Background

One of the most widely used coaching models is Supportive Accountability (SA) which aims to provide intervention users with clear expectations for intervention use, regular monitoring, and a sense that coaches are trustworthy, benevolent, and have domain expertise. However, few measures exist to study the role of the SA model on coached digital interventions. We developed the Supportive Accountability Inventory (SAI) and evaluated the underlying factor structure and psychometric properties of this brief self-report measure.

Method

Using data from a two-arm randomized trial of a remote intervention for major depressive disorder (telephone CBT [tCBT] or a stepped care model of web-based CBT [iCBT] and tCBT), we conducted an Exploratory Factor Analysis on the SAI item pool and explored the final SAI's relationship to iCBT engagement as well as to depression outcomes. Participants in our analyses (n = 52) included those randomized to a receive iCBT, but were not stepped up to tCBT due to insufficient response to iCBT, had not remitted prior to the 10-week assessment point, and completed the pool of 8 potential SAI items.

Results

The best fitting EFA model included only 6 items from the original pool of 8 and contained two factors: Monitoring and Expectation. Final model fit was mixed, but acceptable (χ²(4) = 5.24, p = 0.26; RMSR = 0.03; RMSEA = 0.091; TLI = 0.967). Internal consistency was acceptable at α = 0.68. The SAI demonstrated good convergent and divergent validity. The SAI at the 10-week/mid-treatment mark was significantly associated with the number of days of iCBT use (r = 0.29, p = .037), but, contrary to expectations, was not predictive of either PHQ-9 scores (F(2,46) = 0.14, p = .89) or QIDS-C scores (F(2,46) = 0.84, p = .44) at post-treatment.

Conclusion

The SAI is a brief measure of the SA framework constructs. Continued development to improve the SAI and expand the constructs it assesses is necessary, but the SAI represents the first step towards a measure of a coaching protocol that can support both coached digital mental health intervention adherence and improved outcomes.

中文翻译：

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支持性责任清单：指导性数字干预中支持性责任测量的心理测量特性

背景

最广泛使用的教练模型之一是支持责任 (SA)，旨在为干预用户提供对干预使用的明确期望、定期监控以及教练值得信赖、仁慈且具有领域专业知识的感觉。然而，很少有措施可以研究 SA 模型在指导数字干预中的作用。我们开发了支持性责任清单 (SAI)，并评估了这个简短的自我报告测量的潜在因素结构和心理测量特性。

方法

使用来自对重度抑郁症的远程干预（电话 CBT [tCBT] 或基于网络的 CBT [iCBT] 和 tCBT 的阶梯式护理模型）的双臂随机试验的数据，我们对 SAI 项目进行了探索性因素分析汇集并探讨了最终 SAI 与 iCBT 参与以及抑郁结果的关系。我们分析的参与者 ( n  = 52) 包括那些随机接受 iCBT，但由于对 iCBT 的反应不足而没有升级到 tCBT，在 10 周评估点之前没有汇款，并完成了 8 个潜在的池SAI 项目。

结果

最佳拟合 EFA 模型仅包括原始库中的 8 个项目，并包含两个因素：监控和期望。最终模型拟合是混合的，但可以接受（χ ² (4) = 5.24，p = 0.26；RMSR = 0.03；RMSEA = 0.091；TLI = 0.967）。在 α = 0.68 时，内部一致性是可以接受的。SAI 表现出良好的收敛效度和发散效度。10 周/治疗中期的 SAI 与 iCBT 的使用天数显着相关（r  = 0.29，p  = .037），但与预期相反，不能预测任何 PHQ-9 评分（F ( 2,46 ) = 0.14, p  = .89) 或 QIDS-C 分数 ( F ( 2,46) = 0.84, p  = .44) 在治疗后。

结论

SAI 是 SA 框架结构的简要度量。继续开发以改进 SAI 并扩展其评估的结构是必要的，但 SAI 代表了衡量指导协议的第一步，该协议可以支持指导数字心理健康干预的依从性和改进的结果。