The effect of culinary medicine to enhance protein intake on muscle quality in older adults: a randomized controlled trial

Abstract

Background

Age-related muscle loss can be decreased with increased protein intake. Recent evidence suggests that increasing animal-based protein such as lean beef can be the most effective for age-related muscle repair and growth. Culinary medicine (CM) is a science-based field to teach people the art of food and cooking with the science of medicine to improve health.

Aim

This study aimed to assess the impact of a digital culinary medicine education program emphasizing lean beef on protein intake and muscle quality among community-dwelling senior adults.

Methods

A 16-week randomized study compared a culinary medicine intervention group (CM) to a control group (CN). Among 47 senior adults assessed for eligibility, 28 participants (mean age 72.86 ± 5.22 years) completed the study. The CM invention included weekly cooking demonstration and nutrition education videos. Protein intake, cooking effectiveness, physical activity, and nutrition knowledge were assessed with questionnaires while muscle quality, vitamin B₁₂, folate, and creatinine levels were objectively measured.

Results

Higher protein intake (grams) was seen in the CM group (pre: 52.75 ± 11.93 vs. post: 60.02 ± 21.40) compared to a decrease in protein intake seen among the CN group (pre: 60.68 ± 24.43 vs. post: 52.89 ± 17.85). However, there was no between-group difference in protein intake from the pre-study (P = 0.454). Interestingly, an exploratory measurement of muscle mass, even though not powered to detect modest-sized effects, showed a promising difference in change in muscle mass (kilograms) between the CM group and CN (53.17 ± 13.66 vs. 44.23 ± 4.78, respectively; P = 0.041).

Conclusion

The results suggest this CM intervention might be associated with improved muscle mass. There is also potential for this type of intervention to increase protein intake.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40520-025-03075-8.

Introduction

Aging is associated with a decline in muscle mass, strength, and physical function, which can lead to sarcopenia and frailty [1]. This deterioration of muscle and physical capabilities impacts an individual’s functional independence and quality of life. Dietary protein stimulates muscle protein synthesis (MPS). Evidence suggests that optimal protein intake for an older individual is greater than the Recommended Dietary Allowance (RDA) [2, 3]. In addition, recent research demonstrates that increased oxidation and inflammation play a role in muscle protein breakdown that occurs during aging. Therefore, nutritional interventions that reduce oxidation or inflammation along with higher protein intakes may enhance MPS [4].

Food intake, including protein-rich foods like red meat, has been shown to decline with age [5–7]. Barriers to consuming protein-rich foods include reductions in taste and smell, dentition and dexterity, and changes in living situation [8]. Nutritional interventions that can improve eating behaviors, diet quality, and stimulate MPS in older adults are necessary to help prevent, manage, and promote recovery of sarcopenia. To reduce potential barriers of red meat consumption in community-dwelling older adults, an additional strategy may be the use of cooking demonstrations, or culinary medicine that imparts knowledge about healthy cooking to improve the dietary habits of individuals at risk of sarcopenia. With this approach, people will be educated about age-appropriate, healthy eating behaviors and equipped with basic cooking skills to incorporate nutritious food into their daily diet [9, 10]. A systematic review concluded that culinary interventions such as cooking classes effectively improved attitudes, self-efficacy, and healthy eating in children and adults [11]. A recent study using cooking videos to encourage the consumption of calcium-rich foods showed that the subjects gained knowledge, were motivated to consume calcium-rich foods, and video demonstrations were accepted as an effective communication channel to impart cooking skills [12]. Additionally, it is suggested that cooking at home improves adherence to healthy nutrition, thereby reducing chronic illness risks [13]. Another study showed an association between cooking frequency and improved diet quality with a higher overall Healthy Eating Index (HEI) – 2015 scores [14]. Older adults may not be aware of their changing nutrient needs and therefore may lack the skills to prepare nutritionally adequate foods appropriately. Thus, cooking demonstrations can be a novel strategy to improve diet quality in older adults and promote and augment at-home cooking.

Culinary medicine (CM) is an evidence-based field that combines skills of preparing, cooking, and presenting food with the science of medicine to accomplish potential improvements in eating behaviors and health outcomes [15]. The goal of CM is to help people improve their diet quality which assists them in their medical regimen to produce an effective treatment [10]. A tailored CM program for older adults can be an effective strategy that could reduce barriers to protein intake that will enable older adults to age well and productively.

Different types of protein may have different effects on sarcopenia risk. To accurately assess protein intake and the impact on muscle strength, function, and mass, a grip strength dynamometer, short physical performance battery tests (SPPB), and body composition are promising outcome measurements. Assessing muscle strength, function, and muscle mass is necessary to identify the intervention needed to address the risk of sarcopenia [16]. Therefore, our study aimed to examine how an online CM intervention, emphasizing convenient ways to increase lean beef intake, could improve protein intake to see how this intervention could affect older adults’ muscle strength and mass.

Materials and methods

Study design and study participants

A 16-week single-center, parallel-group, randomized study compared a culinary medicine intervention group (CM) to a control group (CN) on their protein intake, cooking effectiveness, muscle quality, vitamin B₁₂, folate, creatinine levels, physical activity, and nutrition knowledge. The study was conducted at Texas Tech University Nutrition and Metabolic Health Initiative (NMHI), Lubbock, TX.

The study population included independent senior adults who were able to regularly perform physical activity and prepare their meals. Recruitment was conducted in the Lubbock, TX community, at local exercise centers for senior adults, and independent living communities by flyers and social media, which began July 2023 and concluded in January 2024. Participants were excluded from the study if they were less than 65 years old, had limited mobility, intake of nicotine, excessive alcohol, and/or use of drugs (i.e., amphetamines, cocaine, marijuana, and opiates), cancer, transplant, pacemaker, renal disorders, or Type 1 diabetes or Type 2 diabetes with insulin therapy, did not have access to a computer, smartphone, or tablet, or if they scored 4 or more points on the SARC-F sarcopenia screening tool. This is a screening tool for sarcopenia that has been validated by research to adequately identify this in older adults [17]. The study was approved by the institutional review board of Texas Tech University (IRB2023-505). All participants provided written informed consent.

Participants were randomly allocated to two groups, the CM or CN group. Randomization was implemented with a randomly generated sequence created from an online randomizing tool, randomizer.org. After this sequence was generated, every other number was assigned to the CM group starting with the first number. Participants were assigned these numbers with their subject codes in the order they were admitted into the study.

Before the start of the intervention, participants in both groups were provided a handout that offered information on healthful dietary habits for older adults from the Academy of Nutrition and Dietetics (AND) and another one that provided examples of various exercises for different muscle groups and purposes that were designed for seniors from SeniorsMobility.org. Both groups received an activity monitor (Garmin Vivofit 4) to help monitor their activity level.

Both groups were instructed to include one meal of lean beef three times per week (lunch or dinner, 400 kcal/30gm protein). An adequate amount of lean beef was provided for participants, allowing three servings a week to circumvent possible obstacles to consuming beef, like its cost. This beef was purchased from a local organization affiliated with the Texas Tech University College of Agricultural Sciences and Natural Resources and stored in a freezer according to proper USDA guidelines. Before receiving beef, participants were provided a physical handout with instructions from the USDA Kitchen Companion handbook on safely storing and thawing frozen beef at home.

Both groups were provided two lean beef-based recipes each week. Experienced research team members, including two registered dietitians and a culinary professor, collaborated to design this CM program (recipes, cooking demonstrations, and nutritional education videos) to increase awareness and counter common nutritional issues older adults face. The recipes included at least a serving of lean beef, 400 kcal, and 30 g of protein and utilized multiple cuts of beef, including lean ground beef, sirloin, roast beef, chuck roast, and bottom round roast. Preparation technique, time, level of difficulty, and the cost of ingredients were considered, as these are potential barriers to consuming and preparing red meat at home. This program focused on preparation methods that are tender and lean as older adults can experience difficulty in chewing and a decline in appetite with high fat and fibrous red meats. All recipes were tested and evaluated based on sensory characteristics such as appearance, odor, texture, and flavor.

Members of the CN and CM groups received different forms of culinary instruction to promote protein intake. The CN group only received weekly online recipes and exercise recommendation handouts without the CM intervention. While the CN group received only the recipes and exercises, the members of the CM group were additionally provided weekly (1) cooking demonstration videos that provided further step-by-step visual instruction on how to prepare these meals and (2) nutrition education videos covering nutritional topics that were especially relevant to older adults.

Questionnaires

To measure protein intake, a modified version of the rapid self-administered dietary protein food frequency questionnaire was used. This questionnaire contains 20 items evaluating the weekly intake of different types of meat, dairy, eggs, and beans [18]. This outcome measure was changed to the primary outcome after the trial commenced due to reviewers’ comments and recommendations of the authors’ preliminary data that was published stating that this intervention had a strong dietary focus.

The Physical Activity Scale for the Elderly (PASE) questionnaire assessed physical activity habits and how they might have altered throughout the study. This questionnaire has demonstrated a reliable representation of physical activity rates in free-living older adult populations [19].

Based on modifications to a previous questionnaire to assess the effectiveness of the CM intervention in education, a multiple-choice nutrition knowledge questionnaire was used to assess knowledge related to the protein and micronutrient composition of various foods, average dietary needs, and physiological processes related to skeletal muscle maintenance.

The research team also modified a previous resource, the Cooking Effectiveness Questionnaire, to assess the participants’ experience and feelings about cooking. This questionnaire was used to assess the participant’s adherence to the intervention as well as their experience implementing recipes from the intervention. This questionnaire was modified between the groups to include questions related to cooking and nutrition education videos for the CM group.

Information from these questionnaires further assessed the ability of this CM intervention to encourage healthful behavioral changes, like cooking at home, performing physical activity, addressing common nutritional concerns for older adults, as well as the comprehension of nutritional concepts.

Muscle quality

Muscle strength has been determined to be one of the key fields when assessing muscle quality, and handgrip strength has been shown to provide an accurate representation of changes in overall strength [20]. Muscle strength is measured by grip strength using a digital hand-held dynamometer (Camry EH101), focused on the change in strength over four months between the two groups.

A Tanita MC-780U bioelectric impedance analysis (BIA) scale was used to assess changes in multiple fields related to muscle and body composition, including muscle mass in pounds, weight in pounds, and BMI. Previous research has demonstrated that BIA scans accurately assess muscle mass, another key field for determining muscle quality [21]. This machine automatically calculated BMI after inputting height, which researchers measured using a stadiometer. Muscle mass was assessed by BIA. This outcome focused on the change in muscle mass over the four months between CM- and CN-group.

The final major field used to determine muscle quality is muscle function, and changes in this field were assessed with a Short Physical Performance Battery (SPPB) exam. This a resource that has been determined to provide a reliable assessment of muscle function and consists of a series of exercises related to the three categories of balance, gait speed, and chair standing [22]. A researcher read from a script with descriptive instructions on these exercises for the participants, and they were scored from zero to four points in each of these categories based on their performance for a total score ranging from zero to twelve points.

Muscle synthesis

Finally, blood samples were collected at baseline and following the completion of the study. Participants were instructed to fast 8 h before these blood draws were carried out by an experienced phlebotomist. Then, they were analyzed to measure serum vitamin B12, folate, and creatinine values. These micronutrients all act as cofactors in the process of MPS and increases in these values demonstrate improvements in the activity rate of this process.

Sample size and statistical analysis

The study included the final 13 participants in the intervention arm and 15 in the control arm for the analysis. The mean change in protein intake between the intervention and control groups as reported in the literature is the basis for the current study’s power calculation for the primary outcome [23]. Enrolling the participants in each group provides 95% power in estimation to detect 23.2 g/day mean difference of change in the protein intake between the groups with 17.48 g/day pooled standard deviation at 95% Confidence Interval (CI).

Data were entered into Microsoft Excel and analyzed with IBM SPSS version 29.0.0.0. Descriptive statistics were used to describe background characteristics of the participants in terms of mean, standard deviation, and percentage in two groups. Cross-tabulation was done to examine the association between gender and the two groups. All the continuous outcome variables were tested for normality (p > 0.05) using two-sample Kolmogorov-Smirnov test. Independent sample t-test was used for post values and post scores. Univariate ANOVA was used to examine the association of different parameters between the groups after adjusting possible confounders. The probability of significance was set at 5% level of significance.

Results

A total of 47 participants were assessed for eligibility. Ten (21.2%) were excluded during screening due to failing to meet inclusion criteria or losing contact. Thirty-four participants were randomized: 15 to the CM and 19 to the CN. A total of 13 in the CM, compared with 15 in the CN group, completed the 16-week study. Six (17.6%) participants withdrew or dropped out before the completion of the study due to medical reasons unrelated to the study, family reasons, lost contact, or no longer wanting to participate in the study. See the CONSORT study flow diagram (Fig. 1) for the study details.

Fig. 1.

Flow diagram of the study participants

The baseline characteristics of the groups are presented in Table 1. The study included a greater proportion of females [78.6% (22 of 28)]. The CM group’s mean age, weight, and body mass index (BMI) were 71.54 ± 4.48 years, 83.45 ± 19.91 kg, and 29.17 ± 5.12 kg/m², respectively. In the CN group, they were slightly older (74.00 ± 5.68 years) but had lower weight (73.19 ± 10.16 kg) and BMI (27.47 ± 4.31 kg/m²). The CN group has higher PASE and lower knowledge scores than the CM group. Regarding diet, the CN group consumed more protein than the CM group. Meanwhile, the CM group had greater grip strength and muscle mass than the CN group. However, the CN group had slightly higher SPPB scores than the CM group. Finally, the CM group had higher levels of muscle synthesis biomarkers than the CN group.

Table 1.

Baseline characteristics of of different variables between the groups

Variables
	Culinary Medicineb			Controlb			Totalb
Male/Female [#, (%)]	5 (38.5)/8 (61.5)			1 (0.06)/14 (93.3)			6 (21.4)/22 (78.6)
	Mean	N	SD	Mean	N	SD	Mean	N	SD	P c
Age	71.54	13	4.48	74.00	15	5.68	72.86	28	5.22	0.220
Knowledge	69.62	11	9.37	65.61	15	12.60	67.31	26	11.32	0.384
PASE	52.75	10	11.93	59.33	15	16.24	56.70	25	14.77	0.284
Protein (gd)	55.52	11	36.88	60.68	15	24.43	58.50	26	29.75	0.671
MSe dom.f (kgg)	29.17	13	12.22	22.97	15	6.28	25.85	28	9.84	0.117
Non-Dominated Hand (kg)	26.58	13	11.27	21.41	15	6.54	23.81	28	9.25	0.162
Wt (kg)	83.45	13	19.91	73.19	15	10.16	77.95	28	16.03	0.111
BMI (kg/m2)h	29.17	13	5.12	27.47	15	4.31	28.26	28	4.69	0.350
Muscle Mass (kg)	52.91	13	13.5	44.51	15	5.14	48.41	28	10.62	0.052
SPPBi	10.23	13	1.64	10.87	15	0.99	10.57	28	1.35	0.238
Vitamin B12	821.85	13	458.76	776.14	14	379.00	798.15	27	411.70	0.779
Creatinine	0.92	13	0.23	0.84	15	0.16	0.88	28	0.19	0.327
Folate	17.28	13	3.72	17.19	15	4.78	17.23	28	4.25	0.953

^aAll the variables were tested for normality (p > 0.05) using two-sample Kolmogorov-Smirnov test

^bAll values are mean ± standard deviation

^cP value refers to between-group differences were calculated using the chi-squared test for categorical variables or t-test for continuous variables

^dg: gram

^eMS: muscle strength

^fdom.: dominant

^gkg: kilogram

^hkg/m²: kilograms/meter²

^*Statistically significant

^{^}Meaningful difference

ⁱShort Physical Performance Battery tests

The demographic characteristic, age (t=-1.24, p = 0.220) and gender (=2.51, p = 0.113) were homogeneously distributed among the groups. Before the intervention, pre-values and pre-scores were insignificant between the groups (P > 0.05). The incomplete numbers in the CM group are the missing cases.

There was a slight increase of protein intake seen in the CM group compared to a slight decrease in protein intake seen among the CN group. However, there was no between-group difference in protein intake from the pre-study (P = 0.454; Table 2). Similar results were seen with PASE and knowledge scores from baseline (P = 1.00; P = 0.785, respectively). When comparing weight and body composition, there were no significant differences between groups in change from baseline measurements (P = 0.103; P = 0.300, respectively).

Table 2.

Mean characteristics of participants at pre- and post-study ^a

	CM (n = 13)b		CN (n = 15)b
Variable	Pre-Study	Post-Study	Pre-Study	Post-Study	Post-Study Between Group Differences	P c
Knowledge	69.62 ± 9.37	70.47 ± 9.65	65.61 ± 12.60	69.30 ± 8.22	1.17	0.785
PASE	52.75 ± 11.93	60.00 ± 17.32	59.33 ± 16.24	60.00 ± 11.28	0.00	1.00
Protein Intake (gd)	55.52 ± 36.88	60.02 ± 21.40	60.68 ± 24.43	52.89 ± 17.85	7.12	0.454
MSe dom.f (kgg)	29.17 ± 12.22	29.24 ± 10.80	22.97 ± 6.28	23.57 ± 5.98	5.67	0.122
MS non-dom. (kg)	26.58 ± 11.27	26.15 ± 10.22	21.41 ± 6.54	21.89 ± 6.28	4.27	0.207
Weight (kg)	83.45 ± 10.91	83.91 ± 20.43	73.19 ± 10.16	73.19 ± 9.96	23.59	0.103
Body Mass Index (kg/m2)h	29.17 ± 5.12	29.42 ± 5.65	27.47 ± 4.31	27.41 ± 4.36	2.00	0.300
Muscle Mass (kg)	52.91 ± 13.5	53.17 ± 13.66	44.51 ± 5.14	44.23 ± 4.78	8.94	0.041^
SPPBi	10.23 ± 1.64	10.77 ± 1.36	10.87 ± 0.99	11.20 ± 10.8	-0.43	0.360
Vitamin B12	821.85 ± 458.75	657.23 ± 310.99	776.14 ± 379.00	830.27 ± 470.88	-173.04	0.270
Creatinine	0.92 ± 0.23	0.95 ± 0.25	0.84 ± 0.16	0.89 ± 0.15	0.06	0.441
Folate	17.28 ± 3.72	16.39 ± 4.67	17.19 ± 4.78	17.64 ± 3.30	-4.47	0.430

^aThe independent sample t-test was used to compare between-group differences in the post-study

^bAll values are mean ± standard deviation

^cP value refers to between-group differences by independent sample t-test

^dg: gram

^eMS: muscle strength

^fdom.: dominant

^gkg: kilogram

^h kg/m²: kilograms/meter^2*Statistically significant

^{^}Meaningful difference

ⁱShort Physical Performance Battery tests

Muscle quality measurements, as secondary and exploratory measurements, showed a meaningful difference in change in muscle mass between groups (P = 0.041). However, this outcome was only 61.1% powered to detect a minimum difference. For measurements of muscle strength in both dominant and non-dominant hands, there was no between-group difference in the muscle strength change from the pre-study (dominant: P = 0.122 and non-dominant: P = 0.207). Additionally, there were no between-group differences in the muscle function change from baseline (P = 0.360). Lastly, there were no between-group differences in vitamin B₁₂, folate, or creatinine change from pre-study measurements (P = 0.270; P = 0.441; P = 0.430, respectively).

We used Univariate ANOVA to examine the association of different parameters between the groups after adjusting possible confounders (Gender and Age). The Post-Protein Intake was insignificant between the groups, even after adjusting the Gender and Age. The CM group had an average Post-Protein Intake of 60 g (men: ~78 g, women: ~51 g), and the CN group had 53 g (men: 95 g, women: 48 g). These differences presented in Table 3 weren’t statistically significant.

Table 3.

Univariate ANOVA for different parameters between groups adjusting age and gender

Parameter	B	Std. Error	t	Sig.	95% Confidence Interval
					Lower Bound	Upper Bound
Post Protein(gm)
Intercept	94.31	55.17	1.709	0.111	-24.876	213.497
Age	-0.63	0.741	-0.85	0.411	-2.231	0.972
Group	0.238	8.374	0.028	0.978	-17.853	18.33
Sex(M/F)	46.761	14.918	3.135	0.008	14.534	78.989
Group*Gender	-19.135	17.933	-1.067	0.305	-57.877	19.608
Post Dominated Hand (Kg)
Intercept	35.642	14.967	2.381	0.026	4.602	66.682
Age	-0.179	0.201	-0.89	0.383	-0.596	0.238
Group	-0.904	2.499	-0.362	0.721	-6.086	4.279
Sex(M/F)	17.623	5.402	3.262	0.004	6.42	28.825
Group*Gender	0.004	6.235	0.001	1	-12.927	12.934
Post Non-Dominated Hand (Kg)
Intercept	36.163	14.825	2.439	0.023	5.496	66.83
Age	-0.211	0.199	-1.06	0.3	-0.623	0.201
Group	-1.174	2.395	-0.49	0.629	-6.128	3.78
Sex(M/F)	20.252	5.362	3.777	0.001	9.16	31.345
Group*Gender	-3.946	6.155	-0.641	0.528	-16.679	8.788
Post Wt(kg)
Intercept	124.234	75.191	1.652	0.112	-31.311	279.779
Age	0.496	1.011	0.491	0.628	-1.595	2.587
Group	-1.476	12.146	-0.121	0.904	-26.602	23.651
Sex(M/F)	0.946	27.198	0.035	0.973	-55.317	57.209
Group*Gender	67.556	31.22	2.164	0.041	2.972	132.14
Post BMI
Intercept	24.255	14.016	1.731	0.097	-4.739	53.249
Age	0.045	0.188	0.241	0.812	-0.344	0.435
Group	-0.102	2.264	-0.045	0.964	-4.786	4.581
Sex(M/F)	-3.073	5.07	-0.606	0.55	-13.56	7.415
Group*Gender	8.302	5.82	1.427	0.167	-3.736	20.341

Similarly, Post Dominated Hand and Post Non-Dominated Hand had no association with the groups. However, there was a significant association with gender (P < 0.001). Men had significantly stronger hands compared to women in both groups. Non-Dominated Hand Strength showed similar patterns, with men being significantly stronger than women, but no significant differences between the two groups.

The interaction effect of gender and the groups was significant with Post-Weight (P = 0.041). This means that men and women responded differently to the intervention regarding their weight, depending on whether they were in the CM or CN groups. In the CM group, men had a much higher average Post-Weight (~ 103 kg) than women (~ 72 kg). In contrast, in the CN group, men had an average Post-Weight of 73.18 kg, closer to the women’s average Post-Weight (72.72 kg). This difference between men and women across groups was significant.

There was no significant difference in Post-BMI between the groups (CM vs. CN). The average Post-BMI in the CM group was about 29.4 compared to the CN group, which was 27.4. These differences were not statistically meaningful.

There was no significant association of Post-Muscle Mass between the groups; however, it was significant with gender and its interaction effect (P = 0.019; Table 4). While the group alone didn’t impact muscle mass, being male or female, along with the group they were in, significantly influenced muscle mass after the intervention. Men had higher Post-Muscle Mass (68.55 kg in the CM group) than women (43.55 kg). This difference in Post-Muscle Mass between men and women was statistically significant, and the group they were in (CM or CN) further influenced this outcome.

Table 4.

Univariate ANOVA for different parameters between groups adjusting age and gender

Parameter	B	Std. Error	t	Sig.	95% Confidence Interval
					Lower Bound	Upper Bound
Post Muscle Mass (kg)
Intercept	96.816	29.246	3.31	0.003	36.316	157.316
Age	-0.017	0.393	-0.044	0.965	-0.831	0.796
Group	0.212	4.724	0.045	0.965	-9.562	9.985
Sex(M/F)	26.653	10.579	2.519	0.019	4.769	48.537
Group*Gender	28.428	12.143	2.341	0.028	3.308	53.549
Post SPPB
Intercept	16.167	3.062	5.28	0	9.833	22.5
Age	-0.068	0.041	-1.648	0.113	-0.153	0.017
Group	-1.252	0.495	-2.531	0.019	-2.275	-0.229
Sex(M/F)	0.784	1.107	0.708	0.486	-1.506	3.075
Group*Gender	1.052	1.271	0.827	0.417	-1.578	3.681
Post Vit B
Intercept	105.797	1165.067	0.091	0.928	-2304.328	2515.921
Age	9.257	15.66	0.591	0.56	-23.139	41.653
Group	-38.47	188.203	-0.204	0.84	-427.799	350.858
Sex(M/F)	591.419	421.42	1.403	0.174	-280.356	1463.193
Group*Gender	-779.53	483.748	-1.611	0.121	-1780.238	221.179
Post Creatinine
Intercept	1.224	0.448	2.733	0.012	0.297	2.151
Age	-0.005	0.006	-0.759	0.455	-0.017	0.008
Group	-0.101	0.072	-1.402	0.174	-0.251	0.048
Sex(M/F)	0.109	0.162	0.675	0.506	-0.226	0.445
Group*Gender	0.301	0.186	1.62	0.119	-0.083	0.686
Post Folate
Intercept	26.826	11.117	2.413	0.024	3.829	49.823
Age	-0.126	0.149	-0.845	0.407	-0.435	0.183
Group	0.043	1.796	0.024	0.981	-3.672	3.758
Sex(M/F)	2.393	4.021	0.595	0.558	-5.925	10.711
Group*Gender	-6.143	4.616	-1.331	0.196	-15.692	3.405

There was a significant difference in Post-SPPB between the groups (P = 0.019). The CM group had an average Post-SPPB score of 10.77, while the CN group had a higher average score of 11.20. There was no significant difference in Post-Vitamin B Levels, Post-Creatinine Levels, or Post-Folate Levels between the CM and CN groups.

When examining cooking effectiveness before the study, the majority of both groups felt fairly to completely confident, the CM group (64.3%) and CN group (73.3%). Additionally, when asked about cooking attitudes, CM and CN groups had a majority (72.7%, 66.7%, respectively) that enjoyed cooking, thought it was important, and it brought happiness; didn’t think it took too much time, cost too much, or was a burden or stressful. Furthermore, five CM participants (50%) and three CN participants (20%) felt confident and knew what to eat. Lastly, when asked about the time it took to cook, seven CM participants (77.7%) and three CN participants (20%) felt cooking did not take too much time.

When examining cooking effectiveness after the study, four participants did not complete the questionnaire in the CM group and three did not complete the questionnaire in the CN group. The results from those who completed the questionnaire showed that the CM group (100%) felt fairly to completely confident, and the CN group (33.3). Additionally, when asked about cooking attitudes, four CM participants and five CN participants didn’t answer the questionnaire. Of those that answered the questionnaire, seven CM participants and seven CN participants (77.8%, and 70%, respectively) enjoyed cooking, thought it was important, and it brought happiness; didn’t think it took too much time, cost too much, or was a burden or stressful. Furthermore, three CM participants (33.3%) and three CN participants (30%) felt confident and knew what to eat. Lastly, when asked about the time it took to cook, three CM participants (70%) and three CN participants (30%) felt cooking did not take too much time.

At the end of the study, both groups were asked about the main challenges or barriers to managing protein intake and answers included “eating enough protein”; “loving carbs”; “not wanting to cook”; and “knowing the right amount”. Meanwhile, the CM group was asked how the CM videos specifically helped clarify managing their protein intake and answers included “they demonstrated how easy it was to cook the protein to produce a tasty meal”; “they helped with knowing the right portion size”; and “it helped with knowing how to cook in different ways”. Finally, the CM participants were asked what the most memorable or favorite part of the CM videos was, and answers included “impressed with how easy the videos made it seem to cook”; “short and sweet”; “they were not time-consuming”; and “having new recipes”. All CM participants reported having no technical difficulties accessing and watching the videos.

Discussion

To the authors’ knowledge research utilizing CM in a senior adult population is lacking sufficient evidence to demonstrate its impact on improving protein intake and potentially muscle quality. In this study, the CM participants had a notably higher change in muscle mass after 4 months compared to those in the CN group ((116.97 ± 30.05 vs. 97.31 ± 10.52; P = 0.041). Furthermore, although not significant, there was a higher protein intake seen among CM participants and a lower protein intake seen among the CN group after four months (60.02 ± 21.40 vs. 52.89 ± 17.85; P = 0.454). These results suggest that CM emphasizing lean beef could improve muscle mass and protein intake among senior adults. However, there was no additional impact of the CM emphasizing lean beef intervention over the CN group when analyzing the other outcomes. Insufficient consistent protein intake, lack of combining physical activity with nutrition intervention, adherence to the intervention, not recording medications that could influence outcomes, and missing/accuracy of the questionnaires could explain these results. Additionally, there was also a lack of representation of men in this study, which limits generalizability to men. Ethnicity information was also lacking.

The accuracy of each group’s protein questionnaire could play a factor since they were self-administered. Self-administered questionnaires are more susceptible to item non-response [24]. CM participants had up to 30.74% (4 of 11), and CN participants had up to 33.3% (5 of 15) of questionnaires with blank answers, so intake could have been higher and explained better how some outcomes were affected. Additionally, the participants were not asked to change their diet outside their protein intake.

Some evidence suggests that 25–30 g serving of animal protein per meal (e.g., lean beef) can increase MPS by ~ 50% [25, 26]. Another study recommended 1.0 to 1.2 g of protein per kilogram of body weight per day to help senior adults maintain and regain lean body mass and function [27]. Similarly, in the current study, participants were instructed to consume 25–30 gm protein for each meal specifically lean red meat three times per week (lunch or dinner, 400 kcal/30 protein). Although the participants did not achieve the 25–30 gm protein for each meal consistently, the results showed that CM participants increased protein intake after the invention and the CN group decreased protein intake. Further results showed a significant difference between the groups in change in muscle mass after four months. This is similar to Sammarco et al. who showed improvements in body composition and grip strength in the participants who consumed high-protein diets [28]. However, because the participants did not achieve the recommended increased protein intake this could be the reason for nonsignificant increased protein consumption in the CM group. Nevertheless, the current study saw a trend of increased protein intake among those who received the CM intervention compared to a slight decrease in protein intake among the CN group. Reinders et al. also found that participants provided with personalized dietary advice and appropriate high-protein foods increased their protein intake [23]. The current study strengthens previous findings that increased quality protein intake among senior adults can improve muscle quality.

Grip strength and SPPB tests have been used to examine muscle strength and function as components of muscle quality [20]. However, the current study did not find significant differences between groups in muscle strength and function change. Kim et al. found that the amount of change in dietary protein (increase or decrease) was not associated with muscle strength [29]. There are also mixed findings of Samaneh et al. that showed an even distribution of daily protein intake across meals was independently associated with greater muscle strength, but not with the mobility score (i.e., SPPB tests) in older adults [30]. Our current study also did not find that muscle function improved. Once more, because the CM participants did not achieve the recommended protein consumption this could be the reason for no between-group differences in change in muscle strength and function.

Recent evidence suggests that certain biomarkers such as serum vitamin B₁₂, folate, and creatinine are closely associated with muscle health [31–33]. The current study measured these biomarkers to determine the impact of CM on increasing lean beef consumption and found no significant between-group differences in change in these muscle synthesis biomarkers. Both groups consumed animal protein and did not have sarcopenia, which may have played a role. A review by Tosato et al. suggested that creatinine levels are maintained in the presence of stable renal function and animal protein intake, which provides reasoning for the results of nonsignificant between-group differences in the change of creatinine levels in the current study [34]. Moreover, no between-group differences in the change in folate could be a result of nonsignificant differences in change in protein intake. There was a nonsignificant decrease in serum vitamin B₁₂ in the CM group but not in the CN group. This study did not assess multiple factors that could influence serum vitamin B₁₂ levels such as pathophysiological changes (i.e. decreased intrinsic factor and malabsorption) along with medication intake (i.e. gastric acid inhibitors).

Strengths

This study is one of the first to evaluate CM’s effect on enhancing lean beef intake and muscle quality in older adults. This study provided more insight into a CM intervention program to improve knowledge, awareness, and attitude toward animal protein intake within four months. In addition, the feedback from the participants can be applied to future interventions and practices.

Registered dietitians (RDNs), fully trained and qualified with years of experience, developed the whole program with assistance from those with expertise in Hospitality. In addition, a RDN implemented the intervention and provided advice if participants needed clarification about their intervention.

This study objectively determined the impact of a CM education strategy to increase protein intake by measuring the actual achieved change in protein intake by questionnaires, as well as subjectively assessed the appreciation of the CM education strategy by questionnaire. Lastly, providing dietary advice strategies that include whole diet are likely to be more sustainable.

Limitations

Although exercise recommendation handouts were given in this study, this was a diet and nutrition education-focused intervention. An intervention including an exercise component along with digital CM education and nutrition would have given more opportunity for significant differences in muscle strength and function outcomes. The power analysis reveals that there is only 61.1% power to detect a minimum difference of 8.94 kg in average muscle mass with pooled standard deviation of 10.07 kg between the 13 subjects in the CM group and the 15 subjects in the CN group, making this measurement underpowered. Additional research is needed to further investigate with a larger sample size to provide more power, which increases the likelihood of detecting effects of the intervention to prevent and treat senior adults who are at risk for age-related muscle loss. Furthermore, this study may not be representative of the general population with most participants being female (78.6%) and similar age. Additionally, this study focused on lean beef as the primary protein source. Further research could include diversity in meeting protein requirements and addressing individual dietary preferences. The inability to measure potential vitamin B₁₂ absorption issues and the participants’ medications not being recorded were also limitations of this study. There may be a recall bias due to the questionnaires being self-reported. Furthermore, the cooking effectiveness questionnaire results may not be accurate because of the blank questions. Interview-administered questionnaires could improve these limitations. Finally, the CM intervention being delivered by email is not as effective as a digital tool provided by a smartphone application.

Conclusion

The current study is one of the few to examine the outcomes of a digital CM education program with cooking demonstration and nutrition education videos to enhance lean beef intake and muscle quality in older adults. Although the muscle quality measurements were underpowered to show statistical significance, any meaningful response to an intervention that may improve muscle mass in a senior adult population is compelling, and unexpected based on the natural challenges of muscle loss in aging, making it important to share. The results show promising evidence that this intervention could improve muscle mass and increase protein intake. Participants in the intervention group reported that the cooking demonstrations helped with meal preparation of lean beef in the appropriate portions in easy and tasty ways, which can increase confidence in the kitchen and prepare more meals at home.

It would be important to further investigate other factors that could have affected this study. Future studies could include exercise training sessions and a CM app that develops personalized meal plans to determine if it would improve results. It would be ideal to include a diverse age range and ethnicity with an equal gender to better represent the general senior adult population. Once again, interview-administered questionaries would improve protein intake and cooking effectiveness accuracy.

This type of intervention can further knowledge advancement towards CM emphasizing lean beef, sarcopenia, and older adults. Such evidence could significantly link adequate protein intake (i.e., lean beef), physical activity, and sarcopenia. Once a link has been identified, the evidence can confirm that qualified health professionals providing CM emphasizing lean beef to older adults can provide a beneficial strategy for sarcopenia prevention. This link can be vital because research surrounding CM is in its infancy. If CM emphasizing lean beef can influence behavioral change in dietary patterns leading to muscle quality maintenance and improvement, this will be the most significant factor in the intervention’s success and effectiveness. We expect these findings to encourage practitioners to become more educated on CM to ensure that their services can continue to advance as this research becomes more prevalent, in line with the potential advances in CM. Ultimately, this proposal could show how CM emphasizing lean beef could positively benefit public health.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Author contributions

Author ContributionsConceptualization, S.G., A.C., M.A.; methodology, S.G., A.C., M.A., and S.N.; software, S.G., A.C., M.A., and J.C.; validation, J.C., S.G., and S.N.; formal analysis, S.N.; investigation, J.C. and S.G.; resources, J.C. and S.G.; data curation, J.C. and S.G.; writing—original draft preparation, S.G., J.C., and M.A.; writing—review and editing, S.G., A.C., M.A., S.N.; supervision, S.G.; project administration, J.C., S.G. All authors have read and agreed to the published version of the manuscript.

Data availability

Data supporting reported results can be found by contacting the corresponding author. Data will be made available upon request.

Declarations

Competing interests

Drs. Galyean, Childress, and Alcorn are owners of 3 CulinaryMed Docs, LLC. The electronic platform was used as part of the nutrition education to help participants know how to prepare vegetables.

Clinical Trials ID

NCT06157385.