The Role of A1c in Diabetes Management

The A1c test, also known as glycated hemoglobin, measures the percentage of hemoglobin molecules that have glucose attached to them. Because red blood cells typically live about 90 to 120 days, A1c reflects average blood sugar levels over a two- to three-month window. In traditional face-to-face endocrinology or primary care settings, A1c remains a cornerstone for diagnosing prediabetes and diabetes, and for tracking long-term glycemic control. The American Diabetes Association recommends an A1c target of less than 7% for most nonpregnant adults, with individualized goals based on age, comorbidities, and hypoglycemia risk. Clinicians use A1c results to decide medication adjustments, reinforce lifestyle changes, and schedule follow-up intervals. The test is standardized, inexpensive, and widely available, making it a convenient metric in population health management.

However, the transition to telemedicine—accelerated by the COVID-19 pandemic and sustained by patient demand—has exposed fundamental limitations of relying on this single biomarker. In remote care, the absence of direct physical examination, real-time glucose data, and in-person rapport means that providers must depend heavily on patient‑reported information and laboratory results. When A1c becomes the primary, or sole, decision‑making tool, several critical gaps emerge that can compromise the quality and safety of diabetes care delivered via telehealth. While the test provides a useful snapshot of average glucose control over weeks to months, it fails to capture the dynamic, moment-to-moment fluctuations that define a patient’s daily experience with diabetes. This gap is especially problematic in telemedicine, where the traditional safety net of frequent in-person visits and immediate feedback is absent.

Limitations of Relying Solely on A1c in Telemedicine

While A1c is a powerful indicator of average glycemia, it is not designed for the granular, day‑to‑day management decisions that telemedicine demands. The following limitations become especially pronounced in a remote care environment, where real-time data and direct patient observation are limited.

Absence of Real‑Time Glucose Data

A1c provides only a retrospective, smoothed average. It cannot reveal dangerous glucose excursions—episodes of severe hyperglycemia or hypoglycemia—that occur between scheduled visits. For patients on insulin or sulfonylureas, missing these episodes can lead to delayed recognition of hypoglycemia unawareness or insulin titration errors. In telemedicine, where patients may not be pricking their fingers or scanning a continuous glucose monitor (CGM) regularly, the provider has no way to identify acute problems until the next A1c draw, which might be three months away. This latency can be dangerous. Consider a patient whose A1c is 7.2%—seemingly well-controlled—yet who experiences frequent nocturnal hypoglycemia. The A1c would not reveal this pattern, and the provider might continue a regimen that puts the patient at risk for severe hypoglycemic events. With telemedicine, the absence of point-of-care glucose testing and the inability to observe the patient’s immediate condition means that such risks remain hidden.

Inability to Capture Daily Management Insights

Daily blood glucose monitoring is essential for understanding the effects of meals, exercise, stress, illness, and medication timing. Without self‑monitored blood glucose (SMBG) data or CGM readings, clinicians cannot tailor insulin‑to‑carbohydrate ratios, adjust basal rates, or recommend specific lifestyle modifications. In a telemedicine visit, asking “How are your sugars?” often yields vague or inaccurate recollections. The A1c value alone does not inform whether a patient’s morning hyperglycemia is due to the dawn phenomenon, insufficient basal insulin, or late‑night snacking. This lack of context can lead to incorrect therapeutic decisions. For example, if a patient’s A1c is elevated, a clinician might increase basal insulin, but if the underlying problem is actually postprandial hyperglycemia driven by high-carbohydrate meals, the adjustment could increase the risk of hypoglycemia without improving overall control. Only daily glucose data can provide the granularity needed for precise management.

Accuracy Pitfalls and Misinterpretation Risks

A1c results can be misleading in patients with certain hemoglobinopathies, anemia, chronic kidney disease, recent blood transfusions, or pregnancy. For example, in patients with iron deficiency anemia, A1c may be falsely elevated; in those with sickle cell trait, it may be spuriously low. In a telemedicine setting, the clinician may lack immediate access to a complete blood count or hemoglobin electrophoresis to interpret the A1c correctly. Relying solely on a potentially inaccurate number can lead to overtreatment, undertreatment, or misguided medication changes. Furthermore, racial and ethnic differences in the relationship between A1c and average glucose have been documented, raising concerns about health equity when A1c is used as the sole metric. For instance, studies have shown that African American individuals may have higher A1c levels than white individuals at the same average glucose concentration, potentially leading to overclassification of diabetes severity and overtreatment if the provider is unaware of this discrepancy. In a telemedicine encounter, where the clinician cannot verify the results with additional testing or a physical assessment, these inaccuracies pose a real risk to patient safety.

Patient Engagement and Adherence Barriers

Telemedicine depends heavily on patient‑generated health data. When the only measure collected is a quarterly lab draw, there is little incentive for the patient to engage in daily self‑monitoring. Many patients interpret a “good” A1c as permission to relax dietary or medication routines, while a “bad” A1c may cause discouragement without offering actionable feedback. Without real‑time feedback loops, patients lose the motivation that comes from seeing immediate improvements after a healthy meal or a correct insulin dose. This disengagement can lead to a downward spiral of worsening control between visits. The quarterly A1c also creates a psychological distance from the disease. Patients may feel that their daily efforts are invisible, because the only metric that matters to their clinician is a lab value that comes every few months. This can undermine self-efficacy and reduce adherence to glucose monitoring and medication regimens.

Delayed Feedback and Reduced Therapeutic Inertia

In face‑to‑face care, providers can adjust medications at the point of care after reviewing a finger‑stick log. In telemedicine, the three‑month delay between A1c measurements means that ineffective or suboptimal regimens persist longer. Studies have shown that therapeutic inertia—failure to intensify therapy when indicated—is common in diabetes care. Relying only on A1c exacerbates this inertia because the provider must wait for the next lab result to confirm whether an adjustment worked, rather than using weekly glucose trends to make proactive changes. Even when telemedicine visits occur more frequently, the absence of interim glucose data means that each visit is essentially a blind adjustment. A patient might report feeling well, but without objective glucose data, the provider cannot confidently adjust therapy. This delays optimal glycemic control and increases the risk of complications.

Hypoglycemia Detection and Hypoglycemia Unawareness

Hypoglycemia is a serious adverse effect of glucose‑lowering therapies. A1c cannot detect episodes of low blood sugar, and in fact, a low A1c may reflect either excellent glycemic control or frequent hypoglycemia. Patients with hypoglycemia unawareness—common in long‑standing type 1 diabetes—may suffer severe lows without symptoms. In telemedicine, without CGM or frequent SMBG, these episodes remain hidden until an emergency room visit occurs. The result is a care model that is reactive rather than preventive. For older adults or those living alone, undetected hypoglycemia can lead to falls, confusion, or even coma. A telemedicine program that relies exclusively on A1c cannot identify these high-risk patients, and thus cannot intervene before a serious adverse event occurs.

Glycemic Variability: The Missing Piece

A1c also fails to capture glycemic variability—the degree to which a patient’s glucose levels swing between highs and lows. High variability, even in the setting of a normal A1c, is associated with increased oxidative stress, endothelial dysfunction, and a higher risk of hypoglycemia and microvascular complications. In telemedicine, variability can be assessed through SMBG data or CGM-derived metrics like the coefficient of variation (CV). Without these tools, the clinician has no insight into stability, and may incorrectly assume that a patient with a good A1c has well-controlled diabetes. In reality, that patient might be oscillating between dangerous extremes. This oversight can lead to inappropriate medication choices and missed opportunities to address underlying causes of instability, such as erratic meal timing or incorrect insulin dosing.

Complexity of Telemedicine Diabetes Care Beyond A1c

Managing diabetes remotely requires a paradigm shift: from episodic, lab‑driven care to continuous, data‑informed care. Technology has advanced to support this shift, but integration into telehealth workflows remains uneven. Health systems that invest in data infrastructure and remote monitoring technologies can provide safer, more effective care, but those that rely solely on A1c risk falling behind in both outcomes and patient satisfaction.

The Growing Role of Continuous Glucose Monitoring (CGM)

CGM devices provide glucose readings every 5–15 minutes, along with trend arrows and alerts for highs and lows. They generate reports such as the ambulatory glucose profile (AGP), which includes time‑in‑range (TIR)—the percentage of time glucose is within the target range of 70–180 mg/dL. TIR has been recognized by the ADA and international consensus groups as a powerful complement to A1c. Unlike A1c, TIR can show whether a patient is spending most of the day in range or bouncing between extremes. CGM data can be shared via cloud‑based platforms with the care team, enabling remote monitoring and timely interventions. For telemedicine, CGM is transformative because it provides actionable data that A1c cannot. Studies have demonstrated that CGM use improves glycemic control and reduces hypoglycemia in both type 1 and type 2 diabetes, even when used in a virtual care setting. Manufacturers like Dexcom, Abbott, and Medtronic have developed platforms that allow clinicians to view patient data remotely and receive alerts for critical glucose values, making CGM an ideal tool for telemedicine.

Self‑Monitoring of Blood Glucose (SMBG) in Remote Settings

For patients without access to CGM—often due to insurance limitations or cost—regular finger‑stick monitoring remains essential. Telemedicine programs can use connected glucose meters that automatically transmit readings to the healthcare provider’s electronic health record or a dedicated platform. This eliminates the need for patients to keep paper logs or recall numbers during a call. Even basic metrics like fasting glucose, postprandial peaks, and variability indices can guide treatment. The key is that SMBG data, when reviewed in the context of A1c, offers a more complete picture than A1c alone. For example, a patient with an A1c of 8.5% and consistently high fasting glucose may require a basal insulin adjustment, while a patient with the same A1c but widely variable postprandial readings may benefit from a different approach, such as meal-time insulin or dietary counseling. Without SMBG data, these distinctions are lost.

Digital Health Platforms and Data Aggregation

Integrated digital health platforms—such as Glooko, Tidepool, mySugr, and Livongo—consolidate data from glucose meters, CGM, insulin pumps, activity trackers, and food logs. These platforms generate trend reports, dashboards, and decision‑support tools that help clinicians identify patterns quickly during a telemedicine visit. They also enable automated messaging, goal setting, and patient education. When combined with A1c, these tools turn data into actionable insights. However, adoption remains variable due to interoperability challenges, cost, and the need for clinician training. Health systems that prioritize integration of these platforms into their telehealth workflows will be better positioned to deliver comprehensive, data-driven diabetes care. In practice, the most effective programs use a hub-and-spoke model: a central care team reviews incoming data, identifies patients who need intervention, and reaches out proactively, rather than waiting for the next scheduled appointment.

Addressing Social Determinants of Health and Digital Literacy

Telemedicine diabetes care cannot ignore the social context. Patients with limited English proficiency, low health literacy, or unstable housing may struggle to upload data, interpret CGM graphs, or even obtain an A1c lab draw. Relying solely on A1c may inadvertently widen disparities because it does not capture these barriers. Comprehensive telemedicine programs must include care coordinators, community health workers, and technology support to ensure equitable outcomes. For example, a patient’s high A1c might be due to food insecurity rather than medication nonadherence, but without additional data, the clinician may incorrectly intensify therapy. Similarly, a patient with a low A1c might be experiencing frequent hypoglycemia due to inconsistent meal patterns, a problem that would remain hidden without glucose monitoring. Telemedicine programs that fail to address digital literacy and social determinants risk exacerbating health inequities, even as they expand access to care.

Remote Medication Titration and Algorithm-Based Care

One of the most practical benefits of moving beyond A1c in telemedicine is the ability to implement remote medication titration protocols. With regular glucose data, clinicians can adjust doses of insulin and other agents based on predefined algorithms, often without a real-time visit. For instance, a protocol might instruct a patient to increase their basal insulin by 2 units if their fasting glucose averages above 130 mg/dL for three consecutive days. These algorithms can be embedded in digital health platforms and managed by nursing staff or care coordinators under physician supervision. This approach reduces therapeutic inertia and shortens the time to glycemic target. Without glucose data, such proactive adjustments would be impossible, and patients would remain on suboptimal regimens for months at a time.

Evidence-Based Approaches: Combining A1c with Time‑in‑Range and Other Metrics

Research supports that adding TIR and glucose variability metrics to A1c improves diabetes management. An international consensus panel published in Diabetes Technology & Therapeutics (2019) recommended that TIR be used as a key outcome measure in clinical care and research. For most people with type 1 or type 2 diabetes, a TIR above 70% is associated with an A1c of approximately 7.0%, but TIR also reveals whether the patient is achieving that average with minimal hypoglycemia. Studies have shown that TIR correlates with the risk of microvascular complications, similar to A1c. A landmark analysis of data from the Diabetes Control and Complications Trial (DCCT) demonstrated that TIR is strongly associated with the development and progression of retinopathy and nephropathy, providing evidence that this metric has clinical validity as a surrogate endpoint. The consensus panel also emphasized the importance of tracking time below range (TBR, below 70 mg/dL) and time above range (TAR, above 180 mg/dL) to fully characterize a patient’s glycemic status.

Another vital measure is the coefficient of variation (CV) for glucose variability. High variability—even with a good A1c—has been linked to oxidative stress, endothelial dysfunction, and increased hypoglycemia risk. In telemedicine, algorithms can flag patients with high CV for closer monitoring or psychotherapy about consistency. Using only A1c ignores this dimension entirely. For instance, a patient with an A1c of 6.8% but a CV of 45% is at greater risk for both hypoglycemia and long-term complications than a patient with the same A1c and a CV of 25%. Without the variability metric, the clinician would treat both patients identically, missing an opportunity to intervene in the higher-risk case.

Practical Telemedicine Workflows Integrating Multiple Data Sources

Successful telemedicine practices are evolving to include structured, data-driven workflows that leverage the full range of glucose metrics. These workflows should be designed to minimize burden on both clinicians and patients while maximizing clinical impact.

  • Pre‑visit data review: The care team reviews CGM/SMBG data before the virtual visit, focusing on TIR, hypoglycemia frequency, and glucose patterns. This replaces the former reliance on reviewing a paper log during the visit. Typical pre-visit review includes identifying patients with TIR below 60%, those with any level 2 hypoglycemia (below 54 mg/dL), and those with high variability (CV above 36%).
  • Structured data collection: Platforms automatically request a glucose log or CGM upload before the appointment. Live remote monitoring alerts trigger interventions between visits, such as a phone call from a diabetes educator if a patient experiences a severe low glucose event. Automated reminders ensure that data collection becomes routine.
  • Patient empowerment tools: Patients receive real‑time feedback via apps, which improve engagement and self‑efficacy. The quarterly A1c then becomes one of several data points used to verify overall progress, rather than the definitive measure of success. Patients can see their own TIR trends and celebrate improvements between visits, which reinforces positive behaviors.
  • Medication titration protocols: Standardized algorithms based on glucose trends allow clinicians to adjust insulin doses without waiting for A1c. For example, if a patient’s fasting glucose averages over 130 mg/dL for a week, the basal insulin dose can be increased by 2 units. These protocols can be managed by registered nurses or diabetes educators under a collaborative practice agreement, freeing up physician time for complex decision-making.
  • Interdisciplinary team communication: Effective telemedicine diabetes care requires coordination among primary care providers, endocrinologists, diabetes educators, dietitians, and behavioral health specialists. A shared digital platform enables asynchronous communication and ensures that all team members have access to the same data. For instance, a dietitian can review glucose trends and meal logs to provide personalized nutrition counseling between visits.

Overcoming Implementation Barriers

Despite the clear benefits of moving beyond A1c, telemedicine programs face significant barriers. Device costs, insurance coverage gaps, and limited training for clinicians are among the most common obstacles. However, the cost of inaction is higher. Programs that have successfully integrated CGM and digital platforms into telehealth have reported improved glycemic outcomes, reduced emergency department visits, and higher patient satisfaction. For example, the Veterans Health Administration’s telehealth program, which includes CGM for veterans with diabetes, has shown significant reductions in A1c and hypoglycemia-related hospitalizations. Similarly, commercial programs like Virta Health and Onduo have demonstrated that remote diabetes care built on continuous data monitoring can achieve remission in some patients with type 2 diabetes. These examples provide a roadmap for health systems seeking to modernize their telemedicine diabetes care.

Conclusion

The A1c test will remain a valuable tool in diabetes care for the foreseeable future, but it is insufficient as the sole metric in telemedicine. Remote care demands real‑time data, daily insights, and attention to hypoglycemia and variability—all of which lie outside the scope of A1c. Healthcare systems that invest in connected glucose monitoring devices, digital health platforms, and robust telehealth workflows will achieve better outcomes, higher patient satisfaction, and reduced complications. Ultimately, the shift from a quarterly snapshot to a continuous view of glucose empowers both clinicians and patients to manage diabetes more effectively, safely, and personalized manner. The evidence is clear: when telemedicine programs supplement A1c with TIR, SMBG data, and variability metrics, they can deliver care that is more responsive, more equitable, and more aligned with the realities of living with diabetes. The decision to move beyond A1c is not just a technological upgrade—it is a clinical imperative for safe and effective telemedicine diabetes care.

For further reading, see the consensus report on time‑in‑range from the Advanced Technologies & Treatments for Diabetes (ATTD) congress here. The American Diabetes Association’s position on diabetes technology is available here. For a comprehensive review of CGM use in telemedicine, refer to the Endocrine Society’s clinical practice guideline here. Additional insights on health disparities in diabetes technology can be found in the National Institute of Diabetes and Digestive and Kidney Diseases report here.