The effects of telenutrition in overweight and obese adults in a nutritional center in Lima, Peru.

Version Changes

Revised. Amendments from Version 1

Amendments from Version 1: The revised version of this manuscript addresses comments and suggestions that were pointed out by the reviewers. The updates are meant to improve the report and are as follows:  1.    Abstract: Statistical analysis information updated. 2.    Overall manuscript: Clarification on the retrospective nature of the study. 3.    Methods: Allocation clarification given the study design. 4.    Methods: Added information regarding the nutritional plan and monitoring. 5.    Methods: Clarification on outlier management. 6.    Methods: Complementary information on level of education data. 7.    Results: Tables formatting and corresponding text updated accordingly. No results have been eliminated, just redistributed. 8.    Discussion: Additional information on the potential explanations for the findings. 9.    Discussion: Complementary information on the WC changes. 10.    Discussion: Complementary information on the clinical role of RFM parameter. 11.    Discussion: Complementary information on the confounders potential effect. 12.    Discussion: Additional information on the strengths of the study.

Abstract

Background: COVID-19 pandemic has been challenging for health services and systems around the world, including Peru.  A viable alternative in the telemedicine field to guarantee patient nutritional care is telenutrition. Telenutrition involves the interactive use of electronic information and telecommunications technologies to implement the nutrition care process with patients at a remote location. Information regarding the experience with this methodology and its potential effect on patients’ nutritional goals, does not exist in Peru. The aim of the study was to report the effect of the evaluation type (telenutrition vs. in-person) on weight, body mass index (BMI), waist circumference (WC) and relative fat mass (RFM) in overweight and obese adult patients.  Methods: This retrospective study included 100 eligible patients in a single nutritional center, from January 2019 to March 2021. Telenutrition and in-person continuous variables were compared with independent sample t-test or U Mann-Whitney test. Results: There were significant differences in weight, BMI, WC and RFM by the end of follow-up period, in both evaluation modalities. Patients on the telenutrition group had a mean decrease of 6.80 ± 4.87 cm in WC, whereas the mean difference observed for the in-person group was 6.74 ± 4.55 cm. There were no significant differences in the changes of any anthropometric parameters when comparing both systems. Reductions were observed in weight (5.93 ± 3.88 kg vs. 4.92 ± 3.29 kg), BMI (2.23 ± 1.39 kg/ m2 vs. 1.83 ± 1.23 kg/ m2), WC (6.80 ± 4.87 cm vs. 6.74 ± 4.55 cm) and RFM (2.43 ± 1.78 vs. 2.63 ± 1.73) in telenutrition and in-person evaluation, respectively by the end of the follow-up period.  Conclusions: Telenutrition may be regarded as an alternative to in-person evaluation offering anthropometric changes and nutritional goals similar to those reported through the in-person modality, in overweight and obese adult people.

Introduction

Clinical practice has gone through adaptative processes during the COVID-19 pandemic due to isolation and social distancing policies to reduce virus transmission. Telemedicine, a discipline that has been developing for many years, ¹ provides an alternative to ensure continued patients’ access to health services, while minimizing the risks for health workers. ²

Telehealth is the use of electronic information and telecommunication technology to facilitate clinical healthcare and patient education remotely. Whereas telenutrition is defined as a modality of Telehealth that provides an opportunity for a registered dietitian nutritionist to implement patients’ nutritional care remotely. ³

Studies in Western China have shown that telemedicine practices are feasible, acceptable, effective and improves health care outcomes, ⁴ by providing the healthcare worker with information about the patient’s surroundings and how homecare is maintained. In New Mexico, USA, telemedicine-related barriers were identified through a survey that included 2016 nutrition professionals. ⁵ In this study, 29% of the professionals reported lack of client interest, 26% reported not having internet access, and 28% mentioned not being able to perform client assessment or monitor activities by this modality. In regard to the benefits of telemedicine, 66% of the professionals stated that it contributed to social distancing compliance, while 50% acknowledged the flexibility in arranging appointments. ⁵

Australian nutrition professionals regard telehealth evaluations as profitable and well-received by patients. They report that this practice improved healthcare access for people with chronic diseases. ⁶

Regarding potential obstacles in telenutrition practices, anthropometric parameters are key for nutritional assessment and are widely discussed when comparing this modality to in-person evaluation. It is known that standard anthropometric assessment involves direct contact, however, there is evidence that self-reported weight and height measurements may have adequate precision. ⁷ Besides, reports state that self-reported WC measurement may be reliable in cases where scales are not available. ^{8 , 9} Precise measurement can be achieved by providing video instructions for the patient. ^{10 , 11}

In Lima, Peru we are facing these challenges as well. In 2020, the Allikay nutritional center started providing virtual appointments for nutritional care to patients, guaranteeing safe conditions based on the COVID-19 pandemic-related recommendations. There is no information regarding the impact of this modality on the patients’ nutritional status measured by anthropometric parameters in Lima, Peru. The aim of this study was to report the effect of the evaluation type (telenutrition vs. in-person) on anthropometric parameters such as weight, body mass index (BMI), waist circumference (WC) and relative fat mass (RFM), in overweight and obese adult patients in a nutritional center in Lima.

Methods

This is an observational retrospective study that took place between January 2019-March 2021. This study included overweight and obese patients that were assessed by a Registered Dietitian Nutritionist through in-person or virtual appointment, in a nutritional center in Lima. At the moment of the study conception and design, this data had been already generated as part of standard clinical practice and procedures at the nutritional center, which makes this a secondary data analysis.

The samples were obtained through a non-probability convenience sampling. Due to the type of sampling, it is acknowledged that bias is possible and considering this, authors recommend caution when interpreting results in terms of generalization. The final sample size was 100 patients which was arrived by assessing eligibility in every patient evaluated during the established study period (January 2019-March 2021). There were 50 patients evaluated with telenutrition and 50 with in-person assessment. Since this is a secondary analysis, no prospective allocation strategy and no specific eligibility criteria for the allocation was applied. At the moment of the study, there had already been patients evaluated through both modalities and data from those evaluations generated. This study included overweight or obese males and females aged 18 years and older, who had both their baseline and three-month assessment data available. Overweight (BMI ≥ 25) and obesity (BMI ≥ 30) was defined as proposed by the World Health Organization (WHO). ¹² Patients that had bariatric surgery before the recruitment period or were on weight-loss medication at the time of the recruitment, were excluded. All anthropometric parameters were collected twice, at baseline and during the three-month follow-up measurements.

Assessment modalities were either in-person or by telenutrition. The in-person appointment starts with a thorough examination of food habits including two nutritional questionnaires: food consumption frequency and 24-hour dietary recall (Extended data: https://doi.org/10.6084/m9.figshare.14832345.v2). ¹³

Height measurement is self-reported, WC measurement is performed by the nutritionist following standard procedures (middle area between the lower edge of the rib cage and the upper edge of the iliac crest) with a Lufkin measurement tape. Weight measurement was taken by the Inbody 120 scale. ¹⁴ Body fat percentage was calculated with the use of the following RFM formula described by Woolcott OO and Bergman RN: ¹⁵

Male adults: 64−(20 × (height cm/waist circumference (cm))

RFM ≥ 22.8 for obesity in males.

Female adults: 76−(20 × (height cm/waist circumference cm))

RFM ≥ 33.9 for obesity in females.

Once the anthropometric data is taken, the nutritionist creates a personalized nutritional plan adapted to the patients’ nutritional status, food habits and preferences. The nutritional plan was developed based on the patient information on health status, food habits, consumption frequency and attitude towards food obtained during the baseline assessment. Then a set of nutritional goals for the short and long term are established and agreed. The nutritional plan is individualized and provided after every assessment, it includes 10 menu options for the next twenty days with the following macronutrient distribution: 50% of carbohydrates, 20% of proteins and 30% of fat. During the following evaluation a new nutritional plan (same macronutrient distribution) was developed according to the goal achievements. The patients’ indicators that were monitored during the evaluations for both modalities were: weight, WC, BMI and RFM. The follow-up and monitoring are performed through e-mail or by the multimedia messaging application (WhatsApp), once a week. The monitoring was performed through weekly WhatsApp messaging by questioning about plan adherence and providing reminders for weight and WC weekly measurements and reporting. The weekly WhatsApp message started with: “Good day, I hope you are fine. How are you feeling regarding the nutritional plan these past few days? Please let me know”. A new nutritional plan was provided when the weekly weight loss was below 500 g or the reduction in WC was below 0.2 cm, this updated nutritional plan included a carbohydrate reduction to 30 g per day. This procedure was conducted every week until the next in-person or telenutrition assessment. Standard follow-up appointments are performed every 20 days. The three-month follow up assessment includes WC, weight measurements and RFM.

Telenutrition assessment starts one day before the actual appointment by sending an infographic that includes fasting anthropometric measurement instructions to the patient based on the local recommendations. ¹⁴ This is done in order to standardize weight and WC measurements for remote assessments ( Figure 1). During the appointment, height value is self-reported and the nutritionist requests the self-measured values (weight and WC) for the RFM calculation. Following this, the nutritional plan adapted to the patients’ nutritional status, food habits and preferences is sent to the patient by e-mail. The follow-up and monitoring are once a week and it is done by e-mail or WhatsApp. Standard follow-up appointments are performed every 20 days. The three-month follow -up assessment includes self-reported WC, weight measurements and RFM calculation.

Figure 1. Infographic for subjects.

Source: created por Castrillón-Liñán C. based on Aguilar Esenarro L, Contreras Rojas M, Del Canto y Dorador J, Vílchez Dávila W. Guía técnica para la valoración nutricional antropométrica de la persona adulta [Internet]. Instituto Nacional de Salud; 2012. Disponible en: https://repositorio.ins.gob.pe/handle/INS/225.

Data analysis

Data analysis was performed with the use of Stata/SE 12.0 for Windows ( https://www.stata.com/). Continuous variables were examined for outliers with graphic (box plot) and analytic techniques (interquartile ranges). There were three patients in the telenutrition group that showed atypical values in baseline weight (one patient) and follow-up weight (two patients). Once it was confirmed that values were not originated from errors, the decision to handle them during the inference statistical analysis included the use of non-parametric statistical tests (Wilcoxon Rank-sum test or U Mann-Whitney test). Normality was assessed with graphic (histograms) and analytic techniques for skewness and kurtosis (Shapiro Wilk test). For univariate analysis, continuous variables were described with means and standard deviations or medians and ranges. Categorical variables were reported as frequencies. For bivariate analysis, baseline and follow-up continuous variables were compared with paired-samples t-test or Wilcoxon Rank-sum test. Telenutrition and in-person continuous variable values were compared with independent samples t-test or U Mann-Whitney test. Categorical variables were compared with the chi-square test. P-value < 0.05 is considered statistically significant.

Dataset created and analyzed for the study is available as underlying data: https://doi.org/10.6084/m9.figshare.14710296.v1. ¹⁶

Ethical considerations

This study was conducted according to the Declaration of Helsinki principles. Patients were deidentified by being assigned to code numbers, without any reference to the patient information, therefore keeping the data confidential. Patient information was only accessible to the Registered Dietitian Nutritionist who evaluated the patients. Informed consent was not needed for this retrospective study as the data had been anonymized and this was confirmed by the Ethics Committee which approved the study. Ethical approval was obtained from the Institutional Ethics Committee for Research of the Norbert Wiener University with Exp. No 526-2021.

Results

Mean baseline BMI was in the obesity range (31.91 ± 5.53 and 30.36 ± 4.35 kg/m ² for telenutrition and in-person group respectively) ( Table 1).

Table 1. Clinical and anthropometric characteristics according to the assessment group.

Variables	Telenutrition (n = 50)	In-person assessment (n = 50)	p
Sex Men Women	8 (16%) 42 (84%)	15 (30%) 35 (70%)	0.096
Age (years)	40.66 ± 12.69	38.18 ± 10.17	0.2835
Height (cm)	162.72 ± 7.51	164.32 ± 8.59	0.3585
Baseline weight (kg)	84.93 ±18.21	82.22 ± 14.60	0.6994
Baseline BMI (kg/m 2)	31.91 ± 5.53	30.36 ± 4.35	0.1160
Baseline WC (cm)	100.14 ± 14.40	96.30 ± 11.82	0.1482
Baseline RFM	41.03 ± 5.30	37.85 ± 6.53	0.0088

BMI: body mass index, WC: waist circumference, RFM: relative fat mass. Chi square test for sex, U Mann-Whitney test for baseline height, weight and BMI, T-Student test for age, baseline WC and RFM. P < 0.05.

The baseline weight, BMI and WC values were not statistically different between the two assessment groups. However, significant differences were observed in the mean baseline RFM (41.03 ± 5.30 vs. 37.85 ± 6.53) between the assessment groups ( Table 1).

Significant differences between baseline and three-month follow -up weight, BMI, WC and RFM within the assessment groups (p = 0.0000) were identified, the greatest difference reported was in WC, which decreased by 6.80 ± 4.87 cm and 6.74 ± 4.55 cm in telenutrition and in-person assessment, respectively ( Table 2).

Table 2. Anthropometric parameters for subjects evaluated by assessment group.

	Telenutrition (n = 50)			In-person assessment (n = 50)
Variables	Baseline assessment	Follow-up assessment	p	Baseline assessment	Follow-up assessment	p
Weight (kg)	84.93 ± 18.21	79.00 ± 16.62	0.0000	82.22 ± 14.60	77.30 ± 13.95	0.0000
BMI (kg/m 2)	31.91 ± 5.53	29.68 ± 5.02	0.0000	30.36 ± 4.35	28.53 ± 4.12	0.0000
WC (cm)	100.14 ± 14.40	93.34 ± 13.39	0.0000	96.30 ± 11.82	89.56 ± 11.30	0.0000
RFM	41.03 ± 5.30	38.60 ± 5.53	0.0000	37.85 ± 6.53	35.23 ± 6.73	0.0000

BMI: body mass index, WC: waist circumference, RFM: relative fat mass. Wilcoxon Rank-sum test for weight and BMI, T-Student test for WC and RFM. P < 0.05.

In regard to the change in the magnitude of the anthropometric parameter values between the assessment groups, no significant differences were identified for any parameter. Weight (5.93 kg and 4.92 kg), BMI (2.23 kg/m ² and 1.83 kg/m ²), WC (6.80 cm and 6.74 cm), and RFM (2.43 and 2.63) reductions were similar in both telenutrition and in-person assessment, respectively ( Table 3).

Table 3. Difference in anthropometric parameters according to assessment group.

Variables	Telenutrition change	In-person assessment change	p
Weight (kg)	5.93 ± 3.88	4.92 ±3.29	0.1641
BMI (kg/m 2)	2.23 ± 1.39	1.83 ± 1.23	0.1265
WC (cm)	6.80 ± 4.87	6.74 ± 4.55	0.9510
RFM	2.43 ± 1.78	2.63 ± 1.73	0.5741

BMI: body mass index, WC: waist circumference, RFM: relative fat mass. Wilcoxon Rank-sum test for weight and BMI, T-Student test for WC and RFM. P < 0.05.

Discussion

This study was designed to examine variations in the anthropometric parameters of overweight and obese adults treated in a private nutritional center where telenutrition was additionally implemented. The results showed that weight loss was statistically significant in both groups, with no difference in the variation by the three-month follow-up evaluation. Weight loss is a key factor in reducing non-communicable disease risk and COVID-19 complications, the fact that this nutritional goal is achieved through both assessment modalities, offers the patient a viable alternative for health maintenance.

Significant changes in weight, WC, BMI and RFM were observed within each assessment modality in our study. However, comparing the changes in these anthropometric measures between these two assessment modalities, no significant difference in these values was identified. Kuzmar IE et al., reported similar results, with no significant differences in weight loss, BMI and waist-to-height ratio (WHtR), when comparing the in-person assessment to telenutrition, in obese women. ¹⁷

Other studies have identified significant changes in anthropometric parameters, such as Huang et al., who had assessed overweight and obese patients with non-communicable diseases by using telenutrition and observed a significant BMI decrease. ¹⁸ Whereas Beleigoli et al., used a web-based software with patient feedback for 24 weeks in overweight and obese patients in comparison to non-technological interventions to assess weight loss and lifestyle changes, and indicated improvement in food consumption habits, user adherence and significant weight loss. ¹⁹ Likewise, Ventura Marra et al., in a randomized study in cardiovascular patients observed a significant weight loss in a similar follow-up period of 12 weeks, 4.92 ± 3.29 kg and 5.93 ± 3.88 kg for in-person modality and telenutrition, respectively. ²⁰ In five American clinics, a randomized study examined pregnant women to prevent excessive weight gain and promote healthy behavior by comparing telehealth strategies to traditional assessment. Pregnant women assessed traditionally showed an average weekly gestational weight loss of 0.26kg, in comparison to 0.32kg in the telehealth group (the mean difference between the two groups was 0.07 kg per week, CI 95%: −0.09 to −0.04). ²¹

A study that compared video conference health coaching with a focus on physical activity and weight management, to in-person modality for adults with high BMI, showed that the intervention group achieved a significantly greater weight loss (8.23 ± 4.5 kg), ²² within 12 weeks.

Another telehealth program for weight loss that used video conferencing for 12 weeks showed a significant difference between the intervention and control groups in body weight (7.16 ± 4.4 vs. 1.5 ± 4.1%, respectively). The significant weight loss was achieved in nine out of 13 individuals (69.2%) in the intervention group compared to one in 12 (8%) in the control group. ²³

Despite the fact that the nature of the telenutrition modality and other not assessed factors as level of education may impact the precision of the remote measurements, a possible explanation for our results could reside on the fact that the level of concern about their own health due to the pandemic may influence their adherence to the recommendations, besides the additional safety advantage associated to the remote evaluation may explain the similar behavior of the nutritional goals in spite of the assessment modality. Moreover, the same dietitian nutritionist and monitoring methodology for the follow-up were involved on both types of assessments. It is important to emphasize that in our study, the greatest decrease obtained in the telenutrition assessment was for WC (mean difference 6.80 ± 4.87 cm), which is an anthropometric index significantly associated with increased risk to cardiovascular and metabolic diseases, as reported in the literature. ^{24 - 27}

In spite of the fact that it could be argued that this may be associated with a measurement error due to the remote nature of the assessment, we underscore the fact that we also observed statistically significant reductions in weight and BMI values for this group which makes the original WC decrease plausible since it is expected that a weight loss involves a reduction in WC.

Additionally, this study has included RFM which as an anthropometric parameter has not been extensively examined in Lima, Peru. RFM, is a valuable tool for corporal composition evaluation, ^{15 , 28} and for being a great predictor of dyslipidemia and metabolic syndrome. ²⁹ In our study, RFM showed also a significant decrease in both modalities (2.43 ± 1.78 telenutrition vs 2.63 ± 1.73 in-person). Which make it a parameter of interest that could support the nutritional management of patients when assessing and treating metabolic diseases and even preventing potential cardiovascular complications by identifying patients at risk and prompt action with adequate measures.

We consider that local validation initiatives of this parameter may potentially benefit the patient nutritional care. Future studies may address this opportunity.

This study has some limitations, consider the observational and retrospective nature of the study. There may be other variables, such as physical activity, lifestyle changes, previous nutritional consultation or weight-loss concomitant medications, which have not been assessed that could act as confounders. We acknowledge that their potential effects may contribute to the outcome in the parameters assessed besides the sole effect of the evaluation modality. Information on patient’s level of education was not available at the time of the study, we acknowledge this is part of the limitations of our study since level of education may impact the ability to understand instructions and conduct the corresponding measurements remotely as recommended. The study was conducted in a private nutritional center and through a convenience sampling strategy which may impact representativity. The follow-up period was three months, so either stabilization or further variations of the assessed parameters may take place which could be observed with longer follow-up periods.

Our study has some strengths, the fact that nutritional goals can be met despite the assessment modality highlights the fact that the remote evaluable is a feasible option, information that lacking at the local level, furthermore it gives the patient the chance for a safer modality in times of pandemic. Additionally, through the remote use of both questionnaires (consumption frequency and 24-hour dietary recall), we may acknowledge that these tools are applicable and useful through telenutrition. Our study contributes with evidence regarding the applicability of remote anthropometric measurements when associated to standardized remote instructions. Future prospective studies could assess the long-term impact of telenutrition in anthropometric parameters and nutritional health in patients.

Conclusion

In the time of pandemic, telenutrition has become a valuable alternative to nutritional care by reducing the transmission risk through social distancing practices. Telenutrition may well be regarded as a useful tool for current situations such as the COVID-19 pandemic, by offering similar outcomes to those reported as the in-person assessments, while providing ongoing nutritional support to overweight and obese adults in times of isolation and social distancing. Health care providers should attempt to adapt their processes to fulfil the patients’ health demands, in order to prevent excessive weight gain and its related comorbidities through interventions like telenutrition.

Data availability statement

Underlying data

Figshare: The effects of telenutrition in overweight and obese adults in a nutritional center in Lima, Peru.

DOI: https://doi.org/10.6084/m9.figshare.14710296.v1. ¹⁶

The project contains the following underlying data:

Database: The data includes baseline and 3-month follow up demographic and anthropometric parameters for overweight and obese adults according to assessment modality (telenutrition vs in-person).

Extended data

Figshare: The effects of telenutrition in overweight and obese adults in a nutritional center in Lima, Peru.

DOI: https://doi.org/10.6084/m9.figshare.14832345.v2. ¹³

This project contains the following extended data:

File 1: Consumption Frequency Questionnaire

File 2: 24-hour Dietary Recall Questionnaire

Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication): https://creativecommons.org/publicdomain/zero/1.0/.

Funding Statement

The author(s) declared that no grants were involved in supporting this work.