CalmPulse BP focuses on the practical intervention window before stress escalation peaks, turning that short moment into a repeatable sequence: notice, pause, breathe, and reflect.
This reading path makes the problem choice, evidence quality, user framing, execution decisions, and proof trail visible without overstating what the sources support.
watchOS-first behavioral support app for stress-aware micro-pauses, guided breathing, and post-session reflection for young adults with hypertension.
Creates a conservative, non-clinical behavior-support baseline with clear safety boundaries and transparent MVP limitations.
watchOS SwiftUI application organized by state-driven flow (`Onboarding`, `Idle`, `Triggered`, `Breathing`, `Reflection`, `Summary`) with documented domain config and session-log model.
The case-study layer starts with why this problem was selected and how the context justified investment.
Stress-support tools often miss the narrow window when users still have enough bandwidth to run a short intervention.
The project is framed as a watch-first behavioral companion, intentionally separating self-regulation support from clinical diagnosis claims.
It demonstrates product systems thinking under safety constraints, including clear state logic and explicit trust boundaries.
Users often realize stress overload too late, when the best moment for short self-regulation intervention has already passed.
Built a watch-first six-state flow with trigger prompts, guided breathing, quick reflection, and session-summary feedback.
Evidence is surfaced with its source type and credibility note so the recruiter can quickly see what is directly backed versus intentionally constrained.
README explicitly defines the `notice → pause → breathe → reflect` flow and stress-spike timing rationale.
State machine and PRD documents define six app states and runtime defaults.
When formal research artefacts are not available, the page still explains who the work served and why that user framing is justified by the existing sources.
The documented flow and timing logic target quick self-regulation opportunities.
The project publishes explicit PRD, state-machine, and compliance notes.
The goal is to show the trace from research and insight to concrete product or system decisions, then to the outcomes those decisions supported.
Each step keeps the movement from evidence to action explicit before the rationale expands it.
Focused on short intervention timing instead of broad analytics dashboards.
Mapped the user journey into deterministic wearable states with clear transitions.
Embedded explicit non-diagnostic messaging and conservative scope notes.
Each decision keeps the path from insight to execution visible before ending on the outcome signal.
Wearable UX degrades quickly when transition logic is ambiguous.
Implemented a state model spanning onboarding to summary reflection.
Intervention flow is explainable and easier to validate in MVP conditions.
Long protocols reduce completion probability in high-friction moments.
Set a 60-second guided breathing default in runtime config.
The intervention remains realistic for quick daily routine interruptions.
This section preserves the technical and operational substance: architecture, responsibilities, trade-offs, and implementation quality signals.
watchOS SwiftUI application organized by state-driven flow (`Onboarding`, `Idle`, `Triggered`, `Breathing`, `Reflection`, `Summary`) with documented domain config and session-log model.
Creates a conservative, non-clinical behavior-support baseline with clear safety boundaries and transparent MVP limitations.
This visual layer keeps execution readable: how the system or delivery flow was structured and which source-backed outcomes mattered most.
App transitions from idle into prompted pause state when stress signal threshold is inferred.
Short breathing flow provides paced interaction with countdown and state control.
Post-session mood and summary metrics build self-awareness continuity.
Documented in state machine and domain config
Configured breathing duration in runtime settings
Declared transparently in repository documentation
Outcome claims remain conservative and source-backed, while proof records and recruiter-safe links surface the strongest verification trail available.
Six-state intervention flow documented and implemented
Non-diagnostic product scope explicitly documented
Strong case studies show both what was learned and where the current evidence stops.
Next iteration should add hardware validation logs, persistence behavior, and controlled HealthKit integration evidence.