Uneingeschränkter Zugang

Evaluation of mobile applications for fitness training and physical activity in healthy low-trained people - A modular interdisciplinary framework

   | 16. Dez. 2019

Zitieren

Abraham, C. & Michie, S. (2008). A taxonomy of behavior change techniques used in interventions. Health Psychology, 27 (3), 379–387.10.1037/0278-6133.27.3.379Search in Google Scholar

ACSM (2011). Quantity and quality of exercise for developing and maintaining cardiorespiratory, musculoskeletal, and neuromotor fitness in apparently healthy adults: Guidance for prescribing exercise. Medicine & Science in Sports & Exercise, 43 (7), 1334-1359.10.1249/MSS.0b013e318213fefbSearch in Google Scholar

Ainsworth, B. E., Haskell, W. L., Leon, A. S., Jacobs, J. D., Montoye, H. J., Sallis, J. F., & Paffenbarger, J. R. (1993). Compendium of physical activities: Classification of energy costs of human physical activities. Medicine and science in sports and exercise, 25 (1), 71-80.10.1249/00005768-199301000-00011Search in Google Scholar

Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50 (2), 179-211.10.1016/0749-5978(91)90020-TSearch in Google Scholar

Arain, M., Campbell, M. J., Cooper, C. L., & Lancaster, G. A. (2010). What is a pilot or feasibility study? A review of current practice and editorial policy. BMC medical research methodology, 10 (1), 67.10.1186/1471-2288-10-67291292020637084Search in Google Scholar

Arem, H., Moore, S. C., Patel, A., Hartge, P., De Gonzalez, A. B., Visvanathan, K., ... & Linet, M. S. (2015). Leisure time physical activity and mortality: a detailed pooled analysis of the dose-response relationship. JAMA internal medicine, 175 (6), 959-967.10.1001/jamainternmed.2015.0533445143525844730Search in Google Scholar

Atkinson, G., & Nevill, A. M. (1998). Statistical methods for assessing measurement error (reliability) in variables relevant to sports medicine. Sports medicine, 26 (4), 217-238.10.2165/00007256-199826040-000029820922Search in Google Scholar

Baca, A. (2015). Data acquisition and processing. In A. Baca (ed.), Computer Science in Sport: Research and practice (pp.46-81). London: Routledge.10.4324/9781315881782Search in Google Scholar

Bandura, A. (1999). A social cognitive theory of personality. In L. Pervin & O. John (Ed.), Handbook of personality (2nd ed., pp. 154-196). New York: Guilford Publications.Search in Google Scholar

Batacan, R. B., Duncan, M. J., Dalbo, V. J., Tucker, P. S., & Fenning, A. S. (2017). Effects of high-intensity interval training on cardiometabolic health: a systematic review and meta-analysis of intervention studies. British Journal of Sports Medicine, 51(6), 494-503.10.1136/bjsports-2015-09584127797726Search in Google Scholar

Battenberg, A. K., Donohoe, S., Robertson, N., & Schmalzried, T. P. (2017). The accuracy of personal activity monitoring devices. Seminars in Arthroplasty, 28 (2), 71-75.10.1053/j.sart.2017.07.006Search in Google Scholar

Bender, C. G., Hoffstot, J. C., Combs, B. T., Hooshangi, S., & Cappos, J. (2017). Measuring the fitness of fitness trackers. In Sensors Applications Symposium (SAS), 2017 IEEE (pp. 1-6). New York, NY: IEEE.10.1109/SAS.2017.7894077Search in Google Scholar

Bert, F., Giacometti, M., Gualano, M. R., & Siliquini, R. (2014). Smartphones and health promotion: A review of the evidence. Journal of medical systems, 38 (1), 1-11.10.1007/s10916-013-9995-724346929Search in Google Scholar

Bevan, N., Carter, J., Earthy, J., Geis, T., & Harker, S. (2016). New ISO standards for usability, usability reports and usability measures. In International Conference on Human-Computer Interaction (pp. 268-278). Cham: Springer.10.1007/978-3-319-39510-4_25Search in Google Scholar

Bondaronek, P., Alkhaldi, G., Slee, A., Hamilton, F. L., & Murray, E. (2018). Quality of publicly available physical activity apps: Review and content analysis. JMIR mHealth and uHealth, 6(3), e53.10.2196/mhealth.9069588506229563080Search in Google Scholar

Borg, G. (1998). Borg´s perceived exertion and pain scales. Champaign, II.: Human Kinetics.Search in Google Scholar

Case, M. A., Burwick, H. A., Volpp, K. G., & Patel, M. S. (2015). Accuracy of smartphone applications and wearable devices for tracking physical activity data. Journal of the American Medical Association, 313 (6), 625-626.10.1001/jama.2014.1784125668268Search in Google Scholar

Casey, M., Hayes, P. S., Glynn, F., ÓLaighin, G., Heaney, D., Murphy, A. W., & Glynn, L. G. (2014). Patients’ experiences of using a smartphone application to increase physical activity: The SMART MOVE qualitative study in primary care. British Journal of General Practice, 64 (625), e500-e508.10.3399/bjgp14X680989411134325071063Search in Google Scholar

Caspersen, C. J., Powell, K. E., & Christenson, G. M. (1985). Physical activity, exercise, and physical fitness: definitions and distinctions for health-related research. Public health reports, 100 (2), 126.Search in Google Scholar

Champion, V. L. & Skinner, C. S. (2008). The health belief model. In K. Glanz, B.K. Rimer & K. Viswanath (eds.), Health behavior and health education: Theory, research, and practice (pp. 45-65). San Francisco, CA: Wiley.Search in Google Scholar

Chi-Wai, R. K., Sai-Chuen, S. H., So-Ning, T. M., Ka-Shun, P. W., Wing-Kuen, K. L., & Choi-Ki, C. W. (2011). Can mobile virtual fitness apps replace human fitness trainer? In The 5th International Conference on New Trends in Information Science and Service Science (Vol. 1, pp. 56-63). New York, NY: IEEE.Search in Google Scholar

Conroy, D. E., Yang, C. H., & Maher, J. P. (2014). Behavior change techniques in top-ranked mobile apps for physical activity. American journal of preventive medicine, 46 (6), 649-652.10.1016/j.amepre.2014.01.01024842742Search in Google Scholar

Derbyshire, E. & Dancey, D. (2013). Smartphone medical applications for women’s health: What is the evidence-base and feedback? International journal of telemedicine and applications, Article ID 782074.10.1155/2013/782074388069424454354Search in Google Scholar

Direito, A., Dale, L. P., Shields, E., Dobson, R., Whittaker, R., & Maddison, R. (2014). Do physical activity and dietary smartphone applications incorporate evidence-based behaviour change techniques? BMC Public Health, 14 (646), 1-7.10.1186/1471-2458-14-646408069324965805Search in Google Scholar

Donabedian, A. (1988). The quality of care: How can it be assessed? JAMA, 260, 1743-1748.10.1001/jama.260.12.17433045356Search in Google Scholar

Dowd, K. P., Szeklicki, R., Minetto, M. A., Murphy, M. H., Polito, A., Ghigo, E., ... & Tomczak, M. (2018). A systematic literature review of reviews on techniques for physical activity measurement in adults: a DEDIPAC study. International Journal of Behavioral Nutrition and Physical Activity, 15 (1), 15.10.1186/s12966-017-0636-2580627129422051Search in Google Scholar

Düking, P., Fuss, F. K., Holmberg, H. C., & Sperlich, B. (2018). Recommendations for assessment of the reliability, sensitivity, and validity of data provided by wearable sensors designed for monitoring physical activity. JMIR mHealth and uHealth, 6 (4), e102.10.2196/mhealth.9341595211929712629Search in Google Scholar

Fanning, J., Mullen, S. P., & McAuley, E. (2012). Increasing physical activity with mobile devices: A meta-analysis. Journal of medical Internet research, 14(6).10.2196/jmir.2171351484723171838Search in Google Scholar

Farrow, D., & Robertson, S. (2017). Development of a skill acquisition periodisation framework for high-performance sport. Sports Medicine, 47 (6), 1043–1054.10.1007/s40279-016-0646-227873190Search in Google Scholar

Fereidooni, H., Classen, J., Spink, T., Patras, P., Miettinen, M., Sadeghi, A. R., Hollick, M., & Conti, M. (2017). Breaking fitness records without moving: Reverse engineering and spoofing fitbit. In International Symposium on Research in Attacks, Intrusions, and Defenses (pp. 48-69). Cham: Springer.10.1007/978-3-319-66332-6_3Search in Google Scholar

Fokkema, T., Kooiman, T. J., Krijnen, W. P., Schans, C. P. van der, & Groot, M. de (2017). Reliability and validity of ten consumer activity trackers depend on walking speed. Medicine and science in sports and exercise, 49 (4), 793-800.10.1249/MSS.000000000000114628319983Search in Google Scholar

Fritz, T., Huang, E. M., Murphy, G. C., & Zimmermann, T. (2014,). Persuasive technology in the real world: A study of long-term use of activity sensing devices for fitness. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 487-496). New York, NY: ACM.10.1145/2556288.2557383Search in Google Scholar

Fröhlich, M., Müller, F., Schmidtbleicher, D. & Emrich, E. (2009). Outcome-Effekte verschiedener Periodisierungsmodelle im Krafttraining. Deutsche Zeitschrift für Sportmedizin, 60 (10), 307-314.Search in Google Scholar

Fuchs, R., Goehner, W., & Seelig, H. (2011). Long-term effects of a psychological group intervention on physical exercise and health: The MoVo concept. Journal of Physical Activity and Health, 8 (6), 794-803.10.1123/jpah.8.6.79421832294Search in Google Scholar

Fuchs, R., Seelig, H., Göhner, W., Burton, N. W., & Brown, W. J. (2012). Cognitive mediation of intervention effects on physical exercise: Causal models for the adoption and maintenance stage. Psychology & health, 27 (12), 1480-1499.10.1080/08870446.2012.69502022715966Search in Google Scholar

Gao, W., Emaminejad, S., Nyein, H. Y. Y., Challa, S., Chen, K., Peck, A., Fahad, H.M., Ota, H., Shiraki, H., Kiriya, D., Lien, D.-H., Brooks, G.A., Davis, R.W., & Javey, A. (2016). Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis. Nature, 529 (7587), 509.10.1038/nature16521499607926819044Search in Google Scholar

Gibson, A. L., Wagner, D., & Heyward, V. (2018). Advanced Fitness Assessment and Exercise Prescription (8th edition). Champaign, Ill.: Human kinetics.10.5040/9781718220966Search in Google Scholar

Guissard, N., Duchateau, J. & Hainaut, K. (1988). Muscle stretching and motoneuron excitability. European Journal of Applied Physiology, 58, 47-52.10.1007/BF006366023203674Search in Google Scholar

Guyatt, G., Oxman, A. D., Akl, E. A., Kunz, R., Vist, G., Brozek, J., Susan Norris, S., Falck-Ytter, Y., Glasziou, P., deBeer, H., Jaeschke, R., Rind, D., Meerpohl, J., Dahm, P., & Schünemann, H. J. (2011). GRADE guidelines: 1. Introduction—GRADE evidence profiles and summary of findings tables. Journal of clinical epidemiology, 64 (4), 383-394.10.1016/j.jclinepi.2010.04.026Search in Google Scholar

Hagger, M. S. & Chatzisarantis, N. L. (2014). An integrated behavior change model for physical activity. Exercise and Sport Sciences Reviews, 42 (2), 62-69.10.1249/JES.000000000000000824508739Search in Google Scholar

Halson, S. L., Peake, J. M., & Sullivan, J. P. (2016). Wearable technology for athletes: Information overload and pseudoscience? International Journal of Sports Physiology and Performance, 11, 705-706.10.1123/IJSPP.2016-048627701967Search in Google Scholar

He, Y., & Li, Y. (2013). Physical Activity Recognition Utilizing the Built-In Kinematic Sensors of a Smartphone. International Journal of Distributed Sensor Networks, 2013, Article ID 481580.10.1155/2013/481580Search in Google Scholar

Heckhausen, H. (1989). Motivation und Handeln (2nd ed.). [Motivation and action] Berlin: Springer.10.1007/978-3-662-08870-8Search in Google Scholar

Hecksteden, A., Faude, O., Meyer, T., & Donath, L. (2018). How to construct, conduct and analyze an exercise training study? Frontiers in physiology, 9, 1007.10.3389/fphys.2018.01007609497530140237Search in Google Scholar

Heikenfeld, J., Jajack, A., Rogers, J., Gutruf, P., Tian, L., Pan, T., Li, R., Khine, M., Kim, J., Wang, J., & Kim, J. (2018). Wearable sensors: Modalities, challenges, and prospects. Lab on a Chip, 18 (2), 217-248.10.1039/C7LC00914C577184129182185Search in Google Scholar

Helmerhorst, H. H. J., Brage, S., Warren, J., Besson, H., & Ekelund, U. (2012). A systematic review of reliability and objective criterion-related validity of physical activity questionnaires. International Journal of Behavioral Nutrition and Physical Activity, 9 (1), 103.10.1186/1479-5868-9-103349215822938557Search in Google Scholar

Higgins, J. P. & Altman, D. G. (2008). Assessing risk of bias in included studies. In J.P. Higgins & S. Green (eds.), Cochrane handbook for systematic reviews of interventions: Cochrane book series (pp. 187-241). Chichester: Wiley-Blackwell.10.1002/9780470712184.ch8Search in Google Scholar

Higgins, J.P. & Green, S. (eds.). (2008). Cochrane handbook for systematic reviews of interventions: Cochrane book series. Chichester: Wiley-Blackwell.10.1002/9780470712184Search in Google Scholar

Ho, C. L., Fu, Y. C., Lin, M. C., Chan, S. C., Hwang, B., & Jan, S. L. (2014). Smartphone applications (apps) for heart rate measurement in children: Comparison with electrocardiography monitor. Pediatric cardiology, 35 (4), 726-731.10.1007/s00246-013-0844-824259012Search in Google Scholar

Hohmann, A., Lames, M. & Letzelter, M. (2002). Einführung in die Trainingswissenschaft. [Introduction to training science] Wiebelsheim: Limpert.Search in Google Scholar

Janssen, I., & LeBlanc, A. G. (2010). Systematic review of the health benefits of physical activity and fitness in school-aged children and youth. International journal of behavioral nutrition and physical activity, 7 (1), 40.10.1186/1479-5868-7-40288531220459784Search in Google Scholar

Kari, T., Koivunen, S., Frank, L., Makkonen, M., & Moilanen, P. (2016). Critical experiences during the implementation of a self-tracking technology. In PACIS 2016: Proceedings of the 20th Pacific Asia Conference on Information Systems (pp. 129-144). Association for Information Systems. Retrieved from http://aisel.aisnet.org/pacis2016/129/Search in Google Scholar

Kari, T. & Rinne, P. (2018). Influence of digital coaching on physical activity: Motivation and behaviour of physically inactive individuals. In A. Pucihar, M. Kljajič, P. Ravesteijn, J. Seitz, & R. Bons (Eds.), Bled 2018: Proceedings of the 31th Bled eConference. Digital Transformation: Meeting the Challenges (pp. 127-145). Maribor: University of Maribor Press.10.18690/978-961-286-170-4.8Search in Google Scholar

Kassal, P., Steinberg, M. D., & Steinberg, I. M. (2018). Wireless chemical sensors and biosensors: A review. Sensors and Actuators B: Chemical, 266, 228.10.1016/j.snb.2018.03.074Search in Google Scholar

Kellmann, M. & Kallus, K. W. (2001). Recovery-stress questionnaire for athletes: User manual (Vol. 1). Champaign, Il.: Human Kinetics.Search in Google Scholar

Kellmann, M., Bertollo, M., Bosquet, L., Brink, M., Coutts, A. J., Duffield, R., ... & Kallus, K. W. (2018). Recovery and performance in sport: consensus statement. International journal of sports physiology and performance, 13 (2), 240-245.10.1123/ijspp.2017-075929345524Search in Google Scholar

Kendzierski, D. & DeCarlo, K. J. (1991). Physical activity enjoyment scale: Two validation studies. Journal of sport and exercise psychology, 13 (1), 50-64.10.1123/jsep.13.1.50Search in Google Scholar

Kettunen, E., Critchley, W., & Kari, T. (2019). Can digital coaching boost your performance? A qualitative study among physically active people. In Proceedings of the 52nd Hawaii International Conference on System Sciences (HICSS 2019) (pp. 1331-1340). University of Hawai’i at Manoa. Retrieved April 4, 2019 from http://hdl.handle.net/10125/5957410.24251/HICSS.2019.163Search in Google Scholar

Kettunen, E. & Kari, T. (2018). Can sport and wellness technology be my personal trainer? Teenagers and digital coaching. In A. Pucihar, M. Kljajič, P. Ravesteijn, J. Seitz, & R. Bons (Eds.), Bled 2018: Proceedings of the 31th Bled eConference. Digital Transformation: Meeting the Challenges (pp. 463-476). Maribor: University of Maribor Press.10.18690/978-961-286-170-4.32Search in Google Scholar

Khaylis, A., Yiaslas, T., Bergstrom, J., & Gore-Felton, C. (2010). A review of efficacious technology-based weight-loss interventions: five key components. Telemedicine and e-Health, 16 (9), 931-938.10.1089/tmj.2010.0065300090021091286Search in Google Scholar

King, A. C., Hekler, E. B., Grieco, L. A., Winter, S. J., Sheats, J. L., Buman, M. P., ... & Cirimele, J. (2016). Effects of three motivationally targeted mobile device applications on initial physical activity and sedentary behavior change in midlife and older adults: a randomized trial. PloS one, 11(6), e0156370.10.1371/journal.pone.0156370492483827352250Search in Google Scholar

Knight, E., Stuckey, M. I., Prapavessis, H., & Petrella, R. J. (2015). Public health guidelines for physical activity: Is there an app for that? A review of android and apple app stores. JMIR mHealth and uHealth, 3 (2).10.2196/mhealth.4003445648525998158Search in Google Scholar

Kooiman, T. J., Dontje, M. L., Sprenger, S. R., Krijnen, W. P., van der Schans, C. P., & de Groot, M. (2015). Reliability and validity of ten consumer activity trackers. BMC sports science, medicine and rehabilitation, 7 (1), 24.10.1186/s13102-015-0018-5460329626464801Search in Google Scholar

Kranz, M., Möller, A., Hammerla, N., Diewald, S., Plötz, T., Olivier, P., & Roalter, L. (2013). The mobile fitness coach: Towards individualized skill assessment using personalized mobile devices. Pervasive and Mobile Computing, 9 (2), 203-215.10.1016/j.pmcj.2012.06.002Search in Google Scholar

Kühberger, A., Fritz, A., Lermer, E., & Scherndl, T. (2015). The significance fallacy in inferential statistics. BMC research notes, 8 (1), 84.10.1186/s13104-015-1020-4437706825888971Search in Google Scholar

Lachman, M. E., Lipsitz, L., Lubben, J., Castaneda-Sceppa, C., & Jette, A. M. (2018). When adults don’t exercise: Behavioral strategies to increase physical activity in sedentary middle-aged and older adults. Innovation in aging, 2(1), igy007.10.1093/geroni/igy007603704730003146Search in Google Scholar

Lallemand, C., Gronier, G., & Koenig, V. (2015). User experience: A concept without consensus? Exploring practitioners’ perspectives through an international survey. Computers in Human Behavior, 43, 35-48.10.1016/j.chb.2014.10.048Search in Google Scholar

Lang, K. M., & Little, T. D. (2018). Principled missing data treatments. Prevention Science, 19(3), 284-294.10.1007/s11121-016-0644-527040106Search in Google Scholar

Leunes, A. & Burger, J. (2000). Profile of mood states research in sport and exercise psychology: Past, present, and future. Journal of applied sport psychology, 12 (1), 5-15.10.1080/10413200008404210Search in Google Scholar

Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. (2009). The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: Explanation and elaboration. PLoS medicine, 6 (7), e1000100.10.1371/journal.pmed.1000100270701019621070Search in Google Scholar

Ludwig, M., Hoffmann, K., Endler, S., Asteroth, A., & Wiemeyer, J. (2018). Measurement, prediction, and control of individual heart rate responses to exercise—Basics and options for wearable devices. Frontiers in physiology, 9, 778.10.3389/fphys.2018.00778602688429988588Search in Google Scholar

Maher, C. G., Sherrington, C., Herbert, R. D., Moseley, A. M., & Elkins, M. (2003). Reliability of the PEDro scale for rating quality of randomized controlled trials. Physical therapy, 83(8), 713-721.10.1093/ptj/83.8.713Search in Google Scholar

Marshall, S. J. & Biddle, S. J. (2001). The transtheoretical model of behavior change: A meta-analysis of applications to physical activity and exercise. Annals of behavioral medicine, 23 (4), 229-246.10.1207/S15324796ABM2304_211761340Search in Google Scholar

Mateo, G. F., Granado-Font, E., Ferré-Grau, C., & Montaña-Carreras, X. (2015). Mobile phone apps to promote weight loss and increase physical activity: A systematic review and meta-analysis. Journal of medical Internet research, 17 (11), e253.10.2196/jmir.4836Search in Google Scholar

Matthews, J., Win, K. T., Oinas-Kukkonen, H., & Freeman, M. (2016). Persuasive technology in mobile applications promoting physical activity: A systematic review. Journal of medical systems, 40 (3), 72.10.1007/s10916-015-0425-x26748792Search in Google Scholar

McCoy, C. E. (2017). Understanding the intention-to-treat principle in randomized controlled trials. Western Journal of Emergency Medicine, 18 (6), 1075.10.5811/westjem.2017.8.35985565487729085540Search in Google Scholar

McKay, F. H., Cheng, C., Wright, A., Shill, J., Stephens, H., & Uccellini, M. (2018). Evaluating mobile phone applications for health behaviour change: A systematic review. Journal of telemedicine and telecare, 24 (1), 22-30.10.1177/1357633X1667353827760883Search in Google Scholar

McKay, F. H., Slykerman, S., & Dunn, M. (2019). The App Behavior Change Scale: Creation of a scale to assess the potential of apps to promote behavior change. JMIR mHealth and uHealth, 7 (1), e11130.10.2196/11130636767030681967Search in Google Scholar

Mentler, T. & Herczeg, M. (2013). Applying ISO 9241-110 dialogue principles to tablet applications in emergency medical services. In Proceedings of the 10th International ISCRAM Conference – Baden-Baden, Germany, May 2013 (pp.502-506). Baden-Baden: ISCRAM (http://www.iscram.org/content/iscram2013-academic-papers)Search in Google Scholar

Michie, S., Ashford, S., Sniehotta, F. F., Dombrowski, S. U., Bishop, A., & French, D. P. (2011). A refined taxonomy of behaviour change techniques to help people change their physical activity and healthy eating behaviours: The CALO-RE taxonomy. Psychology & Health, 26 (11), 1479-1498.10.1080/08870446.2010.54066421678185Search in Google Scholar

Michie, S., Richardson, M., Johnston, M., Abraham, C., Francis, J., Hardeman, W., Eccles, M. P., Cane, J., & Wood, C. E. (2013). The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: Building an international consensus for the reporting of behavior change interventions. Annals of behavioral medicine, 46 (1), 81-95.10.1007/s12160-013-9486-623512568Search in Google Scholar

Mukhopadhyay, S. C. (2015). Wearable sensors for human activity monitoring: A review. IEEE sensors journal, 15 (3), 1321-1330.10.1109/JSEN.2014.2370945Search in Google Scholar

Munson, S.A. & Consolvo, S. (2012). Exploring goal-setting, rewards, self-monitoring, and sharing to motivate physical activity. In 2012 6th international conference on pervasive computing technologies for healthcare (pervasive health) and workshops (pp. 25-32). New York, NY: IEEE.10.4108/icst.pervasivehealth.2012.248691Search in Google Scholar

Oinas-Kukkonen, H. & Harjumaa, M. (2009). Persuasive systems design: Key issues, process model and system features. Communications of the Association for Information Systems, 24 (1), 28.10.17705/1CAIS.02428Search in Google Scholar

O’Donovan, G., Blazevich, A. J., Boreham, C., Cooper, A. R., Crank, H., Ekelund, U., ... & Hamer, M. (2010). The ABC of Physical Activity for Health: a consensus statement from the British Association of Sport and Exercise Sciences. Journal of sports sciences, 28(6), 573-591.10.1080/0264041100367121220401789Search in Google Scholar

O’Reilly, G. A. & Spruijt-Metz, D. (2013). Current mHealth technologies for physical activity assessment and promotion. American journal of preventive medicine, 45 (4), 501-507.10.1016/j.amepre.2013.05.012419982724050427Search in Google Scholar

Paz, F. & Pow-Sang, J. A. (2016). A systematic mapping review of usability evaluation methods for software development process. International Journal of Software Engineering and Its Applications, 10 (1), 165-178.10.14257/ijseia.2016.10.1.16Search in Google Scholar

Peake, J. M., Kerr, G., & Sullivan, J. P. (2018). A critical review of consumer wearables, mobile applications, and equipment for providing biofeedback, monitoring stress, and sleep in physically active populations. Frontiers in physiology, 9, 743.10.3389/fphys.2018.00743603174630002629Search in Google Scholar

Pelletier, L. G., Tuson, K. M., Fortier, M. S., Vallerand, R. J., Briere, N. M., & Blais, M. R. (1995). Toward a new measure of intrinsic motivation, extrinsic motivation, and amotivation in sports: The Sport Motivation Scale (SMS). Journal of sport and Exercise Psychology, 17 (1), 35-53.10.1123/jsep.17.1.35Search in Google Scholar

Plonczynski, D. J. (2000). Measurement of motivation for exercise. Health Education Research, 15(6), 695-705.10.1093/her/15.6.69511142077Search in Google Scholar

Poitras, V. J., Gray, C. E., Borghese, M. M., Carson, V., Chaput, J. P., Janssen, I., Katzmarzyk, P. T., Pate, R. R., Gorber, S. C., Kho, M. E., Sampson, M., & Tremblay, M.S. (2016). Systematic review of the relationships between objectively measured physical activity and health indicators in school-aged children and youth. Applied Physiology, Nutrition, and Metabolism, 41 (6), S197-S239.10.1139/apnm-2015-066327306431Search in Google Scholar

Preuschl, E., Baca, A., Novatchkov, H., Kornfeind, P., Bichler, S., & Boecskoer, M. (2010). Mobile motion advisor – A feedback system for physical exercise in schools. Procedia Engineering, 2 (2), 2741-2747.10.1016/j.proeng.2010.04.060Search in Google Scholar

Prochaska, JO, Redding, CA, & Evers, K. (2008). The transtheoretical model and stages of change. In K. Glanz, F.M. Lewis, & B.K. Rimer (Eds.), Health behavior and health education (4th ed., pp.97-121). San Francisco: Jossey-Bass.Search in Google Scholar

Reilly, J. J., Penpraze, V., Hislop, J., Davies, G., Grant, S., & Paton, J. Y. (2008). Objective measurement of physical activity and sedentary behaviour: review with new data. Archives of disease in childhood, 93 (7), 614-619.10.1136/adc.2007.13327218305072Search in Google Scholar

Rhea, C. K., Felsberg, D. T., & Maher, J. P. (2018). Toward Evidence-Based Smartphone Apps to Enhance Human Health: Adoption of Behavior Change Techniques. American Journal of Health Education, 49(4), 210-213.10.1080/19325037.2018.1473177Search in Google Scholar

Roda, A., Michelini, E., Zangheri, M., Di Fusco, M., Calabria, D., & Simoni, P. (2016). Smartphone-based biosensors: A critical review and perspectives. TrAC Trends in Analytical Chemistry, 79, 317-325.10.1016/j.trac.2015.10.019Search in Google Scholar

Romeo, A., Edney, S., Plotnikoff, R., Curtis, R., Ryan, J., Sanders, I., ... & Maher, C. (2019). Can Smartphone Apps Increase Physical Activity? Systematic Review and Meta-Analysis. Journal of medical Internet research, 21 (3), e12053.10.2196/12053644421230888321Search in Google Scholar

Rose, S. & Laan, M. J. van der (2009). Why match? Investigating matched case-control study designs with causal effect estimation. The international journal of biostatistics, 5 (1), Article 1.10.2202/1557-4679.1127282789220231866Search in Google Scholar

Ryan, R. M. & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55 (1), 68-78.10.1037/0003-066X.55.1.68Search in Google Scholar

Schmidt, B., Benchea, S., Eichin, R., & Meurisch, C. (2015). Fitness tracker or digital personal coach: How to personalize training. In Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers (pp. 1063-1067). New York, NY: ACM.10.1145/2800835.2800961Search in Google Scholar

Shameli, A., Althoff, T., Saberi, A., & Leskovec, J. (2017). How gamification affects physical activity: Large-scale analysis of walking challenges in a mobile application. In Proceedings of the 26th International Conference on World Wide Web Companion (pp. 455-463). Geneva: International World Wide Web Conferences Steering Committee.10.1145/3041021.3054172Search in Google Scholar

Shea, B. J., Hamel, C., Wells, G. A., Bouter, L. M., Kristjansson, E., Grimshaw, J., Henry, D.A., & Boers, M. (2009). AMSTAR is a reliable and valid measurement tool to assess the methodological quality of systematic reviews. Journal of clinical epidemiology, 62 (10), 1013-1020.10.1016/j.jclinepi.2008.10.00919230606Search in Google Scholar

Stephens, J., & Allen, J. (2013). Mobile phone interventions to increase physical activity and reduce weight: A systematic review. The Journal of cardiovascular nursing, 28 (4), 320.10.1097/JCN.0b013e318250a3e7368180422635061Search in Google Scholar

Tang, L. M., Day, M., Engelen, L., Poronnik, P., Bauman, A., & Kay, J. (2016). Daily & hourly adherence: Towards understanding activity tracker accuracy. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems (pp. 3211-3218). New York, NY: ACM.10.1145/2851581.2892438Search in Google Scholar

Tang, L. M. & Kay, J. (2017). Harnessing long term physical activity data – How long-term trackers use data and how an adherence-based interface supports new insights. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 1 (2), Article 26.10.1145/3090091Search in Google Scholar

Teixeira, P. J., Carraça, E. V., Markland, D., Silva, M. N., & Ryan, R. M. (2012). Exercise, physical activity, and self-determination theory: A systematic review. International journal of behavioral nutrition and physical activity, 9 (1), 78.10.1186/1479-5868-9-78344178322726453Search in Google Scholar

Toigo, M., & Boutellier, U. (2006). New fundamental resistance exercise determinants of molecular and cellular muscle adaptations. European journal of applied physiology, 97 (6), 643-663.10.1007/s00421-006-0238-116845551Search in Google Scholar

Wackerhage, H., Schoenfeld, B. J., Hamilton, D. L., Lehti, M., & Hulmi, J. J. (2018). Stimuli and sensors that initiate skeletal muscle hypertrophy following resistance exercise. Journal of Applied Physiology, 126 (1), 30-43.10.1152/japplphysiol.00685.2018Search in Google Scholar

Wagner, P. (2000). Aussteigen oder Dabeibleiben? [Get off or stay?] Darmstadt: WBG.Search in Google Scholar

Wahl, Y., Düking, P., Droszez, A., Wahl, P., & Mester, J. (2017). Criterion-validity of commercially available physical activity tracker to estimate step count, covered distance and energy expenditure during sports conditions. Frontiers in physiology, 8, 725.10.3389/fphys.2017.00725561530429018355Search in Google Scholar

Wang, J. B., Cataldo, J. K., Ayala, G. X., Natarajan, L., Cadmus-Bertram, L. A., White, M. M., Madanat, H., Nichols, J. F., & Pierce, J. P. (2016). Mobile and wearable device features that matter in promoting physical activity. Journal of mobile technology in medicine, 5 (2), 2-11.10.7309/jmtm.5.2.2496900327493694Search in Google Scholar

Warraich, M. U. (2016). Wellness routines with wearable activity trackers: A systematic review. In MCIS 2016 Proceedings (Article 35). Paphos, Cyprus: http://aisel.aisnet.org/mcis2016/.Search in Google Scholar

Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of personality and social psychology, 54 (6), 1063-1070.10.1037/0022-3514.54.6.1063Search in Google Scholar

WHO (2010). Global recommendations on physical activity for health. Geneva: WHO.Search in Google Scholar

WHO (2018). More active people for a healthier world. Global action plan on physical activity 2018-2030. Geneva: WHO.Search in Google Scholar

Wiemeyer, J. (2018). Fitness Apps – Was erwarten die User? [Fitness apps – What are the users’ expectations?] In D. Link, A. Hermann, M. Lames & V. Senner (eds.), Sportinformatik XII (pp. 90-91). Hamburg: Feldhaus-Czwalina.Search in Google Scholar

Wiemeyer, J., Hatzky, W., Henrich, J. & Seelert, P. (2016). Modern – Mobil – Motivierend = Effektiver & Effizienter? Eine kritische Analyse ausgewählter mobiler Trainings-Applikationen. [Modern – mobile – motivating = more effective and more efficient? A critical analysis of selected applications for mobile training] In K. Witte & J. Edelmann-Nusser (eds.), Sportinformatik XI. (pp.29-34). Aachen: Shaker.Search in Google Scholar

Williams, S. L. & French, D. P. (2011). What are the most effective intervention techniques for changing physical activity self-efficacy and physical activity behaviour – and are they the same? Health Education Research, 26 (2), 308-322.10.1093/her/cyr00521321008Search in Google Scholar

Wong, C., Zhang, Z. Q., Lo, B., & Yang, G. Z. (2015). Wearable sensing for solid biomechanics: A review. IEEE Sensors Journal, 15 (5), 2747-2760.Search in Google Scholar

Yang, C. H., Maher, J. P., & Conroy, D. E. (2015). Implementation of behavior change techniques in mobile applications for physical activity. American journal of preventive medicine, 48 (4), 452-455.10.1016/j.amepre.2014.10.01025576494Search in Google Scholar

Yang, R., Shin, E., Newman, M. W., & Ackerman, M. S. (2015). When fitness trackers don’t ‘fit’: End-user difficulties in the assessment of personal tracking device accuracy. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 623-634). New York, NY: ACM.10.1145/2750858.2804269Search in Google Scholar

Zhou, M., Fukuoka, Y., Mintz, Y., Goldberg, K., Kaminsky, P., Flowers, E., & Oi, A. (2018). Evaluating machine learning–based automated personalized daily step goals delivered through a mobile phone app: Randomized controlled trial. JMIR mHealth and uHealth, 6 (1), e28.10.2196/mhealth.9117580600629371177Search in Google Scholar

eISSN:
1684-4769
Sprache:
Englisch
Zeitrahmen der Veröffentlichung:
2 Hefte pro Jahr
Fachgebiete der Zeitschrift:
Informatik, Datanbanken und Data Mining, andere, Sport und Freizeit, Sportunterricht, other