Maria Wolters

Activity monitoring in patients with depression: A systematic review

BACKGROUND Altered physical activity is an important feature of depression. It is manifested in psychomotor retardation, agitation and withdrawal from engagement in normal activities. Modern devices for activity monitoring (actigraphs) make it possible to monitor physical activity unobtrusively but the validity of actigraphy as an indicator of mood state is uncertain. We carried out a systematic review of digital actigraphy in patients with depression to investigate the associations between measured physical activity and depression. METHODS Systematic review and meta-analysis. Studies were identified from Medline, EMBASE and Psycinfo databases and included if they were either case control or longitudinal studies of actigraphy in adults aged between 18 and 65 diagnosed with a depressive disorder. Outcomes were daytime and night-time activity and actigraphic measures of sleep. RESULTS We identified 19 eligible papers from 16 studies (412 patients). Case control studies showed less daytime activity in patients with depression (standardised mean difference -0.76, 95% confidence intervals -1.05 to -0.47). Longitudinal studies showed moderate increase in daytime activity (0.53, 0.20 to 0.87) and a reduction in night-time activity (-0.36, -0.65 to -0.06) over the course of treatment. LIMITATIONS All study participants were unblinded. Only seven papers included patients treated in the community. CONCLUSIONS Actigraphy is a potentially valuable source of additional information about patients with depression. However, there are no clear guidelines for use of actigraphy in studies of patients with depression. Further studies should investigate patients treated in the community. Additional work to develop algorithms for differentiating behaviour patterns is also needed.

User-centred multimodal reminders for assistive living

While there has been a lot of research on the usability of reminders and alarms in the work context, the home has been somewhat neglected despite the importance of reminder systems for telecare and assistive living systems. We conducted a comprehensive mixed-methods study into the requirements for useable and acceptable reminders in the home. The study consisted of a questionnaire (N=379), 6 focus groups, and 7 home tour interviews. Our results highlight the need for highly flexible and contextualized multimodal and multi-device reminder solutions that build on existing successful strategies for remembering in and around the home. We suggest that developers of home care reminder systems should design for diversity, context, priorities, autonomy, shared spaces, and optimal care.

Reducing working memory load in spoken dialogue systems

We evaluated two strategies for alleviating working memory load for users of voice interfaces: presenting fewer options per turn and providing confirmations. Forty-eight users booked appointments using nine different dialogue systems, which varied in the number of options presented and the confirmation strategy used. Participants also performed four cognitive tests and rated the usability of each dialogue system on a standardised questionnaire. When systems presented more options per turn and avoided explicit confirmation subdialogues, both older and younger users booked appointments more quickly without compromising task success. Users with lower information processing speed were less likely to remember all relevant aspects of the appointment. Working memory span did not affect appointment recall. Older users were slightly less satisfied with the dialogue systems than younger users. We conclude that the number of options is less important than an accurate assessment of the actual cognitive demands of the task at hand.

Being Old Doesn’t Mean Acting Old: How Older Users Interact with Spoken Dialog Systems

Most studies on adapting voice interfaces to older users work top-down by comparing the interaction behavior of older and younger users. In contrast, we present a bottom-up approach. A statistical cluster analysis of 447 appointment scheduling dialogs between 50 older and younger users and 9 simulated spoken dialog systems revealed two main user groups, a “social” group and a “factual” group. “Factual” users adapted quickly to the systems and interacted efficiently with them. “Social” users, on the other hand, were more likely to treat the system like a human, and did not adapt their interaction style. While almost all “social” users were older, over a third of all older users belonged in the “factual” group. Cognitive abilities and gender did not predict group membership. We conclude that spoken dialog systems should adapt to users based on observed behavior, not on age.

Name that tune: musicons as reminders in the home

In this paper we argue that Musicons, short samples from pieces of music are a useful way to present private but memorable reminder messages. We investigated accuracy, memorability and response times for short, medium, and long Musicons. User performance on the Musicons was also compared to short spoken reminders. The study consisted of two sessions a week apart. Quantitative measures were augmented with qualitative questions about associations and memories. Overall, participants achieved a high level of accuracy (89%) on the Musicons. Recognition was stable at 90% or better across sessions for users who were able to construct meaningful links between Musicons and the associated tasks. Optimal response times were achieved for medium-length 0.5 sec. Musicons. We conclude that Musicons are a viable option for alarms and notifications that combine the high learnability of Auditory Icons with the more private nature of Earcons.

Spoken dialogue interfaces for older people

Although speech is a highly natural mode of communication, building robust and usable speech-based interfaces is still a challenge, even if the target user group is restricted to younger users. When designing for older users, there are added complications due to cognitive, physiological, and anatomical ageing. Users may also find it difficult to adapt to the interaction style required by the speech interface. In this chapter, we summarise the work on spoken dialogue interfaces that was carried out during the MATCH project. After a brief overview of relevant aspects of ageing and previous work on spoken dialogue interfaces for older people, we summarise our work on managing spoken interactions (dialogue management), understanding older people’s speech (speech recognition), and generating spoken messages that older people can understand (speech synthesis). We conclude with suggestions for design guidelines that have emerged from our work and suggest directions for future research.

Leveraging large data sets for user requirements analysis

In this paper, we show how a large demographic data set that includes only high-level information about health and disability can be used to specify user requirements for people with specific needs and impairments. As a case study, we consider adapting spoken dialogue systems (SDS) to the needs of older adults. Such interfaces are becoming increasingly prevalent in telecare and home care, where they will often be used by older adults. As our data set, we chose the English Longitudinal Survey of Ageing (ELSA), a large representative survey of the health, wellbeing, and socioeconomic status of English older adults. In an inclusion audit, we show that one in four older people surveyed by ELSA might benefit from SDS due to problems with dexterity, mobility, vision, or literacy. Next, we examine the technology that is available to our target users (technology audit) and estimate factors that might prevent older people from using SDS (exclusion audit). We conclude that while SDS are ideal for solutions that are delivered on the near ubiquitous landlines, they need to be accessible for people with mild to moderate hearing problems, and thus multimodal solutions should be based on the television, a technology even more widespread than landlines.

Pilot randomised controlled trial of Help4Mood, an embodied virtual agent-based system to support treatment of depression

Introduction Help4Mood is an interactive system with an embodied virtual agent (avatar) to assist in self-monitoring of patients receiving treatment for depression. Help4Mood supports self-report and biometric monitoring and includes elements of cognitive behavioural therapy. We aimed to evaluate system use and acceptability, to explore likely recruitment and retention rates in a clinical trial and to obtain an estimate of potential treatment response with a view to conducting a future randomised controlled trial (RCT). Methods We conducted a pilot RCT of Help4Mood in three centres, in Romania, Spain and Scotland, UK. Patients with diagnosed depression (major depressive disorder) and current mild/moderate depressive symptoms were randomised to use the system for four weeks in addition to treatment as usual (TAU) or to TAU alone. Results Twenty-seven individuals were randomised and follow-up data were obtained from 21 participants (12/13 Help4Mood, 9/14 TAU). Half of participants randomised to Help4Mood used it regularly (more than 10 times); none used it every day. Acceptability varied between users. Some valued the emotional responsiveness of the system, while others found it too repetitive. Intention to treat analysis showed a small difference in change of Beck Depression Inventory II (BDI-2) scores (Help4Mood –5.7 points, TAU –4.2). Post-hoc on-treatment analysis suggested that participants who used Help4Mood regularly experienced a median change in BDI-2 of –8 points. Conclusion Help4Mood is acceptable to some patients receiving treatment for depression although none used it as regularly as intended. Changes in depression symptoms in individuals who used the system regularly reached potentially meaningful levels.

The MATCH corpus: a corpus of older and younger users’ interactions with spoken dialogue systems

We present the MATCH corpus, a unique data set of 447 dialogues in which 26 older and 24 younger adults interact with nine different spoken dialogue systems. The systems varied in the number of options presented and the confirmation strategy used. The corpus also contains information about the users’ cognitive abilities and detailed usability assessments of each dialogue system. The corpus, which was collected using a Wizard-of-Oz methodology, has been fully transcribed and annotated with dialogue acts and “Information State Update” (ISU) representations of dialogue context. Dialogue act and ISU annotations were performed semi-automatically. In addition to describing the corpus collection and annotation, we present a quantitative analysis of the interaction behaviour of older and younger users and discuss further applications of the corpus. We expect that the corpus will provide a key resource for modelling older people’s interaction with spoken dialogue systems.

Participatory design of ehealth solutions for women from vulnerable populations with perinatal depression

OBJECTIVE Cultural and health service obstacles affect the quality of pregnancy care that women from vulnerable populations receive. Using a participatory design approach, the Stress in Pregnancy: Improving Results with Interactive Technology group developed specifications for a suite of eHealth applications to improve the quality of perinatal mental health care. MATERIALS AND METHODS We established a longitudinal participatory design group consisting of low-income women with a history of antenatal depression, their prenatal providers, mental health specialists, an app developer, and researchers. The group met 20 times over 24 months. Applications were designed using rapid prototyping. Meetings were documented using field notes. RESULTS AND DISCUSSION The group achieved high levels of continuity and engagement. Three apps were developed by the group: an app to support high-risk women after discharge from hospital, a screening tool for depression, and a patient decision aid for supporting treatment choice. CONCLUSION Longitudinal participatory design groups are a promising, highly feasible approach to developing technology for underserved populations.