Holmusk USA, Inc
Holmusk builds digital behavior change programs and predictive algorithms that offer actionable insights for personalized care and population health management.
Holmusk is a digital health company, focused on solving complex problems in healthcare.
We build innovative, scalable and cost-effective digital behaviour change programs that combine cutting-edge clinical research, technology and design to nudge people toward sustainable changes for better health. We develop powerful predictive algorithms that offer actionable insights for personalised care and population health management.
Our mission is to improve health outcomes.
CaregiverAsia and Holmusk partner to provide customized home care solutions
Caregiver Asia Pte Ltd, an online aggregator of on-demand health and caregiving services, and Holmusk, a digital health and data science company, has partnered to wage war against diabetes – a disease that affects one in nine Singaporeans.
Supplementing the CaregiverPlus Program which is a customized home care management service by CaregiverAsia, Holmusk’s GlycoLeap app will enable the CaregiverAsia user community to prevent or control diabetes through a targeted mobile dietician coach.
“Careseekers who are suffering from diabetes or are concerned about keeping the disease at bay will be recommended to subscribe to the GlycoLeap app by our Nurse Consultants,” said Kumari Rai, CaregiverAsia’s Nurse Manager. Along with a comprehensive care plan provided by CaregiverPlus, a healthier weight and lifestyle can be achieved in three to six months.
Said Yeo Wan Ling, CEO of CaregiverAsia, “With the GlycoLeap app, our customized care plans for diabetes will be augmented by Holmusk’s crew of dietitians who are available in real-time for advice and guidance. Innovative startups working together by leveraging on their unique strengths are an excellent way of providing ground-breaking solutions to everyday problems. We look forward to more exciting partnerships like the one we have with Holmusk.”
Multi-omic data analytics collaboration between the University of Oxford and Holmusk
SINGAPORE, Oct. 2, 2017 — The University of Oxford and Holmusk, a digital health and data analytic company, signed a data transfer agreement to collaborate in investigating metabolism in type 2 diabetes.
The collaboration will establish a working relationship between The University of Oxford and Holmusk to obtain and construct models based on metabolic and metabolomic data in type 2 diabetic hearts. The objective is to produce metabolic pathway models, to predict changes in metabolism in the diabetic heart. This will not only provide insights on the mechanisms that drive abnormal cardiac metabolism but it will also enable us to identify new therapeutic targets for type 2 diabetes.
Dr. Lisa Heather will be leading the research for this collaboration from the University of Oxford while Dr. Latt Mansor, Director, Business Development and Joydeep Sarkar, Director of Data Science will be leading the initiative from Holmusk. Dr. Latt Mansor, who was Dr. Heather’s first DPhil graduate, is excited to work with his alma mater and be a part of the team to expand current understanding of diabetic metabolism using academic research data and data analytics. His passion has always been to bring together academic research and industry expertise.
Nawal Roy, founder and CEO of Holmusk, stated that there should be more collaborations of this nature between industry and academia to optimise the valuable skillsets that both parties can bring to the table. He hopes that the combined international talent pool from both University of Oxford and Holmusk can accelerate knowledge generation as well as create insights for innovation in the healthcare ecosystem.
About the Heather Group, DPAG, University of Oxford
Dr. Lisa Heather is a University Research Lecturer at the Department of Physiology, Anatomy and Genetics and Isobel Laing Fellow at Oriel College. Her group investigates the role abnormal cardiac metabolism plays in the development of heart failure and diastolic dysfunction.