Smartphone-based devices are increasingly recognized to assess disease symptoms in daily life (e.g. ecological momentary assessment, EMA). Despite this development in digital psychiatry, clinical trials are mainly based on point assessments of psychopathology. This study investigated expectable increases in statistical power by intense assessment in randomized controlled trials (RCTs). A simulation study, based on three scenarios and several empirical data sets, estimated power gains of two- or fivefold pre-post-assessment. For each condition, data sets of various effect sizes were generated, and AN(C)OVAs were applied to the sample of interest (N = 50–N = 200). Power increases ranged from 6% to 92%, with higher gains in more underpowered scenarios and with higher number of repeated assessments. ANCOVA profited from a more precise estimation of the baseline covariate, resulting in additional gains in statistical power. Fivefold pre-post EMA resulted in highest absolute statistical power and clearly outperformed traditional questionnaire assessments. For example, ANCOVA of automatized PHQ-9 questionnaire data resulted in absolute power of 55 (for N = 200 and d = 0.3). Fivefold EMA, however, resulted in power of 88.9. Non-parametric and multi-level analyses resulted in comparable outcomes. Besides providing psychological treatment, digital mental health can help optimizing sensitivity in RCT-based research. Intense assessment appears advisable whenever psychopathology needs to be assessed with high precision at pre- and post-assessment (e.g. small sample sizes, small treatment effects, or when applying optimization problems like machine learning). First empiric studies are promising, but more evidence is needed. Simulations for various effects and a short guide for popular power software are provided for study planning.

中文翻译：

---

随机对照试验中激烈评估对统计功效的影响：抑郁症的模拟研究

基于智能手机的设备越来越多地用于评估日常生活中的疾病症状（例如，生态瞬时评估，EMA）。尽管数字精神病学有所发展，但临床试验仍主要基于精神病理学的点评估。这项研究通过对随机对照试验（RCT）进行严格评估，调查了统计能力的预期增长。基于三个方案和几个经验数据集的仿真研究估计了预评估后两倍或五倍的功率增益。对于每种条件，都会生成各种效应大小的数据集，并将AN（C）OVA应用于目标样本（N = 50–N = 200）。功率增加从6％到92％不等，在功率不足的情况下增益更高，重复评估的次数也更多。ANCOVA得益于对基线协变量的更精确估计，从而使统计功效进一步提高。事前EMA的五倍获得了最高的绝对统计能力，并且明显优于传统的问卷调查评估。例如，自动PHQ-9问卷数据的ANCOVA得出的绝对功效为55（对于N = 200和d = 0.3）。但是，五倍EMA的功效为88.9。非参数和多层次分析得出可比较的结果。除了提供心理治疗外，数字心理健康还可以帮助优化基于RCT的研究的敏感性。每当需要在评估前后对精神病理学进行高精度评估（例如小样本量，小的治疗效果，或应用优化问题（例如机器学习）时。最初的经验研究是有希望的，但是还需要更多的证据。提供了各种效果的仿真以及流行电源软件的简短指南，用于研究计划。