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

Open access

Abstract

Numerous mobile applications are available that aim at supporting sustainable physical activity and fitness training in sedentary or low-trained healthy people. However, the evaluation of the quality of these applications often suffers from severe shortcomings such as reduction to selective aspects, lack of theory or suboptimal methods. What is still missing, is a framework that integrates the insights of the relevant scientific disciplines.

In this paper, we propose an integrative framework comprising four modules: training, behavior change techniques, sensors and technology, and evaluation of effects. This framework allows to integrate insights from training science, exercise physiology, social psychology, computer science, and civil engineering as well as methodology. Furthermore, the framework can be flexibly adapted to the specific features of the mobile applications, e.g., regarding training goals and training methods or the relevant behavior change techniques as well as formative or summative evaluation.

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  • Abraham C. & Michie S. (2008). A taxonomy of behavior change techniques used in interventions. Health Psychology 27 (3) 379–387.

  • 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.

  • 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 exercise25 (1) 71-80.

  • Ajzen I. (1991). The theory of planned behavior. Organizational behavior and human decision processes50 (2) 179-211.

  • 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 methodology10 (1) 67.

  • 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 medicine175 (6) 959-967.

  • Atkinson G. & Nevill A. M. (1998). Statistical methods for assessing measurement error (reliability) in variables relevant to sports medicine. Sports medicine26 (4) 217-238.

  • Baca A. (2015). Data acquisition and processing. In A. Baca (ed.) Computer Science in Sport: Research and practice (pp.46-81). London: Routledge.

  • 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.

  • 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 Medicine51(6) 494-503.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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 uHealth6(3) e53.

  • Borg G. (1998). Borg´s perceived exertion and pain scales. Champaign II.: Human Kinetics.

  • 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 Association313 (6) 625-626.

  • 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.

  • 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 reports100 (2) 126.

  • 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.

  • 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.

  • 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 medicine46 (6) 649-652.

  • 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.

  • 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.

  • Donabedian A. (1988). The quality of care: How can it be assessed? JAMA 260 1743-1748.

  • 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 Activity15 (1) 15.

  • 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 uHealth6 (4) e102.

  • Fanning J. Mullen S. P. & McAuley E. (2012). Increasing physical activity with mobile devices: A meta-analysis. Journal of medical Internet research14(6).

  • Farrow D. & Robertson S. (2017). Development of a skill acquisition periodisation framework for high-performance sport. Sports Medicine 47 (6) 1043–1054.

  • 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.

  • 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 exercise49 (4) 793-800.

  • 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.

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

  • 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 Health8 (6) 794-803.

  • 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 & health27 (12) 1480-1499.

  • 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. Nature529 (7587) 509.

  • Gibson A. L. Wagner D. & Heyward V. (2018). Advanced Fitness Assessment and Exercise Prescription (8th edition). Champaign Ill.: Human kinetics.

  • Guissard N. Duchateau J. & Hainaut K. (1988). Muscle stretching and motoneuron excitability. European Journal of Applied Physiology 58 47-52.

  • 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 epidemiology64 (4) 383-394.

  • Hagger M. S. & Chatzisarantis N. L. (2014). An integrated behavior change model for physical activity. Exercise and Sport Sciences Reviews42 (2) 62-69.

  • 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.

  • He Y. & Li Y. (2013). Physical Activity Recognition Utilizing the Built-In Kinematic Sensors of a Smartphone. International Journal of Distributed Sensor Networks2013 Article ID 481580.

  • Heckhausen H. (1989). Motivation und Handeln (2nd ed.). [Motivation and action] Berlin: Springer.

  • Hecksteden A. Faude O. Meyer T. & Donath L. (2018). How to construct conduct and analyze an exercise training study? Frontiers in physiology 9 1007.

  • 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 Chip18 (2) 217-248.

  • 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 Activity9 (1) 103.

  • 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.

  • Higgins J.P. & Green S. (eds.). (2008). Cochrane handbook for systematic reviews of interventions: Cochrane book series. Chichester: Wiley-Blackwell.

  • 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 cardiology35 (4) 726-731.

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

  • 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 activity7 (1) 40.

  • 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/

  • 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.

  • Kassal P. Steinberg M. D. & Steinberg I. M. (2018). Wireless chemical sensors and biosensors: A review. Sensors and Actuators B: Chemical266 228.

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

  • 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 performance13 (2) 240-245.

  • Kendzierski D. & DeCarlo K. J. (1991). Physical activity enjoyment scale: Two validation studies. Journal of sport and exercise psychology13 (1) 50-64.

  • 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/10125/59574

  • 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.

  • 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-Health16 (9) 931-938.

  • 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 one11(6) e0156370.

  • 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 uHealth3 (2).

  • 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 rehabilitation7 (1) 24.

  • 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 Computing9 (2) 203-215.

  • Kühberger A. Fritz A. Lermer E. & Scherndl T. (2015). The significance fallacy in inferential statistics. BMC research notes8 (1) 84.

  • 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 aging2(1) igy007.

  • Lallemand C. Gronier G. & Koenig V. (2015). User experience: A concept without consensus? Exploring practitioners’ perspectives through an international survey. Computers in Human Behavior43 35-48.

  • Lang K. M. & Little T. D. (2018). Principled missing data treatments. Prevention Science19(3) 284-294.

  • Leunes A. & Burger J. (2000). Profile of mood states research in sport and exercise psychology: Past present and future. Journal of applied sport psychology12 (1) 5-15.

  • 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 medicine6 (7) e1000100.

  • 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 physiology9 778.

  • 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 therapy83(8) 713-721.

  • 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 medicine23 (4) 229-246.

  • 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 research17 (11) e253.

  • 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 systems40 (3) 72.

  • McCoy C. E. (2017). Understanding the intention-to-treat principle in randomized controlled trials. Western Journal of Emergency Medicine18 (6) 1075.

  • 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 telecare24 (1) 22-30.

  • 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 uHealth7 (1) e11130.

  • 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)

  • 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 & Health26 (11) 1479-1498.

  • 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 medicine46 (1) 81-95.

  • Mukhopadhyay S. C. (2015). Wearable sensors for human activity monitoring: A review. IEEE sensors journal15 (3) 1321-1330.

  • 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.

  • 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.

  • 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 sciences28(6) 573-591.

  • 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.

  • 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 Applications10 (1) 165-178.

  • 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 physiology9 743.

  • 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 Psychology17 (1) 35-53.

  • Plonczynski D. J. (2000). Measurement of motivation for exercise. Health Education Research15(6) 695-705.

  • 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 Metabolism41 (6) S197-S239.

  • 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 Engineering2 (2) 2741-2747.

  • 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.

  • 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 childhood93 (7) 614-619.

  • 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 Education49(4) 210-213.

  • 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 Chemistry79 317-325.

  • 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 research21 (3) e12053.

  • Rose S. & Laan M. J. van der (2009). Why match? Investigating matched case-control study designs with causal effect estimation. The international journal of biostatistics5 (1) Article 1.

  • 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.

  • 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.

  • 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.

  • 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 epidemiology62 (10) 1013-1020.

  • Stephens J. & Allen J. (2013). Mobile phone interventions to increase physical activity and reduce weight: A systematic review. The Journal of cardiovascular nursing28 (4) 320.

  • 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.

  • 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 Technologies1 (2) Article 26.

  • 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 activity9 (1) 78.

  • Toigo M. & Boutellier U. (2006). New fundamental resistance exercise determinants of molecular and cellular muscle adaptations. European journal of applied physiology97 (6) 643-663.

  • 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 Physiology126 (1) 30-43.

  • Wagner P. (2000). Aussteigen oder Dabeibleiben? [Get off or stay?] Darmstadt: WBG.

  • 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 physiology8 725.

  • 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 medicine5 (2) 2-11.

  • 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/.

  • 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 psychology54 (6) 1063-1070.

  • WHO (2010). Global recommendations on physical activity for health. Geneva: WHO.

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

  • 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.

  • 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.

  • 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 Research26 (2) 308-322.

  • Wong C. Zhang Z. Q. Lo B. & Yang G. Z. (2015). Wearable sensing for solid biomechanics: A review. IEEE Sensors Journal15 (5) 2747-2760.

  • 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.

  • 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.

  • 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 uHealth6 (1) e28.

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