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Citing this Article

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Published on 15.04.15 in Vol 3, No 2 (2015): Apr-Jun

This paper is in the following e-collection/theme issue:

Works citing "Validation of Physical Activity Tracking via Android Smartphones Compared to ActiGraph Accelerometer: Laboratory-Based and Free-Living Validation Studies"

According to Crossref, the following articles are citing this article (DOI 10.2196/mhealth.3505):

(note that this is only a small subset of citations)

  1. Bort-Roig J, Chirveches-Pérez E, Garcia-Cuyàs F, Dowd KP, Puig-Ribera A. Monitoring Occupational Sitting, Standing, and Stepping in Office Employees With the W@W-App and the MetaWearC Sensor: Validation Study. JMIR mHealth and uHealth 2020;8(8):e15338
    CrossRef
  2. Tavares BF, Pires IM, Marques G, Garcia NM, Zdravevski E, Lameski P, Trajkovik V, Jevremovic A. Mobile Applications for Training Plan Using Android Devices: A Systematic Review and a Taxonomy Proposal. Information 2020;11(7):343
    CrossRef
  3. Martin CA, Rivera DE, Hekler EB, Riley WT, Buman MP, Adams MA, Magann AB. Development of a Control-Oriented Model of Social Cognitive Theory for Optimized mHealth Behavioral Interventions. IEEE Transactions on Control Systems Technology 2020;28(2):331
    CrossRef
  4. Katapally TR, Chu LM. Digital epidemiological and citizen science methodology to capture prospective physical activity in free-living conditions: a SMART Platform study. BMJ Open 2020;10(6):e036787
    CrossRef
  5. Silsupadol P, Prupetkaew P, Kamnardsiri T, Lugade V. Smartphone-Based Assessment of Gait During Straight Walking, Turning, and Walking Speed Modulation in Laboratory and Free-Living Environments. IEEE Journal of Biomedical and Health Informatics 2020;24(4):1188
    CrossRef
  6. Höchsmann C, Knaier R, Infanger D, Schmidt-Trucksäss A. Validity of smartphones and activity trackers to measure steps in a free-living setting over three consecutive days. Physiological Measurement 2020;41(1):015001
    CrossRef
  7. Ding D, Ramirez Varela A, Bauman AE, Ekelund U, Lee I, Heath G, Katzmarzyk PT, Reis R, Pratt M. Towards better evidence-informed global action: lessons learnt from the Lancet series and recent developments in physical activity and public health. British Journal of Sports Medicine 2020;54(8):462
    CrossRef
  8. Mitchell M, Lau E, White L, Faulkner G. Commercial app use linked with sustained physical activity in two Canadian provinces: a 12-month quasi-experimental study. International Journal of Behavioral Nutrition and Physical Activity 2020;17(1)
    CrossRef
  9. Kramer J, Künzler F, Mishra V, Smith SN, Kotz D, Scholz U, Fleisch E, Kowatsch T. Which Components of a Smartphone Walking App Help Users to Reach Personalized Step Goals? Results From an Optimization Trial. Annals of Behavioral Medicine 2020;54(7):518
    CrossRef
  10. Li X, Wang Y, Zhang B, Ma J. PSDRNN: An Efficient and Effective HAR Scheme Based on Feature Extraction and Deep Learning. IEEE Transactions on Industrial Informatics 2020;16(10):6703
    CrossRef
  11. Yamamoto K, Matsuda F, Matsukawa T, Yamamoto N, Ishii K, Kurihara T, Yamada S, Matsuki T, Kamijima M, Ebara T. Identifying characteristics of indicators of sedentary behavior using objective measurements. Journal of Occupational Health 2020;62(1)
    CrossRef
  12. Czmil A, Czmil S, Mazur D. A Method to Detect Type 1 Diabetes Based on Physical Activity Measurements Using a Mobile Device. Applied Sciences 2019;9(12):2555
    CrossRef
  13. Kong N, Choi J, Seo WS. Evaluation of Sleep Problems or Disorders Using Sleep Questionnaires. Chronobiology in Medicine 2019;1(4):144
    CrossRef
  14. Amagasa S, Kamada M, Sasai H, Fukushima N, Kikuchi H, Lee I, Inoue S. How Well iPhones Measure Steps in Free-Living Conditions: Cross-Sectional Validation Study. JMIR mHealth and uHealth 2019;7(1):e10418
    CrossRef
  15. Zhou M, Fukuoka Y, Goldberg K, Vittinghoff E, Aswani A. Applying machine learning to predict future adherence to physical activity programs. BMC Medical Informatics and Decision Making 2019;19(1)
    CrossRef
  16. Funk MD, Salazar CL, Martinez M, Gonzalez J, Leyva P, Bassett D, Karabulut M. Validity of Smartphone Applications at Measuring Steps: Does Wear Location Matter?. Journal for the Measurement of Physical Behaviour 2019;2(1):22
    CrossRef
  17. Rodriguez V, Medrano C, Plaza I, Corella C, Abarca A, Julian J. Comparison of Several Algorithms to Estimate Activity Counts with Smartphones as an Indication of Physical Activity Level. IRBM 2019;40(2):95
    CrossRef
  18. Thorpe JR, Forchhammer BH, Maier AM. Development of a Sensor-Based Behavioral Monitoring Solution to Support Dementia Care. JMIR mHealth and uHealth 2019;7(6):e12013
    CrossRef
  19. Park CL, Cho D, Moore PJ. How does education lead to healthier behaviours? Testing the mediational roles of perceived control, health literacy and social support. Psychology & Health 2018;33(11):1416
    CrossRef
  20. Höchsmann C, Knaier R, Eymann J, Hintermann J, Infanger D, Schmidt-Trucksäss A. Validity of activity trackers, smartphones, and phone applications to measure steps in various walking conditions. Scandinavian Journal of Medicine & Science in Sports 2018;28(7):1818
    CrossRef
  21. Perazzo JD, Webel AR, Fichtenbaum CJ, McComsey GA. Bone Health in People Living With HIV: The Role of Exercise and Directions for Future Research. Journal of the Association of Nurses in AIDS Care 2018;29(2):330
    CrossRef
  22. Hekler EB, Rivera DE, Martin CA, Phatak SS, Freigoun MT, Korinek E, Klasnja P, Adams MA, Buman MP. Tutorial for Using Control Systems Engineering to Optimize Adaptive Mobile Health Interventions. Journal of Medical Internet Research 2018;20(6):e214
    CrossRef
  23. Direito A, Walsh D, Hinbarji M, Albatal R, Tooley M, Whittaker R, Maddison R. Using the Intervention Mapping and Behavioral Intervention Technology Frameworks: Development of an mHealth Intervention for Physical Activity and Sedentary Behavior Change. Health Education & Behavior 2018;45(3):331
    CrossRef
  24. Brodie M, Pliner E, Ho A, Li K, Chen Z, Gandevia S, Lord S. Big data vs accurate data in health research: Large-scale physical activity monitoring, smartphones, wearable devices and risk of unconscious bias. Medical Hypotheses 2018;119:32
    CrossRef
  25. Wu R, Liaqat D, de Lara E, Son T, Rudzicz F, Alshaer H, Abed-Esfahani P, Gershon AS. Feasibility of Using a Smartwatch to Intensively Monitor Patients With Chronic Obstructive Pulmonary Disease: Prospective Cohort Study. JMIR mHealth and uHealth 2018;6(6):e10046
    CrossRef
  26. Mitchell M, White L, Lau E, Leahey T, Adams MA, Faulkner G. Evaluating the Carrot Rewards App, a Population-Level Incentive-Based Intervention Promoting Step Counts Across Two Canadian Provinces: Quasi-Experimental Study. JMIR mHealth and uHealth 2018;6(9):e178
    CrossRef
  27. Pope L, Garnett B, Dibble M. Lessons Learned Through the Implementation of an eHealth Physical Activity Gaming Intervention with High School Youth. Games for Health Journal 2018;7(2):136
    CrossRef
  28. Smith MP, Horsch A, Standl M, Heinrich J, Schulz H. Uni- and triaxial accelerometric signals agree during daily routine, but show differences between sports. Scientific Reports 2018;8(1)
    CrossRef
  29. Bort-Roig J, Puig-Ribera A, Contreras RS, Chirveches-Pérez E, Martori JC, Gilson ND, McKenna J. Monitoring sedentary patterns in office employees: validity of an m-health tool (Walk@Work-App) for occupational health. Gaceta Sanitaria 2018;32(6):563
    CrossRef
  30. Sullivan AN, Lachman ME. Behavior Change with Fitness Technology in Sedentary Adults: A Review of the Evidence for Increasing Physical Activity. Frontiers in Public Health 2017;4
    CrossRef
  31. Smith MP, Standl M, Heinrich J, Schulz H, Buchowski M. Accelerometric estimates of physical activity vary unstably with data handling. PLOS ONE 2017;12(11):e0187706
    CrossRef
  32. Lee W, Lin K, Seto E, Migliaccio GC. Wearable sensors for monitoring on-duty and off-duty worker physiological status and activities in construction. Automation in Construction 2017;83:341
    CrossRef
  33. Maddison R, Gemming L, Monedero J, Bolger L, Belton S, Issartel J, Marsh S, Direito A, Solenhill M, Zhao J, Exeter DJ, Vathsangam H, Rawstorn JC. Quantifying Human Movement Using the Movn Smartphone App: Validation and Field Study. JMIR mHealth and uHealth 2017;5(8):e122
    CrossRef
  34. Althoff T, Sosič R, Hicks JL, King AC, Delp SL, Leskovec J. Large-scale physical activity data reveal worldwide activity inequality. Nature 2017;547(7663):336
    CrossRef
  35. Consolvo S, Bentley FR, Hekler EB, Phatak SS. Mobile User Research: A Practical Guide. Synthesis Lectures on Mobile and Pervasive Computing 2017;9(1):i
    CrossRef
  36. Floegel TA, Florez-Pregonero A, Hekler EB, Buman MP. Validation of Consumer-Based Hip and Wrist Activity Monitors in Older Adults With Varied Ambulatory Abilities. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 2017;72(2):229
    CrossRef
  37. Cadmus-Bertram L. Using Fitness Trackers in Clinical Research: What Nurse Practitioners Need to Know. The Journal for Nurse Practitioners 2017;13(1):34
    CrossRef
  38. Heininga VE, van Roekel E, Wichers M, Oldehinkel AJ, Brañas-Garza P. Reward and punishment learning in daily life: A replication study. PLOS ONE 2017;12(10):e0180753
    CrossRef
  39. Olson E, Badder C, Sullivan S, Smith C, Propert K, Margulies SS. Alterations in Daytime and Nighttime Activity in Piglets after Focal and Diffuse Brain Injury. Journal of Neurotrauma 2016;33(8):734
    CrossRef
  40. 손윤선 , Kim Jong Kwang , Daetaek Lee , 황봉연 , 이종도 , 김기언 . Validity Evaluation of the T-REX Triaxial Accelerometer To Measure Physical Activity by Exercise Types and T-REX Attachment Locations in Men and Women. The Korean Journal of Measurement and Evaluation in Physical Education and Sports Science 2016;18(2):1
    CrossRef
  41. Younsun Son , Jong Kwang Kim , Yoon Jung Bae , Bong Yeon Hwang , Dae Taek Lee , Mi Young Lee , Chong-Do Lee , Keyeon KIM . Concurrent Validation of T-REX Accelerometer. IJASS(International Journal of Applied Sports Sciences) 2016;28(2):79
    CrossRef
  42. Åkerberg A, Söderlund A, Lindén M. Investigation of the validity and reliability of a smartphone pedometer application. European Journal of Physiotherapy 2016;18(3):185
    CrossRef
  43. Dadlani V, Levine JA, McCrady-Spitzer SK, Dassau E, Kudva YC. Physical Activity Capture Technology With Potential for Incorporation Into Closed-Loop Control for Type 1 Diabetes. Journal of Diabetes Science and Technology 2015;9(6):1208
    CrossRef
  44. Dabove P, Ghinamo G, Lingua AM. Inertial sensors for smartphones navigation. SpringerPlus 2015;4(1)
    CrossRef
  45. Spruijt-Metz D, Wen CKF, O’Reilly G, Li M, Lee S, Emken BA, Mitra U, Annavaram M, Ragusa G, Narayanan S. Innovations in the Use of Interactive Technology to Support Weight Management. Current Obesity Reports 2015;4(4):510
    CrossRef
  46. Urrea B, Misra S, Plante TB, Kelli HM, Misra S, Blaha MJ, Martin SS. Mobile Health Initiatives to Improve Outcomes in Primary Prevention of Cardiovascular Disease. Current Treatment Options in Cardiovascular Medicine 2015;17(12)
    CrossRef

According to Crossref, the following books are citing this article (DOI 10.2196/mhealth.3505)

:
  1. Zahran L, El-Beltagy M, Saleh M. Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2019. 2020. Chapter 62:673
    CrossRef
  2. Sariyska R, Montag C. Digital Phenotyping and Mobile Sensing. 2019. Chapter 4:45
    CrossRef
  3. Porszasz J, Stringer W, Casaburi R. Clinical Exercise Testing. 2018. 3:59
    CrossRef
  4. Dorsch AK, King CE, Dobkin BH. Neurorehabilitation Technology. 2016. Chapter 29:605
    CrossRef
  5. Rodriguez VH, Medrano C, Plaza I, Corella C, Abarca A, Julian JA. Ambient Intelligence- Software and Applications – 7th International Symposium on Ambient Intelligence (ISAmI 2016). 2016. Chapter 6:49
    CrossRef