Linking Digital Smartphone Behaviour With Brain Function

Study Overview
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Brief Summary
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This study will thus examine daily behaviour based on smartphone use and link it to the neurological and neuropsychological status as well as to neuroradiological studies that are part of the clinical routine. The study will examine behaviour changes before and after surgery, and how this change in measured behaviour with the smartphone relates to today's gold standard, namely professional neuropsychological examination and quantification of brain damage on imaging studies (MRI). This study is a proof-of-principle study that intends to build the basis for larger future observational studies on patients with focal or diffuse brain pathologies.

Detailed Description
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Pathologies of the central nervous system (CNS), as well as their surgical treatment, may interfere with the physiological and behavioural functions of the human brain. Commonly, before and after surgical treatment, the neurosurgeon examines the patient carefully for neurological deficits and additionally asks neuropsychologists to evaluate higher cognitive functions. These examinations, however, only represent the situation at a given point in time, and currently longitudinal or continuous evaluation of physiological and behavioural functions of the human brain is highly limited. Furthermore, in the conventional examinations the complex human behaviour is reduced to very simplified scores (e.g. the NIHSS for neurological or MoCA for neuropsychological functioning). Fluctuations in physiological and behavioural functions are very likely, but are unlikely to be captured with current evaluations at single (discrete) pre- and postoperative points in time. To date, on-line continuous evaluation of brain function in patients undergoing (potentially risky) neurosurgical procedures has not been established. The touchscreen interface of smartphones offers a fresh avenue to capture day-to-day behaviour (engagement of finger tips) by exploiting the technology intrinsic to a smartphone. For instance, the speed of touchscreen use, the distinct behavioural contexts (compartmentalised into Apps) and the corresponding habits can be seamlessly and non-obtrusively captured. More importantly, compared with current discrete approaches of evaluation, this continuous approach can account for - and even exploit - the natural fluctuations in brain functions. Nevertheless, behavioural data from touchscreens is new to scientific exploration and various fundamental questions remain to be answered, such as what are the basic statistical features of smartphone behaviour, how does this behaviour vary from one day to another, and how does this behaviour reflect basic demographic information? This gap in our understanding of smartphone behavioural data also implies that the exact statistical methods to be employed may need to undergo adjustments. For instance, the common central tendency measure of the sample mean may be highly unstable if the parameter/s occupy a power-law distribution rather than a Poisson or Gaussian distribution. In summary, ever-new exploration of the neuroscience of touchscreen behaviour must trigger the right choice of analytical and statistical methods. The focus of this study is laid on patients with pathologies of the CNS. The investigators aim to examine both patients with diffuse and focal pathologies. In order to study diffuse pathologies, the investigators will include patients with hydrocephalus. In order to study focal pathologies, the investigators will include patients with brain tumours or arteriovenous malformations (AVMs) - which are localised and described using clinical neuroimaging. Patients will be examined before and after a neurosurgical procedure. At the UniversitätsSpital Zürich, both patients with hydrocephalus that are scheduled for ventriculo-peritoneal (VP)-shunting and patients with brain tumours/AVMs that are scheduled for microsurgical resection routinely undergo a neurosurgical, neuropsychological and neuroradiological examination (by MRI) preoperatively and at 3 months postoperative (for clinical purpose). Patients that agree to participate in this study will install a free App (programmed by the University of Zurich (UZH) spin-off QuantActions and freely available on the Google Play store) on their smartphone that records their day-to-day physiological and behavioural status associated with use of the hand (smartphone touchscreen). The study will examine behaviour changes before and after surgery, and how this change in measured behaviour with the smartphone relates to the neuropsychological examination and quantification of brain damage on imaging studies (MRI).

Official Title
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Linking Day-to-day Digital Behaviour Captured on the Smartphone With Brain Function in Patients Undergoing Brain Surgery - the Smart Surgery Study

Conditions
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Brain Tumor, Hydrocephalus, Arteriovenous Malformations

Intervention / Treatment
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* Other: There is no study-specific intervention


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Consent of the patient Age: ≥18 Fluent language skills in German Patient is scheduled for either maximum safe resection of a brain tumour/AVM via craniotomy or VP-shunting for hydrocephalus Patient is capable to use a smartphone (based on the Google Android system) and uses a smartphone since at least 3 months Preoperative smartphone-assessed day-to-day behaviour can be recorded for at least 1 week (7 days) Exclusion Criteria: Presence of known neurologic or psychiatric disease other than brain tumour/AVM or hydrocephalus that can potentially influence the performance of a patient while using the smartphone (e.g. dementia, multiple sclerosis, bipolar disorder) Foreseeable difficulties in follow-up due to geographic reasons (e.g. patients living abroad) Patients enrolled in a different clinical trial according to KlinV (participation in another research project according to HFV is allowed, if this is not a burden to the patient)

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Patients With Brain Tumors/AVMs<br>Patients with a brain tumour/AVM scheduled for maximum safe resection via craniotomy. Participants fulfilling all of the following inclusion criteria are eligible for the study: Consent of the patient Age: ≥18 Fluent language skills in German Patient is capable to use a smartphone (based on the Google Android system) and uses a smartphone since at least 3 months Preoperative smartphone-assessed day-to-day behaviour can be recorded for at least 1 week (7 days) | Other: There is no study-specific intervention<br>* There is no study-specific intervention<br>|
| Patients With Hydrocephalus<br>Patients with hydrocephalus scheduled for VP-shunting Participants fulfilling all of the following inclusion criteria are eligible for the study: Consent of the patient Age: ≥18 Fluent language skills in German Patient is capable to use a smartphone (based on the Google Android system) and uses a smartphone since at least 3 months Preoperative smartphone-assessed day-to-day behaviour can be recorded for at least 1 week (7 days) | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Change in pattern of smartphone-assessed day-to-day behaviour | For the primary endpoint, the patterns of smartphone-assessed day-to-day behaviour in the time period before the operation (day -7 until day 0) will be graphically illustrated and compared for obvious differences to the behaviour in the first postoperative week (+7 days) and in the week before the postoperative consultation at three months after the operation. Towards this we shall employ exhaustive statistical and numerical methods that are typical of complex systems research. This includes comparing the patterns in defined parameter space, clustering of patterns dependent on the diseased state and defining new parameter spaces where the data conforms to the working hypothesis. | One week before surgery until the follow-up three months after surgery | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| KPS | For the secondary endpoints, several scores have to be correlated to the complex data generated. Towards this we shall deploy large-scale multivariate approaches that span various time scales - from ms to hours, and that are conducted using sweeping windows across the entire period recording. By using statistical clustering methods, we shall correct for multiple comparison when inferring our data. | One week before surgery until the follow-up three months after surgery | 
| NIHSS | For the secondary endpoints, several scores have to be correlated to the complex data generated. Towards this we shall deploy large-scale multivariate approaches that span various time scales - from ms to hours, and that are conducted using sweeping windows across the entire period recording. By using statistical clustering methods, we shall correct for multiple comparison when inferring our data. | One week before surgery until the follow-up three months after surgery | 
| MoCA score | For the secondary endpoints, several scores have to be correlated to the complex data generated. Towards this we shall deploy large-scale multivariate approaches that span various time scales - from ms to hours, and that are conducted using sweeping windows across the entire period recording. By using statistical clustering methods, we shall correct for multiple comparison when inferring our data. | One week before surgery until the follow-up three months after surgery | 
| Domain-specific z-scores of neuropsychological functioning, age-, sex- and education-adjusted | For the secondary endpoints, several scores have to be correlated to the complex data generated. Towards this we shall deploy large-scale multivariate approaches that span various time scales - from ms to hours, and that are conducted using sweeping windows across the entire period recording. By using statistical clustering methods, we shall correct for multiple comparison when inferring our data. | One week before surgery until the follow-up three months after surgery | 
| For patients with a brain tumour, location of the lesion, as determined on MRI imaging | For the secondary endpoints, several scores have to be correlated to the complex data generated. Towards this we shall deploy large-scale multivariate approaches that span various time scales - from ms to hours, and that are conducted using sweeping windows across the entire period recording. By using statistical clustering methods, we shall correct for multiple comparison when inferring our data. | One week before surgery until the follow-up three months after surgery | 
| For patients with a brain tumour, the size of the lesion | For the secondary endpoints, several scores have to be correlated to the complex data generated. Towards this we shall deploy large-scale multivariate approaches that span various time scales - from ms to hours, and that are conducted using sweeping windows across the entire period recording. By using statistical clustering methods, we shall correct for multiple comparison when inferring our data. | One week before surgery until the follow-up three months after surgery | 
| For patients with a brain tumour, the degree of affected brain tissue as seen on early (within 5 days) or late postoperative MRI (3 months) | For the secondary endpoints, several scores have to be correlated to the complex data generated. Towards this we shall deploy large-scale multivariate approaches that span various time scales - from ms to hours, and that are conducted using sweeping windows across the entire period recording. By using statistical clustering methods, we shall correct for multiple comparison when inferring our data. | One week before surgery until the follow-up three months after surgery | 
| For patients with hydrocephalus, ventricular enlargement, as determined on MRI imaging, according to the Evans index (=A/E); Third ventricle index (= C/E); Cella media index (=D/F); Ventricular score (=(A+B+C+D)/E x 100) | For the secondary endpoints, several scores have to be correlated to the complex data generated. Towards this we shall deploy large-scale multivariate approaches that span various time scales - from ms to hours, and that are conducted using sweeping windows across the entire period recording. By using statistical clustering methods, we shall correct for multiple comparison when inferring our data. | One week before surgery until the follow-up three months after surgery | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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AVM, Behaviour, Brain Tumor, Hydrocephalus, Smartphone, Big Data, Neuropsychology, Neuroradiology, Neurosurgery, Outcome Assessment, Functional Impairment, Cognitive Function, Touchscreen, Arteriovenous malformation