Dance movement therapists use interventions in which participants share synchronous movement to enhance well-being and increase social skills among autistic individuals. However, there is limited research about the effects of synchronized interventions on interpersonal and intrapersonal outcomes of autistic individuals. This field study evaluated the immediate and long-term impacts of a movement-based synchronized group intervention on prosocial behavior, social cohesion, and work-related stress among young autistic adults. A randomized controlled trial was conducted to investigate two movement-based group intervention conditions: synchronous and non-synchronous. Fifty-four young adults, aged 18–22, enrolled in an innovative program integrating young autistic adults into the Israeli army workforce. One-hour-long movement-based intervention sessions took place once a week for six to seven weeks, and data was collected at three time points: before and after the intervention period, and 17 weeks after it ended. Results suggest that the synchronized intervention may be more effective than the non-synchronized intervention in enhancing cooperative behavior after 17 weeks and fostering social closeness with familiar peers post-intervention. However, the synchronized intervention may not be more effective in reducing work-related stress. A holistic approach is discussed, which integrates synchronized and non-synchronized movement-based group interventions for young autistic individuals transitioning into work environments.
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Interpersonal synchrony refers to the coordination and alignment of behaviors, emotions, or sensations—such as gaze, affect, voice, and touch—between two or more individuals during an interaction. One aspect of interpersonal synchrony involves the coordination of body movements. This coordination may be manifested in either precise synchrony, where actions occur concurrently, or lagged synchrony, characterized by responsive behavior (Bernieri & Rosenthal, 1991). Body movements can be linked in terms of space, time, or quality of movement (Amighi et al., 2018), e.g., punching the air together with direct, strong, accelerated movements. Interpersonal synchrony may emerge intentionally, driven by a shared explicit goal, such as maintaining a consistent running pace, or spontaneously during social interactions, such as dancing, singing, or playing together (Schmidt et al., 2011). Described as fostering an experience of "togetherness," this mode of interpersonal engagement is often characterized by a lack of leader-follower hierarchy (Himberg et al., 2018). Given its role in fostering a sense of connection and shared experience, interpersonal synchrony has been closely associated with the development and enhancement of prosocial behaviors.
Prosocial behavior encompasses actions benefiting others, like assistance, cooperation, sharing, and charitable giving (Jensen, 2016). A meta-analysis of 60 studies comparing conditions featuring interpersonal synchrony to control conditions revealed a moderate effect on prosocial attitudes and behaviors, such as belonging, conformity, cooperation, and helping behavior, among non-autistic adults (Rennung & Göritz, 2016). These findings suggest that engaging in synchrony can strengthen social bonds and increase individuals’ willingness to act in the interest of others. Similarly, another meta-analysis comprising 42 studies indicated that interpersonal synchrony within large groups fosters prosocial behavior (Mogan et al., 2017), highlighting that coordinated actions not only enhance interpersonal connections but also promote collective cohesion and positive group-oriented behaviors.
Building on the understanding that interpersonal synchrony can foster prosocial behaviors, therapeutic practices have incorporated synchronized movement as a means to promote social and emotional development, particularly among individuals facing social challenges. For example, dance movement therapists employ synchronized interventions, where participants share rhythmic movements like running or jumping, to enhance body awareness, well-being, and social skills among autistic individuals (Koch et al., 2015; Koehne et al., 2016; Takahashi et al., 2019). Manders et al. (2021) argue that movement-based synchronized interventions might not effectively support social engagement of autistic individuals; they found that, during movement-based mirroring tasks, autistic individuals tended to simply follow instructions to synchronize movements with their partner with little further social engagement. Shuper Engelhard and Vulcan (2021) highlight the potential benefits of movement-based synchronized interventions in improving romantic relationships of autistic individuals. Other studies suggest that interventions based on mirroring movement in autistic adults may enhance social skills (Koch et al., 2015) and specifically emotional inference (Koehne et al., 2016), a fundamental skill underlying prosocial behavior. Yet, there is a lack of scientific evidence for the causal relationship between movement-based synchronized interventions and social cohesion and prosocial behavior of autistic individuals (Amonkar et al., 2021).
While much of the existing research has focused on interpersonal synchrony’s potential to enhance social skills in general contexts, understanding its role in fostering social cohesion within work environments remains particularly important. Social cohesion, the extent of closeness and belongingness within groups (Moustakas, 2023), plays an important role in the successful integration of individuals in work environments (Jiang et al., 2023). Individuals who feel closer to their work team and have a stronger sense of belonging tend to report higher levels of happiness and life satisfaction (Myers, 2000), demonstrate less deviant behavior (Akkerman et al., 2020) and perform more efficiently (Beal et al., 2003). Autistic individuals often find it difficult to adapt to stressful work environments due to their social and emotional challenges (Hendrickx, 2008). According to the International Classification of Functioning, Disability and Health (World Health Organization, 2001), the ability to work significantly impacts individuals’ health, quality of life, and wellbeing. Indeed, there is a great need to support autistic individuals in these environments (Gal et al., 2015; Gorenstein et al., 2020; Hayward et al., 2019). Another critical factor affecting the successful integration and wellbeing of individuals in the workplace, including autistic individuals, is the management of work-related stress. Work-related stress arises when individuals face a challenging gap between the demands of their professional roles and their personal capabilities, straining their coping abilities (Levi & Levi, 2006). A 9-week synchronous movement intervention initially lowered work-related stress levels in a non-autistic work setting, but this effect was not sustained after three months (Göritz & Rennung, 2019). To the best of our knowledge, there exists a dearth of research investigating the effectiveness of synchronized interventions on the work-related stress of autistic individuals.
Understanding the social motivations and needs of autistic individuals is crucial for designing effective interventions that not only address prosocial behavior and work-related stress but also foster a genuine sense of belonging in professional environments. Autistic individuals have diverse social motivations and needs (Chevallier et al., 2012; Cullen, 2015), such as lower or higher levels of desire to belong and be accepted socially (Baumeister & Leary, 2017; Deckers et al., 2017). In a previous qualitative study, autistic female adolescents expressed feeling pressured to adapt their behavior to gain a sense of belonging in a mainstream school (Miles et al., 2019), highlighting the environment’s role in encouraging conformist behavior among autistic individuals with a high need to belong. Furthermore, autistic individuals frequently seek out opportunities to meet others who share similar interests (Cullen, 2015). This suggests that they are more likely to feel a sense of belonging in environments with like-minded peers. In the workplace, social cohesion, prosocial behavior and work-related stress among autistic individuals might be influenced by prior familiarity with team members (Corbett et al., 2014; Lopata et al., 2008). Little is known about the effect of familiarity on social attitudes and behavior of autistic individuals (Stavropoulos & Carver, 2014). Meirsschaut et al. (2011) suggest that social behavior of autistic individuals is not affected by the familiarity of social partners, but rather by a partner’s autism-adapted interaction style. Fujiwara et al. (2020) studied how prior friendship familiarity moderates the cohesiveness effect of movement synchrony in dyadic interactions among non-autistic individuals. The results indicated that synchrony had a high impact on the motivation to cultivate a friendly relationship during interaction with non-familiar partners.
In sum, previous literature indicates that engaging in synchrony promotes prosocial attitudes and behaviors and that synchronized movement interventions have been used therapeutically to support social and emotional development among autistic individuals. However, evidence for the causal impact of such interventions on prosocial behavior, social cohesion, and work-related stress in autistic populations remains limited. In the current study, we aimed to assess these effects among young autistic adults in their transition to a work environment. We hypothesized that the intervention would enhance prosocial behavior (H1) and reduce work-related stress (H2) as evaluated one week after the intervention. However, we anticipated that these effects would not be sustained 17 weeks after the intervention, expecting no significant improvements in prosocial behavior (H3) or reductions in work-related stress (H4) at this later time point. Additionally, we proposed that individual differences would moderate the effects of the intervention. Specifically, we hypothesized that participants with a higher need to belong would exhibit a greater increase in their sense of belonging to the group one week after the intervention (H5). Finally, we hypothesized that prior familiarity with group members would similarly moderate the effects, with those already familiar with other participants showing a greater increase in social closeness to the group (H6).
Method
Setting
This study was conducted in collaboration with the Roim Rachok (Looking Ahead) Program (RRP), an innovative Israeli civilian program designed to integrate young autistic adults without intellectual disability first into the Israeli army workforce, and later into the labor market (Gal et al., 2015). The data was collected during four consecutive program cycles, each comprising 20 to 27 trainees. As part of the program, before joining the army, trainees undergo a 13-week course delivered by an interdisciplinary team covering two main areas: army-related professional skills (e.g., software programming) and army workplace integration skills. Trainees who successfully complete the course are inducted into the army as civilian volunteers for a 17-weeks trial period. This period is intended to provide trainees with the opportunity to experience their prospective work environment before committing to long-term military service. This is why we chose this specific time frame. This study’s intervention sessions were framed as mandatory physical training within the professional course. The trainees’ decision to participate in the study (i.e. respond to questionnaires and perform the behavioral tasks) did not affect their participation in the course or in the physical training intervention.
Participants
Participants (N =57; 51 males, 6 females) were recruited at the beginning of each program cycle. Admission criteria included no severe sensory impairments or physical disability and reconfirmation of an official diagnosis of Autism Spectrum Condition—severity level 1 by a licensed psychiatrist according to DSM-V criteria (American Psychiatric Association, 2013). All participants met additional RRP screening criteria, including independence in ADL (activities of daily living), literacy abilities, basic independence in social communication, ability to appropriately handle classified military materials, approval to volunteer by the IDF mental health authorities, and suitability for one of six RRP vocational fields. The sample size was predetermined based on an a-priori power analysis using the G*Power computer program (Faul et al., 2007). The analysis indicated that a total sample size of 48 participants would be needed to detect medium-sized effects defined as f = 0.25, with 80% power and alpha at 0.05, using a repeated measure, within-between interaction ANOVA with four groups, two measurements and 0.5 correlation assumed. Three participants (1 male, 2 females) who attended fewer than four sessions were removed from the analysis, yielding a final sample of 54 participants (Mage = 19.46, SDage = 0.847, 50 males and 4 females). All participants were Jewish Israeli. Additional data on socioeconomic status was not recorded. According to the participants’ self-report using the Hebrew adaptation of the Edinburgh Handedness questionnaire (Oldfield, 1971), 35% of the participants were right-handed, 4% left-handed, and 61% mixed-handed. During the RRP, participants were trained in one of six army professions: 26% in Electro-optics, 18% in Data Analytics, 17% in Software Programming, 15% in Data Annotation, 13% in Photography Interpretation, and 11% in Geographic Information System. Gender, handedness, and army profession were distributed approximately equally across intervention conditions.
Design
The current study was designed as a randomized controlled trial with two parallel groups (experimental group and treated control group), including a baseline assessment (t1), a primary endpoint one week post-intervention (t2), and a follow-up assessment 17 weeks after the end of the intervention (t3), prior to commitment to long-term military service, as described in Fig. 1. Randomization was performed as block randomization with a 1:1 allocation using a computer-generated randomization procedure developed by Dvir et al. (2024). Participants (N = 54) were randomly assigned to either synchronized (N = 26) or non-synchronized (N = 28) movement-based group interventions (see Table 1). The intervention, led by a professional dance movement therapist, consisted of 60-min adapted physical training sessions once a week for six to seven weeks per program cycle. An RRP alumnus served as a co-instructor for modeling and assistance in all sessions. The sessions were held in a classroom in RRP facilities and were integrated into the RRP course schedule.
Participants’ assignment to intervention conditions by program cycle
No. of sessions
No. of participants
Synchronized
Non-synchronized
Cycle 1
6
6
7
Cycle 2
6
5
8
Cycle 3
7
10
9
Cycle 4
6
5
4
Total
26
28
Intervention Protocol
A structured physical training protocol (Dvir et al., 2024), devised together with an experienced physical trainer, was employed, comprising four segments: 10 min warm-up (e.g. joints rotation, running and jumping), 10 min social game (e.g. “catch”), 30 min main training (e.g. squats, heel raising, and pushups), and 10 min cool-down (e.g. muscle stretching). A foam ball was utilized in the warm-up and social game, while a training mattress was used in the main training and cool-down. During the main training, a Tabata Timer divided the workout into intervals and a metronome set the exercise pace. Instructions and exercises were adapted to suit an autistic audience. For example, exercises were first demonstrated before participants were expected to replicate them; eye contact and physical touch were avoided; detailed visual description of the exercises was provided; modification and alternative positions were permitted; and rhythmic, repetitive movements were incorporated to support regulation.
Synchronized Condition
The synchronized condition involved a group intervention where all participants shared the same body movements simultaneously. Participants and instructors formed a circle facing each other during the warm-up, main workout, and cool-down. They were instructed to execute exercises together at the same pace to foster group synchrony. Each session featured a collaborative game.
Non-synchronized Condition
The non-synchronized condition involved a group intervention in which participants shared the same body movements but at different times. During the warm-up and cool-down, they executed the same exercises as in the synchronized condition but individually. For example, in the cardio warm-up, participants ran in the form of a relay race. In the main workout, they followed the same exercises as in the synchronized condition but in the form of circuit training with seven stations arranged in a circle. Depending on group size, they worked either as dyads or alone at each station. In the dyads, one participant exercised while the other rested. The circuit training involved participants performing various exercises at a different pace across stations. Each session included a solo version of the same game as in the synchronized condition, requiring participants to complete it independently.
Measures
Cooperation
This continuous dependent variable was measured using a cooperative collection task adapted from Jackson et al. (2018) which resembles the classic N-person cooperation dilemma as a “collective action problem of a common good” (Archetti & Scheuring, 2012). In the original version, large groups of participants (N-group = 39–47) worked together to pick up 500 small washers scattered across a 30 m × 20 m experimental area. Cooperation was evaluated by participants’ mean walking speed. In this study, the task was adapted for smaller groups (t1: N-group = 3–8, t2: N-group = 3–8, t3: N-group = 2–5). Task subgroups consisted of participants from the same intervention group, randomly assigned at t1. The same subgroups were used at t2 and t3 unless a subgroup had fewer than two participants due to attrition, in which case two subgroups from the same condition were merged into one. In our task, participants collaborated to collect 100 small flat plastic coins (with a diameter of 4 cm) scattered in a rectangular structure on one side of the classroom (see Fig. 2). They were asked to pick up one coin at a time and put it in a small basket located on the other side of the classroom as fast as possible until all coins were collected. This task illustrates the concept of the common good through the collective collection of coins. While all participants benefit from completing the task efficiently, the minimal and indistinct impact of individual contributions creates an incentive to free ride. This dynamic exemplifies a collective action problem, where some may withhold effort, relying on others. As in the original Jackson et al. (2018) task, cooperation was assessed by measuring participants’ effort to overcome the incentive to conserve energy, operationalized through their step rate (steps per second) using Fitbit Inspire 2 trackers. These wearable fitness trackers were attached to the participants’ wrists and customized with their height and weight to ensure accuracy. Task execution was monitored using one video camera. Cooperation scores ranged from 1.03 to 2.56 steps per second. Higher scores represent higher cooperation.
Fig. 2
A subgroup of participants during the execution of the cooperative collection task, while collecting plastic coins scattered in a rectangular structure on one side of the classroom
This continuous dependent variable was measured using the Hebrew adaptation of the Irritation Scale (Mohr et al., 2005). This scale comprises eight items, with three assessing cognitive irritation and five assessing emotional irritation. Items were rated on a 7-point Likert scale ranging from strongly disagree (1) to strongly agree (7). For the purposes of the present study, the scale was slightly adjusted in that the words “at work” were replaced by “in the course,” for example: “Even when at home, I often think of my problems in the course.” Across the three measurement time points, Cronbach’s alpha ranged from 0.724 to 0.725 for cognitive irritation and from 0.848 to 0.906 for emotional irritation. The items were averaged, with higher scores depicting higher work-related stress. Scores for this measure range from 1–7.
Need to Belong Level
This nominal independent variable was measured using the Hebrew adaptation of the Need to Belong Scale (Leary et al., 2013), comprising ten items rated on a 5-point Likert scale ranging from strongly disagree (1) to strongly agree (5). For example: “I want other people to accept me”; “It bothers me a great deal when I am not included in other people’s plans.” Cronbach’s alpha of need to belong measured at t1 was 0.703. Need to belong level was calculated using a median cut-off of the need to belong measure at t1, so that participants with the 50% highest need to belong score were assigned a high need to belong level (> 3.3) and the rest were assigned a low need to belong level (=< 3.3). Need to belong level was distributed approximately equally across intervention conditions during the randomization process.
Sense of Belonging to the Group
This continuous dependent variable was measured using the Hebrew adaptation of the General Sense of Belonging Scale (Malone et al., 2012). This scale is composed of 12 items that measure sense of belonging (achieved belongingness) which were rated on a 7-point Likert scale ranging from strongly disagree (1) to strongly agree (7). For the purposes of the present study, the scale was adjusted so that the words “other people” or “others” were replaced by the words “participants in the physical training group”; for example: “When I am with participants in the physical training group, I feel included.” The overall reliability of the scale was very good (t1: Cronbach’s alpha = 0.880, t2: Cronbach’s alpha = 0.951). The original version of the General Sense of Belonging Scale (Malone et al., 2012) was also used to validate the adapted one. Validity was measured using Pearson correlation (t1: r = 0.64; t2: r = 0.58, p < 0.01). The items were averaged, with higher scores depicting higher sense of belonging. Scores for this measure range from 1–7.
Prior Familiarity
This nominal independent variable was measured using the first question in the Hebrew adaptation of the Friendship Closeness Inventory (Polimeni et al., 2002). For the purpose of the present study this question was adjusted to include a reference to participants in the small intervention session group alone: “Do you have friends among the participants in the physical training group whom you consider to be ‘close friends’?” Prior familiarity was scored as true when participants indicated having one or more close friends inside the intervention group at baseline (t1).
Closeness to Group Members
This ordinal dependent variable was measured using an adaptation of the Inclusion of Other in Self Scale (Aron et al., 1992; Blanchard et al., 1998). This scale was made up of two Venn diagram-like pictures where one circle represents the participants themselves, and the other circle represents the entire intervention group. The diagrams range from no overlap to near complete overlap in seven stages, and the participants were asked to choose the diagram that best represents how close they feel to the other members in their group. Scores for this measure range from 1–7, where 1 represents no overlap and 7 represents near complete overlap.
Data Collection Procedure
The schedule of the study data collection is described in Fig. 3. Post-allocation assessments took place separately for the two intervention groups and included two parts: the cooperative collection task and questionnaires. Retention was defined as the percentage of participants who completed all three data collection time points. In the synchronized condition, the retention rate was 88% (questionnaires) and 40% (cooperative task). In the non-synchronized condition, the retention rate was 93% (questionnaires) and 54% (cooperative task). The low retention rates for the cooperative task at t3 were attributed to its administration outside the course framework, with some participants unable to attend data collection sessions due to their military service schedules.
Fig. 3
Schedule of enrolment, intervention and assessment. Data for t1 and t2 were collected during the RRP course. Data for t3 were collected 17 weeks after the end of the intervention and outside of the course framework, in a meeting that took place at RRP facilities
Participants attached a fitness tracker two finger widths away from the wrist of their non-dominant hand and stayed seated for five minutes, as the experimenter scattered plastic coins on the floor. The experimenter explained and demonstrated the task rules as described above, emphasizing that, for safety reasons, no running was allowed. After completing the task, participants returned to their seats for another five minutes before detaching the tracker, to ensure accurate step measurement.
Questionnaires Administration
Next, the experimenter sent an online questionnaire (via the Qualtrics software) to the mobile phone of each participant, where they proceeded to answer the questions. While filling up the questionnaire, participants were seated approximately one meter apart to reduce communication. They were encouraged to seek clarification from the experimenter as needed.
Statistical Analysis
First, we sought to ensure that there was no dependency between the dependent variables (cooperation, sense of belonging, and social closeness) and the program cycle (data was collected in four consecutive program cycles, see above). We therefore used Interclass Correlation Coefficient (ICC) analyses at all three time points. For most dependent variables, ICC at all three time points was equal to or less than 0.2, indicating poor dependencies (Koo & Li, 2016). The ICC for cooperation at t1 was equal to 0.52, indicating moderate dependency (Koo & Li, 2016). Therefore, it can be concluded that there was no substantial dependency between the dependent variables and the different program cycles, allowing the use of a non-hierarchical statistical model.
To test the six hypotheses, the effects of the intervention over time were examined using a linear mixed model (LMM) with participants as a random factor (intercepts only), using restricted maximum likelihood estimation method. Mixed models allow both fixed and random effects and are particularly useful in longitudinal studies with repeated measurements. Furthermore, mixed model analysis is flexible in handling missing data and is often preferred over traditional approaches such as repeated measures analysis of variance (Twisk, 2019). Four different two-way LMMs were used, two for H1 and H2, i.e. immediate effects (t1 vs. t2), and another two for H3 and H4, i.e. 17-week-follow-up effects (t1 vs. t3). Fixed factors included intervention condition (between-subjects) and time (within-subjects). To test the fifth hypothesis, the moderating effects of need to belong level over time (t1 vs. t2) were examined using a three-way LMM with two between-subjects fixed factors (intervention condition and need to belong level) and one within-subjects fixed factor (time). To test the sixth hypothesis, the moderating effects of prior familiarity over time (t1 vs. t2) were examined using a three-way LMM with two between-subjects fixed factors (intervention condition and prior familiarity) and one within-subjects fixed factor (time). Statistical significance was defined as p < 0.05. The statistical analyses were performed by SPSS 27.
Results
Demographic Information
Table 2 summarizes the participants' demographic information, including a comparison between the two conditions (synchronized and non-synchronized) using the two-independent sample t-test for continuous measurements, and the chi-square test of frequency distribution for the nominal variables. There were no significant demographic differences between the two conditions at baseline (t1).
Table 2
Demographic information
Synchronized M (SD) or n (%)
Non-synchronized M (SD) or n (%)
p value
Gender
0.34
Male
25 (96%)
25 (89%)
Female
1 (4%)
3 (11%)
Age
19.51 (0.87)
19.42 (0.83)
0.70
Age of ASD diagnosis
9.04 (5.71)
7.50 (5.6)
0.37
Handedness
0.26
Right-handed
11 (42%)
8 (29%)
Left-handed
0 (0%)
2 (7%)
Mixed-handed
15 (58%)
18 (64%)
Birth order
0.81
Eldest
17 (65%)
18 (64%)
Middle
4 (15%)
3 (11%)
Youngest
5 (19%)
4 (25%)
Number of siblings
0.65
None
1 (4%)
3 (11%)
One
9 (35%)
10 (36%)
Two
11 (42%)
7 (25%)
Three or more
5 (19%)
8 (29%)
High school diploma
24 (92%)
23 (82%)
0.27
High school class type
0.81
Regular
13 (50%)
12 (43%)
Integration
4 (15%)
6 (21%)
Special
9 (35%)
10 (36%)
Showing demographic information and statistical tests between the two conditions (synchronized and non-synchronized) at baseline (t1)
Intervention Participation Information
All participants attended between four and seven sessions with 9% attending four sessions, 15% attending five sessions, 48% attending six sessions, and 28% attending seven sessions. Adherence was defined as attendance at a minimum of 75% of the scheduled sessions. The adherence rate was 100% in the synchronized condition and 82% in the non-synchronized condition.
Descriptive Statistics
Table 3 presents the descriptive statistics of the primary dependent variables at t1, t2, and t3 and the secondary dependent variables at t1 and t2. The statistical tests between the two intervention conditions at baseline (t1) are also presented, indicating no significant difference (p > 0.41) for all dependent variables at t1.
Table 3
Means and standard deviations of the study’s dependent variables at the three time points
Dependent variable
T1
T2
T3
Sync
Non-sync
p value
Sync
Non-sync
Sync
Non-sync
Cooperation
1.61 (0.23)
1.71 (0.2)
0.41
1.67 (0.31)
1.68 (0.19)
1.89 (0.23)
1.79 (0.16)
Work-related stress
2.49 (0.94)
2.66 (1.21)
0.56
2.24 (0.95)
2.41 (1.01)
2.27 (0.89)
2.61 (1.11)
Sense of belonging
5.32 (0.82)
5.4 (0.88)
0.73
5.47 (1.01)
6.06 (0.65)
Social closeness
3.92 (1.41)
4.04 (1.4)
0.77
4.12 (1.62)
4.96 (1.43)
Also showing no significant differences between the two intervention conditions at baseline (t1) using two-independent sample t-test analysis for continuous measurements
Cooperation with Group Members
To test the first hypothesis, a two-way LMM model was constructed, with cooperation outcome (step rate) at t1 vs. t2. No significant interaction between intervention condition and time was found (b = 0.08, SE = 0.08, t(45) = 0.98, p = 0.33). The hypothesis that movement-based synchronized group intervention will enhance prosocial behavior as evaluated one week after the end of the intervention (H1) was not confirmed.
To test the second hypothesis, a two-way LMM model was constructed, with cooperation outcome (step rate) at t1 vs. t3. A significant interaction between intervention condition and time (b = 0.2, SE = 0.1, t(33) = 2.09, p = 0.044) was found. The participants increased their step rate from t1 to t3 following both intervention conditions. However, the increase from t1 (Msync = 1.61, SE = 0.04) to t3 (Msync = 1.89, SE = 0.06) was significantly higher following the synchronized intervention than the increase from t1 (Mnon-sync = 1.71, SE = 0.04) to t3 (Mnon-sync = 1.79, SD = 0.05) following the non-synchronized intervention (see Fig. 4). As opposed to H3, this surprising result indicates that movement-based synchronized group intervention enhanced prosocial behavior as evaluated 17 weeks after the end of the intervention.
Fig. 4
Step rate—number of steps per second (means) by intervention condition and time point
To test the third hypothesis, a two-way LMM model was constructed, with work-related stress outcome (irritation) at t1 vs. t2. No significant interaction between intervention condition and time was found (b = 0.04, SE = 0.21, t(51) = 0.17, p = 0.86). The hypothesis that movement-based synchronized group intervention will reduce work-related stress as evaluated one week after the end of the intervention (H2) was not confirmed. However, a significant main effect for time (b = 0.21, SE = 0.15, t(52) = 1.4, p = 0.03) indicated that following both intervention conditions, participants reported lower irritation at t2 (Msync = 2.27, SE = 0.19; Mnon-sync = 2.41, SE = 0.18) compared to t1 (Msync = 2.49, SE = 0.21; Mnon-sync = 2.66, SE = 0.21).
To test the fourth hypothesis, a two-way LMM model was constructed, with work-related stress outcome (irritation) at t1 vs. t3. As predicted by H4, no significant interaction between intervention condition and time was found (b = -0.2, SE = 0.31, t(50) = -0.64, p = 0.53).
Sense of Belonging to the Group
To test the fifth hypothesis, a three-way LMM model was constructed, with sense of belonging outcome at t1 vs. t2. The interaction between intervention condition, need to belong level (high vs. low), and time was analyzed. No significant third-order interaction was found (b = 0.71, SE = 0.47, t(49) = 1.51, p = 0.14). The hypothesis that movement-based synchronized group intervention will enhance sense of belonging of participants with a high need to belong as evaluated one week after the end of the intervention (H5) was not confirmed.
Social Closeness
In the synchronized condition, 11 participants (42%) reported having prior familiarity with some of their group members (i.e., having one or more close friends in their specific intervention group at t1), while in the non-synchronized condition, only eight (28%) reported the same. No significant difference (p = 0.24) was found between four groups of participants (i.e., participants who reported having prior familiarity and participants who reported having no prior familiarity across two intervention conditions) in terms of social closeness outcome at baseline (t1) using one-way ANOVA. To test the sixth hypothesis, a three-way LMM model was constructed, with social closeness outcome at t1 vs. t2. The interaction between intervention condition, prior familiarity, and time was analyzed. A significant third-order interaction was found (b = − 2.4, SE = 0.79, t(49) = − 3.03, p = 0.004). As predicted by H6, following the synchronized intervention, participants who reported having one or more close friends at t1, also reported feeling closer to group members at t2 (see Fig. 5).
Fig. 5
Closeness (Means) to the group’s members by intervention condition and time point. Following synchronized intervention, participants who reported having no close friends at t1, also reported feeling less close to the group at t2 (A), while participants who reported having one or more close friends at t1, also reported feeling closer to the group at t2 (B)
To examine whether prior familiarity with group members moderated the significant effect on cooperation after 17 weeks, a three-way LMM model was constructed, with cooperation outcome (step rate) at t1 vs. t3. The interaction between intervention condition, prior familiarity, and time was analyzed. No significant third-order interaction was found (b = − 0.21, SE = 0.21, t(30) = − 1, p = 0.32). Prior familiarity with group members did not moderate the effect of intervention condition on cooperation after 17 weeks.
Discussion
Results indicate that participants in the synchronized group intervention showed a significant improvement in cooperative behavior 17 weeks post-intervention, compared to those in the non-synchronized group. However, this effect was not moderated by prior familiarity with group members. Thus, the current study suggests that movement-based synchronized group intervention may enhance long-term cooperative behavior, but may not be effective in terms of reducing both immediate and long-term work-related stress among young autistic individuals as they transition into the work environment. The improvement in cooperation may not be contingent upon participants' prior familiarity with one another. These findings are not consistent with findings from prior research conducted with non-autistic individuals, both in laboratory (Reddish et al., 2013; Valdesolo et al., 2010) and work settings (Göritz & Rennung, 2019). These inconsistencies may stem from the unique and less consistent non-verbal communication patterns among autistic individuals, as well as the challenges that these individuals may encounter when asked to imitate or synchronize their movements to a partner’s movement (Lense et al., 2021).
Previous research has shown that being synchronized not only contributed to better cooperation but also enhanced, in this case, children’s social motivation to collaborate in a cooperative button-push task (Rabinowitch & Meltzoff, 2017). Furthermore, Reddish et al. (2013) found that synchrony combined with shared intentionality produced the greatest level of cooperation among groups of neurotypical adults. It is thus possible that the synchronized intervention combined with the joint goal “to exercise together at the same pace” enhanced participants’ social motivation to cooperate, collaborate, and put more work in for the success of the whole group with whom they underwent the synchronized intervention. Interestingly, this effect was only evident 17 weeks post-intervention, but not immediately afterward. This delayed effect may be attributable to participants' increased experience and familiarity with the task rules. Other possible explanations include the gradual internalization of group norms, which may require repeated exposure to shared experiences before being manifested in behavior (Zhou et al., 2015). Additionally, cognitive and emotional processing of prior task sessions may play a role, as individuals might need time to reflect on the consequences of cooperative versus non-cooperative choices (Zhao et al., 2024). Finally, contextual or environmental factors that fluctuate over time, such as changes in perceived group stability or shifts in external stressors, may have modulated participants’ responsiveness to cooperative cues at later stages (Rubenstein, 2011). Future research could consider administering this task at least once during the intervention period to capture changes in cooperative behavior as they emerge.
The results also highlight the role of prior familiarity as a moderator for the immediate effect of movement-based synchronized group intervention in terms of improving social cohesion. These findings are not consistent with findings from prior research conducted with non-autistic individuals in a laboratory setting (Fujiwara et al., 2020) and may be attributed to autistic individuals’ preference for secure environments where peers are familiar to them (Corbett et al., 2014; Lopata et al., 2008), facilitating social engagement in synchronized movement-based interventions (Manders et al., 2021).
Lastly, the results suggest that movement-based synchronized group intervention's immediate effect on the sense of belonging to the group might not be moderated by participants’ need to belong as reported before the intervention. Further research is needed to deepen our understanding regarding interpersonal synchrony, the need to belong, and the sense of belonging among autistic individuals, perhaps also using a bigger sample.
Limitations
The study has several limitations. Firstly, the use of self-report questionnaires (such as the Irritation Scale (Mohr et al., 2005), the Need to Belong Scale (Leary et al., 2013), and the General Sense of Belonging Scale (Malone et al., 2012)) that might not account for neurodiverse perspectives. Secondly, a potential experimenter bias, suggesting that the reported effects might be driven by the therapist’s expectations about the effects of synchrony on prosocial behavior (Atwood et al., 2022). Thirdly, the degree of synchrony achieved in the synchronized group was not objectively measured or analyzed. As such, it is unclear how synchronous participants’ movements actually were. Future studies should include objective measures of synchrony to clarify this relationship. Finally, a key limitation concerns prior familiarity among group members. Participants in the synchronized group were more likely to be familiar with one another compared to those in the non-synchronized group. Future studies should aim to control for prior familiarity across conditions to better isolate the impact of movement synchrony.
Research and Clinical Implications
Further research is needed to deepen our understanding regarding the role of prior familiarity as a moderator of the effect of movement-based synchronized group intervention. Specifically, it is important to better understand how autistic individuals perceive social familiarity, close friendship, and belongingness, as their perceptions may differ from those of non-autistic individuals. Studies should incorporate the “double empathy problem” theory (Milton, 2012), which suggests that difficulties in communication between autistic and non-autistic individuals stem from a lack of mutual understanding. A thorough understanding of autistic individuals’ perceptions is crucial for creating effectively tailored interventions.
Given the findings that participants who were involved in synchronized interventions enhanced their long-term cooperative behavior, we recommend incorporating synchronized group activities—such as exercising, dancing, or singing together—into programs designed to support young autistic individuals transitioning into work environments. Furthermore, based on the findings that participants who were involved in synchronized interventions with at least one familiar peer experienced elevated social cohesion, we propose a nuanced approach to movement-based interventions for young autistic adults. We suggest incorporating movement exercises focused on fostering familiarity among participants, without necessitating group synchronization, at the beginning of an intervention. For instance, in the cardio warm-up, participants can engage in a relay race to acquaint themselves with their peers' running styles and preferences. Once interpersonal familiarity is established, synchronized activities that involve a shared pace can be introduced, to foster cohesiveness. This tailored approach acknowledges the importance of prior familiarity among autistic individuals, encouraging the use of exercises that prioritize familiarity-building, especially in groups where most of the participants are unfamiliar with each other.
In conclusion, it is important to recognize that the prosocial behavior as well as the social attitudes of autistic individuals may be affected by a wide range of factors, beyond the type of intervention, including prior familiarity among participants. Therefore, it is likely that a holistic approach, which integrates synchronized and non-synchronized movement-based group interventions, while considering autistic perceptions, would be the most beneficial in enhancing social cohesion and prosocial behavior among young autistic individuals transitioning to the work environment.
Acknowledgments
The authors thank all participants and the Roim Rachok team for their collaboration and support of this study.
Declarations
Conflict of interest
The authors declare that they have no competing interests.
Compliance with Ethical Standards
This study was approved by the ethics committee for human experiments of the Faculty of Social Welfare & Health Sciences at the University of Haifa (Approval # 017/21). All participants volunteered, and written informed consent was obtained before study participation. Six participants who have a custodian were also required to present the custodian’s approval.
Community Involvement Statement
This research was led by a diverse group that involved two clinical practitioners (C.E. and T.D.) who were responsible for developing the research objectives and for the study design, measures, and interpretation of the findings, together with T-C.R. T.D. was also responsible for the study implementation. The Roim Rachok team and the autistic participants and alumni were involved in the study implementation process.
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