Related Notes: [[How to use potentially use Digital Phenotyping for Addiction Management]] [[Intelligent real-time therapy - harnessing the power of machine learning to optimise the delivery of momentary cognitive-behavioural interventions]] [[Three broad categories of EMA data collection schedules]] [[Ecological Momentary Interventions]] [[Reducing Drinking Among People Experiencing Homelessness Protocol for the Development and Testing of a Just-in-Time Adaptive Intervention]] *We collect sensor data, processed them and compute some sort of prediction, then what? So if we can know craving level, trigger patterns.. can be disrupt it by shifting attention, metacognitive awareness?* Translating digital phenotypes from diagnostic markers into real-time treatment occurs through the design of Just-In-Time Adaptive Interventions (JITAIs). JITAIs deliver personalized, timely support by dynamically adjusting the type, timing, and intensity of interventions to match a user's fluctuating clinical status and contextual vulnerability. ## Basically, HOW to buy a JITAI The architecture of a JITAI is defined by four core operational components: decision points, tailoring variables, intervention options, and decision rules: - **Decision Points:** Highly frequent, predetermined intervals (ranging from once-per-minute sensor sweeps to multiple times-per-day prompt schedules) where an intervention decision is computationally evaluated. - **Tailoring Variables:** Real-time data streams reflecting the user's internal state (e.g., elevated craving levels or physiological stress mapped through heart rate variability) and external context (e.g., proximity to high-risk locations or environmental triggers). - **Intervention Options:** Diverse, clinically validated support modules, including on-demand cognitive reappraisal, mindfulness breathing prompts, and direct clinician alerts. - **Decision Rules:** Algorithmic functions that map specific tailoring variables to appropriate intervention options (e.g., _if_ GPS coordinates indicate proximity to a risk zone _and_ HRV suggests elevated stress, _then_ push an immediate geofenced mindfulness prompt). A prominent example of an active, self-guided mobile intervention is the _Nałogometr_ application. Designed to mitigate relapse risks in substance use and behavioural dependencies, _Nałogometr_ delivers a structured, four-week cognitive-behavioural and mindfulness-based intervention. The application leverages frequent active inputs via EMAs to map cravings and trigger patterns in real time, helping users develop attentional reorientation, metacognitive awareness, and cognitive reappraisal skills. This matches evidence from trials of Mindfulness-Based Relapse Prevention (MBRP), which demonstrate that structured mindfulness practices help decouple cravings from compulsive substance use by enhancing distress tolerance and cue awareness. To expand on these models, contemporary systems are moving towards multimodal, closed-loop designs. Pedro Morouço (Unit for Development, Research and Training, VillaRamadas, Portugal) proposed a novel 3-in-1 integration of digital phenotyping with clinical recovery through the "Digital-PA Loop". Framed within the Change & Grow® Therapeutic Model, this system captures continuous physiological and behavioural inputs via wearable sensors and EMAs. It then applies adaptive machine learning algorithms to trigger personalised interventions across three integrated domains: physical activity adjustments, psychological coping modules, and real-time mindfulness exercises. By incorporating movement and physical activity as a core component of emotional self-regulation, this approach aims to support relapse prevention in high-vulnerability populations.