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Systematic Review of Mobile Health Applications in Rehabilitation

Published:August 29, 2018DOI:https://doi.org/10.1016/j.apmr.2018.07.439

      Abstract

      Objective

      To conduct systematic review to better define how medical mobile applications (apps) have been used in environments relevant to physical medicine and rehabilitation.

      Data Sources

      PUBMED, IEEE, ACM Digital Library, SCOPUS, INSPEC, and EMBASE.

      Study Selection

      A 10-year date limit was used, spanning publication dates from June 1, 2006, to June 30, 2016. Terms related to physical medicine and rehabilitation as well as mobile apps were used in 10 individual search strategies.

      Data Extraction

      Two investigators screened abstracts and applied inclusion and exclusion criteria. Full-length articles were retrieved. Duplicate articles were removed. If a study met all criteria, the article was reviewed in full.

      Data Synthesis

      Specific variables of interest were extracted and added to summary tables. Summary tables were used to categorize studies according themes, and a list of app features was generated.

      Conclusions

      The search yielded abstracts from 8116 studies, and 102 studies were included in the systematic review. Approximately one-third of the studies evaluated apps as interventions, and the remaining two-thirds of the studies assessed functioning of the app or participant interaction with the app. Some apps may have positive benefits when used to deliver exercise or gait training interventions, as self-management systems, or as measurement tools.

      Registration

      The protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO) network (no. CRD42016046672).

      Keywords

      List of abbreviations:

      App (application), COPD (chronic obstructive pulmonary disorder), EHR (electronic health record), FDA (Food and Drug Administration), GPS (Global Positioning System), HIPAA (Health Insurance Portability and Accountability Act of 1996), mHealth (mobile health), PDA (personal digital assistant), PM&R (physical medicine and rehabilitation), RCT (randomized controlled trial), TBI (traumatic brain injury)
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      References

      1. Cisco visual networking index: global mobile data traffic forecast update, 2016-2021. February 7, 2017. Available at: https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/mobile-white-paper-c11-520862.pdf. Accessed October 23, 2018.

        • Lee H.
        • Ahn H.
        • Choi S.
        • Choi W.
        The SAMS: Smartphone Addiction Management System and verification.
        J Medical Sys. 2014; 38: 1
        • Dicianno B.E.
        • Arva J.
        • Lieberman J.M.
        • et al.
        RESNA position on the application of tilt, recline, and elevating legrests for wheelchairs.
        Assist Technol. 2009; 21 (quiz 24): 13-22
        • Cooper R.A.
        • Dicianno B.E.
        • Brewer B.
        • et al.
        A perspective on intelligent devices and environments in medical rehabilitation.
        Med Eng Phys. 2008; 30: 1387-1398
        • Boulos M.N.
        • Wheeler S.
        • Tavares C.
        • Jones R.
        How smartphones are changing the face of mobile and participatory healthcare: an overview, with example from eCAALYX.
        Biomed Eng Online. 2011; 10: 24
      2. International Telecommunication Union, ICT Data and Statistics Division. ICT facts and figures 2017. July 2017. Available at: https://www.itu.int/en/ITU-D/Statistics/Documents/facts/ICTFactsFigures2017.pdf. Accessed October 23, 2018.

      3. Liu B, Lin J, Sadeh N. Reconciling mobile app privacy and usability on smartphones: Could user privacy profiles help? Paper presented at: 23rd International Conference on World Wide Web. April 7-11, 2014; Seoul, Korea.

        • MobiHealth News
        2010 report: the world of health and medical apps.
        (Available at:) (Accessed January 18, 2017)
        • Sporner M.L.
        • Fitzgerald S.G.
        • Dicianno B.E.
        • et al.
        Psychosocial impact of participation in the National Veterans Wheelchair Games and Winter Sports Clinic.
        Disabil Rehabil. 2009; 31: 410-418
        • Dicianno B.E.
        • Tovey E.
        Power mobility device provision: understanding Medicare guidelines and advocating for clients.
        Arch Phys Medicine Rehabil. 2007; 88: 807-816
        • Martinez-Perez B.
        • de la Torre-Diez I.
        • Lopez-Coronado M.
        Mobile health applications for the most prevalent conditions by the World Health Organization: review and analysis.
        J Med Internet Res. 2013; 15: e120
      4. Barendse RJ, van Dam TB, Nelwan SP. Portable platform independent patient monitoring. Paper presented at: Computing in Cardiology 2013; Rotterdam, The Netherlands.

      5. Onashoga S, Sodiya A, Omilani T, Ajisegiri H. A mobile phone-based antenatal care support system. Paper presented at: Systems Engineering (ICSEng) 21st International Conference. August 16-18, 2011; Las Vegas, NV.

      6. de Barros AC, Cevada J, Bayés À, Alcaine S, Mestre B. User-centred design of a mobile self-management solution for Parkinson's disease. Paper presented at: 12th International Conference on Mobile and Ubiquitous Multimedia. December 2-5, 2013; Luleå, Sweden.

        • Zhang M.
        • Cheow E.
        • Ho C.
        • Ng B.Y.
        • Ho R.
        • Cheok C.C.
        Application of low-cost methodologies for mobile phone app development.
        JMIR Mhealth Uhealth. 2014; 2: e55
        • Schmeler M.R.
        • Schein R.M.
        • Fairman A.
        • et al.
        Telerehabilitation.
        Am J Occ Ther. 2010; 64: S92-S102
        • APTA
        Telehealth–definitions and guidelines 2012 BOD G03-06-09-19.
        (Available at:) (Accessed January 18, 2017)
        • Dicianno B.E.
        • Spaeth D.M.
        • Cooper R.A.
        • Fitzgerald S.G.
        • Boninger M.L.
        Advancements in power wheelchair joystick technology: effects of isometric joysticks and signal conditioning on driving performance.
        Am J Phys Med Rehab. 2006; 85: 631-639
        • Boninger M.L.
        • Dicianno B.E.
        • Cooper R.A.
        • Towers J.D.
        • Koontz A.M.
        • Souza A.L.
        Shoulder magnetic resonance imaging abnormalities, wheelchair propulsion, and gender.
        Arch Phys Med Rehabil. 2003; 84: 1615-1620
        • Aitken M.
        • Lyle J.
        Patient adoption of mHealth: use, evidence and remaining barriers to mainstream acceptance.
        IMS Institute for Healthcare Informatics, Parsippany2015
        • Zhang M.W.
        • Ho R.C.
        • Hawa R.
        • Sockalingam S.
        Analysis of the information quality of bariatric surgery smartphone applications using the Silberg scale.
        Obes Surg. 2016; 26: 163-168
        • Dicianno B.E.
        • Parmanto B.
        • Fairman A.D.
        • et al.
        Perspectives on the evolution of mobile (mHealth) technologies and application to rehabilitation.
        Phys Ther. 2015; 95: 397-405
        • Capela N.A.
        • Lemaire E.D.
        • Baddour N.
        • Rudolf M.
        • Goljar N.
        • Burger H.
        Evaluation of a smartphone human activity recognition application with able-bodied and stroke participants.
        J Neuroeng Rehabil. 2016; 13: 5
        • Choi Y.H.
        • Park H.K.
        • Ahn K.H.
        • Son Y.J.
        • Paik N.J.
        A telescreening tool to detect aphasia in patients with stroke.
        Telemed J E Health. 2015; 21: 729-734
        • Choi Y.H.
        • Park H.K.
        • Paik N.J.
        A telerehabilitation approach for chronic aphasia following stroke.
        Telemed J E Health. 2016; 22: 434-440
        • Edgar S.
        • Swyka T.
        • Fulk G.
        • Sazonov E.S.
        Wearable shoe-based device for rehabilitation of stroke patients.
        Conf Proc IEEE Eng Med Biol Soc. 2010; 2010: 3772-3775
        • Foong O.M.
        • Yong J.M.
        • Sulaiman S.
        • Rambli D.R.A.
        Mobile health awareness in pre-detection of mild stroke symptoms.
        J Comput Sci. 2014; 10: 12
      7. Guo J, Smith T, Messing D, Tang Z, Lawson S, Feng JH. ARMStrokes: A mobile app for everyday stroke rehabilitation. In: ACM 2015. Proceedings of the 17th International ACM SIGACCESS Conference on Computers & Accessibility. 2015 Oct 26-28 (pp. 429-430). ACM (Association for Computing Machinery).

      8. How TV, Chee J, Wan E, Mihalidis A. MyWalk: a mobile app for gait asymmetry rehabilitation in the community. In: Proceedings of the 7th International Conference on Pervasive Computing Technologies for Healthcare. 2013 May 5-8 (pp. 73-76). ICST (Institute for Computer Sciences, Social-Informatics, and Telecommunications Engineering).

      9. Lee HY, Ko BW, Song WK, Kim H, Shin JH. Rhythmic auditory stimulation for robot-assisted gait rehabilitation. In: Advanced Intelligent Mechatronics (AIM), 2015 IEEE Internation conference on 2015 Jul 7-11 (pp. 422-426). IEEE (Institute of Electrical and Electronics Engineers).

        • Lee W.W.
        • Yen S.C.
        • Tay A.
        • et al.
        A smartphone-centric system for the range of motion assessment in stroke patients.
        IEEE J Biomed Health Inform. 2014; 18: 1839-1847
      10. Neves D, Vourvopoulos A, Cameirao M, Bermudez I, Badia S. An assistive mobile platform for delivering knowledge of performance feedback. In: Proceedings of the 8th International Conference on Pervasive Computing Technologies for Healthcare 2014 May 20-23 (pp.440-442). ICST (Institute for Computer Sciences, Social-informatics, and Telecommunications Engineering).

        • Oliveira J.
        • Gamito P.
        • Morais D.
        • Brito R.
        • Lopes P.
        • Norberto L.
        Cognitive assessment of stroke patients with mobile apps: a controlled study.
        Stud Health Technol Inform. 2014; 199: 103-107
        • Paul L.
        • Wyke S.
        • Brewster S.
        • et al.
        Increasing physical activity in stroke survivors using STARFISH, an interactive mobile phone application: a pilot study.
        Top Stroke Rehabil. 2016; 23: 170-177
      11. Pedroli E, Serino S, Cipresso P, et al. Neglect App. Usability of a new application for assessment and rehabilitation of neglect. In: Proceedings of the 3rd 2015 Workshop on ICTs for improving Patients Rehabilitation Research Techniques 2015 Oct 1-2 (pp. 139-143). ACM (Association for Computing Machinery).

        • Sureshkumar K.
        • Murthy G.
        • Natarajan S.
        • Naveen C.
        • Goenka S.
        • Kuper H.
        Evaluation of the feasibility and acceptability of the 'Care for Stroke' intervention in India, a smartphone-enabled, carer-supported, educational intervention for management of disability following stroke.
        BMJ Open. 2016; 6e009243
        • Bellamy N.
        • Wilson C.
        • Hendrikz J.
        • Patel B.
        • Dennison S.
        Electronic data capture (EDC) using cellular technology: implications for clinical trials and practice, and preliminary experience with the m-Womac Index in hip and knee OA patients.
        Inflammopharmacology. 2009; 17: 93-99
        • Bellamy N.
        • Wilson C.
        • Hendrikz J.
        • et al.
        Osteoarthritis Index delivered by mobile phone (m-WOMAC) is valid, reliable, and responsive.
        J Clin Epidemiol. 2011; 64: 182-190
      12. Bhachu L, Soldatova LN, Spasic I, Button K. Mobile application KneeCare to support knee rehabilitation. In: IEEE 2014 Science and Information Conference (SAI). 2014 Aug 27-29; London, UK.

      13. Halic T, Kockara S, Demirel D, Willey M, Eichelberger K. MoMiReS: Mobile mixed reality system for physical & occupational therapies for hand and wrist ailments. In: IEEE 2014 Innovations in Technology Conference (InnoTek). 2014 May 16, 2014; Warwick, RI.

        • Nishiguchi S.
        • Ito H.
        • Yamada M.
        • et al.
        Self-assessment tool of disease activity of rheumatoid arthritis by using a smartphone application.
        Telemed J E Health. 2014; 20: 235-240
        • Nishiguchi S.
        • Ito H.
        • Yamada M.
        • et al.
        Self-assessment of rheumatoid arthritis disease activity using a smartphone application. Development and 3-month feasibility study.
        Methods Inf Med. 2016; 55: 65-69
      14. O'Neil C, Dunlop MD, Kerr A. Supporting sit-to-stand rehabilitation using smartphone sensors and Arduino haptic feedback modules. In: Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct 2015 Aug 24-27 (pp. 811-818). ACM (Association for Computing Machinery).

        • Shinohara A.
        • Ito T.
        • Ura T.
        • et al.
        Development of lifelog sharing system for rheumatoid arthritis patients using smartphone.
        Conf Proc IEEE Eng Med Biol Soc. 2013; 2013: 7266-7269
        • Spasić I.
        • Button K.
        • Divoli A.
        • et al.
        TRAK App Suite: A web-based intervention for delivering standard care for the rehabilitation of knee conditions.
        JMIR Res Protoc. 2015; 4: e122
        • Van Reijen M.
        • Vriend I.
        • Zuidema V.
        • van Mechelen W.
        • Verhagen E.A.
        Increasing compliance with neuromuscular training to prevent ankle sprain in sport: does the 'Strengthen your ankle' mobile app make a difference? A randomised controlled trial.
        Br J Sports Med. 2016; 50: 1200-1205
        • Vriend I.
        • Coehoorn I.
        • Verhagen E.
        Implementation of an app-based neuromuscular training programme to prevent ankle sprains: a process evaluation using the RE-AIM Framework.
        Br J Sports Med. 2015; 49: 484-488
        • Dicianno B.E.
        • Fairman A.D.
        • McCue M.
        • et al.
        Feasibility of using mobile health to promote self-management in spina bifida.
        Am J Phys Med Rehabil. 2016; 95: 425-437
        • Fairman A.D.
        • Dicianno B.E.
        • Datt N.
        • Garver A.
        • Parmanto B.
        • McCue M.
        Outcomes of clinicians, caregivers, family members and adults with spina bifida regarding receptivity to use of the iMHere mHealth Solution to promote wellness.
        Int J Telerehabil. 2013; 5: 3-16
        • Wu Y.K.
        • Liu H.Y.
        • Kelleher A.
        • Pearlman J.
        • Cooper R.A.
        Evaluating the usability of a smartphone virtual seating coach application for powered wheelchair users.
        Med Eng Phys. 2016; 38: 569-575
        • Yu D.X.
        • Parmanto B.
        • Dicianno B.E.
        • Pramana G.
        Accessibility of mHealth self-care apps for individuals with spina bifida.
        Perspect Health Info Manag. 2015; 12: 1h
        • Yu D.X.
        • Parmanto B.
        • Dicianno B.E.
        • Watzlaf V.J.
        • Seelman K.D.
        Accessibility needs and challenges of a mHealth system for patients with dexterity impairments.
        Disabil Rehabil Assist Technol. 2015; : 1-9
        • Anderson S.M.
        • Riehle T.H.
        • Lichter P.A.
        • Brown A.W.
        • Panescu D.
        Smartphone-based system to improve transportation access for the cognitively impaired.
        Conf Proc IEEE Eng Med Biol Soc. 2015; 2015: 7760-7763
        • De Joode E.A.
        • Van Heugten C.M.
        • Verhey F.R.
        • Van Boxtel M.P.
        Effectiveness of an electronic cognitive aid in patients with acquired brain injury: a multicentre randomised parallel-group study.
        Neuropsychol Rehabil. 2013; 23: 133-156
        • Dowds M.M.
        • Lee P.H.
        • Sheer J.B.
        • et al.
        Electronic reminding technology following traumatic brain injury: effects on timely task completion.
        J Head Trauma Rehabil. 2011; 26: 339-347
        • Juengst S.B.
        • Graham K.M.
        • Pulantara I.W.
        • et al.
        Pilot feasibility of an mHealth system for conducting ecological momentary assessment of mood-related symptoms following traumatic brain injury.
        Brain Inj. 2015; 29: 1351-1361
      15. Moron MJ, Yanez R, Cascado D, Suarez-Mejias C, Sevilano JL. A mobile memory game for patients with Acquired Brain Damage: a preliminary usability study. In: IEEE. 2014. IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI). 2014 Jun 1-4; Valencia, Spain.

        • Pavliscsak H.
        • Little J.R.
        • Poropatich R.K.
        • et al.
        Assessment of patient engagement with a mobile application among service members in transition.
        J Am Med Inform Assoc. 2016; 23: 110-118
      16. Ruiz-Zafra A, Noguera M, Benghazi K, Garrido JL, Urbano GC, Caracuel A. A mobile cloud-supported e-rehabilitation platform for brain-injured patients. In: IEEE 2013. 2013 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops. 2013 May 5-8; Venice, Italy.

        • Bartlett Y.K.
        • Haywood A.
        • Bentley C.L.
        • et al.
        The SMART personalised self-management system for congestive heart failure: results of a realist evaluation.
        BMC Medi Inform Decis Mak. 2014; 14: 109
        • Forman D.E.
        • LaFond K.
        • Panch T.
        • Allsup K.
        • Manning K.
        • Sattelmair J.
        Utility and efficacy of a smartphone application to enhance the learning and behavior goals of traditional cardiac rehabilitation: a feasibility study.
        J Cardiopulm Rehabil Prev. 2014; 34: 327-334
      17. Gimenez G, Guixeres FJ, Villaescusa J, et al. A new system for integral community cardiac rehabilitation based on technological platforms for the Lifestyle Change Supporting System. In: IEEE 2006. 2006 Computers in Cardiology. 2006 Sep 17-20; Valencia, Spain.

        • Layton A.M.
        • Whitworth J.
        • Peacock J.
        • Bartels M.N.
        • Jellen P.A.
        • Thomashow B.M.
        Feasibility and acceptability of utilizing a smartphone based application to monitor outpatient discharge instruction compliance in cardiac disease patients around discharge from hospitalization.
        Int J Telemed Appl. 2014; 2014: 415868
        • Lee H.E.
        • Wang W.C.
        • Lu S.W.
        • Wu B.Y.
        • Ko L.W.
        Home-based mobile cardio-pulmonary rehabilitation consultant system.
        Conf Proc IEEE Eng Med Biol Soc. 2011; 2011: 989-992
        • Mattila J.
        • Ding H.
        • Mattila E.
        • Särelä A.
        Mobile tools for home-based cardiac rehabilitation based on heart rate and movement activity analysis.
        Conf Proc IEEE Eng Med Biol Soc. 2009; 2009: 6448-6452
        • Pfaeffli L.
        • Maddison R.
        • Jiang Y.
        • Dalleck L.
        • Löf M.
        Measuring physical activity in a cardiac rehabilitation population using a smartphone-based questionnaire.
        J Med Internet Res. 2013; 15: e61
        • Skobel E.
        • Martinez-Romero A.
        • Scheibe B.
        • et al.
        Evaluation of a newly designed shirt-based ECG and breathing sensor for home-based training as part of cardiac rehabilitation for coronary artery disease.
        Eur J Prev Cardiol. 2014; 21: 1332-1340
        • Varnfield M.
        • Karunanithi M.K.
        • Särelä A.
        • et al.
        Uptake of a technology-assisted home-care cardiac rehabilitation program.
        Medical J Aust. 2011; 194: S15-S19
        • Varnfield M.
        • Karunanithi M.
        • Lee C.K.
        • et al.
        Smartphone-based home care model improved use of cardiac rehabilitation in postmyocardial infarction patients: results from a randomised controlled trial.
        Heart. 2014; 100: 1770-1779
        • Vuorinen A.L.
        • Leppänen J.
        • Kaijanranta H.
        • et al.
        Use of home telemonitoring to support multidisciplinary care of heart failure patients in Finland: randomized controlled trial.
        J Med Internet Res. 2014; 16: e282
        • Worringham C.
        • Rojek A.
        • Stewart I.
        Development and feasibility of a smartphone, ECG and GPS based system for remotely monitoring exercise in cardiac rehabilitation.
        PLoS One. 2011; 6e14669
        • Hardinge M.
        • Rutter H.
        • Velardo C.
        • et al.
        Using a mobile health application to support self-management in chronic obstructive pulmonary disease: a six-month cohort study.
        BMC Med Inform Decis Mak. 2015; 15: 46
        • Nguyen H.Q.
        • Donesky-Cuenco D.
        • Wolpin S.
        • et al.
        Randomized controlled trial of an internet-based versus face-to-face dyspnea self-management program for patients with chronic obstructive pulmonary disease: pilot study.
        J Med Internet Res. 2008; 10: e9
      18. Spina G, Huang G, Vaes A, Spruit M, Amft O. COPDTrainer: a smartphone-based motion rehabilitation training system with real-time acoustic feedback. In: Proceedings of the 2013 ACM international joint conference on pervasive and ubiquitous computing 2013 Sep 8-12 (pp.597-606). ACM (Association for Computing Machinery).

        • Verwey R.
        • van der Weegen S.
        • Spreeuwenberg M.
        • Tange H.
        • van der Weijden T.
        • de Witte L.
        Technology combined with a counseling protocol to stimulate physical activity of chronically ill patients in primary care.
        Stud Health Technol Inform. 2014; 201: 264-270
        • Verwey R.
        • van der Weegen S.
        • Spreeuwenberg M.
        • Tange T.
        • van der Weijden T.
        • de Witte L.
        A pilot study of a tool to stimulate physical activity in patients with COPD or type 2 diabetes in primary care.
        J Telemed Telecare. 2014; 20: 6
        • Vorrink S.N.
        • Kort H.S.
        • Troosters T.
        • Lammers J.W.
        A mobile phone app to stimulate daily physical activity in patients with chronic obstructive pulmonary disease: development, feasibility, and pilot studies.
        JMIR Mhealth Uhealth. 2016; 4: e11
        • Wang C.H.
        • Chou P.C.
        • Joa W.C.
        • et al.
        Mobile-phone-based home exercise training program decreases systemic inflammation in COPD: a pilot study.
        BMC Pulm Med. 2014; 14: 142
        • Williams V.
        • Price J.
        • Hardinge M.
        • Tarassenko L.
        • Farmer A.
        Using a mobile health application to support self-management in COPD: a qualitative study.
        Br Journal Gen Pract. 2014; 64: e392-e400
        • Albert M.
        • Toledo S.
        • Shapiro M.
        • Kording K.
        Using mobile phones for activity recognition in Parkinson’s patients.
        Front Neurol. 2012; 3: 7
        • Bray P.
        • Bundy A.C.
        • Ryan M.M.
        • North K.N.
        Feasibility of a computerized method to measure quality of "everyday" life in children with neuromuscular disorders.
        Phys Occup Ther Pediatr. 2010; 30: 43-53
        • Casamassima F.
        • Ferrari A.
        • Milosevic B.
        • Ginis P.
        • Farella E.
        • Rocchi L.
        A wearable system for gait training in subjects with Parkinson's disease.
        Sensors (Basel). 2014; 14: 6229-6246
      19. Chang YJ. Anomaly detection for travelling individuals with cognitive impairments. ACM SIGACCESS Accessibility and Computing. 2010 Jun 1(97):25-32.

      20. Chang YJ, Chen CN, Chou LD, Wang TY. A novel indoor wayfinding system based on passive RFID for individuals with cognitive impairments. In: PervasiveHealth 2008. Second International Conference on Pervasive Computing Technologies for Healthcare 2008 Jan 30-Feb 1 (pp.108-111). IEEE (Institute of Electrical and Electronics Engineers).

      21. Chang YJ, Tsai SK, Wang TY. A context aware handheld wayfinding system for individuals with cognitive impairments. In: Proceedings of the 10th International ACM SIGACCESS Conference on Computers and Accessibility 2008 Oct 13-15 (pp. 27-34). ACM (Association for Computing Machinery.

        • Chang Y.J.
        • Wang T.Y.
        Comparing picture and video prompting in autonomous indoor wayfinding for individuals with cognitive impairments.
        Pers Ubiquitous Comput. 2010; 14: 11
        • Davies D.K.
        • Stock S.E.
        • Holloway S.
        • Wehmeyer M.L.
        Evaluating a GPS-based transportation device to support independent bus travel by people with intellectual disability.
        Intellect Dev Disabil. 2010; 48: 454-463
        • Ellis R.J.
        • Ng Y.S.
        • Zhu S.
        • et al.
        A validated smartphone-based assessment of gait and gait variability in Parkinson's disease.
        PLoS One. 2015; 10e0141694
        • Ferrari A.
        • Ginis P.
        • Hardegger M.
        • Casamassima F.
        • Rocchi L.
        • Chiari L.
        A mobile Kalman-filter based solution for the real-time estimation of spatio-temporal gait parameters.
        IEEE Trans Neural Syst Rehabil Eng. 2016; 24: 764-773
        • Ginis P.
        • Nieuwboer A.
        • Dorfman M.
        • et al.
        Feasibility and effects of home-based smartphone-delivered automated feedback training for gait in people with Parkinson's disease: a pilot randomized controlled trial.
        Parkinsonism Relat Disord. 2016; 22: 28-34
        • Huang K.
        • Sparto P.J.
        • Kiesler S.
        • Siewiorek D.P.
        • Smailagic A.
        iPod-based in-home system for monitoring gaze-stabilization exercise compliance of individuals with vestibular hypofunction.
        J Neuroeng Rehabil. 2014; 11: 69
        • Liddle J.
        • Ireland D.
        • McBride S.J.
        • et al.
        Measuring the lifespace of people with Parkinson's disease using smartphones: proof of principle.
        JMIR Mhealth Uhealth. 2014; 2: e13
      22. Pepa L, Capecci M, Verdini F, Ceravolo MG, Spalazzi L. An architecture to manage motor disorders in Parkinson's disease. In: IEEE 2015. Internet of Things (WF-IoT), 2015 IEEE 2nd World Forum. 2015 Dec 14-16; Milan, Italy.

        • Pu F.
        • Fan X.
        • Yang Y.
        • et al.
        Feedback system based on plantar pressure for monitoring toe-walking strides in children with cerebral palsy.
        Am J Phys Med Rehabil. 2014; 93: 122-129
        • Tacchino A.
        • Pedullà L.
        • Bonzano L.
        • et al.
        A new app for at-home cognitive training: description and pilot testing on patients with multiple sclerosis.
        JMIR Mhealth Uhealth. 2015; 3: e85
        • Dekker-van Weering M.G.
        • Vollenbroek-Hutten M.M.
        • Hermens H.J.
        Do personalized feedback messages about activity patterns stimulate patients with chronic low back pain to change their activity behavior on a short term notice?.
        Appl Psychophysiol Biofeedback. 2012; 37: 81-89
        • Dekker-van Weering M.G.
        • Vollenbroek-Hutten M.M.
        • Hermens H.J.
        A pilot study - the potential value of an activity-based feedback system for treatment of individuals with chronic lower back pain.
        Disabil Rehabil. 2015; 37: 2250-2256
        • Duggan G.B.
        • Keogh E.
        • Mountain G.A.
        • McCullagh P.
        • Leake J.
        • Eccleston C.
        Qualitative evaluation of the SMART2 self-management system for people in chronic pain.
        Disabil Rehabil Assist Technol. 2015; 10: 53-60
        • Timmerman J.G.
        • Tönis T.M.
        • Dekker-van Weering M.G.
        • et al.
        Co-creation of an ICT-supported cancer rehabilitation application for resected lung cancer survivors: design and evaluation.
        BMC Health Serv Res. 2016; 16: 155
        • Boissy P.
        • Jacobs K.
        • Roy S.H.
        Usability of a barcode scanning system as a means of data entry on a PDA for self-report health outcome questionnaires: a pilot study in individuals over 60 years of age.
        BMC Med Inform Decis Mak. 2006; 6: 42
        • Corbi G.
        • Gambassi G.
        • Pagano G.
        • et al.
        Impact of an innovative educational strategy on medication appropriate use and length of stay in elderly patients.
        Medicine (Baltimore). 2015; 94: e918
        • Crotty M.
        • Killington M.
        • van den Berg M.
        • Morris C.
        • Taylor A.
        • Carati C.
        Telerehabilitation for older people using off-the-shelf applications: acceptability and feasibility.
        J Telemed Telecare. 2014; 20: 370-376
        • Des Roches C.A.
        • Balachandran I.
        • Ascenso E.M.
        • Tripodis Y.
        • Kiran S.
        Effectiveness of an impairment-based individualized rehabilitation program using an iPad-based software platform.
        Front Hum Neurosci. 2014; 8: 1015
        • Fleury A.
        • Mourcou Q.
        • Franco C.
        • Diot B.
        • Demongeot J.
        • Vuillerme N.
        Evaluation of a smartphone-based audio-biofeedback system for improving balance in older adults--a pilot study.
        Conf Proc IEEE Eng Med Biol Soc. 2013; 2013: 1198-1201
        • Hsiao K.F.
        • Rashvand H.F.
        Data modeling mobile augmented reality: integrated mind and body rehabilitation.
        Multimed Tools Appl. 2015; 74: 18
      23. Kaenampornpan M, Anuchad T, Supaluck P. Fall detection prototype for Thai elderly in mobile computing era. In: IEEE 2011. Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). 2011 May 17-19; Khon Kaen, Thailand.

        • Redd C.B.
        • Bamberg S.J.M.
        A wireless sensory feedback device for real-time gait feedback and training.
        IEEE. 2012; 17: 8
      24. Santos A, Guimaraes V, Matos N, Cevada J, Ferreira C, Sousa I. Multi-sensor exercise-based interactive games for fall prevention and rehabilitation. In: IEEE 2015. Pervasive Computing Technologies for Healthcare (PervasiveHealth). 2015 May 20-23; Istanbul, Turkey.

        • Sprigle S.
        • Nemeth M.
        • Gajjala A.
        Iterative design and testing of a hand-held, non-contact wound measurement device.
        J Tissue Viability. 2012; 21: 17-26
        • Wu H.H.
        • Lemaire E.D.
        • Baddour N.
        Change-of-state determination to recognize mobility activities using a BlackBerry smartphone.
        Conf Proc IEEE Eng Med Biol Soc. 2011; 2011: 5252-5255
        • Xia Y.
        • Cheung V.
        • Garcia E.
        • Ding H.
        • Karunaithi M.
        Development of an automated physical activity classification application for mobile phones.
        Stud Health Technol Inform. 2011; 168: 188-194
        • Xu J.Y.
        • Nan X.
        • Ebken V.
        • Wang Y.
        • Pottie G.J.
        • Kaiser W.J.
        Integrated inertial sensors and mobile computing for real-time cycling performance guidance via pedaling profile classification.
        IEEE J Biomed Health Inform. 2015; 19: 440-445
        • Bittel A.J.
        • Elazzazi A.
        • Bittel D.C.
        Accuracy and precision of an accelerometer-based smartphone app designed to monitor and record angular movement over time.
        Telemed J E Health. 2016; 22: 302-309
        • Capela N.A.
        • Lemaire E.D.
        • Baddour N.
        Novel algorithm for a smartphone-based 6-minute walk test application: algorithm, application development, and evaluation.
        J Neuroeng Rehabil. 2015; 12: 19
        • Capela N.A.
        • Lemaire E.D.
        • Baddour N.C.
        A smartphone approach for the 2 and 6-minute walk test.
        Conf Proc IEEE Eng Med Biol Soc. 2014; 2014: 958-961
        • Cerrito A.
        • Bichsel L.
        • Radlinger L.
        • Schmid S.
        Reliability and validity of a smartphone-based application for the quantification of the sit-to-stand movement in healthy seniors.
        Gait Posture. 2015; 41: 409-413
        • Cuesta-Vargas A.I.
        • Roldan-Jimenez C.
        Validity and reliability of arm abduction angle measured on smartphone: a cross-sectional study.
        BMC Musculoskelet Disord. 2016; 17: 1
        • Ferriero G.
        • Vercelli S.
        • Sartorio F.
        • et al.
        Reliability of a smartphone-based goniometer for knee joint goniometry.
        Int J Rehabil Res. 2013; 36: 146-151
        • Furrer M.
        • Bichsel L.
        • Niederer M.
        • Baur H.
        • Schmid S.
        Validation of a smartphone-based measurement tool for the quantification of level walking.
        Gait Posture. 2015; 42: 289-294
        • Johnson L.B.
        • Sumner S.
        • Duong T.
        • et al.
        Validity and reliability of smartphone magnetometer-based goniometer evaluation of shoulder abduction—a pilot study.
        Man Ther. 2015; 20: 777-782
        • Krause D.A.
        • Boyd M.S.
        • Hager A.N.
        • Smoyer E.C.
        • Thompson A.T.
        • Hollman J.H.
        Reliability and accuracy of a goniometer mobile device application for video measurement of the functional movement screen deep squat test.
        Int J Sports Phys Ther. 2015; 10: 37-44
        • Milanese S.
        • Gordon S.
        • Buettner P.
        • et al.
        Reliability and concurrent validity of knee angle measurement: smart phone app versus universal goniometer used by experienced and novice clinicians.
        Man Ther. 2014; 19: 569-574
        • Quek J.
        • Brauer S.G.
        • Treleaven J.
        • Pua Y.H.
        • Mentiplay B.
        • Clark R.A.
        Validity and intra-rater reliability of an android phone application to measure cervical range-of-motion.
        J Neuroeng Rehabil. 2014; 11: 65
        • Vohralik S.L.
        • Bowen A.R.
        • Burns J.
        • Hiller C.E.
        • Nightingale E.J.
        Reliability and validity of a smartphone app to measure joint range.
        Am J Phys Med Rehabil. 2015; 94: 325-330
        • Institute for Healthcare Improvement
        The triple aim. Optimizing health, care and cost.
        Healthc Exec. 2009; 24: 64-66
        • Reynoldson C.
        • Stones C.
        • Allsop M.
        • et al.
        Assessing the quality and usability of smartphone apps for pain self-management.
        Pain Med. 2014; 15: 898-909
        • Mendiola M.F.
        • Kalnicki M.
        • Lindenauer S.
        Valuable features in mobile health apps for patients and consumers: content analysis of apps and user ratings.
        JMIR Mhealth Uhealth. 2015; 3: e40
        • Cortez N.G.
        • Cohen I.G.
        • Kesselheim A.S.
        FDA regulation of mobile health technologies.
        N Engl J Med. 2014; 371: 372-379
        • Food and Drug Administration
        Mobile medical applications: guidance for industry and Food and Drug Administration staff.
        Food and Drug Administration, Silver Spring, MD2013