Older Adults Adapt Chewing Muscle Activity Phase by Phase, Food by Food

Chewing is not one uniform action but a sequence of finely tuned adjustments, and a new study suggests that aging reshapes that sequence in ways that depend on both the moment within a chew and the food in the mouth. Using surface electromyography, researchers recorded the activity of the jaw muscles of fifteen younger adults aged 22 to 28 and fifteen older adults aged 65 to 75 as they habitually chewed three commercially available foods with distinctly different textures: soft candy, whole wheat bread, and peanut. The results, published as an open access research article, show that age-related differences in mastication are phase-specific rather than a simple across-the-board decline.

The study, conducted at Huazhong Agricultural University with a cross-sectional design, divided each chewing sequence up to the first swallow into three proportional subphases following an established phased approach: an early phase covering the second to fourth chewing cycles, a middle phase, and a late phase comprising the last three cycles. Electrodes placed over the bilateral masseter and anterior temporalis muscles captured the electrical signatures of the core superficial muscles responsible for force generation and mandibular stability. Signals were filtered, rectified, and smoothed, and a computer-driven peak-detection algorithm identified individual chewing strokes based on burst amplitude and the physiological range of chewing frequency.

From these recordings the researchers derived several dimensionless indices designed to minimize inter-individual variability arising from differences in muscle thickness, electrode placement, and gain. Normalized integrated electromyography, or iEMG, reflects the proportional cumulative muscle effort expended during a given phase relative to the total effort across the entire chewing task. Normalized mean absolute value, or MAV, captures the relative instantaneous muscle activation intensity within a phase compared with the individual’s overall chewing activity. Chewing time was defined as the duration from the peak of the first chewing burst to the peak of the final burst, and chewing rate as the number of cycles divided by that time.

Before any chewing began, instrumental texture profile analysis confirmed that the three foods differed significantly in hardness and cohesiveness, with peanut showing the highest hardness at 3176.93 plus or minus 187.67 grams and soft candy the highest cohesiveness at 0.91 plus or minus 0.00. The researchers also measured salivary flow rate and oral cavity volume in both age groups. Independent-samples t-tests revealed no significant differences between younger and older adults for either measure: salivary flow averaged 0.92 plus or minus 0.51 milliliters per minute in the young group versus 0.78 plus or minus 0.54 in the older group, and oral cavity volume averaged 68.03 plus or minus 13.73 milliliters versus 58.69 plus or minus 14.37 milliliters.

The soft candy produced the simplest pattern. Chewing time and chewing rate were statistically comparable between the age groups, with no significant main effects or interactions. Normalized MAV showed a significant phase effect, declining progressively from early to late chewing, but no age effect. The clearest age-related signal was in normalized iEMG, which was significantly lower in older adults, with young adults exhibiting higher values across all three chewing phases. The authors suggest one possible explanation: a bolus formed from soft candy may require less prolonged structural breakdown than the other tested foods, so the older participants did not need to adjust their timing to manage it.

Bread told a different story. Both age group and chewing phase showed significant main effects on chewing time and rate, and older adults chewed significantly longer than younger adults specifically in the late phase, while also chewing at a higher rate in that phase. Normalized MAV displayed a significant age by phase interaction: older adults showed higher relative activation intensity in the early phase, whereas young adults showed higher values in the middle phase. Normalized iEMG was higher in older adults during the early and middle phases. The authors note that bread’s porous and relatively dry structure could contribute to rapid saliva absorption during chewing and may delay the formation of a well-lubricated bolus, though they caution this interpretation remains tentative because salivary incorporation and dynamic bolus properties were not measured directly.

Peanut, despite having the highest initial hardness of the three foods, produced yet another pattern. Significant age by phase interactions emerged for both chewing time and chewing rate, with older adults exhibiting longer chewing times but lower chewing rates than young adults in both the middle and late phases. Yet normalized MAV showed no significant difference between the age groups, and the age difference in normalized iEMG was confined to the early phase, where young adults had higher values. Taken together with the bread and candy results, this suggests that initial hardness alone may be insufficient to explain masticatory demand across the full chewing sequence; once fragmented, peanut places different oral-processing demands on the chewing system than bread or soft candy.

A consistent thread across all participants and all three foods was a progressive decline in the relative muscle activity allocation, measured by normalized MAV and iEMG, as the chewing phases advanced. The authors interpret this as potentially reflecting the natural physiological progression from food fragmentation and lubrication to bolus formation, a view compatible with the Breakdown Path Model in which swallowability depends on the food’s mechanical structure, the degree of lubrication, and the time course of oral processing. The higher proportion of bilateral chewing observed among the older participants, the authors note, is consistent with previous work suggesting an age-related tendency to favor more balanced chewing patterns.

The authors are careful to frame the study as exploratory and to flag its limits. Food type was not included as a statistical factor, so differences in effect patterns across the three foods are descriptive observations rather than direct statistical comparisons. Only three commercial products were tested, meaning the contribution of any single texture parameter could not be isolated from other matrix properties, and only the initial texture properties were measured, not the dynamic changes in bolus properties during oral processing. The modest sample of thirty participants may have limited statistical power, and the focus on healthy older adults with no more than four missing teeth restricts applicability to people with severe tooth loss, dysphagia, or clinically impaired mastication.

Even with those caveats, the findings carry a practical message for food design aimed at aging populations. Previous work has often identified hardness as the primary determinant of chewing strategy in older people, implying that softer is always better. These results challenge that paradigm: merely reducing hardness may be insufficient, and modulating properties such as cohesiveness, through approaches like hydrocolloid addition or 3D printing, may better support the eating process for people with declining oral function. Because salivary flow and oral cavity volume did not differ significantly between the age groups here, the authors argue that future studies with larger samples, more controlled food models, dynamic bolus characterization, and food type as an explicit statistical factor are needed to clarify which texture-related factors most strongly drive phase-specific chewing adaptations. For now, the study offers a phase-specific lens on how healthy older adults maintain chewing performance through selective adjustments in timing and muscle recruitment rather than a uniform decline.

Subject of Research: Phase-specific chewing muscle activity differences between younger and older adults across three food textures

Article Title: Phase-specific adaptations in masticatory muscle activity in older adults during chewing of foods with different textural properties

Article References: Wang, Y., Xiong, X., Wu, T., & Chen, G. (2026). Phase-specific adaptations in masticatory muscle activity in older adults during chewing of foods with different textural properties. Food, Nutrition and Health, 3(1), Article 25. https://doi.org/10.1007/s44403-026-00075-y

Image Credits: AI Generated

DOI: 10.1007/s44403-026-00075-y

Keywords: mastication, surface electromyography, aging, food texture, masseter, temporalis, bolus formation, chewing rate, food design, oral processing, older adults, iEMG