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- All Subjects: Digital Health
- All Subjects: Interactive computer systems--Psychological aspects.
- Creators: Hekler, Eric B.
Description
Many individual-level behavioral interventions improve health and well-being. However, most interventions exhibit considerable heterogeneity in response. Put differently, what might be effective on average might not be effective for specific individuals. From an individual’s perspective, many healthy behaviors exist that seem to have a positive impact. However, few existing tools support people in identifying interventions that work for them, personally.
One approach to support such personalization is via self-experimentation using single-case designs. ‘Hack Your Health’ is a tool that guides individuals through an 18-day self-experiment to test if an intervention they choose (e.g., meditation, gratitude journaling) improves their own psychological well-being (e.g., stress, happiness), whether it fits in their routine, and whether they enjoy it.
The purpose of this work was to conduct a formative evaluation of Hack Your Health to examine user burden, adherence, and to evaluate its usefulness in supporting decision-making about a health intervention. A mixed-methods approach was used, and two versions of the tool were tested via two waves of participants (Wave 1, N=20; Wave 2, N=8). Participants completed their self-experiments and provided feedback via follow-up surveys (n=26) and interviews (n=20).
Findings indicated that the tool had high usability and low burden overall. Average survey completion rate was 91%, and compliance to protocol was 72%. Overall, participants found the experience useful to test if their chosen intervention helped them. However, there were discrepancies between participants’ intuition about intervention effect and results from analyses. Participants often relied on intuition/lived experience over results for decision-making. This suggested that the usefulness of Hack Your Health in its current form might be through the structure, accountability, and means for self-reflection it provided rather than the specific experimental design/results. Additionally, situations where performing interventions within a rigorous/restrictive experimental set-up may not be appropriate (e.g., when goal is to assess intervention enjoyment) were uncovered. Plausible design implications include: longer experimental and phase durations, accounting for non-compliance, missingness, and proximal/acute effects, and exploring strategies to complement quantitative data with participants’ lived experiences with interventions to effectively support decision-making. Future work should explore ways to balance scientific rigor with participants’ needs for such decision-making.
One approach to support such personalization is via self-experimentation using single-case designs. ‘Hack Your Health’ is a tool that guides individuals through an 18-day self-experiment to test if an intervention they choose (e.g., meditation, gratitude journaling) improves their own psychological well-being (e.g., stress, happiness), whether it fits in their routine, and whether they enjoy it.
The purpose of this work was to conduct a formative evaluation of Hack Your Health to examine user burden, adherence, and to evaluate its usefulness in supporting decision-making about a health intervention. A mixed-methods approach was used, and two versions of the tool were tested via two waves of participants (Wave 1, N=20; Wave 2, N=8). Participants completed their self-experiments and provided feedback via follow-up surveys (n=26) and interviews (n=20).
Findings indicated that the tool had high usability and low burden overall. Average survey completion rate was 91%, and compliance to protocol was 72%. Overall, participants found the experience useful to test if their chosen intervention helped them. However, there were discrepancies between participants’ intuition about intervention effect and results from analyses. Participants often relied on intuition/lived experience over results for decision-making. This suggested that the usefulness of Hack Your Health in its current form might be through the structure, accountability, and means for self-reflection it provided rather than the specific experimental design/results. Additionally, situations where performing interventions within a rigorous/restrictive experimental set-up may not be appropriate (e.g., when goal is to assess intervention enjoyment) were uncovered. Plausible design implications include: longer experimental and phase durations, accounting for non-compliance, missingness, and proximal/acute effects, and exploring strategies to complement quantitative data with participants’ lived experiences with interventions to effectively support decision-making. Future work should explore ways to balance scientific rigor with participants’ needs for such decision-making.
ContributorsPhatak, Sayali Shekhar (Author) / Buman, Matthew P (Thesis advisor) / Hekler, Eric B. (Committee member) / Huberty, Jennifer L (Committee member) / Johnston, Erik W., 1977- (Committee member) / Swan, Pamela D (Committee member) / Arizona State University (Publisher)
Created2019
Description
Desirable outcomes such as health and wellbeing are tightly linked to people’s behaviors, thus inspiring research on technologies that support productively changing those behaviors. Many behavior change technologies are designed by Human-Computer Interaction experts, but this approach makes it difficult to personalize support to each user’s unique goals and needs. As an alternative to the provision of expert-developed pre-fabricated behavior change solutions, the present study aims to empower users’ self-experimentation for behavior change. To this end, two levels of supports were explored. First, the provision of interactive digital materials to support users’ creation of behavioral plans was developed. In the initial step, a tutorial for self-experimentation for behavior change that was fully scripted with images in succession was created. The tutorial focuses on facilitating users’ learning and applying behavior change techniques. Second, users were equipped with a tool to support their implementation of context-aware just-in-time interventions. This tool enables prototyping of sensor-based responsive systems for home environments, integrating simple sensors (two-state magnetic sensors, etc.) and media event components (wireless sound, etc.).
To evaluate the effectiveness of these two approaches, a between-subject trial comparing the approaches to a sleep education control was conducted with 27 participants over 7 weeks. Although results did not reveal significant difference in sleep quality improvement between the conditions, trends indicating greater effectiveness in the two treatment groups were observed. Analysis of the plans participants created and their revision performance also indicated that the two treatment groups developed more specific and personalized plans compared with the control group.
To evaluate the effectiveness of these two approaches, a between-subject trial comparing the approaches to a sleep education control was conducted with 27 participants over 7 weeks. Although results did not reveal significant difference in sleep quality improvement between the conditions, trends indicating greater effectiveness in the two treatment groups were observed. Analysis of the plans participants created and their revision performance also indicated that the two treatment groups developed more specific and personalized plans compared with the control group.
ContributorsLee, Jisoo (Author) / Burleson, Winslow (Thesis advisor) / Hekler, Eric B. (Committee member) / Tinapple, David (Committee member) / Walker, Erin (Committee member) / Arizona State University (Publisher)
Created2016