The study is divided into two phases: pilot and formal study. The overall aim of the study is to
examine the relationship between the use of the LMH – a web-based application to honor the deceased
person’s memory – and symptoms of Prolonged Grief Disorder (PGD). The LMH is based on the idea
that maintaining a continuing bond through posting and interacting with an online website (Bailey et al.,
2015; Irwin, 2015; Kasket, 2012) in which the bereaved person honors the deceased relative and may
share photographs, music and other memories in honor of the deceased, is therapeutic (i.e., associated
with reduction in intensity of symptoms of PGD). However, we hypothesize that the relationship
between amount of time spent visiting the LMH and reduction of PGD symptom severity will be
curvilinear; that is, too little time spent will not provide much comfort and too much time may foster a
lack of investment in reengaging with the living. Furthermore, around 1 out of 10 bereaved could
develop PGD and are at heightened risk of STBs, and we hypothesize that the linguistic and behavioral
data collected from LMH can be used to predict their future suicide attempt risk.
Primary aim: The main aim is to evaluate and determine how usage of the LMH relates to bereavement
Hypothesis: We hypothesize that the relationship between time spent visiting the LMH and PG-13/BCS
scores will be curvilinear such that there is a middle-range that is most therapeutic and either extremes
of too little time visiting the LMH or too much time visiting the LMH will not be associated with lower
Secondary aim: The pilot study will help researchers to identify the unexpected risks of using the Living
Memory Home and address them for the future deployment.
Tertiary aim: We aim to develop and test an automated suicide attempt risk detection Machine Learning
model using the linguistic and behavioral data collected from LMH.
Hypothesis: Linguistic and behavioral data from these activities will support natural language
processing for classifying suicide attempt risk and the development of machine learning models to
predict suicide attempt risk among bereaved online users.