LoopInsights: AI-powered therapy settings analysis#2
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taylorpatterson-T1D wants to merge 10 commits intodevfrom
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LoopInsights: AI-powered therapy settings analysis#2taylorpatterson-T1D wants to merge 10 commits intodevfrom
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Add LoopInsights feature: an AI-driven therapy settings advisor that analyzes glucose, insulin, and carb data to suggest adjustments to Carb Ratios, Insulin Sensitivity Factors, and Basal Rates. Core components: - Dashboard with therapy settings overview, pattern detection, and AI suggestions - Configurable AI provider (OpenAI, Anthropic, Gemini, Grok, self-hosted) - Data aggregation pipeline with test data fixtures from Tidepool - Suggestion lifecycle: pending → applied/dismissed with full history - AI personality settings (Supportive Coach, Clinical Expert, Dry Wit, Tough Love) - Developer mode with auto-apply and test data toggles - Secure API key storage via Keychain - Safety guardrails: max 20% change per adjustment, one setting at a time - Unit tests for models, data aggregation, and suggestion store 22 new files, 4 modified files across Views, View Models, Models, Services, Managers, Resources, and Tests.
…ng, and UI refinements - Wire real therapy settings writes via LoopInsightsSettingsWriter closure - Schedule splitting: insert new entries when AI suggests times not in user's schedule - Revert feature: restore pre-apply settings from suggestion history - Settings Score (0-100) with TIR, Safety, Stability, GMI breakdown - Clinical reasoning framework: AI now understands AID-specific patterns (corrections/day, basal/bolus ratio, time-of-day analysis, cross-setting interactions) - All three settings visible in every AI prompt for cross-setting reasoning - Pre-computed red flags injected into prompt (algorithm workload, basal % alerts) - Stale-data guard: excludes manually reverted changes from recent context - Suggestion merge: consolidates split AI responses into single cards - Pre-Fill Editor: editable proposed values before applying - Auto-applied notification banner - Debug log with Copy Full Log for troubleshooting AI behavior - Temperature forced to 0.0 for deterministic analysis
… advisor UI Add Ask LoopInsights chat with AI advisor powered by therapy context and glucose data. Background monitoring with configurable frequency and notification banners. New Trends & Insights view with Daily/Weekly/Monthly/Stats/Advisor tabs. Dark gradient styling for chat and trends views. Banner now includes Ask button to open chat directly.
…ports Add clinical goal tracking (TIR, A1C, below-range, custom) with progress bars, AI-powered 30-day pattern discovery with sick day and negative basal detection, timestamped reflection journal with mood tags, and HTML-to-PDF report generation with share sheet. Goals & Patterns accessible from the Dashboard navigation section.
… analysis Add HealthKit biometric data (heart rate, HRV, steps, sleep, active energy, weight) to the AI analysis and chat pipelines. Biometrics are read-only, independently authorized, and gracefully degrade when individual types are unavailable. New file: LoopInsights_HealthKitManager.swift Modified: Models, DataAggregator, AIAnalysis, ChatViewModel, Coordinator, FeatureFlags, SettingsView, DashboardView, pbxproj, Localizable.xcstrings
…nsights, Nightscout import - Ambulatory Glucose Profile (AGP) chart with percentile bands and median line - Clarity-style dashboard redesign: Glucose card, Time in Range 5-zone stacked bar, capsule period picker with exact Clarity colors (#C14F0C, #F0CA4C, #74A52E, #D36265, #7F0302) - Caffeine tracker with half-life decay modeling and glucose correlation - Meal insights with food response analysis and per-meal glucose impact - Nightscout data import support - Advanced analyzers for pattern detection - 5-zone TIR breakdown (Very High/High/In Range/Low/Very Low) replacing 3-zone model - Compact list section spacing for tighter dashboard layout - Chat view UI refinements
…card fixes P1: Parallel HealthKit queries via async let (6 concurrent fetches) P2: Single-pass TIR zone counting (5-zone) replacing multiple filter passes P3: Pre-fetch raw data in DataAggregator, cache for cross-component reuse P4: Binary search for glucose lookups in FoodResponseAnalyzer P5: Pre-sorted glucose samples with binary search in AdvancedAnalyzers P6: Pre-compute AGP data in ViewModel instead of SwiftUI view body P7: Static DateFormatter in LoopInsightsTimeBlock.formatTime P8: Pre-sort schedule items before dose loops, pre-sort in ViewModel P9: Pre-convert glucose to parallel arrays avoiding repeated doubleValue calls P10: Pass precomputed hourly averages to circadian profile builder Also: enhanced step/activity data in AI prompts with time-of-day breakdowns and activity-glucose correlation analysis (2h lag), and meal card layout cleanup. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
…y fixes Glucose chart now operates in two modes: standard Ambulatory Glucose Profile (24-hour overlay with percentile bands) for 14-day lookback, and Glucose Profile (multi-day time series) for all other periods. Both modes include an info button explaining the visualization. HealthKit glucose data supplements Loop store for longer analysis periods. Chart data clears on period change to prevent stale labels. Additional fixes across 22 files: improved HealthKit data pipeline reliability, enhanced test data provider, refined food response analysis, and minor bug fixes in background monitor, coordinator, caffeine tracker, and goals/trends views. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
…y fixes Glucose chart now operates in two modes: standard Ambulatory Glucose Profile (24-hour overlay with percentile bands) for 14-day lookback, and Glucose Profile (multi-day time series) for all other periods. Both modes include an info button explaining the visualization. HealthKit glucose data supplements Loop store for longer analysis periods. Chart data clears on period change to prevent stale labels. Additional fixes across 22 files: improved HealthKit data pipeline reliability, enhanced test data provider, refined food response analysis, and minor bug fixes in background monitor, coordinator, caffeine tracker, and goals/trends views.
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Closing — recreating against upstream (LoopKit/Loop) |
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Apr 29, 2026
Implements three programmatic safeguards from Street (2026) "Frontier LLMs require explicit clinical context to avoid training-data anchoring for insulin pump settings" (https://www.diabettech.com/wp-content/uploads/2026/04/diabettech_preprint-3.pdf): 1. Bounds clamping (recommendation #2): Values exceeding the max change % by up to 1.5x are clamped to the limit instead of hard-rejected, with a logged validation note. 2. Citation verification (recommendation LoopKit#3): Glucose values cited in AI reasoning are cross-checked against the actual hourly averages sent to the model (±2 mg/dL tolerance). Misattribution rate is reported as a validation note. 3. Confidence adjustment by data availability: CR analysis with <5 meals or ISF analysis with <3 corrections is capped at Low confidence with an explicit warning about anchor substitution risk. Basal (the most data- driven setting per Street's anchoring spectrum) is not capped. All changes are in the response parsing layer — no prompt modifications: - LoopInsights_AIAnalysis.swift: parseResponse() + extractCitedGlucoseValues() - LoopInsights_Models.swift: validationNotes field on LoopInsightsAnalysisResponse - LoopInsights_DashboardView.swift: validation notes display in UI
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The Problem We're Solving
Managing diabetes with a closed-loop system like Loop requires ongoing tuning of three core therapy settings — Carb Ratio (CR), Insulin Sensitivity Factor (ISF), and Basal Rate (BR). Today, users must manually interpret CGM data, identify patterns (dawn phenomenon, post-meal spikes, overnight lows), and translate those patterns into schedule adjustments. This process is:
Endocrinologists do this work in 15-minute appointments a few times per year. LoopInsights brings that analytical capability to every Loop user, on demand, using their own data.
New Feature Impact
LoopInsights is an AI-powered therapy settings advisor that lives inside Loop's Settings screen. It reads glucose, insulin, and carbohydrate data (read-only), sends aggregated statistics to a user-configured AI provider, and returns specific, time-block-level setting adjustment suggestions with clinical reasoning.
What It Does
User-Configurable Settings
gpt-4o,claude-sonnet-4-5-20250929)Safety Considerations
Feature Architecture
Loop Integration Footprint
Only 3 existing Loop files are modified, with minimal changes:
SettingsView.swiftSettingsViewModel.swiftloopInsightsDataStoresclosure propertyStatusTableViewController.swiftTotal integration footprint: ~28 lines across 3 files.
New Files (40 files)
All LoopInsights code uses the
LoopInsights_prefix and lives inLoopInsights/subdirectories. No LoopKit framework modifications required.Data Flow
Screenshots
Suggested screenshots to include (highest value first):
Video Demo
🎬 A walkthrough video demonstrating the full LoopInsights workflow — from enabling the feature, configuring an AI provider, running analysis, reviewing suggestions, and applying a change — will be linked here.
Beta Test Plan
Prerequisites
feat/LoopInsightsbranch running on a physical devicePhase 1: Basic Functionality
Phase 2: Glucose Charts
Phase 3: Suggestion Workflow
Phase 4: Secondary Features
Phase 5: Edge Cases & Safety
Phase 6: Performance
🤖 Generated with Claude Code