Executive Summary
This is an AI assisted brief for software developers. It asks for proposals to create a 'clinical decision-support and self-management tool' that can meet the growing demands being placed on primary care mental health services.
A digital therapeutic platform is required to help people regain voluntary control over their attentional resources in response to psychologically salient cues associated with addiction, anxiety, depression and other maladaptive behaviours.
The software should operationalise market leading evidence-based psychological principles (CBT, motivational interviewing, relapse prevention, attentional training, and mindfulness-based strategies) for addictions.
Rather than treating symptoms after they develop, the platform will aim to intervene earlier in the cognitive processes associated with an addiction, at the point where emotionally significant cues capture attention.
The software should assist users in recognising attentional capture, understanding its relationship with emotional states (including trauma and attachment histories), unmet needs and cravings, and lead to practising alternative attentional and behavioural responses until these have become habitual.
The platform is intended to complement existing NHS Talking Therapies, Primary Care Mental Health Teams and addiction services rather than replace them.
Clinical Rationale
The software is to be based on the proposition that many common mental health difficulties involve repeated reinforcement of attentional biases towards emotionally significant cues.
Repeated attention to these cues promotes spreading activation within associative memory networks, increasing craving, rumination and maladaptive behaviour.
The therapeutic objective is therefore to strengthen voluntary attentional control before behaviour is initiated.
Repeated successful disengagement from maladaptive cues is expected to increase inhibitory learning, reduce cue salience and strengthen adaptive behavioural responses.
Primary clinical objectives
The software should enable users to:
- recognise emotional vulnerability
- identify high-risk internal and external cues
- detect early attentional capture
- interrupt escalating craving or rumination
- practise alternative attentional responses
- reinforce successful self-regulation
- develop long-term resilience.
Proposed User Journey
Phase 1 – Personal Learning
The software learns:
- situations associated with distress
- times of day
- locations
- emotional states
- people
- activities
- digital behaviours
- language associated with cravings.
The objective is to build an individual attentional profile.
Phase 2 – Cue Recognition
The software teaches users to notice: "I've become drawn towards something" before "I need this."
Exercises focus on recognising:
- bodily sensations
- thoughts
- emotional shifts
- attentional narrowing.
Phase 3 – Attentional Reallocation
Once cues are recognised, the software guides users through evidence-based exercises such as:
- attentional shifting
- urge surfing
- cognitive defusion
- motivational interviewing prompts
- breathing exercises
- behavioural substitution
- value-based decision making.
Phase 4 – Reinforcement
Every successful interruption is positively reinforced.
The software visualises:
- cravings successfully interrupted
- time spent disengaging
- confidence
- resilience growth
- reduction in automatic responding.
The emphasis is on mastery rather than symptom scores.
Artificial Intelligence Component
The AI should personalise rather than diagnose and include:
Detect recurring patterns in:
- emotional states
- environmental contexts
- cue exposure
- behavioural sequences.
Personalise interventions according to:
- previous success
- time of day
- motivation
- current emotional state
- historical relapse patterns
- periods of elevated vulnerability
- preferred pathways of support
Provide clinicians with a clinical dashboard of:
- personalised cue maps
- relapse trajectories
- resilience measures
- engagement statistics
- intervention effectiveness
- patient-defined goals.
The software should be safe and secure enough to:
- comply with NHS GDPR requirements
- store the minimum necessary personal information
- explain every AI recommendation
- allow patients full control over data sharing
- comply with NHS data governance requirements
- avoid commercial advertising or attention-maximising design.
Design Philosophy
Unlike commercial digital platforms that compete for user attention, this platform should be explicitly designed to strengthen users' capacity to regulate, control, and redirect attention voluntarily.
Success is measured not by time spent using the application but by increasing independence from it.
Most digital health apps ask:
"How are you feeling today?"
Your theory suggests a different primary question:
"What has captured your attention today?"
That is a subtle but profound shift.
Mood becomes a consequence of attentional allocation rather than the only thing being measured.
The app could build an attentional diary, not just a mood diary.
Imagine a timeline that records:
08:30 – Poor sleep; attention repeatedly drawn to work emails.
10:15 – Noticed urge to check betting app after stressful meeting. Practised attentional shift to a planned task for 3 minutes; urge reduced.
18:20 – Loneliness triggered thoughts of alcohol. Used urge-surfing exercise and messaged a friend instead.
21:00 – Reflected that cravings passed without acting on them; confidence rating increased.
Over weeks, the person would begin to see not only what they felt, but how attention moved, which cues consistently captured it, and which strategies successfully redirected it. That creates a personalised map of attentional vulnerabilities and strengths.
We can't prevent salient cues from appearing—that's how human perception works—but we can train people to recognise when attention has been captured and to decide, deliberately, whether to continue investing that scarce cognitive resource.
We suspect that framing the intervention as building attentional control will align more closely with cognitive science and make the concept more compelling to clinicians, software developers, and NHS commissioners alike.