Matching Items (2)
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- All Subjects: Sedentary Behaviors
- Creators: Buman, Matthew P
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
Having accurate measurements of sedentary behaviors is important to understand relationships between sedentary behaviors and health outcomes and to evaluate changes in interventions and health promotion programs designed to reduce sedentary behaviors. This dissertation included three projects that examined measurement properties of wearable monitors used to measure sedentary behaviors. Project one examined the validity of three monitors: the ActiGraph GT3X+, activPAL™, and SenseWear 2. None of the monitors were equivalent with the criterion measure of oxygen uptake to estimate the energy cost of sedentary and light-intensity activities. The ActivPAL™ had the best accuracy as compared with the other monitors. In project two, the accuracy of ActiGraph GT3X+and GENEActiv cut-points used to assess sedentary behavior were compared with direct observation during free-living conditions. New vector magnitude cut-points also were developed to classify time spent in sedentary- and stationary behaviors during free-living conditions. The cut-points tested had modest overall accuracy to classify sedentary time as compared to direct observation. New ActiGraph 1-minute vector cut-points increased overall accuracy for classifying sedentary time. Project 3 examined the accuracy of the sedentary sphere by testing various arm elevation- and movement-count configurations using GENEActiv and ActiGraph GT3X+ data obtained during free-living conditions. None of the configurations were equivalent to the criterion measure of direct observation. The best configuration of the GENEActiv was: worn on the dominant wrist at 15 degrees below the horizontal plane with a cut-point <489 for each 15-second interval. The best configuration for the ActiGraph was: worn on the non-dominant wrist at 5° below the horizontal plane with a cut-point of <489 counts for each 15-second interval. Collectively, these findings indicate that the wearable monitors and methods examined in this study are limited in their ability to assess sedentary behaviors and light intensity physical activity. Additional research is needed to further understand the scope and limitations of wearable monitors and methods used to assess sedentary behaviors and light intensity physical activity.
ContributorsFlorez Pregonero, Argemiro Alberto (Author) / Ainsworth, Barbara E (Thesis advisor) / Buman, Matthew P (Committee member) / Hooker, Steven P (Committee member) / Keller, Colleen S (Committee member) / Swan, Pamela (Committee member) / Arizona State University (Publisher)
Created2017