Self-Monitoring Physical Activity, Diet, and Weight Among Adults Who Are Legally Blind: Exploratory Investigation
Bibliographic record
Abstract
BACKGROUND: Obesity is a global pandemic. Lifestyle approaches have been shown effective for weight loss and weight loss maintenance. Central to these evidence-based approaches are increased physical activity, decreased caloric intake, regular self-weighing, and the tracking of these behaviors. OBJECTIVE: This exploratory descriptive study surveyed adults who are legally blind to identify strategies related to tracking physical activity, diet, and weight. These health behaviors are essential components to evidence-based weight loss programs. We also identified areas where we can better support adults who are legally blind in their independent efforts to change these behaviors and improve their health. METHODS: Participants (≥18 years of age) who self-identified as being legally blind were recruited using email announcements in low vision advocacy groups. They completed an interviewer-administered survey on the telephone and an in-person visit for standardized assessment of height and weight. RESULTS: The participants (N=18) had an average age of 31.2 (SD 13.4) years; 50% (9/18) had normal weight (BMI 18.5 to <25); 44% (8/18) were female; 44% (8/18) were Black; and 39% (7/18) were Non-Hispanic White. Most participants (16/18, 89%) used their smartphone to access the internet daily, and 67% (12/18) had at least 150 mins of exercise per week. Although 78% (14/18) of the participants indicated tracking their weight, only 61% (11/18) could indicate how they tracked their weight, and 22% (4/18) indicated they tracked it mentally. Providing individuals with a talking scale was the most consistent recommendation (12/18, 67%) to facilitate independence in managing weight through lifestyle changes. Even though 50% (9/18) of the participants indicated using an app or electronic notes to track some portion of their diet, participants reported challenges with determining portion size and corresponding calorie counts. Most participants (17/18, 94%) reported using apps, electronic notes, smartphones, or wearable devices to track their physical activity. Although strategies such as using wearables and smartphones could provide measurements (eg, step counts) as well as recording data, they also pose financial and technology literacy barriers. CONCLUSIONS: Technology-based solutions were identified for tracking weight, diet, and physical activity for weight management. These strategies have financial and technology literacy barriers. A range of strategies for adopting and tracking health behaviors will be needed to assist individuals with varying skills and life experiences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".