Call Details

Tama

Phone
+16695775590
Scheduled Time
Sep 29, 2025 09:51 PM PDT
Timezone
America/Los_Angeles
Status
completed
Call Type
daily_analysis_update
Created
Sep 29, 2025 09:41 PM PDT
Data Analysis Period
Sep 27, 05:00 PM to Sep 29, 09:41 PM (America/Los_Angeles)

Call Timing Context

Call Time Label
Evening
Is Morning
False
Is Mid-day
False
Current Hour
21

Summary

This evening call reviews your data from the past three days. Overall, you’ve shown strong performance on 09-28 and 09-29 with excellent activity, nutrition, and sleep scores. Your glucose control on 09-29 is outstanding, with an average of 102 mg/dL and a GMI of 5.7%. However, 09-30 shows incomplete data for activity and sleep, so try to log some movement and sleep patterns later today. Nutrition is balanced overall, though occasional high GI items like Chocolate Chip Cookie and Macadamia White Chocolate Cookie should be monitored. Stress tracking is missing and could help complete your health picture. Compared to yesterday, today’s records are a bit lower in logged metrics, so keep an eye on that. Your Heald Score indicates an excellent day with strong overall health. Tip: Consistent data logging across all pillars will support your diabetes reversal journey even further.

Activity Analysis

Highlights

  • 09-28 recorded 14,981 steps and a workout time of 50.3 minutes with a perfect score of 100.
  • 09-29 maintained strong activity with 10,764 steps and an average workout HR of 100.36, keeping the score at 100.
  • 09-30 shows no recorded activity data, likely due to the day being partial so far.

Recommendations

  • If possible, add a short walk or light activity later today to keep the momentum.
  • Aim for consistent logging throughout the day to avoid data gaps.
  • Review your routine to ensure even partial days include at least minimal movement.

Detailed Notes

  • High daily step counts on 09-28 and 09-29 indicate robust physical activity.
  • Workout HR readings were steady, showing effective exercise intensity.
  • Calorie burn values support strong energy expenditure on full-data days.
  • 09-30’s missing data suggests the day is still in progress or not fully recorded.
  • Consistent performance on previous days reflects excellent overall activity habits.

Glucose Analysis

Highlights

  • 09-29 glucose data shows an average of 102 mg/dL with a median of 103 mg/dL.
  • A low GMI of 5.7% indicates very good glycemic control.
  • No hypoglycemic events or glucose spikes were detected.

Recommendations

  • Continue with your current dietary and activity practices to sustain glucose stability.
  • Aim to record glucose data for today to complete your trend.
  • Monitor post-meal readings to catch any subtle spikes early.

Detailed Notes

  • The single day logged (09-29) reflects excellent glycemic balance.
  • Averaged glucose levels remain within target ranges.
  • MAGE is 0, pointing to minimal fluctuations throughout the day.
  • The absence of TBR and TAR events reinforces stable readings.
  • Expanding tracking to today will provide a more comprehensive picture.

Nutrition Analysis

Highlights

  • Macronutrient distribution is balanced at 33% protein, 42% carbs, and 25% fats with an overall score of 91.
  • 09-28 logged 1515 calories with 113g carbs, while 09-29 had 844 calories with 75g carbs.
  • High GI foods detected include Chocolate Chip Cookie and Macadamia White Chocolate Cookie.

Recommendations

  • Strive for more consistent daily calorie logging across all meals.
  • Reduce the consumption of high GI foods to further support blood sugar control.
  • Consider adding fiber-rich foods to smooth out carbohydrate absorption.

Detailed Notes

  • Meal breakdowns indicate good awareness of food timing.
  • Variations in calorie intake suggest areas for more consistency.
  • Glycemic distribution is mostly low with only a small percentage of high GI items.
  • The logged food counts (3-4 per day) provide useful insight.
  • Monitoring high GI items can further optimize nutritional quality.

Sleep Analysis

Highlights

  • Sleep scores on 09-28 (89) and 09-29 (91) indicate good sleep quality.
  • Both nights provided a clear mix of light, REM, and deep sleep stages.
  • 09-30 has no sleep data yet, likely due to the day being incomplete.

Recommendations

  • Maintain a consistent sleep schedule to sustain quality rest.
  • Aim to enhance deep sleep duration especially on nights with lower deep sleep.
  • Log sleep data for the entire day to track nocturnal patterns comprehensively.

Detailed Notes

  • 09-28 showed 1.43 hours of deep sleep and low awake time, supporting recovery.
  • 09-29 had slightly higher light sleep but increased awake minutes.
  • Variability in REM and deep sleep may affect overall restfulness.
  • Consistent sleep data helps identify trends for optimal recovery.
  • Ensuring full-night records will better support your sleep analysis.

Stress Analysis

Highlights

  • Stress metrics for all three days are recorded as 0, which suggests missing data.
  • No variations in strainScore or recoveryScore were observed.
  • This lack of stress data makes it hard to assess daily stress fluctuations.

Recommendations

  • Begin tracking stress levels using a simple subjective scale or journal.
  • Incorporate mindfulness or relaxation exercises to support stress management.
  • Consider using wearable tools that can capture stress-related metrics.

Detailed Notes

  • The absence of stress tracking leaves a gap in understanding overall well-being.
  • Recording even a simple daily stress rating can help detect patterns.
  • Integrating stress data could correlate with sleep and activity metrics.
  • Reliable stress monitoring may inform necessary lifestyle adjustments.
  • Future analyses will benefit from more detailed stress reporting.

Call Logs & Conversation

AI Call Summary

Main Concern(s) Shared: The primary focus was on Shweta's overall health performance for the day, emphasizing her activity levels, glucose control, nutrition logging, sleep quality, and absence of stress data. The goal centered on maintaining metabolic stability and optimizing health metrics through consistent nutrition tracking and stress monitoring. Other Topics Discussed: Additional topics included clarifying step count data for the current day and reinforcing recommendations for increasing daily steps, workout consistency, and incorporating post-workout stretching. Patient Responses: Shweta was generally cooperative and receptive, confirming readiness to receive insights and seeking clarification regarding her step count data. She pointed out a timing inconsistency in the agent's references to 'today' versus 'yesterday,' demonstrating attentiveness and engagement. Beyond that, she did not express further questions or concerns. Health Insights Shared: The AI presented detailed data indicating an excellent overall health score for 2025-09-25, including a perfect activity score of 100, 15,271 steps, increased workout duration to 81.92 minutes with an average heart rate of 66.55 bpm, and improved average glucose level at 80.6 mg/dL down from 90.66 mg/dL. Sleep quality was strong with a score of 86 despite a slight increase in awake time. Nutrition logging was minimal with only two entries totaling 1024 calories, and stress data was absent. Recommendations Given: The AI advised Shweta to improve meal logging consistency to better support her active lifestyle, integrate stress monitoring to complete her health data profile, aim to increase daily steps closer to her previous high of 26,886 by incorporating short walks, maintain structured workouts while enhancing overall daily movement, and consider light stretching post-workout to aid recovery and mobility. Follow-up Needs: The care team should verify stress monitoring implementation and encourage more consistent nutrition tracking. Additionally, clarification on step count measurement timing and reinforcement regarding data interpretation may benefit Shweta to prevent future confusion. Monitoring adherence to recommendations for increasing daily steps and incorporating stretching may also be helpful. Engagement & Overall Assessment: Shweta demonstrated moderate engagement, consenting to receive detailed health insights and actively clarifying data points. While she did not extensively discuss challenges or emotions, her attentiveness to detail suggests good understanding. The conversation effectively delivered actionable guidance tailored to her current metrics and promoted positive health behaviors. Further support may enhance her compliance with nutrition and stress assessment goals.

Call Logs

  • Hey Tama, it's great to connect. I'm Mira from Heald, and I'd love to share some insights from your health data from yesterday when you're ready.

Agent Conversation (text)

Hey Tama, it's great to connect. I'm Mira from Heald, and I'd love to share some insights from your health data from yesterday when you're ready.