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Set up, manage, and control your Google Nest, Google Wifi, Google Home, and Chromecast devices, plus thousands of compatible connected home products like lights, cameras, thermostats, and more – all from the Google Home app. Turn on the lights, adjust the thermostat, or get an alert when there’s a person or package at your front door. Google Home on Wear OS will be available as a preview as we continue to add controls and improvements. One view of your home. The Home tab gives you shortcuts for the things you do most, like playing music or dimming the lights when you want to start a movie. Control it all with just a tap or two – and get to the good stuff faster. The Feed tab highlights important events in your home in one place. Here, you’ll also find ways to get more out of your devices and improve your home setup. Create Routines that allow you to turn on compatible lights, check the weather, play the news, and more with one simple command. See all the active audio and video streams on your compatible home devices in one place, change the volume, skip to the next track, or quickly change which speakers they’re playing from. Understand what’s going on at home with a glance. The Google Home app is designed to show you the status of your home and keep you up to date with what you may have missed. Check in on your home anytime and see a recap of recent events. You can also get a notification if something important happens while you’re away. Set up your Nest Wifi and Google Wifi in minutes using the Google Home app. Run speed tests, set up a guest network, and easily share your Wi-Fi password with family and friends. Use parental controls like Wi-Fi pause to manage online time for the kids. Automatically prioritize video conferencing and gaming traffic on all devices, or decide which devices to prioritize for all traffic types. Get more insights on your network, whether it’s a notification when a new device joins your network or detailed insights for troubleshooting a poor internet connection. A helpful home is a private home. Protecting your privacy starts with one of the world’s most advanced security infrastructures, which we build directly into Google products so that they’re secure by default. The built-in security in your Google Account automatically detects and blocks threats before they reach you, so that your personal information is secure. We build privacy tools that keep you in control. Control your Google Assistant activity, privacy settings, information, and personal preferences. See your activity, delete it manually, or choose to delete it automatically. Control your privacy on Google Assistant with your voice. Ask questions like “Where can I change my privacy settings?” to get answers to the most common privacy and security questions. Visit the Google Nest Safety Center at  to learn more about how we protect your information and respect your privacy. * Some products and features may not be available in all regions. Compatible devices required. google-home/

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HOMA: Often Mentioned, Rarely Defined

I was reviewing some new studies on the use of insulin this past week and every study seemed to use HOMA values. Homeostatic model assessment (HOMA) was first used in 1985 and was derived mathematically by looking at the interaction between beta-cell function and insulin resistance of normoglycemic patients. Alexander Julian, PharmD Candidate, UF College of Pharmacy, put everything in perspective for me and if you Click Here you can see what I learned from him.

HOMA: Often Mentioned, Rarely Defined Alexander Julian Doctor of Pharmacy Candidate University of Florida College of Pharmacy

Homeostatic model assessment (HOMA) is commonly cited in the literature but many clinicians and health professionals are unfamiliar with what it is, what it is used for, and most importantly, why it is used. A pubmed search of the term “HOMA” results in over 3600 hits; many of these are studies that use HOMA to analyze their data and draw conclusions. Simply put, HOMA is a method to quantify insulin resistance and beta-cell function. Originally described in 1985, the original model (HOMA1) was derived mathematically by looking at the interaction between beta-cell function (HOMA1-%B) in response to glucose concentrations, and insulin resistance (HOMA1-IR) in which insulin has diminished suppressive effect on hepatic glucose production. Because a feedback loop between the liver and beta-cells balances insulin secretion and hepatic glucose output, theoretically, it makes it possible to compute steady-state insulin and glucose concentrations. The original model was calibrated to give normal beta-cell function of 100% and normal insulin resistance of 1. Higher insulin resistance is characterized by higher steady state insulin levels and reduced beta-cell function characterized by a decreased compensation to increase glucose levels. By utilizing this interrelationship, any pair of fasting glucose and insulin concentrations can be used to quantify beta-cell function and insulin resistance using the following formulas.

HOMA1-IR = (FPI (mU/L) x FPG (mmol/L))/22.5

HOMA1-%B = (20 x FPI (mU/L))/(FPG (mmol/L) – 3.5)

Where FPI is fasting plasma insulin and FBG is fasting plasma glucose; to convert mg/dL to mmol/L, simply divide by 18.

The original model did not account for differences between hepatic and peripheral insulin sensitivity, increases in insulin secretion or decreases in hepatic glucose production for plasma glucose concentrations above 180 mg/dL, renal glucose losses, or the contribution of circulating proinsulin. An updated HOMA model (HOMA2) has since been created, however it is a computer model and has no simple equation but it adjusted to account for these variations. It models insulin sensitivity (HOMA2-%S) where 100% is normal which is the reciprocal of insulin resistance (100/S%). In addition, the original HOMA model uses equations that were calibrated to insulin assays used in the 1970’s which result in underestimation of %-S and overestimation of %-B. With knowledge of these differences, it is therefore important when evaluating studies to determine whether the HOMA formula or the computer-based HOMA was used to quantify insulin resistance and beta-cell function.

When determining insulin concentrations to compute HOMA, there are some important considerations that should be addressed. C-peptide is a common marker of insulin secretion as it is released in equal amounts as insulin from beta-cells in response to elevated glucose. Therefore, c-peptide may be most useful only in assessing beta-cell function (%B) while specific insulin concentrations are will more useful in determining insulin action or sensitivity (%S). This is especially true if patients use exogenous insulin as part of their diabetes management.

A HOMA2 calculator can be found at the following website: ?maindoc=/homa/

Although the gold standard for assessing insulin resistance remains that of the Hyperinsulinemic Glucose Clamp, it is often too laborious and not feasible for large studies, leaving HOMA to be a method likely used in studies we will read today as well as those in the future. I am hopeful that this column gives basic understanding of the HOMA model, some limitations, and how it’s use in the diabetic literature.

Wallace TM, Levy JC, et al. Use and Abuse of HOMA modeling. Diabetes Care (2004); 27(6): 1487-95

Matthews DR. Insulin resistance and beta-cell function – a clinical perspective. Diabetes, Obesity and Metabolism (2001); 3 (Suppl. 1): S28-S33

Muniyappa R, Lee S, et al. Current approaches for assessing insulin sensitivity and resistance in vivo: advantages, limitations, and appropriate usage. Am J Physiol Endocrinol Metab 294: E15-E26, 2008

Alexander Julian 3rd Year Pharm D. Candidate UF College of Pharmacy