Exploring Traditional Phones as an E-Mail Interface for Older Adults

This article explores the design and development of voice interfaces via traditional telephones as a way of supporting asynchronous online communication for older adults. E-mail is the primary form of online communication for older adults. However, e-mail communication currently requires access to and the ability to use an Internet-connected computer or device, which may be problematic due to barriers of physical access, expense, insufficient computer skill, or other accessibility issues. To address this, the present work leverages the pervasive hardware of traditional phones and familiar nonvisual models of phone-based interaction to create a new e-mail interface for older adults. We examine the potential of e-mail interaction via traditional phones through long-term field observations, prototype testing, and a four-week field-based user study. Our findings indicate that a simple voice e-mail interface accessed through traditional phones is usable for older adults and is a useful way for offline older adults to interact with an e-mail account. The ease of use and convenience of a phone-based interface are important given the “work” of keeping in touch over e-mail, and this approach has the potential to open up new avenues of online access for older adults who are still offline or who have late-life disabilities that make using traditional graphical e-mail systems difficult. This project contributes new insights regarding the ways in which voice interfaces can support asynchronous online communication for older adults and provides design guidance for the development of subsequent voice interfaces.

Harnessing context sensing to develop a mobile intervention for depression

Background

Mobile phone sensors can be used to develop context-aware systems that automatically detect when patients require assistance. Mobile phones can also provide ecological momentary interventions that deliver tailored assistance during problematic situations. However, such approaches have not yet been used to treat major depressive disorder.

Objective

The purpose of this study was to investigate the technical feasibility, functional reliability, and patient satisfaction with Mobilyze!, a mobile phone- and Internet-based intervention including ecological momentary intervention and context sensing.

Methods

We developed a mobile phone application and supporting architecture, in which machine learning models (ie, learners) predicted patients’ mood, emotions, cognitive/motivational states, activities, environmental context, and social context based on at least 38 concurrent phone sensor values (eg, global positioning system, ambient light, recent calls). The website included feedback graphs illustrating correlations between patients’ self-reported states, as well as didactics and tools teaching patients behavioral activation concepts. Brief telephone calls and emails with a clinician were used to promote adherence. We enrolled 8 adults with major depressive disorder in a single-arm pilot study to receive Mobilyze! and complete clinical assessments for 8 weeks.

Results

Promising accuracy rates (60% to 91%) were achieved by learners predicting categorical contextual states (eg, location). For states rated on scales (eg, mood), predictive capability was poor. Participants were satisfied with the phone application and improved significantly on self-reported depressive symptoms (beta_week = –.82, P < .001, per-protocol Cohen d = 3.43) and interview measures of depressive symptoms (beta_week = –.81, P < .001, per-protocol Cohen d = 3.55). Participants also became less likely to meet criteria for major depressive disorder diagnosis (b_week = –.65, P = .03, per-protocol remission rate = 85.71%). Comorbid anxiety symptoms also decreased (beta_week = –.71, P < .001, per-protocol Cohen d = 2.58).

Conclusions

Mobilyze! is a scalable, feasible intervention with preliminary evidence of efficacy. To our knowledge, it is the first ecological momentary intervention for unipolar depression, as well as one of the first attempts to use context sensing to identify mental health-related states. Several lessons learned regarding technical functionality, data mining, and software development process are discussed.

Trial Registration

Clinicaltrials.gov NCT01107041; http://clinicaltrials.gov/ct2/show/NCT01107041 (Archived by WebCite at http://www.webcitation.org/60CVjPH0n)