Smartphone Behavior Based Electronical Scale Validity Assessment Framework
In the study, we developed a smartphone-based electronical scale validity assessment framework. 374 college students are recruited to fill in Beck Depression Inventory. A total of 544 filling of scales are collected, which may be filled accordingly or concealed. Via an electronical scale based WeChat applet and backend application, temporal and spatial behavioral data of subjects during the scale-filling process are collected. We established an assessment model of the validity of the scale-filling based on the behavior data with machine learning approaches. The result shows that smartphone behavior has significant features in the dimension of time and space under different motivations. The framework achieves an valuable assessment of the effectiveness of the scale, whose key indicators such as accuracy, sensitivity and precision are over 80% under multiple dimension behavior data classification. The framework has a good application prospect in the field of psychological screening.
KeywordsValidity assessment Smartphone WeChat applet Behavior data
This work was supported in part by the National Natural Science Foundation of China [Grant No. 61632014, No. 61627808, No. 61210010], in part by the National Basic Research Program of China (973 Program) under Grant 2014CB744600, in part by the Program of Beijing Municipal Science & Technology Commission under Grant Z171100000117005.
- 2.Statistical Report on Internet Development in China (2018)Google Scholar
- 12.Nieuwenhuijsen, K., De Boer, A.G.E.M., Verbeek, J.H.A.M., Blonk, R.W.B., Van Dijk, F.J.H.: The depression anxiety stress scales (DASS): detecting anxiety disorder and depression in employees absent from work because of mental health problems. Occup. Environ. Med. 60(Suppl 1), i77–i82 (2003)CrossRefGoogle Scholar
- 18.Zhang, B., Srihari, S.N.: Fast k-nearest neighbor classification using cluster based trees. IEEE Trans. Pattern Anal. Mach. Intell. 26(4), 525528 (2004)Google Scholar
- 20.Yu, X.-G., Yu, X.-P.: The research on an adaptive k-nearest neighbors classifier. In: 2006 International Conference on Machine Learning and Cybernetics, pp. 1241–1246. IEEE (2006)Google Scholar