How Eating Habits Relate to Well-Being in Japanese Male College Handball Players

This study employed a cross-sectional design. Data were obtained through a web-based questionnaire and a 3-day dietary record to examine associations between eating behaviors, nutrient intake, and subjective well-being in collegiate handball players.

The participant selection process is illustrated in Figure 1.

Eligible participants were male collegiate handball players enrolled in universities registered with the Kanto Student Handball Federation in the greater Tokyo metropolitan area during the 2023 league season. Recruitment was initiated through formal invitations sent to all university handball clubs via the Federation office, supplemented by announcements at league competition venues. A total of 168 athletes consented to participate and completed a web-based questionnaire. All responses were complete, with no missing data. Of these, 97 players subsequently undertook a dietary survey. After exclusion of incomplete or missing dietary records, the final analytic cohort comprised 64 players who completed both the questionnaire and dietary components. Anthropometric data, including height and body weight, were self-reported through the questionnaire. The demographic characteristics and distribution by competitive level of the final 64 participants are presented in Table 1.

Participants represented a wide spectrum of competitive levels. Division 1 teams consisted of elite-level athletes, including several national team members, whereas lower-division teams primarily competed at a recreational level. Heterogeneity in competitive level was observed both across and within divisions. Variability also extended to institutional support structures: although some top-ranked teams employed athletic trainers, no participating team had access to a dedicated sports dietitian. The study protocol was reviewed and approved by the Research Ethics Committee for Human Participants at Wayo Women’s University (Approval No. 2329).

Dietary intake data were collected over 3 consecutive days, representing the participants’ typical schedule, including two training days and one rest day (in any order). A hybrid dietary record method was employed, combining estimated portion size documentation with photographic recording via a smartphone application. Participants were instructed to photograph all consumed food and beverages (excluding water) throughout the day, from waking to bedtime. For commercially prepared items and meals consumed outside the home, participants were additionally asked to photograph nutrition labels or menu descriptions that clearly displayed product names or ingredients. To complement the photographic data, participants recorded the time and location of consumption, dish and ingredient names, and estimated quantities. Additional details were required, such as whether dressing contained oil and the fat content of any milk consumed (e.g., low-fat or fat-free). Nutritional analyses were conducted by an experienced sports dietitian, who reviewed all dietary records and photographs to identify food items and estimate portion sizes. The energy and nutrient items assessed were selected based on those identified as potential challenges in athlete nutrition by the International Olympic Committee (IOC) [30], the American College of Sports Medicine (ACSM) [31], and the International Society of Sports Nutrition (ISSN) [32,33,34], with recommended values for athletes adopted from ACSM guidelines [31]. Energy and nutrient intake were calculated using Calorie Make (Version 1.0.10) and Eiyo Navi (Version 5.3.0) software (Toyo System Science Co., Ltd.), with nutrient composition data drawn from the Standard Tables of Food Composition in Japan, 2020 (8th Revised Edition). When submitted records were unclear or incomplete, participants were contacted directly for clarification.

The questionnaire was administered online through a secure web-based platform accessible via QR code. Items addressed dietary lifestyle, eating habits, training frequency and duration, weekly Rating of Perceived Exertion (wRPE), injury history, and Subjective Health Complaints (SHC). Questions on eating habits and SHC were adapted from instruments used in the authors’ previous studies [35,36] and selected items from the National Health and Nutrition Survey in Japan, with explanatory text provided for each item. For example, in questions concerning snack consumption, specific food examples were provided to facilitate accurate responses. To enhance clarity, “nutrient-dense snacks” were defined as foods beneficial for recovery and rich in carbohydrates, protein, vitamins, and minerals (e.g., rice balls, yogurt, fruit, milk), whereas “unhealthy snacks” were defined as foods high in fat and sugar but low in micronutrients (e.g., potato chips, chocolate, sugar-sweetened beverages). These definitions were made available to the participants within the questionnaire. The Subjective Health Complaints (SHC) checklist used in this study was developed by the authors, drawing on prior research in Japanese collegiate athletes [36,37]. It consists of 27 items reflecting the most frequently reported health complaints in this population, encompassing musculoskeletal pain, gastrointestinal issues, and general fatigue. For each item, participants indicated whether they had experienced the symptom (“Yes”) or not (“No”) within the past two weeks, and the total number of affirmative responses was used as the SHC score for analysis.

To ensure data completeness, the survey interface was designed to prevent progression until all items on a page were answered.

The Hooper Index is a validated subjective tool designed to quantify perceived fatigue and general well-being in athletes [4]. It comprises four dimensions: sleep quality, muscle soreness, stress levels, and overall fatigue, each rated on a 7-point Likert scale. The cumulative Hooper Score ranges from 4 (reflecting optimal well-being) to 28 (reflecting poor well-being). This index is valued for its ability to detect meaningful variations in well-being that may not be identified by objective physiological markers [8]. Its high reliability, sensitivity to fatigue, and methodological simplicity have contributed to its widespread adoption in sports science research [5,6,7,38,39].

To quantify internal training load, the wRPE was employed. Following the final training session of each week, participants were prompted to rate the overall intensity of their weekly training using the Borg CR-10 scale [40], where 0 represents complete rest and 10 denotes maximal effort. Daily RPE tracking was not feasible because athletes trained at various locations; however, this retrospective weekly approach was deemed appropriate based on prior validation studies demonstrating a strong correlation (r = 0.87) between weekly recalled RPE and the mean of daily RPE values [41].

Normality of data distribution was assessed using the Kolmogorov–Smirnov and Shapiro–Wilk tests. Descriptive statistics are presented as mean ± standard deviation (SD). Differences between competitive divisions were assessed using one-way analysis of variance (ANOVA), followed, where significant main effects were observed, by Tukey’s HSD post hoc tests to identify pairwise group differences. Spearman’s rank correlation coefficients were calculated to examine associations between Hooper Score, SHC frequency, and energy/nutrient intakes. Partial correlation analyses were subsequently conducted, adjusting for potential confounders including competitive level, living environment, primary meal preparer, training frequency, duration, intensity, and receipt of dietary guidance. Multiple regression analysis was then performed with Hooper Score as the dependent variable.

All analyses were conducted using the Statistical Package for the Social Sciences (SPSS) for Windows, Version 28.0. Statistical significance was set at p < 0.05. Additionally, a post hoc power analysis was performed using G*Power 3.1 software [42] to evaluate the robustness of the regression model. The analysis, performed with an observed effect size of f² = 0.976, an alpha level of 0.05, a total sample size of 64, and 6 predictors, demonstrated exceptionally high statistical power.

The central finding of this study is that readily measurable, routine eating behaviors emerged as independent predictors of the Hooper Score, a validated index of subjective well-being. This observation is contextualized by the broader finding that participants’ energy and multiple nutrient intakes were consistently below recommended thresholds. Importantly, partial correlation analyses adjusted for potential confounders demonstrated that inadequate intake of both energy and a wide spectrum of nutrients was significantly associated not only with higher Hooper Scores, indicating poorer well-being, but also with an increased number of SHC. The observed associations were not confined to any single nutrient but encompassed a wide range, extending from macronutrients such as energy and protein to micronutrients essential for condition management, including iron and B vitamins. This pattern underscores that overall dietary quality, rather than isolated nutrient intake, is intrinsically linked to the maintenance of athletes’ physical and psychological condition. Consistent with this, a recent scoping review of athlete-focused studies concluded that overall dietary quality contributes to enhanced health, improved performance, and accelerated recovery [43]. The present study extends this evidence by highlighting a previously underexplored dimension: the association between comprehensive dietary quality and subjective well-being. Division level was also associated with well-being in our cohort, with athletes in lower divisions reporting poorer Hooper Scores. However, because this factor was statistically controlled for in the primary regression models, the observed associations between specific eating behaviors and the Hooper Score can be interpreted as independent of competitive level. These findings align with the EA framework introduced as the theoretical context for this study. Poor dietary quality, characterized by inadequate intakes of both macro- and micronutrients, likely contributes to a state of low energy availability, which is known to impair multiple physiological functions and would plausibly manifest as poorer subjective well-being, as reflected in the Hooper Score. These nutritional challenges observed in Japanese collegiate players appear to be consistent with findings from other regions. For instance, studies conducted among both adolescent [17] and elite [44] handball players have similarly reported inadequate energy and carbohydrate intakes, which may compromise athletes’ well-being and recovery. Taken together, these findings suggest that the association between suboptimal dietary patterns and poor subjective condition may represent a widespread issue in handball, regardless of geographical and cultural context.

Multiple regression analysis identified several eating behaviors, considered concrete indicators of overall dietary quality, as independent predictors of the Hooper Score. One such factor was meal regularity, where consistent dinner timing was associated with lower Hooper Scores, reflecting better subjective well-being. This finding highlights the critical role of meal timing in regulating circadian rhythms and maintaining physiological and psychological stability [45]. In contrast, irregular dinner timing may disrupt digestion during sleep and interfere with the secretion of sleep-related hormones, thereby compromising sleep quality—an essential domain of the Hooper Index [46]. Furthermore, late or inconsistent evening meals can suppress morning appetite and increase the likelihood of breakfast omission, compounding the adverse effects of irregular meal patterns on athletes’ overall condition.

Snack quality also emerged as a significant behavioral predictor, with a higher frequency of nutrient-dense snack consumption and reduced intake of unhealthy snacks both associated with lower Hooper Scores. Beyond their immediate nutritional contribution, snack choices can serve as behavioral indicators of athletes’ nutritional literacy and self-regulation [47]. Collegiate athletes, who often manage their diets independently amid fluctuating living conditions, time constraints, and financial pressures, are particularly vulnerable to reliance on readily available, nutrient-poor snacks [48]. Substituting such snacks for nutrient-dense options during the critical post-training recovery period may delay glycogen resynthesis and impair muscle repair, effects that can manifest as greater fatigue and muscle soreness in subsequent Hooper assessments [34].

Adequate protein intake (g/kg body weight) was likewise significantly associated with improved Hooper Scores. While this does not necessarily imply a direct causal effect of protein on subjective well-being, it suggests that protein intake may function as a proxy for broader dietary patterns that incorporate main dishes such as meat, fish, and eggs. These foods are rich sources of essential micronutrients, including iron and B vitamins, both of which were negatively correlated with Hooper Scores in this study [49]. Therefore, sufficient protein intake may enhance multiple dimensions of subjective well-being by facilitating muscle repair and reducing soreness [33], supporting energy metabolism [50], and preventing anemia-related fatigue [51]. This finding underscores the potential value of simple screening questions—such as whether the previous day’s meals included protein-rich main dishes—as practical indicators of dietary quality and subjective well-being, thereby reducing reliance on detailed nutrient calculations in applied settings.

Traditionally, the Hooper Score has been employed in sports science primarily to monitor athlete responses to training load and to detect associated changes in condition [5,6,7,32,33]. The present findings extend this utility by demonstrating that the Hooper Score is also influenced by daily eating behaviors. This observation highlights a new application of the Hooper Score: as a simple, practical screening tool for the early identification of nutritional and behavioral challenges. For example, when an athlete exhibits a deterioration in Hooper Score, practitioners should consider not only training-related factors but also modifiable eating behaviors identified in this study, such as dinner regularity and snack quality. Such an approach could facilitate the identification of underlying contributors to poor condition and inform timely, targeted interventions. Integrating the Hooper Score with key behavior indicators may therefore enhance the delivery of individualized nutritional support in fast-paced sporting environments.

Several limitations must be acknowledged. First, the cross-sectional design of this study precludes causal inference regarding the relationship between eating behaviors and subjective well-being. It remains unclear whether favorable dietary practices improve well-being, or conversely, whether athletes with greater well-being are more likely to adopt healthier behaviors. Longitudinal and dietary intervention studies are required to clarify these causal pathways.

Second, the study population was restricted to male collegiate handball players within a single geographical region, which limits the generalizability of the findings. Associations between eating behaviors and the Hooper Score may differ across competitive levels, sport types, sexes, and age groups. Moreover, the participants may have been more health-conscious than the broader athlete population, potentially introducing selection bias. Future research should therefore include more diverse cohorts to enhance external validity.

Third, the study lacked detailed characterization of participants. Although differences in Hooper Scores were observed across competitive levels, granular information on sporting history (e.g., years of training experience, playing positions) and contextual factors (e.g., training environment, lifestyle, or access to professional support) was not collected. As a result, the mechanisms underlying inter-division differences could not be clarified and remain an important avenue for future research.

Fourth, anthropometric data, including height and body weight, were self-reported. While direct measurement would have been preferable, the logistical challenges of coordinating assessments for more than 60 athletes across multiple universities made this approach unfeasible. Reliance on self-reported data may have introduced reporting bias, and future studies should aim to incorporate standardized direct measurements wherever possible.

Finally, the study relied exclusively on subjective assessments and did not address the physiological mechanisms underlying the observed associations. Future investigations should therefore complement global subjective indicators such as the Hooper Index with more specific assessments of its individual components, as well as objective physiological markers (e.g., blood biomarkers). Such a multifaceted approach would help to elucidate how specific eating behaviors influence stress hormones, inflammatory pathways, and other biological mediators, and how these mechanisms translate into changes in subjective well-being.