Amanda L. Rebar

A meta-meta-analysis of the effect of physical activity on depression and anxiety in non-clinical adult populations.

Amidst strong efforts to promote the therapeutic benefits of physical activity for reducing depression and anxiety in clinical populations, little focus has been directed towards the mental health benefits of activity for non-clinical populations. The objective of this meta-meta-analysis was to systematically aggregate and quantify high-quality meta-analytic findings of the effects of physical activity on depression and anxiety for non-clinical populations. A systematic search identified eight meta-analytic outcomes of randomised trials that investigated the effects of physical activity on depression or anxiety. The subsequent meta-meta-analyses were based on a total of 92 studies with 4310 participants for the effect of physical activity on depression and 306 study effects with 10,755 participants for the effect of physical activity on anxiety. Physical activity reduced depression by a medium effect [standardised mean difference (SMD) = −0.50; 95% CI: −0.93 to −0.06] and anxiety by a small effect (SMD = −0.38; 95% CI: −0.66 to −0.11). Neither effect showed significant heterogeneity across meta-analyses. These findings represent a comprehensive body of high-quality evidence that physical activity reduces depression and anxiety in non-clinical populations.

A systematic review of the effects of non-conscious regulatory processes in physical activity

ABSTRACT Physical activity theories have almost exclusively focused on conscious regulatory processes such as plans, beliefs, and expected value. The aim of this review was to aggregate the burgeoning evidence showing that physical activity is also partially determined by non-conscious processes (e.g., habits, automatic associations, priming effects). A systematic search was conducted and study characteristics, design, measures, effect size of the principle summary measures, and main conclusions of 52 studies were extracted by two independent coders. The findings support that habitual regulatory processes measured via self-report are directly associated with physical activity beyond conscious processes, and that there is likely interdependency between habit strength and intentions. Response latency measures of automatic associations with physical activity were widely disparate, precluding conclusions about specific effects. A small body of evidence demonstrated a variety of priming effects on physical activity. Overall, it is evident that physical activity is partially regulated by non-conscious processes, but there remain many unanswered questions for this area of research. Future research should refine the conceptualisation and measurement of non-conscious regulatory processes and determine how to harness them to promote physical activity.

The subjective experience of habit captured by self-report indexes may lead to inaccuracies in the measurement of habitual action

The habit construct, and proxy measures of habit such as frequency and recency measures of past behaviour (Bagozzi & Warshaw, 1990), has been a topic of considerable interest to health psychology theorists and researchers interested in the factors related to health behaviour (Ajzen, 2002; Chatzisarantis, Hagger, & Smith, 2007; Hagger, Anderson, Kyriakaki, & Darkings, 2007; Hagger, Chatzisarantis, & Biddle, 2001; Hagger, Chatzisarantis, & Harris, 2006; Norman & Conner, 2006; Norman, Conner, & Bell, 2000; Ouellette & Wood, 1998; Verplanken & Faes, 1999). Gardner (2014) recently reviewed the literature on the effects of the habit construct in health-related research. In the review, habit was defined as a ‘as a process by which a stimulus automatically generates an impulse towards action, based on learned stimulus-response associations’ (p. 4). Gardner’s review represents a step forward in understanding the various definitions of habit in the social psychological literature applied to health, and provides considerable insight into the definitions of habit, types of habitual behaviour, effectiveness of previous models and tests of habit in the health-related literature, the limitations of habit research, avenues for future research and implications for interventions. One topic on which Gardner focuses is the various means by which habit has been measured in previous research. He indicates that self-report habit indexes, in which individuals reflect on the automaticity of action through their previous experience, represent the typical means to measure habit (Gardner, Abraham, Lally, & de Bruijn, 2012; Verplanken & Orbell, 2003). Gardner touches upon some of the limitations of such measures, indicating that the measures neglect cues (Sniehotta & Presseau, 2012) and that some of the items likely invite responses consistent with frequency of action rather than automaticity (Gardner & Tang, 2014). In this commentary, we aim to contribute to, and extend, Gardner’s points on the limitations of habit measures. We contend that such measures do not solely capture habitual cues to behaviour or habitual action. Furthermore, we claim that the measures are problematic in that individuals are unlikely to have access or awareness of the cues and associated responses that give rise to habitual

Examining the Interplay of Processes Across Multiple Time-Scales: Illustration With the Intraindividual Study of Affect, Health, and Interpersonal Behavior (iSAHIB)

Human development is characterized by the complex interplay of processes that manifest at multiple levels of analysis and time-scales. The authors introduce the Intraindividual Study of Affect, Health and Interpersonal Behavior as a model for how multiple time-scale study designs facilitate more precise articulation of developmental theory. Combining age heterogeneity, longitudinal panel, daily diary, and experience sampling protocols, the study made use of smartphone and web-based technologies to obtain intensive longitudinal data from 150 persons age 18 to 89 years as they completed three 21-day measurement bursts, spanning 8,557 days and 64,112 social interactions, as they went about their daily lives. The authors illustrate how multiple time-scales of data can be used to articulate bioecological models of development and the interplay among more “distal” processes that manifest at “slower” time-scales (age-related differences and burst-to-burst changes in mental health) and more “proximal” processes that manifest at “faster” time-scales (changes in context that progress in accordance with the weekly calendar and family influence processes).

Enriching Psychological Assessment Using a Person-Specific Analysis of Interpersonal Processes in Daily Life

We present a series of methods and approaches for clinicians interested in tracking their individual patients over time and in the natural settings of their daily lives. The application of person-specific analyses to intensive repeated measurement data can assess some aspects of persons that are distinct from the valuable results obtained from single-occasion assessments. Guided by interpersonal theory, we assess a psychotherapy patient’s interpersonal processes as they unfold in his daily life. We highlight specific contexts that change these processes, use an informant report to examine discrepancies in his reported interpersonal processes, and examine how his interpersonal processes differ as a function of varying levels of self-esteem and anger. We advocate for this approach to complement existing psychological assessments and provide a scoring program to facilitate initial implementation.

Examining the use of evidence-based and social media supported tools in freely accessible physical activity intervention websites

BackgroundIt has been shown that physical activity is more likely to increase if web-based interventions apply evidence-based components (e.g. self-monitoring) and incorporate interactive social media applications (e.g. social networking), but it is unclear to what extent these are being utilized in the publicly available web-based physical activity interventions. The purpose of this study was to evaluate whether freely accessible websites delivering physical activity interventions use evidence-based behavior change techniques and provide social media applications.MethodsIn 2013, a systematic search strategy examined 750 websites. Data was extracted on a wide range of variables (e.g. self-monitoring, goal setting, and social media applications). To evaluate website quality a new tool, comprising three sub-scores (Behavioral Components, Interactivity and User Generated Content), was developed to assess implementation of behavior change techniques and social media applications. An overall website quality scored was obtained by summing the three sub-scores.ResultsForty-six publicly available websites were included in the study. The use of self-monitoring (54.3%), goal setting (41.3%) and provision of feedback (46%) was relatively low given the amount of evidence supporting these features. Whereas the presence of features allowing users to generate content (73.9%), and social media components (Facebook (65.2%), Twitter (47.8%), YouTube (48.7%), smartphone applications (34.8%)) was relatively high considering their innovative and untested nature. Nearly all websites applied some behavioral and social media applications. The average Behavioral Components score was 3.45 (±2.53) out of 10. The average Interactivity score was 3.57 (±2.16) out of 10. The average User Generated Content Score was 4.02 (±2.77) out of 10. The average overall website quality score was 11.04 (±6.92) out of 30. Four websites (8.7%) were classified as high quality, 12 websites (26.1%) were classified as moderate quality, and 30 websites (65.2%) were classified as low quality.ConclusionsDespite large developments in Internet technology and growth in the knowledge of how to develop more effective web-based interventions, overall website quality was low and the majority of freely available physical activity websites lack the components associated with behavior change. However, the results show that website quality can be improved by taking a number of simple steps, and the presence of social media applications in most websites is encouraging.

Cognitive control and the non-conscious regulation of health behavior

We agree with Buckley et al. (2014) that self-control processes are one important aspect of physical activity and sedentary behavior regulation, and that self-control training is an important avenue for health behavior intervention research. However, we believe the role of non-conscious regulatory processes of health behaviors was understated in that the focus was mostly on how non-conscious temptations can bias one toward unhealthy behaviors. We take this opportunity to extend this discussion by highlighting that health behaviors are also regulated by non-conscious processes, and that cognitive control training may also work to regulate behavior through these regulatory pathways. Buckley and colleagues propose that cognitive control abilities are instrumental for the regulation of physical activity, and this is supported by decades of accumulated evidence of the influence of self-regulation processes (e.g., goals, intentions, planning). However, this evidence shows that, at most, half of the variability of physical activity is explained by these constructs (e.g., Brassington et al., 2002; Webb and Sheeran, 2006; Rhodes and Dickau, 2012) suggesting a equally important role of other pathways to physical activity participation and persistence, of which non-conscious pathways are likely to be strong candidates. Health behavior is also driven by non-conscious processes that predispose individuals to act and are the manifestations of well-learned cue-response pairings (Dimmock and Banting, 2009; Rothman et al., 2009; Orbell and Verplanken, 2010; Marteau et al., 2012; Sheeran et al., 2013; Grove et al., 2014; Hagger et al., 2014). In terms of proposed mechanisms, non-conscious processes are related to physical activity by eliciting affective and visceral responses that occur within a fraction of a second after cue perception preceding any controlled deliberation (Murphy and Zajonc, 1993; Bargh et al., 1996; Cunningham et al., 2007). This is not to say that we think physical activity is exclusively determined by non-conscious processes. On the contrary, we propose that physical activity is a complex behavior determined by an interaction of the two. For example, an individual may make a quick, spontaneous, non-deliberative decision to exercise on the basis of the presentation of a well-learned cue (e.g., viewing their exercise program on the wall upon waking), but the knock-on effect of the decision may bring multiple well-learned but consciously-directed decisions into play (e.g., deciding to run or swim, choosing to do it alone or with others). So a non-conscious process may set in motion a series of more consciously-controlled processes leading to action. Attraction and approach responses to physical activity are an important consideration when predicting and understanding physical activity behavior, as outlined by the correlations observed between decision tasks containing activity-related stimuli and physical activity participation (Conroy et al., 2010; Hyde et al., 2012). Research in neuroscience supports the contention that behaviors like physical activity are not exclusively the result of conscious processes and can, to some extent, become guided by automatic processes. Physical activity is often considered to be controlled by deliberative pathways, with concomitant activity in the frontal-parietal and cingulo-oppercular networks. However, subcortical areas of the mesolimbic reward system, which represent reward and emotional valence of stimuli, including the nucleus accumbens, are also activated when individuals engage in acts of self-regulation (Heatherton and Wagner, 2011; Hagger and Chatzisarantis, 2013). This reward system, which likely works outside of a persons conscious awareness (Cunningham et al., 2004), can become trained to respond to certain cues based on learned reward expectancies, and so become hyperactive when salient cues or stimuli are present. For example, repeated presentation of stimuli that are initially controlled by deliberative pathways within the frontal parietal network may accompany feedback from the dopaminergic pathways in the subcortex, such as the mesolimbic dopamine system. Over time, the intrinsic rewards develop and lead to strong reinforced pathways to action. For example, in the early stages of adopting a behavior like physical activity, engagement of the behavior may initially be regulated through conscious control and determined by deliberative pathways, but concomitant responses in the subcortical reward regions of the brain in response to physical activity may reinforce the pathways and compel an individual to return to the activity. After sufficient repetition, the process becomes less deliberative, and the pathways determining the initiation of the action in response to salient cues are met with stronger neural activity relative to competing processes, such that the decision-making processes leading to action are strong and efficient (Miller and Cohen, 2001; Heatherton and Wagner, 2011; Labrecque and Wood, 2015). As highlighted by Buckley et al. (p. 3), there are costs to overexertion of self-regulation, and individuals tend to be confronted with an array of competing demands and alternative goals (Hagger, 2013; Kurzban et al., 2013). We propose that using self-control training, referred to as cognitive control training by Buckley and co-authors, can also enhance the non-conscious regulation of health behavior. Training may be more beneficial for health behavior maintenance than strictly focusing on the enhancement of consciously regulated processes. People may be more effective at maintaining healthy behaviors by shifting more behavior regulation over to non-conscious processes, thereby reducing the need to consciously attend to these processes. A meta-analysis of the effects of self-control on a wide range of behaviors showed that self-control is more strongly linked to non-conscious behaviors than to consciously-regulated behaviors (De Ridder et al., 2012), which highlights the potential for the utilization of self-control training as a means to enhance non-conscious regulation of physical activity via the formation and maintenance of strong habits. This evidence supports the proposition put forth by Hagger and colleagues (Hagger and Chatzisarantis, 2014; Hagger and Luszczynska, 2014) that self-control can also act on behavior through non-conscious means. In summary, we contend that decisions to engage in physical activity may be partially determined by non-conscious processes. This pathway has tended to been neglected in previous research and was not explicitly outlined by Buckley et al. We agree with Buckley et al. that deliberative conscious control is required to overcome the powerful well-learned pathways to sedentary behavior and compete with weaker, less well-defined pathways leading to decisions to be physically active. Together, these processes form part of a holistic approach to understanding neural pathways to physical activity.

Habits Predict Physical Activity on Days When Intentions Are Weak

Physical activity is regulated by controlled processes, such as intentions, and automatic processes, such as habits. Intentions relate to physical activity more strongly for people with weak habits than for people with strong habits, but peoples intentions vary day by day. Physical activity may be regulated by habits unless daily physical activity intentions are strong. University students (N = 128) self-reported their physical activity habit strength and subsequently self-reported daily physical activity intentions and wore an accelerometer for 14 days. On days when people had intentions that were weaker than typical for them, habit strength was positively related to physical activity, but on days when people had typical or stronger intentions than was typical for them, habit strength was unrelated to daily physical activity. Efforts to promote physical activity may need to account for habits and the dynamics of intentions.

Child Sun Safety: Application of an Integrated Behavior Change Model

Objective: Childhood sun exposure increases risk of skin cancer in later life. Parents of young children play an important role in minimizing childhood sun exposure. The aim of the current study was to identify the motivational, volitional, and implicit antecedents of parents’ sun-protective behaviors based on an Integrated Behavior Change model. Method: Parents (N = 373) of 2- to 5-year-old children self-reported their intentions, attitudes, subjective norm, perceived behavioral control, autonomous and controlled motivation, action plans, habit, and past behaviors with respect to sun-protective behaviors for their children. Two weeks later (n = 273), the parents self-reported their participation in sun-protective behaviors for their child. Results: Data were analyzed using variance-based structural equation modeling. Results showed significant direct effects of attitudes, subjective norm, perceived behavioral control, and past behavior on intentions, and significant direct effects of autonomous motivation, perceived behavioral control, intentions, action planning, habit, and past behavior on parents’ participation in sun-protective behaviors for their child. There were also significant total indirect effects of autonomous motivation on intentions mediated by attitudes and subjective norm. Conclusions: Current results indicate that parents’ sun-protective behaviors toward their children are a function of motivational (autonomous motivation, intentions), volitional (action planning), and implicit (habit) factors. The findings from the current study provide formative data to inform the development of behavior change interventions to increase parents’ participation in sun-protective behaviors for their children.

Automatic Evaluation Stimuli – The Most Frequently Used Words to Describe Physical Activity and the Pleasantness of Physical Activity

Physical activity is partially regulated by non-conscious processes including automatic evaluations – the spontaneous affective reactions we have to physical activity that lead us to approach or avoid physical activity opportunities. A sound understanding of which words best represent the concepts of physical activity and pleasantness (as associated with physical activity) is needed to improve the measurement of automatic evaluations and related constructs (e.g., automatic self-schemas, attentional biases). The first aim of this study was to establish population-level evidence of the most common word stimuli for physical activity and pleasantness. Given that response latency measures have been applied to assess automatic evaluations of physical activity and exercise, the second aim was to determine whether people use the same behavior and pleasant descriptors for physical activity and exercise. Australian adults (N = 1,318; 54.3% women; 48.9% aged 55 years or older) were randomly assigned to one of two groups, through a computer-generated 1:1 ratio allocation, to be asked to list either five behaviors and pleasant descriptors of physical activity (n = 686) or of exercise (n = 632). The words were independently coded twice as to whether they were novel words or the same as another (i.e., same stem or same meaning). Intercoder reliability varied between moderate and strong (agreement = 50.1 to 97.8%; κ = 0.48 to 0.82). A list of the 20 most common behavior and pleasantness words were established based on how many people reported them, weighted by the ranking (1–5) people gave them. The words people described as physical activity were mostly the same as those people used to describe exercise. The most common behavior words were ‘walking,’ ‘running,’ ‘swimming,’ ‘bike riding,’ and ‘gardening’; and the most common pleasant descriptor words were ‘relaxing,’ ‘happiness,’ ‘enjoyment,’ ‘exhilarating,’ ‘exhausting,’ and ‘good.’ These sets of stimuli can be utilized as resources for response latency measurement tasks of automatic evaluations and for tools to enhance automatic evaluations of physical activity in evaluative conditioning tasks.