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Oral D-ribose supplementation has been reported to increase adenine nucle-otide synthesis and exercise capacity in certain clinical populations. Theoretically, increasing adenine nucleotide availability may enhance high intensity exercise capacity. This study evaluated the potential ergogenic value of D-ribose supplementation on repetitive high-intensity exercise capacity in 19 trained males. Subjects were familiarized to the testing protocol and performed two practice-testing trials before pre-supplementation testing. Each test involved warming up for 5 min on a cycle ergometer and then performing two 30-s Wingate anaerobic sprint tests on a computerized cycle ergometer separated by 3 min of rest recovery. In the pre- and post-supplementation trials, blood samples were obtained at rest, immediately following the first and second sprints, and following 5 min of recovery from exercise. Subjects were then matched according to body mass and anaerobic capacity and assigned to ingest, in a randomized and double blind manner, capsules containing either 5 g of a dextrose placebo (P) or D-ribose (R) twice daily (10 g/d) for 5 d. Subjects then performed post-supplementation tests on the 6th day. Data were analyzed by ANOVA for repeated measures. Results revealed a significant interaction (p = .04) in total work output. Post hoc analysis revealed that work significantly declined (–18 ± 51 J) during the second post-supplementation sprint in the P group while being maintained in the R group (–0.0 ± 31 J). No significant interactions were observed in peak power, average power, torque, fatigue index, lactate, ammonia, glucose, or uric acid. Results indicate that oral ribose supplementation (10 g/d for 5 d) does not affect anaerobic exercise capacity or metabolic markers in trained subjects as evaluated in this study.

This study examined whether ribose supplementation before and during intense anaerobic exercise impacts anaerobic capacity and metabolic markers. Twelve moderately trained male cyclists (22.3 ± 2.2 y; 181 ± 6 cm, 74.8 ± 9 kg) participated in the study. Subjects were familiarized and fasted for 8 h after standardizing nutritional intake. In a double blind and crossover manner subjects ingested either a 150 mL placebo or ribose (3 g ribose + 150 μg folate). Subjects rested for 25 min and completed 5 × 30 s anaerobic capacity tests with 3 min passive rest. Six capillary blood samples were taken prior to and after sprints for adenine nucleotide breakdown determination. The experiment was repeated 1 wk later with alternative drink. Data were analyzed by repeated measures ANOVA. No significant interactions were observed for any performance or blood variables. D-ribose supplementation has no impact on anaerobic exercise capacity and metabolic markers after high-intensity cycling exercise.

Background: The authors assessed agreement between participant diaries and two automated algorithms applied to activPAL (PAL Technologies Ltd, Glasgow, United Kingdom) data for classifying awake wear time in three age groups. Methods: Study 1 involved 20 youth and 23 adults who, by protocol, removed the activPAL occasionally to create nonwear periods. Study 2 involved 744 older adults who wore the activPAL continuously. Both studies involved multiple assessment days. In-bed, out-of-bed, and nonwear times were recorded in the participant diaries. The CREA (in PAL processing suite) and ProcessingPAL (secondary application) algorithms estimated out-of-bed wear time. Second- and day-level agreement between the algorithms and diary was investigated, as were associations of sedentary variables with self-rated health. Results: The overall accuracy for classifying out-of-bed wear time as compared with the diary was 89.7% (Study 1) to 95% (Study 2) for CREA and 89.4% (Study 1) to 93% (Study 2) for ProcessingPAL. Over 90% of the nonwear time occurring in nonwear periods >165 min was detected by both algorithms, while <11% occurring in periods ≤165 min was detected. For the daily variables, the mean absolute errors for each algorithm were generally within 0–15% of the diary mean. Most Spearman correlations were very large (≥.81). The mean absolute errors and correlations were less favorable for days on which any nonwear time had occurred. The associations between sedentary variables and self-rated health were similar across processing methods. Conclusion: The automated awake wear-time classification algorithms performed similarly to the diary information on days without short (≤2.5–2.75 hr) nonwear periods. Because both diary and algorithm data can have inaccuracies, best practices likely involve integrating diary and algorithm output.