What Engineering Technology Could Do for Quality of Life in Parkinson’s Disease: a Review of...

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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI10.1109/JBHI.2015.2464354, IEEE Journal of Biomedical and Health Informatics

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Abstract—Parkinson’s Disease (PD) involves well known

motor symptoms such as tremor, rigidity, bradykinesia, and

altered gait but there are also non-locomotory motor symptoms

(e.g., changes in handwriting and speech) and even non-motor

symptoms (e.g., disrupted sleep, depression) that can be

measured, monitored, and possibly better managed through

activity based monitoring technologies. This will enhance quality

of life (QoL) in PD through improved self-monitoring, and also

provide information which could be shared with a health care

provider to help better manage treatment. Until recently, non-

motor symptoms (“soft signs”) had been generally overlooked in

clinical management yet these are of primary importance to

patients and their QoL. Day-to-day variability of the condition,

the high variability in symptoms between patients, and the

isolated snapshots of a patient in periodic clinic visits makes

better monitoring essential to the proper management of PD.

Continuously monitored patterns of activity, social interactions,

and daily activities could provide a rich source of information on

status changes, guiding self correction and clinical management.

The same tools can be useful in earlier detection of PD and will

improve clinical studies. Remote medical communications in the

form of telemedicine, sophisticated tracking of medication use,

and assistive technologies that directly compensate for disease

related challenges are examples of other near term technology

solutions to PD problems. Ultimately, a sensor technology is no

good if it is not used. The Parkinson’s community is a

sophisticated early adopter of useful technologies and a group for

which engineers can provide near term gratifying benefits.

Index Terms—review article, activity, nonmotor symptoms,

cognition, depression, sleep quality, exercise, patient perspective

I. INTRODUCTION

N the first World War, Winston Churchill described the gap

between inventors and potential beneficiaries: A hiatus

exists between the inventor who knows what they could invent,

if they only knew what was wanted, and the soldiers who

knew, or ought to know, what they want and would ask for it if

they only knew how much science could do for them [1].

J.A. Stamford is a neuroscientist with Parkinson’s and a co-founder of

Parkinson’s Movement. (e-mail: [email protected]). P.N. Schmidt is a bioengineer and the CIO and Vice President for Research

at the National Parkinson’s Foundation (e-mail: [email protected]).

K.E. Friedl is an endocrine physiologist, currently supported by the ORISE Knowledge Preservation Program for the US Army Research Institute of

Environmental Medicine and a Professor (adjunct) of Neurology at the

University of California, San Francisco (e-mail: [email protected]).

Parkinson’s Disease (PD) has attracted numerous

engineering innovations, especially around classical signals

associated with movement disorders but patients are

concerned about other symptoms that affect their quality of

life. This paper describes problems of people with PD and

technological solutions that might help improve quality of life.

Parkinson’s Disease (PD) is a neurological condition that

has been traditionally characterized by motor symptoms such

as tremor, rigidity, slowed movement (bradykinesia), and

altered gait. Research starting in the 1950’s identified the

association between specific brain regions and motor function

resulting in the characterization of PD as a disease of

dopamine [2]. Dopamine replacement therapies have

revolutionized outcomes of motor function and pre-clinical

research is currently largely focused on characterizing and

targeting effects that go beyond the dopamine system,

including a panoply of motor and non-motor symptoms with

no specific symptom or domain obviously dominating the

patient experience. Better management of many non-motor

symptoms awaits success of these pre-clinical efforts [3-5].

With this as context for the status of Parkinson’s

management, monitoring technologies for PD patients offer

the potential to increase quality of life through objective,

continuous, and on demand self-monitoring. These

technologies also provide important information which could

be shared with a health care provider to help better manage

treatment [6]. Parkinson’s care is typically informed by

history taking based on patient recall, observation of the

patient for the duration of the encounter, and follow-up

intervals timed to sample clinically relevant change.

However, many aspects of the disease fluctuate broadly across

the frequency space, from gait asymmetry (seconds) to

medication pharmacokinetic cycles (hours) to days-to-months

cycles of fatigue, constipation, psychosis, and depression [7].

The high variability in symptoms between patients, and the

isolated snapshots of a patient in periodic clinic visits makes

better monitoring essential to the proper management of PD.

Technologies such as remote medical communications in the

form of telemedicine [8,9] and assistive technologies such as

sophisticated tracking of medication use can also provide near

term solutions to PD problems.

This review outlines some of the current advances in non-

locomotory motion-based technologies and explores additional

needs and opportunities in PD.

What Engineering Technology Could Do for

Quality of Life in Parkinson’s Disease: a

Review of Current Needs and Opportunities

Jonathan A. Stamford, PhD, DSc, Peter N. Schmidt, PhD, and Karl E. Friedl, PhD, Fellow, AIMBE

I



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II. QUALITY OF LIFE PROBLEMS IN PARKINSON’S DISEASE

Parkinson's, in common with many other long-term

disabling conditions, presents a number of general and specific

challenges to the quality of life of patients. These challenges

inevitably have spawned many survey instruments which

purport to quantify and numericise health-related quality of

life in Parkinson's. While many are validated research

instruments, all are inevitably reductionist in nature. Some are

overtly generic and broadly applicable. Others such as the 39

item Parkinson’s Disease Questionnaire (PDQ-39) and the

Parkinson’s Disease Quality of Life Questionnaire (PDQLQ)

are designed specifically for Parkinson's [10].

Standardized instruments all have flaws. Firstly, the

scoring and assessment is often conducted by healthcare

professionals rather than the patients themselves. This induces

an inevitable, if rather comical, disconnect between the patient

and their "quality of life". One might reasonably assume that

the patient was better placed than the physician to pass

judgment on the patient's quality of life.

Secondly, and integral to their need to standardize, the

instruments work best at a population rather than individual

level. The capacity to generalize almost inevitably nullifies the

scope to personalize. At a population level, quality of life

instruments speak reasonably well of the population.

However, weightings given to specific domains may help

validate instruments at the population level but serve poorly as

a "one size fits all" approach to individual quality of life [11].

Thirdly, the distinction between quality of life and health-

related quality of life is a largely clinical construct. Many, if

not most patients fail to recognize the distinction. This is

particularly true for Parkinson's where the treatment drugs can

produce strange alterations in impulse control such as punding

behaviors. For instance, financial difficulties have enormous

bearing on quality of life but, at first sight, are not health-

related. Consider however that these financial difficulties

might arise from Internet gambling due to inappropriately high

doses of prescribed dopamine agonist [12]. Immediately the

perspective is different and this is now a health-related quality

of life issue. Some health quality of life instruments attempt

to identify these related factors.

A. Importance of “soft signs” to PD patients

Non-motor symptoms of PD were largely ignored by

clinicians as recently as during the development of the original

Unified Parkinson’s Disease Rating Scale (UPDRS) rating

scale [13]. Approximately contemporaneously with the

development of the UPDRS, a systematic review of key

disabling symptoms identified by patients resulted in a

questionnaire including indicators of clinical issues beyond

motor symptoms such as speech, depression, anxiety,

psychosis, sleep quality and daytime sleepiness, cognitive

impairment and pain on the PDQ-39 [14]. The patients

surveyed also cited the impact of the disease on social

functioning, citing the importance of the impact of PD on their

ability to function in public, the impact on relationships,

ability to perform eating tasks and dressing, hygiene tasks,

handwriting, and leisure activities. The final PDQ-39

questionnaire was psychometrically optimized to ensure that

each of these domains was statistically independent.

The social functioning domains are clearly important to

patient health-related quality of life however they are difficult

to assess during a clinical interview, where clinicians may

question the patients about difficulty eating rather than

concerns about eating in public and patients may consider

such issues not relevant as clinical concerns. Pain in PD is not

uncommon but rarely identified. In a study of patients

transitioning from a movement disorders clinic to a palliative

care program, investigators found an improvement in Health

Related Quality of Life (HRQL) largely driven by a reduction

in pain [15].

With the diagnoses for many patients in their early 50s [16],

a majority of patients are diagnosed while still at an age where

they expect to be active in the workforce. While motor

impairment is cited in disability claims, for many patients non-

motor symptoms such as daytime sleepiness are cited as the

primary cause of disability [17].

There is good evidence that patient priorities in

symptomatology do not consistently match those of the

treating physician's. Patients often put greater emphasis on

"soft signs" rather than the more readily quantifiable and overt

symptomatology [7]. For instance, in a survey conducted by

Parkinson's Movement (http://parkinsonsmovement.com),

there was little correlation between patient-reported quality of

life and motor symptoms, suggesting that motor symptoms,

the most visible to a physician, are an inadequate measure

upon which to base treatment decisions (figure 1).

FIGURE 1. Motor Symptoms and Patient Quality of Life.

Quality of life in chronic conditions correlates well with the

prevalence of mood disorders [18]. Anxiety and depression are

strong negative predictors of quality of life. Data from

Parkinson's Movement shows that there is an increase in

multiply comorbid mood disorders (figure 2). These survey

data report higher prevalence of mood disorders than

previously recognized. In part this may be due to patients

being more comfortable reporting symptoms to other patients

(in the form of the Parkinson's Movement organization) than

to their treating physicians.



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FIGURE 2. Mood Disorders Reported in Association with Parkinson’s.

This is only an approximate pictorial representation (see text for details).

Patients often underreport symptoms, especially those of an

embarrassing or stigmatizing nature, such as problems with

bladder control. Patients are also squeamish about reporting

mood disorders for fear that this may have stigmatizing

psychiatric implications. This is particularly true for

hallucinations, which are considerably underreported. In the

UK, patients who experience hallucinations may have their

driving license withdrawn, an action which itself has strong

negative implications for quality of life.

Although patients, as a group, tend to prioritize non-motor

symptoms over motor symptoms, the relative importance of

each symptom differs. Patients are as different in their

expression of non-symptoms as they are in their relative

predominance of tremor, rigidity and bradykinesia. Patients

with dystonia for instance will report pain as a major

determinant of quality of life, while those experiencing

chronic fatigue may talk more about sleep and sleep patterns.

These differences are difficult to capture in any single measure

or assessment and this emphasizes the importance of

personalization.

B. Access to care and the value of “virtual house calls”

The major problem with most current forms of clinician-

based assessment is the need for the patient to be physically in

the presence of the physician. For many mobility-impaired

patients this presents significant logistic challenges. Stressful

journeys to the clinic can make patients seem symptomatically

worse than they really are. This is particularly so for confused

and demented patients. Conversely, many patients alter the

timing of their medication or take additional tablets in order to

ensure that they arrive in good shape [19]. But whether the

patients seem worse or better, the issue is accuracy. In a brief

consultation under what are highly artificial circumstances, the

physician is likely to be misled and may consequently make

inappropriate prescribing decisions. To get an accurate

assessment of patients' health, it is best to make these

assessments in the patient's home. Clearly this is incompatible

with most modern medical resource allocation but it can be

elegantly addressed using simple telemedicine [20-22]. Skype

or other forms of audiovisual telecommunication can be

readily used to allow patients in their home to communicate

with physicians in their offices or clinics [22]. The vast

majority of the standard UPDRS assessments can be

conducted under such circumstances [23]. There are

substantial benefits in terms of time, but the real benefit lies in

the quality of information obtained from the patient in their

natural surroundings rather than the artificial environment of

the clinic.

The impact on patients and caregivers of the time and

expense of accessing care is also an important consideration.

In developing a telemonitoring system for depressed patients,

investigators realized that a 15-minute clinical assessment

required patients to dedicate a half day to getting from home

or work to the clinic, parking, walking to the clinic, signing in,

waiting to be seen, and then reversing the steps after the

encounter [24]. Programs offering telemedicine to the home

for PD care have shown the dramatic reduction in travel and

waiting times that can be achieved through telemedicine,

reflecting the longer average distances separating patients

from the scarce clusters of subspecialist neurologists [9].

Such “virtual house calls” are being evaluated in a randomized

controlled trial with one of us (PS) a co-investigator [8].

There are known risks as well as benefits to this approach that

must also be addressed, especially patient privacy and control,

data security, and remote provider training and credentials.

C. Patient Engagement

Patient use of technology and patient engagement in their

health status are implicitly interlinked. As a rule, the more

engaged a patient is with their condition, the more likely they

are to seek technological assistance. The converse however is

also true. Technological solutions are unlikely to appeal to the

disengaged and there is also a technological threshold in the

usage. Confused and demented patients, as well as those

struggling to come to terms with the reality of their illness, are

poor adopters of technology no matter how well meant. This

is reflected to some extent in the poor involvement of the

Parkinson's community in clinical trials [25].

Nonetheless, the diversity of Parkinson's symptoms and the

chronicity of the illness are, at once, a challenge to app

developers and technologists and a rich field of opportunity.

Necessity is the mother of invention and there seems little

doubt that the best solutions will be found when patients’

necessity is the driver for that burst of inventive flair.

Patients want technology that simplifies their lives rather

than complicates. An example of this might be the Lift Labs

spoon [26], designed with a simple servo mechanism that

counteracts the patient's tremor and allows the patient to enjoy

less messy meal times. This is an example of the technology

making life simpler but also addressing a real patient driven

need rather than a technologically driven solution.

Patients also want technology that is passive in the sense of

recording data without having to take active tests or decisions.

Clearly some measures are more amenable to this than others.

For instance, tremor can be monitored passively and

continuously without user intervention. Cognitive function on

ALL 3 SYMPTOMS

10% OF RESPONDENTS

ALL 3 SYMPTOMS

30% OF RESPONDENTS

ANXIETY

DEPRESSION

ANXIETY

DEPRESSION

OBSESSIONALTHOUGHTSOBSESSIONAL

THOUGHTS

ANXIETY &DEPRESSION26%

ANXIETY &DEPRESSION27%

BEFORE PARKINSON’S SINCE PARKINSON’S

NONE OF THESE 3 PROBLEMS: 36%NONE OF THESE 3 PROBLEMS: 13%



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the other hand requires explicit testing and is therefore much

more temporally invasive and intrusive. Of necessity, such

measurements are also discrete rather than continuous.

It is also worth asking the question, Who does the

technology serve? Holter electrocardiographic monitoring

provides data to the physician. A personal fitness monitor

provides data primarily to the user. Patients have an entirely

different relationship with data they ‘own.’

D. Three important QoL problems that could be addressed by

technology

In practice, there are broadly speaking three areas where

technology either could or already does provide assistance.

These are medication monitoring, symptom logging and

cognitive assessment.

Medication monitoring is an area where technology already

provides simple solutions. Parkinson's, more than almost any

other condition, calls for precise timing of medication. There

are several apps available where medication reminders are

provided at user-set times and intervals. Many are generic,

equally usable for any chronic medication condition. Some are

more specific for Parkinson's [27]. However all such apps

make the tacit assumption that accurate reminders mean

accurate compliance although, in reality, there is little reason

to believe that such a close relationship holds. Many apparent

medication failures are in fact compliance failures. A better

system would be one that monitors actual ingestion of tablets.

This is the real problem in search of a solution.

Symptom logging is an area of widespread development

with many apps taking advantage of smart phone gyroscopes

and accelerometers to analyze complex movement patterns

and, with the assistance of complex software solutions, to

tease out tremor, bradykinesia, dyskinesia and gait instability

therefrom [28]. These are often technically elegant and, it has

to be conceded, may be of assistance to physicians in their

decision-making. However, this is also an example of

technology offering solutions ahead of problems. Patients, as

discussed earlier, place far greater emphasis on non-motor

symptoms which have been, so far, less amenable to

gyroscopic or accelerometer analysis. There is the sense that

motor dimensions of symptomatology are given greater weight

by technologists simply because the solutions are already

available.

Mood and cognition monitoring is a tough nut to crack.

Although simple technological solutions for smart phones

exist, all require user intervention either to answer questions

or to perform reasoning based tasks. Such complex biological

constructs as mood and cognition are not amenable to simple

technological solutions. Indeed most such offerings are

fundamentally little more than paper exercises transferred to

the smart phone rather than solutions conceptualized for the

phone. Most such current solutions involve discrete testing at

certain times of day with the logging of the data, either

manually or automatically in some form of diary. It is worth

noting some success in on-line monitoring of depression,

where it was found that the patients most in need of care were

the ones most adherent to the system [24].

III. POTENTIAL TECHNOLOGY SOLUTIONS TO SOME OF THE

SOFT-SIGN (NON-LOCOMOTORY) PROBLEMS

Human behavior is signaled by body movements and other

physical and physiological patterns - so called “honest

signals.” Much of this is detectable body language but more

subtle behavior may be reliably deciphered from continuous

measurements using relatively simple monitors with the aid of

signal analysis and pattern recognition tools. The more

extreme deviations in patterns associated with disease

conditions may also help to identify common features that

reflect performance fluctuations in healthy individuals with

more subtle presentation. In this role, PD is a prototypical

disease for activity studies that include activity-based patterns

of non motor problems. These non motor issues include mood

and depression, cognitive decline and dementia, fatigue, and

various types of disordered sleep. These have not been

typically included as part of the rest of the data analysis in

research studies involving activity measurements in

Parkinson’s, even though these are interrelated and important

to daily functioning for an individual with Parkinson’s

[29,30]. From a technologist’s perspective, it is likely that

many motor and non-motor features of PD can be

stochastically modeled using data as simple as that collected

with a single wearable sensor system involving triaxial

accelerometry [31-34]. Sensitivity and specificity may benefit

from additional data such as heart rate, skin conductance, or

speech components (Table 1).

TABLE 1. NON MOTOR SYMPTOM TRACKING USING ACTIVITY

MONITORING AND OTHER MEASURES.

Note: REM – rapid eye movement sleep stage; SWS – slow wave sleep

Approaches to modeling, mining, and crowd sourcing

behavior from complex monitoring data have been proposed

[58-60]. Similar techniques could be applied to PD data.

A. The importance of purposeful exercise in PD

Exercise can provide remarkable benefits to Parkinson’s

patients, improving clinical evaluation and sustaining or even

improving HRQL, and specifically improving motor function,

mood, and cognition. This non-pharmacological benefit

appears to involve more than one mechanism including

increases in dopamine D2 receptors and BDNF secretion [61-

64]. Walking, Tai Chi, dance, and cycling are popular forms

of exercise in Parkinson’s but all forms of activity appear to

provide benefits, especially if they include high physical

intensity, high-amplitude motion, and cognitive challenges

[65-71]. Monitoring technologies that quantify physical

Problem Feature/Marker Other measures Reference

Exercise activity intensity, duration 35-39

Sleep and alertness REM, SWS

heart rate, skin

conductance 40-43

sleep movements bed monitor 44-47

Fatigue eye tracking 48

Depression voice 49-54

Psychosis 55,56

Cognition memory, confusion,

executive function

instrumented

home, GPS 57



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activity frequency and intensity provide feedback to patients

and care providers to establish the individually useful exercise

prescription. Data from such monitoring will help to further

define optimal exercise dosing. Monitoring also helps to

promote exercise compliance, and this is especially important

in PD because of the recognized therapeutic benefits and

because of barriers to exercise not present in the non-PD

population [36,37,72]. Activity level is also a marker for

declining health and progression of disease, even in the

absence of gait changes, decreasing activity levels are a

marker for the emergence of several clinically relevant

symptoms such as depression [18,38].

Many commercially available devices rely on some form of

actigraphy to provide estimates of activity in steps taken,

distance traveled, or calories expended. Currently available

systems and their applications to PD patients have been well

reviewed elsewhere [33]. Longitudinal monitoring of activity

in PD has revealed differences in intensity and volume of

exercise without changes in frequency or duration, requiring

more than “steps counted” as a useful monitoring approach

[38]. Traditional methods used by researchers to estimate

energy expenditure in free living individuals (ie., outside of a

metabolism chamber) relied on continuous heart rate

monitoring [73] but the combination of continuous heart rate

and actigraphy has been demonstrated to provide greater

accuracy for energy expenditure in healthy active individuals

[74]. Other novel measures such as foot contact time can

accurately assess locomotory activity and categories of

activity [75]. A sensor on a necklace was applied to

quantification of daily activity and energy expenditure

estimates in PD patients [35]. Interpretation of sensor data

requires specific validation with a Parkinson’s population

where altered gait, asymmetrical loss of arm swing, etc. could

conceivably change usual assumptions [76].

The useful interpretation of large continuous data streams

from activity and motion from smart shoes, other wearable

monitors, and home area monitoring, is an important current

focus [31,33]. As activity monitoring increases in the general

population, the observed association between physical activity

and PD risk can also be parsed into whether exercise is

protective or diminished exercise is a result of PD onset [77].

Further development of systems involving social

interaction with peers or coaches and with real or virtual

humans to motivate regular activity could be an important tool

in enhancing quality of life for PD patients [78-80].

B. Sleep quantification and implications for PD

Sleep disturbance is a disabling symptom for many PD

patients and the characterization of sleep disturbance is

important in treatment [7,81-83]. There are effective therapies

for many causes of PD sleep disturbance and rapid

identification of the specific nature of disturbance could speed

beneficial therapy and prevent disability.

Methods to assess sleep quality have been offered in

numerous forms including commercial activity, biometric, and

sleep actigraphy monitors. The simplest form of sleep

monitoring is a well established wearable wrist monitor using

triaxial accelerometry, with movements interpreted using

simple algorithms [40]. In PD patients this also works

reasonably well when compared to the gold standard of

polysomnography in a sleep laboratory, but some of the

measures such as time for sleep onset are complicated by the

disease and scoring algorithms need further work [41].

Inclusion of additional sensor data such as heart rate, skin

conductance, and skin temperature could be expected to

improve sleep assessments, including perhaps isolation of

important phases of sleep (e.g., slow wave sleep; REM sleep).

For example, a simple wearable device that distinguishes

REM sleep with some accuracy would be an important

advance in assessment of REM Sleep Behavior Disorder

(RBD) which occurs in one third of PD patients and is an early

predictive symptom for PD [83].

Another aspect of sleep quality involves large body

movements, measured with instrumented beds or with

accelerometers mounted on the legs or the back [44-46]. In

PD patients compared to healthy controls, smaller and shorter

axial movements are observed but these have not been tied to

any laboratory measures of sleep quality in these studies

[45,46].

Some patients have reported another phenomenon referred

to as “sleep benefit”, where a sleep that is perceived as restful

improves physical symptoms the next day. This effect has

been difficult to establish with questionnaire data and requires

a better practical method of objective quantification of sleep

quality [84].

Sleepiness and sudden sleep onset (“sleep attacks”) have

been reported for some PD patients and this is especially

problematic during driving [85]. Vigiliance monitoring

systems to detect onset of a sleep attack might be beneficial to

PD patients but would require high specificity and rapid

response time; current systems to assess drowsiness may be

inadequate for this purpose.

Identifying poor sleep quality and characterizing other

aspects of the sleep is an important goal for wearable devices

that has not yet been fully achieved but is vitally important for

effective self monitoring. Currently, clinical labs evaluate

characteristics of sleep disorders through electromyographic,

kinemetric, and electroencephalographic measurements

generally involving complex systems. Wearable systems have

been proposed to obtain continuous EEG and manage data

artifacts and these show some promise for the future [86,87].

C. Fatigue - a significant but poorly quantified problem

Fatigue is a separate disabling symptom reported in a

majority of Parkinson’s patients, not necessarily associated

with sleep quality, cognitive impairment, or depression

[10,88,89]. Patients report fatigue as a key limiter in physical

and social functioning [88]. Distinguishing this symptom

from other problems such as daytime sleepiness and

depression through biomonitoring is a key technological

challenge [90]. Novel approaches to unobtrusive detection of

changes in mental state, perhaps including mental fatigue, are

suggested by a recent study that used key-hold times from

normal interactions with a computer keyboard to detect



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psychomotor impairments. These were changes were induced

with testing during night time waking (“sleep inertia”) [91].

D. Mood disorders and psychosis

Depressive mood is associated with lowered activity levels

[50] and disordered sleep [42,29], and there is some evidence

that aspects of mood (depression, anxiety, mania) and

psychotic features (confusion, hallucination, delusion) affect

motor presentation in PD. There is sparse-to-none data on

changes in daily patterns of activity as indicators of depressed

status, yet this seems to be an obvious target for productive

activity-based monitoring research. Combined with other

biological signals such as changes in vocal acoustics, this

could be extraordinarily useful. Changes in speech

characteristics are themselves a symptom of PD motor

changes but specific components have been used to track

depression severity in other patients [54].

Other techniques could evaluate mood and psychosis. Signs

from EEG, ECG, or EMG monitoring have the potential to

assist with identification and may do so in conjunction with

sleep monitoring. One system (iCalm) has explored a

combination of activity, skin conductance, temperature, and

photoplethysmography on wrist and foot to detect changes in

autonomic activity [56]. Another similar system (PSYCHE)

has been tested in bipolar patients to detect mood transitions

including hypomanic and depressive episodes over a course of

treatment [91].

Affect can also be assessed with everyday technologies.

For example, facial expressions in Parkinson’s patients are

often misunderstood because of motor changes affecting facial

muscles (the “Parkinson’s mask”) but an advanced facial

recognition system could conceivably learn its patient in a

webcam application and detect alterations reflecting emotional

changes [80,93,94].

Increasing isolation is another measureable component of

depression. Socialization was monitored in one study by

providing all participants within a network of peers with smart

phone software that detected and recorded proximity to other

software equipped phones, charting frequency of in person

social interactions [43]. In another test of phone-based motion

sensors, developers inadvertently recorded audio data using

the phone’s microphone and analysts evaluated social

interaction using the audio data [95]. The integration of

tracking of broadcast identifiers from 802.11 wifi, Bluetooth,

NFC, or other wireless signature could automate identification

of social interaction through tracking interaction with other

individuals’ devices, tracking frequency and duration of

contact, and a count of unique devices encountered. Many

MAC addresses (wifi or NFC) or numbers (Bluetooth) can be

identified as belonging to a fixed or mobile transmitter (e.g.,

access point or portable client) and in many cases can be

linked to a specific manufacturer, providing more detailed

resolution of social versus infrastructure encounters.

Treatments for depression and anxiety may include

gaming therapies which are being explored for their

effectiveness and lower incidence of side effects compared to

pharmacotherapy [51,52].

E. Mild cognitive impairment and dementia

Many aspects of cognition may be monitored effectively

through gamification of cognitive tests, with set shifting,

memory, and fluency easily tested in engaging ways [96].

Such systems are being developed for a range of

neurocognitive testing applications but could be tailored to

cognitive domains most affected in Parkinson’s, as that

information becomes clearer [97]. Further, activity monitors

may identify erratic behavior patterns characteristic of

executive dysfunction in early cognitive decline and may

compliment cognitive testing or provide insight if the

frequency of dedicated cognitive self-monitoring declines.

Specific types of activities that might be built into an

engaging test system could include simple measures such as

finger tapping. Finger tapping has recently been defined with

29 subtest parameters which can now be characterized in

studies with Parkinson’s patients to define aspects of cognition

and motor function [98]. Impaired driving ability in

Parkinson’s is explained in part by cognitive decline [99].

This can be tested with driving simulations although PD

patients with current licenses are not likely to volunteer to

have their driving assessed in research studies [100]. Driver

assistance systems with warning information about speeds and

following distances improved traffic performance of PD

patients and demonstrate the importance of simple assistive

technologies for safer driving [101].

Monitoring of ambient audio could track verbal fluency

(speech rate) and speaking levels (e.g., polysyllabic words,

sentence structures) to establish baseline and track change in

these markers for cognitive status. Speech content changes

over long duration distinguish declining mental status for

President Reagan compared to President Bush during their

terms in office [102]. Similar evaluations could be conducted

through monitoring the frequency and characteristics of

electronic communications, including e-mail, phone calls, and

online social networking.

Remembering to follow the usually complicated

medication regimes can be a problem in PD. Simple timed

reminders are helpful. Actual detection of pill intake with

individual edible bar codes or other microtags is a feasible

technology that could provide additional help to patients.

IV. BIG DATA STUDIES TECHNOLOGY

A. Previous and current initiatives

Data collected through the approaches identified in section

III will rapidly achieve a scale putting it into the domain of

“big data,” which in this context would suggest very large N

studies with high enough sampling rates as to be considered

effectively continuous. Note that this does not preclude the

huge importance of small sample studies that can identify

large individual effects.

The standard for large dataset studies in neuroscience

was set by the Alzheimer’s Disease Neuroimaging Initiative

(ADNI) [103,104]. This study of 800 subjects collected

longitudinal clinical, genetic, and diverse imaging data from

individuals with Alzheimer’s disease and controls. The ADNI



JBHI-00260-2015-R2

7

has been successful in identifying an imaging biomarker for

Alzheimer’s. The FDA’s tentative but positive-leaning

response has inspired researchers in other diseases to consider

replicating the ADNI approach for the identification of

biomarkers. The ADNI model of public data sharing inspired

the Michael J. Fox Foundation to launch the Parkinson’s

Progression Marker Initiative (PPMI)(http://www.ppmi-

info.org/) [105]. This was also based, in part, on proof of

feasibility in the establishment of an enriched cohort to

identify premotor (prodromal) biomarkers of PD in the Army-

funded Parkinson Associated Risk Syndrome Study

(PARS)[106]. The PPMI, with a cohort expanded beyond the

original 600 PPMI cases and controls, is being supplemented

with body-fixed sensor data.

A different approach builds on a quality improvement

model pioneered by the Northern New England

Cardiovascular Study Group [107] and improved upon in

cystic fibrosis [108]. Using this experimental design, the

National Parkinson Foundation (NPF) launched the

Parkinson’s Outcomes Project (POP). This study is focused

on patient reported outcomes and their clinical care, with the

express purpose of optimizing care [109]. The POP study also

includes mobility as an outcome and has collected data on

subjects using body-fixed sensors [110]. POP, with

approximately 8,000 subjects and 20,000 clinical evaluations,

is the largest clinical study of Parkinson’s disease ever

conducted. With comprehensive clinical characterization,

these studies offer a different set of benefits from the addition

of monitoring technology. Split sample approaches with a

validation dataset held back enable analyses with reduced risk

of spurious findings [111].

A third initiative in Parkinson’s tests the value of wearable

self monitoring devices in improving quality of life for

patients (https://www.michaeljfox.org/). This involves a

collaboration between Intel and Michael J. Fox Foundation.

With patient consent, the data are being aggregated to

compare the device data to patient diaries, medication use, and

clinical observations. The device tested provides 24 hour

measurements of activity but also estimates of sleep quality.

The large volume of data (300 observations/s from each

patient) is managed through a big data analytics cloud

infrastructure at Intel with real time detection of patient status.

Such large scale assessment of free living patients using

wearable monitoring could revolutionize clinical trials. More

importantly, self monitoring technologies could empower

patients to more effectively manage their own healthcare.

Finally, this concept has been extended into the open source

concept in research through Apple’s Research Kit, announced

in March 2015. Research Kit is described as a “software

framework made specifically for medical research” by

providing access to sensors in the iPhone and other Apple

devices [112]. It is too early to determine the success of this

effort which will reflect skew due to (unquantified)

availability bias. Apple’s Research Kit is being utilized in the

mPower tool [113].

B. Importance of frequency-space information

While machine learning will enable new insight to be drawn

from the large studies such as PPMI and POP, their low

frequency of data collection will limit insight into higher

frequency events, signs, and symptoms. Patient data reflects

several frequency-space domains, ranging from falls, which

include delta function-like spikes as ground contact results in

rapid deceleration, through tremor, dyskinesia, and medication

effects to disease progression which is measured on scale of

years (Table 2). TABLE 2

CHARACTERISTIC PERIODS OF SEVERAL FEATURES OF PD

Feature Characteristic Period

Falls

Tremor

Dykinesia

Medication effects

Sleep/fatigue

Hospitalization

Depression

Psychosis

Disease progression

Instantaneous to 1 second

0.25-0.5 seconds

1 second

2-4 hours

1 day

Several days

Months

1-2 years to develop

Years

Continuous data collection from sensors could record these

key events and track motor and other information to

extrapolate the rate of change in features with different

characteristic periods. Such approaches could dramatically

increase the insight available from studies such as PPMI and

POP, identifying some features and tracking change in other

features that are identified through clinical evaluation or

testing. Although currently no kinemetric biomarkers are

available to track many features, a Fourier-transform

approach, analyzing continuous data or a frequency-level

analysis of discontinuous data could be valuable in identifying

to-date unknown associations between monitoring data and

clinically-identified symptoms.

C. Challenges and technology needs

In large-scale characterization studies such as these “big

data” studies, clinical motion data should be collected with

minimal filtering, as the frequency-space associations with PD

features are insufficiently established. A critical need is the

better characterization of these relationships. Key technology

questions are:

How can we address issues of data compression and

power requirements to create more usable sensors that still

provide a sufficient level of resolution to address patient

monitoring needs?

How effectively can unilateral measurement characterize

bilateral symptoms? Are asymmetric features different in the

frequency space depending on position of sensor?

Increasing evidence suggests that motor control

compensation in the forebrain may result in different motor

characteristics. Could frequency-space motor measurement

identify specific pathology?

A recent analysis has shown that not just medication

http://www.ppmi-info.org/

http://www.ppmi-info.org/

https://www.michaeljfox.org/



JBHI-00260-2015-R2

8

effects but differences in effect between medications may be

identifiable through sensitive motor measurement. Can this be

delivered at scale?

How do we organize data flow so that patients have their

desired level of privacy and security controls?

What is the business model that will encourage device

manufacturers to provide raw data, perhaps through an open

API, to access an evolving set of algorithms?

How do we effectively manage genome data for

personalized data interpretation?

V. CONCLUSIONS AND BROAD IMPLICATIONS

Parkinson’s patients want to be involved in decisions about

them. Monitoring technologies can help arm PD patients to

take greater responsibility in their own health care and quality

of life. They may also choose to share these data with health

care providers, especially remotely through emerging

telemedicine capabilities, and this will enable clinicians to

provide better care targeting the specific needs of their

individual patients. The science that can drive development of

important monitoring and other assistive technologies in PD

already largely exists today and is simply waiting for clever

engineering technologists to harness and apply to the right

problems. This paper has outlined the patient perspective

which includes a focus on the often overlooked nonmotor

symptoms and problems. Many of these problems can be

tracked using noninvasive technologies that have been

developed to address each specific symptom (e.g., depressed

mood, inadequate restorative sleep, impaired cognition, etc)

but a combined differential assessment of these various

problems in a single relevant disease (Parkinson’s) has not

been undertaken. The patient community will readily provide

feedback on the desirable features of such a monitoring device

if it is developed and, for the first time, the research

community will be able to access data on free living

populations to address earlier detection and effectiveness and

complications of treatment interventions.

DISCLAIMERS

The opinions and assertions in this paper are those of the

authors and do not necessarily represent the official views or

policies of their institutions. Mention of any specific

commercial products, process, or service by trade name,

trademark, manufacturer, or otherwise does not necessarily

constitute or imply its endorsement, recommendation, or

favoring by the authors or the organizations they represent.

REFERENCES

[1] W. Churchill, The World Crisis, v. 4, p. 192, 1923.

[2] H. Ehringer and O. “Hornykiewicz. Verteilung von Noradrenalin und Dopamin (3-Hydroxytyramin) im Gehirn des Menschen und ihr

Verhalten bei Erkrankungen des extrapyramidalen Systems.” Klinische

Wochenschrift, vol. 38, pp. 1236-1239, 1960. [3] G.C. Cotzias, P.S. Papavasiliou, and R. Gellene. “Modification of

Parkinsonism—chronic treatment with L-dopa.” N Engl J Med, vol.

280, pp. 337-345, 1969.

[4] A.Y. Hung and M.A. Schwarzschild. “Treatment of Parkinson’s

Disease: What’s in the Non-dopaminergic Pipeline?” Neurotherapeutics,

vol. 11, pp. 34-46, 2014.

[5] M.R. Lemke, H.M. Brecht, J. Koester, P.H. Kraus, and H. Reichmann.

“Anhedonia, depression, and motor functioning in Parkinson’s disease during treatment with pramipexole.” J Neuropsychiat, vol. 17, pp. 214-

220, 2005.

[6] P. Wicks, J. Stamford, M.A. Grootenhuis, L. Haverman, and S. Ahmed. “Innovations in e-health.” Qual Life Res, vol. 23, pp. 195-203, 2014.

[7] K.R. Chaudhuri, D.G. Healy, and A.H..V. Schapira. “Non-motor

symptoms of Parkinson's disease: diagnosis and management.” The Lancet Neurology, vol. 5, pp. 235-245, 2006.

[8] M.A. Achey, C.A. Beck, D.B. Beran, et al. “Virtual house calls for

Parkinson disease (Connect.Parkinson): study protocol for a randomized, controlled trial.” Trials, vol. 15, 465 (13 pp), 2014.

[9] M.A. Achey, J.L. Aldred, N. Aljehani, et al. “The past, present, and

future of telemedicine for Parkinson's disease.” Mov Disord, vol. 29, pp. 871-883, 2014.

[10] P. Martinez-Martin, C. Rodriguez‐Blazquez, M.M. Kurtis, and K.

Chaudhuri. “The impact of non‐motor symptoms on health‐related quality of life of patients with Parkinson's disease.” Mov Disord, vol. 26,

pp. 399-406, 2011.

[11] K.R. Chaudhuri, P. Odin, A. Antonini, P. Martinez-Martin. “Parkinson’s disease: the non-motor issues.” Parkinsonism Relat Disord, vol. 17, pp.

717-723, 2011.

[12] M. Samuel, M. Rodriguez‐Oroz, A. Antonini, et al. “Management of

impulse control disorders in Parkinson's disease: Controversies and future approaches.” Mov Disord, vol. 30, pp. 150-159, 2015.

[13] Movement Disorder Society Task Force on Rating Scales for

Parkinson's Disease. “The Unified Parkinson's Disease Rating Scale (UPDRS): status and recommendations.” Mov Disord, vol. 18, p. 738,

2003.

[14] V. Peto, C. Jenkinson, and R. Fitzpatrick. “PDQ-39: a review of the development, validation and application of a Parkinson’s disease quality

of life questionnaire and its associated measures.” J Neurol, vol. 245,

pp. S10-S14, 1998. [15] J.M. Miyasaki, J. Long, D. Mancini, et al. “Palliative care for advanced

Parkinson disease: an interdisciplinary clinic and new scale, the ESAS-

PD.” Parkinsonism Relat Disord, vol. 18, pp. S6-S9, 2012. [16] A. Hassan, S.S. Wu, P. Schmidt, I.A. Malaty, Y.F. Dai, J.M. Miyasaki,

and M.S. Okun. “What are the issues facing Parkinson's disease patients

at ten years of disease and beyond?: Data from the NPF-QII study.” Parkinsonism Relat Disord, vol. 18, pp. S10-S14, 2012.

[17] U. Wüllner, T. Schmitz‐Hübsch, G. Antony, et al. “Autonomic dysfunction in 3414 Parkinson's disease patients enrolled in the German

Network on Parkinson's disease (KNP eV): the effect of ageing.” Eur J Neurol, vol. 14, pp. 1405-1408, 2007.

[18] D. Bega, S.S. Wu, Q. Pei, P.N. Schmidt, and T. Simuni. “Recognition

and Treatment of Depressive Symptoms in Parkinson's Disease: The NPF Dataset.” J Parkinson's Dis, vol. 4, pp. 639-643, 2014.

[19] J.A. Cramer, R.D. Scheyer, R.H. Mattson. “Compliance declines

between clinic visits.” Arch Intern Med, vol. 150, pp. 1509-1510, 1990. [20] V. Venkataraman, S.J. Donohue, K.M. Biglan, P. Wicks, and E. R.

Dorsey. “Virtual visits for Parkinson disease A case series.” Neurology:

Clin Prac, vol. 4, pp. 146-152, 2014. [21] J.P. Hubble, R. Pahwa, D.K. Michalek, C. Thomas, and W.C. Koller.

“Interactive video conferencing: a means of providing interim care to

Parkinson's disease patients.” Mov Disord, vol. 8, pp. 380-382, 1993. [22] E.R. Dorsey, V. Venkataraman, M.J. Grana, et al. “Randomized

controlled clinical trial of “virtual house calls” for Parkinson disease.”

JAMA Neurol, vol. 70, pp. 565-570, 2013. [23] A. Abdolahi, N. Scoglio, A. Killoran, E.R. Dorsey, and K.M. Biglan.

“Potential reliability and validity of a modified version of the unified

Parkinson's disease rating scale that could be administered remotely.” Parkinsonism Relat Disord, vol. 19, pp. 218-221, 2013.

[24] E.M. Hunkeler, W.A. Hargreaves, B. Fireman, et al. “A web-delivered

care management and patient self-management program for recurrent depression: a randomized trial.” Psychiatr Services, vol. 63, pp. 1063-

1071, 2012.

[25] S. Chowdhury, C.C. Meunier, L. Cappelletti and T.B Sherer. “Improving patient participation in Parkinson’s clinical trials: the

experience of the Michael J Fox Foundation.” Clin Invest, vol 4, pp.

185-192, 2014.



JBHI-00260-2015-R2

9

[26] A. Pathak, J.A. Redmond, M. Allen, and K.L. Chou. “A noninvasive

handheld assistive device to accommodate essential tremor: A pilot

study.” Mov Disord, vol. 29, pp. 838-842, 2014.

[27] T. Keränen and S. Liikkanen. “Medication reminder service for mobile

phones: An open feasibility study in patients with Parkinson's disease.” Telemed e-Health vol. 19, pp. 888-890, 2013.

[28] S. Das, B. Amoedo, F. De la Torre, J. Hodgins. “Detecting Parkinsons’

symptoms in uncontrolled home environments: a multiple instance learning approach.” 34th Annual Int Conf IEEE EMBS, San Diego, CA.

Pp. 3688-3691, 2012.

[29] N.J. Weerkamp, G. Tissingh, P.J.E. Poels, S.U. Zuidema, M.Munneke, R.T.C.M. Koopmans, and B.R. Bloem. “Nonmotor symptoms in nursing

home residents with Parkinson's disease: prevalence and effect on

quality of life.” J Am Geriatr Soc, vol. 61, pp. 1714-1721, 2013. [30] D.A. Gallagher, A.J. Lees, and A. Schrag. “What are the most important

nonmotor symptoms in patients with Parkinson's disease and are we

missing them?” Mov Disord, vol. 25, pp. 2493-2500, 2010. [31] E. Fuster-Garcia, A. Bresó, J. Martínez-Miranda, J. Rosell-Ferrer, C.

Matheson, and J.M. García-Gómez. “Fusing actigraphy signals for

outpatient monitoring.” Information Fusion, vol. 23, pp. 69-80, 2015. [32] J. Cancela, M. Pastorino, A.T. Tzallas, M.G. Tsipouras, G. Rigas, M.T.

Arredondo, and D.I. Fotiadis. “Wearability assessment of a wearable

system for Parkinson’s Disease remote monitoring based on a body area network of sensors.” Sensors, v. 14, pp. 17235-17255, 2014.

[33] W. Maetzler, J. Domingos, K. Srulijes, J.J. Ferreira, and B.R. Bloem.

“Quantitative wearable sensors for objective assessment of Parkinson's disease.” Mov Disord, vol. 28, pp. 1628-1637. 2013.

[34] S. Patel, H. Park, P. Bonato, L. Chan, and M. Rodgers. “A review of wearable sensors and systems with application in rehabilitation.” J

Neuroengineer Rehabil, vol. 9, pp. 21, 2012.

[35] M.L. Dontje, M.H.G. de Greef, A.D. Speelman, et al. “Quantifying daily physical activity and determinants in sedentary patients with Parkinson's

disease.” Parkinsonism Related Disord, vol. 19, pp. 878-882, 2013.

[36] M. van Nimwegen, A.D. Speelman, K. Smulders, et al. “Design and

baseline characteristics of the ParkFit study, a randomized controlled

trial evaluating the effectiveness of a multifaceted behavioral program to

increase physical activity in Parkinson patients.” BMC neurology, vol. 10, pp. 70, 2010.

[37] A.D. Speelman, M. van Nimwegen, B.R. Bloem, M. Munneke.

“Evaluation of implementation of the ParkFit program: A multifaceted intervention aimed to promote physical activity in patients with

Parkinson's disease.” Physiotherapy, vol. 100, pp. 134-141, 2014.

[38] J.T. Cavanaugh, T.D. Ellis, G.M. Earhart, M.P. Ford, K.B. Foreman, and L.E. Dibble. “Capturing Ambulatory Activity Decline in Parkinson

Disease.” J Neurologic Physical Ther, vol. 36, pp. 51, 2012.

[39] K.R. Evenson, F. Wen, J.S. Metzger, and A.H. Herring. “Physical activity and sedentary behavior patterns using accelerometry from a

national sample of United States adults.” Int J Behav Nutr Phys Act, vol.

12, pp. 20, 2015. [40] S. Ancoli-Israel, R. Cole, C. Alessi, M. Chambers, W. Moorcroft, C.

Pollak. “The role of actigraphy in the study of sleep and circadian

rhythms. American Academy of Sleep Medicine Review Paper.” Sleep, vol. 26, pp. 342-392, 2003.

[41] J. Maglione, L. Liu, A.B. Neikrug, T. Poon, et al. "Actigraphy for the

Assessment of Sleep Measures in Parkinson's Disease." Sleep, vol. 36, pp. 1209-1217, 2013.

[42] G. Jean–Louis, M.V. Mendlowicz, J.C. Gillin, et al. “Sleep estimation

from wrist activity in patients with major depression.” Physiol Behav, vol. 70, pp. 49-53, 2010.

[43] M. Butt, T.B.M.J. Ouarda, S.F. Quan, A.S. Pentland, and I. Khayal.

“Technologically sensed social exposure related to slow-wave sleep in healthy adults.” Sleep Breath, vol. 19, pp. 255-261, 2015.

[44] A.M. Adami, M. Pavel, T.L. Hayes, A.G. Adami, C. Singer. “A method

for classification of movements in bed.” In Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of

the IEEE, pp. 7881-7884. IEEE, 2011.

[45] M Louter, R.J.G. van Sloun, D.A.A. Pevernagie, J.B.A.M. Arends, P.J. Cluitmans, B.R. Bloem, and S. Overeem. “Subjectively impaired bed

mobility in Parkinson disease affects sleep efficiency.” Sleep Med, vol.

14, pp. 668-674, 2013. [46] M. Louter, W. Maetzler, J. Prinzen, et al. “Accelerometer-based

quantitative analysis of axial nocturnal movements differentiates patients

with Parkinson's disease, but not high-risk individuals, from controls.” J Neurol Neurosurg Psychiatr, vol. 86, pp. 32-37, 2015.

[47] D.T. Plante. “Leg actigraphy to quantify periodic limb movements of

sleep: A systematic review and meta-analysis.” Sleep Med Rev, vol. 18,

pp. 425-434, 2014.

[48] J.I. Hoff, J.J. Van Hilten, H.A.M. Middelkoop, and R.A.C. Roos.

“Fatigue in Parkinson's disease is not associated with reduced physical activity.” Parkinsonism Relat Disord, vol. 3, pp. 51-54, 1997.

[49] A. Korszun, E.A. Young, N.C. Engleberg, C.B. Brucksch, J.F. Greden,

and L.A. Crofford. “Use of actigraphy for monitoring sleep and activity levels in patients with fibromyalgia and depression.” J Psychosom Res,

vol. 52, pp. 439-443, 2002.

[50] J. Kim, T. Nakamura, H. Kikuchi, T. Sasaki, and Y. Yamamoto. “Co-variation of depressive mood and locomotor dynamics evaluated by

ecological momentary assessment in healthy humans.” PloS One, vol. 8,

pp. e74979, 2013. [51] C.V. Russoniello, K. O’Brien, and J.M. Parks. “EEG, HRV and

psychological correlates while playing Bejeweled II: A randomized

controlled study.” Stud Health Technol Inform, vol. 144, pp. 189-92, 2009.

[52] C.V. Russoniello, M. Fish, K. O'Brien. “The efficacy of casual

videogame play in reducing clinical depression: A randomized controlled study.” Games for Health, vol. 2, pp. 341-346, 2013.

[53] A.A. Rizzo, B. Lange, J. G. Buckwalter, et al. “An intelligent virtual

human system for providing healthcare information and support.” Studies Health Technol Informatics, vol. 163, pp. 503, 2011.

[54] J.C. Mundt, A.P. Vogel, D.E. Feltner, and W.R. Lenderking. “Vocal

acoustic biomarkers of depression severity and treatment response.” Biol Psychiatr, vol. 72, pp. 580-587, 2012.

[55] G. Valenza, M. Nardelli, A. Lanata, C. Gentili, G. Bertschy, R. Paradiso, and E.P. Scilingo. “Wearable monitoring for mood recognition in

bipolar disorder based on history-dependent long-term heart rate

variability analysis.” IEEE J Biomed Health Inform, vol. 18, pp. 1625-1635, 2014.

[56] R.R. Fletcher, K. Dobson, M.S. Goodwin, et al. “iCalm: Wearable

sensor and network architecture for wirelessly communicating and

logging autonomic activity.” IEEE Trans Inform Technol Biomed, vol.

14, pp. 215-223, 2010.

[57] D.F. Mahoney, R.B. Purtilo, F.M. Webbe, et al. “In-home monitoring of persons with dementia: Ethical guidelines for technology research and

development.” Alz Dementia, vol. 3, pp. 217-226, 2007.

[58] K. Bousmalis, S. Zafeiriou, L. Morency, and M. Pantic. “Infinite hidden conditional random fields for human behavior analysis.” IEEE Trans

Neural Networks Learning Sys, vol. 24, pp. 170-177, 2013.

[59] M. Chatterjee, G. Stratou, S. Scherer, and L.P. Morency. “Context-based signal descriptors of heart-rate variability for anxiety assessment.” IEEE

Int Conf Acoust, Speech Signal Processing, pp. 3631-3635, 2014.

[60] S. Park, P. Shoemark, and L.P. Morency. “Toward crowdsourcing micro-level behavior annotations: the challenges of interface, training,

and generalization.” Proc 19th Int Conf Intelligent User Interfaces, pp.

37-46, 2014. [61] B.E. Fisher, Q. Li, A. Nacca, et al. “Treadmill exercise elevates striatal

dopamine D2 receptor binding potential in patients with early

Parkinson’s disease.” Neuroreport, vol. 24, pp. 509-514, 2013. [62] G. Frazzitta, P. Balbi, R. Maestri, G. Bertotti, N. Boveri, G. Pezzoli.

“The beneficial role of intensive exercise on Parkinson disease

progression.” Am J Phys Med Rehabil, vol. 92,pp. 523-532, 2013. [63] G. Frazzitta, R. Maestri, M.F. Ghilardi, et al. “Intensive Rehabilitation

Increases BDNF Serum Levels in Parkinsonian Patients.” Neurorehab

Neural Repair, vol. 28,pp. 163-168, 2014. [64] G.M. Petzinger, B.E. Fisher, S. McEwen, J.A. Beeler, J.P. Walsh, M.W.

Jakowec. “Exercise-enhanced neuroplasticity targeting motor and

cognitive circuitry in Parkinson's disease.” The Lancet Neurology, vol. 12, pp. 716-726. 2013.

[65] J.E. Ahlskog. “Does vigorous exercise have a neuroprotective effect in

Parkinson disease?” Neurology, vol. 77, pp. 288-294, 2011. [66] A.D. Speelman, B.P. van de Warrenburg, M. van Nimwegen, G.M.

Petzinger, M. Munneke, B.R. Bloem. “How might physical activity

benefit patients with Parkinson disease?” Nature Reviews Neurology, vol. 7, pp. 528-534, 2011.

[67] Y. Yang, X.Y. Li, L. Gong, Y.L. Zhu, and Y.L. Hao. “Tai chi for

improvement of motor function, balance and gait in Parkinson's disease: a systematic review and meta-analysis.” PloS One, vol. 9, e102942,

2014.

[68] C. Fox, G. Ebersbach, L. Ramig, and S. Sapir. “LSVT LOUD and LSVT BIG: behavioral treatment programs for speech and body movement in



JBHI-00260-2015-R2

10

Parkinson disease.” Parkinson’s Dis, vol. 2012, 12 pp,

http://dx.doi.org.ezproxy.nihlibrary.nih.gov /10.1155/2012/391946.

[69] G. Ebersbach, A. Ebersbach, F. Gandor, B. Wegner, J. Wissel, and A.

Kupsch. “Impact of Physical Exercise on Reaction Time in Patients

With Parkinson's Disease—Data From the Berlin BIG Study.” Arch Phys Med Rehabil, vol. 95, pp. 996-999, 2014.

[70] D. Conradsson, N. Löfgren, H. Nero, M. Hagströmer, A. Ståhle, J. Lökk,

and E. Franzén. “The Effects of Highly Challenging Balance Training in Elderly With Parkinson’s Disease A Randomized Controlled Trial.”

Neurorehabil Neural Repair, 1545968314567150, 2015.

[71] C.J.M. Ayan, A. Cancela, A. Gutiérrez-Santiago, and I. Prieto. “Effects of two different exercise programs on gait parameters in individuals with

Parkinson's disease: a pilot study.” Gait Posture, vol. 39, pp. 648-651,

2014. [72] T. Ellis, J.K. Boudreau, T.R. DeAngelis, et al. “Barriers to exercise in

people with Parkinson disease.” Phys Therapy, vol. 93, pp. 628-636,

2013. [73] G.B. Spurr, A.M. Prentice, P.R. Murgatroyd, G.R. Goldberg, J.C. Reina,

N.T. Christman. “Energy expenditure from minute-by-minute heart-rate

recording: comparison with indirect calorimetry.” Am J Clin Nutr, vol. 48, pp. 552-559. 1988.

[74] S. Brage, N. Brage, P.W. Franks, et al. “Branched equation modeling of

simultaneous accelerometry and heart rate monitoring improves estimate of directly measured physical activity energy expenditure.” J Appl

Physiol, vol. 96, pp. 343-351, 2004.

[75] R.W. Hoyt, M.J. Buller, W.R. Santee, M. Yokota, P.G. Weyand, and J.P. Delany. “Total energy expenditure estimated using foot-ground contact

pedometry.” Diab Technol Therapeutics, vol. 6, pp. 71-81, 2004. [76] M.D. Lewek, R. Poole, J. Johnson, O. Halawa, and X. Huang. “Arm

swing magnitude and asymmetry during gait in the early stages of

Parkinson's disease.” Gait Posture, vol. 31, pp. 256-260, 2010. [77] H. Chen, S.M. Zhang, M.A. Schwarzschild, M.A. Hernan, A. Ascherio.

“Physical activity and the risk of Parkinson disease.” Neurology, vol. 64,

pp. 664-669, 2005.

[78] F. Ofli, G. Kurillo, S. Obdrzalek, R. Bajcsy, H. Jimison, M. Pavel,

“Design and evaluation of an interactive exercise coaching system for

older adults: lessons learned.” IEEE J Biomed Health Informatics, in press, 2015, DOI 10.1109/JBHI.2015.2391671.

[79] A.A. Rizzo, B. Lange, J. G. Buckwalter, et al. “An intelligent virtual

human system for providing healthcare information and support.” Stud Health Technol Informatics vol. 163, pp. 503-509, 2010.

[80] J. Gratch, N. Wang, A. Okhmatovskaia, F. Lamothe, M. Morales, R.J.

van der Werf, and L.P. Morency. “Can virtual humans be more engaging than real ones?” In, Human-Computer Interaction. HCI Intelligent

Multimodal Interaction Environments, pp. 286-297. Springer Berlin

Heidelberg, 2007. [81] S. Paus, H.M. Brecht, J. Köster, G. Seeger, T. Klockgether, and U.

Wüllner. “Sleep attacks, daytime sleepiness, and dopamine agonists in

Parkinson's disease.” Mov Disord, vol. 18, pp. 659-667, 2003.

[82] U. Wüllner, T. Schmitz‐Hübsch, G. Antony, et al. “Autonomic

dysfunction in 3414 Parkinson's disease patients enrolled in the German Network on Parkinson's disease (KNP eV): the effect of ageing.” Eur J

Neurol, vol. 14, pp. 1405-1408, 2007. [83] C.H. Schenck, J. Y. Montplaisir, B. Frauscher, et al. “Rapid eye

movement sleep behavior disorder: devising controlled active treatment

studies for symptomatic and neuroprotective therapy—a consensus statement from the International Rapid Eye Movement Sleep Behavior

Disorder Study Group.” Sleep Med, vol. 14, pp. 795-806, 2013.

[84] M.M. van Gilst, M. Louter, C.R. Baumann, B.R. Bloem, and S. Overeem. “Sleep benefit in Parkinson's disease: time to revive an

enigma?” J Parkinson's Dis, vol. 2, pp. 167-170, 2012.

[85] C. Meindorfner, Y. Körner, J.C. Möller, K. Stiasny‐Kolster, W.H. Oertel, H.P. Krüger. “Driving in Parkinson's disease: mobility,

accidents, and sudden onset of sleep at the wheel.” Mov Disord, vol. 20, pp. 832-842, 2005.

[86] A.J. Casson, D. Yates, S. Smith, J.S. Duncan, and E. Rodriguez-

Villegas. “Wearable electroencephalography.” IEEE Engineer Med Biol Mag, vol. 29, pp. 44-56, 2010.

[87] V. Mihajlovic, B. Grundlehner, R. Vullers, and J. Penders. “Wearable,

Wireless EEG Solutions in Daily Life Applications: What are we Missing?” IEEE J Biomed Health Informatics, vol. 19, pp. 6, 2015.

[88] K. Herlofson and J.P. Larsen. “The influence of fatigue on health-

related quality of life in patients with Parkinson’s disease.” Acta Neurol Scand, v. 107, pp. 1-6, 2003.

[89] M. Amboni, G. Santangelo, P. Barone. “Depression, Apathy,

Anhedonia, and Fatigue in Parkinson’s Disease.” In, Neuropsychiatric Symptoms of Mov Disord, pp. 1-28. Springer Publ, 2015.

[90] W.J. Kop, A. Lyden, A.A. Berlin, et al. “Ambulatory monitoring of

physical activity and symptoms in fibromyalgia and chronic fatigue syndrome.” Arthritis Rheum, vol. 52, pp. 296-303, 2005.

[91] L. Giancardo, A. Sanchez-Ferro, I. Butterworth, C.S. Mendoza, J.M.

Hooker. Psychomotor impairment detection via finger interactions with a computer keyboard during natural typing. Sci Reports, vol. 5, article

no. 9678, 2015.

[92] A. Lanata, G. Valenza, M. Nardelli, C. Gentili, and E.P. Scilingo, “Complexity index from a personalized wearable monitoring system for

assessing remission in mental health.” IEEE J Biomed Health

Informatics, v. 19, pp. 132-139, 2015. [93] T. Baltrusaitis, D. McDuff, N. Banda, M. Mahmoud, R. El Kaliouby, P.

Robinson, and R. Picard. “Real-time inference of mental states from

facial expressions and upper body gestures.” IEEE Int Conf Automatic Face & Gesture Recognition and Workshops, pp. 909-914, 2011.

[94] T. Baltrusaitis, P. Robinson, L. Morency. “3D constrained local model

for rigid and non-rigid facial tracking.” In, IEEE Conf Computer Vision and Pattern Recognition, pp. 2610-2617, 2012.

[95] J. Liddle, D. Ireland, S.J. McBride, et al. “Measuring the lifespace of

people with Parkinson’s disease using smartphones: proof of principle.” JMIR mHealth uHealth, vol. 2, 2014.

[96] S. Hagler. “Computational modeling of cognitive processes for

continuous in-home assessment of cognitive performance.” PhD Dissertation, Oregon Health and Science University. Scholar Archive.

Paper 3559, 2014. http://digitalcommons.ohsu.edu/etd/3559 [97] M. Balzer-Geldsetzer, A. S. Costa, M. Kronenbürger, et al. “Parkinson's

disease and dementia: a longitudinal study (DEMPARK).”

Neuroepidemiology, vol. 37, pp. 168, 2011. [98] D. Austin, J. McNames, K. Klein, H. Jimison, and M. Pavel. “A

Statistical Characterization of the Finger Tapping Test: Modeling,

Estimation, and Applications.” IEEE J Biomed Health Inform, in press, 2015.

[99] A.M. Crizzle, A.M. Myers, E.A. Roy, and Q.J. Almeida. “Drivers with

Parkinson’s disease: are the symptoms of PD associated with restricted driving practices?” J Neurol, vol. 260, pp. 2562-2568, 2013.

[100] A.M. Crizzle, A.M. Myers, and Q.J. Almeida. “Drivers with parkinson's

disease: Who participates in research studies?” Parkinsonism Relat Disord, vol. 18, pp. 833-836, 2012.

[101] M. Dotzauer, S.R. Caljouw, D. De Waard, and W.H. Brouwer. “Longer-

term effects of ADAS use on speed and headway control in drivers diagnosed with Parkinson's disease.” Traffic Injury Prev, vol. 16, pp. 10-

16, 2015.

[102] V. Berisha, S. Wang, A. LaCross, and J. Liss. “Tracking Discourse Complexity Preceding Alzheimer's Disease Diagnosis: A Case Study

Comparing the Press Conferences of Presidents Ronald Reagan and

George Herbert Walker Bush.” J Alz Dis, vol. 45, pp. 959-963, 2015. [103] S.G. Mueller, M.W. Weiner, L.J. Thal, et al. “Ways towards an early

diagnosis in Alzheimer’s disease: The Alzheimer’s Disease

Neuroimaging Initiative.” Alzheimers Dement, vol. 1, pp. 55-66, 2005. [104] G. Miller. “Alzheimer's biomarker initiative hits its stride.” Science, vol.

326, pp. 386-389, 2009.

[105] K. Marek, D. Jennings, S. Lasch, et al. “The Parkinson Progression Marker Initiative (PPMI).” Prog Neurobiol, vol. 95, pp. 629-635, 2011.

[106] D. Jennings, A. Siderowf, M. Stern, J. Seibyl, S. Eberly, D. Oakes, K.

Marek, and the PARS Investigators. “Imaging prodromal Parkinson disease: the Parkinson Associated Risk Syndrome Study.” Neurology,

vol. 4, pp. 1739-46, 2014.

[107] G.T. O'Connor, S.K. Plume, E.M. Olmstead, et al. “A regional prospective study of in-hospital mortality associated with coronary

artery bypass grafting. The Northern New England Cardiovascular

Disease Study Group.” JAMA vol. 266, pp. 803-809, 1991. [108] A. Gawande. “The Bell Curve.” The New Yorker, 6 December 2004.

[109] M.S. Okun, et al. “Piloting the NPF data-driven quality improvement

initiative.” Parkinson’s Related Disorders, vol. 16, pp. 517-521, 2011. [110] P. Schmidt, E.C. Nelson, M. Guttman et al. “The analysis of the use of

kinemetric devices to predict outcomes: Results from NPF-QII”

Movement Disorders Society Annual Congress, 2015 (In press.) [111] S. Young, S. Stanley, and A. Karr. “Deming, data and observational

studies.” Significance, vol. 8, pp. 116-120, 2011.

[112] Apple. https://developer.apple.com/researchkit/ Accessed: 1 July 2015. [113] S.H. Friend, et al. http://parkinsonmpower.org/ Accessed: 1 July 2015.

http://digitalcommons.ohsu.edu/etd/3559

https://developer.apple.com/researchkit/

http://parkinsonmpower.org/



JBHI-00260-2015-R2

11

Jonathan A. Stamford DSc is a

neuroscientist with a double interest in

Parkinson's.

He took a BSc in pharmacology from

the University of Bath (1980). Following

a doctoral thesis on the neurophysiology

of the dopaminergic forebrain (London

Hospital Medical College), and

postdoctoral experience in the UK (LHMC) and USA (Indiana

University and University of California, Riverside), he headed

a research lab (Royal London Hospital) for more than a

decade looking at the effects of drugs on brain dopamine

systems with particular reference to Parkinson's. He was the

author of three academic books and over 200 research

publications and communications.

He was also diagnosed with Parkinson's in 2006 and, since

2011 has been strongly involved in patient advocacy. He

currently works for The Cure Parkinson's Trust and is a

director and co-founder of Parkinson's Movement, a global

patient advocacy collective. He was an ambassador for the

2013 World Parkinson Congress in Montréal.

Since diagnosis, he has also written and published four

books on living with Parkinson's and co-authored a series of

articles on technology tools for Parkinson’s patients including:

P. Wicks, J.A. Stamford, et al. Innovations in e-health. Qual

Life Res, vol. 23, pp. 195-203, 2014; R. Lakshminarayana, et

al. Smartphone-and internet-assisted self-management and

adherence tools to manage Parkinson’s disease (SMART-PD):

study protocol for a randomised controlled trial. Trials, vol.

15, pp. 374, 2014; and R. Narayana, et al. Self-management

in long term conditions using smartphones: A pilot study in

Parkinson’s disease. Int J Integrated Care, vol. 14, 2014.

Dr. Stamford is a frequent invited speaker and also writes a

humorous and influential weekly blog “Slice of Life” about

life with young onset Parkinson's.

Peter N. Schmidt, PhD, earned his

bachelor’s degree at Harvard University

and was awarded an M.S. and Ph.D. from

Cornell University, Sibley School of

Mechanical Engineering where he studied

gait and balance and total joint replacement.

He completed a fellowship at the Hospital

for Special Surgery in New York.

He joined NPF as Chief Information

Officer and Vice President, Research and Professional

Programs in June 2009 where he is responsible for the

Parkinson’s Outcomes Project (POP), a longitudinal study of

Parkinson’s disease with the three aims to (1) identify and

disseminate best practices in care, (2) to identify, track, and

study clinical outcome outliers, and (3) to optimize care in

order to improve the statistical power of clinical trials. With

over 17,500 clinical evaluations of over 7,500 patients, POP is

the largest clinical study of Parkinson’s disease ever

conducted and includes the largest set of patient-reported

outcome measures ever collected in a prospective study.

His career has focused on the intersection of math and

medicine, addressing such problems as engineering

simulations of insults to biological systems, stochastic systems

for chronic disease management, and interoperable electronic

medical records. Along the way, Schmidt contributed to early

versions of Linux, built an encryption system for a Wall Street

brokerage, and published articles on veterinary surgery,

hedging currency risk, and sector-specific characteristics of

stock market valuations. Schmidt holds patents on a total knee

replacement and a mobile device-based video game.

Karl E. Friedl, PhD is an endocrine

physiologist. He received B.A. and M.A.

degrees in zoology from the University of

California at Santa Barbara (UCSB) and

the Ph.D. degree in biology (integrative

physiology) at the Institute of

Environmental Stress, UCSB, in 1984.

From 1983 to 2013, he was a military

officer conducting physiology research and leading medical

research programs for the US Army. His last Army

assignment was as the Director, Telemedicine and Advanced

Technology Research Center (TATRC, 2006-2012). He is

now a physiology consultant in Frederick, Maryland;

Professor (adjunct), Department of Neurology, University of

California, San Francisco; a member of the Board of Directors

for the Parkinson’s Action Network; and a Fellow of the

American Institute of Medical and Biological Engineering.

His research interests include the metabolically optimized

brain, wearable biosensing/physiological models, and limits of

human performance.

Dr. Friedl’s awards and honors include the French Ordre

national du Mérit, US Army Legion of Merit (2nd

OLC), Order

of Military Merit, Morris K. Udall Award for Parkinson’s

Research Advocacy, Ronald and Nancy Reagan Alzheimer’s

Research Award, Diabetes Technology Society Research

Leadership Award, and Outstanding Research and

Development Scientist Award (SAFMLS).

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