Browsing by Author "Morenga LT"

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A co-designed mHealth programme to support healthy lifestyles in Maori and Pasifika peoples in New Zealand (OL@-OR@): a cluster-randomised controlled trial
(Elsevier Ltd, 2019-10) Mhurchu CN; Morenga LT; Tupai-Firestone R; Grey J; Jiang Y; Jull A; Whittaker R; Dobson R; Dalhousie S; Funaki T; Hughes E; Henry A; Lyndon-Tonga L; Pekepo C; Penetito-Hemara D; Tunks M; Verbiest M; Humphrey G; Schumacher J; Goodwin D
Background The OL@-OR@ mobile health programme was co-designed with Māori and Pasifika communities in New Zealand, to support healthy lifestyle behaviours. We aimed to determine whether use of the programme improved adherence to health-related guidelines among Māori and Pasifika communities in New Zealand compared with a control group on a waiting list for the programme. Methods The OL@-OR@ trial was a 12-week, two-arm, cluster-randomised controlled trial. A cluster was defined as any distinct location or setting in New Zealand where people with shared interests or contexts congregated, such as churches, sports clubs, and community groups. Members of a cluster were eligible to participate if they were aged 18 years or older, had regular access to a mobile device or computer, and had regular internet access. Clusters of Māori and of Pasifika (separately) were randomly assigned (1:1) to either the intervention or control condition. The intervention group received the OL@-OR@ mHealth programme (smartphone app and website). The control group received a control version of the app that only collected baseline and outcome data. The primary outcome was self-reported adherence to health-related guidelines, which were measured with a composite health behaviour score (of physical activity, smoking, alcohol intake, and fruit and vegetable intake) at 12 weeks. The secondary outcomes were self-reported adherence to health-related behaviour guidelines at 4 weeks; self-reported bodyweight at 12 weeks; and holistic health and wellbeing status at 12 weeks, in all enrolled individuals in eligible clusters; and user engagement with the app, in individuals allocated to the intervention. Adverse events were not collected. This study is registered with the Australian New Zealand Clinical Trials Registry, ACTRN12617001484336. Findings Between Jan 24 and Aug 14, 2018, we enrolled 337 Māori participants from 19 clusters and 389 Pasifika participants from 18 clusters (n=726 participants) in the intervention group and 320 Māori participants from 15 clusters and 405 Pasifika participants from 17 clusters (n=725 participants) in the control group. Of these participants, 227 (67%) Māori participants and 347 (89%) Pasifika participants (n=574 participants) in the intervention group and 281 (88%) Māori participants and 369 (91%) Pasifika participants (n=650 participants) in the control group completed the 12-week follow-up and were included in the final analysis. Relative to baseline, adherence to health-related behaviour guidelines increased at 12 weeks in both groups (315 [43%] of 726 participants at baseline to 329 [57%] of 574 participants in the intervention group; 331 [46%] of 725 participants to 369 [57%] of 650 participants in the control group); however, there was no significant difference between intervention and control groups in adherence at 12 weeks (odds ratio [OR] 1·13; 95% CI 0·84–1·52; p=0·42). Furthermore, the proportion of participants adhering to guidelines on physical activity (351 [61%] of 574 intervention group participants vs 407 [63%] of 650 control group participants; OR 1·03, 95% CI 0·73–1·45; p=0·88), smoking (434 [76%] participants vs 501 [77%] participants; 1·12, 0·67–1·87; p=0·66), alcohol consumption (518 [90%] participants vs 596 [92%] participants; 0·73, 0·37–1·44; p=0·36), and fruit and vegetable intake (194 [34%] participants vs 196 [30%] participants; 1·08, 0·79–1·49; p=0·64) did not differ between groups. We found no significant differences between the intervention and control groups in any secondary outcome. 147 (26%) intervention group participants engaged with the OL@-OR@ programme (ie, set at least one behaviour change goal online). Interpretation The OL@-OR@ mobile health programme did not improve adherence to health-related behaviour guidelines amongst Māori and Pasifika individuals. Funding Healthier Lives He Oranga Hauora National Science Challenge.
Using the Adapted Levenberg-Marquardt method to determine the validity of ignoring insulin and glucose data that is affected by mixing
(Elsevier B.V., 2021-04-14) Lam N; Docherty PD; Murray R; Chase JG; Morenga LT
Most parameter ID methods use least squares criterion to fit parameter values to observed behavior. However, the least squares criterion can be heavily influenced by outlying data or un-modelled effects. In such cases, least squares estimation can yield poor results. Outlying data is often manually removed to avoid inaccurate outcomes, but this process is complex, tedious and operator dependent. This research presents an adaptation of the Levenberg-Marquardt (L-M) parameter identification method that effectively ignores least-square contributions from outlying data. The adapted method (aL-M) is capable of ignoring outlier data in accordance with the coefficient of variation of the residuals and was thus, capable of operator independent omission of outlier data using the 3 standard deviation rule. The aL-M was compared to the original Levenberg-Marquardt (L-M) method in C-peptide, insulin and glucose data. In total three cases were tested: L-M in the full dataset, L-M in the same data where the points that were suspected to be affected by incomplete mixing at the depot site were removed, and the aL-M in the full data set. There were strong correlations between the aL-M and the reduced dataset from [0.85, 0.71] for the clinically valuable glucose parameters. In contrast, the unreduced data yielded poor residuals and poor correlations with the aL-M [0.44, 0.33]. The aL-M approach provided strong justification for consistent removal of data that was deemed to be affected by mixing.