Validated Digital
Measures
Objective insights from 24-hour movement data
Advancing Research Through Real-World Digital Measures
ActivInsights supports pharmaceutical sponsors, CROs and public health scientists with validated digital measures derived from 24-hour movement monitoring. Our technology services support endpoint qualification and deliver objective clinical outcome assessments in regulated trials. We also transform high quality raw data into meaningful health metrics for population health insights in sleep, physical activity and chronic disease.
All our digital measures are built on a foundation of clinical-grade validation, captured via low-burden, wearable sensors and optimised for scalability, compliance, and reproducibility.
Explore our digital measures—ready for clinical application, FDA/EMA submissions, and real-world research at scale.
What do our Wearables Measure?
All data is generated from our validated wearables: GENEActiv (raw data, high-resolution data for offline analysis) and the ActivInsights Band (real-time, on-device analytics and secure wireless data transmission).
We deliver validated digital measures across:
- Physical Activity & Behaviours
- Sleep & Rest Cycles
- Gait & Mobility Metrics
- Fine Motor Movement & Physical Function
- Daily Life & Circadian Patterns
- Lifestyle & Quality of Life Indicators
These digital measures can be used as validated digital biomarkers, objective outcome assessments and qualification-ready endpoints. They ensure scientific rigour and reproducibility based on open-source algorithms from peer-reviewed research publications.
Digital Health Measures
Each section outlines the core validated variables in our catalogue. For complete validation evidence, request a downloadable guide.
Physical Activity & Behaviours
Objective, continuous insight into activity and estimated energy expenditure
Physical activity and function provide critical insight into a participant’s everyday life. Captured continuously via low-burden wearables, our data reflect real-world activity and movement patterns across all populations—from healthy volunteers to individuals with chronic or mobility-impairing conditions to evaluating intervention effectiveness, cross-cohort comparisons and disease progression.
Unlike traditional assessments such as the 6-minute walk test or self-reported questionnaires, our digital measures offer objective, passive monitoring outside the clinic—reducing patient burden while improving trial scalability and inclusivity.
| Activity | |
|---|---|
| Inactive Duration | Duration of sedentary bouts excluding sleep |
| Active Duration | Time in non-sedentary bouts |
| MVPA Duration | Time in moderate-to-vigorous activity bouts |
| Light Activity Duration | Duration of light intensity bouts |
| Moderate Activity Duration | Duration of moderate intensity bouts |
| Vigorous Activity Duration | Duration of vigorous intensity bouts |
| Activity Intensity | Average Mean intensity of active bouts |
| Most Active 6 Minutes | Mean activity intensity of the most active 6 minutes in a day |
| Active Volume | Total volume (intensity x duration) of active bouts |
| MET Minutes | Total Metabolic equivalents over time |
| Energy Expenditure | Estimated energy expenditure |
Sleep
Reliable, low-burden digital sleep endpoints for long-term tracking
Sleep is a core 24-hour behaviour linked to health, disease, and treatment response. Sleep is the subject of increased research, as both a primary disorder and a symptom of another disease. Our validated digital sleep measures—including timing, duration, efficiency, and regularity—are captured passively using high-resolution accelerometery.
Algorithms detect the rest intervals without the need for sleep diaries, enabling objective, long-term monitoring in real-world settings.
Backed by peer-reviewed evidence and accepted by regulators, these measures support use as digital biomarkers, clinical endpoints, and screening tools across a range of conditions. They enable researchers to compare results across studies and settings beyond traditional actigraphy.
| Sleep | |
|---|---|
| Bed Time | Start of primary rest interval |
| Rise Time | End of primary rest interval |
| Rest Interval Duration | Duration of the primary rest interval (time in bed) |
| Sleep Onset Time | Start of the sleep interval (sleep time) |
| Sleep End Time | End of the sleep interval (wake time) |
| Sleep Interval Duration | Duration of the primary sleep interval |
| Total Sleep Duration | Duration of bouts assessed as sleep in the sleep interval |
| Sleep Efficiency | % of time asleep while resting |
| Sleep Onset Latency | Time to fall asleep |
| WASO Duration | Duration of non-sleep in the sleep interval |
| WASO Count | Number of non-sleep bouts in the sleep interval |
| Sleep Fragmentation | Index score of sleep fragmentation |
| Nap Duration | Duration of sleep outside the primary rest interval |
| Nap Count | Number of sleep bouts outside the primary rest interval |
Gait and Mobility
Unobtrusive, validated gait metrics for remote populations
Gait is a sensitive indicator of motor function and disease progression, offering valuable insight into neurological, musculoskeletal, and aging-related conditions. Subtle changes in walking patterns can signal early-stage decline, making gait analysis a powerful tool in both diagnostics and therapeutic research.
ActivInsights delivers validated digital gait measures using high-resolution accelerometery, collected from the wrist or other body locations. Objective measures quantify key aspects of mobility, such as walking and running behaviour, both in-clinic and remotely for continuous real-world evidence.
| Gait and Mobility | |
|---|---|
| Total Steps | Number of steps |
| Slow Walking Steps | Number of slow walking steps |
| Fast Walking Steps | Number of fast walking steps |
| Cadence | Mean cadence of walking events |
| 95Cadence | The 95th percentile of walking bout cadence |
| Walking Duration | Duration of walking bouts |
| Running Steps | Number of running steps |
| Running Duration | Duration of running bouts |
| Sit-to-stand transitions | Number of running sit-to-stand transitions |
Fine Motor Movement
Detect subtle symptoms in neurology and dermatology studies
With our wearables, data can be collected on even the most minute movements—those that may be visible to the human eye, but which digital measurement captures in a more objective, continuous, and quantifiable way.
Digital measures of fine motor movement can provide valuable insights into tremors, bradykinesia, or repetitive actions, revealing subtle signs of neurological or dermatological conditions.
ActivInsights offers validated digital measures of fine motor activity, commonly used in the study of Parkinson’s disease, atopic dermatitis, and other conditions where small, repetitive movements are clinically significant. Passively captured through our wearables, these measures support biomarker discovery, endpoint development, and behavioural profiling in real-world settings.
| Fine Motor Movement * | |
|---|---|
| Bradykinesia Duration | Total duration in bradykinesia |
| Tremor Duration | Total duration in tremor |
| Tremor Intensity Average | Mean intensity of tremor bouts |
| Tremor Intensity Maximum | Maximum intensity of tremor bouts |
| Nocturnal Scratch Count | Number of nocturnal scratch bouts |
| Nocturnal Scratch Duration | Total duration of nocturnal scratch bouts |
| Nocturnal Scratch Intensity | Mean intensity of nocturnal scratch bouts |
*Available for GENEActiv only.
Within-Day Patterns
Circadian and behavioural variability over time
Understanding within-day activity patterns offers deep insight into behavioural rhythms, circadian health, and deviations that may indicate underlying clinical conditions.
By analysing how activity and inactivity are distributed throughout the day, you can identify patterns that reflect sleep quality, motor function, fatigue, and overall lifestyle regularity.
| Within Day Pattern | |
|---|---|
| Inactive Bout Count | Number of inactive bouts |
| Inactive Bout Duration Variance | Standard deviation of inactive bout durations |
| Active Bout Count | Number of active bouts |
| Active Bout Duration Variance | Standard deviation of active bout durations |
| Walking Bout Count | Number of walking bouts |
| Walking Bout Duration Variance | Standard deviation of walking bout durations |
| Sleep Midpoint | Midpoint of the rest interval |
| Lx Time | Midpoint of the least active x hours |
| Lx Intensity | Mean intensity of the least active x hours |
| Hx Time | Midpoint of the most active x hours |
| Hx Intensity | Mean intensity of the most active x hours |
All daily outputs include validated off-wrist detection with integrated environmental light and near-body temperature measures. Multi-day outputs are also available, such as:
- Sleep Regularity Index
- Interday Stability
- Intradaily Variability
- Rest-Activity Rhythms
- Workday/Weekend/Shift Behaviour Analysis
24-hour movement measures from behavioural bouts
The high-resolution (sensor-level) data output of the GENEActiv allows behavioural bouts to be detected and characterised offline, after data collection. While the ActivInsights Band uses on-board algorithms to process sensor data in real-time, transmitting wirelessly to a secure global data infrastructure.
The behavioural bouts from either of our wearables are then built into 24-hour movement measures, multi-day outputs and visualisations. other connected devices can then be built up into daily summary measures and longer-term measures.
Designed for Population Research & Clinical Trials
Validation First
Regulatory Alignment
Global Deployment
Contact Us
We work with leading pharmaceutical sponsors and digital health innovators to implement clinically meaningful, regulatory-ready digital endpoints. If you’re planning a study or exploratory endpoint strategy, our scientific team can support your design.
