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Objective Various strategies are used to motivate individuals with stroke during rehabilitation. However, how physical therapists select the motivational strategies that they use for each individual is yet to be established. Therefore, this study aimed to explore how physical therapists use different motivational strategies for individuals in stroke rehabilitation programs. Methods A criterion sample of 15 physical therapists who have worked in rehabilitation for over 10 years and were interested in an individual’s motivation participated in one-on-one semi-structured online interviews. The interviews explored their perspectives and experiences regarding the motivational strategies used depending on each individual’s condition. The collected data were analyzed with thematic analysis. Results A total of 9 themes emerged from the data upon thematic analysis and inductive coding. Participants used different strategies to encourage individuals’ active participation in physical therapy depending on (1) their mental health, (2) their physical difficulties, (3) their level of cognitive function, (4) their personality, (5) their activities and participation, (6) their age, (7) their human environment, and (8) the type of rehabilitation service where the individual underwent treatment. For example, in cases where an individual lost self-confidence, participants offered practice tasks that the individual could achieve with little effort to make them experience success. The interviews also revealed (9) motivational strategies used regardless of the individual’s condition. For instance, patient-centered communication was used to build rapport with individuals, irrespective of their condition. Conclusions This qualitative study suggests that physical therapists use different strategies depending on the individual’s mental health conditions, physical problems, level of cognitive function, personality, activities and participation, age, human environment, and the type of rehabilitation service where the individual undergoes treatment to motivate individuals with stroke during physical therapy. Impact The findings of this study can provide experience-based recommendations regarding the selection of motivational strategies for stroke rehabilitation.

This pilot study aimed to investigate the relative and absolute reliability of variables obtained from an acceleration-based gait analysis conducted at comfortable and maximal gait speeds in individuals with chronic stroke. [Participants and Methods] This study included 25 community-dwelling individuals with chronic stroke. The participants wore triaxial accelerometers, while an observed walking trial was performed at comfortable and maximal speeds on two separate days 1 week apart. Relative reliability was evaluated using the intraclass correlation coefficient, and absolute reliability was evaluated using the Bland-Altman analysis, standard error of measurement, and minimal detectable change.[Results] The intraclass correlation coefficient of gait varied according to the acceleration-based gait analysis, ranging from 0.70 to 0.99. The Bland-Altman analysis revealed no systematic bias in both comfortable and maximal gait speed conditions. Most of the minimal detectable changes were smaller at maximal gait speed than at comfortable gait speed. [Conclusion] Acceleration-based gait analysis is a reliable method, particularly in maximal gait speed conditions. It may be used to assess the effect of rehabilitation interventions in individuals with chronic stroke.

Resistance training (RT) progress is determined by an individual’s muscle strength, measured by one-repetition maximum (1RM). However, this evaluation is time-consuming and has some safety concerns. Bioelectrical impedance analysis (BIA) is a valid and easy-to-use method to assess skeletal muscle mass (SMM). Although BIA measurements are often correlated with muscle strength, few studies of 1RM for RT and BIA measurements are available. This observational study examined the relationship between 1RM and BIA measurements and developed BIA-based prediction models for 1RM. Thirty-five healthy young Japanese adults were included. SMM and the skeletal muscle mass index (SMI) were measured using the BIA device. In addition, dominant-leg 1RM was measured using a unilateral leg-press (LP) machine. The correlations between BIA measurements and 1RM were calculated, and simple regression analyses were performed to predict 1RM from the BIA variables. The results showed significant correlations between 1RM and dominant-leg SMM (R = 0.845, P = 0.0001) and SMI (R = 0.910, P = 0.0001). The prediction models for 1RM for LP derived from SMM of the dominant leg and SMI were Y = 8.21x + 8.77 (P = 0.0001), R2 = 0.73, and Y = 15.53x − 36.33 (P = 0.0001), R2 = 0.83, respectively. Our results indicated that BIA-based SMI might be used to predict 1RM for LP accurately.

Skeletal muscle mass (SMM) obtained using bioelectrical impedance analysis (BIA) is often correlated with isometric muscle strength; furthermore, it might also have correlations with one-repetition maximum (1RM). Accurate prediction of 1RM is important because it is useful for determining progress in resistance training. This study examined the relationship between BIA measurements and 1RM and developed prediction models for 1RM using BIA measurements. Thirty-five healthy young Japanese adults were included in this cross-sectional observational study. The SMM of the dominant leg and the skeletal muscle mass index (SMI) were obtained using a BIA device. The 1RM for the dominant leg was measured using a unilateral leg-press (LP) machine. The correlations between BIA measurements and 1RM were calculated, and simple regression analyses were performed to predict 1RM from the BIA variables. The results showed significant correlations between 1RM and dominant-leg SMM (R=0.845, P=0.0001) and SMI (R=0.910, P=0.0001). The prediction models for 1RM for LP derived from SMM of the dominant leg and SMI were Y=8.21x + 8.77 (P=0.0001), R2=0.73, and Y=15.53x − 36.33 (P=0.0001), R2=0.83, respectively. Our results indicate that SMI may be used to accurately predict the 1RM for LP.