ICAMPAM 2024 Oral Sessions

Wednesday, June 19 at 10h15-11h15

Oral Session #1

Clinical Populations 1

O.1.1 – The Relationship Between Gait Capacity and Gait Performance in Neurological Patients
O.1.2 – Wearable Sensors Can Capture Changes in Turning Mobility in Daily Life After Mtbi Rehabilitation
O.1.3 – Alterations in the Daily Living Gait and Mobility During the Day and Night Among Individuals With Cerebellar Ataxia, SCA3: An Exploratory Study
O.1.4 – Associations Between Real World Gait and Pain in Individuals Scheduled for Knee Arthroplasty: Feasibility Study
O.1.5 – Exploring the Relationship Between Fragmentation and Circadian Rhythm of Daily-Living Physical Activity, Functional System Disability Scores, and Physical Fatigue in People With Multiple Sclerosis

Oral Session #2

Measurement Innovations

O.2.1 – Building an Interactive Online Network Application for Harmonising Physical Activity Data From Wearables
O.2.2 – Let’s Do It Again: A New Tool for Adding Spatiotemporal Context to Human Movement Behaviour Data
O.2.3 – Annotating Validation Studies Using Vision-Language Models
O.2.4 – Pilot Evaluation of the Robustness of Ecological Momentary Assessment Labeling for Free-living Physical Activity Data
O.2.5 – A Novel Approach to True Free-living Validation of Accelerometer-Measured Movement Behaviors

Oral Session #3

Predicting health outcomes

O.3.1 – Accelerometer-Measured Daily Physical Activity and Risk of Incident Cancer in the UK Biobank Prospective Cohort
O.3.2 – Temporal Analyses of Physical Behavior and the Association With Health Indicators
O.3.3 – Can Measures of Habitual Activity Intensity Stratify Primary Sjogren’s Syndrome Participants With Persistent Fatigue? Insights From the Brc Tools Study.
O.3.4 – Can Wearable Accelerometers Improve the Prediction of Cardiovascular Disease?
O.3.5 – Estimating BMI From the Complexity of Gait Dynamics in Free Living Data

Oral Session #4

Algorithms 1

O.4.1 – Proof of Concept: Extracting Physical Behaviours From 24-Hour Narrative Data Using Integrated Large Language Models and a Philosophy of Event Approach
O.4.2 – Variation in Step Counts by Different Prediction Methods in Relation to Epidemiologic Studies and Public Health Translation
O.4.3 – Identifying Physical Activity Types Using Thigh-Worn Accelerometry: Comparison of Two No-Code Classification Methods
O.4.4 – Do We Need Age-Specific Human Activity Recognition Models for Classification of Activty Types in Children and Adolecents? –Effect of Age and Length of Activity Bouts
O.4.5 – Heart Rate Monitoring From Motion-Corrupted Photoplethysmography: A Benchmark Study of Open-Source Algorithms

Wednesday, June 19 at 11h45-12h45

Oral Session #5


O.5.1 – Exploring the Impact of Dopaminergic Medication on Real World Digital Mobility Outcomes in People With Parkinson’s
O.5.2 – Walking Recognition in Parkinson’s Disease Populations Using Wrist-Worn Accelerometers
O.5.3 – Can Localisation Information Improve Our Understanding of Real-world Walking in People With Parkinson’s and Older Adults?
O.5.4 – Harnessing Wearable Cameras and Computer Vision to Contextualise Free-living Mobility in Parkinson’s Disease
O.5.5 – Daily-Life Measures of Gait and Turning Across the Spectrum of Normal, Prodromal, Early and Moderate Parkinson’s Disease

Oral Session #6

Digital Mobility

O.6.1 – Requirements for Reliable Estimates of Digital Mobility Outcomes of Walking Activity and Gait From a Lower Back Inertial Measurement Unit in a Large Multi-cohort Study
O.6.2 – The Number of Days Required for a Reliable Estimate of Diverse Digital Mobility Outcomes From Various Gait Domains, Derived From a 1-Week Worn Lower Back Sensor. Analysis Across Different Subject Cohorts
O.6.3 – Walking Slowly – the Accuracy of Real-world Digital Mobility Estimates After a Hip Fracture
O.6.4 – Digital Mobility Outcomes in Hip Fracture Patients the First Year After Hip Fracture Surgery
O.6.5 – Predicting Future Falls Through Digital Mobility Biomarkers in Real-world Monitoring of Community-Dwelling Older Adults

Oral Session #7

Post Joint Replacement

O.7.1 – Brisk Cadence Is Possible and Enhanced Following a Physical Activity Behavior Intervention After Total Knee Arthroplasty
O.7.2 – Improvements in Physical Capacity May Not Indicate Reductions in Real-world Sedentary Activity: A Longitudinal Post-total Hip Arthroplasty Study
O.7.3 – Feasibility of Instrumented Insoles for Long-term Monitoring of Gait After Tibial Fractures
O.7.4 – Monitoring Real-world Gait to Evaluate Changes in Mobility After Total Knee Arthroplasty
O.7.5 – Evaluation of Real-world Mobility Recovery After Hip Fracture Using Digital Mobility Outcomes

Oral Session #8

Sedentary behaviour

O.8.1 – Validation of an Algorithm for Sit-to-Stand and Stand-to-Sit Identification During Activities of Daily Living
O.8.2 – Patterns of Sedentary Time Accumulation According to Age in the United States: A 2003–2006 Nhanes Analysis
O.8.3 – Testing the Consensus Method for Sedentary Time From a Hip-Worn Accelerometer
O.8.4 – Effect of Brief Pedaling Bouts During One Hour of Video Game Play on Popliteal Artery Diameter and Velocity
O.8.5 – Impact of Cut Point Selection on Levels of Physical Activity and Sedentary Time of Toddlers

Wednesday, June 19 at 14h00-15h00

Oral Session #9


O.9.1 – Assessing Physical Behaviour in a Community Led Lifestyle Intervention in a Remote Australian Aboriginal Community: Elcho Island Teachings
O.9.2 – Home vs Community Stepping Patterns: A Measure of Participation
O.9.3 – Enhancing Digital Health Evaluation: Integrating Intensive Longitudinal Monitoring of Physical Activity in a Randomized Controlled Trial – a Case Study From the Dippao Rct.
O.9.4 – Influence of a Tablet-Based, Gamified Exercise Application on Physical Function and Physical Activity for Older Adults: A Feasibility Study
O.9.5 – Wearable Fitness Trackers for Continuous Activity Monitoring in Patients With Haemotological Malignancies Undergoing Stem-Cell Transplant: A 16-Week Intervention Study

Thursday, June 20 at 14h00-15h00

Oral Session #10

Clinical Populations 2

O.10.1 – Wrist Accelerometer Measures Correlate With Disease Activity in Rheumatoid Arthritis and Are Robust to Frequency Downsampling
O.10.2 – The Benefits of Passive Monitoring: A Comparative Analysis of Walking in Real World Using Patient Reported Measures
O.10.3 – Can Feedback From Inertial Measurement Units Enhance Foot Strike Angle and Forward Propulsion in Individuals Recovering From Stroke?
O.10.4 – A Machine Learning Contest Enhances Automated Freezing of Gait Detection and Reveals Time-of-Day Effects
O.10.5 – Measuring and Predicting Daily Physical Activity in People With Chronic Diseases: The Role of Exercise-Related Affective Responses and Perceived Exertion

Oral Session #11


O.11.1 – Concurrent Validity of the Activpal Crea Algorithm and Self-report Diary to Measure Time in Bed and Sleep in Older Adults
O.11.2 – Wearables and Nearables Measuring Sleep and Physical Activity Among Autistic Adults
O.11.3 – Subjective Reports of Sleep Time: Validation Against Accelerometry-Based Estimates
O.11.4 – Using Smartphone Screen Times to Estimate Sleep Times in University Students
O.11.5 – Sleep Timing and Variability: Associations With Other Components of the 24-Hour Activity Cycle

Thursday, June 20 at 15H30-16H30

Oral Session #12

Children and Young People

O.12.1 – Comparison of Machine Learning Techniques for Estimating Energy Expenditure in Preschool Children From Germany and Canada Through Accelerometry Assessment
O.12.2 – Assessing 24-Hour Movement Behaviors in 0-4-Year-Old Children: A Comparative Analysis of Accelerometers and Proxy-Rerport Using the My Little Moves App
O.12.3 – Responsiveness of Accelerometer-Based Physical Activity Estimates in Youth
O.12.4 – Assessing the Accuracy of Activity Classification Using Thigh-Worn Accelerometry: A Validation Study of Actipass in Children
O.12.5 – Machine Learning Models to Detect Physical Activity and Sedentary Time From a Hip-Worn Accelerometer in Toddlers

Oral Session #13

Algorithms 2

O.13.1 – Optimized Machine Learning Models for the Estimation of Energy Expenditure Based on Physiological Signals Measured With Wearables
O.13.2 – Validation of Two Novel Human Activity Recognition Models for Typically Developing Children and Children With Cerebral Palsy
O.13.3 – Innovative Real-world Gait Detection in Huntington’s Disease: Unraveling the Challenges of Chorea Through Deep Learning
O.13.4 – Activity Recognition Using Data From Wearable Sensors and Smart Shoe Devices: Classifying Kick-Board and Skateboard Commuting Behaviors
O.13.5 – Quantifying Daily Head Turns and Head-Trunk Coupling in Healthy Adults

Oral Session #14

24 Hour Behaviours

O.14.1 – Identification of Number of Days Needed to Measure Reliably Fragmentation of Rest Activity Patterns: A Pseudo-Simulation Study Based on Whitehall Accelerometer Sub-study
O.14.2 – Feasibility of Continuous 24-Hour Accelerometry Across Pregnancy
O.14.3 – Combined Associations of Type 2 Diabetes and Vibration Sensation Loss With Device-Measured Physical Activity and Sedentary Behaviour. – the Maastricht Study
O.14.4 – The Assessment of the 24-Hour Physical Behavior Construct via Wearables: A Systematic Review of Validation Studies.
O.14.5 – Daily Physical Activity and Video Game and TV Time on Body Mass Index in Youth: National Youth Fitness Survey

Friday, June 21 at 11h45-12h45

Oral Session #15


O.15.1 – Accuracy and Acceptability of the Desk Positioning System (Dps): A New Sit-Stand Desk Measurement Device
O.15.2 – Device-Based Measurement of Office-Based Physical Behaviour: A Systematic Review
O.15.3 – Association Between Device Measured Physical Activity at Work and Self-perceived Physical Work Demands Among Hospital Workers in Norway – the Stunth Study
O.15.4 – Sitting, Standing and Active Behaviors of Office Workers Participating in an Ergonomic Intervention: How Close Do They Get to a ‘Just Right’ Ergonomic Balance?
O.15.5 – The Effect of a Co-designed Eight-Week Workplace Health Promotion Initiative on Occupational Sedentaty Time, Physical Activity and Glucose Control With Adults Who Hold Desk-Based Occupations: Preliminary Baseline Results

Oral Session #16

Measurement Innovations 2

O.16.1 – Quantifying the Intensity of Free-living Physical Activity in Absolute and Relative Terms: Improving Understanding of Differences in Activity by Age and Associations With Mortality.
O.16.2 – Development of a Screening Tool to Identify Self-report and Device-Based Measures of Habitual Physical Activity for Use in a Systematic Review: The Optima Study
O.16.3 – An Intersectional Approach Towards Accelerometer-Based Physical Behaviour Pattern Analysis: Sequence Mapping
O.16.4 – Identification of Circadian Rhythm Profiles in Older Adults: A Comprehensive Approach Using Data From the Whitehall II Accelerometer Sub-study
O.16.5 – Comparative Evaluation of Ecological Momentary Assessment (Ema), Global Physical Activity Questionnaire (Gpaq), and Bouchard’s Physical Activity Record (Bar) for Measuring Physical Activity: A Multilevel Modeling Approach