, Swette Ctr Environm Biotechnol, Tempe, AZ 85287 USA
[4] Arizona State Univ, Sch Sustainable Engn & Built Environm, Tempe, AZ 85287 USA
关键词
volatile organic compounds (VOCs); resting metabolic rate (RMR); mobile sensors; environmental exposure; RESTING METABOLIC-RATE; VOLATILE ORGANIC-COMPOUNDS; WEIGHT-LOSS; THYROID-HORMONE; ASSOCIATION; SENSOR; ADULTS;
D O I
10.3390/s18082670
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
The development of connected health devices has allowed for a more accurate assessment of a person's state under free-living conditions. In this work, we use two mobile sensing devices and investigate the correlation between individual's resting metabolic rate (RMR) and volatile organic compounds (VOCs) exposure levels. A total of 17 healthy, young, and sedentary office workers were recruited, measured for RMR with a mobile indirect calorimetry (IC) device, and compared with their corresponding predicted RMR values from the Academy of Nutrition and Dietetics' recommended epidemiological equation, the Mifflin-St Jeor equation (MSJE). Individual differences in the RMR values from the IC device and the epidemiological equation were found, and the subjects' RMRs were classified as normal, high, or low based on a cut-off of +/- 200 kcal/day difference with respect to the predicted value. To study the cause of the difference, VOCs exposure levels of each participant's daytime working environment and nighttime resting environment were assessed using a second mobile sensing device for VOCs exposure detection. The results showed that all sedentary office workers had a low VOCs exposure level (<2 ppmC), and there was no obvious correlation between VOCs exposure and the RMR difference. However, an additional participant who was a worker in an auto repair shop, showed high VOCs exposure with respect to the sedentary office worker population and a significant difference between measured and predicted RMR, with a low RMR of 500 kcal/day difference. The mobile sensing devices have been demonstrated to be suitable for the assessment of direct information of human health-environment interactions at free-living conditions.
引用
收藏
页数:11
相关论文
共 50 条
  • [1] Human Health-Environment Interaction Science: An emerging research paradigm
    Spano, Giuseppina
    Giannico, Vincenzo
    Elia, Mario
    Bosco, Andrea
    Lafortezza, Raffaele
    Sanesi, Giovanni
    SCIENCE OF THE TOTAL ENVIRONMENT, 2020, 704
  • [2] Energy Consumption in Personal Mobile Devices Sensing Applications
    Pendao, Cristiano G.
    Moreira, Adriano C.
    Rodrigues, Helena
    2014 7TH IFIP WIRELESS AND MOBILE NETWORKING CONFERENCE (WMNC), 2014,
  • [3] Sensing the Driving Environment with Smart Mobile Devices
    Danescu, Radu Gabriel
    Itu, Razvan
    Petrovai, Andra
    2015 IEEE 11TH INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTER COMMUNICATION AND PROCESSING (ICCP), 2015, : 271 - 278
  • [4] Finger Tracking for Gestural Interaction in Mobile Devices
    Matilainen, Matti
    Hannuksela, Jari
    Fan, Lixin
    IMAGE ANALYSIS, SCIA 2013: 18TH SCANDINAVIAN CONFERENCE, 2013, 7944 : 329 - 338
  • [5] Design and Fabricate Personal Health Sensing Devices
    Zhu, Junyi
    ADJUNCT PROCEEDINGS OF THE 35TH ACM SYMPOSIUM ON USER INTERFACE SOFTWARE & TECHNOLOGY, UIST 2022, 2022,
  • [6] Mobile Sensing Platform for Personal Health Management
    Piyare, Rajeev
    Lee, Seong Ro
    18TH IEEE INTERNATIONAL SYMPOSIUM ON CONSUMER ELECTRONICS (ISCE 2014), 2014,
  • [7] Integrated Personal Mobile Devices to Wireless Weather Sensing Network
    Herrera, Luis
    Mink, Brian
    Sukittanon, Somsak
    IEEE SOUTHEASTCON 2010: ENERGIZING OUR FUTURE, 2010, : 5 - 8
  • [8] A Face Tracking Algorithm for User Interaction in Mobile Devices
    Bulbul, Abdullah
    Cipiloglu, Zeynep
    Capin, Tolga
    2009 INTERNATIONAL CONFERENCE ON CYBERWORLDS, 2009, : 385 - 390
  • [9] Evaluation of a personal mobile coaching service for health tracking
    Gatti, Fabiana
    Brivio, Eleonora
    Galimberti, Carlo
    Studies in Health Technology and Informatics, 2013, 191 : 154 - 157
  • [10] Evaluation of a Personal Mobile Coaching Service for Health Tracking
    Gatti, Fabiana
    Brivio, Eleonora
    Galimberti, Carlo
    ANNUAL REVIEW OF CYBERTHERAPY AND TELEMEDICINE, 2013, 11 : 154 - 157