Predicting Outcomes from First Session Interactions in Remote Cognitive BehavioUral Therapy for Severe Health Anxiety

This is the SPSS 24 dataset and the SPSS syntax used for the analysis. The syntax files provides guidance notes for each stage of analysis.<br>Background. Remote therapy is growing following the COVID-19 epidemic and there are calls to better understand therapeutic processes. Remotely deliver Cognitive Behavioral Therapy (rCBT) for severe health anxiety (SHA) is evidenced as clinically effective, but many who seek help do not benefit. Key interaction-types at the first CBT session predict outcomes in other anxiety disorders, but it is unclear what early interaction-types may be helpful (or hindering) in CBT for SHA. Method. Two trained raters blind to outcome used audio-visual recordings and transcripts to rate each interaction in the first session of CBT for SHA amongst 42 high service utilizers with multimorbidity and SHA. The Consultation Interaction Coding Scheme (CICS) was used to categorize and rate each interaction on the basis of patient activation: a patient’s confidence and perceived ability to manage their own health. Sessions were also assessed using the observer-rated working alliance inventory (WAI-O). Multilevel modelling was used to estimate the predictive validity of CICS and WAI-O scores on health anxiety over 12-month follow-up. Results. Client who gave more positive evaluations of themselves, therapy, or their therapist at the first session showed great reductions in health anxiety across 12-month follow up. Clients more actively engaged in session structuring interactions at the first session also showed greater outcome improvements. Conversely, larger proportions of session 1 spent describing problems predicted poorer outcomes. Models controlled for working alliance and baseline severity. Conclusions. Clients’ involvement in therapeutically active interactions at the first session could contribute to improved outcomes in CBT for SHA.<br>

本数据集为SPSS 24格式数据集，附带分析所用的SPSS语法文件，该语法文件为各分析阶段提供了操作指引说明。
研究背景：新冠疫情后，远程治疗应用愈发广泛，学界呼吁进一步厘清治疗过程的内在机制。针对重度健康焦虑（Severe Health Anxiety, SHA）的远程认知行为疗法（Remote Cognitive Behavioral Therapy, rCBT）已被证实具有临床疗效，但众多寻求治疗的患者并未从中获益。在其他焦虑障碍的治疗中，首次认知行为治疗（CBT）会话中的关键互动类型可预测治疗结局，但目前尚不明确，针对SHA的CBT中，哪些早期互动类型会起到促进或阻碍治疗的作用。
研究方法：由2名对治疗结局设盲的经过培训的编码员，针对42名共病且罹患重度健康焦虑的高频服务使用者，借助首次SHA-CBT会话的音视频记录及转写文本，对每一段互动进行编码评分。本研究采用咨询互动编码方案（Consultation Interaction Coding Scheme, CICS），以患者激活度——即患者对自身健康管理的信心与感知能力——为依据，对所有互动进行分类与评分。同时，采用观察者版工作联盟量表（Observer-rated Working Alliance Inventory, WAI-O）对会话进行评估。本研究采用多层建模方法，估算CICS与WAI-O得分在12个月随访期内对健康焦虑的预测效度。
研究结果：首次会话中对自身、治疗或治疗师给出更多积极评价的患者，在12个月随访期内的健康焦虑水平降幅更为显著。首次会话中更积极参与会话构建互动的患者，其治疗结局改善程度也更高。反之，首次会话中花费更多时间描述问题的患者，其治疗结局则相对更差。所有模型均对工作联盟情况与基线严重程度进行了控制。
研究结论：患者在首次会话中参与治疗性积极互动，或可改善针对SHA的CBT治疗结局。