A sleep diary exploration.


Create an experience to log sleep details everyday

Sleepio is a CBT (Cognitive behaviour therapy) program scientifically proven to help you sleep well without pills or potion. The "Prof" is a virtual doctor who gives the user weekly advice to improve sleep cycles. To give accurate and effective advice, the user needs to log details about sleep everyday. However, logging sleep details adds an extra job in the users daily workflows. Therefore, we need to create an experience which not only has minimum friction but also acknowledges the busy lifestyles of users. 

Duration - 4 hours


What are some of the tasks users have around sleep?

To do some quick user research, I used my own roommate as a subject. I observed user behaviour before, during and after sleep to create a journey map. This journey map identifies critical points about data.



The next step is to brainstorm ideas on how to make this logging experience fit well into their daily journey. I tried to use the eco system of the user to make the experience more intuitive.

Big ideas that emerged

Using Siri to log sleep details
Voice is a natural way of interaction when compared to touch on the screen.

Conversational Chatbot
Users no longer want to open an extra app to get work done. Chatbots get work done within their messages.

3-point sliders instead of a seperate one for each paramter.
We could design a way to handle more than one data point in a single slider. 

Exploring UI patterns for time input

Here's a list of patterns to help the user input time -

  • Circular clock used in Android Alarm
  • iOS alarm slide pattern (currently being used in Sleepio app)
  • Drop down options
  • Circular clock with 2 data points - Samsung Shealth app
  • Text entry fields for each data point

The new sleep diary

We reduce the friction in logging by boiling it down to 7 simple taps. We also introduce "dream catcher", a way for users to keep track of their daily dreams. It could be an interesting read for the user later on. 

3-pointer slider

Using a single slider, we try to get data about when the user got into bed, when he/she start to try sleeping and when they actually slept. 

In the example here, the user taps on "Try sleeping" label and slides it. To convey mode change and point of action we -

- Change background of label to white
- Fill the pointer with white 
- Activate the time label above the pointer.


Dream catcher

During logging, users are already thinking about their sleep. Now might be a good time to prompt them to write down their dreams. This could be an investment that might intrinsically motivate them to log their sleep details. 

Data summary

The data collected from logging could be used to display a timely story to the user. In this screen, the user can quickly assess if there sleep is improving or declining.

The average sleep indicator helps them assess if there sleep is above or below the recommended hours. This is quantitative.

The ratings at the bottom of the bars help them assess the quality of their sleep.

Conversational chat bots

Sometimes, the user might forget to log their sleep. Instead of a notification, we can use a chatbot to get this information. With my research on notifications, I can say that people respond to messages more quickly than other other kinds of notifications. There fore, we could build a chatbot named the prof to ask simple casual questions and get the data from the user in a natural way.

Siri - Voice interaction

Voice is natural way of communicating for humans. We could use this form of communication to log data into our sleep diary. With the recent updates to iOS 10, apps can use Siri to make services more accessible. Here's a sample scenario of how this would work -

"Hey Siri, I went to bed at 11 yesterday. Browsed through some medium articles for about half an hour and fell asleep. I woke up at 4, checked the time and slept again till 8. It was not the most peaceful sleep though. I got up with a headache."

Next steps

These 3 ideas could potential fit well into the users daily workflows. However, we need perform some user testing to understand if Siri and chatbots are efficient. If not, it might not be worth the time and money. Another important aspect of the 3 point slider is interaction discoverability. How can we provide accurate signifiers to show this affordance. Maybe we could use motion to hint the new interaction model. We also perform some A/B testing to test out single point sliders Vs 3 point sliders.