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

mHealth for Data Collection and Research (914) Mobile Health (mhealth) (2765) mHealth for Wellness, Behavior Change and Prevention (2743)

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

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

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

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

Authors of this article:

Eric B Hekler1 Author Orcid Image ;   Matthew P Buman1 Author Orcid Image ;   Lauren Grieco2 Author Orcid Image ;   Mary Rosenberger2 Author Orcid Image ;   Sandra J Winter2 Author Orcid Image ;   William Haskell2 Author Orcid Image ;   Abby C King2 Author Orcid Image
Article Authors Cited by (84) Tweetations (12) Metrics

Journals

  1. Bort-Roig J, Chirveches-Pérez E, Garcia-Cuyàs F, Dowd K, 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 View
  2. Althoff T, Sosič R, Hicks J, King A, Delp S, Leskovec J. Large-scale physical activity data reveal worldwide activity inequality. Nature 2017;547(7663):336 View
  3. 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) View
  4. 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 View
  5. 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 View
  6. Kramer J, Künzler F, Mishra V, Smith S, 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 View
  7. Smith M, Standl M, Heinrich J, Schulz H, Buchowski M. Accelerometric estimates of physical activity vary unstably with data handling. PLOS ONE 2017;12(11):e0187706 View
  8. Spruijt-Metz D, Wen C, O’Reilly G, Li M, Lee S, Emken B, 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 View
  9. 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 View
  10. Dadlani V, Levine J, McCrady-Spitzer S, Dassau E, Kudva Y. 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 View
  11. Mitchell M, White L, Lau E, Leahey T, Adams M, 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 View
  12. Thorpe J, Forchhammer B, Maier A. Development of a Sensor-Based Behavioral Monitoring Solution to Support Dementia Care. JMIR mHealth and uHealth 2019;7(6):e12013 View
  13. Bort-Roig J, Puig-Ribera A, Contreras R, Chirveches-Pérez E, Martori J, Gilson N, 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 View
  14. Smith M, 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) View
  15. Zhai Y, Nasseri N, Pöttgen J, Gezhelbash E, Heesen C, Stellmann J. Smartphone Accelerometry: A Smart and Reliable Measurement of Real-Life Physical Activity in Multiple Sclerosis and Healthy Individuals. Frontiers in Neurology 2020;11 View
  16. 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 View
  17. 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 View
  18. Olson E, Badder C, Sullivan S, Smith C, Propert K, Margulies S. Alterations in Daytime and Nighttime Activity in Piglets after Focal and Diffuse Brain Injury. Journal of Neurotrauma 2016;33(8):734 View
  19. Floegel T, Florez-Pregonero A, Hekler E, Buman M. 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 View
  20. Funk M, Salazar C, 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 View
  21. Urrea B, Misra S, Plante T, Kelli H, Misra S, Blaha M, Martin S. Mobile Health Initiatives to Improve Outcomes in Primary Prevention of Cardiovascular Disease. Current Treatment Options in Cardiovascular Medicine 2015;17(12) View
  22. 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 View
  23. Cadmus-Bertram L. Using Fitness Trackers in Clinical Research: What Nurse Practitioners Need to Know. The Journal for Nurse Practitioners 2017;13(1):34 View
  24. Maddison R, Gemming L, Monedero J, Bolger L, Belton S, Issartel J, Marsh S, Direito A, Solenhill M, Zhao J, Exeter D, Vathsangam H, Rawstorn J. Quantifying Human Movement Using the Movn Smartphone App: Validation and Field Study. JMIR mHealth and uHealth 2017;5(8):e122 View
  25. 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 View
  26. Kong N, Choi J, Seo W. Evaluation of Sleep Problems or Disorders Using Sleep Questionnaires. Chronobiology in Medicine 2019;1(4):144 View
  27. 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) View
  28. Martin C, Rivera D, Hekler E, Riley W, Buman M, Adams M, Magann A. 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 View
  29. Yamamoto K, Ebara T, Matsuda F, Matsukawa T, Yamamoto N, Ishii K, Kurihara T, Yamada S, Matsuki T, Tani N, Kamijima M. Can self-monitoring mobile health apps reduce sedentary behavior? A randomized controlled trial. Journal of Occupational Health 2020;62(1) View
  30. 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 View
  31. Hekler E, Rivera D, Martin C, Phatak S, Freigoun M, Korinek E, Klasnja P, Adams M, Buman M. Tutorial for Using Control Systems Engineering to Optimize Adaptive Mobile Health Interventions. Journal of Medical Internet Research 2018;20(6):e214 View
  32. Katapally T, Chu L. 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 View
  33. Perazzo J, Webel A, Fichtenbaum C, McComsey G. 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 View
  34. Consolvo S, Bentley F, Hekler E, Phatak S. Mobile User Research: A Practical Guide. Synthesis Lectures on Mobile and Pervasive Computing 2017;9(1):i View
  35. 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 View
  36. Ding D, Ramirez Varela A, Bauman A, Ekelund U, Lee I, Heath G, Katzmarzyk P, 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 View
  37. 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 View
  38. Heininga V, van Roekel E, Wichers M, Oldehinkel A, Brañas-Garza P. Reward and punishment learning in daily life: A replication study. PLOS ONE 2017;12(10):e0180753 View
  39. Dabove P, Ghinamo G, Lingua A. Inertial sensors for smartphones navigation. SpringerPlus 2015;4(1) View
  40. Tavares B, Pires I, Marques G, Garcia N, 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 View
  41. 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 View
  42. 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 View
  43. 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 View
  44. Lee W, Lin K, Seto E, Migliaccio G. Wearable sensors for monitoring on-duty and off-duty worker physiological status and activities in construction. Automation in Construction 2017;83:341 View
  45. Park C, Cho D, Moore P. 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 View
  46. 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) View
  47. Sullivan A, Lachman M. Behavior Change with Fitness Technology in Sedentary Adults: A Review of the Evidence for Increasing Physical Activity. Frontiers in Public Health 2017;4 View
  48. Wu R, Liaqat D, de Lara E, Son T, Rudzicz F, Alshaer H, Abed-Esfahani P, Gershon A. 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 View
  49. Pearson E, Prapavessis H, Higgins C, Petrella R, White L, Mitchell M. Adding team-based financial incentives to the Carrot Rewards physical activity app increases daily step count on a population scale: a 24-week matched case control study. International Journal of Behavioral Nutrition and Physical Activity 2020;17(1) View
  50. Kumar D, Galloway J. Feasibility of a home-based environmental enrichment paradigm to enhance purposeful activities in adults with traumatic brain injury: a case series. Disability and Rehabilitation 2022;44(14):3559 View
  51. Muntaner-Mas A, Martinez-Nicolas A, Quesada A, Cadenas-Sanchez C, Ortega F. Smartphone App (2kmFIT-App) for Measuring Cardiorespiratory Fitness: Validity and Reliability Study. JMIR mHealth and uHealth 2021;9(1):e14864 View
  52. Stålesen J, Westergren T, Herman Hansen B, Berntsen S. A Mapping Review of Physical Activity Recordings Derived From Smartphone Accelerometers. Journal of Physical Activity and Health 2020;17(11):1184 View
  53. Mitchell M, Orstad S, Biswas A, Oh P, Jay M, Pakosh M, Faulkner G. Financial incentives for physical activity in adults: systematic review and meta-analysis. British Journal of Sports Medicine 2020;54(21):1259 View
  54. Savi D, Graziano L, Giordani B, Schiavetto S, Vito C, Migliara G, Simmonds N, Palange P, Elborn J. New strategies of physical activity assessment in cystic fibrosis: a pilot study. BMC Pulmonary Medicine 2020;20(1) View
  55. Wang Y, Zhang Y, Bennell K, White D, Wei J, Wu Z, He H, Liu S, Luo X, Hu S, Zeng C, Lei G. Physical Distancing Measures and Walking Activity in Middle-aged and Older Residents in Changsha, China, During the COVID-19 Epidemic Period: Longitudinal Observational Study. Journal of Medical Internet Research 2020;22(10):e21632 View
  56. Wang Y, König L, Reiterer H. A Smartphone App to Support Sedentary Behavior Change by Visualizing Personal Mobility Patterns and Action Planning (SedVis): Development and Pilot Study. JMIR Formative Research 2021;5(1):e15369 View
  57. McCarthy H, Potts H, Fisher A. Physical Activity Behavior Before, During, and After COVID-19 Restrictions: Longitudinal Smartphone-Tracking Study of Adults in the United Kingdom. Journal of Medical Internet Research 2021;23(2):e23701 View
  58. Ho J, Zijlema W, Triguero-Mas M, Donaire-Gonzalez D, Valentín A, Ballester J, Chan E, Goggins W, Mo P, Kruize H, van den Berg M, Gražuleviciene R, Gidlow C, Jerrett M, Seto E, Barrera-Gómez J, Nieuwenhuijsen M. Does surrounding greenness moderate the relationship between apparent temperature and physical activity? Findings from the PHENOTYPE project. Environmental Research 2021;197:110992 View
  59. Mohammed M, Al‐Qahtani M, Takken T. Effects of 12 weeks of recreational football (soccer) with caloric control on glycemia and cardiovascular health of adolescent boys with type 1 diabetes. Pediatric Diabetes 2021;22(4):625 View
  60. Park J, Yoo E, Kim Y, Lee J. What Happened Pre- and during COVID-19 in South Korea? Comparing Physical Activity, Sleep Time, and Body Weight Status. International Journal of Environmental Research and Public Health 2021;18(11):5863 View
  61. de Carvalho Lana R, Ribeiro de Paula A, Souza Silva A, Vieira Costa P, Polese J. Validity of mHealth devices for counting steps in individuals with Parkinson's disease. Journal of Bodywork and Movement Therapies 2021;28:496 View
  62. Pontin F, Lomax N, Clarke G, Morris M. Socio-demographic determinants of physical activity and app usage from smartphone data. Social Science & Medicine 2021;284:114235 View
  63. Lugade V, Kuntapun J, Prupetkaew P, Boripuntakul S, Verner E, Silsupadol P. Three-Day Remote Monitoring of Gait Among Young and Older Adults Using Participants’ Personal Smartphones. Journal of Aging and Physical Activity 2021;29(6):1026 View
  64. Arumugam A, Samara S, Shalash R, Qadah R, Farhani A, Alnajim H, Alkalih H. Does Google Fit provide valid energy expenditure measurements of functional tasks compared to those of Fibion accelerometer in healthy individuals? A cross-sectional study. Diabetes & Metabolic Syndrome: Clinical Research & Reviews 2021;15(6):102301 View
  65. Prado R, Knebel M, Ribeiro E, Teixeira I, Sasaki J, Araújo L, Guerra P, Florindo A. Smartphone apps for tracking physical activity and sedentary behavior: A criterion validity review. Revista Brasileira de Atividade Física & Saúde 2022;27:1 View
  66. Li D, Lee C, Park A, Lee H, Ding Y. Contextual and environmental factors that influence health: A within-subjects field experiment protocol. Frontiers in Public Health 2023;11 View
  67. Ho J, Goggins W, Mo P, Chan E. The effect of temperature on physical activity: an aggregated timeseries analysis of smartphone users in five major Chinese cities. International Journal of Behavioral Nutrition and Physical Activity 2022;19(1) View
  68. Ráthonyi G, Takács V, Szilágyi R, Bácsné Bába É, Müller A, Bács Z, Harangi-Rákos M, Balogh L, Ráthonyi-Odor K. Your Physical Activity Is in Your Hand—Objective Activity Tracking Among University Students in Hungary, One of the Most Obese Countries in Europe. Frontiers in Public Health 2021;9 View
  69. Yao Q, Wang J, Sun Y, Zhang L, Sun S, Cheng M, Yang Q, Wang S, Huang L, Lin T, Jia Y. Accuracy of steps measured by smartphones-based WeRun compared with ActiGraph-GT3X accelerometer in free-living conditions. Frontiers in Public Health 2022;10 View
  70. Vos A, de Bruijn G, Klein M, Lakerveld J, Boerman S, Smit E. SNapp, a Tailored Smartphone App Intervention to Promote Walking in Adults of Low Socioeconomic Position: Development and Qualitative Pilot Study. JMIR Formative Research 2023;7:e40851 View
  71. Goh C, Wang N, Müller A, Yap R, Edney S, Müller-Riemenschneider F. Validation of Smartphones and Different Low-Cost Activity Trackers for Step Counting Under Free-Living Conditions. Journal for the Measurement of Physical Behaviour 2023:1 View
  72. Abdullah S, Arshad J, Khan M, Alazab M, Salah K. PRISED tangle: a privacy-aware framework for smart healthcare data sharing using IOTA tangle. Complex & Intelligent Systems 2023;9(3):3023 View
  73. Atef H, Gaber M. Would the Actigraph Always be Sufficient for Sleep Analysis in Exercise-Based Studies? A Case Report of Negative Response of Sleep to Exercise. Sleep Science 2023;16(02):265 View
  74. Kumar A, Singh R, Chatterjee I, Sharma N, Rana V. Neuroadaptive Incentivization in Healthcare using Blockchain and IoT. SN Computer Science 2023;5(1) View
  75. Di Cesare M, Perpetuini D, Cardone D, Merla A. Assessment of Voice Disorders Using Machine Learning and Vocal Analysis of Voice Samples Recorded through Smartphones. BioMedInformatics 2024;4(1):549 View

Books/Policy Documents

  1. Porszasz J, Stringer W, Casaburi R. Clinical Exercise Testing. View
  2. Dorsch A, King C, Dobkin B. Neurorehabilitation Technology. View
  3. Zahran L, El-Beltagy M, Saleh M. Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2019. View
  4. Sariyska R, Montag C. Digital Phenotyping and Mobile Sensing. View
  5. Rodriguez V, Medrano C, Plaza I, Corella C, Abarca A, Julian J. Ambient Intelligence- Software and Applications – 7th International Symposium on Ambient Intelligence (ISAmI 2016). View
  6. Saito T, Ono R. Physical Therapy and Research in Patients with Cancer. View
  7. Sariyska R, Montag C. Digital Phenotyping and Mobile Sensing. View
  8. Romano F, Perpetuini D, Cardone D, Merla A. 9th European Medical and Biological Engineering Conference. View