Less is More: Leveraging Digital Behavioral Markers for Real-Time Identification of Loneliness in Resource-Limited Settings

Abstract

The resource-constrained nature of developing regions and also the positive impact of early intervention show the need for a minimal and faster system to identify loneliness. However, existing pervasive device-based promising systems’ requirement to run in the background for prolonged periods can be costly in terms of resources and also may not be effective for early intervention. Thus, we conducted a study (N = 105) in Bangladesh by developing a minimal system that can retrieve the past 7 days’ app usage behavioral data within a second (Mean = 0.31 s, SD = 1.1 s). Leveraging only the instantly accessed data, we developed models through features selected by 3 different methods and exploration of 14 diverse machine learning (ML) algorithms including 8-tree-based algorithms. We found that the Gaussian Naïve Bayes model, developed by filter method Information Gain selected features, can identify 90.7% of lonely participants correctly with an F1 score of 82.4%. Through SHapley Additive exPlanations (SHAP), we explained the ML models showing how the features impacted the model’s outcome. Due to being minimal, faster, and explainable, our system can play a role in resource-limited settings for early identification of loneliness which may create a positive impact by mitigating the loneliness rate.