Mobile and pervasive computing technologies and the future of Alzheimer’s clinical trials

Mobile and pervasive computing technologies and the future of Alzheimer’s clinical trials www.nature.com/npjdigitalmed PERSPECTIVE OPEN Mobile and pervasive computing technologies and the future of Alzheimer’s clinical trials 1 2 2 P. Murali Doraiswamy , Vaibhav A. Narayan and Husseini K. Manji The rapid growth of mobile phones, automated speech recognizing personal assistants, and internet access among the elderly provides new opportunities for incorporating such technologies into clinical research and personalized medical care. Alzheimer’s disease is a good test case given the need for early detection, the high rate of clinical trial failures, the need to more efficiently recruit patients for trials, and the need for sensitive and ecologically valid trial outcomes. npj Digital Medicine (2018) 1:1 ; doi:10.1038/s41746-017-0008-y INTRODUCTION Industries and Associations, plans to register 24,000 people to identify a Europe-wide cohort of >6000 high-risk participants, of Alzheimer’s disease (AD) affects an estimated 45 million people which 1500 will be invited to participate in a technology-enabled worldwide, and despite substantial research investments, there trial to test new treatments for the prevention of AD. In the US, are no therapies to prevent or slow disease progression. The the Alzheimer’s Association TrialMatch (https://trialmatch.alz.org/ 99.6% failure rate in recent AD clinical trials highlights the need for find-clinical-trials#createaccount) allows caregivers and subjects innovation and efficiency. It is believed that the best chance to to customize their search for trials and receive alerts. The intervene therapeutically is by targeting the preclinical or Brain Health Registry (BHR) at USCF is pioneering a broad-based prodromal stages of Alzheimer’s. Some of these challenges may internet-based approach for recruiting and monitoring individuals potentially be amenable to technological solutions. at risk for AD. The registry is currently being used for pilot The revolution in mobile technologies such as smart phones/ validation studies of mobile cognitive tests, and ultimately, all tablets, cloud-based platforms, and deep learning-driven software subjects enrolled in a large national biomarker trial (ADNI-3) will be algorithms and miniaturized automated physiological sensors is 3–7 given the option to participate in BHR. The Dominant Inherited poised to profoundly disrupt medicine and, by extension, many 5–8 Alzheimer’s Network (DIAN) and the Alzheimer’s Prevention Initiative aspects of neuropsychiatry and AD care and clinical research. (API) registries have successfully recruited subjects with specified In this Perspective, we highlight four areas in which such genetic mutations. The Human Cognition Project (with >40 million technology could facilitate AD clinical trials: (1) Mobilizing subjects of all ages from 180 countries) provides a model for the recruitment, (2) Mobile cognitive measures, (3) Digital functional kind of global brain laboratory that can be harnessed using mobile outcomes, and (4) Integrated informatics platforms. For further tools. This registry was recently used successfully to recruit and information on how technology may help with care and caregiver 4,5 conduct a purely online randomized clinical trial of cognitive support, readers are referred elsewhere. training. Research apps such as Apple’s Research Kit now also allow participants a simple way to consent, participate, and share their MOBILIZING CLINICAL TRIAL RECRUITMENT data, and these apps can be adapted for consenting caregivers ClinicalTrials.gov lists >450 active Alzheimer’s trials needing some and legal representatives. Ultimately, it is hoped such registries do 70,000 volunteers—posing the challenge of how to efficiently not exist in silos, are able to recruit samples representative of the identify, consent, and screen subjects. Prevention trials increas- population, and keep subjects engaged over long periods to ingly rely on expensive brain scans or spinal fluid biomarkers to minimize selection and attrition biases. identify at-risk subjects but such methods have high screen fail rates—because only about a third of asymptomatic subjects may test positive. Thus time for subject recruitment may take up 30% 8,9 MOBILE COGNITIVE OUTCOMES of drug development costs and delay trials by ≥2 years. Brain health registries are one such solution wherein at-risk A second challenge is that small, but clinically meaningful, subjects are registered, self-consented, and prescreened via treatment effects on cognition are often difficult to measure in cognitive or even genetic self-tests. While academic AD centers preclinical AD or mild cognitive impairment (MCI) due to learning have always had registries, the emergence of national registries effects, ceiling effects, heterogeneity, and normal fluctuations. could be a catalyst. The European Prevention of Alzheimer’s Composite endpoints formed by combining scores from existing Dementia (EPAD) initiative, funded by the Innovative Medicines neuro-psychological batteries have been proposed as primary Initiative and the European Federation of Pharmaceutical endpoints for such trials. For example, the Alzheimer’s Disease 1 2 Neurocognitive Disorders Program, Department of Psychiatry, Duke University Health System, and the Duke Institute for Brain Sciences, Durham, NC 27710, USA and Janssen Research & Development, LLC, Raritan, New Jersey 08869, USA Correspondence: P. Murali Doraiswamy (murali.doraiswamy@duke.edu) Received: 31 July 2017 Revised: 12 September 2017 Accepted: 12 September 2017 Published in partnership with the Scripps Translational Science Institute Mobile technologies for Alzheimer’s trials PM Doraiswamy et al. Fig. 1 Figure depicts selected mobile or pervasive computing technologies that are being studied for use in AD research with examples in each area. This is not intended to be a comprehensive listing and the degree of validation that each technology has undergone is variable. The examples are intended to merely provide readers an overview of the developments in the field. HRV heart rate variability, ADAS refers to Alzheimer’s Disease Assessment Scale, EDA refers to electrodermal activity, FAQ refers to functional activities questionnaire, VR refers to virtual reality, other abbreviations are listed in the text Cooperative Studies-Preclinical Alzheimer’s Cognitive Composite, progression in a sample of 1200 subjects ranging from normal to the primary outcome measure in the Anti-Amyloid Treatment in MCI to mild AD dementia. Asymptomatic Alzheimer’s study, is a composite of episodic memory, list learning, digit symbol substitution, and the Mini MOBILE ASSESSMENT OF FUNCTION (ACTIVITIES OF DAILY Mental State Examination. While it has shown promise in LIVING) AND BEHAVIOR retrospective analyses of observational studies, it has not yet been fully validated. Another key challenge in Alzheimer’s prevention trials is to Automated mobile cognitive tests may offer advantages such demonstrate an impact on function (daily activities of living) in as individualized scaling, automatic and error free scoring, and populations that have minimal to no functional deficits. Long- multiplicity of test versions and, if combined with gaming itudinal studies have charted the timeline of functional losses from elements, may be less anxiety provoking. Many neuro- preclinical stage to MCI to AD and many activities of daily living psychological tests and self-rated measures of cognition have (ADLs; e.g., spatial navigation, financial calculations, using phone been adapted for use on a tablet or phone, undergone varying or computer, driving) can be measured using phone sensors, GPS, degrees of qualification or validation (Fig. 1), and are already used wearables, or home sensors. For example, in a 6-week study of the in research and clinical trials. For example, a recent study of a iPad Proactive Activity Toolkit (PROACT), investigators tagged objects based self-test called C3-PAD (Computerized Cognitive Composite in a home with 108 radiofrequency identification tags and for Preclinical Alzheimer’s Disease) in 49 cognitively normal elderly analyzed signals by a prototype glove worn by a group of healthy subjects (mean age 71 years, 20% non-Caucasian) showed high adults. PROACT accurately identified the performance of an ADL in reliability among the test versions (Cronbach alpha coefficient = >80% of subjects and the specific ADLs in 73% of cases. Another 0.93). In all, 98% of subjects completed four out of five sessions study, CART, has installed strategically placed sensors in >480 correctly, and there was a high correlation between in-clinic and 2 homes of seniors and has been continuously monitoring gait, at-home C3-PAD assessments (r = 0.508, p < 0.0001), suggesting mobility, sleep, and activity for >10 years to develop signatures of promise for clinical trial use. The relationship with standardized cognitive and functional decline (http://www.ohsu.edu/xd/ tests covering similar cognitive domains was also significant but research/centers-institutes/orcatech/index.cfm). A number of pre- less robust (r = 0.168, p < 0.003), suggesting that the tests are in viously validated paper and pencil functional scales are also now need for some refinement. A number of other large epidemio- available in online or mobile versions. Electronic organizer tools logical studies (e.g., Framingham study) and clinical trials have also such as the Google Calender and AP@LZ have been tested in incorporated mobile cognitive tests as exploratory outcomes. proof-of-concept studies with small samples to ameliorate ADNI-3, a national biomarker study, is directly comparing an at- 17–20 home computerized cognitive self-test measure vs. standard functional and memory deficits, which in turn may improve paper and pencil tests as well as pathological markers of AD trial compliance. npj Digital Medicine (2018) 1 Published in partnership with the Scripps Translational Science Institute 1234567890 Mobile technologies for Alzheimer’s trials PM Doraiswamy et al. Many behavioral changes seen in AD, such as wandering, sleep, access, and ethics of sharing biometric data are examples of some circadian rhythm changes, depression, and agitation, can also other limitations. Further, uptake of smart phone digital technol- potentially be tracked through use of mobile devices and sensors ogies by some elderly subgroups (such as those on fixed incomes) (Fig. 1). For example, the outcome of a 4-week phase-2 clinical trial remains low. And despite large numbers of people expressing of a dual orexin receptor antagonist in mild-to-moderate AD initial interest in registries or apps, due to large drop outs only a patients (N = 125) with irregular sleep wake rhythm disorder is small fraction may continue over time. Clinicians and CROs who sleep efficiency and wake efficiency measured using actigraphy conduct AD trials may not be well informed about the technical through a wrist device (https://clinicaltrials.gov/ct2/show/NCT limitations of mobile devices requiring the building of new 03001557). Another pilot study evaluated the utility of a wrist partnerships with engineers. Last but not least, regulatory and sensor (Philips DTI-2) and digital dashboard to track agitation ethical guidelines often lag behind the rapid pace at which and stress in six nursing home patients with dementia technology is evolving. Groups such as the Software as a Medical over 2 months (total recorded time was 142 h across 37 days). Device International Regulators Forum, Mobile Clinical Trials Wrist sensor data (galvanic skin conductance, accelerometry, subcommittee of the Clinical Trials Transformation Initiative, and skin and environment temperature, and ambient light) were the ISTAART Technology PIA (https://act.alz.org/site/SPageServer? extracted weekly and compared with 24 h observations made by pagename=ISTAART_PIA_Technology) are addressing some of nursing staff study across four parameters—sleep, aggression, these challenges. The pilot digital health technology precertifica- stress, and normal. These data allowed the authors to develop tion program (https://www.fda.gov/MedicalDevices/DigitalHealth/ objective thresholds with sensor data for defining “stress” and UCM567265) announced by the U.S. Food and Drug Administra- “agitation” in AD patients and develop a dashboard that allows a tion may also provide greater regulatory clarity. clinician to run a stress analysis for a given patient over a given time period. CONCLUSIONS Other researchers are studying the utility of a wearable 22 23 camera and non-immersive virtual environments to detect Given the promise of mobile technologies across multiple areas everyday changes in function or behavior. For example, one study of AD clinical trials, we call for a more rapid systematic validation compared the performance of 24 subjects with mild-to-moderate and a multi-stakeholder public–private partnership—involving AD vs. 32 normal controls on a laptop-based virtual coffee making caregivers and patients, academia, clinicians, industry, regulators, task in a virtual reality kitchen. AD patients performed worse than ethicists—to develop a framework for the optimal deployment of controls on the virtual test, and their errors were correlated with such tools. Technology is advancing rapidly and automatic both scores on standardized functional tests and caregiver ratings speech recognition personal assistant devices, powered by of their impairments. Such tools may in future offer promise due artificial intelligence, may be game changers with regards to to their ecological validity and potential to provide evidence to how the elderly will interact with devices in the future. payers of real-world outcomes. Ultimately, it is hoped that such innovations will accelerate the testing and development of effective therapies to delay the onset of AD. CLOUD-BASED ANALYTIC PLATFORMS AND TRIAL NETWORKS FOR MOBILE AD TRIALS AUTHOR CONTRIBUTIONS The fourth challenge is one of data sharing and bioinformatics. P.M.D. did the first draft, with additional drafting by V.A.N., and all the authors The vast majority of raw data from Alzheimer’s trials, even those contributed to edits. funded by government agencies, are not readily accessible to the wider scientific or public community. One of the exceptions has been ADNI, which has made data sharing a priority from day 1 and 11 ADDITIONAL INFORMATION led to >800 publications. Today, many AD-related informatics Competing interests: The technologies described in this article were selected to be algorithms are trained using ADNI data, but there is no equivalent illustrative of the advances being made. P.M.D. is a minor shareholder and advisor to public resource for replication or validation. Further, databases Anthrotronix and a former advisor and grant recipient from Neuronetrix. He has also created over the past two decades for AD trials may lack received a speaking fee from CEOs Against Alzheimer’s (but has no involvement with contemporary features, such as a patient portal, integrated sensor their trial platform). He has also received research grants and/or advisory/speaking and smart phone data, patient engagement tools, and secure data fees from several pharmaceutical, CRO, and technology companies for other projects, sharing. Most such data platforms exist in silos with limited or no and he owns shares in Maxwell Health, Evidation, Muses Labs, Turtle Shell, and cross-integration ability. The Global Alzheimer’s Platform (GAP) Adverse Events Inc. whose products are not discussed here. He has served as an advisor and received grants from Johnson and Johnson in the past for other projects network in the US and Dementia Platform in the UK (DPUK) but not in the past 3 years. V.A.N. and H.K.M. are employees and shareholders of (https://www.dementiasplatform.uk/about) are examples of some Janssen (Johnson & Johnson) and have no direct financial conflicts of interest to recent efforts to overcome these limitations. The WeCareAdvisor, a declare as individuals in the technologies or registries mentioned. Janssen as a web-based clinical research platform aimed at interventions for company is active in the area of Alzheimer’s research and drug development. It has behavioral disturbances in in dementia, is being tested in a financially supported mobile technologies and tools being developed (mostly by randomized trial. The NIH will soon fund a clinical trials network other academics or entities) for Alzheimer’s diagnosis, monitoring, and care-giver and coordinating center for mobile cognitive trials (https://grants. support as well as patient registries and platforms, such as BHR and GAP. ReVeRe is nih.gov/grants/guide/rfa-files/RFA-AG-18-012.html). being developed by Janssen. Outside of Alzheimer’s, JNJ also supports a range of mobile technologies for other therapeutic areas. EVIDENCE GAPS AND CHALLENGES OF MOBILE Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims TECHNOLOGIES FOR AD TRIALS in published maps and institutional affiliations. Technology comes with both promises and limitations. New dementia mobile technologies often undergo initial feasibility and REFERENCES acceptability testing but are rarely subject to rigorous randomized 1. Cova, I. et al. Worldwide trends in the prevalence of dementia. J. Neurol. Sci. 37, trials comparing them to traditional methods. There is also lack of 259–260 (2017). regulatory clarity over how biometric and digital data should be 2. Cummings, J. L., & Morstorf, T. & Lee, G. Alzheimer’s drug-development pipeline: applied in late-stage registration trials. Lack of interoperability of 2016. Alzheimers Dement. 2, 222–232 (2016). devices, operating systems and platforms, privacy, hacking risks, Published in partnership with the Scripps Translational Science Institute npj Digital Medicine (2018) 1 Mobile technologies for Alzheimer’s trials PM Doraiswamy et al. 3. Topol, E. Digital medicine: empowering both patients and clinicians. Lancet 388, 19. Imbeault, H. et al. Impact of AP@ LZ in the daily life of three persons with 740–741 (2016). 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Pervasive computing technologies to well-being in patients with mild Alzheimer disease. Int. Psychogeriatr. 29, 741–754 continuously assess Alzheimer’s disease progression and intervention efficacy. (2017). Front. Aging Neurosci. 7, 102 (2015). 23. García-Betances, R. I. et al. A succinct overview of virtual reality technology use in 8. Cummings, J. et al. Re-engineering Alzheimer clinical trials: Global Alzheimer’s Alzheimer’s disease. Front. Aging Neurosci. 7, 80 (2015). Platform Network. J. Prev. Alzheimers Dis. 3, 114–120 (2016). 24. Allain, P. et al. Detecting everyday action deficits in Alzheimer’s disease using 9. Ray Dorsey, E. et al. Novel methods and technologies for 21st-century clinical a nonimmersive virtual reality kitchen. J. Int. Neuropsychol. Soc. 20, 468–477 trials: a review. JAMA Neurol. 72, 582–588 (2015). (2014). 10. Ritchie, C. W. et al. Development of interventions for the secondary prevention of 25. Gitlin, L. N. et al. A randomized trial of a web-based platform to help families Alzheimer’s dementia: the European Prevention of Alzheimer’s Dementia (EPAD) manage dementia-related behavioral symptoms: the WeCareAdvisor™. Contemp. project. Lancet Psychiatry 3, 179–186 (2016). Clin. Trials 62,27–36 (2017). 11. Weiner, M. W. et al. The Alzheimer’s Disease Neuroimaging Initiative 3: continued innovation for clinical trial improvement. Alzheimers Dement. 13, 561–571 (2017). 12. Hardy, J. L. et al. Enhancing cognitive abilities with comprehensive training: a Open Access This article is licensed under a Creative Commons large, online, randomized, active-controlled trial. PLoS ONE 10, e0134467 (2015). Attribution 4.0 International License, which permits use, sharing, 13. Bot, B. M. et al. The mPower study, Parkinson disease mobile data collected using adaptation, distribution and reproduction in any medium or format, as long as you give ResearchKit. Sci. 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Med. 42, 109–120 (2008). from the copyright holder. To view a copy of this license, visit http://creativecommons. 17. El Haj, M., Gallouj, K. & Antoine, P. Google Calendar enhances prospective memory org/licenses/by/4.0/. in Alzheimer’s disease: a case report. J. Alzheimers Dis. 57, 285–291 (2017). 18. El Haj, M. Google Calendar to alleviate prospective memory compromise in a © The Author(s) 2018 patient with very mild Alzheimer’s disease. Front. Psychol. 8, 361 (2017). npj Digital Medicine (2018) 1 Published in partnership with the Scripps Translational Science Institute http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png npj Digital Medicine Springer Journals

Mobile and pervasive computing technologies and the future of Alzheimer’s clinical trials

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Abstract

www.nature.com/npjdigitalmed PERSPECTIVE OPEN Mobile and pervasive computing technologies and the future of Alzheimer’s clinical trials 1 2 2 P. Murali Doraiswamy , Vaibhav A. Narayan and Husseini K. Manji The rapid growth of mobile phones, automated speech recognizing personal assistants, and internet access among the elderly provides new opportunities for incorporating such technologies into clinical research and personalized medical care. Alzheimer’s disease is a good test case given the need for early detection, the high rate of clinical trial failures, the need to more efficiently recruit patients for trials, and the need for sensitive and ecologically valid trial outcomes. npj Digital Medicine (2018) 1:1 ; doi:10.1038/s41746-017-0008-y INTRODUCTION Industries and Associations, plans to register 24,000 people to identify a Europe-wide cohort of >6000 high-risk participants, of Alzheimer’s disease (AD) affects an estimated 45 million people which 1500 will be invited to participate in a technology-enabled worldwide, and despite substantial research investments, there trial to test new treatments for the prevention of AD. In the US, are no therapies to prevent or slow disease progression. The the Alzheimer’s Association TrialMatch (https://trialmatch.alz.org/ 99.6% failure rate in recent AD clinical trials highlights the need for find-clinical-trials#createaccount) allows caregivers and subjects innovation and efficiency. It is believed that the best chance to to customize their search for trials and receive alerts. The intervene therapeutically is by targeting the preclinical or Brain Health Registry (BHR) at USCF is pioneering a broad-based prodromal stages of Alzheimer’s. Some of these challenges may internet-based approach for recruiting and monitoring individuals potentially be amenable to technological solutions. at risk for AD. The registry is currently being used for pilot The revolution in mobile technologies such as smart phones/ validation studies of mobile cognitive tests, and ultimately, all tablets, cloud-based platforms, and deep learning-driven software subjects enrolled in a large national biomarker trial (ADNI-3) will be algorithms and miniaturized automated physiological sensors is 3–7 given the option to participate in BHR. The Dominant Inherited poised to profoundly disrupt medicine and, by extension, many 5–8 Alzheimer’s Network (DIAN) and the Alzheimer’s Prevention Initiative aspects of neuropsychiatry and AD care and clinical research. (API) registries have successfully recruited subjects with specified In this Perspective, we highlight four areas in which such genetic mutations. The Human Cognition Project (with >40 million technology could facilitate AD clinical trials: (1) Mobilizing subjects of all ages from 180 countries) provides a model for the recruitment, (2) Mobile cognitive measures, (3) Digital functional kind of global brain laboratory that can be harnessed using mobile outcomes, and (4) Integrated informatics platforms. For further tools. This registry was recently used successfully to recruit and information on how technology may help with care and caregiver 4,5 conduct a purely online randomized clinical trial of cognitive support, readers are referred elsewhere. training. Research apps such as Apple’s Research Kit now also allow participants a simple way to consent, participate, and share their MOBILIZING CLINICAL TRIAL RECRUITMENT data, and these apps can be adapted for consenting caregivers ClinicalTrials.gov lists >450 active Alzheimer’s trials needing some and legal representatives. Ultimately, it is hoped such registries do 70,000 volunteers—posing the challenge of how to efficiently not exist in silos, are able to recruit samples representative of the identify, consent, and screen subjects. Prevention trials increas- population, and keep subjects engaged over long periods to ingly rely on expensive brain scans or spinal fluid biomarkers to minimize selection and attrition biases. identify at-risk subjects but such methods have high screen fail rates—because only about a third of asymptomatic subjects may test positive. Thus time for subject recruitment may take up 30% 8,9 MOBILE COGNITIVE OUTCOMES of drug development costs and delay trials by ≥2 years. Brain health registries are one such solution wherein at-risk A second challenge is that small, but clinically meaningful, subjects are registered, self-consented, and prescreened via treatment effects on cognition are often difficult to measure in cognitive or even genetic self-tests. While academic AD centers preclinical AD or mild cognitive impairment (MCI) due to learning have always had registries, the emergence of national registries effects, ceiling effects, heterogeneity, and normal fluctuations. could be a catalyst. The European Prevention of Alzheimer’s Composite endpoints formed by combining scores from existing Dementia (EPAD) initiative, funded by the Innovative Medicines neuro-psychological batteries have been proposed as primary Initiative and the European Federation of Pharmaceutical endpoints for such trials. For example, the Alzheimer’s Disease 1 2 Neurocognitive Disorders Program, Department of Psychiatry, Duke University Health System, and the Duke Institute for Brain Sciences, Durham, NC 27710, USA and Janssen Research & Development, LLC, Raritan, New Jersey 08869, USA Correspondence: P. Murali Doraiswamy (murali.doraiswamy@duke.edu) Received: 31 July 2017 Revised: 12 September 2017 Accepted: 12 September 2017 Published in partnership with the Scripps Translational Science Institute Mobile technologies for Alzheimer’s trials PM Doraiswamy et al. Fig. 1 Figure depicts selected mobile or pervasive computing technologies that are being studied for use in AD research with examples in each area. This is not intended to be a comprehensive listing and the degree of validation that each technology has undergone is variable. The examples are intended to merely provide readers an overview of the developments in the field. HRV heart rate variability, ADAS refers to Alzheimer’s Disease Assessment Scale, EDA refers to electrodermal activity, FAQ refers to functional activities questionnaire, VR refers to virtual reality, other abbreviations are listed in the text Cooperative Studies-Preclinical Alzheimer’s Cognitive Composite, progression in a sample of 1200 subjects ranging from normal to the primary outcome measure in the Anti-Amyloid Treatment in MCI to mild AD dementia. Asymptomatic Alzheimer’s study, is a composite of episodic memory, list learning, digit symbol substitution, and the Mini MOBILE ASSESSMENT OF FUNCTION (ACTIVITIES OF DAILY Mental State Examination. While it has shown promise in LIVING) AND BEHAVIOR retrospective analyses of observational studies, it has not yet been fully validated. Another key challenge in Alzheimer’s prevention trials is to Automated mobile cognitive tests may offer advantages such demonstrate an impact on function (daily activities of living) in as individualized scaling, automatic and error free scoring, and populations that have minimal to no functional deficits. Long- multiplicity of test versions and, if combined with gaming itudinal studies have charted the timeline of functional losses from elements, may be less anxiety provoking. Many neuro- preclinical stage to MCI to AD and many activities of daily living psychological tests and self-rated measures of cognition have (ADLs; e.g., spatial navigation, financial calculations, using phone been adapted for use on a tablet or phone, undergone varying or computer, driving) can be measured using phone sensors, GPS, degrees of qualification or validation (Fig. 1), and are already used wearables, or home sensors. For example, in a 6-week study of the in research and clinical trials. For example, a recent study of a iPad Proactive Activity Toolkit (PROACT), investigators tagged objects based self-test called C3-PAD (Computerized Cognitive Composite in a home with 108 radiofrequency identification tags and for Preclinical Alzheimer’s Disease) in 49 cognitively normal elderly analyzed signals by a prototype glove worn by a group of healthy subjects (mean age 71 years, 20% non-Caucasian) showed high adults. PROACT accurately identified the performance of an ADL in reliability among the test versions (Cronbach alpha coefficient = >80% of subjects and the specific ADLs in 73% of cases. Another 0.93). In all, 98% of subjects completed four out of five sessions study, CART, has installed strategically placed sensors in >480 correctly, and there was a high correlation between in-clinic and 2 homes of seniors and has been continuously monitoring gait, at-home C3-PAD assessments (r = 0.508, p < 0.0001), suggesting mobility, sleep, and activity for >10 years to develop signatures of promise for clinical trial use. The relationship with standardized cognitive and functional decline (http://www.ohsu.edu/xd/ tests covering similar cognitive domains was also significant but research/centers-institutes/orcatech/index.cfm). A number of pre- less robust (r = 0.168, p < 0.003), suggesting that the tests are in viously validated paper and pencil functional scales are also now need for some refinement. A number of other large epidemio- available in online or mobile versions. Electronic organizer tools logical studies (e.g., Framingham study) and clinical trials have also such as the Google Calender and AP@LZ have been tested in incorporated mobile cognitive tests as exploratory outcomes. proof-of-concept studies with small samples to ameliorate ADNI-3, a national biomarker study, is directly comparing an at- 17–20 home computerized cognitive self-test measure vs. standard functional and memory deficits, which in turn may improve paper and pencil tests as well as pathological markers of AD trial compliance. npj Digital Medicine (2018) 1 Published in partnership with the Scripps Translational Science Institute 1234567890 Mobile technologies for Alzheimer’s trials PM Doraiswamy et al. Many behavioral changes seen in AD, such as wandering, sleep, access, and ethics of sharing biometric data are examples of some circadian rhythm changes, depression, and agitation, can also other limitations. Further, uptake of smart phone digital technol- potentially be tracked through use of mobile devices and sensors ogies by some elderly subgroups (such as those on fixed incomes) (Fig. 1). For example, the outcome of a 4-week phase-2 clinical trial remains low. And despite large numbers of people expressing of a dual orexin receptor antagonist in mild-to-moderate AD initial interest in registries or apps, due to large drop outs only a patients (N = 125) with irregular sleep wake rhythm disorder is small fraction may continue over time. Clinicians and CROs who sleep efficiency and wake efficiency measured using actigraphy conduct AD trials may not be well informed about the technical through a wrist device (https://clinicaltrials.gov/ct2/show/NCT limitations of mobile devices requiring the building of new 03001557). Another pilot study evaluated the utility of a wrist partnerships with engineers. Last but not least, regulatory and sensor (Philips DTI-2) and digital dashboard to track agitation ethical guidelines often lag behind the rapid pace at which and stress in six nursing home patients with dementia technology is evolving. Groups such as the Software as a Medical over 2 months (total recorded time was 142 h across 37 days). Device International Regulators Forum, Mobile Clinical Trials Wrist sensor data (galvanic skin conductance, accelerometry, subcommittee of the Clinical Trials Transformation Initiative, and skin and environment temperature, and ambient light) were the ISTAART Technology PIA (https://act.alz.org/site/SPageServer? extracted weekly and compared with 24 h observations made by pagename=ISTAART_PIA_Technology) are addressing some of nursing staff study across four parameters—sleep, aggression, these challenges. The pilot digital health technology precertifica- stress, and normal. These data allowed the authors to develop tion program (https://www.fda.gov/MedicalDevices/DigitalHealth/ objective thresholds with sensor data for defining “stress” and UCM567265) announced by the U.S. Food and Drug Administra- “agitation” in AD patients and develop a dashboard that allows a tion may also provide greater regulatory clarity. clinician to run a stress analysis for a given patient over a given time period. CONCLUSIONS Other researchers are studying the utility of a wearable 22 23 camera and non-immersive virtual environments to detect Given the promise of mobile technologies across multiple areas everyday changes in function or behavior. For example, one study of AD clinical trials, we call for a more rapid systematic validation compared the performance of 24 subjects with mild-to-moderate and a multi-stakeholder public–private partnership—involving AD vs. 32 normal controls on a laptop-based virtual coffee making caregivers and patients, academia, clinicians, industry, regulators, task in a virtual reality kitchen. AD patients performed worse than ethicists—to develop a framework for the optimal deployment of controls on the virtual test, and their errors were correlated with such tools. Technology is advancing rapidly and automatic both scores on standardized functional tests and caregiver ratings speech recognition personal assistant devices, powered by of their impairments. Such tools may in future offer promise due artificial intelligence, may be game changers with regards to to their ecological validity and potential to provide evidence to how the elderly will interact with devices in the future. payers of real-world outcomes. Ultimately, it is hoped that such innovations will accelerate the testing and development of effective therapies to delay the onset of AD. CLOUD-BASED ANALYTIC PLATFORMS AND TRIAL NETWORKS FOR MOBILE AD TRIALS AUTHOR CONTRIBUTIONS The fourth challenge is one of data sharing and bioinformatics. P.M.D. did the first draft, with additional drafting by V.A.N., and all the authors The vast majority of raw data from Alzheimer’s trials, even those contributed to edits. funded by government agencies, are not readily accessible to the wider scientific or public community. One of the exceptions has been ADNI, which has made data sharing a priority from day 1 and 11 ADDITIONAL INFORMATION led to >800 publications. Today, many AD-related informatics Competing interests: The technologies described in this article were selected to be algorithms are trained using ADNI data, but there is no equivalent illustrative of the advances being made. P.M.D. is a minor shareholder and advisor to public resource for replication or validation. Further, databases Anthrotronix and a former advisor and grant recipient from Neuronetrix. He has also created over the past two decades for AD trials may lack received a speaking fee from CEOs Against Alzheimer’s (but has no involvement with contemporary features, such as a patient portal, integrated sensor their trial platform). He has also received research grants and/or advisory/speaking and smart phone data, patient engagement tools, and secure data fees from several pharmaceutical, CRO, and technology companies for other projects, sharing. Most such data platforms exist in silos with limited or no and he owns shares in Maxwell Health, Evidation, Muses Labs, Turtle Shell, and cross-integration ability. The Global Alzheimer’s Platform (GAP) Adverse Events Inc. whose products are not discussed here. He has served as an advisor and received grants from Johnson and Johnson in the past for other projects network in the US and Dementia Platform in the UK (DPUK) but not in the past 3 years. V.A.N. and H.K.M. are employees and shareholders of (https://www.dementiasplatform.uk/about) are examples of some Janssen (Johnson & Johnson) and have no direct financial conflicts of interest to recent efforts to overcome these limitations. The WeCareAdvisor, a declare as individuals in the technologies or registries mentioned. Janssen as a web-based clinical research platform aimed at interventions for company is active in the area of Alzheimer’s research and drug development. It has behavioral disturbances in in dementia, is being tested in a financially supported mobile technologies and tools being developed (mostly by randomized trial. The NIH will soon fund a clinical trials network other academics or entities) for Alzheimer’s diagnosis, monitoring, and care-giver and coordinating center for mobile cognitive trials (https://grants. support as well as patient registries and platforms, such as BHR and GAP. ReVeRe is nih.gov/grants/guide/rfa-files/RFA-AG-18-012.html). being developed by Janssen. Outside of Alzheimer’s, JNJ also supports a range of mobile technologies for other therapeutic areas. EVIDENCE GAPS AND CHALLENGES OF MOBILE Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims TECHNOLOGIES FOR AD TRIALS in published maps and institutional affiliations. Technology comes with both promises and limitations. 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npj Digital MedicineSpringer Journals

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