A healthy lifestyle has the ability not only to give you more energy and help you look and feel better, but it also has the ability to help you live longer and prevent disease, such as obesity and pressure ulcers. This is particularly important for the elderly population, as a healthier lifestyle would enable independent living to occur for a longer period of time. However, providing a direct link between increasing physical activity and positive health outcomes is a problem. The effect of leading an increasing sedentary lifestyle is also not evident straightaway. Effects of this behavior often occur over years and decades, as opposed to days or months. Therefore, there is very little willingness to change, if instant results are not seen. There is a need to provide a mechanism that is able to monitor an individual and provide a visual indication of his or her behavior. It is envisioned that the area of human digital memories is capable of providing such a system. This article explores how sedentary behavior and journey information can be collected, from different environments, so that an illustration of a user's habits can be seen and changes can occur. A successful prototype has also been developed that evaluates the applicability of the approach.
Get full access to this article
View all access options for this article.
References
1.
OwenN, SugiyamaT, EakinEE, GardinerPA, TremblayMS, SallisJF. Adults' sedentary behavior: Determinants and interventions. Am J Prev Med, 2011; 41:189–196.
2.
ClarkBK, SugiyamaT, HealyGN, SalmonJ, DunstanDW, OwenN. Validity and reliability of measures of television viewing time and other non-occupational sedentary behaviour of adults: A review. Obes Rev, 2009; 10:7–16.
3.
Office for National Statistics. Healthy life expectancy: Living longer in poor health. 2010. www.statistics.gov.uk/cci/nugget.asp?id=2159. 2011 August 24.
4.
MurphySL, XuJ, KochanekKD. Deaths: Preliminary data for 2010. Natl Vital Stat Rep, 2012; 60,(4).
5.
BiddleSJH, PearsonN, RossGM, BraithwaiteR. Tracking of sedentary behaviours of young people: A systematic review. Prev Med, 2010; 51:345–351.
6.
National Health Service. Pressure ulcers—Causes. www.nhs.uk/Conditions/Pressure-ulcers/Pages/Causes.aspx. 2011 December 10.
7.
WangT-Y, ChenS-L, HuangH-C, KuoS-H, ShiuY-J. The development of an intelligent monitoring and caution system for pressure ulcer prevention. 2011International Conference on Machine Learning and Cybernetics, Guilin, China. IEEE, 2011; 566–571.
8.
BennettG, DealeyC, PosnettJ. The cost of pressure ulcers in the UK. Age Ageing, 2004; 33:230–235.
9.
Agency for Healthcare Research and Quality. Evidence-based Practice Center Systematic Review Protocol: Pressure ulcer risk assessment and prevention: A comparative effectiveness review. Rockville, MD: Agency for Healthcare Research and Quality, 2012.
10.
International Council of Nurses, Pfizer Inc.A global survey of nurses (May 2009)Nurses in the Workplace: Expectations and Needs, 2009. www.icn.ch/images/stories/documents/news/advocacy/nurses_in_the_workplace/survey.swf. 2011 December 10.
11.
DobbinsC, MerabtiM, FergusP, Llewellyn-JonesD. Towards a framework for capturing and distributing rich interactive human digital memories. Proceedings of the 12th Annual PostGraduate Symposium on the Convergence of Telecommunications, Networking andBroadcasting (PGNet’11), Liverpool, England. Liverpool John Moores University (L JMU), 2011.
12.
DobbinsC, FergusP, MerabtiM, Llewellyn-JonesD. Monitoring and measuring sedentary behaviour with the aid of human digital memories. Proceedings of the 9th Annual IEEE Consumer Communications & Networking Conference (CCNC’12), 2nd IEEE International Workshop on Consumer eHealth Platforms, Services and Applications (CeHPSA), Las Vegas, NV. IEEE, 2012; 395–398.
13.
OuelletteJA, WoodW. Habit and intention in everyday life: The multiple processes by which past behavior predicts future behavior. Psychol Bull, 1998; 124:54–74.
14.
WeiserM. The computer for the 21st century. ACM SIGMOBILE Mobile Comput Commun Rev, 1999; 3,3:3–11.
LeeHet al.Constructing a SenseCam visual diary as a media process. Multimedia Systems, 2008; 14:341–349.
17.
DohertyAR, MoulinCJA, SmeatonAF. Automatically assisting human memory: A SenseCam browser. Memory, 2010; Sept:1–11.
18.
SellenA, FoggA, AitkenM, HodgesS, RotherC, WoodK. Do life-logging technologies support memory for the past? An experimental study using SenseCam. Conference on Human Factors in Computing Systems CHI ’07, New York. ACM, 2007; 81–90.
19.
DohertyARet al.Passively recognising human activities through lifelogging. Comput Hum Behav, 2011; 27:1948–1958.
20.
LindleySE, RandallD, SharrockW, GlancyM, SmythN, HarperR. Narrative, memory and practice: Tensions and choices in the use of a digital artefact. Proceedings of the 23rd British HCI Group Annual Conference on People and Computers: Celebrating People and Technology, Swinton, UK. British Computer Society, 2009; 1–9.
21.
KalnikaiteV, SellenA, WhittakerS, KirkD. Now let me see where I was: Understanding how lifelogs mediate memory. Proceedings of the 28th International Conference on Human Factors in Computing Systems CHI ’10, New York. ACM, 2010; 2045.
22.
KellyP, DohertyA, BerryE, HodgesS, BatterhamAM, FosterC. Can we use digital life-log images to investigate active and sedentary travel behaviour? Results from a pilot study. Int J Behav Nutr Phys Act, 2011; 8:44.
23.
ByrneD, JonesGJF. Towards computational autobiographical narratives through human digital memories. Proceedings of the 2nd ACM International Workshop on Story Representation, Mechanism and Context SRMC ’08, New York. ACM, 2008; 9.
24.
KikhiaB, HallbergJ, BengtssonJE, SävenstedtS, SynnesK. Building digital life stories for memory support. Int J Comput Healthc, 2010; 1:161–176.
25.
PantelopoulosA, BourbakisNG. A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Trans Systems Man Cybernet C, 2010; 40:1–12.
26.
TaraldsenKet al.Evaluation of a body-worn sensor system to measure physical activity in older people with impaired function. Phys Ther, 2011; 91:277–285.
27.
LordS, ChastinSFM, McInnesL, LittleL, BriggsP, RochesterL. Exploring patterns of daily physical and sedentary behaviour in community-dwelling older adults. Age Ageing, 2011; 40:205–210.
28.
CavalheriVet al.Energy expenditure during daily activities as measured by two motion sensors in patients with COPD. Respir Med, 2011; 105:922–929.
29.
QuonBSet al.Monitoring habitual physical activity in cystic fibrosis patients using pedometers. Am J Respir Crit Care Med, 2011; 183:A1121.
30.
PittaF, TroostersT, ProbstVS, SpruitMA, DecramerM, GosselinkR. Quantifying physical activity in daily life with questionnaires and motion sensors in COPD. Eur Respir J, 2006; 27:1040–1055.
31.
AoS-Iet al.Review of daily physical activity monitoring system based on single triaxial accelerometer and portable data measurement unit. Machine Learn Syst Eng, 2010; 68:569–580.
32.
BassettDR. Validity and reliability issues in objective monitoring of physical activity. Res Q Exerc Sport, 2000; 71:S30–S36.
33.
DwyerTJ, AlisonJA, McKeoughAJ, ElkinsMR, ByePTP. Evaluation of the SenseWear activity monitor during exercise in cystic fibrosis and in health. Respir Med, 2009; 103:1511–1517.
34.
AlmeidaGJM, WaskoMCM, JeongK, MooreCG, PivaSR. Physical activity measured by the SenseWear armband in women with rheumatoid arthritis. Phys Ther, 2011; 91:1367–1376.
35.
DobbinsC, MerabtiM, FergusP, Llewellyn-JonesD. Capturing and sharing human digital memories with the aid of ubiquitous peer-to-peer mobile services. Proceedings of the 10th Annual IEEE International Conference on Pervasive Computing and Communications (PerCom’12), 9th IEEE International Workshop on Managing Ubiquitous Communications and Services (MUCS), Lugano, Switzerland. IEEE, 2012; 64–69.
36.
FergusP, AttwoodA, DobbinsC, StrattonG, HussainA, Al-JumeilyD, RandlesM. Monitoring and measuring physical activity and sedentary behaviour. Int J Healthc Technol Manage, 2013(in press).
37.
DobbinsC, MerabtiM, FergusP, Llewellyn-JonesD. Creating human digital memories for a richer recall of life experiences. Proceedings of the 10th IEEE International Conference on Networking, Sensing and Control (ICNSC 13), Paris, France. IEEE, 2013.
38.
Della ValleE, CeriS, van HarmelenF, FenselD. It's a streaming world! Reasoning upon rapidly changing information. IEEE Intell Syst, 2009; 24:83–89.
39.
BarbieriDF, BragaD, CeriS, Della ValleE, GrossniklausM. C-SPARQL: A continuous query language for RDF data streams. Int J Semantic Comput, 2010; 4:3–25.
40.
CliffordGD, AzuajeF, McSharryPE. Advanced methods and tools for ECG data analysis. London: Artech House Publishers, 2006.
41.
DropBox.com. What is Dropbox ? The story of Dropbox how Dropbox works features. www.dropbox.com/static/docs/DropboxFactSheet.pdf. 2012 February 12.
42.
W3C. SPARQL query language for RDF. 2008. www.w3.org/TR/rdf-sparql-query/. 2011 December 10.
43.
semsol/Benjamin Nowack. Easy RDF and SPARQL for LAMP systems. 2012. https://github.com/semsol/arc2/wiki. 2012 May 7.
