Which behaviour change techniques are effective to promote physical activity and reduce sedentary behaviour in adults: a factorial randomized trial of an e- and m-health intervention.

BACKGROUND: E- and m-health interventions are promising to change health behaviour. Many of these interventions use a large variety of behaviour change techniques (BCTs), but it's not known which BCTs or which combination of BCTs contribute to their efficacy. Therefore, this experimental study investigated the efficacy of three BCTs (i.e. action planning, coping planning and self-monitoring) and their combinations on physical activity (PA) and sedentary behaviour (SB) against a background set of other BCTs. METHODS: In a 2 (action planning: present vs absent) × 2 (coping planning: present vs absent) × 2 (self-monitoring: present vs absent) factorial trial, 473 adults from the general population used the self-regulation based e- and m-health intervention 'MyPlan2.0' for five weeks. All combinations of BCTs were considered, resulting in eight groups. Participants selected their preferred target behaviour, either PA (n = 335, age = 35.8, 28.1% men) or SB (n = 138, age = 37.8, 37.7% men), and were then randomly allocated to the experimental groups. Levels of PA (MVPA in minutes/week) or SB (total sedentary time in hours/day) were assessed at baseline and post-intervention using self-reported questionnaires. Linear mixed-effect models were fitted to assess the impact of the different combinations of the BCTs on PA and SB. RESULTS: First, overall efficacy of each BCT was examined. The delivery of self-monitoring increased PA (t = 2.735, p = 0.007) and reduced SB (t = - 2.573, p = 0.012) compared with no delivery of self-monitoring. Also, the delivery of coping planning increased PA (t = 2.302, p = 0.022) compared with no delivery of coping planning. Second, we investigated to what extent adding BCTs increased efficacy. Using the combination of the three BCTs was most effective to increase PA (x2 = 8849, p = 0.003) whereas the combination of action planning and self-monitoring was most effective to decrease SB (x2 = 3.918, p = 0.048). To increase PA, action planning was always more effective in combination with coping planning (x2 = 5.590, p = 0.014; x2 = 17.722, p < 0.001; x2 = 4.552, p = 0.033) compared with using action planning without coping planning. Of note, the use of action planning alone reduced PA compared with using coping planning alone (x2 = 4.389, p = 0.031) and self-monitoring alone (x2 = 8.858, p = 003), respectively. CONCLUSIONS: This study provides indications that different (combinations of) BCTs may be effective to promote PA and reduce SB. More experimental research to investigate the effectiveness of BCTs is needed, which can contribute to improved design and more effective e- and m-health interventions in the future. TRIAL REGISTRATION: This study was preregistered as a clinical trial (ID number: NCT03274271 ). Release date: 20 October 2017.

Physical activity to improve cognition in older adults: can physical activity programs enriched with cognitive challenges enhance the effects? A systematic review and meta-analysis.

BACKGROUND: Aging-related cognitive decline and cognitive impairment greatly impacts older adults' daily life. The worldwide ageing of the population and associated wave of dementia urgently calls for prevention strategies to reduce the risk of cognitive decline. Physical activity (PA) is known to improve cognitive function at older age through processes of neuroplasticity. Yet, emerging studies suggest that larger cognitive gains may be induced when PA interventions are combined with cognitive activity (CA). This meta-analysis evaluates these potential synergistic effects by comparing cognitive effects following combined PA + CA interventions to PA interventions (PA only), CA interventions (CA only) and control groups. METHODS: Pubmed, Embase, PsycInfo, CINAHL and Sportdiscus were searched for English peer-reviewed papers until April 2018. Data were extracted on cognition and factors potentially influencing the cognitive effects: mode of PA + CA combination (sequential or simultaneous), session frequency and duration, intervention length and study quality. Differences between older adults with and without mild cognitive impairments were also explored. RESULTS: Forty-one studies were included. Relative to the control group, combined PA + CA intervention showed significantly larger gains in cognition (g = 0.316; 95% CI 0.188-0.443; p < .001). Studies that compared combined PA + CA with PA only, showed small but significantly greater cognitive improvement in favor of combined interventions (g = 0.160; 95% CI 0.041-0.279; p = .008). No significant difference was found between combined PA + CA and CA only interventions. Furthermore, cognitive effects tended to be more pronounced for studies using simultaneous designs (g = 0.385; 95%CI 0.214-0.555; p < .001) versus sequential designs (g = 0.114; 95%CI -0.102- 0.331, p = .301). Effects were not moderated by session frequency, session duration, intervention length or study quality. Also, no differences in effects were found between older adults with and without mild cognitive impairments. CONCLUSION: Findings of the current meta-analysis suggest that PA programs for older adults could integrate challenging cognitive exercises to improve cognitive health. Combined PA + CA programs should be promoted as a modality for preventing as well as treating cognitive decline in older adults. Sufficient cognitive challenge seems more important to obtain cognitive effects than high doses of intervention sessions.

Physical Activity, Sedentary Behavior, and Diet-Related eHealth and mHealth Research: Bibliometric Analysis.

BACKGROUND: Electronic health (eHealth) and mobile health (mHealth) approaches to address low physical activity levels, sedentary behavior, and unhealthy diets have received significant research attention. However, attempts to systematically map the entirety of the research field are lacking. This gap can be filled with a bibliometric study, where publication-specific data such as citations, journals, authors, and keywords are used to provide a systematic overview of a specific field. Such analyses will help researchers better position their work. OBJECTIVE: The objective of this review was to use bibliometric data to provide an overview of the eHealth and mHealth research field related to physical activity, sedentary behavior, and diet. METHODS: The Web of Science (WoS) Core Collection was searched to retrieve all existing and highly cited (as defined by WoS) physical activity, sedentary behavior, and diet related eHealth and mHealth research papers published in English between January 1, 2000 and December 31, 2016. Retrieved titles were screened for eligibility, using the abstract and full-text where needed. We described publication trends over time, which included journals, authors, and countries of eligible papers, as well as their keywords and subject categories. Citations of eligible papers were compared with those expected based on published data. Additionally, we described highly-cited papers of the field (ie, top ranked 1%). RESULTS: The search identified 4805 hits, of which 1712 (including 42 highly-cited papers) were included in the analyses. Publication output increased on an average of 26% per year since 2000, with 49.00% (839/1712) of papers being published between 2014 and 2016. Overall and throughout the years, eHealth and mHealth papers related to physical activity, sedentary behavior, and diet received more citations than expected compared with papers in the same WoS subject categories. The Journal of Medical Internet Research published most papers in the field (9.58%, 164/1712). Most papers originated from high-income countries (96.90%, 1659/1717), in particular the United States (48.83%, 836/1712). Most papers were trials and studied physical activity. Beginning in 2013, research on Generation 2 technologies (eg, smartphones, wearables) sharply increased, while research on Generation 1 (eg, text messages) technologies increased at a reduced pace. Reviews accounted for 20 of the 42 highly-cited papers (n=19 systematic reviews). Social media, smartphone apps, and wearable activity trackers used to encourage physical activity, less sedentary behavior, and/or healthy eating were the focus of 14 highly-cited papers. CONCLUSIONS: This study highlighted the rapid growth of the eHealth and mHealth physical activity, sedentary behavior, and diet research field, emphasized the sizeable contribution of research from high-income countries, and pointed to the increased research interest in Generation 2 technologies. It is expected that the field will grow and diversify further and that reviews and research on most recent technologies will continue to strongly impact the field.

Users' thoughts and opinions about a self-regulation-based eHealth intervention targeting physical activity and the intake of fruit and vegetables: A qualitative study.

PURPOSE: EHealth interventions are effective in changing health behaviours, such as increasing physical activity and altering dietary habits, but suffer from high attrition rates. In order to create interventions that are adapted to end-users, in-depth investigations about their opinions and preferences are required. As opinions and preferences may vary for different target groups, we explored these in two groups: the general population and a clinical sample. METHODS: Twenty adults from the general population (mean age = 42.65, 11 women) and twenty adults with type 2 diabetes (mean age = 64.30, 12 women) performed 'MyPlan 1.0', which is a self-regulation-based eHealth intervention designed to increase physical activity and the intake of fruit and vegetables in the general population. The opinions and preferences of end-users were explored using a think aloud procedure and a questionnaire. During a home visit, participants were invited to think aloud while performing 'MyPlan 1.0'. The thoughts were transcribed verbatim and inductive thematic analysis was applied. RESULTS: Both groups had similar opinions regarding health behaviours and 'MyPlan 1.0'. Participants generally liked the website, but often experienced it as time-consuming. Furthermore, they regularly mentioned that a mobile application would be useful to remind them about their goals on a daily basis. Finally, users' ideas about how to pursue health behaviours often hindered them to correctly use the website. CONCLUSIONS: Although originally created for the general population, 'MyPlan 1.0' can also be used in adults with type 2 diabetes. Nevertheless, more adaptations are needed to make the eHealth intervention more convenient and less time-consuming. Furthermore, users' ideas regarding a healthy lifestyle should be taken into account when designing online interventions.