Capturing Measures That Matter: The Potential Value of Digital Measures of Physical Behavior for Alzheimer’s Disease Drug Development

Abstract

Alzheimer’s disease (AD) is a devastating neurodegenerative disease and the primary cause of dementia worldwide. Despite the magnitude of AD’s impact on patients, caregivers, and society, nearly all AD clinical trials fail. A potential contributor to this high rate of failure is that established clinical outcome assessments fail to capture subtle clinical changes, entail high burden for patients and their caregivers, and ineffectively address the aspects of health deemed important by patients and their caregivers. AD progression is associated with widespread changes in physical behavior that have impacts on the ability to function independently, which is a meaningful aspect of health for patients with AD and important for diagnosis. However, established assessments of functional independence remain underutilized in AD clinical trials and are limited by subjective biases and ceiling effects. Digital measures of real-world physical behavior assessed passively, continuously, and remotely using digital health technologies have the potential to address some of these limitations and to capture aspects of functional independence in patients with AD. In particular, measures of real-world gait, physical activity, and life-space mobility captured with wearable sensors may offer value. Additional research is needed to understand the validity, feasibility, and acceptability of these measures in AD clinical research.

Keywords

Alzheimer’s disease digital measure drug development gait life-space mobility meaningful aspect of health physical behavior physical function wearable sensor

ALZHEIMER’S DISEASE: A LEADING CAUSE OF DEATH WITH FEW EFFECTIVE THERAPIES

Alzheimer’s disease (AD) is the primary cause of dementia and the seventh leading cause of death worldwide [1]. AD is a progressive and neurodegenerative condition, which ultimately affects every aspect of daily life for patients and their caregivers. In addition, it has major economic impacts, with the cost of AD care estimated to total $321 billion in 2022 in the US alone [2]. Despite the magnitude of AD’s impacts, it remains the only leading cause of death with very few therapies to prevent or slow disease progression. Between 2002 and 2012, 99.6% of AD clinical trials failed [3, 4]. Although this low success rate reflects many factors including AD’s complex pathophysiology, a lack of validated biomarkers, and underpowered trials [5], it also reflects the failure of established AD clinical outcome assessments to guide the development of potentially valuable therapeutic interventions. Given the costs associated with drug development, which are estimated to total $42.5 billion since 1995 [6], and the lack of effective AD therapies, innovation is paramount. In this review, we articulate an approach to developing novel outcome measures reflecting functional independence that are suitable for AD drug development. First, we summarize why more sensitive and ecologically valid outcome measures reflecting functional independence are needed in AD clinical research. We then describe how wearable sensors deployed passively, continuously, and remotely have the potential to capture changes in real-world physical behavior that occur in AD and impact the ability to function independently. Finally, we outline our hypothesis that measures of real-world physical behavior provide an opportunity for the AD clinical research community to better understand functional independence in AD and summarize the research needed to develop, validate, and implement these measures.

FUNCTIONAL INDEPENDENCE IN AD: CRITICAL FOR DIAGNOSIS AND MEANINGFUL TO PATIENTS

In the progression of AD, cognitive and behavioral changes are coupled with declines in the ability to perform activities of daily living (ADLs) and to function independently. Impairments in instrumental ADLs, which include complex tasks such as performing household chores, preparing meals, and traveling outside the home, may be evident during the mild cognitive impairment (MCI) stage and intensify in disease progression [7]. Basic ADLs, such as grooming, bathing, and ambulation, decline in moderate-to-severe disease stages [8]. Accordingly, functional impairment is one criterion required for a diagnosis of AD dementia [7, 9] and is encompassed in the diagnostic criteria for MCI due to AD [10]. The ability to perform ADLs and function independently is also something that patients with AD and their caregivers want to maintain in disease progression. Across recent qualitative studies, patients have reported their desire to continue carrying out everyday behaviors such as walking, gardening, and caring for oneself [11 –13]. Yet despite functional impairment being a key diagnostic criterion and something that both patients and their caregivers want to prevent, in AD drug development, less emphasis is placed on functional outcomes compared to cognitive and neuropsychiatric outcomes. For instance, among clinical trials conducted from 2004–2014, only 32% of dementia trials and 16% of MCI trials included an outcome reflecting functioning [14].

ESTABLISHED ASSESSMENTS OF FUNCTIONAL INDEPENDENCE IN AD CLINICAL RESEARCH

When functional independence is assessed in AD clinical research, it is traditionally captured through informant-based assessments of a patient’s competence at performing basic and/or instrumental ADLs. ADL assessments used commonly in clinical trials include the AD Cooperative Study –ADL Scale (ADCS-ADL) [15], the instrumental subscale of the ADCS (ADCS-iADL), and the version of the ADCS for use in MCI (ADCS-MCI-ADL) [16]. Other commonly used ADL assessments are the Amsterdam Instrumental ADL Questionnaire [17] and the Integrated AD Rating Scale (iADRS), a composite assessment that combines items from the widely-used AD Assessment Scale –Cognitive Subscale and the ADCS-iADL [18].

Although these assessments capture some of AD’s impacts on functional independence, there are limitations associated with their use. For one, the majority of these assessments are dependent on informant reports [19, 20], which increases caregiver burden and introduces bias [21]. In addition, established assessments of ADLs only capture a limited scope of activities and are assessed in the clinic, limiting their ability to capture the complexities of patients’ experiences, such as the pace and diurnal patterning of behavior [22]. Finally, these assessments are prone to ceiling effects at early disease stages, when changes in day-to-day function may be challenging to quantify [21]. These effects are especially relevant as most current clinical trials target individuals early in disease progression, with 45% of Phase III and 59% of Phase II trials enrolling individuals with MCI due to AD or mild AD, and a subset of trials enrolling individuals who are cognitively normal but at risk for AD [23]. In these stages, episodic assessments of ADLs may therefore fail to detect subtle, but important, clinical changes over time. Together, these challenges necessitate innovative approaches to measure functional independence in AD more holistically and with greater sensitivity.

CHANGES IN PHYSICAL FUNCTION IN AD

AD has a significant impact on physical function, which we define here as an individual’s ability to perform physical tasks such as locomotor behaviors [24]. The changes in physical function that occur in AD, together with cognitive and behavioral changes, contribute to patients’ ability to perform ADLs [25 –27]. Historically, physical function in AD has been evaluated in laboratory- or clinic-based settings. Compared to otherwise healthy older adults, individuals with MCI and AD exhibit slower and altered gait patterns when assessed in the clinic [28 –34]. Furthermore, gait parameters differ significantly in individuals with MCI compared to those with overt dementia [35, 36], suggesting that changes in physical function progress as cognitive function declines. Declines in physical function assessed with in-clinic physical performance tests are associated with risk of developing AD, and among individuals with MCI, motor function impairment is related to risk of progressing to overt dementia [37 –39]. Furthermore, AD-related changes in physical function may be detectable prior to the onset of cognitive changes; for instance, among those who develop AD, declines in grip strength measured in the clinic and a history of falls may be evident more than 5 years prior to diagnosis [40]. Although these findings have been generated largely from cross-sectional studies, they support the clinical relevance of changes in physical function in patients with AD.

There are limitations in translating evidence from in-clinic physical function tests to the real-world. For instance, gait speed and stride regularity are lower when measured with accelerometry in older adults’ real-world environments than when measured in the laboratory [41 –43]. Accordingly, there has been an increase in the use of digital health technologies to quantify physical function through the remote monitoring of patients’ real-world physical behavior. In the sections below, we provide evidence that assessing real-world physical behavior may offer value for capturing functional independence in AD clinical research.

THE POTENTIAL FOR DIGITAL MEASURES OF REAL-WORLD PHYSICAL BEHAVIOR TO CAPTURE FUNCTIONAL INDEPENDENCE IN AD

Under the umbrella of digital medicine [44], digital health technologies can be deployed remotely in clinical research to capture health information continuously, characterize disease manifestation and progression, enable decentralized clinical trials, and facilitate recruitment of diverse and underrepresented populations [45 –47]. One class of digital health technologies with significant potential for AD clinical research is wearable sensors such as accelerometers and Global Position System (GPS) trackers. These tools can be deployed to assess real-world physical behavior passively, remotely, and continuously. In the case of AD, digital measures of real-world physical behavior, derived from wearable sensors, have the potential to provide rich insights into functional independence throughout disease progression [22, 48]. In particular, we hypothesize that digital measures of real-world physical behavior have the potential to capture meaningful aspects of functional independence and to complement established assessments to improve monitoring of functional independence in AD clinical research [48 –51].

As previously described, established assessments of ADLs often rely on informant reports, require active participation from patients and/or their caregivers, may rely on recall, and are captured infrequently. In contrast, wearable sensors can provide remote and passive assessments and offer the potential to capture everyday physical behavior with high granularity and low patient and caregiver burden [52]. As such, deploying these measures could increase sensitivity to detect subtle changes in functional independence over time [53, 54], especially at early disease stages, which could lower sample size requirements and costs in AD clinical trials [55]. Furthermore, mobility and walking behavior are key aspects of functional independence. Since wearable sensors allow for monitoring of the mobility and physical challenges encountered by patients in their real-world environments, digital measures of real-world physical behavior may provide rich insights into aspects of ADLs that are meaningful for patients and their caregivers. Below, we outline specific domains of real-world physical behavior with potential value for assessment in AD: gait, physical activity and sedentary behavior, and life-space mobility. The exact concept(s) of interest that these measures reflect must be systematically investigated.

Gait

Assessments of real-world gait have confirmed findings from the clinic, demonstrating that individuals with MCI and AD exhibit slower gait speed and shorter stride length relative to cognitively normal older adults [56, 57]. Variability in step velocity and cadence assessed from real-world accelerometry is also higher in individuals with mild AD compared to cognitively normal older adults [58]. In addition, stride time variability and within-bout stride regularity in real-world walking patterns is lower in individuals with MCI relative to controls [34, 56], suggesting that real-world gait patterns are sensitive to clinical change in early disease stages. Real-world gait speed, stride length, and stride regularity have also been associated with cognitive performance in MCI [56, 59]. Collectively, this evidence suggests that assessing real-world gait parameters such as gait speed, gait variability, and stride length, or a composite set of these metrics with wearable sensors [60, 61] may capture clinical changes across the AD continuum.

Physical activity and sedentary behavior

Assessments of real-world physical behavior have also demonstrated that physical activity and sedentary behavior are affected in the progression of AD. Compared to controls, individuals with MCI and dementia take fewer steps per day [57], spend less time walking [34], and spend more time in sedentary activity bouts [62]. There is also evidence that AD is associated with changes in diurnal physical activity patterns measured using accelerometry. In one study, individuals with AD were less active in the morning compared to controls [62], and in another investigation, individuals with MCI exhibited lower levels of physical activity during both the morning and evening hours compared to otherwise healthy older adults [63]. Further, diurnal distributions of physical activity may provide novel insights into ecologically relevant symptoms. For instance, based on a 1-week observation in older adults’ home environments, fragmentation of physical activity patterns was more strongly related to subjective memory complaints than physical activity volume alone [64]. Finally, assessing the patterns and variability of physical behavior with wearable sensors may also provide insights into habitual activity engagement [65], a component of functional independence for individuals with AD. Many habitual ADLs, such as washing dishes, brushing teeth, and folding laundry, are performed in a standing position, with occasional stepping, for extended periods of time. Wearable sensors that can differentiate sitting from other upright behaviors could be used to infer performance characteristics of ADLs. This possibility opens the door to the discovery of metrics which could provide insights into how functional independence is affected in the progression of AD.

Life-space mobility

Investigations utilizing wearable GPS trackers have also provided evidence for AD-related changes in life-space mobility, defined as the geographic area in which life is carried out [66]. In one study, GPS-derived area and mean distance traveled from home were both lower for individuals with AD compared to otherwise healthy older adults; these measures were also associated with accelerometer-derived measures of physical behavior [67]. In addition, combining wearable sensors with ensembles of non-wearable sensors may enable more holistic assessments of life-space mobility than a wearable sensor alone [68, 69]. In one study, spending less time outside the home, as captured by in-home motion sensors, predicted major transitions in care among older adults [70]. In another investigation, distributions of time spent in different rooms, as captured with non-wearable motion sensors, detected MCI with high accuracy [71]. This suggests that incorporating objective measures of how a person moves throughout their life space may add an important dimension to the assessment of functional independence and have clinical value.

PRECEDENCE FOR THE DEPLOYMENT OF DIGITAL MEASURES OF REAL-WORLD PHYSICAL BEHAVIOR IN CLINICAL TRIALS

Although there is evidence supporting AD-related changes in real-world measures of gait, physical activity, and life-space mobility, the majority of evidence supporting the value of such measures comes from cross-sectional studies in laboratory or clinic settings [60]. As such, to date, these measures have not yet been deployed in drug development studies. However, there is precedent for deploying digital measures of real-world gait and physical activity as outcome measures in clinical trials for Parkinson’s disease [72], osteoarthritis [73, 74], and Major Depressive Disorder [75]. Furthermore, stride velocity 95th centile, captured continuously with a validated ankle-worn device, was recently qualified by the European Medicines Agency for use as a secondary efficacy endpoint in clinical trials for Duchenne Muscular Dystrophy [76]. This was the first regulatory qualification of a digital clinical measure and serves as a precedent for the development of novel endpoints in other therapeutic areas. For instance, wearable sensor-derived gait speed, collected in patients’ real-world environments, is undergoing qualification procedures with the FDA and European Medicines Agency for use as a measure of walking behavior in sarcopenia and multiple sclerosis, conditions in which mobility is affected [43 , 78]. In alignment with this effort, the Mobilise-D clinical validation study is being conducted to understand the construct validity, predictive capacity, and sensitivity of real-world measures reflecting walking behavior across a number of therapeutic areas, with the aim of identifying outcomes that can be deployed for regulatory decision-making [77]. Similar efforts are needed to understand whether measures of real-world gait, physical activity, and life-space mobility can also provide relevant information about the efficacy of novel therapeutics targeting AD.

THE POTENTIAL FOR DIGITAL MEASURES OF REAL-WORLD PHYSICAL BEHAVIOR TO ENRICH COGNITIVE ASSESSMENTS

Beyond providing standalone insights into functional independence, digital measures of real-world physical behavior may enrich established measures of cognition in individuals with AD. Current evidence suggests that combining assessments of cognitive and physical function may provide richer insights into disease progression than either type of assessment alone. For instance, combining assessments of cognition and instrumental activities of daily living outperforms cognitive assessments alone for predicting conversion from MCI to overt dementia [79]. Supplementing established cognitive outcomes with digital measures of real-world physical behavior could also be a means of demonstrating the meaningfulness of an identified cognitive change [80, 81]. In addition, this approach may also help to address limitations of established assessments such as ceiling effects. For example, the Cognitive-Functional Composite, which combines items from existing assessments probing memory, executive function, and ADLs, has fewer range restrictions compared to established cognitive assessments and therefore offers greater sensitivity to clinical progression [82].

A ROADMAP TO DEVELOP AND VALIDATE DIGITAL MEASURES OF REAL-WORLD PHYSICAL BEHAVIOR IN AD

We have summarized evidence that measures of real-world physical behavior captured with wearable sensors may be capable of complementing established assessments in AD clinical trials to better capture aspects of functional independence that matter to patients with AD and their caregivers. However, developing, validating, and implementing these measures in regulated clinical trials comes with challenges. Broadly, a digital clinical measure requires substantial evidence demonstrating that it: 1) represents something meaningful to patients, 2) can be derived accurately and reliably with a specific digital health technology, 3) is feasible to derive with that digital health technology in the relevant clinical setting, and 4) is linked to gold-standard clinical outcomes, such as clinical events or survival. Frameworks for generating such evidence have been established in recent years [83 –85]. Based on these frameworks, we outline a roadmap of steps required to develop and validate digital measures that can be deployed in clinical trials and highlight challenges specific to the case of developing measures of real-world physical behavior in AD.

Identifying aspects of functional independence that matter to patients with AD and their caregivers

A key tenet of patient-focused drug development, the US Food and Drug Administration’s framework for incorporating the patient perspective into all phases of drug development [86], is the notion that what matters to patients should inform the development of outcome measures and, in turn, the evaluation of novel therapeutics [87]. In practice, this occurs by directly engaging patients to identify meaningful aspects of health, which are the symptoms and impacts of disease that patients want to improve, prevent, or protect from becoming worse [88]. Recent studies aimed at understanding meaningful aspects of health in AD have demonstrated that functional independence is something that patients and their caregivers wish to maintain in disease progression [89 –92], but few studies have systematically examined how specific aspects of functional independence change along the AD continuum [89]. Additional research in this domain will be critical for the development of digital measures of real-world physical behavior with clinical utility across the AD continuum. Once these aspects of functioning are identified, a conceptual framework should be developed which maps those aspects to concept(s) of interest that can be measured with a specific wearable sensor [88 , 93–95].

Evaluating analytical validity to derive outcome measures of interest

Once outcome measures and measurement technologies are identified, evidence should be generated to demonstrate that the measure(s) of interest are analytically valid—that is, that they can be captured accurately and reliably with the selected measurement technology [84]. In the case of generating digital measures of real-world physical behavior in AD, this step is especially important, as many existing algorithms to derive measures of physical behavior have been developed and validated in healthy populations. When these algorithms are applied to clinical populations with slow or altered gait patterns, such as individuals with AD, algorithmic bias may exist [96]. Therefore, it is critical that analytical validation and reliability studies are conducted in representative samples of patients with AD.

Establish feasibility of wearable sensors in an aging and forgetful population

Understanding the feasibility and acceptability of a digital health technology that will be used to derive an outcome measure is an essential step in the process to establish that technology is fit-for-purpose [97]. Wearable sensors can be used to derive digital measures of real-world physical behavior, but their feasibility and acceptability in AD is not well understood. A number of studies have demonstrated that the short-term use of wearable sensors among older adults with MCI and AD is both feasible and acceptable [98 –102]; for instance, one study demonstrated the feasibility of using wearable sensors to assess gait over a 7-day period in individuals with mild AD [103]. However, other studies have documented challenges with using wearable sensors among individuals in this population, including ease of device use, the need for assistance from a caregiver, and forgetting to charge the device [101, 104]. Studies examining barriers and facilitators to using wearable sensors in patients with AD for extended periods of time will be important for understanding their applicability for capturing measures of real-world physical behavior.

Determining clinical validity of measures of real-world physical behavior in AD

Once it is established that the measure of interest is analytically valid and feasible to capture, a critical step is establishing evidence of the measure’s clinical validity—that is, its relationship with gold standard clinical outcomes of interest [84]. In the case of AD, generating this evidence will require long-term wearable sensor deployment in individuals with AD to assess relationships between digital measures of real-world physical behavior and clinical benefit over time. Within this step, collecting data from representative samples of individuals with AD, across the AD continuum, will be also critical to ensure that digital measures found to be clinically valid also reflect patients’ collective experiences [48]. Although generating the evidence along this roadmap will require significant investment from many stakeholders [97], this investment is necessary to realize the potential value of digital measures of real-world physical behavior in AD and to promote their appropriate deployment in AD clinical research.

CONCLUSION

In the case of AD, where disease-modifying therapies are in high demand, there is mounting pressure to move away from the status quo in drug development. Amid a digital revolution in clinical research, digital measures of real-world physical behavior captured with wearable sensors have the potential to advance AD drug development. Because they can be captured passively, continuously, and remotely, these measures have the potential to complement established outcomes of functional independence in AD clinical trials. In particular, measures of real-world gait, physical activity, and life-space mobility may provide novel insights into aspects of functional independence that matter to patients with AD. Additional research is necessary to understand the analytical and clinical validity, feasibility, and acceptability of digital measures of real-world physical behavior in AD clinical research and drug development.

Footnotes

ACKNOWLEDGMENTS

The authors have no acknowledgments to report.

FUNDING

This work was supported by funding from the National Institute on Aging and the Massachusetts Artificial Intelligence and Technology Center for Connected Care in Aging & Alzheimer’s Disease (grant number 5P30AG073107-02 Pilot A2 and Pilot A3). MB’s work was also supported by the Army Research Laboratory Cooperative Agreement #W911NF2120208 and the Massachusetts Life Sciences Center.

CONFLICT OF INTEREST

SLB, JMB, KL, and IC are employees of VivoSense, Inc. IC is on the Editorial Board of Karger Digital Biomarkers and the Scientific Advisory Board for IMI IDEA FAST, and has received fees for lectures and consulting on digital health at ETH Zürich and FHNW Muttenz. All other authors have no conflict of interest to report.

References

National Institute on Aging. Alzheimer’s Disease Fact Sheet, National Institutes of Health.

Alzheimer’s

Association

(2022) 2022 Alzheimer’s disease facts and figures. Alzheimers Dement 18, 700–789.

Cummings

, Ritter

, Zhong

(2018) Clinical trials for disease-modifying therapies in Alzheimer’s disease: A primer, lessons learned, and a blueprint for the future. , . J Alzheimers Dis 64, S3–S22.

Cummings

, Morstorf

, Zhong

(2014) Alzheimer’s disease drug-development pipeline: Few candidates, frequent failures. Alzheimers Res Ther 6, 37.

Cummings

, Feldman

, Scheltens

(2019) The “rights” of precision drug development for Alzheimer’s disease. Alzheimers Res Ther 11, 76.

Cummings

, Goldman

, Simmons-Stern

, Ponton

(2022) The costs of developing treatments for Alzheimer’s disease: A retrospective exploration. Alzheimers Dement 18, 469–477.

Marshall

, Amariglio

, Sperling

, Rentz

(2012) Activities of daily living: Where do they fit in the diagnosis of Alzheimer’s disease? Neurodegener Dis Manag 2, 483–491.

Mlinac

, Feng

(2016) Assessment of activities of daily living, self-care, and independence. Arch Clin Neuropsychol 31, 506–516.

McKhann

, Knopman

, Chertkow

, Hyman

, Jack

, Kawas

, Klunk

, Koroshetz

, Manly

, Mayeux

, Mohs

, Morris

, Rossor

, Scheltens

, Carrillo

, Thies

, Weintraub

, Phelps

(2011) The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 263–269.

10.

Albert

, DeKosky

, Dickson

, Dubois

, Feldman

, Fox

, Gamst

, Holtzman

, Jagust

, Petersen

, Snyder

, Carrillo

, Thies

, Phelps

(2011) The diagnosis of mild cognitive impairment due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 270–279.

11.

de Boer

, Hertogh

CMPM

, Dröes

R-M

, Riphagen

, Jonker

, Eefsting

(2007) Suffering from dementia –the patient’s perspective: A review of the literature. Int Psychogeriatr 19, 1021–1039.

12.

Mank

, van Maurik

, Bakker

, van de Glind

EMM

, Jönsson

, Kramberger

, Novak

, Diaz

, Gove

, Scheltens

, van der Flier

, Visser

LNC

(2021) Identifying relevant outcomes in the progression of Alzheimer’s disease; what do patients and care partners want to know about prognosis? Alzheimers Dement (N Y) 7, e12189.

13.

Tochel

, Smith

, Baldwin

, Gustavsson

, Ly

, Bexelius

, Nelson

, Bintener

, Fantoni

, Garre-Olmo

, Janssen

, Jindra

, Jørgensen

, McKeown

, Öztürk

, Ponjoan

, Potashman

, Reed

, Roncancio-Diaz

, Vos

, Sudlow

, theROADMAP consortium (2019) What outcomes are important to patients with mild cognitive impairment or Alzheimer’s disease, their caregivers, and health-care professionals? A systematic review. Alzheimers Dement (Amst) 11, 231–247.

14.

Harrison

, Noel-Storr

, Demeyere

, Reynish

, Quinn

(2016) Outcomes measures in a decade of dementia and mild cognitive impairment trials. Alzheimers Res Ther 8, 48.

15.

Galasko

, Schmitt

, Thomas

, Jin

, Bennett

, Alzheimer’s

Disease Cooperative Study

(2005) Detailed assessment of activities of daily living in moderate to severe Alzheimer’s disease. J Int Neuropsychol Soc 11, 446–453.

16.

Potashman

, Pang

, Tahir

, Shahraz

, Dichter

, Perneczky

, Nolte

(2023) Psychometric properties of the Alzheimer’s Disease Cooperative Study –Activities of Daily Living for Mild Cognitive Impairment (ADCS-MCI-ADL) scale: A analysis of the ADCS ADC-008 trial. BMC Geriatr 23, 124.

17.

Sikkes

SAM

, Knol

, Pijnenburg

YAL

, de Lange-de Klerk

ESM

, Uitdehaag

BMJ

, Scheltens

(2013) Validation of the Amsterdam IADL Questionnaire© a new tool to measure instrumental activities of daily living in dementia. Neuroepidemiology 41, 35–41.

18.

Wessels

, Siemers

, Yu

, Andersen

, Holdridge

, Sims

, Sundell

, Stern

, Rentz

, Dubois

, Jones

, Cummings

, Aisen

(2015) A combined measure of cognition and function for clinical trials: The Integrated Alzheimer’s Disease Rating Scale (iADRS). J Prev Alzheimers Dis 2, 227–241.

19.

Alzheimer’s Association (2020) Alzheimer’s Disease Caregivers: Fact Sheet.

20.

Mohamed

, Rosenheck

, Lyketsos

, Schneider

(2010) Caregiver burden in Alzheimer disease: Cross-sectional and longitudinal patient correlates. Am J Geriatr Psychiatry 18, 917–927.

21.

Sikkes

, Rotrou

J de

(2014) A qualitative review of instrumental activities of daily living in dementia: What’s cooking? Neurodegener Dis Manag 4, 393–400.

22.

Owens

, Hinds

, Manyakov

, Stavropoulos

, Lavelle

, Gove

, Diaz-Ponce

, Aarsland

(2020) Selecting remote measurement technologies to optimize assessment of function in early Alzheimer’s disease: A case study. Front Psychiatry 11, 582207.

23.

Cummings

, Zhou

, Lee

, Zhong

, Fonseca

, Cheng

(2023) Alzheimer’s disease drug development pipeline: 2023. Alzheimers Dement (N Y) 9, e12385.

24.

Justice

, Cesari

, Seals

, Shively

, Carter

(2016) Comparative approaches to understanding the relation between aging and physical function. J Gerontol A Biol Sci Med Sci 71, 1243–1253.

25.

Dubbelman

, Jutten

, Tomaszewski Farias

, Amariglio

, Buckley

, Visser

, Rentz

, Johnson

, Properzi

, Schultz

, Donovan

, Gatchell

, Teunissen

, Van Berckel

BNM

, Van der Flier

, Sperling

, Papp

, Scheltens

, Marshall

, Sikkes

SAM

, Alzheimer Disease Neuroimaging Initiative, National Alzheimer’s Coordinating Center, the Harvard Aging Brain Study, the Alzheimer Dementia Cohort (2020) Decline in cognitively complex everyday activities accelerates along the Alzheimer’s disease continuum. Alzheimers Res Ther 12, 138.

26.

Bruderer-Hofstetter

, Gorus

, Cornelis

, Meichtry

, De Vriendt

(2022) Influencing factors on instrumental activities of daily living functioning in people with mild cognitive disorder –a secondary investigation of cross-sectional data. BMC Geriatr 22, 791.

27.

Wang

DXM

, Yao

, Zirek

, Reijnierse

, Maier

(2020) Muscle mass, strength, and physical performance predicting activities of daily living: A meta-analysis. J Cachexia Sarcopenia Muscle 11, 3–25.

28.

Alexander

, Mollo

, Giordani

, Ashton-Miller

, Schultz

, Grunawalt

, Foster

(1995) Maintenance of balance, gait patterns, and obstacle clearance in Alzheimer’s disease. Neurology 45, 908–914.

29.

VISSER

(1983) Gait and balance in senile dementia of Alzheimer’s type. Age Ageing 12, 296–301.

30.

Pau

, Mulas

, Putzu

, Asoni

, Viale

, Mameli

, Leban

, Allali

(2020) Smoothness of gait in healthy and cognitively impaired individuals: A study on italian elderly using wearable inertial sensor. Sensors 20, E3577.

31.

Kuan

Y-C

, Huang

L-K

, Wang

Y-H

, Hu

C-J

, Tseng

I-J

, Chen

H-C

, Lin

L-F

(2021) Balance and gait performance in older adults with early-stage cognitive impairment. Eur J Phys Rehabil Med 57, 560–567.

32.

Verghese

, Robbins

, Holtzer

, Zimmerman

, Wang

, Xue

, Lipton

(2008) Gait dysfunction in mild cognitive impairment syndromes. J Am Geriatr Soc 56, 1244–1251.

33.

Bahureksa

, Najafi

, Saleh

, Sabbagh

, Coon

, Mohler

, Schwenk

(2017) The impact of mild cognitive impairment on gait and balance: A systematic review and meta-analysis of studies using instrumented assessment. Gerontology 63, 67–83.

34.

Hausdorff

, Hillel

, Shustak

, Del Din

, Bekkers

EMJ

, Pelosin

, Nieuwhof

, Rochester

, Mirelman

(2018) Everyday stepping quantity and quality among older adult fallers with and without mild cognitive impairment: Initial evidence for new motor markers of cognitive deficits? . J Gerontol A Biol Sci Med Sci 73, 1078–1082.

35.

König

, Klaming

, Pijl

, Demeurraux

, David

, Robert

(2017) Objective measurement of gait parameters in healthy and cognitively impaired elderly using the dual-task paradigm. Aging Clin Exp Res 29, 1181–1189.

36.

Gillain

, Dramé

, Lekeu

, Wojtasik

, Ricour

, Croisier

J-L

, Salmon

, Petermans

(2016) Gait speed or gait variability, which one to use as a marker of risk to develop Alzheimer disease? A pilot study. Aging Clin Exp Res 28, 249–255.

37.

Aggarwal

, Wilson

, Beck

, Bienias

, Bennett

(2006) Motor dysfunction in mild cognitive impairment and the risk of incident Alzheimer disease. Arch Neurol 63, 1763–1769.

38.

Bardenheier

, Resnik

, Jutkowitz

, Gravenstein

(2022) Patterns of limitation in physical function in late midlife associated with late-onset Alzheimer’s disease and related dementias: A cluster analysis. J Alzheimers Dis 89, 1331–1338.

39.

Wang

, Larson

, Bowen

, van Belle

(2006) Performance-based physical function and future dementia in older people. Arch Intern Med 166, 1115–1120.

40.

Swaddiwudhipong

, Whiteside

, Hezemans

, Street

, Rowe

, Rittman

(2023) Pre-diagnostic cognitive and functional impairment in multiple sporadic neurodegenerative diseases. Alzheimers Dement 19, 1752–1763.

41.

Hillel

, Gazit

, Nieuwboer

, Avanzino

, Rochester

, Cereatti

, Croce

, Rikkert

, Bloem

, Pelosin

, Del Din

, Ginis

, Giladi

, Mirelman

, Hausdorff

(2019) Is every-day walking in older adults more analogous to dual-task walking or to usual walking? Elucidating the gaps between gait performance in the lab and during 24/7 monitoring. Eur Rev Aging Phys Act 16, 6.

42.

Takayanagi

, Sudo

, Yamashiro

, Lee

, Kobayashi

, Niki

, Shimada

(2019) Relationship between daily and in-laboratory gait speed among healthy community-dwelling older adults. Sci Rep 9, 3496.

43.

Mueller

, Hoefling

, Muaremi

, Praestgaard

, Walsh

, Bunte

, Huber

, Fürmetz

, Keppler

, Schieker

, Böcker

, Roubenoff

, Brachat

, Rooks

, Clay

(2019) Continuous digital monitoring of walking speed in frail elderly patients: Noninterventional validation study and longitudinal clinical trial. JMIR MHealth UHealth 7, e15191.

44.

Coravos

, Goldsack

, Karlin

, Nebeker

, Perakslis

, Zimmerman

, Erb

(2019) Digital medicine: A primer on measurement. Digit Biomark 3, 31–71.

45.

Stehlik

, Schmalfuss

, Bozkurt

, Nativi-Nicolau

, Wohlfahrt

, Wegerich

, Rose

, Ray

, Schofield

, Deswal

, Sekaric

, Anand

, Richards

, Hanson

, Pipke

, Pham

(2020) Continuous wearable monitoring analytics predict heart failure hospitalization. Circ Heart Fail 13, e006513.

46.

Marra

, Chen

, Coravos

, Stern

(2020) Quantifying the use of connected digital products in clinical research. NPJ Digit Med 3, 50.

47.

Dockendorf

, Hansen

, Bateman

, Moyer

, Shah

, Shipley

(2021) Digitally enabled, patient-centric clinical trials: Shifting the drug development paradigm. Clin Transl Sci 14, 445–459.

48.

Gold

, Amatniek

, Carrillo

, Cedarbaum

, Hendrix

, Miller

, Robillard

, Rice

, Soares

, Tome

, Tarnanas

, Vargas

, Bain

, Czaja

(2018) Digital technologies as biomarkers, clinical outcomes assessment, and recruitment tools in Alzheimer’s disease clinical trials. Alzheimers Dement (N Y) 4, 234–242.

49.

Doraiswamy

, Narayan

, Manji

(2018) Mobile and pervasive computing technologies and the future of Alzheimer’s clinical trials. NPJ Digit Med 1, 1.

50.

Leurent

, Ehlers

(2015) Digital technologies for cognitive assessment to accelerate drug development in Alzheimer’s disease. Clin Pharmacol Ther 98, 475–476.

51.

Mc Carthy

, Schueler

(2019) Editorial: Can digital technology advance the development of treatments for Alzheimer’s disease? J Prev Alzheimers Dis 6, 217–220.

52.

Reinertsen

, Clifford

(2018) A review of physiological and behavioral monitoring with digital sensors for neuropsychiatric illnesses. Physiol Meas 39, 05TR01.

53.

C-Y

, Beattie

, Mattek

, Sharma

, Kaye

, Dodge

(2021) Reproducibility and replicability of high-frequency, in-home digital biomarkers in reducing sample sizes for clinical trials. Alzheimers Dement (N Y) 7, e12220.

54.

Manley

, Bayen

, Braley

, Merrilees

, Clark

, Zylstra

, Schaffer

, Bayen

, Possin

, Miller

, Schenk

, Bonasera

(2020) Long-term digital device-enabled monitoring of functional status: Implications for management of persons with Alzheimer’s disease. Alzheimers Dement (N Y) 6, e12017.

55.

Bellerophon Announces FDA Acceptance of Change to Ongoing Phase 3 REBUILD Study of INOpulse® for Treatment of Fibrotic Interstitial Lung Disease, Bellerophon Therapeutics, Inc.

56.

Xie

, Wang

, Tao

, Huang

, Zhang

, Lv

(2019) Wearable sensor-based daily life walking assessment of gait for distinguishing individuals with amnestic mild cognitive impairment. Front Aging Neurosci 11, 285.

57.

Taylor

, Brodie

, van Schooten

, Delbaere

, Close

JCT

, Payne

, Webster

, Chow

, McInerney

, Kurrle

, Lord

(2019) Older people with dementia have reduced daily-life activity and impaired daily-life gait when compared to age-sex matched controls. J Alzheimers Dis 71, S125–S135.

58.

Varma

, Ghosal

, Hillel

, Volfson

, Weiss

, Urbanek

, Hausdorff

, Zipunnikov

, Watts

(2021) Continuous gait monitoring discriminates community-dwelling mild Alzheimer’s disease from cognitively normal controls. Alzheimers Dement (N Y) 7, e12131.

59.

Miyazaki

, Kiyama

, Nakai

, Kawada

, Takeshita

, Araki

, Makizako

(2021) Relationships between gait regularity and cognitive function, including cognitive domains and mild cognitive impairment, in community-dwelling older people. Healthcare (Basel) 9, 1571.

60.

Kourtis

, Regele

, Wright

, Jones

(2019) Digital biomarkers for Alzheimer’s disease: The mobile/ wearable devices opportunity. NPJ Digit Med 2, 9.

61.

Yoneyama

, Mitoma

, Sanjo

, Higuma

, Terashi

, Yokota

(2016) Ambulatory gait behavior in patients with dementia: A comparison with Parkinson’s disease. IEEE Trans Neural Syst Rehabil Eng 24, 817–826.

62.

, Harris

, Shiroma

, Leung

, Kwok

(2018) Patterns of physical activity and sedentary behavior for older adults with Alzheimer’s disease, mild cognitive impairment, and cognitively normal in Hong Kong. J Alzheimers Dis 66, 1453–1462.

63.

Rackoll

, Neumann

, Passmann

, Grittner

, Külzow

, Ladenbauer

, Flöel

(2021) Applying time series analyses on continuous accelerometry data-A clinical example in older adults with and without cognitive impairment. PloS One 16, e0251544.

64.

Del Pozo Cruz

, Del Pozo-Cruz

(2021) Associations between activity fragmentation and subjective memory complaints in middle-aged and older adults. Exp Gerontol 148, 111288.

65.

Mc Ardle

, Del Din

, Donaghy

, Galna

, Thomas

, Rochester

(2020) Factors that influence habitual activity in mild cognitive impairment and dementia. Gerontology 66, 197–208.

66.

Liddle

, Ireland

, Krysinska

, Harrison

, Lamont

, Karunanithi

, Kang

, Reppermund

, Sachdev

, Gustafsson

, Brauer

, Pachana

, Brodaty

(2021) Lifespace metrics of older adults with mild cognitive impairment and dementia recorded via geolocation data. Australas J Ageing 40, e341–e346.

67.

Tung

, Rose

, Gammada

, Lam

, Roy

, Black

, Poupart

(2014) Measuring life space in older adults with mild-to-moderate Alzheimer’s disease using mobile phone GPS. Gerontology 60, 154–162.

68.

Beattie

, Miller

, Almirola

, Au-Yeung

W-TM

, Bernard

, Cosgrove

, Dodge

, Gamboa

, Golonka

, Gothard

, Harbison

, Irish

, Kornfeld

, Lee

, Marcoe

, Mattek

, Quinn

, Reynolds

, Riley

, Rodrigues

, Sharma

, Siqueland

, Thomas

, Truty

, Wall

, Wild

, Wu

C-Y

, Karlawish

, Silverberg

, Barnes

, Czaja

, Silbert

, Kaye

(2020) The Collaborative Aging Research Using Technology Initiative: An open, sharable, technology-agnostic platform for the research community. Digit Biomark 4, 100–118.

69.

Kaye

, Reynolds

, Bowman

, Sharma

, Riley

, Golonka

, Lee

, Quinn

, Beattie

, Austin

, Seelye

, Wild

, Mattek

(2018) Methodology for establishing a community-wide life laboratory for capturing unobtrusive and continuous remote activity and health data. J Vis Exp e56942.

70.

Au-Yeung

W-TM

, Miller

, Steele

, Mattek

, Beattie

, Silbert

, Kaye

(2022) Predicting major decline in older adults: The role of time spent out of home. Alzheimers Dement 18, e066879.

71.

Akl

, Chikhaoui

, Mattek

, Kaye

, Austin

, Mihailidis

(2016) Clustering home activity distributions for automatic detection of mild cognitive impairment in older adults. J Ambient Intell Smart Environ 8, 437–451.

72.

AbbVie (2022) A Randomized, Double-Blind, Double Dummy, Active-Controlled Study Comparing the Efficacy, Safety and Tolerability of ABBV-951 to Oral Carbidopa/Levodopa in Advanced Parkinson’s Disease Patients, clinicaltrials.gov.

73.

Pfizer (2020) A phase 3 randomized, double-blind, active controlled, multicenter study of the long-term safety and efficacy of subcutaneous administration of tanezumab in subjects with osteoarthritis of the hip or knee, clinicaltrials.gov.

74.

Harden

(2015) Milnacipran for the pain, sensory sensitization and mood changes in knee osteoarthritis, clinicaltrials.gov.

75.

Takeda (2019) The exploratory study to investigate the effect of ramelteon for insomnia patients with major depressive disorder by using actigraphy, clinicaltrials.gov.

76.

Servais

, Camino

, Clement

, McDonald

, Lukawy

, Lowes

, Eggenspieler

, Cerreta

, Strijbos

(2021) First regulatory qualification of a novel digital endpoint in duchenne muscular dystrophy: A multi-stakeholder perspective on the impact for patients and for drug development in neuromuscular diseases. Digit Biomark 5, 183–190.

77.

Mikolaizak

, Rochester

, Maetzler

, Sharrack

, Demeyer

, Mazzà

, Caulfield

, Garcia-Aymerich

, Vereijken

, Arnera

, Miller

, Piraino

, Ammour

, Gordon

, Troosters

, Yarnall

, Alcock

, Gaßner

, Winkler

, Klucken

, Schlenstedt

, Watz

, Kirsten

A-M

, Vogiatzis

, Chynkiamis

, Hume

, Megaritis

, Nieuwboer

, Ginis

, Buckley

, Brittain

, Comi

, Leocani

, Helbostad

, Johnsen

, Taraldsen

, Blain

, Driss

, Frei

, Puhan

, Polhemus

, Basea de

, Gimeno

, Hopkinson

, Buttery

, Hausdorff

, Mirelman

, Evers

, Neatrour

, Singleton

, Schwickert

, Becker

, Jansen

C-P

, clinical validation study (WP4) on behalf of Mobilise-D consortium (2022) Connecting real-world digital mobility assessment to clinical outcomes for regulatory and clinical endorsement–the Mobilise-D study protocol. PLoS One 17, e0269615.

78.

Rochester

, Mazzà

, Mueller

, Caulfield

, McCarthy

, Becker

, Miller

, Piraino

, Viceconti

, Dartee

, Garcia-Aymerich

, Aydemir

, Vereijken

, Arnera

, Ammour

, Jackson

, Hache

, Roubenoff

(2020) A roadmap to inform development, validation and approval of digital mobility outcomes: The Mobilise-D Approach. Digit Biomark 4, 13–27.

79.

Cloutier

, Chertkow

, Kergoat

M-J

, Gélinas

, Gauthier

, Belleville

(2021) Trajectories of decline on instrumental activities of daily living prior to dementia in persons with mild cognitive impairment. Int J Geriatr Psychiatry 36, 314–323.

80.

Edgar

, Vradenburg

, Hassenstab

(2019) The 2018 revised FDA guidance for early Alzheimer’s disease: Establishing the meaningfulness of treatment effects. J Prev Alzheimers Dis 6, 223–227.

81.

Food and Drug Administration (2018) Early Alzheimer’s disease: Developing drugs for treatment guidance for industry.

82.

Jutten

, Harrison

, Lee Meeuw Kjoe

, Ingala

, Vreeswijk

, van Deelen

RAJ

, de Jong

, Opmeer

, Aleman

, Ritchie

, Scheltens

, Sikkes

SAM

(2019) Assessing cognition and daily function in early dementia using the cognitive-functional composite: Findings from the Catch-Cog study cohort. Alzheimers Res Ther 11, 45.

83.

Coravos

, Doerr

, Goldsack

, Manta

, Shervey

, Woods

, Wood

(2020) Modernizing and designing evaluation frameworks for connected sensor technologies in medicine. NPJ Digit Med 3, 37.

84.

Goldsack

, Coravos

, Bakker

, Bent

, Dowling

, Fitzer-Attas

, Godfrey

, Godino

, Gujar

, Izmailova

, Manta

, Peterson

, Vandendriessche

, Wood

, Wang

, Dunn

(2020) Verification, analytical validation, and clinical validation (V3): The foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs). NPJ Digit Med 3, 55.

85.

Izmailova

, Wagner

, Ammour

, Amondikar

, Bell-Vlasov

, Berman

, Bloomfield

, Brady

, Cai

, Calle

, Campbell

, Cerreta

, Clay

, Foschini

, Furlong

, Goldel

, Goldsack

, Groenen

PMA

, Folarin

, Heemskerk

, Honig

, Hotopf

, Kamphaus

, Karlin

, Leptak

, Liu

, Manji

, Mather

, Menetski

, Narayan

, Papadopoulos

, Patel

, Patrick-Lake

, Podichetty

, Pratap

, Servais

, Stephenson

, Tenaerts

, Tromberg

, Usdin

, Vasudevan

, Zipunnikov

, Hoffmann

(2021) Remote digital monitoring for medical product development. Clin Transl Sci 14, 94–101.

86.

Center for Drug Evaluation and Research (2020) FDA patient-focused drug development guidance series for enhancing the incorporation of the patient’s voice in medical product development and regulatory decision making. FDA.

87.

Perfetto

, Burke

, Oehrlein

, Epstein

(2015) Patient-focused drug development: A new direction for collaboration. Med Care 53, 9–17.

88.

Manta

, Patrick-Lake

, Goldsack

(2020) Digital measures that matter to patients: A framework to guide the selection and development of digital measures of health. Digit Biomark 4, 69–77.

89.

DiBenedetti

, Slota

, Wronski

, Vradenburg

, Comer

, Callahan

, Winfield

, Rubino

, Krasa

, Hartry

, Wieberg

, Kremer

, Lappin

, Martin

, Frangiosa

, Biggar

, Hauber

(2020) Assessing what matters most to patients with or at risk for Alzheimer’s and care partners: A qualitative study evaluating symptoms, impacts, and outcomes. Alzheimers Res Ther 12, 90.

90.

Jessen

, Georges

, Wortmann

, Benham-Hermetz

(2022) What matters to patients with Alzheimer’s disease and their care partners? Implications for understanding the value of future interventions. J Prev Alzheimers Dis 9, 550–555.

91.

Saunders

, Sheehan

, Muniz-Terrera

, Luz

, Ritchie

(2022) Impact of clinical symptoms and diagnosis: The electronic Person-Specific Outcome Measure (ePSOM) development programme. J Patient-Rep Outcomes 6, 33.

92.

Watson

, Saunders

, Muniz Terrer

, Ritchie

, Evans

, Luz

, Clarke

(2019) What matters to people with memory problems, healthy volunteers and health and social care professionals in the context of developing treatment to prevent Alzheimer’s dementia? A qualitative study. Health Expect 22, 504–517.

93.

Coyne

, Wyrwich

(2015) ISPOR task force for clinical outcomes assessment: Clinical outcome assessments: Conceptual foundation—report of the ISPOR clinical outcomes assessment –emerging good practices for outcomes research task force. Value Health 18, 739–740.

94.

Center for Drug Evaluation and Research (2020) Patient-Focused Drug Development Glossary. FDA.

95.

FDA-NIH BiomarkerWorking (2016) BEST (Biomarkers, Endpoints, and other Tools) Resources [Internet], Food and Drug Administration (US), Silver Spring (MD).

96.

Cyarto

, Myers

, Tudor-Locke

(2004) Pedometer accuracy in nursing home and community-dwelling older adults. Med Sci Sports Exerc 36, 205–209.

97.

Goldsack

, Dowling

, Samuelson

, Patrick-Lake

, Clay

(2021) Evaluation, acceptance, and qualification of digital measures: From proof of concept to endpoint. Digit Biomark 5, 53–64.

98.

Chen

J-H

, Lauderdale

(2019) Cognitive function, consent for participation, and compliance with wearable device protocols in older adults. J Gerontol Ser A 74, 269–273.

99.

Kamil

, Bakar

, Ehrenburg

, Wei

, Pletnikova

, Xiao

, Oh

, Mancini

, Agrawal

(2021) Detection of wandering behaviors using a body-worn inertial sensor in patients with cognitive impairment: A feasibility study. Front Neurol 12, 529661.

100.

Saif

, Yan

, Niotis

, Scheyer

, Rahman

, Berkowitz

, Krikorian

, Hristov

, Sadek

, Bellara

, Isaacson

(2020) Feasibility of using a wearable biosensor device in patients at risk for Alzheimer’s disease dementia. J Prev Alzheimers Dis 7, 104–111.

101.

Stavropoulos

, Lazarou

, Diaz

, Gove

, Georges

, Manyakov

, Pich

, Hinds

, Tsolaki

, Nikolopoulos

, Kompatsiaris

, The

RADAR-AD Consortium

(2021) Wearable devices for assessing function in Alzheimer’s disease: A European public involvement activity about the features and preferences of patients and caregivers. Front Aging Neurosci 13, 643135.

102.

Zylstra

, Netscher

, Jacquemot

, Schaffer

, Shen

, Bowhay

, Braley

, Possin

, Miller

, Bayen

, Bonasera

, Schenk

(2018) Extended, continuous measures of functional status in community dwelling persons with Alzheimer’s and related dementia: Infrastructure, performance, tradeoffs, preliminary data, and promise. J Neurosci Methods 300, 59–67.

103.

Mc Ardle

, Morris

, Hickey

, Del Din

, Koychev

, Gunn

, Lawson

, Zamboni

, Ridha

, Sahakian

, Rowe

, Thomas

, Zetterberg

, MacKay

, Lovestone

, Rochesteron

, Deep

and Frequent Phenotyping study team (

(2018) Gait in mild Alzheimer’s disease: Feasibility of multi-center measurement in the clinic and home with body-worn sensors: A pilot study. J Alzheimers Dis 63, 331–341.

104.

van der Wardt

, Hancox

, Burgon

, Bajwa

, Goldberg

, Harwood

(2021) Measuring physical activity levels in people with mild cognitive impairment or mild dementia. J Aging Phys Act 29, 10–16.