Prior guided multi view clustering characterizes smartphone use behavioral profiles from heterogeneous questionnaire data in adults aged 60 to 69 years

Questionnaire surveys often contain heterogeneous modules whose roles in behavioral profiling differ. This study evaluated a prior-guided multi-view clustering procedure using a community survey of smartphone use among 428 adults aged 60–69 years in Chongqing, China. The clustering inputs were organized into a 26-item smartphone-use behavior view, a demographic and sociological view, and a lifestyle and health-habit view; smartphone-use context variables were excluded from cluster construction and examined only after clustering. Candidate solutions with two to six clusters were examined using internal validity and repeated 80% subsampling. The two-cluster solution showed the strongest internal separation. The three-cluster solution had the highest mean subsampling ARI (0.940), retained three nontrivial groups, and was selected as the primary exploratory representation; the five-cluster solution showed similarly high stability but contained a small group of 35 respondents. The three profiles contained 219, 112, and 97 respondents. Conditional on the prespecified prior \(\varvec{\pi }=(0.70,0.15,0.15)^\top \) , the estimated view contribution weights were 0.8054, 0.1060, and 0.0886. Behavior-view-only spectral clustering was highly concordant with the full solution (ARI \(=0.908\) ; profile correlation \(=0.999\) ), showing that the behavior view supplied the core partition while the auxiliary views provided limited, explicitly constrained contextual refinement rather than a qualitatively different structure. Variables used in clustering are reported as profile characteristics rather than independent validation. Smartphone-use duration, timing, location, and function variables that were excluded from clustering showed exploratory post hoc associations with profile membership. The findings support transparent prior-guided integration of heterogeneous questionnaire modules while emphasizing dependence on the specified prior, the dominant behavioral view, data-quality decisions, and the exploratory nature of the profiles.