A peer-level look at how endocrinologists and diabetes specialists interpret HbA1c alongside continuous glucose monitoring: when time in range, hypoglycemia, glycemic variability, and glucose patterns change the treatment decision..
Audience: Endocrinologists
Countries: 6
Completion Rate: 75.7%
SGID: 8975620
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– Quick Read — Key Findings
HbA1c remains central to diabetes care, but CGM can reveal the timing, direction, and variability behind an average that may otherwise look reassuring.
The 2026 American Diabetes Association Standards of Care recommend assessing glycemic status with A1C and/or CGM metrics such as time in range, time above range, and time below range, and for insulin-treated diabetes or hypoglycemia risk, CGM adds information a periodic average cannot show. The MDForLives survey asks a narrower question: when both are available, what do endocrinologists weight most? The answer is not that HbA1c is disappearing, but that it is increasingly read beside glucose patterns, hypoglycemia, variability, and the patient context in which those signals occur.
54.2% say HbA1c remains important but is increasingly interpreted alongside other measures.
The largest group does not call HbA1c obsolete; they treat it as one part of a broader assessment, while 28.8% still use it as the primary measure for most decisions and 13.6% say CGM metrics can matter more in selected patients. The distinction is practical: HbA1c summarizes longer-term exposure, while CGM adds the daily pattern, when glucose is in range, when lows occur, when highs cluster, and how much it fluctuates. Endocrinology is increasingly using the two views together rather than as competing endpoints.
66.1% say TIR/TBR/TAR and glycemic patterns most influence their interpretation when HbA1c and CGM-derived measures appear discordant.
Hypoglycemia and glycemic variability come next at 18.6%, then GMI or mean glucose at 10.2% and symptoms or clinical context at 5.1%. So discordance is rarely resolved by picking another average; clinicians move toward range and timing instead. That matters because the same HbA1c can sit above very different profiles: a patient may spend substantial time both low and high, or show pronounced post-meal excursions, while the average compresses those extremes into one number.
49.2% identify time in range as the single most clinically actionable CGM-derived measure in day-to-day diabetes practice.
AGP and overall pattern analysis rank second at 20.3%, followed by time below or above range at 18.6% and glycemic variability at 11.9%. TIR is the clearest single signal, but the spread shows why a dashboard helps: no one metric explains every decision. CGM has changed assessment most in type 1 or insulin-treated type 2 diabetes (62.7%), followed by recurrent or unrecognized hypoglycemia and marked variability, aligning its value with situations where timing and safety matter as much as the average.
66.7% are likely to modify treatment when HbA1c appears acceptable but CGM reveals significant variability or hypoglycemia.
43.1% select incomplete CGM data or patient adherence as a barrier to effective routine use, while 39.7% select access, reimbursement, or interoperability limitations.
Because respondents could pick up to two challenges, these barriers often coexist: incomplete data alongside limited time to interpret it, or a clear need for CGM alongside difficulty sustaining access or moving data into the workflow. Looking ahead, 61.4% expect wider CGM use and tighter integration of TIR/TBR/TAR with HbA1c to change assessment most over the next two to three years, so the opportunity is less about more sensors than about making the growing stream of glucose data easier to interpret, integrate, and act on.
The survey does not describe HbA1c being replaced; it describes a more layered form of glycemic assessment. Most respondents still consider HbA1c important, but when the average and the CGM story diverge, range patterns, hypoglycemia, and variability move to the foreground.
For endocrinologists and diabetes specialists, the challenge is now interpretation. The value of CGM depends on enough usable data, access that can be sustained, a workflow that can absorb the information, and judgment that can turn a pattern into the right action for the individual patient.
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