Volume 30 Issue 6
Jun.  2017
Turn off MathJax
Article Contents

ZHANG Qun, YANG Xi, LIU Dan, ZHAO Wen Hua. Measurement and Assessment of Physical Activity by Information and Communication Technology[J]. Biomedical and Environmental Sciences, 2017, 30(6): 465-472. doi: 10.3967/bes2017.062
Citation: ZHANG Qun, YANG Xi, LIU Dan, ZHAO Wen Hua. Measurement and Assessment of Physical Activity by Information and Communication Technology[J]. Biomedical and Environmental Sciences, 2017, 30(6): 465-472. doi: 10.3967/bes2017.062

Measurement and Assessment of Physical Activity by Information and Communication Technology

doi: 10.3967/bes2017.062
More Information
  • Author Bio:

    ZHANG Qun, male, born in 1973, PhD, majoring in medical informatics, E-mail: zhangqun@chinacdc.cn

  • Corresponding author: ZHAO Wen Hua, Tel: 86-10-67791292, E-mail: zhaowh@chinacdc.cn
  • Received Date: 2017-05-01
  • Accepted Date: 2017-05-31
  • This study provides explorative insights into the information and communication technology (ICT) for promoting the physical activity level. ICT has provided innovative ideas and perspectives for PA measurement, assessment, evaluation and health intervention. ICT that aims to increase exercise for the entire population should be of a well-oriented and easy-to-use design with the options of tailored and personalized feedback, coaching, and ranking and supporting; it should be capable of setting goals and working with a schedule and be accompanied by a website to provide overviews of the users' exercise results and progress.
  • 加载中
  • [1] WHO. Global Strategy on Diet, Physical Activity and Health L. 2004. 05 http://www.who.int/dietphysicalactivity/en/. [2017-05-25]
    [2] Bauman A, Merom D, Bull FC, et al. Updating the Evidence for Physical Activity: Summative Reviews of the Epidemiological Evidence, Prevalence, and Interventions to Promote 'Active Aging'. Gerontologist, 2016, 56, S268-80. doi:  10.1093/geront/gnw031
    [3] Cook I, Alberts M, Lambert EV. Relationship between adiposity and pedometer-assessed ambulatory activity in adult, rural African women. Int J Obes, 2008, 32; 1327-30. doi:  10.1038/ijo.2008.26
    [4] Warburton DE, Charlesworth S, Ivey A, et al. A systematic review of the evidence for Canada's Physical Activity Guidelines for Adults. Int J Behav Nutr Phys Act, 2010, 7, 39. doi:  10.1186/1479-5868-7-39
    [5] WHO. Prevalence of insufficient physical activity. 2010 http://www.who.int/gho/ncd/risk_factors/physical_activity/en/. [2017-05-25]
    [6] UNION I T. ICT facts and figures 2016. 2016 http://www.itu.int/en/ITU-D/Statistics/Documents/facts/ICTFactsFigures2016.pdf. [2017-05-25]
    [7] Sallis JF, Saelens BE. Assessment of physical activity by self-report: status, limitations, and future directions. Res Q Exerc Sport, 2000, 71, S1-14. https://www.ncbi.nlm.nih.gov/pubmed/?term=Assessment+of+physical+activity+by+self-report%3A+status%2C+limitations%2C+and+future+directions.
    [8] Riddoch CJ, Mattocks C, Deere K, et al. Objective measurement of levels and patterns of physical activity. Arch Dis Child, 2007, 92:963-9. doi:  10.1136/adc.2006.112136
    [9] Bergouignan A, Momken I, Lefai E, et al. Activity energy expenditure is a major determinant of dietary fat oxidation and trafficking, but the deleterious effect of detraining is more marked than the beneficial effect of training at current recommendations. Am J Clin Nutr, 2013, 98:648-58. doi:  10.3945/ajcn.112.057075
    [10] Mensah K, Maire A, Oppert JM, et al. Assessment of sedentary behaviors and transport-related activities by questionnaire: a validation study. BMC Public Health, 2016, 16, 753. doi:  10.1186/s12889-016-3412-3
    [11] Chen KY, Janz KF, Zhu W, et al. Redefining the roles of sensors in objective physical activity monitoring. Med Sci Sports Exerc, 2012, 44, S13-23. doi:  10.1249/MSS.0b013e3182399bc8
    [12] Keytel LR, Goedecke JH, Noakes TD, et al. Prediction of energy expenditure from heart rate monitoring during submaximal exercise. J Sports Sci, 2005, 23:289-97. doi:  10.1080/02640410470001730089
    [13] Westerterp KR. Assessment of physical activity: a critical appraisal. Eur J Appl Physiol, 2009, 105: 823-8. http://cat.inist.fr/?aModele=afficheN&cpsidt=21266848
    [14] Husted HM, Llewellyn TL. The Accuracy of Pedometers in Measuring Walking Steps on a Treadmill in College Students. Int J Exerc Sci, 2017, 10:146-53. http://digitalcommons.wku.edu/cgi/viewcontent.cgi?article=1904&context=ijes
    [15] Kelly P, Fitzsimons C, Baker G. Should we reframe how we think about physical activity and sedentary behaviour measurement? Validity and reliability reconsidered. Int J Behav Nutr Phys Act, 2016, 13, 32. doi:  10.1186/s12966-016-0351-4
    [16] Slootmaker SM, Schuit AJ, Chinapaw MJM, et al. Disagreement in physical activity assessed by accelerometer and self-report in subgroups of age, gender, education and weight status. Int J Beha Nutr Phys Act, 2009, 6, 17. doi:  10.1186/1479-5868-6-17
    [17] Case MA, Burwick HA, Volpp KG, et al. Accuracy of smartphone applications and wearable devices for tracking physical activity data. JAMA, 2015, 313:625-6. doi:  10.1001/jama.2014.17841
    [18] Colpani V, Spritzer PM, Lodi AP, et al. Physical activity in climacteric women: comparison between self-reporting and pedometer. Rev Saúde Pública, 2014, 48:258-65. doi:  10.1590/S0034-8910.2014048004765
    [19] Sweet SN, Fortier MS. Improving Physical Activity and Dietary Behaviours with Single or Multiple Health Behaviour Interventions? A Synthesis of Meta-Analyses and Reviews. Int J Environ Res Public Health, 2010, 7:1720-43. doi:  10.3390/ijerph7041720
    [20] King AC, Haskell WL, Taylor CB, et al. Group-vs. home-based exercise training in healthy older men and women. JAMA, 1991, 266:1535-42. doi:  10.1001/jama.1991.03470110081037
    [21] Noell J, Glasgow RE. Interactive technology applications for behavioral counseling1: Issues and opportunities for health care settings. Am J Prev Med, 1999, 17:269-74. doi:  10.1016/S0749-3797(99)00093-8
    [22] Patrick K. Information technology and the future of preventive medicine. Am J Prev Med, 2000, 19:132-5. doi:  10.1016/S0749-3797(00)00189-6
    [23] Marcus B, Owen N, Forsyth L, et al. Physical activity interventions using mass media, print media, and information technology. Am J Prev Med, 1998, 15:362-78. doi:  10.1016/S0749-3797(98)00079-8
    [24] Writing Group for the Activity Counseling Trial Research Group. Effects of physical activity counseling in primary care: The activity counseling trial: a randomized controlled trial. JAMA, 2001, 286:677-87. doi:  10.1001/jama.286.6.677
    [25] Marcus BH, Lewis BA, Williams DM, et al. Step into Motion: A randomized trial examining the relative efficacy of Internet vs. print-based physical activity interventions. Contemp Clin Trials, 2007, 28:737-47. doi:  10.1016/j.cct.2007.04.003
    [26] Cushing CC, Steele RG. A Meta-Analytic Review of eHealth Interventions for Pediatric Health Promoting and Maintaining Behaviors. J Pediatr Psychol, 2010, 35:937-49. doi:  10.1093/jpepsy/jsq023
    [27] Vandelanotte C, M LLER A M, SHORT C E, et al. Past, Present, and Future of eHealth and mHealth Research to Improve Physical Activity and Dietary Behaviors. J Nutr Educ Behav, 2016, 48:219-28, e1. doi:  10.1016/j.jneb.2015.12.006
    [28] Rehman H, Kamal AK, Sayani S, et al. Using Mobile Health (mHealth) Technology in the Management of Diabetes Mellitus, Physical Inactivity, and Smoking. Curr Atheroscler Rep, 2017, 19, 16. doi:  10.1007/s11883-017-0650-5
    [29] Davies CA, Spence JC, Vandelanotte C, et al. Meta-analysis of internet-delivered interventions to increase physical activity levels. Int J Behav Nutr PhysAct, 2012, 9, 52. doi:  10.1186/1479-5868-9-52
    [30] Webb TL, Joseph J, Yardley L, et al. Using the Internet to Promote Health Behavior Change: A Systematic Review and Meta-analysis of the Impact of Theoretical Basis, Use of Behavior Change Techniques, and Mode of Delivery on Efficacy. J Med Internet Res, 2010, 12, e4. doi:  10.2196/jmir.1376
    [31] Conn VS, Hafdahl AR, Mehr DR. Interventions to Increase Physical Activity among Healthy Adults: Meta-Analysis of Outcomes. Am j Public Health, 2011, 101:751-8. doi:  10.2105/AJPH.2010.194381
    [32] Brouwer W, Kroeze W, Crutzen R, et al. Which Intervention Characteristics are Related to More Exposure to Internet-Delivered Healthy Lifestyle Promotion Interventions? A Systematic Review. J Med Internet Res, 2011, 13, e2. doi:  10.2196/jmir.1639
    [33] S. FOX M D. Pew Research Center's Internet & American Life Project, Washington DC. Mobile health 2012. https://www.benton.org/taxonomy/term/2126
    [34] Bender JL, Yue RY, To MJ, et al. A Lot of Action, But Not in the Right Direction: Systematic Review and Content Analysis of Smartphone Applications for the Prevention, Detection, and Management of Cancer. J Med Internet Res, 2013, 15, e287. doi:  10.2196/jmir.2661
    [35] Conroy DE, Yang CH, Maher JP. Behavior Change Techniques in Top-Ranked Mobile Apps for Physical Activity. Am J Prev Med, 2014, 46:649-52. doi:  10.1016/j.amepre.2014.01.010
    [36] Coughlin SS, Whitehead M, Sheats JQ, et al. A Review of Smartphone Applications for Promoting Physical Activity. Jacobs J Community Med, 2016, 2, 021. http://www.ncbi.nlm.nih.gov/pmc/articles/pmid/27034992/
    [37] Bort-Roig J, Gilson ND, Puig-Ribera A, et al. Measuring and Influencing Physical Activity with Smartphone Technology: A Systematic Review. Sports Med, 2014, 44:671-86. doi:  10.1007/s40279-014-0142-5
    [38] Middelweerd A, van der Laan DM, van Stralen MM, et al. What features do Dutch university students prefer in a smartphone application for promotion of physical activity? A qualitative approach. Int J Behav Nutr Phys Act, 2015, 12, 31. doi:  10.1186/s12966-015-0189-1
    [39] Glynn LG, Hayes PS, Casey M, et al. Effectiveness of a smartphone application to promote physical activity in primary care: the SMART MOVE randomised controlled trial. Br J Gen Pract, 2014, 64, e384-e91. doi:  10.3399/bjgp14X680461
    [40] Kirwan M, Duncan MJ, Vandelanotte C, et al. Using Smartphone Technology to Monitor Physical Activity in the 10, 000 Steps Program: A Matched Case–Control Trial. J Med Internet Res, 2012, 14, e55. doi:  10.2196/jmir.1950
    [41] Ehlers DK, Huberty JL. Middle-Aged Women's Preferred Theory-Based Features in Mobile Physical Activity Applications. J Phys Act Health, 2014, 11:1379-85. doi:  10.1123/jpah.2012-0435
    [42] Rabin C, Bock B. Desired Features of Smartphone Applications Promoting Physical Activity. Telemed J E Health, 2011, 17:801-3. doi:  10.1089/tmj.2011.0055
    [43] King AC, Hekler EB, Castro CM, et al. Exercise advice by humans versus computers: Maintenance effects at 18 months. Health Psychol, 2014, 33:192-6. doi:  10.1037/a0030646
    [44] Lyons EJ, Lewis ZH, Mayrsohn BG, et al. Behavior Change Techniques Implemented in Electronic Lifestyle Activity Monitors: A Systematic Content Analysis. J Med Internet Res, 2014, 16, e192. doi:  10.2196/jmir.3469
    [45] Webb RC, Bonifas AP, Behnaz A, et al. Ultrathin conformal devices for precise and continuous thermal characterization of human skin. Nat Mater, 2013, 12:938-44. doi:  10.1038/nmat3755
    [46] Trung TQ, Lee NE. Flexible and Stretchable Physical Sensor Integrated Platforms for Wearable Human-Activity Monitoringand Personal Healthcare. Adv Mater, 2016, 28:4338-72. doi:  10.1002/adma.v28.22
    [47] Pasinetti GM, Ksiezak-Reding H, Santa-Maria I, et al. Development of a grape seed polyphenolic extract with anti-oligomeric activity as a novel treatment in progressive supranuclear palsy and other tauopathies. J Neurochem, 2010, 114:1557-68. doi:  10.1111/j.1471-4159.2010.06875.x
    [48] Crowley O, Pugliese L, Kachnowski S. The Impact of Wearable Device Enabled Health Initiative on Physical Activity and Sleep. Cureus, 2016, 8, e825. http://assets.cureus.com/uploads/original_article/pdf/4832/1476193389-20161011-5148-1bjv12s.pdf
    [49] Bravata DM, Smith-Spangler C, Sundaram V, et al. Using pedometers to increase physical activity and improve health: A systematic review. JAMA, 2007, 298:2296-304. doi:  10.1001/jama.298.19.2296
    [50] Pekmezi D, Dunsiger S, Gaskins R, et al. Feasibility and acceptability of using pedometers as an intervention tool for Latin. J Phys Act Health, 2013, 10:451-7. doi:  10.1123/jpah.10.3.451
    [51] Mendoza L, Horta P, Espinoza J, et al. Pedometers to enhance physical activity in COPD: a randomised controlled trial. Eur Respir J, 2015, 45:347-54. doi:  10.1183/09031936.00084514
    [52] Whitney R, Bhan H, Persadie N, et al. Feasibility of Pedometer Use to Assess Physical Activity and Its Relationship With Quality of Life in Children With Epilepsy: A Pilot Study. Pediatr Neurol, 2013, 49:370-3. doi:  10.1016/j.pediatrneurol.2013.06.002
    [53] Rollo ME, Aguiar EJ, Williams RL, et al. eHealth technologies to support nutrition and physical activity behaviors in diabetes self-management. Diabetes Metab Syndr Obes, 2016, 9:381-90. doi:  10.2147/DMSO
    [54] Doherty AR, Kelly P, Kerr J, et al. Using wearable cameras to categorise type and context of accelerometer-identified episodes of physical activity. Int J Behav Nutr Phys Act, 2013, 10, 22. doi:  10.1186/1479-5868-10-22
    [55] Årsand E, Muzny M, Bradway M, et al. Performance of the First Combined Smartwatch and Smartphone Diabetes Diary Application Study. J Diabetes Sci Technol, 2015, 9:556-63. doi:  10.1177/1932296814567708
    [56] Deutsch JE, Myslinski MJ, Kafri M, et al. Feasibility of Virtual Reality Augmented Cycling for Health Promotion of People Post-Stroke. J Neurol Phys Ther, 2013, 37:118-24. doi:  10.1097/NPT.0b013e3182a0a078
    [57] Lin CY, Chang YM. Interactive augmented reality using Scratch 2.0 to improve physical activities for children with developmental disabilities. Res Dev Disabil, 2015, 37:1-8. doi:  10.1016/j.ridd.2014.10.016
    [58] Miller KJ, Adair BS, Pearce AJ, et al. Effectiveness and feasibility of virtual reality and gaming system use at home by older adults for enabling physical activity to improve health-related domains: a systematic review. Age Ageing, 2014, 43:188-95. doi:  10.1093/ageing/aft194
    [59] Hakala S, Rintala A, Immonen J, et al. Effectiveness of technology-based distance interventions promoting physical activity: Systematic review, meta-analysis and meta-regression. Eur J Phys Rehabil Med, 2017, 49:97-105. doi:  10.2340/16501977-2195
    [60] Bravata DM, Smith-Spangler C, Sundaram V, et al. Using pedometers to increase physical activity and improve health: a systematic review. JAMA, 2007, 298:2296-304. doi:  10.1001/jama.298.19.2296
    [61] Coughlin SS, Whitehead M, Sheats JQ, et al. Smartphone Applications for Promoting Healthy Diet and Nutrition: A Literature Review. Jacobs J Food Nutr, 2015, 2, 021. http://europepmc.org/abstract/MED/26819969
    [62] Flores Mateo G, Granado-Font E. Mobile Phone Apps to Promote Weight Loss and Increase Physical Activity: A Systematic Review and Meta-Analysis. J Med Internet Res, 2015, 17, e253. doi:  10.2196/jmir.4836
    [63] Richards J, Thorogood M, Hillsdon M, et al. Face-to-face versus remote and web 2. 0 interventions for promoting physical activity. Cochrane Database Syst Rev, 2013, 30, CD010393. http://www.cochrane.org/CD010393/VASC_face-to-face-versus-remote-and-web-2.0-interventions-for-promoting-physical-activity
  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Tables(1)

Article Metrics

Article views(675) PDF downloads(11) Cited by()

Proportional views
Related

Measurement and Assessment of Physical Activity by Information and Communication Technology

doi: 10.3967/bes2017.062
  • Author Bio:

  • Corresponding author: ZHAO Wen Hua, Tel: 86-10-67791292, E-mail: zhaowh@chinacdc.cn

Abstract: This study provides explorative insights into the information and communication technology (ICT) for promoting the physical activity level. ICT has provided innovative ideas and perspectives for PA measurement, assessment, evaluation and health intervention. ICT that aims to increase exercise for the entire population should be of a well-oriented and easy-to-use design with the options of tailored and personalized feedback, coaching, and ranking and supporting; it should be capable of setting goals and working with a schedule and be accompanied by a website to provide overviews of the users' exercise results and progress.

ZHANG Qun, YANG Xi, LIU Dan, ZHAO Wen Hua. Measurement and Assessment of Physical Activity by Information and Communication Technology[J]. Biomedical and Environmental Sciences, 2017, 30(6): 465-472. doi: 10.3967/bes2017.062
Citation: ZHANG Qun, YANG Xi, LIU Dan, ZHAO Wen Hua. Measurement and Assessment of Physical Activity by Information and Communication Technology[J]. Biomedical and Environmental Sciences, 2017, 30(6): 465-472. doi: 10.3967/bes2017.062
  • Sedentary behavior and physical activity (PA) are important issues of public health. It is well known that PA reduces the risk for cardiovascular diseases and diabetes and has substantial benefits for several conditions, including those that are associated with obesity, and that a passive lifestyle subsequently decreases the quality of life by increasing hypertension, obesity, and type 2 diabetes mellitus[1-2].

    An overall evaluation of the evidence suggests that important health benefits accrue in most children and youths who daily accumulate 60 or more minutes of moderate to vigorous PA (MVPA). For adults and elderly individuals, risk reduction routinely occurs at levels of 150 min of at least moderate to intensity PA per week[3-4]. There is strong scientific evidence that regular PA extensively benefits the health in adults aged 18-64 years and in older adults aged ≥ 65 years. In some cases, the evidence of health benefits is the strongest in older adults because the outcomes related to inactivity are more common in them.

    Globally, in 2010, approximately 23% of the adults aged > 18 years and more than 80% of the school-going adolescents aged 11-17 years had insufficient PA[5]. Therefore, it is important to increase the engagement in PA with the most effective methods to achieve this goal. The objective measurement and evaluation of the daily PA level is of great significance for the development of health interventions.

    With rapid growth in Internet accessibility and improvements in technology, a growing population of research has employed information and communication technology (ICT), such as mobile health and wearable devices, to promote PA for addressing the increasing rates of obesity and chronic diseases. ICT has no universal definition, and it is related to technologies facilitating the transfer of information and various types of electronically-mediated communications. At the end of 2016, seven billion people (95% of the global population) were living in an area that had mobile-cellular network coverage, and it was estimated that globally there were approximately 3.6 billion mobile-broadband subscriptions[6]. So it is not surprising that research on the use of ICT intervention has escalated in the past decade.

    Effective measurement is the basis and key for conducting relevant research. Measurement and assessment have been transformed from traditional methods to ICT, and precision and accuracy have also improved in practice. This article attempts to analyze various current measurement instruments and PA intervention researches to explore efficient methods among them to accordingly make recommendations for different populations to promote their PA levels and to focus on promoting further research in the field of public health.

  • Measurement methods of PA can be divided into subjective and objective evaluation methods. Subjective methods include diaries, logs, and questionnaires; e.g., international physical activity questionnaires. Subjective measurement, as part of a self-report survey, is the most extensively used. The most common method for assessing the PA level is the questionnaire, but as a subjective measurement method, several kinds of bias often occur[7], such as the recall bias to quantify PA for children and elderly individuals. Therefore, objective methods, such as accelerometers, provide a considerably greater precision of measurement[8]. Thus, seeking efficient methods to quantify PA has become a necessity.

    Objective methods include observations, doubly labeled water (DLW), motion sensors (e.g., pedometers and accelerometers), and heart rate (HR) monitors. DLW is the most effective and reliable method to measure the total energy expenditure of free-living conditions, which is the gold standard[9-10]. HR monitors are based on physiological sensors which is useful as a physiological variable because it linearly and proportionately increases with exercise intensity and subsequently with oxygen uptake[11]. Some studies have concluded that the energy expenditure can be predicted from HR after adjusting for age, sex, and body mass[12], because of the low correlation between energy expenditure and HR in light-intensity PA.

    Movement sensors (e.g., pedometers and accelerometers) report their outcomes in activity counts per unit of time, which are the product of the frequency and intensity of movement. Therefore, movement sensors not only can provide temporal information regarding specific variables, such as the total amount, frequency, and duration of PA[13], but also monitor the accumulation of MVPA and/or sedentary behavior with the development of population-specific cut-off points for activity counts per minute. Early pedometers were mechanical with a 2D-vibration sensor; currently, 3D-electronic pedometers have replaced them and have become a popular way for people to keep track of their recommended 10, 000 daily steps; these are worn around the waist or on the wrist and include Omron™, Walking FIT™[14]. Among them, wrist pedometers, such as the Basis B1 health watch and Fit Flex, are popular and have been used as devices for monitoring PA. They are effective in detecting the total movement over a given timeframe, but may be less effective in distinguishing the types of PA. In other words, they might be effective in detecting the duration of walking or sitting, but are unable to detect cycling or the difference between sitting and watching TV/working[15]. However, cycling, which is classified as a MVPA, is poorly measured by accelerometers[16].

    The growing affordable, multi-sensor technologies, including the combination of physiological, contextual, and motion sensors, seem to have a great potential in recording PA, sleeping time, HR, and other daily activities. Recently, the use of smartphones with applications (apps) or a WeChat tool has also become popular. The users need not buy any pedometer device because a smartphone directly provides the pedometer function. A comparative study of accuracy between pedometers, wearable devices, and smartphone apps reported the relative differences in mean steps during 500-and 1, 500-step trails ranging from -0.3% to 1.0%, -22.7% to 1.5%, and -6.7% to 6.2%. Compared with actual step counts, data from wearable devices differed more than from smartphone apps[17]. Moreover, an assessment using a questionnaire overestimated PA strata than that using an objective method, such as using a pedometer[18]. Gradually, the development of ICT has provided new measures for assessing PA. The researchers have begun to try ICT methods, which are gradually being used in epidemiologic research to validate traditional methods, such as questionnaires (Table 1).

    Common Types 2D Pedometers 3D Pedometers Sport Band Smartphone-pedometers Apps Watch-pedometers
    Movement Mechanical-pendulum sensor 3D-acceleration electronic sensor Acceleration sensor + Physiological sensor + GPS Acceleration sensor Acceleration sensor + GPS 3D-acceleration electronic sensor + physiological sensor + GPS
    Data storage None Yes Yes Yes Yes Yes
    Calories, number of steps, distance Yes Yes Yes Yes Yes Yes
    Speed None None Yes Yes Yes Yes
    Wearable mode Must be vertical with a belt clip Free or use a band fixed on the wrist Wrist Free - Wrist
    Brand Manpo-meter from Japan Walking FIT, Omron Fitbit, Mi band WeChat movement, QQ movement Nike + running, Codoon, Langdong Apple watch
    Other functions None Timing Heart rate, sleep time, cycling - Cycling, climbing (partial have) Heart rate, sleep time

    Table 1.  Common Types of Measuring Physical Activity

  • To address the growing rates of chronic disease and to affect the global burden of non-communicable disease, most interventions targeting behavior change that aim to improve PA have been developed[19]. Traditionally, face-to-face interventions (i.e., a structured PA program and counseling) are considered to be the optimal means for changing health-related behavior in group-based conditions because of their effectiveness in promoting fitness and exercise duration[20]. However, time constraints, cost limitations, geographic restric-tions, and competing demands make inter-personal contacts difficult, and hence, such interventions are not effective in a large population[21-22].

    Mass media (i.e., TV, radio, and advertisements) has been considerably used to guide and assist behavioral changes because it has the potential to assess large number of individuals, while the generic content and feedback was characterized by a lack of personalization, which was mentioned as the reason why they were successful only in raising awareness but not in increasing the PA level[23]. Interventions through mass mailing[24] and print media[25] did not seem to have a significant impact on health behavior.

    Nowadays, the rapid development of electronic technology is resulting in interactive and computerized health communications for promoting PA and improving health behavior. Numerous inspections have revealed that ICT intervention through a computer or an independent electronic device incorporating behavioral principles has contributed to large effect sizes on health behavior and a PA increase, while solely education intervention failed to produce the significant effects[26]. This paper fills a gap in the literature by empirically examining the impact of various types of ICT interventions among various PA promotions and their associated outcomes, including both behavioral process variables and health outcomes.

    Mobile health (mHealth) or electronic health have become nearly ubiquitous, including the Internet, closed computer systems, mobile devices, and smartphone apps, which are referred to as the promising means of mobile computing and communication technologies that seek to either improve the understanding of health information or to use technology as a surrogate for health service delivery[27]. Utilizing mHealth leverage technology to improve the level of PA has been refined as an efficacious methodology[28].

  • The rapid growth in Internet coverage and improvements in technology has provided millions of people with the accessibility to vast amounts of health information. Internet and computer-based intervention have the potential benefits in improving the effectiveness of PA, the most important of which may be the cost savings associated with a lesser time consumption, and the computer is likely to increase the willingness to disclose sensitive information and to improve the consistency of exercise and acquisition of salient information. Nevertheless, machines lack flexibility and are unlikely to receive appropriate responses.

    Meta-analysis of Internet-delivered interventions have supported the public health impact of producing small but significant increases in PA across a population that has the potential for obvious positive changes to reach large populations, especially to sedentary individuals[29]. The effectiveness of Internet-based PA promotion is associated with a more extensive use of planned behavior theory, an inclusion of more behavior change techniques, and an application of participant interaction methods[30]. Furthermore, enhancing website engagement was an important factor in relation to potential improvements, and a clear linear association was identified between the intensity of intervention and the healthy behavioral changes[31]. Intervention features, such as structured educational materials, tailored content, and interactive elements, were identified to enhance the effectiveness of intervention to produce a greater PA change, while email reminders and updated content tended to be not necessarily included in an attempt to optimize a behavior change[32].

  • Rapid developments in mobile phones have encouraged the use of smartphone apps in health promotion. Approximately 31% of the smartphone users have used their phone to search for health information in 2012 compared with 17% in 2015, and 38% of health app users have downloaded an app for PA[33]. A smartphone not only combines the voice and text messaging functions but also provides powerful computing technology that can receive Internet access, wireless connectivity, sensing with other devices, and support of third-party applications, all which have opened up a new era for healthcare[34]. PA-related apps are available from major smartphone providers, such as iPhone, Android, and Nokia. Hundreds of exercise-focused apps have the potential to enhance the efforts to conveniently promote behavior change and provide real-time support at a low cost. The range of app interventions involving goal-setting, instruction, coaching, modeling, motivating, tailored feedback and social support towards exercise performance were identified as the most useful strategies to promote PA[35].

    Several reviews on app interventions for promoting PA revealed that smartphone apps can be efficacious, although the magnitude of the intervention affect is limited. As for a different frequency, intensity, and duration of PA, apps that automatically track the taken steps and the progress to PA goals seemed to be preferred at various ages and genders, and they are user-friendly and sufficiently flexible for use[36]. A smartphone-based assessment of PA was revealed to have an average-to-excellent level of accuracy for various types of behavior[37].

    College and university students preferred a PA app for detailed information on their progress that enable competition with friends by ranking or earning rewards in a private community, while they are not willing to share their regular PA accomplishments through social media[38]. PA app interventions commonly used as supplementary tools indicated a significant increase in the daily step activity[39], and they enhanced engagement and increased the levels of exercise in addition to website-delivered intervention[40]. Self-regulation features may be the most beneficial in a PA app for middle-aged women (mean age, 40.7 years; SD, 10.3 years) with opportunities to interact with others, and game-like activities seem to be attractive[41]. Features that target self-regulatory principles and integrate a music feature could successfully increase the overall MVPA in young and aging adults[42-43].

  • The escalating field of healthcare information technology has developed a wide range of portable devices allowing comprehensive monitoring and regulation of individual movements. PA assessment method using an on-body sensing system, such as an external accelerometer, not only measures the steps taken and hours of sleep attained and keeps track of activity data but also detects temperature, HR, pressure, and strain. The latest wearable devices enable self-monitoring of daily or long-term goals and provide feedback on PA performance[44]. Physical sensors that measure and quantify electro-exercises have currently been of interest because of their unique characteristics, such as ultra-thinness, low modulus, light weight, high flexibility, and stretching ability[45]. They exploit the interfacing to the skin or to the organs and facilitate a low-cost wearable unobtrusive solution with electrical signals continuously generated by human activities, which provide a new opportunity for PA monitoring and promotion[46]. It has been estimated that by 2018, more than 13 million wearable devices will be integrated into wellness programs, and already employee wellness plans with the use of wearable devices are becoming increasingly popular to promote PA[47].

    In fact, recent systematic reviews have demonstrated that the use of wearable activity monitoring devices, even the simplest one such as pedometers, is associated with significant increases in PA and reductions in the in-body mass index, blood pressure, disease burden, and healthcare costs[48-49]. Pedometers have been found to be the most sensitive to walking behavior, which is simple in output, literacy-friendly, and time-wise understandable to end-users[50]. Specifically, pedometers are both a reliable and a valid tool for encouraging the self-monitoring of PA and raising the PA level in risk groups[51], children, and adolescents[52]. Wearable cameras, consumer-grade accelerometers, and smart-watches are emerging as novel ways to impact an increasing PA[53]. Wearable cameras offer a perfect objective method available to capture and categorize the social and environmental context of activity behavior[54], while smart-watches have been investigated to provide immediate feedback, reminders, and alerts in supporting exercise[55].

  • Virtual reality (VR) is the digital creation of a complete virtual environment, which enables the merging of virtual objects with real objects and lowers the barrier to entry for individuals engaging in virtual content. VR provides opportunities for repetitive, contextual practice and feedback consistent with the conditions for successful activity acquisition, so that the use of VR has increasingly attracted public interest for enhancing PA engagement. In contrast to VR, augmented reality (AR) inhibits the personal experience of reality through the superimposition of virtual elements on the current physical environment.

    Existing evidence has supported the feasibility of a VR system promoting PA to address activity limitation, impairment, and weak participation in healthy individuals, elderly people and for rehabilitation in clinical populations[56]. Popular, commercially available gaming technologies have been commonly employed to encourage PA, which indicated a significant and positive effect on the PA of children[57]. The effectiveness of an AR feature was systematically reviewed in patients with diabetes to reveal that exercise involvement, insulin, blood glucose levels, and health quality were significantly improved[58].

  • On account that physical inactivity imposes a heavy social and financial burden, it is essential to find innovative ways to promote PA at both individual and societal levels. Digitalization has given rise to the idea that PA could be promoted, among other means, through the use of technology. This has led to an increased interest in the potential of technological devices and applications as a component of PA interventions[59]. The technology that has been used in interventions to promote PA has included a broad range of software applications and services. Examples of such remote technologies are smartphones, computers, tablet computers, activity monitors, and pedometers.

    Using ICT on PA intervention is an important method to improve human health, which has provided innovative ideas and perspectives for PA promotion. First of all, ICT, as the research of information acquisition, processing, and transmission, has a great influence on public health information accessibility, dissemination, and application. Secondly, ICT improve the techniques of personal information collection and analyses, and enhance the capacity and specificity of physician expertise, and then increase the quality of individual chronic disease risk assessment. Thirdly, ICT has a profound influence on the improvement of the health information literacy, which can formulate the comprehensive understanding and raise the self-management efficacy, and in the meantime reduce the burden of medical expenses. Furthermore, the innovative ICT can measure common health indicators and PA features objectively, identify human health subtle changes accurately, promote fitness and exercise involvement, and illustrate to produce healthy behavior autonomously.

    Evidence on the effectiveness of using technology to promote PA is conflicting. Pedometers[60] and other Internet-based interventions have been found to be more promising than placebo or minimal treatments. Smartphone apps may be effective in increasing PA[61], although a systematic review with meta-analysis demonstrated the effect to be non-significant[62]. The present review demonstrates consistent evidence supporting the efficacy of improving psychosocial variables through ICT-based interventions (e.g., self-efficacy). For behavioral variables (the PA level), the evidence was less consistent. Unfortunately, there is insufficient information explaining the underlying mechanisms for change because several of the included studies have an incomplete theoretical foundation. There is insufficient information on the relative effectiveness of Internet vs. traditional interventions (e.g., a face-to-face method)[63]. Overall, it is important to obtain statistically relevant, up-to-date, and both scientifically and practically important information on the effect of ICT in promoting PA. Therefore, gaining information on the effect of technology in increasing PA requires sufficiently comparative studies.

    Research using various PA devices, such as activity monitors or smartphone apps, has increased together with rapid advances in technology. Therefore, data need to be constantly updated to enable a comparison of the effectiveness of various features between the products. Future studies should compare the effectiveness of various technologies according to their features, such as interactivity. Another important factor to consider is the cost-effectiveness, which has not generally been investigated in the domain of technology and PA.

    However, while valid information on the effect of technology requires studies with comparative frameworks, it is also important to examine whether a specific technology is more effective or acceptable in population and individual health intervention research. Valid measurement of PA is challenging. The present review could not confirm the effect of an ICT initiation strategy on intervention exposure and adherence rate because these data were not available in most of the included studies. Clearly, more studies are needed to investigate the impact of an ICT initiation strategy on an intervention exposure rate, adherence rate, and efficacy. Considering the rapid change and revolution in electronic devices and techniques, there is a huge concern that anything that is developed today may soon become obsolete and outdated. High quality and long-term research into ICT to support PA and health behavior should be emphasized and explored.

    In conclusion, it is important for a healthy China infrastructure to develop theory-based, effective, and engaging, but a simple and well-ordered, ICT with a structured layout that is based on user preferences and opinions focusing on key features facilitating PA engagement, goal-setting, real-time feedback and motivation, expert consultation, and social networking. Health improvement based on ICT will promote social development and economic prosperity. The combination of ICT and PA research can effectively promote relevant national health policy-making and health information services. Future research should focus on behavior change to analyze the function, interface, and interaction of ICT to improve information technology for implementing PA intervention and for bringing to realization the healthy planning objectives in China.

Reference (63)

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return