Handbook of Digital Technologies in Movement Disorders.

Format:

Book

Author/Creator:

Bhidayasiri, Roongroj.

Contributor:

Maetzler, Walter.

Language:

English

Subjects (All):

Parkinson's disease.

Movement disorders.

Physical Description:

1 online resource (366 pages)

Edition:

1st ed.

Place of Publication:

San Diego : Elsevier Science & Technology, 2024.

Summary:

This comprehensive handbook explores the integration of digital technologies in the management of movement disorders, with a focus on Parkinson's disease. Edited by Roongroj Bhidayasiri and Walter Maetzler, it addresses the challenges and opportunities presented by digital health tools, artificial intelligence, and novel analytics in clinical practice and research. The book is intended for medical professionals, researchers, and healthcare providers seeking to enhance patient care and improve diagnostic and therapeutic strategies. It covers topics such as digital outcomes, AI applications, unmet clinical needs, and regulatory considerations, providing a thorough understanding of the current landscape and future directions in the field of movement disorders. Generated by AI.

Contents:

Front Cover

Handbook of Digital Technologies in Movement Disorders

Copyright

Dedication

Contents

List of contributors

Preface

Acknowledgments

I - Digital technologies: The primer

1 - We are living in the Parkinson's pandemic: how can multi-stakeholders utilize digital technologies effectively?

Parkinson's disease as a prototypical hypokinetic disorder: recognized for more than 200 years we now know more and more ab ...

Living in the era of a double (Parkinson and COVID-19) pandemic: an upcoming global challenge

There is no one size-fits-all solution with differences in each region, country, society, and system: where do digital tech ...

Digital solutions in Parkinson's disease and other movement disorders: global, community, and individual considerations

There are things that we know about PD and movement disorders, and there are also many unknowns, but we should balance the ...

Toward a real-life implementation of clinically relevant multi-modal digital markers

Conclusion

References

2 - Embracing the promise of artificial intelligence to improve patient care in movement disorders

Introduction

Clinical reasoning in movement disorders: Neurologist's view

Toward the simulation of neurologist's clinical reasoning in movement disorders

Toward a better way for neurologists to onboard artificial intelligence: AI as assistant, monitor, coach, and teammate

Toward postCOVID-19 pandemic conditions: how artificial intelligence impacts movement disorders care and research

Neurologists and artificial intelligence: the two paths joining together

II - Different stakeholders' perspectives on technology in movement disorders

3 - Medical professional's viewpoint and clinical adoption.

Current need for digital health technology-based tools in movement disorders

Applications of digital health technology-based tools in movement disorders and current role in clinical practice

Clinical needs and potential future applications of digital health technology-based tools

Critical appraisal of digital health technology for movement disorder diseases

4 - Applying technologies to unmet clinical needs in movement disorders

Unmet clinical need #1: accurately diagnosing, characterizing, measuring, and monitoring movement disorders ("deep phenotyp ...

Unmet clinical need #2: non-motor disability, wellbeing, and integrated care

Unmet clinical need #3: lack of proper incentives in healthcare reimbursement models

Unmet clinical need #4: access to subspecialty care and advanced treatments in movement disorders

Unmet clinical need #5: home-bound segment of the movement disorder patient population

Unmet clinical need #6: the need to adopt artificial intelligence

Conclusions

5 - Drug development for movement disorders: using digital measures for decision making

Supporting drug development

Barriers to drug development

Capturing variability

Improving sensitivity

Reducing rater bias

Enriching clinical trials

Internal decision making

Regulatory decision making

Role of regulators

Study endpoints

Clinical outcome assessments and biomarkers

Indirect measures

Evidential requirements

Regulatory guidances

Regulatory pathways

Future directions

Use in regulatory decision making

Market access

Precompetitive collaborations

6 - Novel analytics in the management of movement disorders

The need for robust data management infrastructure.

Clinical perspective: why do we need novel analytics for movement disorders clinical management?

Managing and handling data collected with digital technologies

Data processing: examples of application for mobility and gait analysis

Digital device data calibration

Traditional approaches for movement detection and characterization

Machine deep learning techniques for gait event detection and characterization

Extracting meaningful information from a continuous stream of data

Current feature engineering approaches for gait analysis

Traditional and established analytics - the "white box"

Advanced novel techniques for information extraction from digital devices

Novel approaches: novel features and data fusion - the "gray box"

Using analytics for early diagnosis of movement disorder

Leveraging state-of-the-art analytics like machine learning and digital technologies

Potential of digital technologies and gait analysis as a diagnostic tool

Opportunities for prognosis of clinical endpoints

Monitoring the progression of PD

Techniques and tools for longitudinal analysis

Missing data and dropouts

Classification of falls risk using machine learning

Machine deep learning for real-time fall risk assessment

Daily life management of movement disorder: Parkinson's disease

Tracking medication adherence and novel analytics: potential and future applications

Approaches for PD disease severity assessment from real-world data

Recommendations and future trends

III - Technologies in movement disorders: Detecting disease, monitoring symptoms and treatment response, and measuring p ...

7 - Digital outcomes: potentials and clinical considerations

Digital outcomes: potentials and clinical considerations

Definitions

The benefits of digital outcomes

The potential of digital outcomes.

Early diagnosis

Monitoring progression and prediction of deterioration

Patient reported outcomes

Digital outcomes in clinical trials

Current challenges in implementing digital outcomes

Challenges relating to data collection and data processing

Challenges relating to data interpretation

Challenges relating to implementation in clinical care and clinical trials

The roadmap for implementation

Methodological road map: addressing challenges relating to data

Toward biomarkers, clinical outcome assessments, patient reported outcomes and endpoints

8 - Technologies for identification of prodromal movement disorder phases and at-risk individuals

Prodromal Parkinson's disease

Premanifest Huntington's disease

Motor function

Nonmotor symptoms

Other prodromal and premanifest movement disorders

9 - Toward digitalization of clinical rating scales

History of the development of rating scales and the transition from paper to electronic

Digital data collection: evolution and applications

Methodological advantages of using electronic rating scales

Regulatory implications

Practical steps for implementing a rating scale in an electronic format

Development of electronic clinical scales

Implementation of electronic clinical scales

Equivalence studies

Digitalization of rating scales in movement disorders: real case studies

Movement disorders society scales experience

Proprietary electronic data capture systems

10 - Patient diaries in movement disorders: opportunities and limitations

Disease state diaries: what are they?

Existing disease diaries in movement disorders: the Parkinson's disease experience.

Caveats of existing diaries: granularity, contextual, and clinimetric limitations

Granularity limitations: absence of nonmotor behaviors, OFF and ON definitions, underrepresentation of partial states, and ...

Contextual limitations: limited cognitive debriefing, heterogeneous assessment frequency and status determination, problems ...

Clinimetric limitations: heterogeneous measurement formats and validation methods

E-diaries: more than just a change in format

E-diaries: co-creation approach

E-diaries: available digital technologies

E-diaries: rigorous and innovative clinimetric validation

E-diaries: compliance with regulatory agencies

E-diaries: core limitations

E-diaries: modular development approach

E-diaries: application in other movement disorders

11 - Wearables for diagnosis and predicting clinical milestones

Introductory overview

Supporting diagnosis

Symptoms suggestive of a diagnosis of Parkinson's disease or other movement disorders

Current evidence for the utility of wearable sensors to detect Parkinson's disease symptoms

Sleep disturbances, including difficulty turning in bed, derived from wearable sensors

Cardinal motor symptoms (tremor, bradykinesia) derived from wearable sensors

Current evidence for the utility of wearable sensors to detect balance and gait changes in Parkinson's disease

Balance and gait impairments derived with wearable sensors: from prodromal/early stage to later disease stages

Potential for wearable sensors to aid in diagnosis

Predicting clinical milestones

Freezing of gait derived from wearable sensors

Cognitive decline detected from changes in gait

Motor complications derived from wearable sensors.

Summary of evidence for the utility of sensors to support diagnosis and prediction of milestones for Parkinson's disease.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

Other Format:

Print version: Bhidayasiri, Roongroj Handbook of Digital Technologies in Movement Disorders

ISBN:

9780323994958

0323994954

OCLC:

1419055113