Research

The thinking behind the loop.

Medication gets people started. Staying with it is a daily, behavioral problem. Here is the design thesis behind Nishi, in plain language, and the working paper it comes from.

Working paper Health Economics & Systems Architecture October 2026
The problem

GLP-1 works. Staying on it is the hard part.

GLP-1 medications regulate appetite and blood sugar well. But the paper's starting point is a sobering one: a large share of people, by some estimates up to half, stop within the first year. Rarely because the drug failed. Usually because daily life got in the way.

Side effects

Shifting digestion and recurring mild nausea or fatigue wear people down and push them to quit early.

Nutrition gaps

Too little protein during rapid weight loss costs lean muscle and slows metabolism, the opposite of the goal.

App fatigue

Logging every meal and symptom into a blank prompt is friction. People abandon the tracking long before the plan.

The architecture

An active observation loop, not a blank prompt.

The idea at the center of the paper: flip the app from reactive to proactive. Instead of waiting for you to type, the system watches context in the background and surfaces one low-friction, useful step at a time.

It starts quietly. Nishi reads background signals from Apple Health and Health Connect, sleep, activity, body metrics, with no manual logging.
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Morning

State check

Reads overnight recovery and where you are in your dose, then suggests a gentle, protein-forward breakfast that is easy on nausea.

Midday

Pattern match

Tracks protein and energy as the day builds and nudges a small adjustment before the afternoon dip sets in.

Evening

Quiet execution

Checks what is in your kitchen against your targets, queues gentle recipes, and fills in what is missing for your grocery cart.

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And it adds up for your doctor. Weekly dose, symptoms, weight trend, and nutrition fold into a one-tap summary you can share with your endocrinologist or dietitian.
The difference

Two ways to run the same regimen.

 
Traditional, unassisted
Active observation
Daily experience
High-friction manual logging and blank prompts
A proactive, three-beat daily loop
Staying on therapy
High dropout in the first 6 to 12 months
Support aimed at the symptom and nutrition hurdles that cause it
Clinical visits
Fragmented, from-memory patient reports
A structured, one-tap trend summary
Cost of care
Sunk cost when courses are abandoned
More value from each course that is completed
Read it in full

The working paper.

Working paper · Pre-print

An Active Biometric Observation Architecture for Improving Pharmacological Adherence and Reducing Macroeconomic Healthcare Expenditure

Julian Rodriguez, Jr. (ORCID), Royal Economic Society / Royal Society of Arts

Satyam Bansal (ORCID), Nishi

The global rollout of GLP-1 receptor agonists is a major advance in chronic disease management, but it shares one operational failure point across patients and health systems: high discontinuation. The paper sets out an active biometric observation architecture designed to run alongside treatment, replacing passive, high-friction manual logging with a proactive observation loop of background telemetry, real-time symptom management, and automated dietary execution.

This is a working paper that lays out a design thesis. It argues a model for supporting adherence and makes an economic case for it. It is not a clinical trial, and it does not claim proven medical outcomes. Nishi is a wellness companion, not a medical service.

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