skip to main content

Title: Planning Habit: Daily Planning Prompts with Alexa
The widespread adoption of intelligent voice assistants (IVAs), like Amazon’s Alexa or Google’s Assistant, presents new opportunities for designers of persuasive technologies to explore how to support people’s behavior change goals and habits with voice technology. In this work, we explore how to use planning prompts, a technique from behavior science to make specific and effective plans, with IVAs. We design and conduct usability testing (N = 13) on a voice app called Planning Habit that encourages users to formulate daily plans out loud. We identify strategies that make it possible to successfully adapt planning prompts to voice format. We then conduct a week-long online deployment (N = 40) of the voice app in the context of daily productivity. Overall, we find that traditional forms of planning prompts can be adapted to and enhanced by IVA technology.  more » « less
Award ID(s):
Author(s) / Creator(s):
; ;
Ali, Raian; Lugrin, Birgit; Charles, Fred
Date Published:
Journal Name:
Persuasive Technology (PERSUASIVE 2021)
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Procrastination is a major issue faced by students which can lead to negative impacts on their academic performance and mental health. Productivity tools aim to help individuals to alleviate this behavior by providing self-regulatory support. However, the processes of how these applications help students conquer academic procrastination are under-explored. Particularly, it is essential to understand what aspects of these applications help which kinds of students in accomplishing their academic tasks. In this paper, we address this gap by presenting an academic planning and time management app (Proccoli) and a study designed to understand the association between student procrastination modeling, in-app behaviors, and perceived performance with app evaluation. As the core of our study, we analyze student perceptions of Proccoli and its impact on their study tasks and time management skills. Then, we model student procrastination behaviors by Hawkes process mining, assess student in-app behaviors by specifying planning and performance-related measures and evaluate the relationship between student behaviors and the evaluation survey results. Our study shows a need for personalized self-regulation support in Proccoli, as students with different in-app studying behaviors are found to have different perceptions of the app functionalities and the association between the prompts for social accountability students received by using Proccoli and their procrastination behavior is significant. 
    more » « less
  2. null (Ed.)
    Smart speakers such as Amazon Echo present promising opportunities for exploring voice interaction in the domain of in-home exercise tracking. In this work, we examine if and how voice interaction complements and augments a mobile app in promoting consistent exercise. We designed and developed TandemTrack, which combines a mobile app and an Alexa skill to support exercise regimen, data capture, feedback, and reminder. We then conducted a four-week between-subjects study deploying TandemTrack to 22 participants who were instructed to follow a short daily exercise regimen: one group used only the mobile app and the other group used both the app and the skill. We collected rich data on individuals' exercise adherence and performance, and their use of voice and visual interactions, while examining how TandemTrack as a whole influenced their exercise experience. Reflecting on these data, we discuss the benefits and challenges of incorporating voice interaction to assist daily exercise, and implications for designing effective multimodal systems to support self-tracking and promote consistent exercise. 
    more » « less
  3. Intelligent voice assistants, and the thirdparty apps (aka “skills” or “actions”) that power them, are increasing in popularity and beginning to experiment with the ability to continuously listen to users. This paper studies how privacy concerns related to such always-listening voice assistants might affect consumer behavior and whether certain privacy mitigations would render them more acceptable. To explore these questions with more realistic user choices, we built an interactive app store that allowed users to install apps for a hypothetical always-listening voice assistant. In a study with 214 participants, we asked users to browse the app store and install apps for different voice assistants that offered varying levels of privacy protections. We found that users were generally more willing to install continuously-listening apps when there were greater privacy protections, but this effect was not universally present. The majority did not review any permissions in detail, but still expressed a preference for stronger privacy protections. Our results suggest that privacy factors into user choice, but many people choose to skip this information. 
    more » « less
  4. Mobile and web apps are increasingly relying on the data generated or provided by users such as from their uploaded documents and images. Unfortunately, those apps may raise significant user privacy concerns. Specifically, to train or adapt their models for accurately processing huge amounts of data continuously collected from millions of app users, app or service providers have widely adopted the approach of crowdsourcing for recruiting crowd workers to manually annotate or transcribe the sampled ever-changing user data. However, when users' data are uploaded through apps and then become widely accessible to hundreds of thousands of anonymous crowd workers, many human-in-the-loop related privacy questions arise concerning both the app user community and the crowd worker community. In this paper, we propose to investigate the privacy risks brought by this significant trend of large-scale crowd-powered processing of app users' data generated in their daily activities. We consider the representative case of receipt scanning apps that have millions of users, and focus on the corresponding receipt transcription tasks that appear popularly on crowdsourcing platforms. We design and conduct an app user survey study (n=108) to explore how app users perceive privacy in the context of using receipt scanning apps. We also design and conduct a crowd worker survey study (n=102) to explore crowd workers' experiences on receipt and other types of transcription tasks as well as their attitudes towards such tasks. Overall, we found that most app users and crowd workers expressed strong concerns about the potential privacy risks to receipt owners, and they also had a very high level of agreement with the need for protecting receipt owners' privacy. Our work provides insights on app users' potential privacy risks in crowdsourcing, and highlights the need and challenges for protecting third party users' privacy on crowdsourcing platforms. We have responsibly disclosed our findings to the related crowdsourcing platform and app providers.

    more » « less
  5. Abstract

    The planning, design, and maintenance of stormwater infrastructure must be informed by changing rainfall patterns due to climate change. However, there is little consensus on how future climate information should be used, or how uncertainties introduced by use of different methods and datasets should be characterized or managed. These uncertainties exacerbate existing challenges to using climate information on local or municipal scales. Here we analyze major cities in the U.S., 48 of which developed climate adaptation and resilience plans. Given the prevalence of depth duration frequency (DDF) curves for planning infrastructure for rainfall, we then assessed the underlying climate information used in these 48 plans to show how DDF curves used for resilience planning and the resulting outcomes can be affected by stakeholders’ methodological choices and datasets. For rainfall extremes, many resilience plans varied by trend detection method, data preprocessing steps, and size of study area, and all used only one of the available downscaled climate projection datasets. We evaluate the implications of uncertainties across five available climate datasets and show the level of climate resilience to extreme rainfall depends on the dataset selected for each city. We produce risk matrices for a broader set of 77 U.S. cities to highlight how local resilience strategies and decisions are sensitive to the climate projection dataset used in local adaptation plans. To help overcome barriers to using climate information, we provide an open dataset of future daily rainfall values for 2-, 5-, 10-, 25-, 50-, and 100 years annual recurrence intervals for 77 cities and compare resilience outcomes across available climate datasets that each city can use for comparison and for robust resilience planning. Because of uncertainty in climate projections, our results highlight the importance of no-regret and flexible resilience strategies that can be adjusted with new climate information.

    more » « less