The impact of nutrition counselling via online application on nutrient intake among Thai older adults during COVID-19: pilot study of randomised controlled trial

Abstract

Background

During COVID-19, Thai older adults were restricted to travel due to fragile and easy to infect. Dental services and other healthcare also provided a limited scope for all. This study aimed to investigate the impact of online nutrition counselling on nutrient intake and nutritional status in older adults and the feasibility of online nutrition counselling among Thai older adults.

Methods

A pilot randomised controlled trial was conducted among 30 older adults (aged ≥ 60 years) at Mahidol Dental Hospital, Bangkok, Thailand. Eligible participants were Thai nationals who had recently received dental services, were able to use a mobile phone with the LINE Official Account application, and could send food-related photos or messages. Participants were randomly assigned (1:1) via computer-generated randomisation to either an intervention group (n = 15) or a control group (n = 15). The intervention group received tailored dietary advice based on a three-day food diary, followed up monthly for three months. The control group received general health advice, which was also provided to the intervention group. Outcome assessors were blinded to group allocation. Primary outcomes included changes in nutrient intake, body measurements (weight, height, waist and hip circumference), and nutritional status assessed by the Mini Nutritional Assessment-Short Form (MNA-SF). Nutrient analysis was performed using INMUCAL-nutrient V4.0 software. Within- and between-group differences were assessed using the Wilcoxon signed-rank test.

Results

All participants completed the study (100% retention). Both groups showed a significant decline in daily energy and nutrient intakes after three months (p < 0.05), but no significant between-group differences were observed for nutrient intake or body measurements. A trend toward reduced waist circumference was noted in the intervention group, whereas no such change was observed in the control group.

Conclusions

This study showed that online nutritional counselling impacted food consumption and nutrients intake among Thai older adults during COVID-19. The combination of online tailored diet advice and dental treatment was feasible, highly acceptable and practical to participants to improve healthy diet consumption.

Trial registration

MU-IRB DTPY 2021/DT100 (COA:2021/082.2209): 22 /09/2021; Retrospectively registered on Thai Clinical Trial Registry (TCTR20231101003): 01/11/2023.

Background

COVID-19 is a global pandemic that affects various aspects of people’s life. Food consumption is also another part that changed due to the pandemic situation. The concern of follow-up health status among older adults was raised as the inaccessibility of health services and limitation of face-to-face consultation.

The chronic conditions following health behaviours, especially food consumption, were reported in various countries, similar to Thailand [1]. Even the improvement of functional dentition due to a denture or dental prostheses was applied. The nutritional status of the older population could not show a significant change similar to study among older adults in Ireland [2]. Worstman and colleagues’ study [3] found that combining dental implants and nutritional advice could help better nutritional status, healthy behaviours, and quality of life after 12 months of study. In addition, the changing body measurements included greater weight and food consumption during the pandemic, as study in Brazil [4].

In Thailand, current nutritional counselling for older adults is face-to-face tailored consultation including exploring problems and behavioural adjustments. Most nutritional education includes motivation to adopt healthy eating, maintenance of healthy eating, and monitoring healthy eating. However, face-to-face consultation has limitations to monitor behaviour when patients are at their homes. Telenutrition is an alternative way to provide nutritional counselling that could reduce the gap in face-to-face consultation such as nutrition history with image-assisted information could reduce potential recall bias from patients [5, 6]. Moreover, the study in mobile phone-based behaviour change program among adults [7] showed 3-month period could be measured as the immediate effect after intervention similar to other face-to-face behavior change program studies which used 3-month period to explore the change during studies [8, 9].

In addition, the pandemic situation and the benefits of diet counselling for older adults were a concern. Telemedicine is the most valuable and appropriate tool during a difficult time to provide access for people, especially older adults or fragile groups [10, 11]. The pilot of virtual nutritional counselling among patients who received dental service in Thailand was aimed to explore the feasibility and impact of intervention during COVID-19.

Methods

This study is a pilot for a 3-month trial conducted among older adults in Bangkok, Thailand, from October 2021 to March 2022. The study was a pre-post individual-level comparison with a control group, and the feasibility and acceptability were reported. Before starting the process of the study, all participants signed written informed consent.

Study population

Thirty older adults were recruited in Dental Hospital, Faculty of Dentistry, Mahidol University, Bangkok Thailand, via social media platforms and the public relation board at the hospital. They all agreed to participate in the study. Participants were included if they were Thai nationals, aged 60 years old and over, received dental service within 3 months, could use a mobile phone with the Line application, and could take a photo or send a message about the food they consumed via the Line application. Participants were excluded if they were diagnosed with a mental disorder affecting the follow-up period, had already consulted with a doctor or nutritionist for nutritional counselling, and were uncomfortable giving information to researchers during the study period. The intervention group would report a 3-day food diary and receive tailored diet advice every month. All participants were invited to a follow-up examination for three months, and there was no drop-out participant (100% retention rate).

The minimum number of participants for the pilot study was 30, following flat rules of thumb [12]. In addition, Sim and Lewis suggested that the minimum sample size for the pilot study should be 24, 12 in both intervention and control groups. This number could be used for a pilot study, and the findings could be the preliminary result for further studies.

Feasibility of study

The criteria were set to successfully recruit at 80% of the target sample size within the recruitment period. The retention should achieve at least 70% of participants through the end of the study and 80% adherence to the study protocol. Data collection should be usable data from at least 85% of participants who completed the trial. The intervention acceptability was planned to obtain feedback from participants after trial and set criteria for positive feedback for at least 70% of participants. If the pilot trial meets or exceeds the criteria in all or most categories, indicating the feasibility and potential success of a full-scale RCT: Process as Planned. If the trial meets some criteria but falls short in others, suggesting that specific adjustments are needed before progressing: Proceed with Modification. If the trial fails to meet key criteria, indicating significant challenges that may not be addressable in a full RCT : Do not Proceed [13].

Randomisation

Participants in this study were randomly assigned to either the intervention group (n = 15) or the control group (n = 15) using a simple randomization method. This process was designed to ensure that each participant had an equal chance of being placed in either group, thereby minimising selection bias and helping to achieve comparable groups.

Allocation sequence

The allocation sequence was generated using an online-based computer software designed for randomization purposes. The specific steps involved in the procedure are as follows:

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Preparation: Prior to the start of the study, a list of all eligible participants was compiled. Each participant was assigned a unique identification number to maintain confidentiality and facilitate the randomisation process.

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Random sequence generation: The computer software was configured to generate a random allocation sequence. This was done using a random number generator, which assigned participants to either the intervention or control group based on a predetermined ratio of 1:1. The software ensured that the sequence was unpredictable and unbiased.

As each participant enrolled in the study, they were assigned to a group according to the next number in the random allocation sequence. This process continued until all participants were allocated to either the intervention or control group.

*

Blinding: To maintain the integrity of the randomisation process and prevent any potential bias, the allocation sequence was concealed from the researchers who enrolled participants. This was achieved by using a research assistant to manage the allocation process or by using sealed, opaque envelopes containing the group assignments.

Data collection

Data were collected via the questionnaires, body measurements and a 3-day food diary at baseline and follow-up examination (Fig. 1). The questionnaires collected information on demographic characteristics (sex and age), socioeconomic status (education attainment and household income), health behaviours (smoking status and physical activity), chronic conditions, and nutritional assessment via a short-form of mini-nutritional assessment (MNA-SF) [14] and review dental record examination.

The MNA-SF [15] is a screening tool that includes food intake declined, involuntary weight loss, mobility, psychological stress or acute disease, neuropsychological problems, body mass index (BMI), and calf circumference. The MNA-SF scores could screen participants as malnourished (0–7 points), at risk of malnutrition (8–11 points) and normal nutrition status (12–14 points).

Anthropometric measurements including weight, height, waist and hip circumferences were measured by trained researcher (SS) who were calibrated with expert (gold standard). Participants wore indoor clothing without shoes, using digital standing scale (Tanita Body Fat Scale Beauty Fit BC-G12 White) which was calibrated before every use for weighting. Waist and hip circumference were measured by tape measure in centimetres (cm). The retraining and revision of processes were done monthly to minimise reporting biases. The BMI was calculated and categorised following following World Health Organisation Asia-Pacific Criteria [16]. Waist to Hip Ratio (WHR) was calculated by dividing waist circumference by hip circumference.

The dental record was updated from the dental examination in the hospital file to determine the number of teeth, condition of teeth and recent dental treatment within 6 months at the baseline examination [17].

The 3-day food diary was collected via the Line application for all participants at baseline and 3-month follow-up examinations. The food diary needed to include 2 weekdays and one day of the weekend (either Saturday or Sunday) consecutively. Intervention group had to provide every month, while control group only provided at baseline and 3-month follow-up examination. All participants will be given the example of 3-day food diary following the validated 3-day Thai food diary form which used among Thai older adults in public hospital. The food diary details included meals, food lists, food groups and quantity [18]. At the same time, the intervention group (tailored diet counselling) had to send a 3-day food diary every month before 1 week before diet counselling with reminder notification via application. The average daily nutrient data was calculated via INMUCAL nutrient V4.0 software. The INMUCAL nutrient V4.0 was developed by the Institution of Nutrition, Mahidol University (INMU), Thailand, for analysing dietary intake, including macronutrients, micronutrients, and energy intake based on Thai Recommended Daily Intake. The validity of the nutrient analysis relies on the Thai food composition database. INMUCAL uses a database developed and maintained by INMU, which is regularly updated with new food composition data obtained through laboratory analysis. Additionally, INMUCAL has undergone various validation studies to ensure its reliability. These studies typically compare the software’s output with laboratory analysis of food samples and other established dietary assessment tools [19].

Intervention

Monthly, the average nutrient intake from a 3-day food diary was calculated from a 3-day food diary via INMUCAL nutrient V4.0 software [20] and considered a part of diet advice. The nutrient intake information was determined by other factors, including food choice, chronic conditions, and individual habits. Then, the diet advice was finalised and approved by a nutritionist from the Institute of Nutrition, Mahidol University. After that, the online tailored diet counselling was sent to participants in the intervention group via the Line official application within a week. Intervention group would receive 3 tailored online nutritional counselling over the study. For all participants, the general health information as an infographic from the Ministry of Public Health and Mahidol University was sent weekly to all participants (Fig. 2).

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Outcome measurements

Primary outcome measurement is nutritional status, including body measurements (body mass index, waist circumference, and hip circumference), were administered at baseline and post-intervention or 3-month follow-up examinations. Secondary outcomes are average nutrient intake which is also collected at baseline and post-intervention.

All procedures involving research study participants were approved by the Faculty of Dentistry/Pharmacy Institutional Review Board (COA.No. MU-DT/PY-IRB 2021/082.2209 on 22/09/2021; The TCTR identification number is TCTR20231101003 on 01/112023). Written informed consent was obtained from all participants.

Statistical analyses

All analyses were conducted using STATA 16 (Stata Corp., College Station, TX). Thirty participants provided complete data at baseline and follow-up intervention. Pearson’s chi-square tests and independent t-tests were used to examine baseline differences between control and intervention groups in sociodemographic characteristics, health behaviours, health conditions and outcome measures. The normality test was tested by Kolmogorov-Smirnov test showing that all data were not normal distribution. Spearman correlation was used to test the difference in the median between groups and Wilcoxon Signed Rank test was used to test the difference between pre-post study among groups. The differences in nutritional status, including body measurements, nutrient intake between groups, and time(pre-post study) were explored. Regression analyses were used to see the association between change of nutritional status or energy intake and groups of participants. The potential confounders including sex, age, educational level, household income, health behaviours (smoking status, alcohol consumption, frequency of health check-up), comorbidity, and remaining teeth were adjusted in models. The statistically significant level was 0.05 for all analyses.

Results

Sample characteristics

The study sample consisted of thirty older adults who were randomly assigned to intervention (n = 15) and control groups (n = 15) equally. No participant losses to follow-up (100% retention rate). This study started to recruit on 18 October 2021 and the follow-up examination started on 18 January 2022. This pilot trial ended on 30 April 2022. Most of the participants in the study were a woman (70%), youngest-old or 60–74 (86.67%), 30,000–49,999 Thai Bath (average income in Thailand, 2021 was 27,000 Thai Bath) as a household income and university graduates (86.67%). The mean age was 68.4 years. Table 1 presents the baseline characteristics of the intervention and control groups. No difference between demographic characteristics, socioeconomic status, health behaviours (smoking status, alcohol consumption, frequency of health check-up), comorbidity, remaining teeth and nutritional status groups indicated adequate randomisation.

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For health behaviours, most participants never smoke 96.7%. For participants from intervention group, reported ‘did not drink’ alcoholic beverages (73.3%) as the most proportion, while every participant in control group drank occasionally. Most of them attended health check-ups within a year (93.4%). The most chronic condition was hypertension (60.0%). Most participants had 20 teeth and above (93.3%) and used dentures (80.0%). Comparing BMI categories at baseline examination, there was no difference between intervention and control groups. Only 1 participant from intervention group was underweight (BMI < 18.5 kg/m2) and other participants in intervention group were evenly distributed, 7 participants for each category, to be overweight (BMI: 23.0–24.9 kg/m2) and obese (BMI ≥ 25 kg/m2). For control group, seven participants were obese and other eight were evenly categorised into normal and overweight groups.

Intervention effects for participants

Changes in body measurements

Table 2 shows the comparison of body measurements between intervention and control groups both in baseline and follow-up examinations. For baseline measurement, the average weight was 58.6 kg, and BMI was 23.3 kg/m2, which was in the normal range for the Thai population. The average waist and hip circumferences were 82.8 and 96.6 centimetres, respectively. To compare between groups, the interventional group had all body measurements more prominent than the control group. However, there was no statistically significant difference between the groups. For waist circumference, hip circumference and waist-to-hip ratio, the average waist circumference and WHR among the intervention group increased after the intervention. In comparison, in the control group, the average weight, BMI, WC, and WHR increased after joining the study. According to BMI following Asia-Pacific Criteria [16], One participant in the intervention group changed from underweight (BMI < 18.5) to normal (BMI:18.5–22.9) and one participant changed from overweight (BMI: 23.0-24.9) to obese status (BMI:25 and above). Others (normal: n = 6; overweight: n = 2; obesity level: n = 4) remained the same. For the control group, results remained the same including 5 participants who were at normal nutritional status, 2 participants who had overweight status, and 3 obese participants. Participants in the control group showed worse status including 3 participant who changed from normal to overweight and 2 participants who showed overweight to obesity. The status following BMI values before and after intervention showed that 1 participant had worse nutritional status while 1 participant in the intervention group had better nutritional status. Both groups had 13 participants who had no change in nutritional status following BMI values. This finding was not statistically significant to see differences between groups after study. While within group, the change of nutritional status showed statistically significant differences between baseline and follow-up examinations in both intervention and control groups (p < 0.001; results not shown).

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Changes in nutrients intake

Tables 3 and 4 show the average daily energy with macronutrients and micronutrients, respectively. For the energy and macronutrients, both groups showed a statistically significant decrease in the average intake of energy, protein, carbohydrate, and fat except for cholesterol after the study (p < 0.05). In addition, the average consumption of micronutrients, including calcium, phosphorus, and potassium, also statistically decreased in both groups in follow-up examination compared to baseline examination. There was also no significant difference between groups for nutrients intake.

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The association between groups and nutritional status

Table 5 shows the associations between BMI category changes and different groups of samples (p < 0.05). However, there was no association between changes in BMI and WC. Only WHR change showed significant association in adjusted model. For all nutrients except phosphorus showed no association between groups.

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Feasibility of study

The trial successfully recruited 30 participants in total within the planned recruitment period. For retention and adherence, all participants (100%) completed the trial in each group and also strongly adhered to telenutrition protocol following the prescribed study guidelines. Informal positive feedback was received from 23 participants (76.7%) who accepted the trial. Thus, the decision is to proceed as planned to a full RCT.

Discussion

Virtual nutritional counselling was used for 3-month as the intervention of this study during the COVID-19 situation. The changes in energy, almost all macronutrients and micronutrients, were decreased in both groups after participating in the study. Body measurements were slightly changed, and the remaining teeth were not related to nutritional status among participants. This study revealed the feasibility of using chat applications to communicate about health, especially nutrition, with older adults who were the high-risk population during the pandemic. The barriers to nutritional advice in person were reduced via this alternative, and the multidisciplinary health professions included physicians, nutritionists, dentists, and other healthcare workers.

For the comparison between intervention and control groups, there was no statistically significant difference which could be resulted from participants who were keen to improve their health after dental services and also take general health information strictly. Thus, the intervention and control groups’ differences could not be seen distinguish similar to findings from study of educational intervention on health-promoting lifestyle in Iran [21]. However, the control group which received a proper general health information criticised as strong design, and possibility of ethical issues diminished.

Telenutrition or virtual nutritional counselling in this study could have a positive impact on various health outcomes including diabetes [22] cardiovascular disease [23] weight management [24] and mental health [22]. As virtual nutrition has role to help in daily meal plan, monitoring behaviours and also support mental health for older adults to get proper service at home. Additionally, the costs of in-person meetings, such as transportation, time, and miscellaneous, were reduced due to virtual nutritional counselling. The chat application also helped nutritionists investigate the portion size, the living environment, and leisure activities while they were at home via asking for photos or having conversations. The photos could be taken immediately before and after a meal, reducing the recall bias from only written records. Thus, combining food records with photos could be more accurate when calculating the average nutrient intake than the traditional food record.

Comparing body measurements between pre-post studies found that 2 participants in control group and only 1 participant from intervention group changed from overweight to obesity level. While 2 participants from both intervention and control also changed to better nutritional status (average weight), the rest of the participants could maintain nutritional status, especially BMI, reducing the mortality rate [20]. However, the waist circumference and hip circumference in both groups were increased which similar to study among older adults in Japan and Brazil reported waist circumference increase overtime even body weight decreased [25, 26]. This could be related to the increase in fat body mass, specifically in abdominal cavity [27].

For the telehealth and telenutrition, the weekly health information for all participants could encourage them to be concerned about their health status, which could change their health behaviours [28]. Apart from body measurements, the trends of average nutrient intake were reducing, especially intervention group. The informal feedback showed that the preparation of food and photo taking was a concern among participants because they realised that these data would be calculated and related to their upcoming health and nutritional counselling [29]. Participants in this study appraised that their physical and mental health was better because they could talk about their routine lives and leisure time activities. They also asked about additional health information, such as non-evidence health advice from friends or social media. Thus, the researchers could also confirm or correct the information based on evidence on each topic.

However, some limitations need to be aware in this study. First, the intervention was based on the internet connection, which depends on the skill of mobile phone usage and the stability of the connection. The pandemic also impacted the accessibility of food resources and physical exercises. The diet advice was adapted and tailored for each participant to the highest efficiency [30, 31]. The generalisability should be concerned with the small number of participants. Second, all participants were recruited with the consent to receive and send health information as well as diet consumption for 3-month period which need a commitment to work. This could be a potential bias for selection bias for participants who keen to keep healthy behaviours which might lead to change among both groups and unable to see difference between group after intervention [32]. Third, the short follow-up study could result in no significant improvement of nutritional status and dietary intake for participants; however, the feasibility of telenutrition study among older adults could be seen in this study. Additionally, patients’ satisfaction on nutritional counselling should be done to evaluate in further study rather than informal feedback.

Intervention implementation

Online health and nutritional counseling impact on people’s behaviours such as the food selection and physical exercises. High retention and adherence, strong data quality, and positive participant feedback, the decision is to proceed as planned to a definitive randomised controlled trial. These results indicate that telenutrition intervention is feasible and holds promise for improving health outcomes in a larger population. The full-scale RCT will allow for a more rigorous evaluation of the intervention’s efficacy and impact.

Conclusion

This study showed that online nutritional counselling impacted food consumption and nutrients intake among Thai older adults during COVID-19. The combination of online tailored diet advice and dental treatment was feasible, highly acceptable, and practical to participants to improve healthy diet consumption. The longer follow-up and adaptation in the healthcare setting could help the older adult population have better health behaviour.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

COVID-19:

Coronavirus disease 2019

MNA-SF:

Mini nutritional assessment-short form

RCT:

Randomised control trial

cm:

Centimetre

BMI:

Body mass index

WHR:

Waist to hip ratio

INMU:

The Institution of Nutrition, Mahidol University

Kcal :

kilocalories

g :

Gram

mg:

milligram

SD:

Standard deviation

References

Akepalakorn WP, Thaikla H, Satheannoppakao K. W. The 5th Thai National Health Examination Survey 2014. Health System Research Institute; 2016.

Wallace S, Samietz S, Abbas M, et al. Impact of prosthodontic rehabilitation on the masticatory performance of partially dentate older patients: can it predict nutritional state? Results from a RCT. J Dent. 2018;68:66–71. 2017/11/14.

Wostmann B, Simon T, Neuhauser-Berthold M, et al. Pilot study on the influence of nutritional counselling and implant therapy on the nutritional status in dentally compromised patients. PLoS One. 2016;11:e0147193. https://doi.org/10.1371/journal.pone.0147193.

Nehme PA, Marot LP, Nogueira LFR, et al. A nutritional counseling program prevents an increase in workers’ dietary intake and body weight during the COVID-19 pandemic. Front Physiol. 2021;12:703862. 2021/08/10.

Tanweer A, Khan S, Mustafa FN, et al. Improving dietary data collection tools for better nutritional assessment – a systematic review. Computer Methods and Programs in Biomedicine Update. 2022;2:100067. https://doi.org/10.1016/j.cmpbup.2022.100067.

Whitton C, Healy JD, Collins CE, et al. Accuracy and cost-effectiveness of technology-assisted dietary assessment comparing the automated self-administered dietary assessment tool, Intake24, and an image-assisted mobile food record 24-hour recall relative to observed intake: protocol for a randomized crossover feeding study. JMIR Res Protoc. 2021;10(12):e32891. https://doi.org/10.2196/32891.

Ek A, Alexandrou C, Soderstrom E, et al. Effectiveness of a 3-month mobile phone-based behavior change program on active transportation and physical activity in adults: randomized controlled trial. JMIR Mhealth Uhealth. 2020;8(6):e18531. https://doi.org/10.2196/18531.

O’Dwyer T, Monaghan A, Moran J, et al. Behaviour change intervention increases physical activity, spinal mobility and quality of life in adults with ankylosing spondylitis: a randomised trial. J Physiother. 2017;63:30–9. 2016/12/19.

Rogers LQ, Hopkins-Price P, Vicari S, et al. Physical activity and health outcomes three months after completing a physical activity behavior change intervention: persistent and delayed effects. Cancer Epidemiol Biomarkers Prev. 2009;18:1410–8. 2009/04/23.

Chaichuay W. Elderlies’ experience in using LINE application: a phenomenological study. Veridian E-J. 2017;10:905–18 .

Contreras CM, Metzger GA, Beane JD, et al. Telemedicine: Patient-Provider clinical engagement during the COVID-19 pandemic and beyond. J Gastrointest Surg. 2020;24:1692–7. 2020/05/10.

Sim J, Lewis M. The size of a pilot study for a clinical trial should be calculated in relation to considerations of precision and efficiency. J Clin Epidemiol. 2012;65:301–8. https://doi.org/10.1016/j.jclinepi.2011.07.011.

Avery KN, Williamson PR, Gamble C, et al. Informing efficient randomised controlled trials: exploration of challenges in developing progression criteria for internal pilot studies. BMJ Open. 2017;7:e013537. 2017/02/19.

Moynihan P, Thomason M, Walls A, et al. Researching the impact of oral health on diet and nutritional status: methodological issues. J Dent. 2009;37:237–49. https://doi.org/10.1016/j.jdent.2008.12.003.

Rubenstein LZ, Harker JO, Salvà A, Guigoz Y, Vellas B. MNA®-SF (Mini Nutritional Assessment – Short Form) User Guide. Nestlé Nutrition Institute; 2009. Available from: https://www.mna-elderly.com/sites/default/files/2021-10/mna-guide-english-sf.pdf.

Pan WH, Yeh WT. How to define obesity? Evidence-based multiple action points for public awareness, screening, and treatment: an extension of Asian-Pacific recommendations. Asia Pac J Clin Nutr. 2008;17:370–4. 2008/09/27.

Bureau of Dental Health . The 8th Thailand National Oral Health Survey 2017. Bangkok: Department of Health Ministry of Public Health; 2018:330.

Rajavithi hospital. Health record. 2018. Available from: https://www.rajavithi.go.th/rj/?page_id=11916.

Institute of Nutrition Mahidol university. INMUCAL-Nutrients. V.4.0. 2024. Available from: https://inmu.mahidol.ac.th/inmucal/.

Institute of Nutrition Mahidol university. Nutrient calculation computer software INMUCAL-Nutrients . Version4 database NB.3.1. Thailand. 2018. Retrieved from https://inmu.mahidol.ac.th/inmucal/.

Solhi M, Fard Azar FE, Abolghasemi J, et al. The effect of educational intervention on health-promoting lifestyle: intervention mapping approach. J Educ Health Promot. 2020;9:196. 2020/10/17.

Arafat MY, Zaman S, Hawlader MDH. Telemedicine improves mental health in COVID-19 pandemic. J Glob Health. 2021;11:03004. https://doi.org/10.7189/jogh.11.03004.

Trivedi R, Elshafie S, Tackett R, et al. Effectiveness and feasibility of telehealth-based dietary interventions targeting cardiovascular disease risk factors: systematic review and meta-analysis. J Med Internet Res. 2024;26:e49178. https://doi.org/10.2196/49178.

Ventura Marra M, Lilly CL, Nelson KR, et al. A pilot randomized controlled trial of a telenutrition weight loss intervention in middle-aged and older men with multiple risk factors for cardiovascular disease. Nutrients. 2019;11:2019/01/27. https://doi.org/10.3390/nu11020229.

Howel D. Waist circumference and abdominal obesity among older adults: patterns, prevalence and trends. PLoS One. 2012;7:e48528. https://doi.org/10.1371/journal.pone.0048528.

Vargas PM, Schneider BC, Costa CS, et al. Age is the most important factor for change in body mass index and waist circumference in older people in Southern Brazil. Nutrition. 2023;109:111956. https://doi.org/10.1016/j.nut.2022.111956.

Zamboni M, Zoico E, Scartezzini T, et al. Body composition changes in stable-weight elderly subjects: the effect of sex. Aging Clin Exp Res. 2003;15:321–7. 2003/12/10.

Bradbury J, Thomason JM, Jepson NJ, et al. Nutrition counseling increases fruit and vegetable intake in the edentulous. J Dent Res. 2006;85(5):463–8. https://doi.org/10.1177/154405910608500513.

Krondl M, Lau D, Coleman P, et al. Tailoring of nutritional support for older adults in the community. J Nutr Elder. 2003;23(2):17–32. https://doi.org/10.1300/J052v23n02_02.

Kaufman-Shriqui V, Sherf-Dagan S, Boaz M, et al. Virtual nutrition consultation: what can we learn from the COVID-19 pandemic? Public Health Nutr. 2021;24:1166–73. 2021/01/14.

Komagamine Y, Kanazawa M, Iwaki M, et al. Combined effect of new complete dentures and simple dietary advice on nutritional status in edentulous patients: study protocol for a randomized controlled trial. Trials. 2016;17:539. 2016/11/11.

Popovic A, Huecker MR, Study Bias. In: StatPearls. Treasure Island, FL: StatPearls Publishing; 2023.