Health App + AI
The apps you already use, finally readable.
One practical guide per wellness app. Each one shows how to stack a chat assistant, a notebook tool and an agent on top of the data the app already collects — so you can read it, not just look at it.
Track with a device rather than an app? Browse the Devices + AI guides →
AI + Claude
The careful chat model, configured to externalise your executive function.
A viral post claims Claude has a hidden ADHD Executive Function Mode that lets you finish a week of work in four hours. It does not — there is no secret mode, and no button to press. What is real is the set of prompts underneath the hype. They work because they do the one thing a scattered brain cannot do for itself: hold the plan outside your head and hand it back one piece at a time. Here is how to stack Claude into a focus-and-health system that survives a bad week.
AI + Hume Health
AI + Hume Health: read the whole band, not one more score.
The Hume band tracks sleep, heart rate, recovery and body composition — then hands most people a daily score and a nudge. A small stack of free AI tools lets you read the data underneath, across every metric at once, and decide what actually matters.
AI + Lifesum
AI + Lifesum: how to actually use the nutrition data your app already collects.
Lifesum expertly tracks your food intake and provides personalised guidance. However, many users rarely move beyond the in-app summaries. By adding a small stack of AI tools, you can interpret your long-term patterns and gain actionable insights from your own nutrition data.
AI + Carb Manager
AI + Carb Manager: how to actually use your nutrition data for informed decisions.
Carb Manager collects a wealth of detail on your diet and health. Yet, for many, this valuable information remains locked within the app, unexamined beyond daily logging. A simple AI stack can transform raw numbers into actionable insights, helping you understand your eating patterns and their impact.
AI + MyFitnessPal
AI + MyFitnessPal: read the data your nutrition app already collects.
MyFitnessPal collects a vast amount of nutrition data. For most users, this information remains largely unanalysed, hidden within the app’s interface. A concise stack of AI tools can transform your raw logged data into actionable insights, helping you understand your nutrition patterns more deeply.
AI + Cronometer
AI + Cronometer: read the data your nutrition app already collects, with a little help from AI.
Cronometer meticulously logs details about your dietary intake, yet for many, this rich daily record remains largely unexamined. By integrating a small stack of AI tools, you can transform raw nutrition data into interpretable insights, understanding the subtle patterns of your personal health.
AI + Zoe
AI + Zoe: how to read the nutrition data your app already collects.
Many nutrition apps, including Zoe, collect an abundance of personal health data. Yet, for most users, this information remains largely unanalysed beyond the app’s default summaries. A simple stack of AI tools can transform your raw Zoe data into meaningful, actionable insights, helping you understand your unique nutritional landscape.
AI + Levels
AI + Levels: Understand your continuous glucose monitoring data with a simple AI stack.
Levels provides a wealth of continuous glucose monitoring data, offering a window into your metabolic health. However, much of this granular information often remains unexamined beyond the in-app summaries. By responsibly stacking a few AI tools, you can extract deeper, more actionable insights from your existing Levels data.
AI + Hedia
AI + Hedia: how to integrate your CGM companion app into a personal health data system.
Hedia reliably collects vital data for diabetes management, but most users only interact with a fraction of its potential. By combining Hedia with a small stack of AI tools, you can move beyond simple logging to understand trends and patterns in your own health data. This guide shows you how.
AI + Lifesum
AI + Lifesum: how to actually read the nutrition data your app already stores.
Lifesum diligently logs your dietary intake, but many users simply track without truly understanding their patterns. This often leaves valuable insights dormant within the app. By constructing a small AI stack, you can transform raw nutrition logs into actionable, personalised insights.
AI + Rise Science
AI + Rise Science: practical steps to integrate your sleep data for real insights.
Most health apps diligently collect your personal data, yet for many, this information remains largely unexamined, locked away within the app's interface. Imagine transforming that dormant data into actionable insights, without needing to become a data scientist. By combining your Rise Science data with a simple AI stack, you can finally contextualise and understand your sleep patterns, making informed adjustments to your daily routine.
AI + Oura Ring
AI + Oura Ring: how to understand the data your wearable already collects.
Your Oura Ring generates a wealth of personal health data daily, yet most users only glance at the app's summary. This guide explains how to use a small stack of AI tools to move beyond superficial statistics, enabling you to derive practical, ongoing insights from your own health information.
AI + WHOOP
AI + WHOOP: How to actually use your wearable_companion data to understand your health.
WHOOP gathers extensive data on your recovery, strain, and sleep, but raw numbers often remain unexamined. Most users interact with the app's summary, missing deeper insights within their own records. By layering a small AI stack, you can transform this raw data into meaningful, personalised understanding of your health patterns.
AI + Calm
AI + Calm: How to actually use your mindfulness data
Calm gathers valuable insights into your meditation habits, sleep patterns, and stress responses. Most users engage with the app daily but rarely reflect on this aggregated data. By connecting Calm with a simple stack of AI tools, you can transform raw app activity into meaningful personal understanding.
AI + Headspace
AI + Headspace: how to make sense of the mindfulness data you already collect.
Headspace records every meditation session, sleepcast, and focus exercise you complete, yet most users never review this rich dataset. By integrating a small stack of AI tools, you can transform these raw logs into actionable insights, helping you understand your patterns and refine your mindfulness practice.
AI + Hatch Restore
AI + Hatch Restore: How to actually use your sleep data
Hatch Restore collects valuable data on your sleep environment, but for many, this data remains unexamined within the app. By applying a small stack of AI tools, you can transform raw numbers into actionable insights, helping you understand and refine your sleep patterns.
AI + AutoSleep Tracker for Watch
AI + AutoSleep Tracker for Watch: How to actually use your sleep data
Your Apple Watch meticulously tracks your sleep, courtesy of apps like AutoSleep Tracker. Yet, for many, this rich stream of personal health data remains an unread ledger. By integrating a straightforward AI stack composed of a chat assistant, a notebook tool, and an agent layer, you can transform raw metrics into actionable insights about your rest.
AI + Sleep Cycle
AI + Sleep Cycle: read the data your sleep app already collects.
Sleep Cycle provides a wealth of personal sleep data, but many users simply glance at the 'sleep score' and move on. This guide demonstrates how a small stack of AI tools can transform your raw sleep data into actionable insights. By learning to retrieve and interpret your own information, you can begin to understand your unique sleep patterns with greater clarity.
AI + Calm
AI + Calm: How to actually use your mindfulness data
Modern mindfulness applications like Calm offer more than guided meditations and sleep stories; they collect valuable personal data. This guide demonstrates how to combine Calm's insights with AI tools to better understand and utilise your own wellness journey.
AI + Headspace
AI + Headspace: How to actually use your mindfulness data
Headspace offers a structured approach to mindfulness and meditation, but the insights it provides are often general. By integrating AI tools, you can extract personalised patterns and actionable observations from your engagement data. This guide demonstrates a practical method to deepen your understanding of your own mindfulness journey.
AI + BetterMe
AI + BetterMe: How to actually use your fitness data for personal well-being
BetterMe offers structured fitness, nutrition, and habit coaching. We show you how to move beyond merely following its plans and instead, harness your personal data to understand what truly works for your body. This guide details how to stack AI tools on top of BetterMe, transforming raw data into actionable insights.
AI + MyFitnessPal
AI + MyFitnessPal: How to actually use your nutrition data for better insights
MyFitnessPal offers valuable nutrition tracking, but its data often remains in silos. By integrating AI tools, you can move beyond simple logging to understand patterns, identify trends, and derive actionable insights from your dietary habits. This guide outlines a practical method for doing just that.
AI + Sleep Cycle
AI + Sleep Cycle: How to actually understand your sleep data
Sleep Cycle offers valuable insights into your nightly rest. However, raw data often needs careful interpretation to become truly useful. By integrating AI, you can move beyond simple metrics and begin to discern meaningful patterns in your sleep.
AI + Flo Period Tracker
AI + Flo Period Tracker: Understanding your menstrual data with a multi-tool approach
Flo Period Tracker collects valuable information about your menstrual cycle, but raw data alone can be challenging to interpret. By integrating AI tools, you can move beyond simple tracking to genuinely understand the patterns and fluctuations within your own health data. This guide outlines a practical, evidence-based method to do so.
AI + Strava
AI + Strava: How to use your fitness data for insightful understanding
For many, Strava is the primary record of their physical activity. While the app provides a good overview, extracting deeper, personalised insights often requires additional effort. This guide explains how to use AI tools alongside Strava to move beyond simple activity logging and genuinely understand your fitness patterns.