Of the 167 raw accelerometry data files, 157 had valid data for at least one weekday and one weekend day. Once combined with the questionnaire and biological data, 139 participants had complete data.
Females consumed more SSB (sugar-sweetened beverages), experienced poorer sleep quality, and were more physically active and insulin resistant (Table 1, p≤0.07). Males were leaner, used more alcohol and tobacco products and experienced lower people-to-bedroom density (p≤0.040). The JIS high risk group had a higher SES, a lower HIV+ prevalence, were mostly obese, more insulin resistant, and likely to be in a partnered relationship (Table 1, p≤0.040).
In bi-variate analysis, sex, SES, CI, WC, BMI, SSB and WASO were significantly associated with HOMA-IR (p≤0.05). SES, BMI, CI, WC and HIV status were significantly related to MetSz (p≤0.027).
Except for the MetSz models (Table 2), all other models were significant (Table 2, Figure 1A-F, Figure S1A-F - see Additional file 1, p≤0.0203). The binary logistic regression models (Figure 1A-C, Figure S1A-C - see Additional file 1) showed good fit (Hosmer-Lemeshow: p≥0.3744; AUC: 0.808-0.877).
Poor sleep quality was significantly associated with poor metabolic health (increasing HOMA-IR levels) (p<0.05), while TST was significantly and directly related to HOMA-IR (p<0.05) (Table 2). Males tended to have lower HOMA-IR, and a higher SES was associated with a higher HOMA-IR (p<0.05). SSB was significantly and directly associated with HOMA-IR (p<0.05). Increasing levels of PA tended to be associated with reduced levels of HOMA-IR. Only in one model did sleep quantity and quality parameters concurrently and significantly contribute to explaining the variance in HOMA-IR (Table 2).
The MetSz models were not associated with any sleep variable (Table 2). While sex was not significant, HIV+ status was quite consistently associated with lower (healthier) MetSz. As with the HOMA-IR model, SES status was positively associated with a higher (poorer) MetSz (Table 2).
There was a significant non-linear trend (U-shaped) between JIS-MetS risk and NST (p=0.0196) (Figure 1A), such that for NST Q2, there was 80% less likelihood of being at high risk for JIS-MetS. There was also a significant difference in frequencies between NST Q1 and Q2 (p=0.021) (Figure 1A).
There was a significant positive, linear trend between JIS-MetS risk and SFI (p=0.0001) (Figure 1C). The odds of high risk JIS-MetS for SFI Q3 was high (OR= 69.81, 8.44; 577.63), however the confidence intervals were wide. There were significant differences between the frequencies for Q3 versus Q1 and Q2 (p<0.001) (Figure 1C).
Being a current user of both alcohol and tobacco products carried a significant higher odds for JIS-MetS high risk in the NST (OR=4.51, p=0.034) and SFI (OR=6.55, p =0.023) models (Figure 1A-C).
In binary logistic regression models (Figure 1A-C) males had significantly lower odds for JIS-MetS high risk (OR≈0.15, p≤ 0.006), and similarly for HIV+ in the NST and SFI models (OR=0.25, p≤0.0035). Once adjusted for adiposity (CI), sex was no longer a significant factor (p≥0.097). However, HIV+ status remained a significant factor in most models (OR≈0.26, p≤0.048) (Figure 1A-C).
There was a significant linear trend between TST and HOMA-IR levels (p=0.0444) (Figure 1D). Compared to TST Q1 and Q2, being in TST Q3 (longest sleep time) increased the odds of being in the highest HOMA-IR level (Q3) by a factor of 2.84 (p=0.044) (Figure 1D).
There were significant linear relationships between SE and WASO, and HOMA-IR (p≤0.007) (Figure 1E). A high SE (Q3) was 85% less likely to result in HOMA-IR Q3 (p=0.001). Being in WASO Q2 and Q3 significantly increased the odds for HOMA-IR Q3; OR=3.17 and OR=6.75, respectively (p≤0.019).
Both TST Q3 and SE Q3 were concurrently and independently significant within the TST and SE models (p≤0.0044) (Figure 1A-B).
SFI and AC were significantly associated with increasing HOMA-IR levels (p≤0.0201) (Figure 1F). Being in SFI Q2 and Q3 significantly increased the odds for HOMA-IR Q3, OR=4.67 (p=0.004) and OR=10.91 (p<0.001), respectively. For AC Q3 the odds of being in the HOMA-IR Q3 was 3.01 (p=0.020) (Figure 1F).
In ordinal logistic regression models (Figure 1D-F), being in the VM Q2 level, significantly decreased the odds of being in HOMA-IR Q3 by a factor of ≈0.31 (p≤0.039). In contrast, for both CI Q2 and Q3 levels, the odds of being in HOMA-IR Q3 were significantly increased; Q2 OR≈2.9 and Q3 OR≈3.79, respectively (p≤0.045) (Figure 1D-F).
Expressing sleep quantity and quality parameters in terms of sleep health guidelines, we found significant non-linear associations with JIS-MetS risk (p≤0.0308) (Figure S1A-F, see Additional file 1). The relationship between sleep categories for both TST and NST, and JIS-MetS risk were U-shaped, the nadir at 7-9 hours of sleep (Figure S1A-B, see Additional file 1).
In binary logistic regression models (Figure S1A-C, see Additional file 1) males had significantly lower odds for JIS-MetS high risk (OR≈0.14, p= 0.004), and similarly for HIV+ (OR≈0.26, p≤0.0034). Once adjusted for adiposity (CI), sex was no longer a significant factor (p≥0.121). However, HIV+ status remained a significant factor in most models (OR≈0.23, p≤0.045) (Figure S1A-C, see Additional file 1).
In ordinal logistic regression models (Figure S1D-F, see Additional file 1) physical activity (VM Q2) significantly reduced the odds of being in HOMA-IR Q3 by a factor of ≈0.26 (p≤0.010). For both CI Q2 and Q3 levels, the odds of being in HOMA-IR Q3 were significantly increased (Q2 OR≈3.16, Q3 OR≈3.89, p≤0.022). For SE ≥85%, there was a 76% lower likelihood of being in HOMA-IR Q3. Significantly more participants were classified as low HOMA-IR (Q1) in the SE ≥85% category (Figure S1D, see Additional file 1).