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Inicio Neurología (English Edition) Mobile phone applications in Parkinson's disease: a systematic review
Journal Information
Vol. 34. Issue 1.
Pages 38-54 (January - February 2019)
Vol. 34. Issue 1.
Pages 38-54 (January - February 2019)
Review article
Open Access
Mobile phone applications in Parkinson's disease: a systematic review
Aplicaciones móviles en la enfermedad de Parkinson: una revisión sistemática
M. Linares-del Reya, L. Vela-Desojob,c, R. Cano-de la Cuerdaa,
Corresponding author

Corresponding author.
a Departamento de Fisioterapia, Terapia Ocupacional, Rehabilitación y Medicina Física, Facultad de Ciencias de la Salud, Universidad Rey Juan Carlos, Madrid, Spain
b Unidad de Trastornos del Movimiento, Servicio de Neurología, Hospital Universitario Fundación Alcorcón, Madrid, Spain
c CINAC HM Puerta del Sur, Móstoles, Madrid, Spain
Article information
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Tables (2)
Table 1. Main characteristics of the studies included in the review.
Table 2. Main characteristics of mobile applications for Parkinson's disease.
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Parkinson's disease (PD) is the second most common neurodegenerative disease. However, diagnosing, assessing, and treating these patients is a complex process requiring continuous monitoring. In this context, smartphones may be useful in the management of patients with PD.


The purpose of this study is to perform a systematic review of the literature addressing the use of mobile phone applications (apps) in PD.

Materials and methods

We conducted a literature search of articles published in English or Spanish between 2011 and 2016 analysing or validating apps specifically designed for or useful in PD. In addition, we searched for apps potentially useful for PD patients in the leading app stores.


The literature and app searches yielded a total of 125 apps, 56 of which were classified as potentially useful in PD and 69 as specifically designed for PD (23 information apps, 29 assessment apps, 13 treatment apps, and 4 assessment and treatment apps). Numerous mobile apps are potentially useful in or specifically designed for PD management. However, scientific evidence of their usefulness is scarce and of poor quality. Further studies are needed to validate these tools and regulate their use.

Mobile phone applications
Parkinson's disease

La enfermedad de Parkinson (EP) es la segunda enfermedad neurodegenerativa más frecuente, pero su diagnóstico, valoración y tratamiento son complejos y requiere de una atención sanitaria continuada en el tiempo. En este sentido, las características de los teléfonos móviles inteligentes o smartphones hacen que se plantee su uso en el ámbito de la asistencia del paciente con EP.


El objetivo del presente trabajo es realizar una revisión sistemática sobre el uso de aplicaciones móviles (apps) en la EP.

Material y métodos

Se llevó a cabo una búsqueda bibliográfica incluyendo artículos publicados en inglés o castellano, del año 2011 hasta el 2016, y que presentasen, analizasen o validasen un sistema basado en una app con utilidad o diseño específico para la EP. A su vez, se llevó a cabo una búsqueda de aplicaciones móviles en los principales mercados de aplicaciones móviles.


Se encontraron mediante ambas búsquedas 125 aplicaciones, de las cuales 56 se clasificaron con potencial utilidad en la EP, y 69 con un diseño específico para la EP, siendo 23 apps de información sobre EP, 29 apps de valoración, 13 apps de tratamiento y 4 apps de valoración y tratamiento. Existen un gran número de aplicaciones móviles con potencial utilidad y diseño específico en la EP; sin embargo; la evidencia científica acerca de los mismos es escasa y de baja calidad. Son necesarios estudios posteriores para validar esta tecnología, así como una regulación por parte de organismos acerca de su uso.

Palabras clave:
Aplicaciones móviles
Enfermedad de Parkinson
Full Text

Parkinson's disease (PD) is a chronic neurodegenerative disease characterised by 4 motor symptoms: bradykinesia, resting tremor, musculoskeletal stiffness, and postural instability. However, patients also display non-motor symptoms, including psychiatric alterations, neurovegetative symptoms, and cognitive alterations.1

PD is currently the second most frequent neurodegenerative disease, after Alzheimer disease.2 In 2005, the World Health Organization estimated worldwide incidence of PD at 4.5-19 new cases per 100000 person-years, with prevalence of 100-200 cases per 100000 population.1 The most recent report from the European Parkinson's Disease Association predicts a worldwide prevalence of 8.7 million to 9.3 million cases by 2030.3 In Spain, around 2% of people aged over 65 are affected by PD4; 115000 disabled people are diagnosed with the disease, according to the National Statistics Institute.5 Given that PD is a chronic disease, and the constantly improving life expectancy in developed countries, these figures and the associated social and healthcare costs will inevitably rise. The costs associated with the disease range from €7000 to €17000 per patient per year, depending on the sources consulted and the factors considered.4,6,7

Although the aetiology of PD is unknown, the latest theories consider it to be multifactorial, involving a combination of genetic predisposition and a range of environmental triggers.8

Diagnosis of PD is mainly based on clinical criteria, as no biomarkers are able to confirm the presence of the disease. This can be an obstacle in the differential diagnosis of PD, resulting in treatment delays.9 In most cases, treatment entails the administration of a combination of levodopa and other dopaminergic drugs to compensate for dopamine depletion, the cause of most PD symptoms.10 These patients also require complementary rehabilitation therapy, including physiotherapy, occupational therapy, speech therapy, and psychological treatment. Multidisciplinary management of the disease is of great importance11: in addition to pharmacological treatment of symptoms, the other therapeutic approaches aim to slow or reduce disability, improving patients’ functional status and quality of life as far as possible.12 Neurorehabilitation has been demonstrated to be effective and plays a major role in the treatment of PD.13 Measuring the effects of this therapy requires continuous, appropriate evaluation using objective tools; this enables us to provide the best possible care and treatment at all times.14

Recent years have seen an increase in the use of information technology in healthcare. In the area of neurological disease, research is being conducted into new assessment and treatment technologies based on motion analysis, robotic systems, virtual reality, and telerehabilitation.14 However, these solutions are often very costly, limiting their use in clinical practice, regardless of their effectiveness. The mass adoption of smartphones raises the question of their usefulness as a clinical tool. In PD, possible uses of the devices include increasing collaboration between team members, reducing time-/distance-related limitations in communication between patients and healthcare professionals, and tracking patient progression.8


We performed a systematic review of published studies on mobile applications (apps) either directly related to PD or potentially useful for management of the disease; the purpose of the review was to describe, analyse, and classify the applications identified to increase understanding of them.

Material and methods

The systematic review analysed information from biomedical databases as well as sources specific to mobile applications and new technology.

Literature search

We gathered published scientific articles addressing the design, development, and evaluation of mobile applications related to PD. The search was performed on the Academic Search Premier, MEDLINE, CINAHL, and PubMed databases, with the keywords “Parkinson's disease,” “Parkinson,” “smartphone,” “mobile application,” and “app.”

The review included only those articles published in English or Spanish between 2011 and 2016 (2011 was selected as the lower limit because the first reference related to an app developed specifically for PD was published that year). We excluded studies with no direct relevance to PD.

The Jadad scale was used to assess the methodological quality of the articles included. The scale, also known as the Oxford Quality Scoring System,15 is validated, simple, and quick to apply. A number of questions are used to classify studies by methodological quality: whether the study is randomised and whether the method of randomisation is described; whether the study is double blind and whether the method of blinding is described; and whether withdrawals and dropouts are described. A score of 3 or higher indicates acceptable methodological quality.

Search of other information sources

In addition to the literature search, we searched the main app marketplaces (Google Play, Apple App Store, and Windows Store) for mobile applications related to PD.

As no specific method has been described for searching and classifying mobile applications, we identified apps described in a review of neurorehabilitation apps16 and in a list of the “50 best Spanish-language health apps.”17

We initially included all those apps described in these sources that were related to neurorehabilitation, regardless of language or country of development. We then searched the terms “Parkinson's disease” and “Parkinson” in the marketplaces listed above in order to identify apps either directly related to PD or potentially useful in management of the disease, selecting apps that were available in English or Spanish. This process accounted for the therapeutic usefulness, content, quality, design, and usability of the applications. Finally, apps were categorised by purpose according to the following classification:

  • 1.

    Applications useful for PD: apps not specifically designed for PD but potentially useful in managing the disease.

  • 2.

    Applications specifically designed for PD: including 3 subcategories:

  • Information apps: apps providing information on the disease, targeting healthcare professionals, patients, families, or carers.

  • Assessment apps: apps including various tests for assessing patients with PD, analysing gait, balance, tremor, speech, and upper limb coordination, among other parameters.

  • Treatment apps: apps providing patients and healthcare professionals with a series of guidelines for pharmacological treatment of PD or neurorehabilitation, including physiotherapy, cognitive therapy, and speech therapy.

This classification was also used for applications identified through the literature review. It should also be noted that certain applications could be assigned to more than one category. Some applications require the use of peripheral devices, such as additional sensors or fastening mechanisms; this was also noted in the analysis of results.

ResultsLiterature search

We gathered a total of 34 articles; 26 were finally selected following application of the inclusion and exclusion criteria.18–43 Eight articles were excluded for either not presenting an application with usefulness for PD,44 or not providing sufficient information on the application or the study.45–51 The flow chart in Fig. 1 shows the number of studies identified in each database and the number of studies excluded.

Figure 1.

Flow chart illustrating the article selection process.


Of the 26 studies identified, 23 were available in full-text format; only the abstracts were available for the remaining 3 articles.35,40,43 Five of the final sample of articles present a mobile application or system potentially useful for PD20 or specifically designed for management of the disease25,28,37,38; no results have yet been published on the usefulness of these apps for human patients. The system presented by Casamassima et al.23 was evaluated in 2 subsequent studies.31,41

The tool developed by Palmerini et al.19 and the Takac et al.22 system were studied only in healthy individuals. The study by Butson et al.21 targeted healthcare professionals. In several studies, all participants were patients with PD.24,26,27,32,33,36,39,41,43 The remaining studies included both patients and healthy controls.18,29–31,34,35,40,42 The total sample for all studies included 420 patients diagnosed with PD and 232 controls.

The articles gathered were very heterogeneous. Two evaluated applications which were not specifically designed for patients with PD but were potentially useful in this context. One presented a tool for performing the Timed Up and Go test,19 and the other presented an application for gait assessment.20

Twenty-one studies assessed the usefulness of applications specifically designed for PD in assessing and treating various physical and cognitive symptoms of the disease.18,23–38,40–43 Butson et al.21 analyse the clinical usefulness of an application designed to assist in setting the parameters of deep brain stimulation. Takac et al.22 evaluate a system for detecting potential episodes of freezing of gait based on an analysis of the patient's spatial context. Finally, the study by Broen et al.39 aimed to obtain real-time data on patients’ affective states.

According to the classification described above, the applications presented in the gathered studies can be classified as follows:

Assessment applications: applications for assessing resting tremor,18,24,34,40 bradykinesia,27 upper limb dexterity,43 various gait parameters,23,31,33,41,42 visual acuity,35 various combinations of physical, cognitive, and vocal aspects of PD,3,28,29,32,38 the spatial context of the patient,22 and the patient's affective state.39

Treatment applications: these apps included 2 subcategories. Firstly, the group of applications related to medical and pharmacological treatment included an app designed by Lakshminarayana et al.25 to improve self-management and treatment adherence, and an app designed by Butson et al.21 to facilitate clinical decision-making regarding deep brain stimulation parameters. The second subcategory were apps focusing on gait rehabilitation. These included apps using rhythmic auditory23,26,28,41 or vibratory cueing42 for training gait and to treat freezing of gait. Another rehabilitation therapy application, described by Van der Kolk et al.,37 was designed to improve motivation in an exercise-based treatment programme. The CuPid system, described by Casamassima et al.,23 Ferrari et al.,31 and Ginis et al.,41 may be classified either as a treatment app or as an assessment app; the same is true of the REMPARK app presented by Sama et al.28

We identified no publications on apps providing information on PD. The studies gathered do not address the costs of the applications nor whether their use was restricted to healthcare professionals or patients. It should also be noted that 4 of the systems presented required additional motion sensors,20,23,26,31,32,41 while other studies used devices for fastening the smartphone to the patient's body.

The methodological quality of the studies was poor, with the study by Ginis et al.41 being the only one scoring 3 on the Jadad scale. Certain studies could not be assessed with the scale, either because they only described an action protocol for future lines of research, or because the full texts were not available.

Table 1 shows the most relevant characteristics of the articles included in the literature review. To summarise, we analysed 26 articles presenting 24 different systems based on mobile applications; 2 were useful for PD management and 22 were specifically designed for the disease. The latter group included 15 assessment apps, 5 treatment apps, and 2 applications for both assessment and treatment.

Table 1.

Main characteristics of the studies included in the review.

Article  Participants  Intervention  Results  Jadad score 
Arora et al.29  N=20 (10 healthy individuals+10 PD)  Evaluation of an app comprising various tests (voice, posture, gait, finger tapping, and response time) in patients with PD and in controls  The system showed 96.2% sensitivity and 96.9% specificity for discriminating between patients and controls. 
Bot et al.38  N=Presentation of a protocol to validate mPower, an application designed to assess memory, tapping, voice, and gait in patients with PD  Not available  Not applicable 
Broen et al.39  N=5 (PD)  Evaluation of the use of smartphones to apply a sampling method  The sampling method was shown to be able to detect diurnal motor fluctuations through data from the questions included. 
Butson et al.21  N=5 (healthcare professionals)  Study of an app-based system for selecting the most appropriate deep brain stimulation parameters in 4 patients  A significant reduction was observed in decision-making time for all patients. 
Casamassima et al.23  N=Presentation of a system for assessing and treating gait alterations through biofeedback. Additional sensors required  Not available  Not applicable 
Ellis et al.30  N=24 (12 healthy individuals+12 PD)  Evaluation of an app-based system for assessing and treating gait alterations, comparing against data obtained with footswitch sensors and a pressure sensor mat, in different situations  All measurement tools identified significant differences between the patient and control groups for gait parameters. 
Ferrari et al.31  N=28 (12 healthy individuals+16 PD)  Evaluation of a system for assessing gait, comparing against data obtained with a mat and with cameras. Additional sensors required  The different measurement systems returned similar data. The system showed high test-retest reliability. 
Ferreira et al.32  N=22 (PD)  Evaluation of the viability and usability of the SENSE-Park system in PD patients. Additional sensors required  Patients had a good opinion of the application's assessment system. 
Fraiwan et al.40  N=42 (21 healthy individuals+21 PD)  Evaluation of the usefulness of an application for detecting resting tremor  Results from the app showed 95% accuracy.  Not applicable 
Ginis et al.41  N=40 (PD)  Evaluation of the CuPid system for gait rehabilitation in various conditions, with and without the application. The researchers evaluated balance, gait, endurance, and quality of life. Additional sensors required  Both groups showed improvements in primary outcomes. The group using the application significantly improved in balance and maintained quality of life. 
Ivkovic et al.42  N=20 (10 healthy individuals+10 PD)  Evaluation of the usefulness of an app in gait rehabilitation using tactile cueing  The system was effective for regulating heel tapping. 
Kim et al.33  N=15 (PD)  Evaluation of the sensitivity of an application for detecting freezing of gait  The application showed high sensitivity and specificity for detecting freezing of gait with the smartphone in different positions. 
Kostikis et al.18  N=20 (10 healthy individuals+10 PD)  Presentation of a system (web application) for detecting resting tremor of the hand in patients with different diseases  Significant differences were observed between patients and controls in the measurement of acceleration with the application. The application enabled detection of tremor with the accelerometer. 
Kostikis et al.24  N=23 (PD)  Presentation of a system (web application) for detecting resting tremor of the hand in patients with PD, compared against the UPDRS  A correlation was observed between data obtained from the app and UPDRS scores. 
Kostikis et al.34  N=45 (20 healthy individuals+25 PD)  Evaluation of a system for detecting resting tremor in patients with PD and controls  The application very accurately discriminated between patients and controls. 
Lakshminarayana et al.25  N=Presentation of an application focusing on self-management of PD  Not available  Not applicable 
Lee et al.43  N=103 (PD)  Validation of an application measuring motor function, compared against the MDS-UPDRS  A strong correlation was observed between data obtained from the app and MDS-UPDRS scores.  Not applicable 
Lin et al.35  N=103 (71 healthy individuals+32 PD)  Evaluation of the usefulness of an app measuring visual acuity in PD patients  The app may be useful for assessing visual acuity.  Not applicable 
Lopez et al.26  N=10 (PD)  Evaluation of the ListenMee system for gait rehabilitation in patients with PD. Additional sensors required  Gait velocity, cadence, and stride length improved significantly. 
Palmerini et al.19  N=49 (healthy individuals)  Participants performed the Timed Up and Go test while wearing the smartphone.  The test showed a strong correlation with the app's measurements, particularly for step duration (R=0.93). 
Pan et al.36  N=40 (PD)  Presentation of an application designed to assess disease severity by measuring tremor and gait difficulty  The application showed high sensitivity and specificity. 
Printy et al.27  N=26 (PD)  Presentation of an application for assessing bradykinesia in PD patients, compared against the UPDRS motor subscore  The application was able to classify disease status as more severe or less severe with an accuracy of 94.5%. 
Sama et al.28  N=Presentation of the REMPARK system for assessing and treating gait alterations  Not available  Not applicable 
Takac et al.22  N=12 (healthy individuals)  Evaluation of a system for assessing patients’ environment, tracking position and orientation, and detecting freezing of gait  The application accurately tracked patients’ position and orientation. 
Van der Kolk et al.37  N=Presentation of an application designed to motivate PD patients taking an exercise programme  Not available  Not applicable 
Wagner and Ganz20  N=Presentation of a system based on insoles with footswitches for assessing gait  Not available  Not applicable 
Search of other information sources

In our search of the main mobile application marketplaces, we identified 103 available apps related to PD. Of these, 49 were available only for Apple devices, 39 for Android devices, and 6 for Windows Phone devices; 11 applications were available for both Apple and Android devices; and one was available in all 3 marketplaces. Fig. 2 illustrates the search process.

Figure 2.

Results of the search of mobile application marketplaces.


In terms of cost, 74 apps were free and 29 were paid. Apps targeted patients with PD in 45 cases, healthcare professionals in 34 (but did not include any filter or identity authentication to prevent use by non-professionals), and both groups in 24.

By category, 54 apps were not specifically designed for management of PD but were considered useful, and 49 were specifically designed for the disease. Table 2 summarises the characteristics of the applications identified.

Table 2.

Main characteristics of mobile applications for Parkinson's disease.

Name  Operating system  Costa  Description  Users  Classification 
AAC Text to Speech  iOS  Free  Voice recording to increase volume or speech  Patients  Useful for PD 
ARAT Action Research Arm Test  Android  Free  Upper limb assessment  HC professionals+patients  Useful for PD 
DAF Assistant  iOS  9.99  Feedback to improve speech  Patients  Useful for PD 
DAF Assistant Legacy  iOS  9.99  Stimuli to improve clarity and fluency of speech  Patients  Useful for PD 
DAF Professional  iOS/Android  4.99  Designed to improve speech and oral expression  Patients  Useful for PD 
DAF Professional Lite  Android  Free  Clearer speech and reduced stuttering  Patients  Useful for PD 
Daily Rx  Android  Free  Information on numerous diseases  HC professionals+patients  Useful for PD 
Brain Injury  Android  Free  Guidelines and advice for patients with neurological diseases  HC professionals+patients  Useful for PD 
Easy Speak AAC  iOS  39.99  Support for spoken communication by telephone  Patients  Useful for PD 
Tremor  Android  Free  Information on tremor  HC professionals+patients  Useful for PD 
Genomapp  Android  Free  Information on genetic diseases  HC professionals+patients  Useful for PD 
Guideline Central  Android  Free  Collection of clinical practice guidelines  HC professionals  Useful for PD 
Handbook of Natural Medicine  Android  Free  Information on alternative medicine  HC professionals+patients  Useful for PD 
Help Talk  Android  Free  Assistance for oral communication  Patients  Useful for PD 
Homeopathic Guide  Android  Free  Information on homeopathy  Patients  Useful for PD 
iReflex  iOS  0.99  Assessment of reflexes  Patients  Useful for PD 
MedOcloc Pill Reminder  Android  Free  Medication self-management  Patients  Useful for PD 
Mega Icon Launcher  Android  Free  Improved smartphone usability  Patients  Useful for PD 
Miniatlas Sistema Nervioso Central  iOS  9.99  Atlas of the nervous system and information on neurological diseases  HC professionals+patients  Useful for PD 
Montfort iTUG clinic  iOS  Free  Instrumented Get Up and Go Test  HC professionals  Useful for PD 
Movement Disorders  iOS  Free  Scientific publication on movement disorders  HC professionals+patients  Useful for PD 
MyTherapy Pastillero  Android  Free  Medication self-management  Patients  Useful for PD 
NeuroLinks  iOS  Free  Assistance in diagnosis and treatment of PD  HC professionals  Useful for PD 
Neurología en preguntas cortas  iOS  0.99  Information on neurological diseases  HC professionals  Useful for PD 
Neurology Now  Android  Free  Official publication of the American Academy of Neurology  HC professionals  Useful for PD 
Neurology Pocket  iOS  9.99  Information on various neurological diseases  HC professionals  Useful for PD 
Neurology Today  Android  Free  Official publication of the American Academy of Neurology  HC professionals  Useful for PD 
NeuroScores  iOS/Android  Free  Scales used in neurological examinations  HC professionals  Useful for PD 
One Ring  iOS  Free  Device for monitoring resting tremor  HC professionals  Useful for PD 
Pacing Board Pocket  iOS  1.99  Stimuli to improve speech  Patients  Useful for PD 
ParkPen  Android  Free  Pen designed for PD patients  Patients  Useful for PD 
Prognosis: Neurology  Android  Free  Information on neurological diseases  HC professionals  Useful for PD 
Portafolio Médico  Android  Free  Information on numerous diseases  HC professionals  Useful for PD 
Response Measurement  Android  Free  Measurement of response time to stimuli  HC professionals+patients  Useful for PD 
Rx Wiki  Android  Free  Information on numerous diseases and drugs  Patients  Useful for PD 
Senior Care Manager  iOS  Free  Patient follow-up system  Patients  Useful for PD 
Sináptica TEVA  iOS/Android  Free  Application for the Sináptica congress  HC professionals  Useful for PD 
Speak Better  iOS  24.99  Tactile stimuli to improve clarity and fluency of speech  Patients  Useful for PD 
Speech Pacesetter  iOS  9.99  Tactile stimuli to improve clarity and fluency of speech  Patients  Useful for PD 
Speech Pacesetter Lite  iOS  Free  Tactile stimuli to improve clarity and fluency of speech  Patients  Useful for PD 
Speech Tool  iOS  Free  Voice volume training  Patients  Useful for PD 
SpeechCompanion  Android  1.49  Speech therapy exercises  Patients  Useful for PD 
StudyMyTremor  iOS  3.99  Resting tremor monitoring  Patients  Useful for PD 
Swallow Prompt  iOS/Android  1.99  Assistance with saliva control  Patients  Useful for PD 
Taptimal  iOS  Free  Finger tapping test  Patients  Useful for PD 
Tippy Tap-Alfabeto  iOS  Free  Finger tapping game  Patients  Useful for PD 
Tremor Test  iOS  9.99  Measurement of resting tremor  HC professionals+patients  Useful for PD 
Voice Analyst  iOS  12.99  Voice tone and volume analysis  HC professionals  Useful for PD 
Voice Analyst Lite  Android  Free  Voice recording and tone and volume analysis  HC professionals+patients  Useful for PD 
Word or Color Dot  Android  0.99  Cognitive exercises  Patients  Useful for PD 
Workstation en trastornos del movimiento  iOS/Android  Free  Database with information on movement disorders  HC professionals  Useful for PD 
World Neurology  iOS  Free  Official publication of the World Federation of Neurology  HC professionals+patients  Useful for PD 
Miniatlas Psiquiatría  Windows Phone  2.99  Atlas of the central nervous system  HC professionals  Useful for PD 
Neurology  Windows Phone  Free  Information on neurological diseases  HC professionals  Useful for PD 
AD/PD 2015  iOS/Android  Free  Official application for the 12th International Conference on Alzheimer's & Parkinson's Diseases and Related Neurological Disorders  HC professionals  Information 
Alzheimer's and Parkinson's Disease  iOS  Free  Information on PD and Alzheimer disease  HC professionals+patients  Information 
Azilect Realidad Aumentada  Android  Free  Demonstration of rasagaline action  HC professionals+patients  Information 
Learning Neurology Quiz  iOS  1.99  Information on neurological diseases  HC professionals  Information 
MDS Congress  Android  Free  Official application of the 2014 congress of the Movement Disorder Society  HC professionals  Information 
Parkinson  Windows Phone  0.99  Information on PD  Patients  Information 
Parkinson Enfermedad  Android  Free  Information on research  HC professionals+patients  Information 
Parkinson Info App  iOS  Free  Database with information on PD  HC professionals+patients  Information 
Parkinson's Central  iOS/Android  Free  Information on PD  Patients  Information 
Parkinson's Disease  iOS/Android  1.99  Information on PD  HC professionals  Information 
Parkinson's Disease  Android  Free  Information on PD  HC professionals  Information 
Parkinson's Disease  Windows Phone  2.99  Information on PD  Patients  Information 
Parkinson's Disease an Overview  Windows Phone  Free  Information on PD  HC professionals+patients  Information 
Parkinson's Disease Monitor  iOS  Free  Scientific publication on PD  HC professionals  Information 
Parkinson's Disease Point of Care  iOS  Free  Information on PD diagnosis, treatment, and management  HC professionals  Information 
Parkinson's Easy Call  Android  Free  Assisted telephone dialling  Patients  Information 
Parkinson's Toolkit  iOS/Android  Free  Clinical practice guidelines for treatment of PD  HC professionals  Information 
Parkison's Disease Facts  Android  Free  Collection of PD statistics, relevant details, and treatment information  HC professionals+patients  Information 
PD Headline News  iOS  Free  Publications on PD  HC professionals+patients  Information 
Rides for Parkinson's  iOS  Free  Transport for PD patients  Patients  Information 
TEVA Parkinson  Android  Free  Organisation of clinical histories  HC professionals  Information 
World Parkinson Congress  iOS  Free  Programme of the 2010 World Parkinson Congress  HC professionals  Information 
Earlystimulus  Android  Free  Criteria for deep brain stimulation  HC professionals  Information 
DopaFit  Android  Free  Exercises for PD patients  Patients  Treatment 
iParkinsons  iOS  99.99  Treatment for speech dysfunction in PD  Patients  Treatment 
ListenMee App  Android  121  Cueing to improve gait  Patients  Treatment 
Music Therapy  Android  Free  Cueing to improve gait  Patients  Treatment 
Parkinson Exercises  iOS/Android/Windows Phone  4.22  Exercise videos for PD patients  Patients  Treatment 
Parkinson's Speech Aid  iOS  Free  Repeats voice at greater volume or speed  Patients  Treatment 
uMotif  iOS/Android  Free  Follow-up of symptoms, medication, and activity  HC professionals+patients  Treatment 
PD Warrior  Android  Free  Exercises for PD patients  Patients  Treatment 
DigiTap  iOS  2.99  Finger tapping test  HC professionals  Assessment 
Lift Pulse  iOS  Free  Recording of resting tremor  HC professionals  Assessment 
MDS UPDRS  iOS  5.99  MSD-UPDRS scale  HC professionals  Assessment 
My Parkinson's Disease Manager  iOS  Free  Monitoring and storage of PD data  Patients  Assessment 
myHealthPal  iOS  Free  Monitoring and self-management of PD  Patients  Assessment 
myParkinson's  Android  Free  Measurement of upper limb resting tremor  HC professionals+patients  Assessment 
Parkinson: Symptom Graph Create  Android  Free  Graph plotting to monitor PD  HC professionals+patients  Assessment 
Parkinsons  iOS  5.99  Scales used in assessment of PD  HC professionals  Assessment 
Parkinson's Diary  iOS  Free  Monitoring of ADL, mood, and physical fitness  Patients  Assessment 
Parkinson's Test  Android  Free  Test for diagnosing and assessing PD  HC professionals+patients  Assessment 
PD Me  iOS  Free  PD monitoring  Patients  Assessment 
Prognosis  Windows Phone  Free  Tests for assessing gait, sleep, voice, and upper limbs  HC professionals  Assessment 
TR_Meter  iOS  Free  Measurement of resting tremor  HC professionals  Assessment 
Tremor Tracer  iOS  3.99  Tremor assessment  HC professionals+patients  Assessment 
Tremor12  iOS  Free  Resting tremor monitoring  Patients  Assessment 
UPDRS  iOS  Free  UPDRS questionnaire  HC professionals  Assessment 
Beats Medical Parkinsons Treatment  iOS/Android  Free  Monitoring and treatment of movement, speech, and dexterity  Patients  Assessment+treatment 
Fox Insight App  Android  Free  Monitoring of activity, tremor, and sleep  Patients  Assessment+treatment 

HC: healthcare.


Costs shown in euros.

The first group includes 23 apps providing information on various neurological diseases, 15 for improving speech, 7 for testing upper limb function, 2 addressing self-management of medication and symptom monitoring, and 2 for administering various neurological assessment scales. The remaining apps are designed for improving telephone usability and saliva control, performing cognitive exercises, following up patients, and to serve as design tools for the 3D printing of objects to assist patients with neurological diseases.

The second group includes 23 applications providing information on the disease, 16 designed to assess various signs and symptoms, and 8 assisting in treatment. A further 2 applications were assigned to both the treatment and the assessment categories.

Eight of the assessment apps tested multiple aspects of PD (sleep, gait, voice, activities of daily living, and mood), 5 assessed resting tremor, 2 included the UPDRS scale, and one included a finger tapping test. Treatment applications were based on physical exercise in 3 cases, speech therapy in 2, auditory cueing for treating gait in 2, and self-management and monitoring of pharmacological treatment in one case. One of the applications included in both the assessment and treatment groups monitored movement, tremor, and sleep and included a metronome for gait rehabilitation; the other assessed gait and also included a metronome. Fig. 3 includes a graphical representation of the mobile applications for PD available in the main marketplaces.

Figure 3.

Graphical representation of the classification of apps for Parkinson's disease.


Two of the applications found in our search of the app marketplaces have previously been analysed by Lakshminarayana et al.25 and Lopez et al.26; therefore, the applications identified through both searches amounted to a total of 125, of which 56 were potentially useful for PD and 69 were specifically designed for management of the disease (23 information apps, 29 assessment apps, 13 treatment apps, and 4 assessment and treatment apps).


The term “eHealth” refers to the use of information technology in the field of health, either for follow-up and treatment of various diseases or for public health surveillance and stimulating new research.52 New technology may constitute a response to one of the greatest challenges of neurodegenerative diseases: long-term treatment viability and patient assessment. These tools are intended to be universally accessible, including for disabled people. Another advantage of these devices is that they improve communication between healthcare professionals and patients, as well as within both groups. There is ample opportunity for PD management to benefit from new technology. As the disease mainly affects motor function, a majority of patients present functional difficulties which constitute an obstacle to attending consultations and receiving continuous follow-up. Therefore, technological advances may be of use in following up these patients, for example in the analysis of tremor, motor fluctuations, or freezing.53

The World Health Organization defines mHealth, one component of eHealth, as medical or public health practice supported by the use of mobile devices and the systems incorporated in these devices.54 Sales of mobile devices have increased rapidly in recent years; in 2015, there were over 7.3 billion devices, more than one per person worldwide.55 This has led to an increase in the number of applications available in the main marketplaces, with the Google Play and Apple App Store platforms offering over 2 million apps.55 Around 97000 of these are dedicated to health and medicine.17 In the present review, we selected applications cited in articles indexed in biomedical databases or listed on the app marketplaces as being related to PD. However, given the large number and variety of apps available, it is probable that other apps would be useful in this field. These would include those dedicated to physical exercise, cognitive tasks, or medication or dietary monitoring. Reasons for the non-inclusion of such apps in our study would be the difficulty of classifying them and the constant appearance of new applications and updates. According to the latest Doctoralia report,56 the most widely used applications are those providing health information (86% of survey respondents), followed by apps focusing on physical exercise (77%), diet (66%), and medication management (66%). These data coincide with those obtained in our search of the main app marketplaces: both in the group of specifically designed apps and the group of useful apps for PD, information apps constituted the largest subgroup. However, the literature search identified no studies on this type of app; it is therefore necessary to consider the accuracy of the information provided by mHealth apps.

While some applications were developed by foundations or associations specialising in PD, such as the National Parkinson Foundation (Parkinson's Toolkit and Parkinson's Central) and the Michael J. Fox Foundation (Fox Insight App), many lack the opinions, approval, or support of such bodies. In 2013, the United States Food and Drug Administration (FDA) published user and developer guidelines (updated in 2015) on future regulations in the sector,57 in the light of the potential of mobile applications to transform healthcare and the consequent need to review their safety. The European Commission also considers that recommendations about health apps require them to be classified and verified; to that end, it has published a directory containing information on numerous health applications.58

The results of our literature search show that mobile devices are able to track the position and orientation of healthy individuals22 and may be used to perform certain tests, such as the Timed Up and Go19 and some tasks from the UPDRS.43 Other applications are able to discriminate between controls and patients with PD with a reasonable degree of accuracy,18,29–31,34 as well as between different stages of the disease.27,36 Some apps have been shown to be able to accurately detect resting tremor.24,40 Gait assessment apps have also been found to be effective for detecting heel taps42 and freezing of gait.33 Some studies have demonstrated significant improvements in several aspects of gait.26,41 However, their findings should be interpreted with caution given the poor methodological quality of the studies and the small number of participants.

Other literature reviews have addressed the use and usefulness of mobile apps for other diseases. A 2012 Cochrane review concluded that there was very limited evidence that mobile phone messaging interventions may have a positive impact on self-management in patients with chronic diseases.59 In 2013, another Cochrane review on the effectiveness and viability of using apps to facilitate self-management among patients with asthma60 found insufficient evidence to advise either healthcare professionals or patients about the approach. Whitehead and Seaton61 published a systematic review on self-management apps for chronic diseases, finding that the applications have potential to improve symptom management in patients with cardiovascular disease, chronic lung disease, and diabetes mellitus.

In the field of motion analysis in neurorehabilitation, 2 reviews support the use of sensors and mobile devices in patients with neurological diseases. According to Bloc et al.,62 it is possible to use sensors to monitor physical activity in neurological patients, even those with severe conditions. With reference to PD specifically, Hubble et al.63 support the use of sensors to detect differences in balance between healthy individuals and patients with the disease; this may be useful for early detection of the disease and in assessing the risk of falls. These findings may explain the use of additional sensors in 4 of the systems studied in the present review.

In the context of mHealth, Zaki and Drazin64 found 111 apps potentially useful for neurosurgery students and professionals. However, the authors stress the lack of reliable opinions on the majority of the applications and note that this type of system may entail some degree of ethical and legal risk with respect to the protection of patients’ data. The study by Sánchez-Rodríguez et al.16 is the only systematic review performed to date on mobile applications for neurorehabilitation. These researchers classified applications into 5 categories, concluding that the available evidence supporting their use as adjuvant therapy in certain neurological diseases was of low quality.

App usability is another important factor to consider. Only one study analysed the feasibility of implementation and the usability of an application for patients with PD.32 The review by Zapata et al.65 demonstrates the importance of adapting applications to the real needs of target users to improve their implementation and usefulness.

All the reviews cited above note the need for additional, better quality research in this field in order to provide useful, reliable tools for patients and healthcare professionals. Our literature search also found protocols for validation studies of various applications.25,37,38 Furthermore, the European Commission recently launched initiatives to encourage research into new technologies for PD; these include the CuPid23,31,41 and REMPARK28 systems. This line of research is currently being explored through the development of the PD_manager platform and the mPark application66 by the Universidad Politécnica de Madrid's Life Supporting Technologies research group. Other projects are being conducted by private organisations; these include the Mementum application.67 Finally, it should be noted that large companies are also conducting projects including PD assessment apps; these include Apple, with the above mentioned mPower app, and the pharmaceutical company Roche.68 The available evidence on these tools is likely to expand greatly in the coming years; analysis of these data is necessary to confirm the usefulness of these health apps.

Our search of the main mobile application marketplaces identified 103 apps that are potentially useful in the management of PD, of which 49 are available only for Apple devices, 39 for Android devices, and 6 for Windows Phone devices; 8 are available both for Apple and for Android devices; and one is available for all 3 operating systems. Seventy-four apps are free of cost and 29 are paid; 45 target patients with PD, 34 target healthcare professionals, and 24 target both groups. However, apps targeting healthcare professionals exclusively do not limit access by other users; including this type of control, for example by requiring professional association membership numbers or passwords, or through other mechanisms, may be beneficial to ensuring that the content of such apps is better exploited. As mentioned previously, numerous authors and such regulatory agencies as the FDA state the need to review these applications in order to ensure patient safety. Meulendijk et al.69 describe the 9 essential requirements for health apps from the perspective of patients: accessibility, certifiability, portability, privacy, safety, security, stability, trustability, and usability. Most of the apps found in the application marketplaces have not been validated in context; nor have they been supervised or approved by healthcare agencies, as proposed by Meulendijk et al.69

This review has several limitations. Firstly, the studies analysed are of poor methodological quality and include small samples. Furthermore, apps were selected according to developers’ descriptions; information may therefore be incomplete. The constant change and evolution of app marketplaces means that the applications presented in this review may become unavailable in the future, or other apps may not have been included as they were not registered at the time of the search.


Numerous mobile applications are potentially useful or specifically designed for the management of PD. However, despite the available evidence, the poor methodological quality of the studies identified prevents us from recommending generalised use of these apps. We identified 125 applications cited in published articles in biomedical databases and in app marketplaces; of these, 56 were potentially useful in PD and 69 were specifically designed for the disease. Of the latter group, 23 offered information on the disease, 29 were based around assessment, 13 were designed to treat aspects of the disease, and 4 offered both assessment and treatment.

The potential benefits and risks associated with mHealth give rise to a need for official regulation and for further research in the field; this would provide both healthcare professionals and patients with safe, reliable tools for the care and management of PD.

Conflicts of interest


World Health Organization.
Neurological disorders: public health challenges.
K. Wirdefeldt, H. Adami, P. Cole, D. Trichopoulos, J. Mandel.
Epidemiology and etiology of Parkinson's disease: a review of the evidence.
Eur J Epidemiol, 26 (2011), pp. S1-S58
European Parkinson's Disease Association (EPDA).
Projected number of people with Parkinson disease in the most populous nations.
Fundación Española de Enfermedades Neurológicas.
Informe de la fundación del cerebro sobre el impacto social de la enfermedad de Parkinson en España.
Instituto Nacional de Estadística. Encuesta de discapacidad, autonomía personal y situaciones de dependencia; 2008 [accessed 10.12.15]. Available from: www.ine.es
E. Cubo, P.M. Martin, M. González, B. Frades.
Impacto de los síntomas motores y no motores en los costes directos de la enfermedad de Parkinson.
Neurología, 24 (2009), pp. 15-23
Fundación Española de Enfermedades Neurológicas.
Impacto sociosanitario de las enfermedades neurológicas en España.
Real Patronato sobre Discapacidad (Ministerio de Sanidad, Servicios Sociales e Igualdad).
El libro blanco del párkinson en España. Aproximación, análisis y propuesta de futuro.
Ministerio de Sanidad, Servicios Sociales e Igualdad, (2015),
National Institute for Health and Care Excellence.
Parkinson's disease diagnosis and management in primary and secondary care.
Royal College of Physicians, (2006),
F. Micheli, M. Fernández-Pardal.
Médica Panamericana, (2010),
M. Morris.
Movement disorders in people with Parkinson Disease: a model for physical therapy.
Phys Ther, 80 (2000), pp. 578-597
R. Cano-de la Cuerda, A.I. Macías Jiménez, V. Crespo Sánchez, M. Morales Cabezas.
Escalas de valoración y tratamiento fisioterápico en la enfermedad de Parkinson.
Fisioterapia, 26 (2004), pp. 201-210
H. Gage, L. Storey.
Rehabilitation for Parkinson's disease: a systematic review of available evidence.
Clin Rehabil, 18 (2004), pp. 463-482
R. Cano-de la Cuerda, S. Collado-Vázquez.
Neurorrehabilitación: Métodos específicos de valoración y tratamiento.
Médica Panamericana, (2012),
A.R. Jadad, R.A. Moore, D. Carroll, C. Jenkinson, D.J.M. Reynols, D.J. Gavaghan, et al.
Assesing the quality of reports of randomized clinical trials: is blinding necessary?.
Control Clin Trials, 17 (1996), pp. 1-12
M.T. Sánchez Rodríguez, S. Collado-Vázquez, P. Martín Casas, R. Cano-de la Cuerda.
Apps en neurorrehabilitación. Una revisión sistemática de aplicaciones móviles.
[Epub ahead of print]
The App Intelligence. Informe apps salud en español. En: Mugarza F. Informe de las mejores 50 apps de salud en español. Madrid: Zeltia; 2014.
N. Kostikis, D. Hristu-Varsakelis, M. Arnaoutoglou, C. Kotsavasiloglou, S. Baloyiannis.
Towards remote evaluation of movement disorders via smartphones.
Conf Proc IEEE Eng Med Biol Soc 2011, 2011 (2011), pp. 5240-5243
L. Palmerini, S. Mellone, L. Rocchi, L. Chiari.
Dimensionality reduction for the quantitative evaluation of a smartphone-based Timed Up and Go test.
Conf Proc IEEE Eng Med Biol Soc 2011, 2011 (2011), pp. 7179-7182
R. Wagner, A. Ganz.
PAGAS: portable and accurate gait analysis system.
Conf Proc IEEE Eng Med Biol Soc 2012, 2012 (2012), pp. 280-283
C.R. Butson, G. Tamm, S. Jain, T. Fogal, J. Kruger.
Evaluation of interactive visualization on mobile computing platforms for selection of deep brain stimulation parameters.
IEEE Trans Vis Comput Graph, 19 (2013), pp. 108-117
B. Takac, A. Catala, D. Rodriguez Martin, N. van der Aa, W. Chen, M. Rauterberg.
Position and orientation tracking in a ubiquitous monitoring system for Parkinson disease patients with freezing of gait symptom.
JMIR Mhealth Uhealth, 1 (2013), pp. e14
F. Casamassima, A. Ferrari, B. Milosevic, P. Ginis, E. Farella, L. Rocchi.
A wearable system for gait training in subjects with Parkinson's disease.
Sensors (Basel), 14 (2014), pp. 6229-6246
N. Kostikis, D. Hristu-Varsakelis, M. Arnaoutoglou, C. Kotsavasiloglou.
Smartphone-based evaluation of parkinsonian hand tremor: quantitative measurements vs clinical assessment scores.
Conf Proc IEEE Eng Med Biol Soc 2014, 2014 (2014), pp. 906-909
R. Lakshminarayana, D. Wang, D. Burn, K.R. Chaudhuri, G. Cummins, C. Galtrey, et al.
Smartphone- and internet-assisted self-management and adherence tools to manage Parkinson's disease (SMART-PD): study protocol for a randomised controlled trial (v7; 15 August 2014).
W.O.C. Lopez, C.A.E. Higuera, E.T. Fonoff, O.S. de, U. Albicker, J.A.E. Martinez.
Listenmee® and Listenmee® smartphone application: synchronizing walking to rhythmic auditory cues to improve gait in Parkinson's disease.
Hum Mov Sci, 37 (2014), pp. 147-156
B.P. Printy, L.M. Renken, J.P. Herrmann, I. Lee, B. Johnson, E. Knight, et al.
Smartphone application for classification of motor impairment severity in Parkinson's disease.
Conf Proc IEEE Eng Med Biol Soc 2014, 2014 (2014), pp. 2686-2689
A. Sama, C. Perez-Lopez, D. Rodriguez-Martin, J.M. Moreno-Arostegui, J. Rovira, C. Ahlrichs, et al.
A double closed loop to enhance the quality of life of Parkinson's Disease patients: REMPARK system.
Stud Health Technol Inform, 207 (2014), pp. 115-124
S. Arora, V. Venkataraman, A. Zhan, S. Donohue, K.M. Biglan, E.R. Dorsey, et al.
Detecting and monitoring the symptoms of Parkinson's disease using smartphones: a pilot study.
Parkinsonism Relat Disord, 21 (2015), pp. 650
R.J. Ellis, Y.S. Ng, S. Zhu, D.M. Tan, B. Anderson, G. Schlaug, et al.
A validated smartphone-based assessment of gait and gait variability in Parkinson's disease.
PLoS One, 10 (2015), pp. e0141694
A. Ferrari, P. Ginis, M. Hardegger, F. Casamassima, L. Rocchi, L. Chiari.
A mobile kalman-filter based solution for the real-time estimation of spatio-temporal gait parameters.
IEEE Trans Neural Syst Rehabil Eng, 24 (2016), pp. 764-773
J.J. Ferreira, C. Godinho, A.T. Santos, J. Domingos, D. Abreu, R. Lobo, et al.
Quantitative home-based assessment of Parkinson's symptoms: the SENSE-PARK feasibility and usability study.
H. Kim, H.J. Lee, W. Lee, S. Kwon, S.K. Kim, H.S. Jeon, et al.
Unconstrained detection of freezing of Gait in Parkinson's disease patients using smartphone.
Conf Proc IEEE Eng Med Biol Soc 2015, 2015 (2015), pp. 3751-3754
N. Kostikis, D. Hristu-Varsakelis, M. Arnaoutoglou, C. Kotsavasiloglou.
A smartphone-based tool for assessing parkinsonian hand tremor.
IEEE J Biomed Health Inform, 19 (2015), pp. 1835-1842
T.P. Lin, H. Rigby, J.S. Adler, J.G. Hentz, L.J. Balcer, S.L. Galetta, et al.
Abnormal visual contrast acuity in Parkinson's disease.
J Parkinsons Dis, 5 (2015), pp. 125-130
D. Pan, R. Dhall, A. Lieberman, D.B. Petitti.
A mobile cloud-based Parkinson's disease assessment system for home-based monitoring.
JMIR Mhealth Uhealth, 3 (2015), pp. e29
N.M. Van der Kolk, S. Overeem, N.M. de Vries, R.P. Kessels, R. Donders, M. Brouwer, et al.
Design of the Park-in-Shape study: a phase ii double blind randomized controlled trial evaluating the effects of exercise on motor and non-motor symptoms in Parkinson's disease.
B.M. Bot, C. Suver, E.C. Neto, M. Kellen, A. Klein, C. Bare, et al.
The mPower study Parkinson disease mobile data collected using ResearchKit.
Sci Data, 3 (2016), pp. 160011
M.P. Broen, V.A. Marsman, M.L. Kuijf, R.J. van Oostenbrugge, J. van Os, A.F. Leentjens.
Unraveling the relationship between motor symptoms affective states and contextual factors in Parkinson's disease: a feasibility study of the experience sampling method.
PLoS One, 11 (2016), pp. e0151195
L. Fraiwan, R. Khnouf, A.R. Mashagbeh.
Parkinson's disease hand tremor detection system for mobile application.
J Med Eng Technol, 40 (2016), pp. 127-134
P. Ginis, A. Nieuwboer, M. Dorfman, A. Ferrari, E. Gazit, C.G. Canning, 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, 22 (2016), pp. 28-34
V. Ivkovic, S. Fisher, W.H. Paloski.
Smartphone-based tactile cueing improves motor performance in Parkinson's disease.
Parkinsonism Relat Disord, 22 (2016), pp. 42-47
W. Lee, A. Evans, D.R. Williams.
Validation of a smartphone application measuring motor function in Parkinson's disease.
J Parkinsons Dis, 6 (2016), pp. 371-382
P. Pierleoni, L. Pernini, A. Belli, L. Palma.
An android-based heart monitoring system for the elderly and for patients with heart disease.
Int J Telemed Appl, 2014 (2014), pp. 625156
P. Nolan, S. Hoskins, J. Johnson, V. Powell, K.R. Chaudhuri, R. Eglin.
Implicit theory manipulations affecting efficacy of a smartphone application aiding speech therapy for Parkinson's patients.
Stud Health Technol Inform, 181 (2012), pp. 138-142
S.A. Antos, M.V. Albert, K.P. Kording.
Hand, belt, pocket or bag: practical activity tracking with mobile phones.
J Neurosci Methods, 231 (2014), pp. 22-30
B. Carignan, J.F. Daneault, C. Duval.
Measuring tremor with a smartphone.
Methods Mol Biol, 1256 (2015), pp. 359-374
R. LeMoyne, T. Mastroianni.
Use of smartphones and portable media devices for quantifying human movement characteristics of gait, tendon reflex response, and Parkinson's disease hand tremor.
Methods Mol Biol, 1256 (2015), pp. 335-358
E.R. Dorsey, Y.F. Yvonne Chan, M.V. McConnell, S.Y. Shaw, A.D. Trister, S.H. Friend.
The use of smartphones for health research.
Acad Med, 92 (2017), pp. 157-160
C. Medrano, I. Plaza, R. Igual, A. Sanchez, M. Castro.
The effect of personalization on smartphone-based fall detectors.
Sensors (Basel), 16 (2016), pp. e117
P. Raknim, K.C. Lan.
Gait monitoring for early neurological disorder detection using sensors in a smartphone: validation and a case study of parkinsonism.
Telemed J E Health, 22 (2016), pp. 75-81
World Health Organization. eHealth. Avaialable from: http://www.who.int/topics/ehealth/en/ [accessed 15.06.16].
M. Achey, M. Guttman, A. Hassan, S.M. Khandhar, Z. Mari, M. Spindler, et al.
The past, present, and future of telemedicine for Parkinson's disease.
Mov Disord, 29 (2014), pp. 871-883
World Health Organization.
mHealth new horizons for health through mobile technologies.
Ditrendia. Informe Mobile en España y en el Mundo 2015; 2015. [consultado 1 Ago 2016]. Disponible en: http://www.ditrendia.es/wp-content/uploads/2015/07/Ditrendia-Informe-Mobile-en-Espa%C3%B1a-y-en-el-Mundo-2015.pdf
Doctoralia. Primer informe Doctoralia: salud e Internet 2015; 2015. [accessed 01.08.16]. Available from: http://insights.doctoralia.es/informe-doctoralia-sobre-salud-e-internet-2015/
Food and Drug Administration's.
The mobile medical applications. Guidance for Industry and Food and Drug Administration Staff.
The myhealthapps directory 2015–2016.
Available from: http://www.patient-view.com/-bull-directories.html [accessed 01.08.16]
T. Jongh, I. Gurol-Urganci, V. Vodopivec-Jamsek, J. Car, R. Atun.
Mobile phone messaging for facilitating selfmanagement of long-term illnesses.
Cochr Datab Syst Rev, 12 (2012),
J.S. Marcano Belisario, K. Huckvale, G. Greenfield, J. Car, L.H. Gunn.
Smartphone and tablet self management apps for asthma.
Cochr Datab Syst Rev, 11 (2013),
L. Whitehead, P. Seaton.
The effectiveness of self-management mobile phone and tablet apps in long-term condition management: a systematic review.
J Med Internet Res, 18 (2016), pp. e97
V.A.J. Block, E. Pitsch, P. Tahir, B.A.C. Cree, D.D. Allen, J.M. Gelfand.
Remote physical activity monitoring in neurological disease: a systematic review.
PLoS ONE, 11 (2016), pp. e0154335
R.P. Hubble, G.A. Naughton, P.A. Silburn, M.H. Cole.
Wearable sensor use for assessing standing balance and walking stability in people with parkinson's disease: a systematic review.
PLoS ONE, 10 (2016), pp. e0123705
M. Zaki, D. Drazin.
Smartphone use in neurosurgery? APP-solutely.
Surg Neurol Int, 5 (2014), pp. 113
B. Zapata, J. Fernandez-Aleman, A. Idri, A. Toval.
Empirical studies on usability of mHealth apps: a systematic literature review.
J Med Syst, 39 (2015), pp. 1-19
Proyecto PD_manager [web] [accessed 15.06.16]. Available from: http://www.parkinson-manager.eu/.
Mememtum [web] [accessed 15.06.16]. Available from: http://mememtum.com/.
Meulendijk M, Meulendijks J, Paul A, Edwin N, Mattijs E, Marco R. What concerns users of medical apps? Exploring non-functional requirements of medical mobile applications. Available from: http://ecis2014.eu/E-poster/files/0263-file1.pdf [accessed 15.06.16].

Please cite this article as: Linares-del Rey M, Vela-Desojo L, Cano-de la Cuerda R. Aplicaciones móviles en la enfermedad de Parkinson: una revisión sistemática. Neurología. 2019;34:38–54.

Copyright © 2017. Sociedad Española de Neurología
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