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- All Subjects: Digital Health
- All Subjects: Oxidative Stress
- Creators: Swan, Pamela D
Description
Background. Effects of lifestyle interventions on early biomarkers of oxidative stress and CVD risk in youth with prediabetes are unknown. Objective. To evaluate the effects of a lifestyle intervention to prevent type 2 diabetes among obese prediabetic Latino adolescents on oxidized lipoproteins. Design: In a quasi-experimental design, 35 adolescents (51.4% male, age 15.5(1.0) y, body mass index (BMI) percentile 98.5(1.2), and glucose 2 hours after an oral glucose tolerance test-OGTT 141.2(12.2) mg/dL) participated in a 12-week intervention that included weekly exercise (three 60 min-sessions) and nutrition education (one 60 min-session). Outcomes measured at baseline and post-intervention were: fasting oxidized LDL and oxidized HDL (oxLDL and oxHDL) as oxidative stress variables; dietary intake of fresh fruit and vegetable (F&V) and fitness (VO2max) as behavioral variables; weight, BMI, body fat, and waist circumference as anthropometric variables; fasting glucose and insulin, 2hour glucose and insulin after an OGTT, insulin resistance (HOMA-IR), and lipid panel (triglycerides, total cholesterol, VLDL-c, LDL-c, HDL-c, and Non-HDL) as cardiometabolic variables. Results. Comparing baseline to post-intervention, significant decreases in oxLDL concentration were shown (51.0(14.0) and 48.7(12.8) U/L, p=0.022); however, the intervention did not decrease oxHDL (395.2(94.6) and 416.1(98.4) ng/mL, p=0.944). F&V dietary intake (116.4(97.0) and 165.8(91.0) g/d, p=0.025) and VO2max (29.7(5.0) and 31.6(4.7) ml*kg-1*min-1, p<0.001) significantly increased. Within-subjects correlations between changes in F&V intake and oxidized lipoproteins, adjusted for VO2max changes, were non-significant (R=-0.15, p=0.52 for oxLDL; R=0.22, p=0.25 for oxHDL). Anthropometric variables were significantly reduced (weight -1.3% p=0.042; BMI -2.2% and BMI percentile -0.4%, p=0.001; body fat -6.6% and waist circumference -1.8%, p=0.025). Cardiometabolic variables significantly improved, including reductions in glucose 2hour (-19.3% p<0.001), fasting insulin (-12.9% p=0.008), insulin 2hour (-53.5% p<0.001), and HOMA-IR (-12.5% p=0.015), with 23 participants (66%) that reverted toward a normal glucose tolerance status. Most lipid panel significantly changed (triglycerides -10.2% p=0.032; total cholesterol -5.4% p=0.002; VLDL-c -10.4% p=0.029; HDL-c -3.2% p=0.022; and Non-HDL -5.5% p=0.0007). Conclusion. The intervention resulted in differential effects on oxidized lipoproteins and significant improvements in behavioral, anthropometric and cardiometabolic variables, reducing the high metabolic risk of obese prediabetic kids.
ContributorsRenteria Mexia, Ana Maria (Author) / Shaibi, Gabriel Q (Thesis advisor) / Vega-Lopez, Sonia (Committee member) / Swan, Pamela D (Committee member) / Olson, Micah L (Committee member) / Lee, Chong (Committee member) / Arizona State University (Publisher)
Created2017
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