Which indicators for measuring the daily physical activity? An overview on the challenges and technology limits for Telehealth applications

Abstract

BACKGROUND:

Obesity is one of the biggest drivers of preventable chronic diseases and healthcare costs in Worldwide. Different prevention activities are suggested. By monitoring daily energy expenditure (EE) could be possible make personalized diets and programming physical activity. In this, physical inactivity is one of the most important public health problems. Some studies refer the effort of the international community in promoting physical activities. Physical activity can be promoted only by increasing citizens' empowerment on taking care of their health, and it passes from the improving of individual information. Technology can offer solutions and metrics for monitoring and measuring daily activity by interacting with individuals, sharing information and feedbacks.

OBJECTIVE:

In this study we review indicators of total energy expenditure and weaknesses of available devices in assessing these parameters.

METHODS

: Literature review and technology testing EuNetHta core model.

RESULTS:

For the clinical aspects, it is fundamental to take into account all the factor that can influence the personal energy expenditure as: heart rate, blood pressure and thermoregulation (influenced by the body temperature).

DISCUSSION:

In this study we focused the attention on the importance of tools to encourage the physical activity. We made an analysis of the factor that can influence the right analysis of energy expenditure and at the same time the energy regime. A punctual monitoring of the exercise regime could be helpful in Telemedicine application as Telemonitorig. More study are needed to value the impact of physical activity tracker in Telemonitorig protocols.

CONCLUSION:

On the assessment of the energy expenditure, critical issues are related to the physiological data acquisition. Sensors connected with mobile devices could be important tools for disease prevention and interventions affecting health behaviors. New devices applications are potential useful for telemedicine assistance, but security of data and the related communication protocol limits should be taking into account.

Keywords

Physical activity motor activities psychomotor performance energy expenditure energy metabolisms instruments and apparatus telemedicine mobile health Telehealth

Get full access to this article

View all access options for this article.

References

Barnes

P.M.

and Schoenborn

C.A.

. Trends in adults receiving a recommendation for exercise or other physical activity from a physician or other health professional. NCHS Data Brief (86): 1-8.

Blair

S.N.

Physical inactivity: the biggest public health problem of the 21st century. Br J Sports Med 2009; 43(1): 1-2.

Benzo

R.P.

, Chang

C.C.

, et al. Physical activity, health status and risk of hospitalization in patients with severe chronic obstructive pulmonary disease. Respiration 80(1): 10-8.

Fernandes

R.A.

, Coelho

E.S.M.J.

, et al. Possible underestimation by sports medicine of the effects of early physical exercise practice on the prevention of diseases in adulthood. Curr Diabetes Rev 11(3): 2015.

Kahlmeier

, Wijnhoven

T.M.

, et al. National physical activity recommendations: systematic overview and analysis of the situation in European countries. BMC Public Health 15: 133.

Muntaner

, Vidal-Conti

, et al. Increasing physical activity through mobile device interventions: A systematic review, Health Informatic J. February 3 (2015), doi: 10.1177/1460458214567004.

Roux

, Pratt

, et al. (2008). Cost effectiveness of community-based physical activity interventions. Am J Prev Med 35(6): 578-88.

Blasco

R.R.

Resting energy expenditure; assessment methods and applications. Nutr Hosp 31(Suppl 3): 245-54.

Cunningham

J.J.

(1980). A reanalysis of the factors influencing basal metabolic rate in normal adults. Am J Clin Nutr 33(11): 2372-4.

10.

Bamberga

, Rizzi

, et al. Relationship between energy expenditure, physical activity and weight loss during CPAP treatment in obese OSA subjects. Respir Med 109(4): 540-5.

11.

Hins

, Series

, et al. (2006). Relationship between severity of nocturnal desaturation and adaptive thermogenesis: preliminary data of apneic patients tested in a whole-body indirect calorimetry chamber. Int J Obes (Lond) 30(3): 574-7.

12.

Ryan

J.M.

, Walsh

, et al. A comparison of three accelerometry-based devices for estimating energy expenditure in adults and children with cerebral palsy. J Neuroeng Rehabil 11: 116. %

13.

Ryan

J.M.

, % Walsh

, et al. %A comparison of three %accelerometry-based devices for estimating energy expenditure in adults and %children with cerebral palsy. J Neuroeng Rehabil 11: 116.

14.

Jesus

, Achamrah

, et al. Validity of predictive equations for resting energy expenditure according to the body mass index in a population of 1726 patients followed in a Nutrition Unit. Clin Nutr 34(3): 529-35.

15.

Siervo

, Bertoli

, et al. Accuracy of predictive equations for the measurement of resting energy expenditure in older subjects. Clin Nutr 33(4): 613-9.

16.

Ocobock

The allocation and interaction model: A new model for predicting total energy expenditure of highly active humans in natural environments. Am J Hum Biol. 2015 Oct 12. doi: 10.1002/ajhb.22797. [Epub ahead of print].

17.

Lee

Y.-D.

, Jeong

D.-U.

, A novel method for energy expenditure using multisensor based activity monitoring. Computer Sciences and Convergence Information Technology (ICCIT), 2011 6th International Conference on, vol., no., pp. 103, 106, Nov. 29 2011-Dec. 1 2011.

18.

Arsand

, Muzny

, et al. Performance of the first combined smartwatch and smartphone diabetes diary application study. J Diabetes Sci Technol 9(3): 556-63.

19.

Murgia

, Corona

, et al. The application of telemedicine in the follow-up of lung transplantation in a patient with cystic fibrosis. Clin Ter 165(5): e382-3.

20.

Solvoll

, Scholl

, et al. Physicians interrupted by mobile devices in hospitals: understanding the interaction between devices, roles, and duties. J Med Internet Res 15(3): e56.

21.

Solvoll

and Scholl

(2008). Strategies to reduce interruptions from mobile communication systems in surgical wards. J Telemed Telecare 14(7): 389-92.

22.

van Lieshout

E.J.

, van der Veer

S.N.

, et al. (2007). Interference by new-generation mobile phones on critical care medical equipment. Crit Care 11(5): R98.

23.

Hanada

, Watanabe

, et al. (2000). A screening gate to prevent entry of mobile telephone handsets in the speaking/stand-by mode into prohibited and restricted areas. IEEE Trans Inf Technol Biomed 4(4): 320-3.

24.

Solvoll

, et al. (2013). CallMeSmart: A VoIP Softphone on Android Based Mobile Devices Using SIP. eTELEMED 2013, The Fifth International Conference on eHealth, Telemedicine, and Social Medicine, International Academy, Research and Industry Association (IARIA). 15: 198-203.

25.

Schiaffini

, Tagliente

, et al. Impact of long-term use of eHealth systems in adolescents with type 1 diabetes treated with sensor-augmented pump therapy. J Telemed Telecare. 2015 Aug 18. pii: 1357633X15598425. [Epub ahead of print].

26.

Manini

T.M.

Energy expenditure and aging. Ageing Res Rev 9(1): 1-11. % Manini

T.M.

%Energy Expenditure and Aging. Ageing Res Rev. 2010 %January; 9(1): 1. doi: 10.1016/j.arr.2009.08.002.

27.

Cutillo

, et al. Predicted and Measured Resting Energy Expenditure in Children with Spinal Muscular Atrophy 2, The Journal of Pediatrics, 164(5): 1228-1230.

28.

Patil

S.R.

and Bharadwaj

. Development of new equations for basal metabolic rate for adolescent student Indian population. J Postgrad Med 59(1): 25-9.

29.

Bamberga

, et al. Relationship between energy expenditure, physical activity and weight loss during CPAP treatment in obese OSA subjects, Respiratory Medicine (2015), http://dx.doi.org/10.1016/j.rmed.2015.02.010.

30.

Ryan

J.M.

, Walsh

, et al. A comparison of three accelerometry-based devices for estimating energy expenditure in adults and children with cerebral palsy. J Neuroeng Rehabil 11: 116.

31.

Drougard

, Fournel

, et al. Impact of hypothalamic reactive oxygen species in the regulation of energy metabolism and food intake. Front Neurosci 9: 56.

32.

Lupinsky

, et al. Comparison between two metabolic monitors in the measurement of resting energy expenditure and oxygen consumption in diabetic and non-diabetic ambulatory and hospitalized patients. Nutrition 31 (2015): 176-179.

33.

Guttormsen

A.B.

and Pichard

. Determining energy requirements in the ICU. Curr Opin Clin Nutr Metab Care 17(2): 171-6.

34.

Orrow

, Kinmonth

A.L.

, et al. Effectiveness of physical activity promotion based in primary care: systematic review and meta-analysis of randomised controlled trials. Bmj 344: e1389.

35.

Marcus

B.H.

, Williams

D.M.

, et al. Physical Activity Intervention Studies: What We Know and What We Need to Know: A Scientific Statement From the American Heart Association Council on Nutrition, Physical Activity, and Metabolism (Subcommittee on Physical Activity); Council on Cardiovascular Disease in the Young; and the Interdisciplinary Working Group on Quality of Care and Outcomes Research. Circulation. 2006; 114: 2739-2752, published online before print December 4 2006, doi: 10.1161/CIRCULATIONAHA.106.179683.

36.

Drougard

, Fournel

, et al. Impact of hypothalamic reactive oxygen species in the regulation of energy metabolism and food intake. Front Neurosci 9: 56.

37.

Guttormsen

A.B.

and Pichard

. Determining energy requirements in the ICU. Curr Opin Clin Nutr Metab Care 17(2): 171-6.