Association Between Weight Loss and Glycemic Outcomes: A Post Hoc Analysis of a Remote Patient Monitoring Program for Diabetes Management

Abstract

Background

: Evidence-based guidelines for the management of type 2 diabetes (T2D) consist of blood glucose monitoring, medication adherence, and lifestyle modifications that may particularly benefit from reminders, consultation, education, and behavioral reinforcements through remote patient monitoring (RPM).

Objectives

: To identify predictors of weight loss and to examine the association between weight loss and hemoglobin A1C (HbA1C) outcomes for T2D patients who were enrolled in an RPM program for diabetes management.

Materials and Methods

: The study applied logistic and ordinary least-squares regression models to examine the relationship between baseline characteristics and the likelihood of weight loss during the RPM, and how the magnitude of weight loss was related to changes in HbA1C outcomes for 1,103 T2D patients who went through 3 months of RPM from 2014 to 2017.

Results

: Older patients were 3% more likely to have weight loss (odds ratio [OR], 1.03; 95% confidence interval [CI], 1.02–1.05), whereas patients with higher baseline HbA1C had 9% reduced odds (OR, 0.91; 95% CI, 0.85–0.97) of experiencing weight loss. For every pound of weight lost, there was a 0.02-point (95% CI, 0.01–0.03) reduction on the HbA1C measured at the end of the RPM. Moreover, compared with those who had weight loss of ≤3%, participants who had lost 5–7%, or >7% of their baseline weight had a 0.37- and 0.58-point reduction in HbA1C, respectively.

Conclusions

: This study revealed a notable relationship between weight loss and positive HbA1C outcomes for T2D patients in an RPM-facilitated diabetes management program, which pointed to the potential of integrating evidence-based lifestyle modification programs into future telemedicine programs to improve diabetes management outcomes.

Introduction

An estimated 23.1 million people in the United States have been diagnosed with type 2 diabetes (T2D), which is projected to increase to 39.7 million by 2030.¹ Among individuals with T2D, 87.5% were overweight or obese,² which are important risk factors of morbidity and mortality for T2D, cardiovascular diseases, stroke, and cancers.³

T2D is a chronic progressive metabolic disorder characterized by hyperglycemia mainly due to relative deficiency of insulin hormone and insulin resistance. Glycated hemoglobin A1C (HbA1C), which reflects an individual's plasma glucose concentration averaged over a 3-month span, is used as a benchmark of glycemic control in diabetes management.

Extensive research has established the importance of weight management in the treatment of T2D through better glycemic control, which results in increased insulin sensitivity and signaling,^4

–8 and delayed diabetes-associated complications.^9

–13 For example, in the Look AHEAD trial of 5,145 participants, it was reported that even modest weight loss (5–10% of initial body weight) can reduce HbA1C level, improve cardiovascular disease risk factors, and decrease the use of diabetes, hypertension, and lipid-lowering medications.^14,15 Accordingly, achieving weight loss or preventing weight gain followed by weight loss is recommended for risk reduction and management of T2D along with controlling blood glucose, blood pressure, and cholesterol.^16,17

Telemedicine, including remote patient monitoring (RPM), has emerged as an increasingly widely adopted mode of care delivery, especially for remote patients who might not have the needed time or affordable transportation for them to overcome the geographic distance easily.^18
–20 RPM is the use of telecommunication devices that transmit health information to trained health care professionals for instant services and may be particularly successful in disease management and cost-effective for patients with T2D. Disease management via RPM can improve adherence to complex care regimens such as blood glucose monitoring, medications, and a system approach to supporting patients' lifestyle changes (control of calorie intake, especially carbohydrate/lipid intakes, and regular physical activity), which may benefit from reminders, consultation, education, and behavioral reinforcements through regular contact with the care team.^21,22

Using data from a large-scale RPM diabetes management program, this study aimed to (1) identify the predictors of weight loss and (2) examine the association between weight loss and HbA1C outcomes.

Materials and Methods

Description of RPM Program

Details of the RPM program have been documented elsewhere.²³ In brief, patients with T2D, and a recent hospital admission for any reason, were recruited in the program no later than 1 month after discharge from a Midwest health care system. Other enrollment criteria of the program included the following: (1) at least 19 years of age; (2) ability to use their own glucometer and take insulin and/or other prescribed medications; (3) not pregnant; (4) no history of addiction; (5) both English-speaking and able to read English; (6) hospital discharge to home; and (7) ability to express a basic understanding of and successfully use RPM equipment.

The 3-month RPM program entailed daily remote monitoring of biometric data, including blood pressure, weight, and blood glucose, and weekly phone calls or instant calls from nurse coaches when an alert was issued for out-of-range biometric results by the monitoring system. During the regular weekly phone calls, nurse coaches provided individualized education services based on patients' experiences, which included diet, exercise, medication, coping, and problem solving through the course of intervention. All the education materials followed the American Diabetes Association guideline.²⁴

At the end of the program, patients were seen at one of three local community health centers where they received diabetic retinopathy screening, along with a virtual nutritional counseling session and foot examination from a certified diabetes educator (assisted by an on-site medical assistant) remotely located at the telehealth hub. Remote monitoring equipment (Cardiocom^®; Medtronic, Inc., Minneapolis, MN) included a cellular base unit, blood pressure cuff, blood glucose meter, weight scale, and necessary cords.

Study Design and Study Sample

In this retrospective observational study, the analysis sample was confined to participants with complete records of variables of interest at the baseline and end of the monitoring period. In total, 1,103 out of 1,883 enrolled patients who had completed the RPM program in 2015–2017 were included in this study. Data used in this study were collected primarily from three sources: (1) participant biometric data (weight, HbA1C, and blood pressure), which were automatically uploaded to the monitoring platform via the remote monitoring device when participants took the measurements; (2) participant electronic medical records for demographic characteristics; and (3) self-administrated surveys for patient activation measurement at both baseline and the completion of the intervention. The RPM program was an outcome improvement study. As a result, the study protocol was waived by the institutional review board.

Outcome Variables

Weight change

Weight change was defined as the difference in weight (pounds) between the baseline and end of RPM (weight at end of RPM minus weight at baseline). Positive values indicated a weight gain, whereas negative values suggested weight loss. We further categorized patients who experienced weight loss by percentage of weight lost into four groups: ≤3%, >3 and ≤5% (3–5%), >5 and ≤7% (5–7%), and >7%, based on varying levels of clinically meaningful weight loss (i.e., 5–10%).^25
–27

Hemoglobin A1C

HbA1C was measured at study baseline and after completion of the RPM program with HbA1C >9% considered poor glycemic control.²⁸

Predictive Variables

Demographic information included age at baseline, sex, and self-reported race/ethnicity (i.e., white, African American, Hispanic, Asian, American Indian or Alaska Native, Native Hawaiian or other Pacific Islander, and other). We further categorized them into non-Hispanic white versus nonwhite due to the small sample size in certain categories. Primary health insurance was categorized into public insurance (Medicare and Medicaid) and private insurance. High blood pressure at the baseline was defined as >140/90 mmHg.

We used the Patient Activation Measure-13 (PAM-13)²⁹ to measure patient activation. PAM-13 is a Likert scale with five response categories with scores ranging from 1 to 5 for each of the 13 items: (1) strongly disagree, (2) disagree, (3) agree, (4) strongly agree, and (5) not applicable. We further used its four levels of activation in the analysis: level 1: the patients do not yet believe they are active or have an important role in managing their health (<47.0); level 2: the patients lack confidence and knowledge to take action to manage their health (47.1–55.1); level 3: the patients are beginning to take action to manage their health (55.2–67.0); and level 4: the patients are maintaining actions to manage their health over time (≥67.1).³⁰ Changes in the PAM-13 activation scores were calculated as the difference at baseline and the completion of the RPM program. Upload frequency to document monitoring was calculated by using the total number of uploads of biometric data during the program period divided by the total number of intervention days. Previous work found that higher upload frequency (better patient engagement) was associated with better glycemic control outcomes.³¹

Analysis

In the descriptive statistics, we reported the mean and standard deviation (SD) for continuous variables, and frequency and percentage for categorical variables.

In the multivariable analysis, specifically, we used the following: (1) logistic regression models to examine the relationship between baseline demographic and clinical characteristics, and the likelihood of weight loss during the RPM; (2) ordinary least-squares (OLS) regression models to determine the association between weight change during RPM and the HbA1C level after completion of RPM, controlling for baseline demographic and clinical characteristics, patient activation scores, and upload frequency; and (3) OLS regression models to assess the relationship between weight loss during RPM on the HbA1C level after completion of RPM, controlling for baseline demographic and clinical characteristics, patient activation scores, and upload frequency.

All analyses were conducted using Stata^® version 14 (StataCorp, College Station, TX). Two-tailed p-values of less than 0.05 were considered statistically significant.

Results

Sample Characteristics

Table 1 describes the sociodemographic and clinical characteristics of the study sample. Of the 1,103 participants included in the study, the average age at baseline was 60.5 (±11.4) years, 55% were female, 69% were non-Hispanic white, and 26% were African American; 52% had public health insurance. In terms of clinical factors, the average HbA1C at baseline was 7.6 (± 1.9), 19% had baseline HbA1C >9%, the average weight at baseline was 222 pounds (±53), and 24% had blood pressure >140/90 mmHg at baseline. The average increase of PAM score from the baseline to the end of RPM was 5.4 (± 16.3). As for upload frequency, the average number of uploads per day was 0.76 (± 0.22).

Table 1.

Demographic and Clinical Characteristics of the Analysis Sample by Weight Change Status (Weight Loss vs. Weight Gain)

VARIABLE	WEIGHT LOSS n = 685	WEIGHT GAIN n = 418	TOTAL n = 1,103	p-VALUE
Age at baseline, mean (SD)	61.7 (10.8)	58.4 (11.9)	60.5 (11.4)	<0.001
Female, N (%)	366 (53)	243 (58)	609 (55)	0.128
Race, N (%)				0.097
Non-Hispanic whites	490 (72)	269 (64)	759 (69)
African American	160 (23)	129 (31)	289 (26)
Native^a	8 (1)	3 (1)	11 (1)
Multiraces	7 (1)	3 (1)	10 (1)
Other	20 (3)	14 (3)	34 (3)
Health insurance, N (%)				0.663
Medicare	344 (50)	200 (48)	544 (49)
Medicaid	20 (3)	15 (3)	35 (3)
Private	321 (47)	203 (49)	524 (48)
HbA1c at baseline, mean (SD)	7.4 (1.7)	7.9 (2.2)	7.6 (1.9)	<0.001
Baseline HbA1c >9%, N (%)				<0.001
No	580 (85)	317 (76)	897 (81)
Yes	105 (15)	101 (24)	206 (19)
HbA1c at the end of RPM, mean (SD)	6.9 (1.4)	7.4 (1.6)	7.1 (1.5)	<0.001
Weight at baseline (lb), mean (SD)	224 (51)	219 (55)	222 (53)	0.11
Weight at the end of RPM (lb), mean (SD)	217 (49)	225 (56)	220 (52)	0.013
High blood pressure at baseline, N (%)				0.766
No	519 (76)	320 (77)	839 (76)
Yes	166 (24)	98 (23)	264 (24)
PAM score category at baseline, N (%)				0.485
Level 1 (≤47)	66 (10)	38 (9)	104 (9)
Level 2 (47.1–55.1)	117 (17)	84 (20)	201 (18)
Level 3 (55.2–67.0)	234 (34)	148 (35)	382 (35)
Level 4 (≥67.1)	268 (39)	148 (35)	416 (38)
PAM score during RPM, mean (SD)	5.1 (16.2)	5.7 (16.6)	5.4 (16.3)	0.564
Daily upload frequency, mean (SD)	0.8 (0.2)	0.7 (0.20)	0.8 (0.2)	0.011
Degree of weight loss during RPM, N (%)
≤3%	397 (58)	—	—
3–5% (>3% and ≤5%)	150 (22)	—	—
5–7% (>5% and ≤7%)	70 (10)	—	—
>7%	68 (10)	—	—

Native included American Indian, Alaska Native, Native Hawaiian, and other Pacific Islander.

PAM, patient activation measure; RPM, remote patient monitoring; SD, standard deviation.

We compared the characteristics of the study subjects by their weight change status (Table 1), and found significant differences in terms of age at baseline, HbA1C at baseline, HbA1C at end of RPM, and upload frequency between weight loss and weight gain groups.

Predictors of Weight Loss

Table 2 shows the logistic regression results of the likelihood of weight loss in terms of participants' sociodemographic and clinical characteristics. Older patients were more likely to have weight loss after completion of RPM (odds ratio [OR], 1.03; 95% confidence interval [CI], 1.02–1.05); and participants with higher baseline HbA1C had 9% lower odds (OR, 0.91; 95% CI, 0.85–0.97) of having weight loss after completion of RPM.

Table 2.

Predictors of Weight Loss at the End of Remote Patient Monitoring, n = 1,103

VARIABLE	OR	p-VALUE	95% CI
Age at baseline	1.03	0.000	1.02–1.05
HbA1C at baseline	0.91	0.005	0.85–0.97
Gender (ref = female)
Male	1.03	0.806	0.79–1.35
Race/ethnicity (ref = non-Hispanic whites)
Nonwhite^a	0.88	0.380	0.67–1.17
Health insurance (ref = public insurance^b)
Private insurance	1.23	0.146	0.93–1.63
Baseline weight (lb)	1.00	0.009	1.001–1.01
Having high blood pressure at baseline (ref = no)	1.10	0.516	0.82–1.48
Baseline patient activation score (ref Level 1 [≤47])
Level 2 (47.1–55.1)	0.71	0.187	0.43–1.18
Level 3 (55.2–67.0)	0.85	0.494	0.53–1.36
Level 4 (≥67.1)	1.00	0.999	0.60–1.66
PAM score change during RPM	1.00	0.814	0.99–1.01
Daily upload frequency	1.26	0.444	0.70–2.27

Nonwhite included African American, American Indian or Alaska Native, Native Hawaiian or other Pacific Islander, multiraces, and others.

Public health insurance included Medicare and Medicaid.

CI, confidence interval; OR, odds ratio; PAM, patient activation measurement; RPM, remote patient monitoring.

Association Between Weight Change and HbA1C

OLS regression results showed that there is a significant association between weight change and postintervention HbA1C (Table 3). For every 1-U weight loss, there was a 0.02-point (95% CI, 0.01–0.03) reduction on the HbA1C after completion of RPM, controlling for participant characteristics, activation, and other clinical outcomes. In addition, older age, a higher baseline HbA1C, being nonwhite, a higher baseline weight, having hypertension at baseline, and a lower number of upload frequencies were found to be associated with a higher postintervention HbA1C (Table 3). Moreover, HbA1C decrease was greater in participants with baseline HbA1C >9% (mean = 0.11, 95% CI, 0.02–0.17; data not shown) for each pound of weight lost.

Table 3.

Association Between Weight Change Status and HbA1c Level at the End of Remote Patient Monitoring, n = 1,103

VARIABLE	COEFFICIENT	p	95% CI
Age at baseline	0.01	0.011	0.003 to 0.02
HbA1C at baseline	0.41	<0.001	0.36 to 0.47
Gender (ref = female)
Male	−0.13	0.168	−0.31 to 0.05
Race/ethnicity (ref = non-Hispanic whites)
Nonwhite^a	0.20	0.042	0.01 to 0.40
Health insurance (ref = public insurance^b)
Private insurance	0.04	0.709	−0.16 to 0.23
Baseline weight (lb)	0.00	0.157	−0.001 to 0.003
Weight change (lb)	0.02	<0.001	0.01 to 0.03
Having high blood pressure at baseline (ref = no)	0.29	0.004	0.09 to 0.49
Baseline patient activation score (ref Level 1 [≤47])
Level 2 (47.1–55.1)	−0.01	0.939	−0.36 to 0.33
Level 3 (55.2–67.0)	−0.11	0.481	−0.44 to 0.21
Level 4 (≥67.1)	−0.06	0.722	−0.40 to 0.28
PAM score change during RPM	0.00	0.243	−0.01 to 0.00
Daily upload frequency	−0.85	<0.001	−1.27 to −0.43

Nonwhite included African American, American Indian or Alaska Native, Native Hawaiian or other Pacific Islander, multiraces, and others.

Public health insurance included Medicare and Medicaid.

CI, confidence interval; PAM, patient activation measure; RPM, remote patient monitoring.

Degree of Weight Loss and HbA1C

Table 4 presents the OLS regression results of association between degree of weight loss and the postintervention HbA1C. We found a dose/effect relationship (higher percentage of weight loss and greater reduction of HbA1C) between weight loss and postintervention HbA1C. Specifically, participants who had lost 3–5%, 5–7%, or >7% baseline weight were associated with 0.21-, 0.37-, and 0.58-point reduction of HbA1C after completion of RPM, respectively, compared with those who had weight loss of ≤3%.

Table 4.

Degree of Weight Loss and HbA1c Level at the End of Remote Patient Monitoring, n = 685

VARIABLE	COEFFICIENT	p-VALUE	95% CI
Age at baseline	0.01	0.010	0.003 to 0.02
HbA1C at baseline	0.41	0.000	0.36 to 0.46
Gender (ref = female)
Male	−0.12	0.210	−0.30 to 0.07
Race/ethnicity (ref = non-Hispanic whites)
Minorities (nonwhite^a)	0.20	0.044	0.01 to 0.40
Health insurance (ref = public insurance^b)
Private insurance	0.03	0.768	−0.16 to 0.22
Baseline weight (lb)	0.00	0.557	−0.001 to 0.002
Degree of weight loss (ref ≤3%)
3–5% (>3% and ≤5%)	−0.21	0.056	−0.42 to 0.01
5–7% (>5% and ≤7%)	−0.37	0.012	−0.66 to −0.08
>7%	−0.58	0.000	−0.87 to −0.29
Having high blood pressure at baseline (ref = no)	0.29	0.005	0.09 to 0.49
Baseline patient activation score (ref Level [≤47])
Level 2 (47.1–55.1)	−0.02	0.912	−0.37 to 0.33
Level 3 (55.2–67.0)	−0.12	0.467	−0.44 to 0.20
Level 4 (≥67.1)	−0.07	0.701	−0.41 to 0.27
PAM score change during RPM	0.00	0.245	−0.01 to 0.002
Daily upload frequency	−0.84	0.000	−1.25 to −0.42

Nonwhite included African American, American Indian or Alaska Native, Native Hawaiian or other Pacific Islander, multiraces, and others.

Public health insurance included Medicare and Medicaid.

CI, confidence interval; PAM, patient activation measure; RPM, remote patient monitoring; SD, standard deviation.

Discussion

In this study, we used data from a recently completed RPM program to identify predictors of weight loss and to examine the association between weight change and HbA1C outcomes for patients with T2D.

With a large sample of participants (n = 1,103), results indicated that participants who were older or had lower baseline HbA1C level were more likely to lose weight after completion of the program. Moreover, we found that for every pound of weight loss, the mean HbA1C was reduced by 0.02 points and the effect was greater among participants whose baseline HbA1C was >9%. These results are comparable with a recent systematic review using aggregated clinical trial data,⁶ which reported a linear relationship between weight loss and HbA1C reduction, with an estimated mean HbA1C reduction of 0.1 point for each 1 kg (∼2.2 pounds) of reduced body weight for the nonsurgical group and a positive relationship between baseline HbA1C and the effect of HbA1C lowering with the same degree of weight loss. Among participants who had lost weight, we further identified a dose/response relationship—higher levels of weight loss resulted in better HbA1C outcomes. These results were consistent with a study by Shantha et al.³² that consisted of 72 overweight or obese patients with T2D who participated in university-based weight management programs. In their study, authors reported that with a percentage weight loss of 0.5%, 8.7%, and 10.3%, participants were able to reduce HbA1C by 0.5, 1, and 1.5 in a time period of 5.6, 8.7, and 10.1 months, respectively. In our study, >3 and ≤5%, >5 and ≤7%, and >7% weight loss was associated with a 0.21- (nonsignificant), 0.37-, and 0.58-point HbA1C reduction, respectively, in a 3-month time period. The reason for our stronger relationship with respect to shorter periods of time may be that in addition to the lifestyle modification component (healthy eating and being physically active) through self-management coaching and education, our RPM program also entailed the monitoring of blood glucose, blood pressure, weight, medication adherence assessment, coping, and problem solving facilitated with telemedicine technology. The difference in outcomes demonstrated the importance of designing and delivering comprehensive individualized, patient-centered disease management support services for better diabetes management and control.^33,34

Results of our study also point to the potential of integrating technology-facilitated lifestyle interventions into RPM to improve diabetes management outcomes for overweight or obese T2D patients early in the disease stage. With the average age in the analysis sample being 60.5 years and incidence of T2D increases with age until the age 65 years,³⁵ it is likely that many patients with T2D in our study were still at an earlier disease stage in which patients can benefit greatly from weight loss in terms of glycemic control. Studies showed that weight loss-induced improvements in glycemia are most likely to occur early in the natural history of T2D when obesity-associated insulin resistance has caused reversible β cell dysfunction, but the insulin secretory capacity remains relatively preserved.^36
–38 In the later stage of the disease, the goal of lifestyle management may switch to the prevention of weight gain,³⁸ or sustained weight loss,³⁹ which may introduce different disease management strategies.

The findings from this study highlight the benefits of addressing and improving lifestyle modification components in patients with T2D using telemedicine or telemonitoring to increase patient engagement and satisfaction.^40

–43 Components of a successful diabetes management program may include, but are not limited to, healthy diets, regular physical activity, education and support, and pharmacotherapy. This study represents a rare effort to examine the association between weight loss and HbA1C while simultaneously accounting for the effect of patient engagement in the telemedicine component, a metric not commonly available in similar studies. Our findings coincide with previous studies,^31,44 which found that patients with T2D who participated in telemedicine programs had better HbA1C control with a higher level of engagement compared with their counterparts who engaged less.

Some limitations need to be noted. First, duration of diabetes, an important variable in the analysis on factors associated with HbA1C outcomes, was not available. Also, the analysis sample, obtained from a quality improvement project, might have introduced a selection bias by enrolling motivated participants. Finally, we were not able to account for the effects of weight-reducing medications that may also lower HbA1C or the potential reduction in use of glucose-lowering medication as a result of weight loss in this study due to data unavailability. This may in turn overestimate and underestimate the impact of weight loss on glycemic control, respectively.

Conclusion

This study revealed a notable relationship between weight loss and positive HbA1C for T2D patients in an RPM-facilitated diabetes management program with a standard lifestyle modification component. Our findings call for a potential integration of evidence-based lifestyle modification programs into standard diabetes management facilitated by the use of telemedicine technology to improve patient engagement. Furthermore, with the increasing use of telemedicine in diabetes management, future research should focus on the effects of weight loss and subsequent glycemic control by disease stages, with telemedicine as a mean to inform the improvement, adoption, and dissemination of similar programs. This could open up doors to broader populations, including patients who are obese, or patients with prediabetes or varying severity of diabetes.

Footnotes

Acknowledgments

The RPM program as described in this study was supported by grant number 1C1CMS331344 from the Department of Health and Human Services, Centers for Medicare & Medicaid Services (CMS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the article. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the U.S. Department of Health and Human Services or any of its agencies. The research presented here was conducted by the awardee. Findings from this study might or might not be consistent with or confirmed by the findings of the independent evaluation contractor hired by the CMS.

Disclosure Statement

No competing financial interests exist.

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