A4W is a project started in the years of my journey with anxiety. It’s based on the idea of a “AtoZ of mental health” that is able to accompany the reader from poor mental heath, through change to a point of long term wellbeing. Now in early alpha, the content is published in a series of apps that the user can collect as they wish … forming their own collection of wellbeing resources. The apps are able to host intelligent interactions that help the reader through education, resolution to conclusion forming their own personalised wellbeing routine. The project currently has to main branches as outlined below. 


    • research content & outcomes
    • research case studies, problem mapping, change planning & outcomes
    • research data modelling, analysis & machine learning
    • development web, plugin and app
    • design visual communication & animation

    Welcome to A4W

    where would you like to begin?

    Anxiety Toolkit
    Body and Mind
    Career Management
    Creativity Techniques
    Communication Skills
    Decision Making
    Depression Toolkit
    Emotional Intelligence
    Fears and Phobias
    Failure Management
      Learning skills
      People Skills
      Problem Solving & Solution Finding
      Project Management
      Self Skills
      Strategy and Analysis
      Thinking Errors
      Time Management
      Stress Management
      Success Management


        A4W is able to record a users progress through their day with a number of Key Performance Indicators. At key points analysis interactions are triggered and subsequent responses recorded. The following snapshots illustrate such trigger / responses

        In this first example a Interaction is triggered in the first session where the user reports a series of low wellbeing scores. Recovery is recorded in the next hour, but ongoing interactions are needed to maintain a good state.

        In this example the overlap of a users last session at 02.30 and their next at 11.00 am. A interaction is triggered and they are returned to a generally well balanced state for the rest of their day.


        This example shows the user has recorded a low score through their morning. A interaction is triggered in their lunch hour and recovery can be observed through their afternoon.