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This cross-sectional study included healthy participants aged between 18 and 35 years. The exclusion criteria were as follows: any chronic disease (e.g., COPD, asthma, hypertension, diabetes) treated with medication; any hearing and/or severe visual impairment; pregnancy; body mass index (BMI) ≥ 35 kg/m², BMI was calculated using weight and height (BMI = weight in kg/(height in meters²)); any acute or chronic pain (e.g., osteoarthritis, after injury); participants with amputation of any limb; and participants using any walking aids (e.g., walker, walking stick). To assess physical and mental health, subjects self-rated their condition on a scale of 1 to 10, with 1 being very poor and 10 being very good.
Measurements were made on a standardized and study-approved treadmill that is an integral part of the Motion Analysis Laboratory in the Department of Geriatric Medicine (type: Zebris FDM-THQM3i, CE certified). Parameters were recorded by the participants at a self-selected speed. A self-selected speed was measured on the ground using a stopwatch and markers. The speed was then transferred to the treadmill. Each participant was warmed up on the treadmill for 1 min before starting the recording. The ball test was used to determine the dominant leg for each subject [11]. Data from this leg were used for statistical processing.
Spatiotemporal parameters such as duration of the stance and swing phase, loading response, mid and terminal stance, pre-swing phase time, step speed, step frequency, step width, step length, and step time were analyzed. Coefficients of variation “CoV” [%] = (standard deviation/mean) *100 were calculated for step time, step length, step width and cadence, indicating the variability between individual steps. In addition, the average of the maximum force achieved in Newtons and the maximum pressure in Newtons per square centimeter were calculated for the forefoot, midfoot, and heel. These parameters were measured with the help of an instrumented treadmill while walking, once for 5 min with over-ear headphones without audio, as the headphones themselves can influence gait parameters, and once for 5 min with an audiobook through the over-ear headphones.
Before the measurement with the audiobook, the subjects were informed about a post-test to check the played information, so that the subjects concentrate on the audiobook and do not concentrate too much on walking. However, the test is not administered at the end.
The order of the two measures, with and without listening to the audiobook, was determined through a simple randomization process. Prior to the experiment, 40 numbers were written down and participants were randomly assigned a number between 1 and 40. Those who drew a number between 1 and 20 were measured with the audiobook first (dual-task condition), while participants who drew a number between 21 and 40 were measured without the audiobook first (single-task condition).This randomization ensured an equal number of subjects for comparison.
Questions about the participants’ behavior while listening to audio and walking during daily life were used to determine how often and for how long the subjects listened to audio files. The participants were divided into frequent and infrequent users by asking, “How often do you listen to audio while walking in your daily life?” There were 4 choices: 1) I do not listen to audio; 2) rarely (1–4 times a month); 3) often (2–3 times a week); and 4) very often (more than 3 times a week). The difference in spatiotemporal gait parameters between participants who listened to audio rarely and those who listened to audio often was calculated. Participants who did not listen to audio and those who listened to audio very often were excluded from the analysis, as they were categorized as extremes. All participants listened to an audiobook of classical literature.
We also used the NASA Task Load Index (NASA-TLK), a standardized questionnaire designed to assess perceived workload [12]. Subjects were asked to complete this questionnaire after each measurement with and without listening to the audiobook. In the analysis, the participants were divided according to the NASA scale into those who experienced “little” subjective load (NASA ≤ 33) and those who experienced “high” subjective load (NASA ≥ 34) while listening to an audiobook while walking.
Statistical methods
The Zebris FDM-THQM3i treadmill is equipped with proprietary software (Zebris FDM software) for the data derivation. The statistical analysis was prepared using SPSS 26.0 for Windows. The gait parameters are presented as the mean values with standard deviation over the entire measurement period of 5 min each. The results of the Shapiro-Wilk test indicated that the data were normally distributed (Appendix 2). The distribution of data for gait phases and foot pressure was not normal for the whole sample (n = 40), but with a sufficient number (n > 30) of subjects, we made a further calculation than for normally distributed data. Therefore, the paired t-test was used to compare single- and dual-task conditions, with a significance level of p ≤ 0.05. In addition, the dual-tasking costs (DTCs) for individual parameters were calculated. DTC is the cost of performing several tasks at the same time, in our case two tasks – one motor and one cognitive. DTC was calculated according to the following formula: DTC (%) = (100 * (single-task score – dual-task score)/single-task score).
The study was approved by the Ethics Committee of the Faculty of Medicine at RWTH Aachen University (approval number: EK 310/21), and informed consent was obtained from all participants. Data collection for this study started in November 2021 and was finalized in February 2022 and study results are reported according to the CONSORT statement.
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