Research & Science
Foundational research
Sahha's technology is backed by cutting-edge research in digital phenotyping. We are actively involved in internal and external research projects collaborating with leading universities and research organisations. Our commitment to rigorous scientific inquiry ensures that our products are reliable and effective.
Evidence-based Approach
Guided by State-of-art Literature
We rigorously review and select findings from reputable scientific studies
Verified with Internal Studies
We conduct our own studies to verify these findings using our extensive dataset
Driven by Domain Expertise
Our team of PhDs collaborate with leading professors to ensure the highest standards of research and development
Our Flagship Research
5000+ Participants
Across a diverse range of ages (18-65), genders, and nationalities (UK, US, CA, AU, NZ, and more)
50K+ Clinical Samples
Tens of thousands of clinical assessments collected, allowing us to build foundational models
Validated by 100K+
Our models are validated in the real world by hundreds of thousands of people globally
Smartphone-based stress detection
This study explores the relationship between smartphone-derived sensor data to evaluate stress levels with the aim of providing an accessible solution for stress monitoring without relying on wearable technology. The study, involving 820 participants across multiple countries, collected behavioral data including physical activity, sleep patterns, and phone usage metrics over six months. The logistic regression model demonstrated strong predictive capability with an AUC of 0.76 and an Average Precision score of 0.78, effectively distinguishing between healthy and stressed individuals based on their behavioral patterns.
Phone activity & depression
This study explores the relationship between smartphone-derived activity patterns and depression levels. The study involved 2,864 participants and collected 26,888 PHQ-9 samples. The findings indicate that higher active hours, early morning activity, and regular physical activity are associated with lower depression levels, while nighttime activity correlates with higher depression levels. The research concludes that inferring depression levels from smartphone activity data is scientifically feasible.
Key Findings
Association of Daily Steps and DASS-21
Association of Sleep Timing and DASS-21
Research & Science
Foundational research
Sahha's technology is backed by cutting-edge research in digital phenotyping. We are actively involved in internal and external research projects collaborating with leading universities and research organisations. Our commitment to rigorous scientific inquiry ensures that our products are reliable and effective.
Evidence-based Approach
Guided by State-of-art Literature
We rigorously review and select findings from reputable scientific studies
Verified with Internal Studies
We conduct our own studies to verify these findings using our extensive dataset
Driven by Domain Expertise
Our team of PhDs collaborate with leading professors to ensure the highest standards of research and development
Our Flagship Research
5000+ Participants
Across a diverse range of ages (18-65), genders, and nationalities (UK, US, CA, AU, NZ, and more)
50K+ Clinical Samples
Tens of thousands of clinical assessments collected, allowing us to build foundational models
Validated by 100K+
Our models are validated in the real world by hundreds of thousands of people globally
Smartphone-based stress detection
This study explores the relationship between smartphone-derived sensor data to evaluate stress levels with the aim of providing an accessible solution for stress monitoring without relying on wearable technology. The study, involving 820 participants across multiple countries, collected behavioral data including physical activity, sleep patterns, and phone usage metrics over six months. The logistic regression model demonstrated strong predictive capability with an AUC of 0.76 and an Average Precision score of 0.78, effectively distinguishing between healthy and stressed individuals based on their behavioral patterns.
Phone activity & depression
This study explores the relationship between smartphone-derived activity patterns and depression levels. The study involved 2,864 participants and collected 26,888 PHQ-9 samples. The findings indicate that higher active hours, early morning activity, and regular physical activity are associated with lower depression levels, while nighttime activity correlates with higher depression levels. The research concludes that inferring depression levels from smartphone activity data is scientifically feasible.
Key Findings
Association of Daily Steps and DASS-21
Association of Sleep Timing and DASS-21
We regularly publish scientific white papers, blogs and other articles on the topic of digital phenotyping within applications.
Scientific Advisors
Backed the original PhD in digital phenotyping, our advisory board brings expertise from a multitude of desciplines to make the Sahha mission come true.
A. Professor Mathew Kiang
Stanford University
Computational Epidemiology Lab, Digital Phenotyping
Professor Nathan Berg
Otago University
Applied Statistics, Behavioural Economics
Dr Christina Maher
University of Sydney
Behavioral Science, Computational Neuroscience
Research Team
We're a team of PhDs and researchers, bringing advances made in research labs to the real world.
Gurleen Singh
Head of Product and Science
Computational Neuroscience, PhD
Sugam Budhraja
Head of Machine Learning
Machine Learning applied to Mental Health, PhD
Samuel Tan
Lead Research Scientist
Machine Learning applied to Mental Health, PhD
Vaibhav Rawat
Lead Data Engineer
We regularly publish scientific white papers, blogs and other articles on the topic of digital phenotyping within applications.
We regularly publish scientific white papers, blogs and other articles on the topic of digital phenotyping within applications.