ATTUNE (Attentional Training for Understanding Needs and Emotional Regulation)

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, with or without any comorbid anxiety, depression or other psychiatric conditions.

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, understand its relationship to their  emotional states (including trauma and attachment histories), identify and tolerate unmet needs and cravings, and assist the practising of 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 therapeutic target is not necessarily the low mood or even the addictive behaviour itself, but the person's ability to tolerate cues and cravings without acting on them. This may have previously been  referred to as inhibitory control or response inhibition in the literature.

The basic ideas involved have strong support from several therapeutic traditions:

·        CBT teaches people to identify triggers and test alternative responses.

·        Motivational Interviewing strengthens commitment to change.

·        Relapse Prevention focuses on anticipating high-risk situations.

·        Acceptance and Commitment Therapy (ACT) teaches willingness to experience cravings without acting on them.

·        Mindfulness-Based Relapse Prevention explicitly trains people to "surf" urges until they pass.

·        Marlatt’s "urge surfing" refers to learning that cravings are temporary internal events that do not require action.

Despite the psychotherapeutic theory, meta-analyses of scientific studies have struggled to show reliable effects; although this may be because baseline traits of impulsivity were not controlled for (re: Moeller et al 2015; Burton et al. 2021; Mehta et al 2021; Fascher et al 2023; Thai et al 2024; Di Rosa et al 2026).

Thus, this call is for software to be developed that is based on the proposition that:

1)     Many common mental health difficulties involve repeated reinforcement of attentional biases towards emotionally significant cues.

2)     Attentional biases can be changed without changing the underlying attachment and trauma histories or the predisposing structure of any relevant personality or neurodevelopmental traits.

3)     Behavioural reinforcement of cognitive cues promotes repeated attention, and spreading activation within associative memory networks, increasing craving, rumination, and expectations for future reinforcements.

4)     The therapeutic objective is therefore to strengthen voluntary attentional control before behaviour is initiated.

5)     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.

THE PROPOSED USER JOURNEY

Phase 1 – Personal Learning

The objective is to build an individual attentional profile. The software learns:

·        situations associated with distress

·        times of day

·        locations

·        emotional states

·        people

·        activities

·        digital behaviours

·        language associated with cravings.

 

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 emphasis is on mastery rather than symptom scores. The software visualises:

·        cravings successfully interrupted

·        time spent disengaging

·        confidence

·        resilience growth

·        reduction in automatic responding.

 

ARTIFICIAL INTELLIGENCE COMPONENT

The AI should personalise rather than diagnose and:

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.

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?"

This 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.