supplemental_material_S2 – Supplemental material for Development and psychometric testing of an instrument to assess psychosocial determinants of sleep hygiene practice

Supplemental material, supplemental_material_S2 for Development and psychometric testing of an instrument to assess psychosocial determinants of sleep hygiene practice by Beatrice Murawski, Ronald C Plotnikoff and Mitch J Duncan in Journal of Health Psychology</p

supplemental_material_S1 – Supplemental material for Development and psychometric testing of an instrument to assess psychosocial determinants of sleep hygiene practice

Supplemental material, supplemental_material_S1 for Development and psychometric testing of an instrument to assess psychosocial determinants of sleep hygiene practice by Beatrice Murawski, Ronald C Plotnikoff and Mitch J Duncan in Journal of Health Psychology</p

Examining social-cognitive theory constructs as mediators of behaviour change in the active team smartphone physical activity program: A mediation analysis

Background: Regular engagement in physical activity has well-established physical and psychological health benefits. Despite this, over a quarter of the global adult population is insufficiently physically active. Physical activity interventions grounded in behaviour change theory, such as the social-cognitive theory, are widely considered to be more effective than non-theoretical approaches. Such interventions set out to intervene on the ultimate outcome (physical activity), but also influence intermediate factors (social-cognitive theory constructs) which in turn, are believed to influence physical activity behaviour. The primary aim of the study was to use mediation analysis to examine whether changes in the social-cognitive theory and related constructs, in particular self-efficacy, outcome expectations, intentions, barriers and goal setting, mediated the effects of a smartphone-based social networking physical activity intervention. Methods: Mediation analyses were conducted using the PROCESS Macro in SPSS to (i) calculate the regression coefficients for the effect of the independent variable (group allocation) on the hypothesised mediators (social-cognitive theory constructs), (ii) calculate the regression coefficient for the effect of the hypothesised mediators (social-cognitive theory constructs) on the dependent variable (objectively measured physical activity or self-report physical activity), independent of group assignment and (iii) determine the total, direct and indirect intervention effects. Results: Data from 243 participants were included in the mediation analysis. There was no evidence of mediation for change in objectively measured MVPA or self-reported MVPA. Conclusions: There was no conclusive evidence that any of the social-cognitive theory constructs mediated the relationship between an app-based intervention and change in physical activity. Ongoing efforts to develop and understand components that make physical activity app-based interventions effective are recommended. Trial registration: This trial was registered with the Australian and New Zealand Clinical Trial Registry (ACTRN12617000113358, date of registration 23 January, 2017). © 2021, The Author(s)

Effect of a physical activity and sleep m-health intervention on a composite activity-sleep behaviour score and mental health: A mediation analysis of two randomised controlled trials

Background: To examine if a composite activity-sleep behaviour index (ASI) mediates the effects of a combined physical activity and sleep intervention on symptoms of depression, anxiety, or stress, quality of life (QOL), energy and fatigue in adults. Methods: This analysis used data pooled from two studies: Synergy and Refresh. Synergy: Physically inactive adults (18–65 years) who reported poor sleep quality were recruited for a two-arm Randomised Controlled Trial (RCT) (Physical Activity and Sleep Health (PAS; n = 80), or Wait-list Control (CON; n = 80) groups). Refresh: Physically inactive adults (40–65 years) who reported poor sleep quality were recruited for a three-arm RCT (PAS (n = 110), Sleep Health-Only (SO; n = 110) or CON (n = 55) groups). The SO group was omitted from this study. The PAS groups received a pedometer, and accessed a smartphone/tablet “app” using behaviour change strategies (e.g., self-monitoring, goal setting, action planning), with additional email/SMS support. The ASI score comprised self-reported moderate-to-vigorous-intensity physical activity, resistance training, sitting time, sleep duration, efficiency, quality and timing. Outcomes were assessed using DASS-21 (depression, anxiety, stress), SF-12 (QOL-physical, QOL-mental) and SF-36 (Energy & Fatigue). Assessments were conducted at baseline, 3 months (primary time-point), and 6 months. Mediation effects were examined using Structural Equation Modelling and the product of coefficients approach (AB), with significance set at 0.05. Results: At 3 months there were no direct intervention effects on mental health, QOL or energy and fatigue (all p > 0.05), and the intervention significantly improved the ASI (all p < 0.05). A more favourable ASI score was associated with improved symptoms of depression, anxiety, stress, QOL-mental and of energy and fatigue (all p < 0.05). The intervention effects on symptoms of depression ([AB; 95%CI] -0.31; − 0.60,-0.11), anxiety (− 0.11; − 0.27,-0.01), stress (− 0.37; − 0.65,-0.174), QOL-mental (0.53; 0.22, 1.01) and ratings of energy and fatigue (0.85; 0.33, 1.63) were mediated by ASI. At 6 months the magnitude of association was larger although the overall pattern of results remained similar. Conclusions: Improvements in the overall physical activity and sleep behaviours of adults partially mediated the intervention effects on mental health and quality of life outcomes. This highlights the potential benefit of improving the overall pattern of physical activity and sleep on these outcomes. Trial registration: Australian New Zealand Clinical Trial Registry: ACTRN12617000680369; ACTRN12617000376347. Universal Trial number: U1111–1194-2680; U1111–1186-6588. Human Research Ethics Committee Approval: H-2016-0267; H-2016–0181

sj-pdf-2-hpq-10.1177_13591053221137184 – for Does matching a personally tailored physical activity intervention to participants’ learning style improve intervention effectiveness and engagement?

sj-pdf-2-hpq-10.1177_13591053221137184 for Does matching a personally tailored physical activity intervention to participants’ learning style improve intervention effectiveness and engagement? by Stephanie Alley, Ronald C Plotnikoff, Mitch J Duncan, Camille E Short, Kerry Mummery, Quyen G To, Stephanie Schoeppe, Amanda Rebar and Corneel Vandelanotte in Journal of Health Psychology</p

sj-sav-1-hpq-10.1177_13591053221137184 – for Does matching a personally tailored physical activity intervention to participants’ learning style improve intervention effectiveness and engagement?

sj-sav-1-hpq-10.1177_13591053221137184 for Does matching a personally tailored physical activity intervention to participants’ learning style improve intervention effectiveness and engagement? by Stephanie Alley, Ronald C Plotnikoff, Mitch J Duncan, Camille E Short, Kerry Mummery, Quyen G To, Stephanie Schoeppe, Amanda Rebar and Corneel Vandelanotte in Journal of Health Psychology</p

sj-sps-4-hpq-10.1177_13591053221137184 – for Does matching a personally tailored physical activity intervention to participants’ learning style improve intervention effectiveness and engagement?

sj-sps-4-hpq-10.1177_13591053221137184 for Does matching a personally tailored physical activity intervention to participants’ learning style improve intervention effectiveness and engagement? by Stephanie Alley, Ronald C Plotnikoff, Mitch J Duncan, Camille E Short, Kerry Mummery, Quyen G To, Stephanie Schoeppe, Amanda Rebar and Corneel Vandelanotte in Journal of Health Psychology</p

sj-spv-3-hpq-10.1177_13591053221137184 – for Does matching a personally tailored physical activity intervention to participants’ learning style improve intervention effectiveness and engagement?

sj-spv-3-hpq-10.1177_13591053221137184 for Does matching a personally tailored physical activity intervention to participants’ learning style improve intervention effectiveness and engagement? by Stephanie Alley, Ronald C Plotnikoff, Mitch J Duncan, Camille E Short, Kerry Mummery, Quyen G To, Stephanie Schoeppe, Amanda Rebar and Corneel Vandelanotte in Journal of Health Psychology</p

Examining moderators of the effectiveness of a web- and video-based computer-tailored physical activity intervention

Understanding for whom behaviour change interventions work is important, however there is a lack of studies examining potential moderators in such interventions. This study investigated potential moderators on the effectiveness of a computer-tailored intervention to increase physical activity among Australian adults. People who had <150 min of moderate-vigorous physical activity (MVPA) a week, able to speak and read English, aged ≥18 years, lived in Australia, and had internet access were eligible to participate. Participants recruited through social media, emails, and third-party databases, were randomly assigned to either the control (n = 167) or intervention groups (n = 334). Physical activity was measured objectively by ActiGraph GT3X and also by self-report at baseline and three months. Three-way interaction terms were tested to identify moderators (i.e., demographic characteristics, BMI, and perceived neighbourhood walkability). The results showed that the three-way interaction was marginally significant for sex on accelerometer measured MVPA/week (p = 0.061) and steps/day (p = 0.047). The intervention appeared to be more effective for women compared to men. No significant three-way interactions were found for the other potential moderators. Strategies to improve levels of personalisation may be needed so that physical activity interventions can be better tailored to different subgroups, especially sex, and therefore improve intervention effectiveness

Associations between app usage and behaviour change in a m-health intervention to improve physical activity and sleep health in adults: Secondary analyses from two randomised controlled trials

Background To examine associations between user engagement and activity-sleep patterns in a 12-week m-health behavioural intervention targeting physical activity and sleep. Methods This secondary analysis used data pooled from two Randomised Control Trials (RCT, [Synergy and Refresh]) that aimed to improve physical activity and sleep (PAS) among physically inactive adults with poor sleep. Both RCTs include a PAS intervention group (n = 190 [Synergy n = 80; Refresh n = 110]) and a wait list Control (CON n = 135 [Synergy n = 80; Refresh n = 55]). The PAS groups received a pedometer and accessed a smartphone/tablet “app” with behaviour change strategies, and email/SMS support. Activity-sleep patterns were quantified using the activity-sleep behaviour index (ASI) based on self-report measures. Intervention usage was quantified as a composite score of the frequency, intensity and duration of app usage during intervention (range: 0–30). Assessments were conducted at baseline, 3 and 6 months. Relationships between usage and ASI were examined using generalised linear models. Differences in ASI between the control group and intervention usage groups (Low [0–10.0], Mid [10.1–20.0], High [20.1–30.0]) were examined using generalised linear mixed models adjusted for baseline values of the outcome. Trial Registration: ACTRN12617000376347; ACTRN12617000680369. Results During the 3-month intervention, the mean (± sd) usage score was 18.9 ± 9.5. At 3 months (regression coefficient [95%CI]: 0.45 [0.22, 0.68]) and 6 months (0.48 [0.22, 0.74]) there was a weak association between usage score and ASI in the intervention group. At 3 months, ASI scores in the Mid (Mean [95%CI] = 57.51 [53.99, 61.04]) and High (60.09 [57.52, 62.67]) usage groups were significantly higher (better) than the control group (51.91 [49.58, 54.24]), but not the Low usage group (47.49 [41.87, 53.12]). Only differences between the high usage and control group remained at 6 months. Conclusion These findings suggests that while higher intervention usage is associated with improvements in behaviour, the weak magnitude of this association suggests that other factors are also likely to influence behaviour change in m-health interventions