Supplementary Table 1. Detailed characteristics of the included studies by analytical category

This dataset summarizes the detailed characteristics of studies included in a review on training load monitoring, recovery, fatigue, readiness, periodization, predictive modelling, and decision-support systems in handball and comparable team sports. The table organizes the literature into six analytical categories: internal and external load monitoring; microcycle structure, competitive congestion, and weekly periodization; well-being, recovery, fatigue, and readiness; predictive models, machine learning, and decision-support systems; handball-specific studies; and complementary evidence from comparable team sports. For each study, the dataset records the year of publication, authors, title, research objective, study design, methodology, main results, and conclusions. Overall, the table shows that contemporary team-sport research increasingly supports integrated, multivariable approaches to training-load management. Across the included studies, internal load measures, external load indicators, wellness questionnaires, recovery markers, neuromuscular assessments, contextual factors, and predictive models are commonly combined to better understand athlete responses and support individualized training decisions. The evidence suggests that training load and recovery responses vary according to sport, playing position, player role, competition time, microcycle structure, match congestion, and season phase. In handball-specific studies, particular emphasis is placed on the need to individualize monitoring and recovery strategies according to positional demands, match exposure, accumulated workload, and congestion. The dataset can be used to support evidence mapping, literature synthesis, systematic review coding, and the development of multicomponent models for training planning and load regulation in team sports.本数据集汇总了一项综述所纳入的全部研究的详细特征，该综述聚焦于手球（handball）及同类型团体项目中的训练负荷（training load）监测、恢复（recovery）、疲劳（fatigue）、竞技准备（readiness）、周期化训练（periodization）、预测建模（predictive modelling）与决策支持系统（decision-support systems）相关研究。该数据集配套的表格将相关文献划分为六大分析类别：内部负荷（internal load）与外部负荷（external load）监测；小周期训练（microcycle）结构、赛事密集度（competitive congestion）与周度周期化训练；健康状态、恢复、疲劳与竞技准备；预测模型、机器学习（machine learning）与决策支持系统；手球专项研究；以及同类型团体项目的补充佐证研究。 针对每一项纳入研究，本数据集均记录了其发表年份、作者、论文标题、研究目标、研究设计、研究方法、主要结果与结论。整体而言，该表格显示，当代团体项目运动研究愈发支持采用整合式多变量方法开展训练负荷管理。在本次纳入的所有研究中，研究者通常会结合内部负荷测量指标、外部负荷表征指标、健康问卷（wellness questionnaires）、恢复标志物（recovery markers）、神经肌肉评估（neuromuscular assessments）、情境因素（contextual factors）以及预测模型，以更深入地解析运动员的机体反应，并辅助制定个性化训练决策。 现有研究证据表明，训练负荷与恢复反应会因运动项目、场上位置、球员角色、赛事时段、小周期训练结构、赛事密集度以及赛季阶段的不同而存在差异。在手球专项研究中，相关文献特别强调，需根据场上位置的需求、比赛出场暴露量（match exposure）、累积训练负荷（accumulated workload）以及赛事密集度，定制个性化的监测与恢复策略。本数据集可用于辅助开展证据图谱绘制（evidence mapping）、文献综合分析（literature synthesis）、系统综述编码（systematic review coding），以及开发用于团体项目训练规划与负荷调控的多组分模型（multicomponent models）。