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A Systematic Review of Balance and Fall Risk Assessments With Mobile Phone Technology

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Gait dysfunctions and rest impairments are key fall risk factors and associated with reduced quality of life in individuals with Parkinson's Disease (PD). Smartphone-based assessments prove potential to increase remote monitoring of the disease. This review aimed to summarize the validity, reliability, and discriminative abilities of smartphone applications to appraise gait, remainder, and falls in PD. Two contained reviewers screened articles systematically identified through PubMed, Web of Science, Scopus, CINAHL, and SportDiscuss. Studies that used smartphone-based gait, balance, or fall applications in PD were retrieved. The validity, reliability, and discriminative abilities of the smartphone applications were summarized and qualitatively discussed. Methodological quality appraisal of the studies was performed using the quality assessment tool for observational cohort and cantankerous-sectional studies. Thirty-one articles were included in this review. The studies present by and large with depression adventure of bias. In total, 52% of the studies reported validity, 22% reported reliability, and 55% reported discriminative abilities of smartphone applications to evaluate gait, balance, and falls in PD. Those studies reported strong validity, good to excellent reliability, and good discriminative properties of smartphone applications. Simply 19% of the studies formally evaluated the usability of their smartphone applications. The current testify supports the use of smartphone to assess gait and balance, and observe freezing of gait in PD. More studies are needed to explore the use of smartphone to predict falls in this population. Further studies are also warranted to evaluate the usability of smartphone applications to ameliorate remote monitoring in this population.

Registration: PROSPERO CRD 42020198510

Introduction

Parkinson's disease (PD) is the 2d near common neurodegenerative disorder in the The states of America behind Alzheimer's disease [1]. It is associated with 4 cardinal well-known motor symptoms: resting tremor, bradykinesia, postural instability, and musculoskeletal stiffness [2]. These motor symptoms lead to gait dysfunctions and rest impairments which have been linked to an increased risk of falls [3], mortality, and morbidity in individuals diagnosed with PD [4]. Due to the consequences associated with gait dysfunctions and balance impairments within individuals with PD, accurate assessments widely available to a variety of clinicians and healthcare providers is disquisitional for monitoring the overall progression of the disease and providing targeted intendance.

Significant effort by clinicians and researchers has focused on the accurate and valid measurement of gait and rest in individuals with PD. Functioning-based clinical measures commonly used include the Timed Up and Get (TUG) test, Berg Balance Calibration (Bbs), Mini Residual Evaluation Systems Examination (Mini-BESTest), and Functional Gait Assessment (FGA) [5,6,7]. These clinical outcomes are quick, piece of cake to administer, and suitable for clinical practice. However, the clinical test's scoring criteria are often subjective, and it is recommended that trained healthcare professionals such as physical and occupational therapists administer the tests [8]. In improver, clinical outcomes are frequently insensitive to subtle dysfunctional changes and have poor reliability [9]. Within a laboratory-based setting, gait alterations and balance impairments have been accurately evaluated using move capture, wearable sensor systems, or forcefulness plate [10,11,12]. These advanced technologies crave specialized equipment and avant-garde noesis to translate their results. Due to their excessive cost and the requirement of specialized training earlier implementation and interpretation of the data, they are not ever suitable for clinical practice [8, thirteen]. Additionally, clinical and laboratory-based assessments crave in-person contact which is not possible during a global health crisis such as the COVID-19 pandemic [14].

With the progress of technology, smartphones tin can serve as an culling to the utilize of the aforementioned expensive and complex technologies. Smartphones are ubiquitous, portable, and affordable. Smartphones also offer the potential to perform remote assessments in home environments to gather more insight into an private'southward true functional abilities and limitations. Smartphones are equipped with triaxial accelerometers and gyroscopes that use applications that can be installed on smartphones. This technology has been used to assess gait and residue in various clinical populations including older adults [fifteen], not-ambulatory individuals [16], individuals with traumatic brain injury [17], and people with multiple sclerosis [xviii]. Previous systematic reviews by Linares-Del Rey et al. [xix] and Zapata et al. [twenty] indicated that smartphone and mHealth applications presented with potential to be used to monitor medication and provide PD-related information. However, these reviews did not synthetize the validity, reliability, and discriminative abilities of smartphone applications to appraise gait and balance in this population. Therefore, the primary purpose of this systematic review was to synthetize the current state of smartphone applications to evaluate gait and balance in individuals with PD. Specifically, this review summarized the validity, reliability, and discriminative abilities of smartphone applications in PD. As a secondary aim, due to relationship between gait, balance, and falls, this review besides aimed to report on smartphone applications power to predict hereafter falls in PD.

Methods

Review Protocol and Registration

The systematic review was conducted in accord with the Cochrane Handbook [21]. The reporting of the written report followed the instructions suggested by the Preferred Reporting Items for Systematic Reviews and Meta-Assay (PRISMA) [22]. The review protocol was pre-registered with PROSPERO, the International prospective register of systematic review (CRD: 42020198510).

Information Sources and Searches

The following databases search were searched from inception to February 2021: PubMed/Medline, Scopus, Web of Scientific discipline, CINAHL, and SportDiscuss. The search strategies were adjusted for the various databases and filters were added to exclude studies such equally animal studies. The search algorithm included possible combinations of "smartphone", or "cell telephone", or "mobile phone", and "gait", or "ambulation", or "continuing" or "fall", or "postural control", or "rest", and "Parkinson's Affliction". The search strategies developed for each database are presented in Appendix A.

(EW) and (SMD) independently examined championship and abstract of articles retrieved from the databases to identify potential articles for this review. They each compiled a list of potentially eligible studies. The full papers on both lists were retrieved and both review authors independently culled on the basis of the full text articles. A manual search in the reference lists (forwards and astern searches) of included articles was conducted to identify other relevant studies. Any discrepancies were discussed until consensus was reached with a third author (LA).

Written report Choice

Studies meeting the eligibility criteria were included in the systematic review. The eligibility criteria were adult based on the populations, interventions or exposures, comparators, outcomes, and written report designs (PICOS). Participants: adults (over the age of 18 years) with the diagnosis of Parkinson's Affliction; Interventions or Exposures: not applicable; Comparators: data collected with a smartphone and compared with whatsoever clinical gait and balance outcome measures or with whatever validated applied science such equally standalone accelerometer, inertial measurement unit of measurement (IMU), 3-D move capture, or force plate; Outcomes: gait measures such every bit 10-m Walking Examination (10MWT), Freezing of Gait (FoG) measure, TUG; residual measures such every bit BBS and Mini-BESTest; and prospective falls; Study designs: cross-sectional and prospective cohort observational studies and intervention studies. Studies were excluded if they were non-man trials, reviews, abstracts, briefing proceedings, case–control studies, and protocol papers without data drove. Studies published in languages other than English, French, Spanish, Italian, and Portuguese were intended to be excluded due to the inability of the research squad to fully understand the content of these eventual studies.

Information Extraction and Assay

2 review authors (EW) and (JP) independently extracted the following data: authors, publication year, study design, participant characteristics (Hoehn and Yahr, percentage of males, and duration of disease), mean age of participants, sample size, smartphone outcome measures, and types of smartphone, gait and balance outcome measures, measure of validity, reliability, sensitivity, and specificity, main results, smartphone article of clothing site, intended user (i.due east., clinicians or individuals with PD) and locations of cess. Different smartphone applications based on accelerometers and gyroscopes were extracted. Whatever discrepancies between the authors during data extraction were resolved through word with a tertiary author (LA).

Methodological Quality Assessment

The methodological quality assessment of the included studies was performed independently past ii review authors (JP) and (RA) using the Quality Assessment Tool for Observational Cohort and Cross-sectional Studies developed by the National Institute of Health-NIH [23]. This tool includes 14 criteria that are rated according to 3 responses: Yep, No, or Other (cannot determine, not reported, or not available). The fourteen criteria are listed and defined in Appendix B. Overall, the studies are rated as skilful, off-white, or poor methodological quality [23]. Studies with 8 or more than 50% of the full applicable questions responded as "aye" were considered "good quality," studies with five–7 or less than 50% of the total applicative questions responded as "yes" were considered "fair quality." Studies with < 5 questions responded as "yep" were considered "low quality" [24]. Whatever discrepancies betwixt the authors during methodological quality cess were resolved through discussion with a third author (LA).

Results

Study Selection

The databases search yielded a full of 376 manufactures and 4 articles were plant following forward and astern searches. After removing duplicates, 252 articles underwent title and abstract screening. Following initial screening, 71 articles were retrieved for full text review. A full of xl articles were excluded after the full text review due to the post-obit reason: did not use a smartphone to assess fall, gait, or remainder (northward = 19), did non include individuals with PD (n = 17), and protocol papers (n = 4). Finally, 31 articles were included in this review. Figure 1 presents a detailed illustration of the study selection in accordance with PRISMA guidelines.

Fig. 1
figure 1

PRISMA period chart

Full size paradigm

Study Analysis

Table 1 presents the characteristics of the studies included in this review. Sample size varied from nine to 334 participants amid the included studies. Nine studies were prospective accomplice, and the remaining 22 studies were cantankerous-sectional including individuals with PD. The mean age of participants varied from fifty.vii to 77.0 years old. Hoehn and Yahr (H&Y) scale evaluating the level of disability and how the symptoms of PD progress varied from 1 to iv beyond the studies. Ten studies did non written report the level of disability of their participants. Most participants with PD in the studies were males. Percentage of males varied from 43 to 86%. Six studies did not provide data near the gender of the participants. The mean duration of affliction varied from 3.5 to 15.five years. Seven studies did not written report the mean duration of PD.

Tabular array 1 Characteristics of the included studies

Full size table

Table 2 presents the details of the smartphone effect measures, models of smartphone used, gait and balance outcome measures, measures of validity, reliability, sensitivity, and specificity of the smartphone measures, the main results, as well as the intended users of the smartphone, and locations of assessment. Simply i of the included studies used a smartphone awarding to specifically predict future falls among individuals with PD [8], four studies used smartphone applications to specifically evaluate remainder impairments [25,26,27,28], and thirteen studies used smartphone applications to specifically assess gait including freezing of gait (FoG) [9, 29,30,31,32,33,34,35,36,37,38,39,40]. In addition, 11 studies used smartphone applications to assess gait and remainder simultaneously [41,42,43,44,45,46,47,48,49,50,51] and two studies used smartphone applications to assess gait, rest, and predict time to come falls amidst individuals with PD [52, 53].

Tabular array 2 Summary of smartphone consequence measures, validity, reliability, discriminative ability, and main results of the included studies

Full size table

Gait and residue measurements trough clinical and biomechanics outcomes differed greatly between the studies. The most common gait and rest outcome measures included the gait, FoG, and postural stability items of the Unified Parkinson's Disease Rating Scale-Three (UPDRS-Three), FoG, TUG, 10MWT, and 6-Minute Walking Exam (6MWT). Seventeen studies (55%) completed the smartphone assessments in laboratory settings, ten studies (32%) completed the assessments in home settings, and four (xiii%) completed the assessments in both dwelling house and laboratory settings. Smartphone models too varied greatly from iOS to Android Bone beyond the studies reviewed. iOS models included iPod Touch 4thursday and 5th generation, iPhone v, 6, and 6 plus and Android OS models included Samsung S3, S5, S8, J7, LG, LG Optimus, Sony Xperia, Google Nexus, Huawei Arise, and Moto G4. Vi studies did not specify the smartphone models used [26, 28, 34, 38, 50, 51]. Nineteen (61%) out the 31 studies included in this review reported individuals with PD as their intended users, vii (23%) reported clinicians as intended users, and five (xvi%) reported both individuals with PD and clinicians every bit their intended users. Although almost of the studies mentioned their intended users, only six out the 31 included studies formally evaluated the usability, feasibility, or satisfaction of their smartphone applications [27, 35, 38, 44, 46, 50]. Too, smartphone article of clothing site during gait and residuum assessments differed profoundly among the studies and included the device secured to the chest, placed in front pocket, or mounted at the hip, navel, ankle, thigh, and lower back. Ii studies did non specify the smartphone article of clothing site during their assessments [29, 46].

Validity and Reliability of Smartphone Applications

Xvi (52%) out of the 31 studies included in this review evaluated the validity of smartphone assessments past comparing their results to gait and balance clinical tests, standalone accelerometers, or force plate (See Table 2). Fiems et al. [eight, 25] reported concurrent validity through meaning correlations betwixt an assessment performed with the Sway Rest™ smartphone awarding (balance and fall protocols) and IMUs placed at pelvic and thoracic regions (ρ = –0.61 to –0.92, p < 0.001). Su et al. [9] found significant correlations between a customized smartphone awarding and mobility laboratory measures, including step time and stride variability, and total UPDRS-Iii (r = 0.98 – 0.99, p < 0.001 and β = 0.37 – 0.39, p < 0.001). Similarly, Borzì et al. [41] reported significant correlations between a smartphone derived quality of motion (QoM) alphabetize, 6MWT, and gait and postural stability items of the UPDRS III (r = 0.4 – 0.61, p < 0.0001). Ellis et al. [36] reported concurrent validity of the SmartMOVE application with step durations and stride displacements. Also, Capecci et al. [37] reported meaning correlations between a FoG detection smartphone application and step cadence. Chen et al. [43] and Lipsmeier et al. [45] reported that the Roche PD application significantly correlated with gait and postural stability items of UPDRS (r = 0.54, p < 0.001) for severity of gait and residuum assessments. Additionally, Clavijo-Buendía et al. [44] reported construct validity of the RUNZI application for gait measurements but not for rest assessments (p > 0.05). Elm et al. [46] found meaning correlations betwixt the Pull a fast one on Vesture Companion (FWC) application paired with a smartwatch and balance and walking items of UPDRS (r = 0.43 – 0.54, p's < 0.01). Tang et al. [40] reported strong correlation between a customized smartphone application and a research-grade accelerometer measuring gait and FoG detection (r = 0.86 – 0.97, p < 0.05). Yahalom et al. [48, 49] reported discriminant validity between individuals with PD and salubrious control using the EncephaLog application measures. In addition, the authors reported high correlation between EncephaLog application measures and 4-axial motor UPDRS and UPDRS items arising from chair, posture, and gait measures (r = 0.xiv – 0.46, p < 0.05) [49]. Borzì et al. [26] reported high correlation between a SensorLog application and UPDRS postural instability clinical score (r = 0.76, p < 0.0001). Similarly, Ozinga et al. [28] reported a high understanding betwixt a mobile device center of mass (COM) acceleration and NeuroCom force plate measures with a difference close to 0. Finally, Zhan et al. [29] reported correlation betwixt the Hopkins PD application and UPDRS, TUG Test, and H &Y (r = 0.72 – 0.91, p'south < 0.01).

Seven (22%) out of the 31 studies included in this review evaluated some measure out of reliability using a smartphone. Fiems et al. [8, 25], Clavijo-Buendía et al. [44], Lipsmeier et al. [45], Ozinga et al. [28] reported skillful to excellent test–retest reliability (ICC = 0.64 – 0.98) of smartphone applications (Sway Balance™, RUNZI application, Roche PD awarding, mobile device COM acceleration) to appraise gait and residue in individuals with PD. Additionally, Tang et al. [twoscore] reported good reliability in detecting FoG (ICC = 0.82) using a customized smartphone-based assessment. Finally, Serra-Añó et al. [51] reported good reliability for postural control (ICC = 0.62 – 0.71) and splendid for gait (ICC = 0.89 – 0.92) using the FallSkip awarding.

Discriminative abilities of Smartphone Applications

Seventeen (55%) out of the 31 studies included in this review evaluated some discriminative power of smartphone applications to assess falls, gait, and/or balance among individuals with PD. Fiems et al. [8] indicated that the Sway Balance™ smartphone awarding showed an accurateness of 0.65 to predict future falls. This prediction operation is lower than the prediction performance of the ABC (0.76), Mini-BESTest (0.72), MDS-UPDRS (0.66), and autumn history (0.83) [viii]. In contrast, Lo et al. [52] reported a high accuracy of 0.94 of a customized smartphone-based cess to predict future falls. Lo et al. [52] besides reported an accurateness of 0.95 and 0.nine to predict FoG and postural instability in individuals with PD. Similarly, Pepa et al. [34, 39], Borzì et al. [31], Capecci et al. [37], Tang et al. [xl], Abujrida et al. [47], and Kim et al. [30] reported good predictive abilities of smartphone applications to notice FoG (sensitivity: 0.lxx—0.96, specificity, 0.85—0.99, and accuracy: 0.81—0.99).

Additionally, Borzì et al. [41], Arora et al. [32, 53], Abujrida et al. [47], reported good to first-class ability of smartphone applications to discriminate gait and postural instability betwixt individuals with PD and good for you controls (sensitivity: 0.85–1, specificity: 0.88–i). Also, Borzi et al. [31, 41], Chomiak et al. [33], Chen et al. [43], Lipsmeier et al. [45], and Abujrida et al. [47] indicated good to fantabulous power of smartphone applications to discriminate between individuals with PD with different levels of postural stability, dynamic gait instability, gait wheel breakdown, resting tremor, dexterity, and leg dexterity (sensitivity: 0.58–1, specificity: 0.79–ane, accuracy: 0.92 – 0.97). Finally, Fiems et al. [25] and Bayés et al. [38] reported moderate to good ability of smartphone applications to predict H & Y levels (sensitivity: 0, specificity: 1, accuracy: 0.57) and to recognize on–off motor states (sensitivity: 97% and specificity: 88%), respectively.

Methodological Quality Assessment

Appendix C presents the details of the methodological quality assessment of the included studies. Xx-eight (90%) out the 31 studies presented with good methodological quality indicating a low hazard of bias. Different levels of exposure and blinding of outcome measures were non applicable to whatsoever of the included studies and were not accounted in the overall quality rating. Lack of sample size justification was the most common deficiency reported in the included studies.

Discussion

The purpose of this report was to synthesize the current bear witness of smartphone applications to assess gait, balance, and falls among individuals with PD. Smartphone-based assessments may be an appropriate culling to conventional gait and balance analysis methods when such methods are cost prohibitive or not possible. In addition, the importance of remote monitoring and assessments have become clear in calorie-free of the COVID-xix pandemic. As a result of this systematic review, smartphone applications accept shown to present with stiff validity, reliability, and discriminative abilities to evaluate gait and remainder and detect FoG among individuals with PD. The ability of smartphone applications to predict time to come falls in this population is inconclusive and deserves further exploration.

Inside this review, sixteen studies evaluated the validity of smartphone applications against clinical and biomechanics measures to assess gait and remainder in individuals with PD. The results indicate strong concurrent and discriminant validities of smartphone applications in detecting FoG, gait alterations, and postural instability in this population. Also, the results indicate that smartphone applications are valid to differentiate gait and postural instability between salubrious command and individuals with PD. Only 1 study reported depression correlations between cadence derived from the EncephaLog application and arising and gait axial motor UPDRS measures [49]. These low correlations may be explained by the measurement errors of the EncephaLog application that needs to be refined to appropriately reflect gait constructs in individuals with PD. The EncephaLog application was not fully automated and some human preprocessing before every measurement was however required prior to analysis [49]. In summary, the validity of thirteen smartphone applications to evaluate gait and/or rest in PD has been reported in the literature. Sway Remainder™ [8, 25], SensorLog awarding [26], and a smartphone-based accelerometer awarding without a specific name [28] have been validated to appraise postural instability in PD. Also, SmartMOVE awarding [36], RUNZI application [44], Hopkins PD application [29], a Fuzzy logic algorithm embedded in a smartphone [39], and ii other smartphone-based accelerometer applications without specific names [9, 40] have been validated to appraise gait alterations and FoG in PD. While Roche PD application [43, 45], Play a joke on Wearable Companion application [46], EncephaLog application [49], and a FallSkip system based on accelerometer and gyroscope [51] have been validated to evaluate both gait and postural instability in this population.

Similar to the validity of smartphone applications, the seven studies that evaluated the reliability of smartphone applications indicated practiced to excellent reliability to assess gait and balance, and in detecting FoG among individuals with PD. Although the studies reported loftier reliability of smartphone applications which minimizes the measurement errors between assessments, simply 22% of the included studies in this review evaluated reliability. This is like to the results of previous reviews indicating that the reliability of smartphone applications was merely evaluated amid few studies including older adults [15] and people with multiple sclerosis [xviii]. To increase the use of smartphone applications inside clinical settings and provide an appropriate remote monitoring of gait and balance in individuals with PD, more than studies are warranted to explore the reliability of smartphone applications in PD.

Additionally, smartphone applications showed good to excellent abilities to discriminate gait and postural instability between healthy controls and individuals with PD. Also, several of the included studies reported the ability of smartphone applications to appropriately discriminate between individuals with PD with dissimilar levels of postural stability, dynamic gait instability, gait cycle breakup, rest tremor, dexterity, and leg dexterity. These results are also consistent with the results reported in older adults [15] and people with multiple sclerosis [18] indicating that smartphone applications are sensitive, specific, and accurate in discriminating gait and postural control within subgroups of individuals with PD and betwixt healthy control and individuals with PD. Still, the discriminative ability of smartphone applications to predict falls is inconclusive. While Fiems et al. [8] indicate that the Sway Balance™ mobile application is not equally accurate as history of falls and other clinical measures including ABC and Mini-BESTest in predicting falls and should not exist used as culling, Lo et al. [52] reported a high accuracy of a customized smartphone-based cess to predict time to come falls. This contradictory result should be further clarified past using smartphone applications to track falls in prospective studies. Gait dysfunctions, FoG, and balance impairments are the primary fall risks among individuals with PD [54] and therefore, their relationship should be explored using smartphone-based assessments. This volition increase the clinical utilise of smartphone applications amongst people with PD.

The type of smartphone applications (i.e., RUNZI, EncephaLog, Roche PD, FWC) and model of smartphones (i.eastward., iOS-based and Android-based smartphones) differed greatly among the studies included in this review. This may influence the results of the validity, reliability, and discriminative properties described in this study. While no comparisons tin can be made based on the studies included in this review because of the dissimilar conditions of data collection, future studies should investigate the differences between smartphone applications and/or smartphone models. Likewise, the smartphone wear sites while evaluating gait and balance varied profoundly among the studies reviewed. In nearly studies, participants carried smartphones in their front end pant pocket or secured at the chest or waist. Smartphone placement has been reported every bit an important factor during gait and residue assessments in people with multiple sclerosis [55]. In individuals with PD, Kim et al. [30] compared dissimilar smartphone placements during FoG detection and reported that location of smartphone did non influence the results. The authors furthermore reported that smartphones mounted at ankle, waist, or pocket provided similar FoG detection results in this population [thirty]. Since only one study has investigated the influence of unlike smartphone locations on gait and balance assessments in PD, this topic also deserves further exploration.

Lastly, but 19% of the included studies formally evaluated the usability, feasibility, or satisfaction of their smartphone applications and 32% completed the assessments in home settings. In general, the studies reported feasibility, good acceptability, and high satisfaction of the smartphone applications in unsupervised home settings [27, 35, 38, 44, 46, fifty]. However, in individuals with advanced PD, common motor features including tremor, bradykinesia, rigidity, and postural instability [ii] are probable to be exacerbated which may lead to an increased difficulty using smartphone engineering. Due to the importance of the intended users in the development of smartphone applications for remote monitoring [20], more studies are needed to investigate the usability of smartphone applications to appraise gait and residual in individuals with PD. Adaptations of smartphone applications based on user'south needs may help to overcome the challenge of incorporating smartphone applications into clinical practice and clinical decision making.

Limitations

They are some limitations associated with this systematic review that should be taken into consideration when interpretating the results presented. Start, the results of validity and discriminative abilities reported in this review are based on only approximately 50% of the total number of studies included. The other 50% did not report any results of validity and discriminative abilities of smartphone applications. Similarly, approximately eighty% of the included studies did not report any results of reliability of smartphone applications in PD. Given the importance of the psychometric backdrop of an outcome measures regarding measurement errors and measurement construct, the results presented may exist overstated. It is essential that more studies compare smartphone application outcomes with clinical outcomes and/or biomechanics measures to approve the results presented in this review. Additionally, none of the included studies performed a sample size adding before recruiting participants. This is a crucial limitation as the ability of the analysis in the included studies may be hindered. Finally, our review did not specify which smartphone applications are more appropriate to assess gait and residue according to the stage and severity of PD. This was mainly because most of the studies included in this review did not focus on differentiating gait and balance assessments based on participant'southward characteristics using smartphone applications. Nosotros recommend that time to come studies explore the differences between the various stages and severity of PD using smartphone applications to facilitate their incorporation into clinical decision making.

Determination

This review provides strong bear witness regarding the potential use of smartphone applications to assess gait and balance among individuals with PD in the home or laboratory. The results indicate that smartphone applications nowadays with stiff validity, reliability, and discriminative abilities to monitor gait dysfunctions and remainder impairments and to notice FoG in individuals with PD. This review too highlights the need for further use of smartphone applications to monitor fall gamble factors in this population. Additionally, virtually studies did not formally investigate the usability of smartphone applications. Due to the importance of the acceptability and satisfaction of smartphone applications in these assessments, farther studies are warranted to investigate the usability of smartphone applications to assess gait and balance in PD. This will help to efficiently employ smartphone applications equally an unobstructive engineering science for gait and residuum assessments during individuals with PD daily life activities in habitual setting. Consequently, remote monitoring of PD will help provide targeted care to individuals with PD.

Availability of Data and Fabric

Not applicable.

Code Availability

Non applicable.

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Funding

This research did not receive whatever specific grand from funding agencies in the public, commercial, or not-for-profit sectors.

Author data

Affiliations

  1. Section of Kinesiology and Community Health, Higher of Applied Wellness Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, United states of america

    Libak Abou, Joseph Peters, Ellyce Wong & Laura A. Rice

  2. Department of Rehabilitation Scientific discipline, University of Oklahoma Health Sciences Eye, Oklahoma Metropolis, OK, U.s.a.

    Rebecca Akers

  3. Centre National Hospitalier et Universitaire de Pneumo-Phtisiologie, Cotonou, Littoral, Benin

    Mauricette Sènan Dossou

  4. Department of Physical Therapy and Rehabilitation Science, School of Wellness Professions, Medical Center, University of Kansas, Kansas City, KS, U.s.a.

    Jacob J. Sosnoff

Contributions

LA was responsible for designing the study, protocol registration, mediating any data screening and extraction discrepancies, interpreting the results, and writing the initial manuscript. JP, EW, SMD, and RA were responsible for screening the studies, extracting the data, and assessing the methodological quality assessment of the included studies at unlike levels. JJS and LAR contributed to the study pattern and provided feedback on the initial review protocol and the manuscript.

Corresponding author

Correspondence to Laura A. Rice.

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Jacob J. Sosnoff is principal owner of Sosnoff Technologies LLC.

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Abou, Fifty., Peters, J., Wong, E. et al. Gait and Balance Assessments using Smartphone Applications in Parkinson's Disease: A Systematic Review. J Med Syst 45, 87 (2021). https://doi.org/10.1007/s10916-021-01760-5

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  • DOI : https://doi.org/x.1007/s10916-021-01760-five

Keywords

  • Smartphone
  • Parkinson's Affliction
  • Gait
  • Postural command
  • mHealth

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