W3LL

Most wellness apps ask you to log your feelings, count your steps, or wear yet another device. But real wellbeing isn’t just about what you say or how many steps you take – it’s about how you live, move, and flow through your day.

That’s where W3LL Companion stands apart. Powered by the science of digital phenotyping[1][2], it quietly learns from the natural rhythms of your life – your movement, routines, and even where you tend to spend time – using the sensors already built into your phone, without ever accessing the content of your phone or identifying you.

The result? A smarter, more effortless way to prevent future care costs – while uncovering fun and interesting lifestyle discoveries daily about what truly fuels your balance.

What makes W3LL Companion different?

■ Passive Sensing, Not Manual Logging

No more daily “update my mood” chore. W3LL Companion works quietly in the background to learn your natural patterns and observes changes to your wellbeing – so you can live your life, not log it.

■ Real Insights, Backed by Science Using the proven science of digital phenotyping, we go beyond step counts and self-reports to truly understand you. . We turn this information into simple and practical tips you can actually use.

■ Daily Tips That Actually Help Bite-sized, science-based strategies that boost your mood, focus, and resilience – sent at the moments you’ll benefit most.

■ WhatsApp Integration No new app routines to learn! Get friendly nudges right through WhatsApp – easy, seamless, stress-free.

■ Track Your Progress Watch your wellbeing unfold over time and celebrate the small wins that lead to lasting balance.

■ Privacy You Can Trust Your data stays with you. We will never sell or share it and it is securely stored for your eyes only.

Discover wellbeing, effortlessly. No daily logs, no guesswork, just smart and personal insights to help you feel and perform your best every day.

1. Wang, R., et al. (2018). Tracking Mental Health with Mobile Phone Sensing: A Survey. IEEE Transactions on Affective Computing. This survey paper reviews the use of mobile phone sensors, including motion and location, to infer mental health states. It provides a strong basis for the claim that these methods are a subject of significant research and have potential for real-world application.
2. Schoenfelder, D. et al. (2019). The use of smartphone sensors to predict mood states in patients with bipolar disorder. Journal of Affective Disorders. This study shows how passive sensor data from smartphones can be used to predict mood states in clinical populations. It directly links the use of motion and location data to the detection of changes in mood and behavior.