‘Watkins’ & ‘Watkins2.0’: Smart Phone Applications (Apps) For Gait- Assessment in Normal Pressure Hydrocephalus And Decompensated Long-Standing Overt Ventriculomegaly

‘Watkins’ & ‘Watkins2.0’: Smart Phone Applications (Apps) For Gait- Assessment in Normal Pressure Hydrocephalus And Decompensated Long-Standing Overt Ventriculomegaly | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article ‘Watkins’ & ‘Watkins2.0’: Smart Phone Applications (Apps) For Gait- Assessment in Normal Pressure Hydrocephalus And Decompensated Long-Standing Overt Ventriculomegaly Kanza Tariq, Lewis Thorne, Ahmed Toma, Laurence Watkins This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4701792/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Sep, 2024 Read the published version in Acta Neurochirurgica → Version 1 posted 9 You are reading this latest preprint version Abstract Objective Gait disturbance is one of the features of normal pressure hydrocephalus (NPH) and decompensated long-standing overt ventriculomegaly (LOVA). The timed-up-and-go (TUG) test and the timed-10-meter-walking test (10MWT) are frequently used assessments tools for gait and balance disturbances in NPH and LOVA, as well as several other disorders. We aimed to make smart-phone apps which perform both the 10MWT and the TUG-test and record the results for individual patients, thus making it possible for patients to have an objective assessment of their progress. Patients with a suitable smart phone can perform repeat assessments in their home environment, providing a measure of progress for them and for their clinical team. Methods 10MWT and TUG-test were performed by 50 healthy adults, 67 NPH and 10 LOVA patients, as well as 5 elderly patients as part of falls risk assessment using the Watkins2.0 app. The 10MWT was assessed with timed slow-pace and fast-pace. Statistical analysis used SPSS (version 25.0, IBM) by paired t-test, comparing the healthy and the NPH cohorts. Level of precision of the app as compared to a clinical observer using a stopwatch was evaluated using receiver operating characteristics curve. Results As compared to a clinical observer using a stopwatch, in 10MWT the app showed 100% accuracy in the measure of time taken to cover distance in whole seconds, 95% accuracy in the number of steps taken with an error ± 1–3 steps, and 97% accuracy in the measure of total distance covered with error of ± 0.25–0.50 meter. The TUG test has 100% accuracy in time taken to complete the test in whole seconds, 97% accuracy in the number of steps with an error of ± 1–2 steps and 87.5% accuracy in the distance covered with error of ± 0.50 meter. In the measure of time, the app was found to have equal sensitivity as an observer. In measure of number of steps and distance, the app demonstrated high sensitivity and precision (AUC > 0.9). The app also showed significant level of discrimination between healthy and gait-impaired individuals. Conclusion ‘Watkins’ and ‘Watkins2.0’ are efficient apps for objective performance of 10MWT and the TUG-test in NPH and LOVA patients and has application in several other pathologies characterised by gait and balance disturbance. Figures Figure 1 Figure 2 Introduction Normal pressure hydrocephalus (NPH) and long-standing overt ventriculomegaly (LOVA) represent forms of chronic hydrocephalus of adult life. Both conditions feature ventriculomegaly and characteristic symptomatology including disturbances of gait and balance, urinary incontinence and cognitive decline [ 1 – 3 ] . Early diagnosis and treatment can ameliorate symptoms or inhibit further deterioration [ 1 – 3 ] . Gait and balance disturbance is not only the earliest manifested and most frequent feature of NPH but is also the most amenable to treatment [ 4 ] . It is therefore regarded as obligatory in the diagnosis of NPH [ 2 , 4 ] . The timed-10-meter-walking test (10MWT) and the timed-up-and-go (TUG) test are frequently used diagnostic and prognostic tools for gait and balance disturbances in NPH and LOVA [ 2 , 5 , 6 ] . 10MWT requires the patient to walk a 10 meter distance in a straight line. The test can be performed at normal pace or fast pace and heed is paid to the time taken to cover the distance, number of steps taken and the use of any mobility/balance assistance devices such as walking frames or sticks [ 2 ] . TUG test entails having a seated patient stand-up and walk 3 meters, turn around 180 degrees, walk back and sit down again. Results are analysed by measuring time to complete test as a reflection of gait-velocity [ 7 ] . Several studies, including guidelines from the American College of Rheumatology, have aimed to provide an age-matched reference range of average values for both normal pace and fast pace gait speed in both 10MWT [ 8 , 9 ] and TUG test [ 10 ] . The results not only help discriminate between normal and impaired gait, but also facilitate in identifying the risk of fall, frailty, decline in functional activity and cognitive impairment [ 9 ] . Comparison of serial results pre- and post-intervention help clinicians monitor the degree and duration of improvement [ 5 , 6 ] . Traditionally these tests are performed in a controlled clinical environment under direct supervision of a member of the clinical team. A 2021 study by Kuruvithadam et al showed that patients demonstrated much slower and dysfunctional gait in home environment as compared to clinical environment, possibly due to psychological effect of performance pressure and the relative lack of obstacles and distractions in clinical settings. Notably, real-world measurements displayed superior discrimination of NPH patients as compared to clinical measurements [ 1 ] . Recently, technical advances in mobile computing have made it possible to administer these tests in home environment and to compliment them in the clinic. Kuruvithadam et al performed 10MWT in the patient’s home environment by attaching wearable inertial measurement units (IMUs) to the wrists, ankles and chest of the patient, and extracted the data using a computerised algorithm [ 1 ] . Instrumented TUG test (iTUG), utilizing wearable IMUs, accelerometers and gyroscopes, has been performed by several investigators over the decades studying mobility [ 11 ] , frailty, balance and risk of falls in the geriatric population [ 12 – 14 ] or in various pathological conditions such as Parkinson’s disease, stroke, cerebral palsy, movement disorders, multiple sclerosis, spinal cord injury and brain injury [ 15 – 17 ] . The usage of smart-phones for the performance of iTUG has, to date, been undertaken by 5 investigators, all utilizing the built-in accelerometer and gyroscopes of the smart-phone for clinical assessment in such disorders as frailty syndrome, falls-risk, and gait velocity in NPH [ 14 , 15 , 18 – 24 ] . With the exception of Milosevic et al and Yamada et al, all wearable and smart phone iTUG assessments required the data to be migrated from the device and evaluated by skilled personnel using a computerised algorithm. In 2013 Milosevic et al developed the first automated smart phone application (app) capable of performing self-administrable TUG tests, aimed at mobility examining in Parkinson’s disease [ 18 ] . The app was commercially available on Android systems as smartphone-TUG (sTUG) between 2013 to 2016. Yamada et al in 2017 designed a self-administrable automated smart-phone app ‘Hacaro-iTUG’ using iPhone’s built-in accelerometer and gyroscope for gait evaluation in NPH [ 19 ] . The test requires for the iPhone to be mounted completely still on the umbilicus with a special belt to prevent any shaking during mobilisation and the placement of a cone at 3 meter distance. The results are displayed as a function of time and individuals are assigned a score based on their performance. The scoring system is generated by the developers [ 19 ] . In this study we introduce ‘Watkins’ and ‘Watkins2.0’, smart-phone walking apps capable of performing both the 10MWT and the TUG-test, developed for both iOS and Android operating systems, using built-in accelerometers, pedometers and gyroscope. The apps are completed with verbal instructions and hence do not require marking of pre-set distance. It also does not require the smart-phone to be mounted on any specific part of the body. The non-purchase apps calculate and display results as a function of distance covered, time and number of steps taken to cover the distance. To the best of our knowledge these are the first smart-phone apps to include these features and mark the next generation in gait-assessment apps. Methodology ‘Watkins’ & ‘Watkins2.0’ development There were two separate applications created for both Android and iOS users. To achieve better accuracy, both the applications were written in native languages for smooth communication of software with hardware, which allowed for data extraction at intervals of 0.005 seconds. The interaction of hardware with software is described in detail below in their respective sections. Android Android, due to its flexibility and compatibility with almost all devices, has become an essential component of all the hand held devices except Apple devices. There are several platforms available for developers to build applications for android users including react-native, flutter, java, kotlin etc. Out of them, there are few languages that are flexible to both android and iOS and provide utility of cross platform execution. However, when it comes to the usage of hardware, it is difficult to use such languages, and native languages are preferred. In android, the two native languages used to create apps are java and kotlin. The step counter application was written in kotlin. In order to count the steps, several application programming interfaces (APIs) were used to detect the movement of user. iOS iOS possess much high security and complex structure as compared to Android. However, there are several native platforms to make applications, namely Swift or objective C. Step counter application was written in Swift language. The interface was similar to Android and had the same flow. Description In mobile devices, there are several hardware components that are used for specific movements e.g. gyroscope is used to detect the rotation, accelerometer is used for movement in x,y,z axis and pedometer is used for step-count. Although these hardware components are useful to detect the mobile movement, they have their limitation which prevent them from being used directly. In our application, we used accelerometer, pedometer and gyroscope to detect the linear and angular movement and calculate the number of steps. It is not possible to interact with these hardware components directly. Kotlin provides API calls that interact with hardware following permission coding. Once permitted to access the hardware, we use the APIs to get a response from the hardware. Several open-source libraries were used to get data from the sensors. An API based call was sent to the hardware that responded by providing coordinates according to the movement of the user. This raw data is then processed and based on the output it is decided whether the user mobilised or not. For Android this was achieved through libraries such as android.hardware.SensorEventListener and android.hardware.SensorManager . For iOS, Core Motion library was used to create events required to communicate with the hardware. One of the main class is the CMMotionManager that manages all the services required to detect the motion. The main hardware used to count steps was pedometer. CMDeviceMotion class provides the attitude, rotation rate, and acceleration of a device. CMPedometer is the most important part of this application, as this class was used to interact with pedometer to detect the movement of the mobile phone based on which it decides how many steps user has taken. Based on the measurements received, steps and distance are calculated. Functioning The application starts with the interface that asks the user for very basic demographics to maintain individual progress records. Following this, the app allows its user to choose which test to perform; TUG test or timed 10-meter test. The timed 10-meter test The 10MWT is set at default 10 metres, but the walking distance can be manually changed. When distance is set, the app gives verbal countdown notice of 3 seconds before it verbally instructs to ‘start walking’. An animation is displayed that gives information of what the user is doing for example walking forward, backward. When the pre-set distance is covered in a straight line, the app verbally informs the patient to ‘stop’, and displays the result in measure of distance covered, time taken to cover set distance and number of steps taken. TUG test The TUG tests is set at default distance of 3 meters which can be manually changed. On selecting start, the app verbally countdowns from 3 before asking the user to ‘get up and walk’. Once 3 meters are covered the app verbally instructs the user to ‘turn around and walk back to the chair’. Once the required distance is covered the app verbally advises to ‘sit down’ and displays results in terms of time and number of steps taken to cover the distance. The results can be saved and compared with future results. Ethical approval: Formal ethical approval, institutional board review and informed consent from participants was not required prior to the development of the apps. This was confirmed by the Health-Research Authority, UK. Validation: The app was validated in 50 healthy adults, 67 NPH and 10 LOVA patients, as well as 5 elderly patients as part of falls risk assessment. 50/67 NPH and 6/10 LOVA patients were invited to use the app in their home-environment and results were compared to walking tests performed under supervision in clinics. The 10MWT was assessed with timed slow-pace and fast-pace. The results were compared with a clinical observer using a stop-watch. Statistical analysis: Statistical analysis used SPSS (version 25.0, IBM) by paired t-test, comparing the healthy and the NPH cohorts. Level of precision of the app as compared to a clinical observer using a stopwatch was evaluated using receiver operating characteristics curve. Results Participant characteristics: The median age of the 50 healthy adults was 48 years (range: 22-56), with 22% (11/50) older than 45, 23M:27F, 4% (2/50) obese (BMI > 30). The median age of the 67 NPH patients was 77 years (range: 64-89), with 73% (49/67) older than 70, 37M:30F, 44.7% (30/67) obese (BMI > 30). The median age of the 10 LOVA patients was 47 years (range: 44-56), with 70% (7/10) older than 45, 2M:8F, 50% (5/10) obese (BMI > 30). The median age of the 5 elderly adults (Parkinson’s disease =2; dystonia = 2, subdural haematoma = 1) was 62 years (range: 52-74), with 80% (4/5) older than 55, 4M:1F, none obese. 50 NPH and 6 LOVA patients described the apps as simple, efficient, easy-to-use, eliciting confidence and peace of mind in home settings and highly likely to recommend to others, through patient-reported feedback. Table 1. Participant Characteristics Participant characteristics Healthy adults Normal pressure hydrocephalus Long-standing overt ventriculomegaly Elderly adults Participant number 50 67 10 5 Median age (yrs) 48 77 47 62 M:F 23M:27F 37M:30F 2M:8F 4M:1F Percentage obese 4% 44.70% 50% 0% Application characteristics: Timed -10-meter-walking test performed primarily with Watkins: As compared to a clinical observer using a stopwatch the app showed 100% accuracy in the measure of time taken to cover distance (sensitivity 100%, specificity 100%, AUC 1.0), 94.9% accuracy in the number of steps taken with an error ± 1-3 steps (sensitivity 100%, specificity 92.6%, AUC 0.942), and 97.4% accuracy in the measure of total distance covered with error of ± 0.25-0.50 meter (sensitivity 100%, specificity 96.3%, AUC 0.978). The timed-up-and-go test performed primarily with Watkins2.0: As compared to a clinical observer using a stopwatch the app has 100% accuracy in time taken to complete the test (sensitivity 100%, specificity 100%, AUC 1.0), 97.2% accuracy in the number of steps with an error of ± 1-2 steps (sensitivity 100%, specificity 95.6%, AUC 0.948), and 87.5% accuracy in the distance covered with error of ± 0.50 meter (sensitivity 100%, specificity 83.3%, AUC 0.861). Significant discrimination in all parameters was seen between healthy and gait-impaired individuals (p < 0.001), with NPH patients having longer duration of time to complete test and a greater number of steps. Table 2. Application characteristics Accuracy Error Sensitivity Specificity AUC p-value Timed 10-meter-walking test Time (s) 100% 0.00 100% 100% 1.00 p < 0.001 Number of steps 94.90% ± 1-3 steps 100% 92.60% 0.94 p < 0.001 Distance (m) 97.40% ± 0.25-0.50 meter 100% 96.30% 0.978 p < 0.001 Timed-up-and-go test Time (s) 100% 0.00 100% 100% 1.00 p < 0.001 Number of steps 97.20% ± 1-2 steps 100% 95.60% 0.95 p < 0.001 Distance (m) 87.50% ± 0.50 meter 100% 83.30% 0.86 p < 0.001 Discussion Principal findings : In the current study we present the first self-administrable automated non-purchase gait assessment smart-phone applications capable of performing both 10MWT and TUG test independently with outstanding accuracy, compatible with both Android and iOS systems. Watkins2.0 did, however, have an 87.5% accuracy in measuring the distance covered with error of ± 0.50 meter. Similar to previous work conducted by other investigators, the margin of error can be decreased and the percentage of accuracy significantly improved by increasing the total length of distance beyond 3 meters [12] . This is due to the subsequent increase in time afforded by the APIs to extract data from the hardware. Both ‘Watkins’ and ‘Watkins2.0’ not only look at the time required to cover a fixed distance but also the number of steps taken to cover the distance. The apps are also capable of calculating the distance covered in meters through the duel input by accelerometer and pedometer. This makes the need to mark a set distance redundant, as the apps, depending on the test being performed, verbally inform the participants to either stop or change direction once the set distance is covered. Unlike other studies, the phone can be held in hand at the time of the assessment and does not need to be fixed on the body [13, 16, 18, 19, 24] . Therefore no additional hardware in the form of special belts or sleeves is required. The results are immediately displayed, as measures of easily understandable parameters such as time, number of steps and distance, instead of complicated scoring systems [19, 24] . This provides ease to the participants in interpreting their results and monitoring their progress and contributes to reassurance. Due to their easy utility and high precision, both apps, specially ‘Watkins’, they were highly recommended to others by every participant who used them in their home. We believe that these are very useful apps and have application even beyond NPH and LOVA. Utility and literature review: : In recent decades global demographics have changed with a sharp rise in the elderly population [15] . This has also impacted health-care services producing a need to assess health, level of independent function, mobility, balance, cognition and environmental safety in the geriatric population and to identify the vulnerable [15] . Evaluation of functional impediment and performance level of daily activity through measurements of walking speed and assessment of gait pattern are frequently employed tools to predict health status and vulnerability in the elderly [1, 9] . Patients with NPH often feel symptomatic improvement following shunt insertion surgery or spinal-tap test which may last up to six months [4-6] , after which patients may begin to plateau which in some cases causes distress, anxiety and apprehension regarding shunt function and disease prognosis. ‘Watkins’ and ‘Watkins2.0’ allow patients to perform repeat assessments in their home and have an objective estimation of their progress. These tests have further clinical application in predicting risk of falls in suspected NPH patients [2] , helping with early diagnosis of dementia [25] , studying frailty in chronic obstructive pulmonary disease [26] , monitoring the efficacy of physiotherapy in improving gait in patients with neurological impairment secondary to traumatic brain injury [27] and even in predicting death in the elderly [28] . In 2021 De-Bruijn et al published a study showing that a majority of the educated population used health apps regardless of age and gender and that they displayed healthier habits [29] . Thus, the app can also be used to help patients improve gait and functional mobility by repeated practice and self-monitoring [30] . Strengths, limitations and future work: Smart-phone apps are difficult and expensive to make [31] , with an ever-increasing amount of technical skill required to develop and improve the app. This, on one hand, results in just a couple of smart-phone apps globally capable of conducting self-administrated clinical assessments accurately and with precision, making ‘Watkins’ and ‘Watkins2.0’ the first of their kind, and on the other hand, greater time will be required to upgrade the apps to improve accuracy in the calculation of distance in the TUG-test and to include gait pattern assessment and supplementary angular measurements of trunk position during different phases of walking. ‘Watkins’ and ‘Watkins2.0’ have the potential to be used as a regular clinical assessment tool in hospitals as well as homes in a variety of neurosurgical, neurological and movement disorders as well as in healthy geriatric population, and future upgrades would aim to include specific features to facilitate assessments in these various conditions. Conclusion ‘ Watkins’ and ‘ Watkins2.0’ represent the next generation in smart-phone apps, capable of objective and efficient performance of 10MWT and the TUG-test in NPH and LOVA patients and have application in several other pathologies featuring gait and balance disturbance. Declarations Ethical Approval: The development of the apps and the use of all human data collected through the app has been approved by the appropriate ethics committee and have therefore been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. All patients have their informed consent prior to their data being included in the study. All patients have their informed consent to publish their data anonymously in the study. Competing interests: ‘Watkins’ and ‘Watkins2.0’ are non-profit smart phone applications. The authors have no financial or personal interest in or benefit from the app. The authors have no competing interests to declare that are relevant to the content of this article. Conflict Of Interest: The authors declare that they have no conflict of interest. Authors Contributions: Dr Kanza Tariq identified the need of the apps, conceptualised and developed the apps, collected and analysed the data, performed bug-sweeps and developed the newer version of the app. Mr Lewis Thorne, Mr Ahmed Toma and Mr Laurence Watkins collected and analysed the data, reported bugs and tested the subsequent version of the app. They also reviewed the manuscript. Fundings: The development of both ‘Watkins’ and ‘Watkins2.0’ was funded by the International Charity for Digital Health Availability of data and materials: The link to the apps programme and the data repository can be found in the supplementary data section. 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JMIR Hum Factors 9(2):e29767 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Sep, 2024 Read the published version in Acta Neurochirurgica → Version 1 posted Editorial decision: Accepted 17 Sep, 2024 Reviews received at journal 17 Sep, 2024 Reviewers agreed at journal 11 Sep, 2024 Reviews received at journal 11 Aug, 2024 Reviewers agreed at journal 23 Jul, 2024 Reviewers invited by journal 17 Jul, 2024 Editor assigned by journal 17 Jul, 2024 Submission checks completed at journal 17 Jul, 2024 First submitted to journal 07 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4701792","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":334159194,"identity":"9c8e7ff3-2850-4ad5-bd4c-b6db7a7608ef","order_by":0,"name":"Kanza Tariq","email":"data:image/png;base64,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","orcid":"","institution":"National Hospital for Neurology and Neurosurgery","correspondingAuthor":true,"prefix":"","firstName":"Kanza","middleName":"","lastName":"Tariq","suffix":""},{"id":334159196,"identity":"28edc066-b53c-40a2-bb5c-0b1d25a69d64","order_by":1,"name":"Lewis Thorne","email":"","orcid":"","institution":"National Hospital for Neurology and Neurosurgery","correspondingAuthor":false,"prefix":"","firstName":"Lewis","middleName":"","lastName":"Thorne","suffix":""},{"id":334159200,"identity":"3e4d0e6a-e02c-4999-89da-129d9a0ccb9c","order_by":2,"name":"Ahmed Toma","email":"","orcid":"","institution":"National Hospital for Neurology and Neurosurgery","correspondingAuthor":false,"prefix":"","firstName":"Ahmed","middleName":"","lastName":"Toma","suffix":""},{"id":334159201,"identity":"2421bbf9-1424-4934-a501-29e63d4f0bff","order_by":3,"name":"Laurence Watkins","email":"","orcid":"","institution":"National Hospital for Neurology and Neurosurgery","correspondingAuthor":false,"prefix":"","firstName":"Laurence","middleName":"","lastName":"Watkins","suffix":""}],"badges":[],"createdAt":"2024-07-07 23:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4701792/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4701792/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00701-024-06275-9","type":"published","date":"2024-09-28T15:57:17+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62653986,"identity":"b4a3f20d-ce97-48ae-bb1f-07c1403cc49f","added_by":"auto","created_at":"2024-08-17 01:20:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":143763,"visible":true,"origin":"","legend":"\u003cp\u003eThe timed 10-meter test in Watkins \u0026amp; Watkins2.0\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4701792/v1/03a9b19c0a41f305f13adaa7.png"},{"id":62653985,"identity":"edd8fbd1-441c-4f34-a852-021cc3d8855a","added_by":"auto","created_at":"2024-08-17 01:20:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":147704,"visible":true,"origin":"","legend":"\u003cp\u003eThe TUG test in Watkins2.0\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4701792/v1/10fef70b88e3322052904bb9.png"},{"id":65627142,"identity":"f60884f8-7da8-4656-ad19-004eaa2ca98a","added_by":"auto","created_at":"2024-09-30 16:12:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":819638,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4701792/v1/865ac8ae-e2f5-45a4-a1f2-f42d9083bbb0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"‘Watkins’ \u0026 ‘Watkins2.0’: Smart Phone Applications (Apps) For Gait- Assessment in Normal Pressure Hydrocephalus And Decompensated Long-Standing Overt Ventriculomegaly","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNormal pressure hydrocephalus (NPH) and long-standing overt ventriculomegaly (LOVA) represent forms of chronic hydrocephalus of adult life. Both conditions feature ventriculomegaly and characteristic symptomatology including disturbances of gait and balance, urinary incontinence and cognitive decline\u003csup\u003e[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Early diagnosis and treatment can ameliorate symptoms or inhibit further deterioration\u003csup\u003e[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Gait and balance disturbance is not only the earliest manifested and most frequent feature of NPH but is also the most amenable to treatment\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. It is therefore regarded as obligatory in the diagnosis of NPH\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. The timed-10-meter-walking test (10MWT) and the timed-up-and-go (TUG) test are frequently used diagnostic and prognostic tools for gait and balance disturbances in NPH and LOVA\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. 10MWT requires the patient to walk a 10 meter distance in a straight line. The test can be performed at normal pace or fast pace and heed is paid to the time taken to cover the distance, number of steps taken and the use of any mobility/balance assistance devices such as walking frames or sticks\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. TUG test entails having a seated patient stand-up and walk 3 meters, turn around 180 degrees, walk back and sit down again. Results are analysed by measuring time to complete test as a reflection of gait-velocity\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Several studies, including guidelines from the American College of Rheumatology, have aimed to provide an age-matched reference range of average values for both normal pace and fast pace gait speed in both 10MWT\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e and TUG test\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. The results not only help discriminate between normal and impaired gait, but also facilitate in identifying the risk of fall, frailty, decline in functional activity and cognitive impairment\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Comparison of serial results pre- and post-intervention help clinicians monitor the degree and duration of improvement\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTraditionally these tests are performed in a controlled clinical environment under direct supervision of a member of the clinical team. A 2021 study by Kuruvithadam et al showed that patients demonstrated much slower and dysfunctional gait in home environment as compared to clinical environment, possibly due to psychological effect of performance pressure and the relative lack of obstacles and distractions in clinical settings. Notably, real-world measurements displayed superior discrimination of NPH patients as compared to clinical measurements\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecently, technical advances in mobile computing have made it possible to administer these tests in home environment and to compliment them in the clinic. Kuruvithadam et al performed 10MWT in the patient\u0026rsquo;s home environment by attaching wearable inertial measurement units (IMUs) to the wrists, ankles and chest of the patient, and extracted the data using a computerised algorithm\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Instrumented TUG test (iTUG), utilizing wearable IMUs, accelerometers and gyroscopes, has been performed by several investigators over the decades studying mobility\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, frailty, balance and risk of falls in the geriatric population\u003csup\u003e[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e or in various pathological conditions such as Parkinson\u0026rsquo;s disease, stroke, cerebral palsy, movement disorders, multiple sclerosis, spinal cord injury and brain injury\u003csup\u003e[\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe usage of smart-phones for the performance of iTUG has, to date, been undertaken by 5 investigators, all utilizing the built-in accelerometer and gyroscopes of the smart-phone for clinical assessment in such disorders as frailty syndrome, falls-risk, and gait velocity in NPH \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19 CR20 CR21 CR22 CR23\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. With the exception of Milosevic et al and Yamada et al, all wearable and smart phone iTUG assessments required the data to be migrated from the device and evaluated by skilled personnel using a computerised algorithm.\u003c/p\u003e \u003cp\u003eIn 2013 Milosevic et al developed the first automated smart phone application (app) capable of performing self-administrable TUG tests, aimed at mobility examining in Parkinson\u0026rsquo;s disease \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. The app was commercially available on Android systems as smartphone-TUG (sTUG) between 2013 to 2016.\u003c/p\u003e \u003cp\u003eYamada et al in 2017 designed a self-administrable automated smart-phone app \u0026lsquo;Hacaro-iTUG\u0026rsquo; using iPhone\u0026rsquo;s built-in accelerometer and gyroscope for gait evaluation in NPH\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. The test requires for the iPhone to be mounted completely still on the umbilicus with a special belt to prevent any shaking during mobilisation and the placement of a cone at 3 meter distance. The results are displayed as a function of time and individuals are assigned a score based on their performance. The scoring system is generated by the developers\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study we introduce \u0026lsquo;Watkins\u0026rsquo; and \u0026lsquo;Watkins2.0\u0026rsquo;, smart-phone walking apps capable of performing both the 10MWT and the TUG-test, developed for both iOS and Android operating systems, using built-in accelerometers, pedometers and gyroscope. The apps are completed with verbal instructions and hence do not require marking of pre-set distance. It also does not require the smart-phone to be mounted on any specific part of the body. The non-purchase apps calculate and display results as a function of distance covered, time and number of steps taken to cover the distance. To the best of our knowledge these are the first smart-phone apps to include these features and mark the next generation in gait-assessment apps.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003e \u003cstrong\u003e\u0026lsquo;Watkins\u0026rsquo; \u0026amp; \u0026lsquo;Watkins2.0\u0026rsquo; development\u003c/strong\u003e \u003cp\u003eThere were two separate applications created for both Android and iOS users. To achieve better accuracy, both the applications were written in native languages for smooth communication of software with hardware, which allowed for data extraction at intervals of 0.005 seconds. The interaction of hardware with software is described in detail below in their respective sections.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAndroid\u003c/strong\u003e \u003cp\u003eAndroid, due to its flexibility and compatibility with almost all devices, has become an essential component of all the hand held devices except Apple devices. There are several platforms available for developers to build applications for android users including react-native, flutter, java, kotlin etc. Out of them, there are few languages that are flexible to both android and iOS and provide utility of cross platform execution. However, when it comes to the usage of hardware, it is difficult to use such languages, and native languages are preferred. In android, the two native languages used to create apps are java and kotlin. The step counter application was written in kotlin. In order to count the steps, several application programming interfaces (APIs) were used to detect the movement of user.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eiOS\u003c/strong\u003e \u003cp\u003eiOS possess much high security and complex structure as compared to Android. However, there are several native platforms to make applications, namely Swift or objective C. Step counter application was written in Swift language. The interface was similar to Android and had the same flow.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDescription\u003c/strong\u003e \u003cp\u003eIn mobile devices, there are several hardware components that are used for specific movements e.g. gyroscope is used to detect the rotation, accelerometer is used for movement in x,y,z axis and pedometer is used for step-count. Although these hardware components are useful to detect the mobile movement, they have their limitation which prevent them from being used directly. In our application, we used accelerometer, pedometer and gyroscope to detect the linear and angular movement and calculate the number of steps. It is not possible to interact with these hardware components directly. Kotlin provides API calls that interact with hardware following permission coding. Once permitted to access the hardware, we use the APIs to get a response from the hardware. Several open-source libraries were used to get data from the sensors.\u003c/p\u003e \u003cp\u003e An API based call was sent to the hardware that responded by providing coordinates according to the movement of the user. This raw data is then processed and based on the output it is decided whether the user mobilised or not.\u003c/p\u003e \u003cp\u003eFor Android this was achieved through libraries such as \u003cem\u003eandroid.hardware.SensorEventListener\u003c/em\u003e and \u003cem\u003eandroid.hardware.SensorManager\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eFor iOS, Core Motion library was used to create events required to communicate with the hardware. One of the main class is the CMMotionManager that manages all the services required to detect the motion. The main hardware used to count steps was pedometer. CMDeviceMotion class provides the attitude, rotation rate, and acceleration of a device. CMPedometer is the most important part of this application, as this class was used to interact with pedometer to detect the movement of the mobile phone based on which it decides how many steps user has taken. Based on the measurements received, steps and distance are calculated.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFunctioning\u003c/strong\u003e \u003cp\u003eThe application starts with the interface that asks the user for very basic demographics to maintain individual progress records. Following this, the app allows its user to choose which test to perform; TUG test or timed 10-meter test.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eThe timed 10-meter test\u003c/strong\u003e \u003cp\u003eThe 10MWT is set at default 10 metres, but the walking distance can be manually changed. When distance is set, the app gives verbal countdown notice of 3 seconds before it verbally instructs to \u0026lsquo;start walking\u0026rsquo;. An animation is displayed that gives information of what the user is doing for example walking forward, backward. When the pre-set distance is covered in a straight line, the app verbally informs the patient to \u0026lsquo;stop\u0026rsquo;, and displays the result in measure of distance covered, time taken to cover set distance and number of steps taken.\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eTUG test\u003c/strong\u003e \u003cp\u003eThe TUG tests is set at default distance of 3 meters which can be manually changed. On selecting start, the app verbally countdowns from 3 before asking the user to \u0026lsquo;get up and walk\u0026rsquo;. Once 3 meters are covered the app verbally instructs the user to \u0026lsquo;turn around and walk back to the chair\u0026rsquo;. Once the required distance is covered the app verbally advises to \u0026lsquo;sit down\u0026rsquo; and displays results in terms of time and number of steps taken to cover the distance. The results can be saved and compared with future results.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthical approval:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eFormal ethical approval, institutional board review and informed consent from participants was not required prior to the development of the apps. This was confirmed by the Health-Research Authority, UK.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eValidation:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eThe\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eapp was validated in 50 healthy adults, 67 NPH and 10 LOVA patients, as well as 5 elderly patients as part of falls risk assessment. 50/67 NPH and 6/10 LOVA patients were invited to use the app in their home-environment and results were compared to walking tests performed under supervision in clinics. The 10MWT was assessed with timed slow-pace and fast-pace. The results were compared with a clinical observer using a stop-watch.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical analysis:\u003c/em\u003e\u003c/strong\u003e Statistical analysis used SPSS (version 25.0, IBM) by paired t-test, comparing the healthy and the NPH cohorts. Level of precision of the app as compared to a clinical observer using a stopwatch was evaluated using receiver operating characteristics curve.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eParticipant characteristics:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eThe median age of the 50 healthy adults was 48 years (range: 22-56), with 22% (11/50) older than 45, 23M:27F, 4% (2/50) obese (BMI \u0026gt; 30). The median age of the 67 NPH patients was 77 years (range: 64-89), with 73% (49/67) older than 70, 37M:30F, 44.7% (30/67) obese (BMI \u0026gt; 30). The median age of the 10 LOVA patients was 47 years (range: 44-56), with 70% (7/10) older than 45, 2M:8F, 50% (5/10) obese (BMI \u0026gt; 30). The median age of the 5 elderly adults (Parkinson\u0026rsquo;s disease =2; dystonia = 2, subdural haematoma = 1) was 62 years (range: 52-74), with 80% (4/5) older than 55, 4M:1F, none obese. 50 NPH and 6 LOVA patients described the apps as simple, efficient, easy-to-use, eliciting confidence and peace of mind in home settings and highly likely to recommend to others, through patient-reported feedback.\u003c/p\u003e\n\u003cp\u003eTable 1. Participant Characteristics\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"465\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.602150537634408%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eParticipant characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.763440860215054%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealthy adults\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43010752688172%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eNormal pressure hydrocephalus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.440860215053764%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eLong-standing overt ventriculomegaly\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.763440860215054%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eElderly adults\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.602150537634408%\" valign=\"bottom\"\u003e\n \u003cp\u003eParticipant number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.763440860215054%\" valign=\"bottom\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43010752688172%\" valign=\"bottom\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.440860215053764%\" valign=\"bottom\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.763440860215054%\" valign=\"bottom\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.602150537634408%\" valign=\"bottom\"\u003e\n \u003cp\u003eMedian age (yrs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.763440860215054%\" valign=\"bottom\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43010752688172%\" valign=\"bottom\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.440860215053764%\" valign=\"bottom\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.763440860215054%\" valign=\"bottom\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.602150537634408%\" valign=\"bottom\"\u003e\n \u003cp\u003eM:F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.763440860215054%\" valign=\"bottom\"\u003e\n \u003cp\u003e23M:27F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43010752688172%\" valign=\"bottom\"\u003e\n \u003cp\u003e37M:30F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.440860215053764%\" valign=\"bottom\"\u003e\n \u003cp\u003e2M:8F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.763440860215054%\" valign=\"bottom\"\u003e\n \u003cp\u003e4M:1F\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.602150537634408%\" valign=\"bottom\"\u003e\n \u003cp\u003ePercentage obese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.763440860215054%\" valign=\"bottom\"\u003e\n \u003cp\u003e4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43010752688172%\" valign=\"bottom\"\u003e\n \u003cp\u003e44.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.440860215053764%\" valign=\"bottom\"\u003e\n \u003cp\u003e50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.763440860215054%\" valign=\"bottom\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eApplication characteristics:\u003c/em\u003e\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTimed -10-meter-walking test performed primarily with Watkins:\u003c/em\u003e\u003c/strong\u003e As compared to a clinical observer using a stopwatch the app showed 100% accuracy in the measure of time taken to cover distance (sensitivity 100%, specificity 100%, AUC 1.0), 94.9% accuracy in the number of steps taken with an error \u0026plusmn; 1-3 steps (sensitivity 100%, specificity 92.6%, AUC 0.942), and 97.4% accuracy in the measure of total distance covered with error of \u0026plusmn; 0.25-0.50 meter (sensitivity 100%, specificity 96.3%, AUC 0.978).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eThe timed-up-and-go test performed primarily with Watkins2.0:\u003c/em\u003e\u003c/strong\u003e As compared to a clinical observer using a stopwatch the app has 100% accuracy in time taken to complete the test (sensitivity 100%, specificity 100%, AUC 1.0), 97.2% accuracy in the number of steps with an error of \u0026plusmn; 1-2 steps (sensitivity 100%, specificity 95.6%, AUC 0.948), and 87.5% accuracy in the distance covered with error of \u0026plusmn; 0.50 meter (sensitivity 100%, specificity 83.3%, AUC 0.861).\u003c/p\u003e\n\u003cp\u003eSignificant discrimination in all parameters was seen between healthy and gait-impaired individuals (p \u0026lt; 0.001), with NPH patients having longer duration of time to complete test and a greater number of steps.\u003c/p\u003e\n\u003cp\u003eTable 2. Application characteristics\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"661\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.643504531722055%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.329305135951662%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccuracy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.6012084592145%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eError\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.818731117824774%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.120845921450151%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.667673716012084%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.818731117824774%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.643504531722055%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTimed 10-meter-walking test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.329305135951662%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.6012084592145%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.818731117824774%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.120845921450151%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.667673716012084%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.818731117824774%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.643504531722055%\" valign=\"bottom\"\u003e\n \u003cp\u003eTime (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.329305135951662%\" valign=\"bottom\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.6012084592145%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.818731117824774%\" valign=\"bottom\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.120845921450151%\" valign=\"bottom\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.667673716012084%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.818731117824774%\" valign=\"bottom\"\u003e\n \u003cp\u003ep \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.643504531722055%\" valign=\"bottom\"\u003e\n \u003cp\u003eNumber of steps\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.329305135951662%\" valign=\"bottom\"\u003e\n \u003cp\u003e94.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.6012084592145%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026plusmn; 1-3 steps\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.818731117824774%\" valign=\"bottom\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.120845921450151%\" valign=\"bottom\"\u003e\n \u003cp\u003e92.60%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.667673716012084%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.818731117824774%\" valign=\"bottom\"\u003e\n \u003cp\u003ep \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.643504531722055%\" valign=\"bottom\"\u003e\n \u003cp\u003eDistance (m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.329305135951662%\" valign=\"bottom\"\u003e\n \u003cp\u003e97.40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.6012084592145%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026plusmn; 0.25-0.50 meter\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.818731117824774%\" valign=\"bottom\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.120845921450151%\" valign=\"bottom\"\u003e\n \u003cp\u003e96.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.667673716012084%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.818731117824774%\" valign=\"bottom\"\u003e\n \u003cp\u003ep \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.643504531722055%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTimed-up-and-go test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.329305135951662%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.6012084592145%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.818731117824774%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.120845921450151%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.667673716012084%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.818731117824774%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.643504531722055%\" valign=\"bottom\"\u003e\n \u003cp\u003eTime (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.329305135951662%\" valign=\"bottom\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.6012084592145%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.818731117824774%\" valign=\"bottom\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.120845921450151%\" valign=\"bottom\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.667673716012084%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.818731117824774%\" valign=\"bottom\"\u003e\n \u003cp\u003ep \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.643504531722055%\" valign=\"bottom\"\u003e\n \u003cp\u003eNumber of steps\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.329305135951662%\" valign=\"bottom\"\u003e\n \u003cp\u003e97.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.6012084592145%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026plusmn; 1-2 steps\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.818731117824774%\" valign=\"bottom\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.120845921450151%\" valign=\"bottom\"\u003e\n \u003cp\u003e95.60%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.667673716012084%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.818731117824774%\" valign=\"bottom\"\u003e\n \u003cp\u003ep \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.643504531722055%\" valign=\"bottom\"\u003e\n \u003cp\u003eDistance (m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.329305135951662%\" valign=\"bottom\"\u003e\n \u003cp\u003e87.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.6012084592145%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026plusmn; 0.50 meter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.818731117824774%\" valign=\"bottom\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.120845921450151%\" valign=\"bottom\"\u003e\n \u003cp\u003e83.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.667673716012084%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.818731117824774%\" valign=\"bottom\"\u003e\n \u003cp\u003ep \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePrincipal findings\u003c/em\u003e\u003c/strong\u003e: In the current study we present the first self-administrable automated non-purchase gait assessment smart-phone applications capable of performing both 10MWT and TUG test independently with outstanding accuracy, compatible with both Android and iOS systems. Watkins2.0 did, however, have an 87.5% accuracy in measuring the distance covered with error of \u0026plusmn; 0.50 meter. Similar to previous work conducted by other investigators, the margin of error can be decreased and the percentage of accuracy significantly improved by increasing the total length of distance beyond 3 meters\u003csup\u003e[12]\u003c/sup\u003e. This is due to the subsequent increase in time afforded by the APIs to extract data from the hardware. \u0026nbsp;Both \u0026lsquo;Watkins\u0026rsquo; and \u0026lsquo;Watkins2.0\u0026rsquo; \u0026nbsp;not only look at the time required to cover a fixed distance but also the number of steps taken to cover the distance. The apps are also capable of calculating the distance covered in meters through the duel input by accelerometer and pedometer. This makes the need to mark a set distance redundant, as the apps, depending on the test being performed, verbally \u0026nbsp;inform the participants to either stop or change direction once the set distance is covered. Unlike other studies, the phone can be held in hand at the time of the assessment and does not need to be fixed on the body\u003csup\u003e[13, 16, 18, 19, 24]\u003c/sup\u003e. Therefore no additional hardware in the form of special belts or sleeves is required. The results are immediately displayed, as measures of easily understandable parameters such as time, number of steps and distance, instead of complicated scoring systems\u003csup\u003e[19, 24]\u003c/sup\u003e. This provides ease to the participants in interpreting their results and monitoring their progress and contributes to reassurance. Due to their easy utility and high precision, both apps, specially \u0026lsquo;Watkins\u0026rsquo;, they were highly recommended to others by every participant who used them in their home. We believe that these are very useful apps and have application even beyond NPH and LOVA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eUtility and literature review: :\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eIn recent decades global demographics have changed with a sharp rise in the elderly population\u003csup\u003e[15]\u003c/sup\u003e. This has also impacted health-care services producing a need to assess health, level of independent function, mobility, balance, cognition and environmental safety in the geriatric population and to identify the vulnerable\u003csup\u003e[15]\u003c/sup\u003e. \u0026nbsp;Evaluation of functional impediment and performance level of daily activity through measurements of walking speed and assessment of gait pattern are frequently employed tools to predict health status and vulnerability in the elderly\u003csup\u003e[1, 9]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003ePatients with NPH often feel symptomatic improvement following shunt insertion surgery or spinal-tap test which may last up to six months\u003csup\u003e[4-6]\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e after which patients may begin to plateau which in some cases causes distress, anxiety and apprehension regarding shunt function and disease prognosis. \u0026lsquo;Watkins\u0026rsquo; and \u0026lsquo;Watkins2.0\u0026rsquo; allow patients to perform repeat assessments in their home and have an objective estimation of their progress.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese tests have further clinical application in predicting risk of falls in suspected NPH patients\u003csup\u003e[2]\u003c/sup\u003e, helping with early diagnosis of dementia\u003csup\u003e[25]\u003c/sup\u003e, studying frailty in chronic obstructive pulmonary disease\u003csup\u003e[26]\u003c/sup\u003e, monitoring the efficacy of physiotherapy in improving gait in patients with neurological impairment secondary to traumatic brain injury\u003csup\u003e[27]\u003c/sup\u003e and even in predicting death in the elderly\u003csup\u003e[28]\u003c/sup\u003e. In 2021 De-Bruijn et al published a study showing that a majority of the educated population used health apps regardless of age and gender and that they displayed healthier habits\u003csup\u003e[29]\u003c/sup\u003e. Thus, the app can also be used to help patients improve gait and functional mobility by repeated practice and self-monitoring\u003csup\u003e[30]\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStrengths, limitations and future work:\u003c/em\u003e\u003c/strong\u003e Smart-phone apps are difficult and expensive to make\u003csup\u003e[31]\u003c/sup\u003e, with an ever-increasing amount of technical skill required to develop and improve the app. This, on one hand, results in just a couple of smart-phone apps globally capable of conducting self-administrated clinical assessments accurately and with precision, making \u0026lsquo;Watkins\u0026rsquo; and \u0026lsquo;Watkins2.0\u0026rsquo; the first of their kind, and on the other hand, greater time will be required to upgrade the apps to improve accuracy in the calculation of distance in the TUG-test and to include gait pattern assessment and supplementary angular measurements of trunk position during different phases of walking. \u0026lsquo;Watkins\u0026rsquo; and \u0026lsquo;Watkins2.0\u0026rsquo; have the potential to be used as a regular clinical assessment tool in hospitals as well as homes in a variety of neurosurgical, neurological and movement disorders as well as in healthy geriatric population, and future upgrades would aim to include specific features to facilitate assessments in these various conditions.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003e\u003cstrong\u003e\u0026lsquo;\u003c/strong\u003eWatkins\u0026rsquo; and\u003cstrong\u003e\u0026nbsp;\u0026lsquo;\u003c/strong\u003eWatkins2.0\u0026rsquo; represent the next generation in smart-phone apps, capable of objective and efficient performance of 10MWT and the TUG-test in NPH and LOVA patients and have application in several other pathologies featuring gait and balance disturbance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval:\u0026nbsp;\u003c/strong\u003eThe development of the apps and the use of all human data collected through the app has been approved by the appropriate ethics committee and have therefore been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.\u003c/p\u003e\n\u003cp\u003eAll patients have their informed consent prior to their data being included in the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll patients have their informed consent to publish their data anonymously in the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003e\u0026lsquo;Watkins\u0026rsquo; and \u0026lsquo;Watkins2.0\u0026rsquo; are non-profit smart phone applications. The authors have no financial or personal interest in or benefit from the app.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict Of Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contributions:\u0026nbsp;\u003c/strong\u003eDr Kanza Tariq identified the need of the apps, conceptualised and developed the apps, collected and analysed the data, performed bug-sweeps and developed the newer version of the app.\u003c/p\u003e\n\u003cp\u003eMr Lewis Thorne, Mr Ahmed Toma and Mr Laurence Watkins collected and analysed the data, reported bugs and tested the subsequent version of the app. They also reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFundings:\u0026nbsp;\u003c/strong\u003eThe development of both \u0026lsquo;Watkins\u0026rsquo; and \u0026lsquo;Watkins2.0\u0026rsquo; was funded by the\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eInternational Charity for Digital Health\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe link to the apps programme and the data repository can be found in the supplementary data section.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ehttps://github.com/drkanzatariq17/Watkins-App\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKuruvithadam K et al (2021) \u003cem\u003eData-Driven Investigation of Gait Patterns in Individuals Affected by Normal Pressure Hydrocephalus.\u003c/em\u003e Sensors (Basel), 21(19)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavis A et al (2021) Assessing the predictive value of common gait measure for predicting falls in patients presenting with suspected normal pressure hydrocephalus. BMC Neurol 21(1):60\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalandri G et al (2021) Longstanding overt ventriculomegaly in adults (LOVA) with patent aqueduct: surgical outcome and etiopathogenesis of a possibly distinct form of chronic hydrocephalus. Acta Neurochir (Wien) 163(12):3343\u0026ndash;3352\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbram K et al (2016) The Effect of Spinal Tap Test on Different Sensory Modalities of Postural Stability in Idiopathic Normal Pressure Hydrocephalus. Dement Geriatr Cogn Dis Extra 6(3):447\u0026ndash;457\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHallqvist C, Gronstedt H, Arvidsson L (2022) Gait, falls, cognitive function, and health-related quality of life after shunt-treated idiopathic normal pressure hydrocephalus-a single-center study. Acta Neurochir (Wien) 164(9):2367\u0026ndash;2373\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRydja J et al (2022) Physical Capacity and Activity in Patients With Idiopathic Normal Pressure Hydrocephalus. Front Neurol 13:845976\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePodsiadlo D, Richardson S (1991) The timed Up \u0026amp; Go: a test of basic functional mobility for frail elderly persons. J Am Geriatr Soc 39(2):142\u0026ndash;148\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBohannon RW (1997) Comfortable and maximum walking speed of adults aged 20\u0026ndash;79 years: reference values and determinants. Age Ageing 26(1):15\u0026ndash;19\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen AT et al (2022) Walking Speed Assessed by 4-Meter Walk Test in the Community-Dwelling Oldest Old Population in Vietnam. Int J Environ Res Public Health, 19(16)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBischoff HA et al (2003) Identifying a cut-off point for normal mobility: a comparison of the timed 'up and go' test in community-dwelling and institutionalised elderly women. Age Ageing 32(3):315\u0026ndash;320\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBergquist R et al (2020) Predicting Advanced Balance Ability and Mobility with an Instrumented Timed Up and Go Test. Sensors, 20\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrtega-Bastidas P et al (2023) Instrumented Timed Up and Go Test (iTUG)-More Than Assessing Time to Predict Falls: A Systematic Review. Sens (Basel), 23(7)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoi J et al (2021) Wearable Sensor-Based Prediction Model of Timed up and Go Test in Older Adults. Sens (Basel), 21(20).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilva J, Sousa I (2016) \u003cem\u003eInstrumented timed up and go: Fall risk assessment based on inertial wearable sensors\u003c/em\u003e. 1\u0026ndash;6\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSprint G, Cook DJ, Weeks DL (2015) Toward Automating Clinical Assessments: A Survey of the Timed Up and Go. IEEE Rev Biomed Eng 8:64\u0026ndash;77\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePonciano V et al (2020) Mobile Computing Technologies for Health and Mobility Assessment: Research Design and Results of the Timed Up and Go Test in Older Adults. Sens (Basel), 20(12)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePonciano V et al (2020) Data acquisition of timed-up and go test with older adults: accelerometer, magnetometer, electrocardiography and electroencephalography sensors' data. Data Brief 32:106306\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMilosevic M, Jovanov E, Milenkovic A (2013) \u003cem\u003eQuantifying Timed-Up-and-Go test: A smartphone implementation\u003c/em\u003e. 1\u0026ndash;6\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamada S et al (2018) Quantitative Evaluation of Gait Disturbance on an Instrumented Timed Up-and-go Test. Aging Disease, 10\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCampillay J, Silva R, Guzman-Venegas R (2017) \u003cem\u003eReproducibilidad de los tiempos de ejecuci\u0026oacute;n de la prueba de Timed Up and Go, medidos con aceler\u0026oacute;metros de smartphones en personas mayores residentes en la comunidad.\u003c/em\u003e Revista Espa\u0026ntilde;ola de Geriatr\u0026iacute;a y Gerontolog\u0026iacute;a, 52\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGal\u0026aacute;n-Mercant A, Cuesta-Vargas A (2015) Clinical frailty syndrome assessment using inertial sensors embedded in smartphones. Physiol Meas, 36\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGal\u0026aacute;n-Mercant A, Cuesta-Vargas A (2013) \u003cem\u003eDifferences in Trunk Kinematic between Frail and Nonfrail Elderly Persons during Turn Transition Based on a Smartphone Inertial Sensor.\u003c/em\u003e BioMed Research International, 2013\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGal\u0026aacute;n-Mercant A, Cuesta-Vargas A (2014) Differences in trunk accelerometry between frail and non-frail elderly persons in functional tasks. BMC Res Notes 7:100\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamada S et al (2021) Gait Assessment Using Three-Dimensional Acceleration of the Trunk in Idiopathic Normal Pressure Hydrocephalus. Front Aging Neurosci, 13\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCedervall Y et al (2020) Timed Up-and-Go Dual-Task Testing in the Assessment of Cognitive Function: A Mixed Methods Observational Study for Development of the UDDGait Protocol. Int J Environ Res Public Health, 17(5)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlbarrati AM et al (2022) The Timed Up and Go test predicts frailty in patients with COPD. NPJ Prim Care Respir Med 32(1):24\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWatson M (2002) Refining the Ten-metre Walking Test for Use with Neurologically Impaired People. Physiotherapy 88:386\u0026ndash;397\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAscencio EJ et al (2022) Timed up and go test predicts mortality in older adults in Peru: a population-based cohort study. BMC Geriatr 22(1):61\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Bruijn GJ, Dallinga JM, Deutekom M (2021) Predictors of Walking App Users With Comparison of Current Users, Previous Users, and Informed Nonusers in a Sample of Dutch Adults: Questionnaire Study, vol 9. JMIR Mhealth Uhealth, p e13391. 5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcKay FH et al (2019) Using Health and Well-Being Apps for Behavior Change: A Systematic Search and Rating of Apps. JMIR Mhealth Uhealth 7(7):e11926\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlwakeel L, Lano K (2022) Functional and Technical Aspects of Self-management mHealth Apps: Systematic App Search and Literature Review. JMIR Hum Factors 9(2):e29767\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"acta-neurochirurgica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"anch","sideBox":"Learn more about [Acta Neurochirurgica](http://link.springer.com/journal/701)","snPcode":"701","submissionUrl":"https://submission.springernature.com/new-submission/701/3","title":"Acta Neurochirurgica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4701792/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4701792/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eGait disturbance is one of the features of normal pressure hydrocephalus (NPH) and decompensated long-standing overt ventriculomegaly (LOVA). The timed-up-and-go (TUG) test and the timed-10-meter-walking test (10MWT) are frequently used assessments tools for gait and balance disturbances in NPH and LOVA, as well as several other disorders. We aimed to make smart-phone apps which perform both the 10MWT and the TUG-test and record the results for individual patients, thus making it possible for patients to have an objective assessment of their progress. Patients with a suitable smart phone can perform repeat assessments in their home environment, providing a measure of progress for them and for their clinical team.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e10MWT and TUG-test were performed by 50 healthy adults, 67 NPH and 10 LOVA patients, as well as 5 elderly patients as part of falls risk assessment using the Watkins2.0 app. The 10MWT was assessed with timed slow-pace and fast-pace. Statistical analysis used SPSS (version 25.0, IBM) by paired t-test, comparing the healthy and the NPH cohorts. Level of precision of the app as compared to a clinical observer using a stopwatch was evaluated using receiver operating characteristics curve.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAs compared to a clinical observer using a stopwatch, in 10MWT the app showed 100% accuracy in the measure of time taken to cover distance in whole seconds, 95% accuracy in the number of steps taken with an error\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026ndash;3 steps, and 97% accuracy in the measure of total distance covered with error of \u0026plusmn;\u0026thinsp;0.25\u0026ndash;0.50 meter. The TUG test has 100% accuracy in time taken to complete the test in whole seconds, 97% accuracy in the number of steps with an error of \u0026plusmn;\u0026thinsp;1\u0026ndash;2 steps and 87.5% accuracy in the distance covered with error of \u0026plusmn;\u0026thinsp;0.50 meter. In the measure of time, the app was found to have equal sensitivity as an observer. In measure of number of steps and distance, the app demonstrated high sensitivity and precision (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.9). The app also showed significant level of discrimination between healthy and gait-impaired individuals.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003e\u0026lsquo;Watkins\u0026rsquo; and \u0026lsquo;Watkins2.0\u0026rsquo; are efficient apps for objective performance of 10MWT and the TUG-test in NPH and LOVA patients and has application in several other pathologies characterised by gait and balance disturbance.\u003c/p\u003e","manuscriptTitle":"‘Watkins’ \u0026amp; ‘Watkins2.0’: Smart Phone Applications (Apps) For Gait- Assessment in Normal Pressure Hydrocephalus And Decompensated Long-Standing Overt Ventriculomegaly","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-17 01:20:31","doi":"10.21203/rs.3.rs-4701792/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2024-09-17T11:50:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-17T11:29:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"259471006942596811254574553878552867862","date":"2024-09-11T21:15:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-11T18:33:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"45380637269162268450976461948211423878","date":"2024-07-23T09:35:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-17T12:25:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-17T04:40:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-17T04:39:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Acta Neurochirurgica","date":"2024-07-07T23:31:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"acta-neurochirurgica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"anch","sideBox":"Learn more about [Acta Neurochirurgica](http://link.springer.com/journal/701)","snPcode":"701","submissionUrl":"https://submission.springernature.com/new-submission/701/3","title":"Acta Neurochirurgica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"20454085-08a2-4e31-8577-1abb69190bd6","owner":[],"postedDate":"August 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-30T16:01:11+00:00","versionOfRecord":{"articleIdentity":"rs-4701792","link":"https://doi.org/10.1007/s00701-024-06275-9","journal":{"identity":"acta-neurochirurgica","isVorOnly":false,"title":"Acta Neurochirurgica"},"publishedOn":"2024-09-28 15:57:17","publishedOnDateReadable":"September 28th, 2024"},"versionCreatedAt":"2024-08-17 01:20:31","video":"","vorDoi":"10.1007/s00701-024-06275-9","vorDoiUrl":"https://doi.org/10.1007/s00701-024-06275-9","workflowStages":[]},"version":"v1","identity":"rs-4701792","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4701792","identity":"rs-4701792","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}