Research & Approach

The science behind Lina

What we do, how we do it, and the research behind our decisions.

hello@findlina.com

What actually is Lina?

Lina is a daily companion for people on GLP-1 medications like Ozempic, Wegovy, Mounjaro, and Zepbound. We help you track what actually matters: protein intake, hydration, side effects, and the non-scale victories that keep you going. Everything is built on behavioral science research. We never make medical claims or give dosing guidance. Think of it as daily support between your doctor visits.


The gap between prescription and persistence

GLP-1 medications work well when patients stay on them. But staying on them is harder than most people expect.

66%

of patients report feeling shame about using weight loss medication, leading them to hide what they're doing and quit treatment early.

Rubino et al., Lancet Diabetes & Endocrinology, 2020

4-6

weeks is when side effects peak and dropout rates spike. Without support during this window, many patients conclude the medication "isn't for them."

GLP-1 Clinical Literature Review


The emotional side

Clinical trials don't capture what it actually feels like. Many people hide their GLP-1 use, which creates isolation right when they need support most. The internal dialogue doesn't stop: "Is this cheating? What happens when I stop?"

Your prescriber has fifteen minutes with you every few months. The emotional side of this process needs daily support.

Lina fills this gap. We're there every day, between the appointments where clinical care happens.


How tracking non-scale victories builds lasting habits

The scale fluctuates with hydration, hormones, and time of day. It can't tell you that your energy is better or your clothes fit differently. Non-scale victories are often the more meaningful signs of change.

66

days on average

How long it takes for a new behavior to become automatic. Not 21 days as the myth goes.

Lally et al., 2010

2-3x

better follow-through

When people decide in advance when and where they'll perform a behavior.

Gollwitzer, 1999

50-100%

improved outcomes

Self-monitoring improves weight management outcomes. The single strongest predictor of success.

Burke et al., 2011


The Lina habit loop

1. Track a non-scale victory

Notice something good that has nothing to do with a number. More energy, jeans fitting differently, making it through the afternoon without sugar. These moments slip by when you're only watching the scale.

Celebrating small wins triggers dopamine release, reinforcing the behavior. Woolley and Fishbach (2018) found this makes habit formation 2.5x more likely to succeed.

2. Build pattern awareness

Patterns emerge that were always invisible. Your energy crashes without breakfast protein. Your mood lifts when you drink enough water. Lina surfaces these connections so you can actually see what's working.

Burke et al. (2011) found self-monitoring is the strongest predictor of successful weight management, improving outcomes by 50-100%.

3. Create daily consistency

One good day doesn't do much. A hundred ordinary days does. Lina's check-ins take less than a minute, but that minute repeated across months adds up. The streak system makes consistency visible.

Lally's study (2010) found it takes an average of 66 days for a behavior to become automatic, ranging from 18-254 days depending on complexity.

4. Stack new habits

Attach new habits to ones you already have. Log protein while you eat breakfast. Do your check-in while coffee brews. You're not adding more to your day, you're fitting things into gaps that already exist.

Gollwitzer's research (1999) showed "when-then" planning increases follow-through by 2-3x.

5. Build health identity

At some point, you stop saying "I'm trying to be healthier" and start saying "I take care of my health." That shift comes from repeated action, not affirmation. What you do every day becomes who you are.

Oyserman's research (2006) found connecting behavior to identity improves long-term adherence by 40%.

6. Achieve sustainable outcomes

The weight changes. The health scores improve. But by this point, those outcomes feel like side effects of how you live, not goals you're grinding toward. The habits carry the weight.

Ryan and Deci (2000) found intrinsic motivation predicts 3x better long-term weight maintenance than extrinsic motivation.

Why the scale fails

People who focus on how they feel (energy, mood, daily function) show 3x better long-term weight maintenance than those focused on scale numbers. The scale tells you what happened. Non-scale victories tell you what's actually changing.

Ryan & Deci, 2000

Small wins compound

Each non-scale victory triggers dopamine release, and your brain starts encoding the behavior that led to it. Over time, healthy choices become rewarding on their own instead of something you have to force.

Woolley & Fishbach, 2018


What Lina tracks

Clinically relevant data points

Daily check-ins

  • Mood and energy levels (5-point scale)
  • Side effects (user-selected from common GLP-1 effects)
  • Non-scale victories
  • Sleep quality

Nutrition tracking

  • Protein intake (g) with personalized goals
  • Water intake (oz/ml) with personalized goals
  • Meal logging with AI-powered nutritional estimates

Medication adherence

  • Injection tracking (date, time, dose if user enters)
  • Injection site rotation reminders
  • Next dose reminders

Progress visualization

  • Weight trends (with smart smoothing)
  • Progress photos with side-by-side comparison
  • Streak tracking for daily engagement

All data is user-reported and user-controlled. We never access prescription records, pharmacy data, or medical records.


Everything Lina does

A condensed look at the full feature set

Home & dashboard

  • Dynamic greeting with dose/streak badges
  • Scrollable calendar strip with protein rings and check-in indicators
  • Reorderable dashboard cards
  • Daily rotating insights from 100+ entry library

Meal scanning & analysis

  • Camera or text-based logging
  • AI nutritional analysis using USDA values
  • Health score (0-100), macro breakdown, glucose forecast
  • Portion adjustment and GLP-1-specific meal insights

Daily check-ins

  • 5-step flow: mood, feelings, side effects (40+ options), notes, non-scale victories
  • Celebration feedback and personalized streak messages

Water & protein

  • Daily goals with progress tracking
  • Manual and auto-logging from scanned meals
  • Customizable targets

Medication tracking

  • Dose recording with injection site rotation
  • Dual medication support
  • Configurable alarms
  • Medication blood level chart with pharmacokinetic modeling

Exercise & fitness

  • Workout timer, 200+ exercise library
  • Strength/cardio/flexibility inputs
  • Apple Health step sync
  • Weekly activity calendar

Weight & body

  • Weight trend charts
  • Ethnicity-adjusted BMI
  • Stats cards (current, start, goal, progress)
  • Home card with percentage toggle

Progress photos

  • Camera capture and body measurements (chest, waist, hips, thighs, arms)
  • Photo comparison with draggable slider
  • Grid repository

Journal

  • Daily timeline with date-browsable history
  • Mood-colored calendar dots
  • Aggregated nutrition, meal, check-in, water, and exercise entries

Insights library

  • 100+ insights across 5 categories (mindset, body, nutrition, education, community)
  • Collect and share functionality

Streaks

  • Dual-trigger system (check-in or protein goal)
  • Streak freezes earned at milestones
  • Detailed streak modal

Notifications

  • Check-in reminders, dose reminders and alarms
  • Goal nudges and re-engagement system
  • Supportive (non-guilt) messaging

Data export

  • Full PDF export of all data (check-ins, doses, meals, weight, water, streaks, photos)
  • Date range filters

Profile & settings

  • Daily targets and medication config
  • Physical stats, unit preferences
  • Apple Health toggle, glucose forecasting toggle
  • Health profile and 7 unlockable app icons

How we calculate your goals

Personalized targets based on published research

Protein Goals

Preserving muscle during GLP-1-induced weight loss matters. Lina calculates protein goals using:

Formula: 1.2g protein per kg of current body weight

This multiplier sits in the middle of the 1.0-1.6g/kg range recommended for muscle preservation during weight loss. Goals automatically adjust as users log new weights.

Minimum: 50g/day

Absolute floor for any adult

Maximum: 120g/day

Practical ceiling accounting for GLP-1 appetite suppression

References:

  • Phillips SM, et al. "Protein requirements for muscle preservation." Am J Clin Nutr. 2016
  • Westerterp-Plantenga MS, et al. "Dietary protein and weight management." Annu Rev Nutr. 2009;29:21-41

Calorie Goals

For users who opt into calorie tracking, Lina calculates goals using the Mifflin-St Jeor equation, the most widely validated formula for estimating BMR.

BMR = (10 × weight in kg) + (6.25 × height in cm) - (5 × age) - 161

TDEE = BMR × activity multiplier

Goal = TDEE - 500 kcal (moderate deficit for ~0.5kg/week loss)

Activity multipliers (Harris-Benedict revised):

Sedentary

1.2

Lightly active

1.375

Moderately active

1.55

Very active

1.725

Note: GLP-1 medications already reduce appetite, so a moderate deficit is appropriate. We never push users toward extreme restriction.

BMI with ethnicity-adjusted thresholds

Standard BMI cutoffs were developed primarily using White European populations and don't reflect equivalent cardiometabolic risk across ethnicities. Lina uses research-based ethnicity-specific thresholds.

Thresholds (based on equivalent T2DM risk to BMI 30 in White populations):

White

30

Black

30

South Asian

23.9

Chinese

26.9

Reference: Lancet Diabetes & Endocrinology, 2021

All BMI displays include a disclaimer: "Lina is not medical advice. Always consult your healthcare provider for personalized guidance."


Health score and AI food insights

How we help patients understand their food choices

Lina's AI meal analysis gives each logged meal a Health Score from 0-100. The score reflects nutritional quality without moral judgment.

How it works

  1. Users photograph or describe their meal
  2. AI estimates nutritional content using USDA standard reference values
  3. A health score is calculated based on protein/fiber content, sugar/sodium levels, and whole vs. processed food indicators

Score ranges

70-100

Nutritious

High protein, fiber, whole foods

40-69

Moderate

Some processed elements

0-39

Consider Alternatives

High sugar, fried, ultra-processed

Important framing: The score is labeled as "an estimate to help guide healthier choices," not a medical assessment. The goal is to inform without shaming.


How we can work together

Digital tools and clinical care are better when they're connected

Patient recommendation

You can recommend Lina as a between-visit support tool for patients on GLP-1 medications. The app is free to start, with premium features on subscription.

Content collaboration

If you're interested in contributing educational content, get in touch. Everything is reviewed for compliance before publication.

Research partnerships

We're open to research collaborations that help us better understand GLP-1 patient outcomes.

Feedback

We welcome clinical feedback on our approach. If you see something that concerns you, we want to know.

To discuss any of these opportunities:

hello@findlina.com

Frequently asked questions

Common questions from clinicians and patients