AppsForWellbeing

INTRODUCTION

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.

PROJECT REQUIREMENTS TO DATE

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?

Addiction

Anxiety Toolkit

Body and Mind

Career Management

Creativity Techniques

Communication Skills

Decision Making

Depression Toolkit

Emotional Intelligence

Fears and Phobias

Failure Management

Leadership

Learning skills

People Skills

Problem Solving & Solution Finding

Project Management

Self Skills

Strategy and Analysis

Thinking Errors

Time Management

Stress Management

Success Management

BRANCH 1 : PRIVATE TRIALS

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.