Using ayodee as between-session homework: a guide for clinicians

Self-monitoring is one of the most evidence-supported components of CBT, DBT, and motivational interviewing for substance use. It's also the component that most consistently fails in practice , because paper records don't travel to the situations where the behaviour occurs, because retrospective recall produces systematically inaccurate data, and because there's no prompt to complete the record in the moment when it matters.

ayodee addresses all three of these problems. This guide is for practitioners who want to integrate it into their clinical work.

What ayodee provides that paper records don't

In-the-moment recording. The app is on the client's phone, present in the situations where substance use and urges actually occur. The clinical literature is consistent: self-monitoring completed at or near the event is more accurate and more therapeutically potent than retrospective completion. ayodee's daily reminder system prompts recording at a time the client selects.

Antecedent and consequence data. The daily log captures mood, stress, energy, and sleep , both as antecedents (the emotional state before use) and consequences (next-day mood and sleep quality). This is the A and C of the ABC model, collected automatically alongside the B. Paper records require client effort to complete all three fields; ayodee structures the record to capture them as a matter of course.

Urge logging independent of use. Clients can log an urge with a single tap, recording intensity and context without necessarily logging substance use. This is therapeutically significant: the urge data separates the craving from the behaviour, making the cue-craving-response sequence legible in a way that use-only records cannot produce.

Longitudinal pattern data. After three to four weeks of consistent logging, the app surfaces correlations , mood preceding heavier use, sleep cost following it, urge timing patterns. This pattern data arrives in session ready for collaborative examination rather than requiring the clinician and client to reconstruct it from memory or incomplete records.

Validated assessment data. AUDIT, DAST, DASS-21, and PHQ-9 are delivered at evidence-based intervals. The scores arrive with the session data. Baseline and follow-up comparisons are available without separate administration.

How to assign it

Frame it in CBT terms if that's your modality. "I'd like you to keep a daily self-monitoring record between our sessions. This is one of the central tools in CBT for exactly this kind of work , the evidence shows that the act of recording consistently changes the behaviour. The app does the structural work; you just need to log each day."

Frame it in DBT terms if appropriate. "This is essentially a diary card , the same daily record of emotions, urges, and behaviours that's core to DBT. The difference is it's on your phone, so it's there in the moments when things are actually happening."

Frame it as feedback for MI. "I'd like to have accurate data about your use and your mood patterns before our next session. This app generates the kind of personalised feedback that's a central component of motivational interviewing , your actual data, in context, rather than estimates."

In all cases, emphasise: timing matters. The therapeutic effect of self-monitoring is strongest when recording happens at or near the event. Once-a-day logging at the end of the day is far better than reconstructing the week before a session.

What to look for in the data at the next session

Review the data before the session begins if the client shares a report. Focus on:

Antecedent patterns. What were the mood/stress ratings on the days before the heaviest-use days? The correlation between emotional state and subsequent use is almost always there in the data and is rarely visible to the client before they see it.

Consequence data. Sleep quality and next-day mood scores following heavier use versus lighter or no use. This data is often where the client's stated belief ("it helps me relax / sleep") is most directly challenged by their own evidence. Use it collaboratively, not confrontationally: "What do you make of the difference in sleep scores on these nights versus these ones?"

Urge patterns. When do urges cluster? The time-of-day and contextual pattern of urge data is typically quite specific and often surprises clients who had experienced their cravings as unpredictable. Identifying the high-risk windows is the first step in Marlatt's relapse prevention work.

Exception data. Identify the lower-use or zero-use periods. What was different? Solution-focused examination of exceptions is often more productive than deficit analysis of heavy days, particularly with clients who are ambivalent about change.

Trends. Is consumption stable, increasing, or decreasing across the logged period? A trend , visible in the data but not in memory , is often the piece of information that shifts a client's self-assessment most significantly.

Which client profiles benefit most

Pre-contemplation and contemplation stage clients. The app is designed for people who haven't decided to change. The data it generates , accurate, non-judgemental, their own , is the most effective tool available for moving clients from "I don't think it's a problem" to "I can see there's a pattern here." The MI feedback component is most potent in this group.

Clients who minimise or underestimate their use. Memory-based self-report systematically underestimates consumption. Clients whose self-reported use seems inconsistent with their presentation , whose affect, sleep, or functioning suggests heavier use than they're describing , often show a significant gap between their estimate and their logged total. This gap is clinical data and a useful therapeutic focus.

Clients between more intensive treatment phases. For clients who have completed residential or day programme treatment, the app provides structured between-session monitoring during the consolidation phase. The Marlatt risk-map function is particularly relevant here: the high-risk situation data is most valuable when the client has some investment in not using it.

Clients who struggle with recall or completion of paper records. The phone-based format, the daily reminder, and the structured fields remove most of the practical barriers to consistent self-monitoring.

A note on privacy

ayodee collects no identifying information , the client registers with a passphrase only. No name, no date of birth, no email. This means:

The client may be more willing to log accurately, knowing the record is not attached to their identity. Clients who express anxiety about records , particularly those with legal, employment, or child protection considerations , can be reassured that the app architecture makes identification impossible.

The clinician receives only what the client chooses to share. The client generates a PDF report within the app and sends it directly to you. Nothing is automatic. Nothing is shared without deliberate client action.

This design is clinically useful: the self-monitoring data belongs to the client, which supports the autonomy and self-efficacy that both SDT and MI research identifies as predictive of sustained change.

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ayodee is available at ayodee.com. For information about the clinician dashboard and batch reporting features, visit the clinicians section of the website.