Use of Passive Sensing in Psychotherapy Studies in Late Life: A Pilot Example, Opportunities and Challenges

Improved outcomes for depressed elder abuse victims with video-delivered psychotherapy during COVID-19

OBJECTIVE

There is a lack of evidence-based scalable therapies for elder abuse victims, with no current remotely delivered tailored psychotherapy. The purpose of this manuscript is to (a) examine the effectiveness of a brief therapy for depression for elder abuse victims, and (b) to compare remote intervention delivery via phone or video to the traditional in-person delivery.

METHOD

PROTECT, Providing Options to Elderly Clients Together, is a brief therapy developed in collaboration with partners at the Department for the Aging (DFTA) of New York City. During the COVID-19 outbreak, PROTECT delivery shifted from in-person to phone or video delivery. Depression severity was tracked using the Patient Health Questionaire-9 (PHQ-9). Reduction in depression severity was evaluated using a linear mixed effects model with non-inferiority test to compare the effectiveness of video vs in-person delivery of PROTECT.

RESULTS

PROTECT reduced depression (average 5.15 PHQ-9 points). Video and phone delivery were non-inferior to in-person delivery. The video group completed therapy more quickly than the in-person group and had a more rapid improvement in depression symptoms.

CONCLUSIONS

PROTECT therapy delivered remotely reduces depression among diverse elder abuse victims. Video delivery of PROTECT could increase reach and scalability to serve more vulnerable older depressed victims.

2SpamH: A Two-Stage Pre-Processing Algorithm for Passively Sensed mHealth Data

Recent advancements in mobile health (mHealth) technology and the ubiquity of wearable devices and smartphones have expanded a market for digital health and have emerged as innovative tools for data collection on individualized behavior. Heterogeneous levels of device usage across users and across days within a single user may result in different degrees of underestimation in passive sensing data, subsequently introducing biases if analyzed without addressing this issue. In this work, we propose an unsupervised 2-Stage Pre-processing Algorithm for Passively Sensed mHealth Data (2SpamH) algorithm that uses device usage variables to infer the quality of passive sensing data from mobile devices. This article provides a series of simulation studies to show the utility of the proposed algorithm compared to existing methods. Application to a real clinical dataset is also illustrated.