A scoping review of bicycling interventions’ impacts on well-being
This scoping review synthesized findings from 87 intervention studies to examine the impact of bicycling on psychological, social, affective, and cognitive well-being. The results showed that bicycling interventions, particularly those conducted outdoors and over multiple sessions, led to improvements in mood, reduced depressive symptoms, increased social connection, and enhanced cognitive functioning. The authors conclude that bicycling is a multidimensional activity that promotes holistic well-being, highlighting the need for more translational and community-based research beyond indoor settings.
## Abstract **Introduction: ** Bicycling, inclusive of cycling on trail or road, and stationary forms, is recognized for its substantial physical benefits and has gained attention for its broader impact on dimensions of well-being. Evidence demonstrates that bicycling improves cardiovascular fitness, body composition, and metabolic health while serving as an effective strategy for exercise promotion and disease prevention. Beyond physical outcomes, growing evidence supports bicycling's impact on psychological and cognitive domains of well-being from large associative studies, active travel, and bicycling programs. **Methods: ** This scoping review synthesizes research examining the impact of bicycling interventions on psychological, social, affective, and cognitive domains of well-being. Database searches of EBSCOhost, ProQuest, PsycINFO, PsycArticles, and PubMed identified relevant studies. Of the 1,653 studies identified, 87 studies met inclusion criteria. **Results: ** A majority of studies implemented acute, indoor bicycling interventions assessing cognitive outcomes. Synthesized results indicate positive impacts of bicycling on well-being, including improved mood, reduced depressive symptoms, increased social connection, and enhanced cognitive functioning, especially among interventions occurring outdoors and over multiple sessions. Affective and cognitive outcomes varied by intervention, context, and population. **Discussion: ** This scoping review reinforces bicycling as a multidimensional modality for promoting well-being by synthesizing findings from 87 intervention studies across 19 countries. Notable improvements across well-being domains emerged, particularly in multi-session, outdoor interventions. Findings underscore the necessity for translational and inclusive community-based research beyond indoor environments, and exploration of how unique bicycling features (i.e., outdoor exposure, social opportunity, and meaningful routine) promote holistic well-being across the lifespan. ## 1 Introduction Bicycling, whether on mountain or trail, road, or stationary bikes, and whether for transportation, recreation, or competition, has been widely studied for its contributions to physical, emotional, and psychological well-being (, ). As interest grows in the relationship between movement and mental health, bicycling has emerged as a practical, accessible, adaptable, and sustainable activity that supports holistic wellness across diverse populations and settings (). Beyond its well-documented physical health benefits (), bicycling has been increasingly recognized for its positive effects on mental and emotional well-being (, ). Physical activity, including bicycling, has been associated with reduced symptoms of depression and anxiety, improved mood, and enhanced emotional regulation (, ). Neurobiological mechanisms, such as the release of endorphins and other neurotransmitters, may underlie some of these effects (). Additionally, bicycling often facilitates social interaction and community engagement, contributing to greater social cohesion and a sense of belonging (, ). Much of the existing literature relies on population-level survey data to examine associations between bicycling and mental well-being (, ). For example, Friel et al. () conducted a prospective longitudinal study and found that commuting via bicycle was associated with a lower risk of all-cause mortality, as well as a lower likelihood of having a prescription for mental health problems compared to non-bicyclist commuters. These studies offer important evidence on the role bicycling may play in supporting well-being, but they are limited in their ability to establish causal relationships. In contrast, intervention-based studies, which involve structured programs or activities designed to assess the direct impact of bicycling activities, can provide stronger evidence of the intentional effects of bicycling on specific health outcomes. Yet, to date, these intervention studies remain fragmented and under-synthesized. This scoping review aims to address this gap by systematically mapping the existing literature on bicycling-related interventions and their impacts on psychological, social, affective, and cognitive well-being. By focusing specifically on interventions, we seek to better understand how bicycling can be intentionally leveraged to promote these domains of well-being across the lifespan. Several frameworks describe the interaction between physical activity and cognitive, psychological, and behavioral outcomes. Self-determination theory suggests physical activity enhances well-being by satisfying core psychological needs for autonomy, competence, and relatedness (). Whereas a biopsychosocial model suggests that psychological benefits, such as improvements in mood, are promoted by physiological changes, such as the release of endorphins that occur during and after physical activity (). Together, these theories highlight the complex interplay between physical activity and various domains of well-being. In practice, bicycling programs may engage multiple domains of well-being from individual to community levels (). However, despite these frameworks, how bicycling interventions are intentionally designed to leverage these mechanisms, particularly for non-physical outcomes, has yet to be synthesized. Given the range in how bicycling interventions are researched, designed, implemented, and evaluated across different populations and outcome domains, a scoping review is warranted to map the available evidence, identify key themes, and highlight gaps in the literature. For the purposes of this review, we adopt a broad definition of “bicycling interventions” as the purposeful or structured use of a bipedal vehicle (e.g., road, mountain, gravel, and other bicycles used outdoors or indoor stationary bicycles) to facilitate changes across four domains of well-being (psychological, affective, social, and cognitive, see [Table 1](#T1)). Under this definition, the included interventions span a wide range of aims, intensities, time scales, and settings. For example, indoor, laboratory-based acute cycling studies often prioritize internal validity to isolate specific effects and mechanistic insight. On the other hand, multi-session outdoor programs often prioritize ecological context and real-world implementation. Including these dimensions (i.e., indoor vs. outdoor; acute vs. multi-session) allows this review to map the full range of pathways through which bicycling may influence well-being. All age groups, clinical and non-clinical populations, and international studies published in English were included to capture global trends and translational work. Table 1 | Well-Being Domain | Description | Examples of Subareas | Example Measures from the Literature | |---|---|---|---| | Psychological Well-Being | The broader state of mental and emotional health, including self-esteem, purpose, and self-efficacy. | Self-esteem, self-efficacy | 36-Item Short Form Health Survey (), General Self-Efficacy Scale (), World Health Quality Assessment Instrument-100 () | | Social Well-Being | Social well-being refers to how individuals perceive the quality of their relationships and their integration within social systems, including family, peers, and communities. Strong social wellness is associated with stronger support networks and a reduced risk of mental health challenges such as loneliness and depression. | Feelings of social support, feelings of isolation, sense of belonging, community engagement. | Trier Social Stress Test for Groups (), Social Interaction Anxiety Scale (), Lubben Social Networking Scale () | | Affective Well-Being | Affective well-being concerns the balance of positive and negative emotions and mood. It is a core component of subjective well-being and closely tied to emotional regulation capacities. | Mood, depressive and anxiety symptoms, emotional regulation. | Positive and Negative Affect Schedule (), Beck Depression Inventory (), State and Trait Anxiety Inventory () | | Cognitive Well-Being | Cognitive health refers to the ability to think, learn, and remember, encompassing mental functions such as attention, memory, processing speed, executive function, and problem-solving. It is an important component of overall brain health. Maintaining strong cognitive health is especially important across the lifespan to prevent cognitive decline and support adaptive functioning. | Attention, memory, and executive functioning; cognitive flexibility and processing efficiency; risk of cognitive impairment. | Wisconsin Card Sorting Test, Stroop Task, N-Back Task, Tower of London, Trail Making Test, Digit Span, Auditory Oddball, Task (P300) EEG, fMRI, fNIRS | Descriptions of the domains of well-being. The table describes the domains of well-being and provides examples of related subareas and measures used to assess each domain. This comprehensive approach aligns with the goals of this scoping review to synthesize what is known about bicycling interventions, highlight what dimensions may shape different outcomes, and identify gaps to inform future research and practice. Specifically, this review addresses how bicycling interventions differ across indoor and outdoor settings, how acute, short-term, and long-term interventions assess different domains of well-being, and which populations are represented across the literature. ## 2 Methods ### 2.1 Data sources and search strategy This scoping review was conducted by integrating the Joanna Briggs Institute (JBI) methodology () with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) (). A comprehensive search strategy was utilized to identify literature examining the relationship between bicycling (including mountain biking, road biking, and stationary biking) and four domains of well-being including psychological, social, affective, and cognitive well-being. A database search of EBSCOhost (Academic Search Complete, Academic Search Elite, Academic Search Premier, CINAHL Complete, ERIC, MEDLINE, Psychology and Behavioral Sciences Collection), ProQuest, PsycINFO, PsycArticles, and PubMed was completed using key search terms. The following search string was implemented: “Mountain Biking” OR “Road Biking” OR “Road Cycling” OR “Bicycling” BY TITLE OR ABSTRACT AND “Mental Health” OR “Mental Wellbeing” OR “Health” OR “Wellbeing” OR “Physical Health” OR “Physical Wellbeing” OR “Psychological Wellbeing” OR “Psychological Health” OR “Cognitive” OR “Neurological” OR “Physical” OR “Social Emotional Health” OR “EEG” BY ABSTRACT Medical Subject Headings (MeSH) terms “Bicycling/physiology” OR “Bicycling/psychology” were also incorporated into the EBSCOhost and PubMed searches; however, such options were not available through PsycINFO or ProQuest. To ensure relevancy and recency, the publication date range was limited to a 20-year period ranging from September 27, 2004, to September 27, 2024, based on the date of the initial search. The final search was conducted on February 20, 2025. ### 2.2 Study inclusion criteria The research question was developed using The Population, Concept, and Context (PCC) framework (). For this Scoping Review, the population consisted of individuals of all ages and included clinical populations; the concept was any bicycling intervention that assessed psychological, social, affective, and/or cognitive well-being outcomes; the context included all geographic locations and participant demographics so long as the bicycling intervention primarily assessed one of the above domains of well-being. Studies that were not accessible in English were excluded. Additional details regarding study design are reported in [Table 2](#T2). Table 2 | Author, Year | Country | n | Intervention | Well-Being Domain(s) | Selection of Measures | Results | Bike, Location | |---|---|---|---|---|---|---|---| | Aguirre-Loaiza et al. () | Colombia | 19 | 2 Groups (Physical Exercise group of 1 session of 20 min cycling, Control: rest) | Cognitive | Stroop Task | Reduced RTs in Physical Exercise Group | Stationary, Indoor | | Akaiwa et al. ( | [120](#B120))[121](#B121))[104](#B104))[122](#B122))[123](#B123))[116](#B116))[60](#B60))[88](#B88))[74](#B74))[92](#B92))[124](#B124))[101](#B101))[125](#B125))[76](#B76))[126](#B126))[102](#B102))**ö** tting et al. ([127](#B127))[128](#B128))[129](#B129))[130](#B130))[99](#B99))[93](#B93))[94](#B94))[97](#B97))[131](#B131))[132](#B132))[133](#B133))[75](#B75))[134](#B134))[69](#B69))[135](#B135))[95](#B95))[136](#B136))[79](#B79))[137](#B137))[138](#B138))[77](#B77))[73](#B73))[139](#B139))[86](#B86))[78](#B78))[90](#B90))[140](#B140))[84](#B84))[141](#B141))[98](#B98))[142](#B142))[143](#B143))[72](#B72))[96](#B96))[57](#B57))[100](#B100))[144](#B144))[83](#B83))Characteristics of included studies: The table summarizes characteristics of the 87 studies included in this scoping review reporting data for the author and year, country, number of participants (n), intervention design, Well-Being Domain(s) assessed, selection of well-being measures, highlight of results, type of bicycle used, and whether the intervention primarily occurred indoor or outdoor. The scope of inclusion was narrowed to studies in which bicycling (e.g., mountain biking, e-biking, road cycling, or stationary bicycling) was a primary intervention activity. Additionally, the study needed to assess bicycling's impact on a psychological, social, affective, and/or cognitive well-being outcome (see [Table 1](#T1) for definitions of these domains). In terms of exclusion criteria, gray literature and reviews (systematic, scoping, or narrative) were excluded to ensure all studies met the specific inclusion criteria of this review, although primary studies cited within relevant reviews were screened individually. Studies in which bicycling was incidental, secondary to another intervention, or where health outcomes could not be directly attributed to bicycling (i.e., combined effects of bicycling and walking) were excluded. Several records examining transportation and urban infrastructure were excluded for not meeting inclusion criteria, as they either did not include a specific intervention component, or did not assess one of the defined outcomes of interest. Several studies focused on competitive athletic performance were also excluded when bicycling was used to examine other outcomes, such as performance or heart rate, rather than the outcomes of interest. This review also excluded interventions that implemented stationary desk bikes and hand cycles. ### 2.3 Charting the data Data extraction and charting were completed by Author LS with key characteristics of interest identified by the research team prior to data extraction. Data categories extracted included title, author, year of publication, country, sample size, participant age range, gender of population, whether race/ethnicity demographics were reported, whether a clinical population was studied, intervention time scale (acute vs. short-term vs. long-term), intervention details, selection of social, affective, psychological, or cognitive outcome measures, general results reported, type of bicycle, and whether the intervention occurred primarily indoors or outdoors. Acute interventions were defined as single or two-session interventions. Short-term interventions were defined as including at least three bicycling-specific sessions over at least two weeks and up to twelve weeks. Long-term interventions were defined as any intervention lasting more than twelve weeks. Each relevant outcome measure was coded as positive, negative, neutral, or inconclusive, with some studies including more than one outcome measure. Data extraction was verified by Author EW. ## 3 Results ### 3.1 Scientific literature search A combined total of 1,653 studies were identified across all searches and imported into the Rayyan review management platform (). After importing all studies, 110 duplicates were removed, leaving 1,543 studies to be screened. These studies underwent a blinded, independent screening of title and abstracts by two reviewers (Authors LS and BR). Conflicts were resolved through discussion with a third and fourth reviewer (Authors EW and CB) to reach consensus. Following screening, 102 full-text records were reviewed by the Author LS and assessed for inclusion eligibility. Of these, after full-text review, 15 records were excluded (See [Figure 1](#F1)). A total of 87 studies were included in the final scoping review for meeting the review's inclusion criteria and aim of investigating the effects of bicycling interventions on psychological, social, affective, and cognitive well-being ([Figure 1](#F1)). Figure 1 ### 3.2 Descriptive characteristics of included studies Nineteen countries were represented among the 87 studies, with a total of 134 well-being outcomes represented across the studies ([Table 2](#T2)). The studies were conducted in the following countries: United States (*n* = 30; 35%), Germany (*n* = 16; 18%), Japan (*n* = 6; 7%), Australia and China (*n* = 5; 6%), Canada and the United Kingdom (*n* = 4; 5%), France (*n* = 3; 4%); Belgium, Italy, and South Africa each with 2 studies (2%) and Austria, Brazil, Colombia, Hungary, Netherlands, Republic of Korea, Russia, and Spain with 1 study (1%). In terms of the population demographics, participant age ranges varied across studies, with 12% of studies including participants under 18 years of age, 83% of studies including adults between 18 and 55 years of age, 20% of studies including older adults greater than 55 years of age, and 15% of studies including two or more age groups. Among the included studies, 81% examined both males and females, 3% examined only females, 13% examined only males, 3% did not report on gender, and 2% included non-binary participants ([Table 3](#T3)). Only 5% of studies mentioned participant race and/or ethnicity. Table 3 | Category | Subcomponent | Number of Studies (%) | |---|---|---| Population Age Range | Under 18 years old | 11 (13%) | | Adults 18–55 years old | 72 (83%) | | | Older adults >55 years old | 17 (20%) | | | Two or more age groups | 13 (15%) * | | Study Design | Acute intervention | 54 (62%) | | Short-term intervention | 29 (33%) | | | Long-term intervention | 4 (5%) | | Gender | Female and Male Participants | 70 (81%) | | Female Participants only | 3 (3%) | | | Male Participants only | 11 (13%) | | | Did not report gender | 3 (3%) | | | Non-binary Participants | 2 (2%) | | Population | Not Clinical | 61 (70%) | | Clinical Group | 26 (30%) | | Race/Ethnicity | Reported | 4 (5%) | | Not Reported | 83 (95%) | General characteristics of included studies. The table includes general characteristics of the 87 included studies across population age range, study design type, participant gender, and whether a clinical population was included. *Some studies included two or more age groups, causing percentages to total over 100%. Clinical populations were the focus in 30% of studies including individuals with metabolic conditions (high BMI, pre-diabetes, obesity), neurological disorders (Parkinson’s disease, Cerebral Palsy, Alzheimer’s disease, Dementia, chronic post-stroke conditions), psychiatric conditions (Schizophrenia, methamphetamine use), intellectual or developmental disabilities (Down Syndrome, intellectual disability), cancer in elderly adults, persistent fatigue and diagnosed mobility limitations ([Table 3](#T3)). In terms of study design, sample sizes within studies ranged from 7 to 1,148 participants. The majority of interventions were acute by design, with 62% coded as acute interventions, 33% coded as short-term interventions involving multiple sessions up to twelve weeks, and 5% of the included studies were long-term interventions with programming duration greater than 12 weeks ([Table 3](#T3)). Most studies utilized stationary bicycles for the intervention (84%), while fewer studies used 2-wheel bicycles (14%), e-bikes (5%), or a combination of bicycle types (2%). Additionally, 84% of studies were conducted indoors, while 16% of studies were conducted outdoors ([Table 2](#T2)). ### 3.3 Well-Being outcomes #### 3.3.1 General psychological well-being Psychological well-being outcomes were measured eighteen times, with 3 occurring in acute interventions, 13 in short-term interventions, and 2 in long-term interventions ([Tables 2](#T2), [4](#T4)). Of the studies, 67% reported positive impacts, 22% neutral, and 11% negative impacts of bicycling overall ([Table 5](#T5)). For acute studies, 33% reported positive results, 33% neutral, and 33% negative. Short-term interventions reported 77% positive results, 15% neutral, and 8% negative. For long-term interventions, one reported positive results and the other reported neutral results ([Table 4](#T4)). Indoor interventions reported 63% positive, 25% neutral, and 13% negative effects. Outdoor interventions reported similar results with 70% positive, 20% neutral, and 10% negative impacts ([Table 6](#T6)). Three studies (–) evaluated general psychological well-being using subscales from the 36-Item Short Form Health Survey () and found improvements among general well-being. Significant improvements in 36-Item Short Form Health Survey (SF-36) scores were found from pre to post intervention in the bicycling and high-intensity agility exergaming groups compared to the control group after 25 sessions (). Neumeier et al. () reported significant positive changes in the intervention group, which actively commuted for 12 months, across four subcomponents of the SF-36 including physical functioning, mental health, vitality, and general health as well as the physical component summary score. Leyland et al. () reported a marginal interaction between session and group for the mental health component of the SF-36, with the e-bike cyclists increasing in this score, but no differences among pedal cyclists and non-bicycling controls. There was a significant improvement in mental health scores after the interventions, but this improvement did not differ between the groups. Table 4 | Well-Being Domain | Total | Acute | Short-Term | Long-Term | |||||||||||| |---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| | Positive | Neutral | Negative | Inconclusive | Total | Positive | Neutral | Negative | Inconclusive | Total | Positive | Neutral | Negative | Inconclusive | Total | || | n | n (%) | n (%) | n (%) | ||||||||||||| | Psychological | 18 | 1 (33%) | 1 (33%) | 1 (33%) | 0 (0%) | 3 | 10 (77%) | 2 (15%) | 1 (8%) | 0 (0%) | 13 | 1 (50%) | 1 (50%) | 0 (0%) | 0 (0%) | 2 | | Social | 10 | 2 (100%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 | 8 (100%) | 0 (0%) | 0 (0%) | 0 (0%) | 8 | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 | | Affective | 25 | 1 (10%) | 3 (30%) | 6 (60%) | 0 (0%) | 10 | 12 (86%) | 2 (14%) | 0 (0%) | 0 (0%) | 14 | 1 (100%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 | | Cognitive | 81 | 27 (45%) | 12 (20%) | 12 (20%) | 9 (15%) | 60 | 12 (67%) | 3 (17%) | 0 (0%) | 3 (0%) | 18 | 3 (100%) | 0 (0%) | 0 (0%) | 0 (0%) | 3 | Summary outcome measures by study duration. Counts and proportions of outcome measures for each domain of well-being (psychological, social, affective, and cognitive), and whether the results were considered positive, neutral, negative, or inconclusive for study duration (acute, short-term, long-term). Note that each study may include more than one outcome measure and may include different outcome measures across different domains. Table 5 | Well-Being Domain | Positive | Neutral | Negative | Inconclusive | |---|---|---|---|---| | n (%) | |||| | Psychological | 12 (67%) | 4 (22%) | 2 (11%) | 0 (0%) | | Social | 10 (100%) | 0 (0%) | 0 (0%) | 0 (0%) | | Affective | 14 (56%) | 5 (20%) | 6 (24%) | 0 (0%) | | Cognitive | 42 (52%) | 15 (19%) | 12 (15%) | 12 (15%) | Summary of results across well-being domains. Counts and proportions of outcome measures for each domain of well-being (psychological, social, affective, and cognitive), and whether the results were considered positive, neutral, negative, or inconclusive. Note that each study may include more than one outcome measure and may include different outcome measures across different domains. Table 6 | Well-Being Domain | Total | Indoor | Outdoor | |||||||| |---|---|---|---|---|---|---|---|---|---|---|---| | Positive | Neutral | Negative | Inconclusive | Total | Positive | Neutral | Negative | Inconclusive | Total | || | n | n (%) | n (%) | ||||||||| | Psychological | 18 | 5 (63%) | 2 (25%) | 1 (13%) | 0 (0%) | 8 ( | 7 (70%) | 2 (20%) | 1 (10%) | 0 (0%) | 10 | | Social | 10 | 5 (100%) | 0 (0%) | 0 (0%) | 0 (0%) | 5 | 5 (100%) | 0 (0%) | 0 (0%) | 0 (0%) | 5 | | Affective | 25 | 6 (35%) | 5 (29%) | 6 (35%) | 0 (0%) | 17 | 8 (100%) | 0 (0%) | 0 (0%) | 0 (0%) | 8 | | Cognitive | 81 | 38 (50%) | 14 (18%) | 12 (16%) | 12 (16%) | 76 | 4 (80%) | 1 (20%) | 0 (0%) | 0 (0%) | 5 | Summary outcome measures by domain and location. Counts and proportions of outcome measures for each domain of well-being (psychological, social, affective, and cognitive), and whether the results were positive, neutral, negative, or inconclusive by location in either the indoor or outdoor setting. Note that each study may include more than one outcome measure and may include different outcome measures across different domains. Four studies examined self-efficacy among participants using four different self-report measures. Two studies developed their measures following Bandura's recommended measurement approach (). Samendinger et al. () demonstrated the importance of previous self-efficacy on future performance, while Focht et al. () found that sedentary adults and older adults reported lower self-efficacy after the bicycling intervention. One study () did not find significant effects of bicycling on self-efficacy when using a subscale of the Physical Activity and Self Efficacy Survey () while participants from Brookman et al. () had improvements in self-efficacy after a bicycling intervention when using the General Self Efficacy Scale (). Combined, these results demonstrate that the set and setting may be important for self-efficacy and highlight the need for standardized use of self-efficacy questionnaires. Ryu et al. () examined whether a 16-week bicycling intervention impacted well-being using the World Health Quality Assessment Instrument-100 () and reported no significant differences across an outdoor bicycling group when compared to an occupational therapy group of persons with Schizophrenia. Two studies using qualitative interviews also reported primary themes of improved well-being among participants (, ). Specifically, Brown et al. () reported themes of increased confidence and courage, motivation, and general mental well-being. Similarly, participants from Zander et al. () reported increased confidence after the intervention. #### 3.3.2 Social well-being Social well-being outcomes were measured 10 times, with 2 occurring in acute interventions and 8 in short-term interventions ([Tables 2](#T2), [4](#T4)). Two studies found positive impacts of acute interventions on social well-being, while 8 studies found positive impacts on social well-being in short-term interventions ([Table 4](#T4)). Of the 10 studies examining this well-being domain, 100% reported positive impacts of cycling on social well-being ([Table 5](#T5)), with 50% of interventions occurring indoors, and 50% occurring outdoors ([Table 6](#T6)). Six studies used a variety of self-report measures to capture different components of social well-being, including social isolation, sense of belonging, and community engagement. Five studies employed qualitative interviews where respondents reported on the impact of bicycling on social functioning. There was a lack of consensus on how to measure social functioning, as each study employed a different instrument. Wunsch et al. () used the Trier Social Stress Test for Groups (), Li et al. () used the Social Interaction Anxiety Scale () and The Prisoner's Dilemma Task (), Geard et al. () used the VR-12 (), Brookman et al. () used the Lubben Social Networking Scale (), and Page & Nilsson () used The Flourishing Scale () and the GHQ-12 (). Despite using different questionnaires, all studies reported that bicycling had positive effects on social well-being. Specifically, multi-session bicycling interventions may yield greater positive effects for expanding social network sizes and reducing loneliness (, ), while acute bicycling interventions may improve prosocial behaviors among participants (). Participants across five qualitative studies (, , –) described multiple social health benefits of bicycling including exposure to new friendships and community, improved social skills, increased feelings of connectedness, and a greater sense of “fitting in”. Overall, self-reported social well-being scores across the scales indicated that bicycling contributed to positive effects on social well-being. #### 3.3.3 Affective well-being Affective well-being outcomes were measured 25 times, with 10 occurring in acute interventions, 14 in short-term interventions, and 1 in a long-term intervention ([Tables 2](#T2), [4](#T4)). In total, 56% of studies found positive effects of bicycling on affective well-being, 20% neutral effects, and 24% negative effects ([Table 5](#T5)). Of the acute studies, 10% reported positive impacts on affective well-being and 30% neutral effects, with the majority of studies (60%) reporting negative impacts, though these interventions often induced exhaustion or fatigue ([Table 4](#T4)). For short-term interventions, 86% reported positive impacts and 14% neutral. One long-term intervention reported positive impacts. Of the indoor interventions, 35% reported positive results, 29% neutral, 35% negative. In contrast, 100% of outdoor interventions reported positive impacts on affective well-being ([Table 6](#T6)). Among the studies, 14 examined psychiatric symptoms, 5 assessed acute mood and affective states, and four employed qualitative interviews. Eight studies examined the impact of bicycling on self-reported depression, with seven studies utilizing the Beck Depression Inventory () and one study utilizing the Geriatric Depression Scale (). Three interventions reported improvements in depression symptoms scores, with lower scores indicating fewer symptoms. After a 16-week bicycling intervention, Ryu et al. () reported reductions in depression scores. Tollár et al. () reported reductions in BDI scores from pre to post intervention after a 5-week bicycling intervention that included thirty 1-hour sessions. Brookman et al. () found that post-intervention depression scores decreased or remained neutral for a majority of participants. Two studies did not find significant differences in depression scores, one employing an acute bicycling intervention () and the other a short-term intervention (). Three studies (, , ) used the BDI as a baseline screening assessment. Six studies examined participants’ self-reported anxiety during their bicycling intervention. Four of these implemented the State and Trait Anxiety Inventory (), one used the Geriatric Anxiety Inventory (), and one used the Beck Anxiety Inventory (). One study measured anxiety solely at baseline (). Three studies found no significant differences in anxiety from pre to post intervention (, , ). A 16-week program noted improvements for anxiety symptom scores (). One study found that anxiety scores increased (i.e., symptoms worsened) in participants who completed a challenging mental task while they were bicycling ([57](#B57)). Of the three studies employing variations of the Profile of Mood States (), two found no differences in fatigue or energy states (, ). However, Dishman et al. ([60](#B60)) noted improvements in vigor following both low and moderate intensity exercise among participants with persistent fatigue. Two studies examined affect through the Positive and Negative Affect Schedule (). Leyland et al. () included the PANAS in their 8-week bicycling intervention for older adults and found a positive shift in PANAS scores across all groups (non-bicycling control, pedal bicyclists, and e-bike bicyclists). Szabo et al. () found acute positive changes in affect, with increases in positive PANAS scores and decreases in negative PANAS scores following a 35-minute indoor bicycling exercise. Four studies utilizing qualitative interviews reported participant themes suggesting the bicycling program improved affective well-being. Main themes reported by Anderson et al. () included “feeling good,” “feeling happy,” and “more energy” following a 12-week e-bike loan intervention. Interviews reported by Zander et al. () revealed that female participants in particular, felt empowered and proud, and many reported improvements in energy and mood following the 12-week bicycling intervention. Parents in Armstrong et al. () reported their children with Cerebral Palsy demonstrated improved motivation, mood, and mindset following an 8-week training program. Similarly, qualitative interventions in Brown et al. () revealed themes of improved confidence, courage, and motivation among participants. ### 3.4 Cognitive well-being Cognitive well-being outcomes were implemented 81 times, with 60 occurring in acute interventions, 18 in short-term interventions, and 3 in long-term interventions ([Tables 2](#T2), [4](#T4)). Forty-five percent of acute interventions reported positive impacts of bicycling on well-being, while 20% were neutral, 20% negative, and 15% inconclusive ([Table 4](#T4)). Of the short-term interventions, 67% reported positive impacts, 17% neutral, and 17% inconclusive. Three long-term interventions (100%) reported positive impacts of bicycling on cognitive well-being. Overall, 52% found positive impacts, 19% neutral, 15% negative, and 15% inconclusive impacts on cognition ([Table 5](#T5)). With regards to location, of the indoor studies, 50% found positive impacts of bicycling on well-being, 18% neutral impacts, 16% negative impacts, and 16% were inconclusive. In contrast to the indoor interventions, 80% of outdoor interventions reported positive impacts of the bicycling intervention on cognition, and 20% of studies reported neutral impacts ([Table 6](#T6)). Thirty-seven studies used only behavioral measures of cognition, most frequently assessing processing speed (reaction time) and accuracy. Thirty-two studies used various methods to examine brain activity to understand bicycling's impact on cognitive function. Of these, twenty-five studies used electroencephalogram (EEG), five studies used functional near infrared spectroscopy (fNIRS), and two studies used functional magnetic resonance imaging (fMRI). Two studies combined behavioral responses with measures of brain-derived neurotrophic factor (BDNF). Below, we summarize key findings for both behavioral outcomes (reaction time, accuracy) and brain activity (EEG, fNIRS, fMRI). #### 3.4.1 Behavioral outcomes Researchers used a wide variety of tasks to assess cognitive outcomes before, during, or after bicycling. The most common tasks were the Stroop Task (11 studies), Auditory Oddball Task (9 studies), Wisconsin Card Sorting Task (7 studies), Flanker Task (6 studies), Trail Making Test (5 studies), Digit Span Test (5 studies), n-back Task (4 studies), and the Tower of London Task (3 studies). ##### 3.4.1.1 Reaction time Nineteen studies examined processing speed by measuring reaction time on tests of executive function. Eleven acute interventions (–[73](#B73)) noted improved reaction time after participating in bicycling interventions. Two acute interventions (, [74](#B74)) found no significant differences in reaction time. One acute intervention, designed to induce fatigue, noted impaired reaction time ([75](#B75)). ##### 3.4.1.2 Accuracy Eight studies examined the impact of bicycling interventions on accuracy across various cognitive tasks. Three studies (, [76](#B76), [77](#B77)) reported significant improvements in accuracy after a bicycling intervention, while five studies (, , , [74](#B74), [78](#B78)) found no significant differences in accuracy. Even in an exercise-induced fatigue group that bicycled for one hour at a 90% ventilation threshold, accuracy did not differ from the rest group ([75](#B75)). Three studies (, [69](#B69), [79](#B79)) noted impaired accuracy. Additionally, Grego et al. () reported reduced accuracy after 120 min of bicycling. #### 3.4.2 Brain activity ##### 3.4.2.1 Electroencephalogram and event related potentials Electroencephalogram (EEG) is a technique used to capture electrical activity from the brain. EEG can also be used to generate event related potentials (ERPs) which measure changes in averaged EEG signals in response to specific stimuli or “events” and are often associated with both low-level sensory processing and cognitive processing ([80](#B80)). Fourteen studies measured ERPs, while 11 measured general cortical oscillatory activity ([Table 2](#T2)). Twenty studies examined cortical activity *during* bicycling, 5 compared activity before and after bicycling, and 5 compared activity during and before or after bicycling. Out of the 25 studies, 22 were conducted in an indoor setting using stationary bicycles. Findings regarding the impact *while* bicycling on ERP components have also been reported in a previous Systematic Review ([81](#B81)). Eleven studies examined the P300 ERP component (, , [69](#B69), [76](#B76), [78](#B78), [79](#B79), [82](#B82)–[84](#B84), [86](#B86), [125](#B125)). The P300 component is typically elicited using oddball paradigms, in which responses to less common stimuli in the midst of repetitive stimuli are recorded and is commonly interpreted as reflecting attentional allocation and context updating ([85](#B85)). Larger amplitudes generally reflect greater allocation of attentional resources, whereas longer latencies may indicate slower stimulus evaluation. Environmental complexity (e.g., traffic) and cadence modulated P300 effects, with high-load environments reducing attentional allocation and reducing P300 amplitude ([78](#B78)) and optimal cadence enhancing attention compared to fast and slow conditions ([82](#B82)). In total, seven studies found increases in P300 amplitudes during or after bicycling (, , , [69](#B69), [76](#B76), [79](#B79), [84](#B84)), and four found decreases in P300 amplitude ([78](#B78), [82](#B82), [83](#B83), [86](#B86)). Four studies examined the N1 component ([78](#B78), [79](#B79), [83](#B83), [86](#B86)). The N1 component reflects early sensory and attentional processing and is sensitive to stimulus discrimination and selective attention ([87](#B87)). In total, two studies found increases in N1 amplitude, one found no differences, and one study found a decrease. N1 amplitude was larger in high-traffic environments ([78](#B78), [86](#B86)), consistent with enhanced early sensory processing under conditions of increased environmental vigilance. Conversely, one study assessing ERPs after a bout of bicycling found reduced N1 amplitud**e** at post-test relative to rest ([79](#B79)), which may reflect temporary post-exercise fatigue. Importantly, these differences highlight that during-exercise and post-exercise effects may not be directly comparable. Four studies examined the N2 component ([69](#B69), [72](#B72), [79](#B79), [88](#B88)). The N2 component is often associated with conflict monitoring, response inhibition, and aspects of cognitive control ([89](#B89)). Overall, N2 amplitude decreased during low or vigorous cycling in three studies. Moderate cycling neutralized the N2 in one study. Two studies reported shorter N2 latencies following bicycling ([72](#B72), [79](#B79)), a pattern sometimes interpreted as more efficient conflict monitoring. Another study found greater negative N2 amplitudes after two bicycling conditions compared to rest ([69](#B69)), suggesting stronger cognitive control. Dodwell et al. ([88](#B88)), examined the posterior-contralateral negativity (PCN), a sub-type of the N2, and found an inverted-U relationship where rest and vigorous bicycling reduced its amplitude or increased the latency, while moderate bicycling eliminated these effects. One study examined the N400 component ([90](#B90)), a marker of **s** emantic processing, and found an increase in amplitude ([91](#B91)). During a vocabulary-encoding task performed while bicycling, N400 amplitude increased relative to a non-bicycling condition ([90](#B90)). This suggests that concurrent physical activity can modulate the neural resources recruited for semantic encoding, although more work is needed to determine whether bicycling enhances or increases the demands of semantic processing. In addition to ERPs, 11 studies examined cortical oscillatory activity through EEG ([Table 2](#T2)). Across these studies, alpha power commonly increased during warm-up and moderate bicycling (7 studies), which may reflect shifts toward more internally oriented attention or reduced visual processing demands ([60](#B60), , [92](#B92)–[96](#B96)). At higher exercise intensities, two studies reported decreases in alpha power ([93](#B93), [97](#B97)), a pattern typically interpreted as greater cortical activation and heightened task engagement. One study similarly reported reduced alpha activity during higher intensity bicycling compared to lower intensity bicycling ([60](#B60)). In addition to alpha activity, increases in beta and gamma power were observed during bicycling, particularly in frontal and parietal regions, and were associated with enhanced motor and attentional control ([92](#B92)). ##### 3.4.2.2 Functional near-infrared spectroscopy Five studies used functional near-infrared spectroscopy (fNIRS) to examine brain activity during bicycling ([Table 2](#T2)). One study demonstrated that fNIRS is a feasible and valid method for measuring cortical activation *during bi*cycling, showing that hemodynamic responses associated with both motor and cognitive function changed with exercise intensity ([98](#B98)). Two other studies found changes in brain activation across brain regions including the prefrontal cortex and primary motor cortex ([99](#B99), [100](#B100)), consistent with executive functioning and sustained attentional engagement. ##### 3.4.2.3 Functional magnetic resonance imaging Two studies used functional magnetic resonance imaging (fMRI) to examine neural activity in relation to bicycling. One study reported changes in activation in the cerebellum, precentral gyrus, and postcentral gyrus during periods of bicycling ([101](#B101)). The other study found that brain activity during successful spatial learning was modulated by the participant's physical fitness across a 6-month intervention comparing a bicycling (aerobic activity) group to a stretching group ([102](#B102)). ## 4 Discussion ### 4.1 Summary This scoping review includes synthesized evidence from 87 intervention studies performed across 19 countries, emphasizing the multidimensional potential of bicycling interventions to promote well-being across psychological, social, affective, and cognitive dimensions. Summarized results across all studies demonstrated that bicycling interventions have a majority of positive effects on the different domains of well-being across both indoor and outdoor environments, as well as across different lengths of interventions. Positive results were more consistent among outdoor and short- and long-term bicycling interventions. While several studies reported negative effects of bicycling in the indoor environments, those study protocols often included tests to exhaustion, or vigorous cycling, demonstrating an inverted- U effect of bicycling in the acute high intensity settings indoors. These study protocols were intentionally designed to induce negative valence and to challenge participants, leading to the higher proportions of negative impacts reported among acute interventions. Inconclusive results were reported for studies that only included a brain imaging measure, where it could not be determined if the effect of bicycling was positive, neutral, or negative. Similarly, in a scoping review focused on mountain biking, Kuklinski et al. ([103](#B103)) identified 116 studies spanning 24 countries that were used to identify five domains of influence (i.e., physical, psychological, social, environmental, and cognitive). Their analysis highlighted how mountain biking research increasingly conceptualizes bicycling as a complex biopsychosocial phenomenon rather than a purely recreational or physical pursuit. With the expansion of our review to focus more broadly on all bicycling-based interventions, the convergence between the reviews further supports bicycling's relevance for emotional regulation, stress reduction, and social connectedness. Several patterns emerge from our review, illuminating the holistic impact of bicycling on human well-being, including improvements in participant mental health, depressive symptomology, mood, and cognitive processes. Taken together, this impact may be attributed to factors above and beyond physical activity, including several unique features of bicycling such as outdoor exposure, opportunities for social interaction, and engagement in meaningful routines (, ). #### 4.1.1 Psychological Across the reviewed studies, bicycling's impact on psychological well-being, particularly self-efficacy, was context dependent. One intervention demonstrated improved self-efficacy using the validated General Self-Efficacy Scale () after a bicycling intervention, consistent with findings from Brown et al. (, [104](#B104)), who reported enhanced self-efficacy among middle school students in mountain bike programs. However, other studies showed a mix of positive, neutral, and even contra-therapeutic outcomes, particularly among sedentary adults and older populations (). This variability underscores the critical importance of “set and setting” in intervention design, suggesting that outcomes are significantly influenced by the psychological and environmental context in which bicycling occurs. It also highlights the importance of designing interventions for older adults that reduce barriers to physical exercise and that make it appealing, particularly for those who have not recently engaged in physical activity. Together, these findings suggest that bicycling interventions are most effective when they foster self-efficacy and align with self-determination theory, supporting autonomy, competence, and relatedness, key drivers of sustained motivation and well-being. While general mental health outcomes were not frequently reported in this review, three studies that did measure general mental health reported positive increases (–). Notably, these studies were not solely acute in nature, with programming spanning the course of several weeks. The results indicate that general mental health may be more positively impacted by interventions with multi-session programming compared to acute programming, which is consistent with previous work demonstrating a dose-response relationship between physical activity and mental health ([105](#B105)). Future work should examine optimal program length to achieve benefits, as this can be important for practitioners to incorporate into program design. Together, these findings align with preexisting literature emphasizing psychological well-being as a multidimensional construct encompassing self-acceptance, environmental mastery, autonomy, and personal growth ([106](#B106), [107](#B107)). This supports the assertion that interventions focused on fostering psychological well-being can lead to better coping strategies and improved mental health outcomes which can be leveraged to inform future program development and research (). Previous work suggests techniques for practitioners to adjust programming to enhance self-efficacy by setting challenging but reachable goals that build on each other, creating social groups within an intervention to encourage participation, incorporating self-regulation techniques, and reducing exacerbation of physical limitations ([108](#B108)). The adaptability of bicycling factors such as riding location, bicycle type, and opportunities for social engagement support its effectiveness as a tool from which self-efficacy interventions can be tailored. Indeed, Kuklinski et al. ([103](#B103)) highlighted self-efficacy and perceived competence as the most frequently cited psychological constructs across mountain biking studies, with outcomes strongly influenced by environmental conditions, risk perception, and social support. They found that experiences fostering mastery and autonomy, particularly in nature-rich settings, were associated with improved confidence, vitality, and identity development. #### 4.1.2 Social Although social outcomes were only explored among 10 measures, the findings are encouraging. Participants in short-term bicycling interventions consistently demonstrated the ability to expand social networks and reduce loneliness, while those in acute interventions showed potential to foster prosocial behaviors. Additionally, benefits to social well-being were found across both indoor and outdoor studies, highlighting how stationary bikes can also facilitate opportunities for social engagement. Qualitative findings suggested powerful themes around community integration, skill development, and social connectedness, consistent with prior research demonstrating that social interactions and engagement in community activities facilitated through bicycling contribute to greater social cohesion and a sense of belonging (, , [104](#B104)). These findings support the conceptualization of social well-being as encompassing the quality of an individual's relationships and interactions within their community, including intrapersonal feelings of belonging and connection ([103](#B103), [109](#B109)). Kuklinski et al. ([103](#B103)) found that group-based riding promotes prosocial identity formation and inclusion, particularly in participants in youth programs. #### 4.1.3 Affective In terms of affective well-being, findings indicated differential effects between depression and anxiety outcomes. Depressive symptoms demonstrated consistent improvement across multiple studies using validated instruments, supporting prior research linking physical activity, including bicycling, to reduced depressive symptoms and enhanced mood (). Neurotransmitter secretion during physical activity, such as bicycling, has been linked to improved emotional well-being (), potentially explaining the consistent improvements in depressive symptoms observed across studies. Observed changes in anxiety symptoms after bicycling, on the other hand, were more variable and appeared to be context dependent. For instance, dual-task paradigms, such as bicycling while performing a cognitive task, have been shown to increase anxiety levels, suggesting that cognitive demands during bicycling may moderate affective outcomes ([57](#B57)). Many of the acute studies incorporated anxiety or stress inducing components within their interventions, contributing to the higher proportion of negative results among affective well-being. Kuklinski et al. ([103](#B103)) reported similar trends in their review, showing that moderate challenge and outdoor exposure during mountain biking enhance affective states through flow state and self-determination, whereas excessive demands, such as a poorly designed or overly competitive environments, could potentially elicit anxiety or fear, further illustrating the importance of emotional regulation and perceived control in bicycling-based interventions. Multi-session interventions were distinct from acute sessions as they reported higher proportions of positive effects of bicycling on improving mood and psychiatric symptoms. These interventions often did not include components that induced negative emotional states within participants, leading to higher proportions of positive results. Similar contrasts were found between outdoor and indoor interventions. These findings highlight the complexity of affective well-being, encompassing both the presence of positive emotions, such as joy and contentment, and the absence of negative emotions, such as anxiety and despair ([110](#B110)). #### 4.1.4 Cognitive In relation to domains of cognitive functioning, several patterns emerged that illustrate the holistic impact of bicycling on well-being. Processing speed on a variety of cognitive tasks improved after participating in both acute and short-term bicycling interventions, though accuracy largely remained unchanged. This aligns with research suggesting the positive impact of physical activity on cognitive processes and executive function ([111](#B111)). Across event related potential (ERPs) components, high environmental load (e.g., busy traffic, complex surroundings) appears to enhance early sensory processing (increased N1) while reducing later attentional allocation (reduced P300). In contrast, moderate-intensity or optimal-cadence bicycling may support or enhance cognitive control processes, whereas high-intensity bicycling or fatigue-inducing protocols tend to impair attentional allocation in acute settings. This aligns with an “inverted-U-shaped” relationship between bicycling intensity and cognitive performance, where moderate levels of stimulation optimized neural efficiency while excessive or insufficient intensity may have impaired cognitive outcomes ([88](#B88)). This phenomenon was also observed in behavioral measures ([75](#B75)). Similar to affective well-being results, multi-session studies reported higher proportions of positive outcomes on cognitive well-being, highlighting how consistent bicycling over time can support brain health and cognitive function. Altogether, these findings suggest that the impact of bicycling on neural processing is influenced by environmental complexity, exercise intensity, consistency, and task demands. Cognitive outcomes were overrepresented in this scoping review, appearing in most studies. However, many of these studies were conducted indoors on stationary bicycles, highlighting the need for translational research to better understand how cognition may be impacted from bicycling outdoors. Indeed, Kuklinski et al. ([103](#B103)) noted that only 4% of included outdoor mountain biking studies employed objective neurocognitive measures, highlighting a critical gap for future research. Promising results from Tempest and Reiss ([98](#B98)) validated fNIRS as a solution to understanding brain activity while bicycling outside of the lab and found that bicycling modulates cognitive processing. Additionally, 15 studies using EEG were conducted *during* bicycling, providing evidence that measuring meaningful brain activity during exercise is possible. These findings underscore the importance of conceptualizing cognitive processing as distinct from, yet connected to, psychological and affective responses, as effective cognitive functioning facilitates emotional regulation and supports overall mental health ([110](#B110)). Furthermore, research supports the notion that bicycling interventions that seek to enhance cognitive processing can significantly contribute to both learning outcomes and psychological development among participants across various contexts ([112](#B112)). #### 4.1.5 Additional considerations As noted in the results, almost all included studies did not report on participants’ race or ethnicity. Reporting participant racial and ethnic demographics is critical for understanding how bicycling interventions may differently impact people across diverse backgrounds. Previous research has demonstrated lower rates of bicycling participation amongst racial and ethnic minorities in addition to older adults who own bicycles ([113](#B113)). Individuals who do not currently identify as bicyclists may experience greater health benefits if they adopt bicycling as a regular lifestyle behavior. Similarly, promising results indicate that bicycling programs can benefit women, who typically participate at lower rates than men, especially in areas where cycling mode share is low ([114](#B114)), by improving mood, confidence, and related outcomes (). Future studies should include comparative analyses across gender and explore tailored strategies to address barriers that may differ across groups. Adolescents and older adults were underrepresented in the studies reviewed, yet both groups face critical life transitions that affect well-being. Cycling programs may offer meaningful benefits, including improved focus and well-being in youth () and enhanced cognitive function, mobility, and mental health in older adults ([115](#B115)). Including these populations is essential to understand cycling's benefits across the lifespan, and program design should account for age-specific considerations and barriers, such as safety, motivators, and access to appropriate equipment. Improving access to bicycling and engaging underrepresented populations in research is crucial for understanding how bicycling impacts well-being domains across different groups, while also identifying the associated barriers and facilitators. Community bicycling initiatives and bicycling-related non-profits can play an essential role in expanding access to bicycling and its associated benefits. Clanton et al. () analyzed how local organizations advance community well-being through bicycling and found that they not only support bicycling itself but also can enhance community well-being across multiple domains. This emphasizes the importance of community-based participatory research, connecting researchers and bicycling organizations, to better understand how real-world bicycling programs impact the well-being of individuals and their communities. Currently published program evaluations and studies of these initiatives demonstrate promising results (, [116](#B116)), however, there is a need for additional research on bicycling interventions, examining all domains of well-being and how they interact. Despite the majority of included studies examining cognitive outcomes, many of these were acute, on stationary bikes, and in a lab setting. There is opportunity to translate this research into real-world interventions, especially on the impacts of bicycling interventions on the cognition of older adults, as recent work has highlighted how improving lifestyle factors, like increased physical activity, can improve cognition ([117](#B117)). ### 4.2 Limitations and future directions One methodological limitation of this review arose during our search for relevant studies. The term “cycling” was removed from the key terms list because it connotes biological cycle processes. In addition, specific types of bicycles (e.g., e-bikes, tricycles) were not included among our search terms. However, several articles including these types of bikes underwent the screening process. To refine our search, we added in the MeSH terms when available to improve study identification. In addition, bicycling is often referred to as active travel, aerobic exercise, or physical exercise. To improve future searches, authors should ensure appropriate indexing and clearly define the type of physical activity implemented (i.e., bicycling, stationary bike) to improve discoverability and accessibility of the literature. While most studies employed acute interventions using stationary bicycles within a lab setting with adult age groups, future research has exciting opportunities to expand understanding of how bicycling interventions impact domains of well-being. Moving beyond lab settings allows researchers opportunities to examine the potential long-term benefits in ecological contexts, such as outdoor bicycling and active transportation, capturing sustained, real-world benefits beyond acute exercise. Finally, a shift toward naturalistic settings and diverse population sampling, including youth, older adults, racial and ethnic minorities, and individuals with differing abilities or clinical conditions, will strengthen knowledge of bicycling's therapeutic potential across the lifespan. Results from studies engaging clinical populations, such as participants with neurological disorders or metabolic disease, highlight bicycling as a promising therapeutic intervention, however, additional research should replicate and build upon current findings. While many of the included studies assessed the cognitive domain of well-being, fewer studies examined psychological, social, and affective outcomes on well-being. Incorporating objective measures alongside self-report instruments targeting these less studied domains would enhance comparability across studies and reduce potential bias, ultimately building a more robust evidence base for bicycling interventions. Considering the post-pandemic shifts in bicycling patterns and community perceptions ([118](#B118), [119](#B119)), longitudinal research examining sustained engagement and long-term outcomes would be particularly valuable. ### 4.3 Conclusions Based on this scoping review, bicycling interventions demonstrate significant promise for promoting well-being across psychological, affective, social, and cognitive domains. While a majority of studies emphasized cognitive effects within an indoor environment, a growing number of studies explored psychological, social, and affective outcomes. Overall, though fewer studies were conducted in outdoor settings, a high percentage of studies reported positive effects regardless of setting. Evidence suggests that bicycling's benefits extend beyond physical health (, , , ) to encompass emotional regulation, stress reduction, social connectedness, and cognitive enhancement. However, realizing this potential requires intentional program design, equitable infrastructure development, and continued research across populations in real-world settings. By strategically implementing bicycling interventions within educational, clinical, and community contexts, we can leverage this accessible, low-cost activity to address pressing public health challenges while fostering individual and community well-being. As bicycling continues to gain recognition as both a practical mode of transportation and a therapeutic intervention, stakeholders across sectors have an opportunity to harness its holistic benefits to promote health equity and enhance quality of life across diverse populations. ## Statements ### Data availability statement The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author. ### Author contributions LS: Conceptualization, Data curation, Investigation, Methodology, Project administration, Visualization, Writing – original draft, Writing – review & editing. BR: Conceptualization, Data curation, Investigation, Writing – original draft, Writing – review & editing. EW: Conceptualization, Funding acquisition, Methodology, Supervision, Writing – original draft, Writing – review & editing. SetW: Conceptualization, Writing – review & editing. SeaW: Conceptualization, Writing – review & editing. CB: Conceptualization, Funding acquisition, Methodology, Supervision, Writing – original draft, Writing – review & editing. ### Funding The author(s) declared that financial support was received for this work and/or its publication. This work was supported in part by a REI Cooperative Action Fund grant (2024). ### Conflict of interest LS and EW are employees of Outride, a nonprofit organization funded in part by Specialized Bicycle Components. This relationship may be perceived as a competing interest with respect to research on bicycling. The authors affirm that Specialized and all other funders had no role in the study design; data collection, analysis, or interpretation; manuscript preparation; or the decision to submit the manuscript for publication. All other authors (BR, SetW, SeaW, CB) declare that they have no commercial or financial relationships that could be construed as a potential conflict of interest. The author SW declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision. ### Generative AI statement The author(s) declared that generative AI was not used in the creation of this manuscript. Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us. ### Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. 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Living* 8:1807791. doi: [10.3389/fspor.2026.1807791](http://dx.doi.org/10.3389/fspor.2026.1807791) Received 10 February 2026 Revised 24 April 2026 Accepted 29 April 2026 Published 19 May 2026 Volume 8 - 2026 Edited by [Luis Felipe Reynoso Sánchez](https://loop.frontiersin.org/people/1108568/overview), Autonomous University of Occident, Mexico Reviewed by [Oleksandr P. Romanchuk](https://loop.frontiersin.org/people/2051596/overview), Lesya Ukrainka Volyn National University, Ukraine [James A. Green](https://loop.frontiersin.org/people/478604/overview), University of Limerick, Ireland Updates Copyright © 2026 Schuck, Reisman, Walker, Wiafe, Wilson and Brown. This is an open-access article distributed under the terms of the [Creative Commons Attribution License (CC BY)](https://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. ******* Correspondence:** Lauren Schuck [laurens@outridebike.org](mailto:laurens@outridebike.org) **†** ORCID Lauren Schuck [orcid.org/0009-0001-3445-7530](https://orcid.org/0009-0001-3445-7530) Brandie Reisman [orcid.org/0009-0001-2902-2006](https://orcid.org/0009-0001-2902-2006) Esther Walker [orcid.org/0009-0001-6982-3641](https://orcid.org/0009-0001-6982-3641) Seth Wiafe [orcid.org/0000-0001-6085-9405](https://orcid.org/0000-0001-6085-9405) Sean M. Wilson [orcid.org/0000-0003-4404-1173](https://orcid.org/0000-0003-4404-1173) Cian L. Brown [orcid.org/0000-0003-0837-0295](https://orcid.org/0000-0003-0837-0295) Disclaimer All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.