A mobile healthy lifestyle intervention to promote mental health in adolescence: a mixed-methods evaluation.

BACKGROUND: A healthy lifestyle may improve mental health. It is yet not known whether and how a mobile intervention can be of help in achieving this in adolescents. This study investigated the effectiveness and perceived underlying mechanisms of the mobile health (mHealth) intervention #LIFEGOALS to promote healthy lifestyles and mental health. #LIFEGOALS is an evidence-based app with activity tracker, including self-regulation techniques, gamification elements, a support chatbot, and health narrative videos. METHODS: A quasi-randomized controlled trial (N = 279) with 12-week intervention period and process evaluation interviews (n = 13) took place during the COVID-19 pandemic. Adolescents (12-15y) from the general population were allocated at school-level to the intervention (n = 184) or to a no-intervention group (n = 95). Health-related quality of life (HRQoL), psychological well-being, mood, self-perception, peer support, resilience, depressed feelings, sleep quality and breakfast frequency were assessed via a web-based survey; physical activity, sedentary time, and sleep routine via Axivity accelerometers. Multilevel generalized linear models were fitted to investigate intervention effects and moderation by pandemic-related measures. Interviews were coded using thematic analysis. RESULTS: Non-usage attrition was high: 18% of the participants in the intervention group never used the app. An additional 30% stopped usage by the second week. Beneficial intervention effects were found for physical activity (χ21 = 4.36, P = .04), sedentary behavior (χ21 = 6.44, P = .01), sleep quality (χ21 = 6.11, P = .01), and mood (χ21 = 2.30, P = .02). However, effects on activity-related behavior were only present for adolescents having normal sports access, and effects on mood only for adolescents with full in-school education. HRQoL (χ22 = 14.72, P < .001), mood (χ21 = 6.03, P = .01), and peer support (χ21 = 13.69, P < .001) worsened in adolescents with pandemic-induced remote-education. Interviewees reported that the reward system, self-regulation guidance, and increased health awareness had contributed to their behavior change. They also pointed to the importance of social factors, quality of technology and autonomy for mHealth effectiveness. CONCLUSIONS: #LIFEGOALS showed mixed results on health behaviors and mental health. The findings highlight the role of contextual factors for mHealth promotion in adolescence, and provide suggestions to optimize support by a chatbot and narrative episodes. TRIAL REGISTRATION: ClinicalTrials.gov [NCT04719858], registered on 22/01/2021.

Content and quality of physical activity ontologies: a systematic review.

INTRODUCTION: Ontologies are a formal way to represent knowledge in a particular field and have the potential to transform the field of health promotion and digital interventions. However, few researchers in physical activity (PA) are familiar with ontologies, and the field can be difficult to navigate. This systematic review aims to (1) identify ontologies in the field of PA, (2) assess their content and (3) assess their quality. METHODS: Databases were searched for ontologies on PA. Ontologies were included if they described PA or sedentary behavior, and were available in English language. We coded whether ontologies covered the user profile, activity, or context domain. For the assessment of quality, we used 12 criteria informed by the Open Biological and Biomedical Ontology (OBO) Foundry principles of good ontology practice. RESULTS: Twenty-eight ontologies met the inclusion criteria. All ontologies covered PA, and 19 included information on the user profile. Context was covered by 17 ontologies (physical context, n = 12; temporal context, n = 14; social context: n = 5). Ontologies met an average of 4.3 out of 12 quality criteria. No ontology met all quality criteria. DISCUSSION: This review did not identify a single comprehensive ontology of PA that allowed reuse. Nonetheless, several ontologies may serve as a good starting point for the promotion of PA. We provide several recommendations about the identification, evaluation, and adaptation of ontologies for their further development and use.

Neuroticism may not reflect emotional variability.

Neuroticism is one of the major traits describing human personality, and a predictor of mental and physical disorders with profound public health significance. Individual differences in emotional variability are thought to reflect the core of neuroticism. However, the empirical relation between emotional variability and neuroticism may be partially the result of a measurement artifact reflecting neuroticism's relation with higher mean levels-rather than greater variability-of negative emotion. When emotional intensity is measured using bounded scales, there is a dependency between variability and mean levels: at low (or high) intensity, it is impossible to demonstrate high variability. As neuroticism is positively associated with mean levels of negative emotion, this may account for the relation between neuroticism and emotional variability. In a metaanalysis of 11 studies (N = 1,205 participants; 83,411 observations), we tested whether the association between neuroticism and negative emotional variability was clouded by a dependency between variability and the mean. We found a medium-sized positive association between neuroticism and negative emotional variability, but, when using a relative variability index to correct for mean negative emotion, this association disappeared. This indicated that neuroticism was associated with experiencing more intense, but not more variable, negative emotions. Our findings call into question theory, measurement scales, and data suggesting that emotional variability is central to neuroticism. In doing so, they provide a revisionary perspective for understanding how this individual difference may predispose to mental and physical disorders.

Assessing sleep-related attitudes with the implicit association test: A prospective study in young adults.

The measurement of automatic attitudes towards sleep, in addition to reflective self-reports, might improve our ability to predict and explain sleep-hindering practices. Two types of implicit association tests (IATs), a sleep-related evaluations IAT and a sleep-related self-identity IAT, were developed to evaluate their efficacy for assessing automatic sleep-related attitudes. In addition, a speeded self-report measure of sleep evaluations was explored as a means to assess automatic sleep-related attitudes. The study included 136 young adults (age = 21.70 ± 2.22, 43% female). At baseline, the two IATs, the speeded self-report, and standard self-reports of sleep determinants (reflective attitudes, self-efficacy, intention and action planning for sleep-promoting behaviour), sleep hygiene practices, sleep quality, and sleep duration were assessed. All variables except for the sleep determinants were assessed again at 2-week follow-up. The results demonstrated good reliability of the two IAT versions, but both IATs were unrelated to the speeded self-report, the sleep determinants, sleep practices, sleep quality or sleep duration. The speeded self-report correlated significantly with the standard self-reports of sleep determinants. Baseline scores on the IATs or speeded self-report did not predict sleep hygiene practices, sleep duration or sleep quality at follow-up. The findings indicate that sleep-related IATs might not be suited to assess automatic sleep-related attitudes. Further investigation is needed to determine whether speeded self-reports are valid measures of automatic attitudes. Moreover, more empirical research is required to clarify the role of automatic processes for sleep hygiene behaviours.

Towards more personalized digital health interventions: a clustering method of action and coping plans to promote physical activity.

BACKGROUND: Despite effectiveness of action and coping planning in digital health interventions to promote physical activity (PA), attrition rates remain high. Indeed, support to make plans is often abstract and similar for each individual. Nevertheless, people are different, and context varies. Tailored support at the content level, involving suggestions of specific plans that are personalized to the individual, may reduce attrition and improve outcomes in digital health interventions. The aim of this study was to investigate whether user information relates toward specific action and coping plans using a clustering method. In doing so, we demonstrate how knowledge can be acquired in order to develop a knowledge-base, which might provide personalized suggestions in a later phase. METHODS: To establish proof-of-concept for this approach, data of 65 healthy adults, including 222 action plans and 204 coping plans, were used and were collected as part of the digital health intervention MyPlan 2.0 to promote PA. As a first step, clusters of action plans, clusters of coping plans and clusters of combinations of action plans and barriers of coping plans were identified using hierarchical clustering. As a second step, relations with user information (i.e. gender, motivational stage, ...) were examined using anova's and chi2-tests. RESULTS: First, three clusters of action plans, eight clusters of coping plans and eight clusters of the combination of action and coping plans were identified. Second, relating these clusters to user information was possible for action plans: 1) Users with a higher BMI related more to outdoor leisure activities (F = 13.40, P < .001), 2) Women, users that didn't perform PA regularly yet, or users with a job related more to household activities (X2 = 16.92, P < .001; X2 = 20.34, P < .001; X2 = 10.79, P = .004; respectively), 3) Younger users related more to active transport and different sports activities (F = 14.40, P < .001). However, relating clusters to user information proved difficult for the coping plans and combination of action and coping plans. CONCLUSIONS: The approach used in this study might be a feasible approach to acquire input for a knowledge-base, however more data (i.e. contextual and dynamic user information) from possible end users should be acquired in future research. This might result in a first type of context-aware personalized suggestions on the content level. TRIAL REGISTRATION: The digital health intervention MyPlan 2.0 was preregistered as a clinical trial (ID:NCT03274271). Release date: 6-September-2017.

Classification of painful or painless diabetic peripheral neuropathy and identification of the most powerful predictors using machine learning models in large cross-sectional cohorts.

BACKGROUND: To improve the treatment of painful Diabetic Peripheral Neuropathy (DPN) and associated co-morbidities, a better understanding of the pathophysiology and risk factors for painful DPN is required. Using harmonised cohorts (N = 1230) we have built models that classify painful versus painless DPN using quality of life (EQ5D), lifestyle (smoking, alcohol consumption), demographics (age, gender), personality and psychology traits (anxiety, depression, personality traits), biochemical (HbA1c) and clinical variables (BMI, hospital stay and trauma at young age) as predictors. METHODS: The Random Forest, Adaptive Regression Splines and Naive Bayes machine learning models were trained for classifying painful/painless DPN. Their performance was estimated using cross-validation in large cross-sectional cohorts (N = 935) and externally validated in a large population-based cohort (N = 295). Variables were ranked for importance using model specific metrics and marginal effects of predictors were aggregated and assessed at the global level. Model selection was carried out using the Mathews Correlation Coefficient (MCC) and model performance was quantified in the validation set using MCC, the area under the precision/recall curve (AUPRC) and accuracy. RESULTS: Random Forest (MCC = 0.28, AUPRC = 0.76) and Adaptive Regression Splines (MCC = 0.29, AUPRC = 0.77) were the best performing models and showed the smallest reduction in performance between the training and validation dataset. EQ5D index, the 10-item personality dimensions, HbA1c, Depression and Anxiety t-scores, age and Body Mass Index were consistently amongst the most powerful predictors in classifying painful vs painless DPN. CONCLUSIONS: Machine learning models trained on large cross-sectional cohorts were able to accurately classify painful or painless DPN on an independent population-based dataset. Painful DPN is associated with more depression, anxiety and certain personality traits. It is also associated with poorer self-reported quality of life, younger age, poor glucose control and high Body Mass Index (BMI). The models showed good performance in realistic conditions in the presence of missing values and noisy datasets. These models can be used either in the clinical context to assist patient stratification based on the risk of painful DPN or return broad risk categories based on user input. Model's performance and calibration suggest that in both cases they could potentially improve diagnosis and outcomes by changing modifiable factors like BMI and HbA1c control and institute earlier preventive or supportive measures like psychological interventions.

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.

Behavioral Conceptualization and Treatment of Chronic Pain.

Pain is considered a hardwired signal of bodily disturbance belonging to a basic motivational system that urges the individual to act and to restore the body's integrity, rather than just a sensory and emotional experience. Given its eminent survival value, pain is a strong motivator for learning. Response to repeated pain increases when harm risks are high (sensitization) and decreases in the absence of such risks (habituation). Discovering relations between pain and other events provides the possibility to predict (Pavlovian conditioning) and control (operant conditioning) harmful events. Avoidance is adaptive in the short term but paradoxically may have detrimental long-term effects. Pain and pain-related responses compete with other demands in the environment. Exposure-based treatments share the aim of facilitating or restoring the pursuit of individual valued life goals in the face of persistent pain, and further improvements in pain treatment may require a paradigm shift toward more personalized approaches.

Goal reengagement is related to mental well-being, life satisfaction and acceptance in people with an acquired brain injury.

OBJECTIVE: After an acquired brain injury (ABI), the achievement of previous life goals may no longer be feasible. This study examined whether self-reported disengagement from previous goals and reengagement towards new, more feasible goals, are associated with higher quality of life (QOL) and life satisfaction. We also examined whether acceptance mediated these relationships. METHODS: Eighty-two individuals (18-68 years of age) with an ABI completed a battery of questionnaires. We investigated the relations between goal disengagement and reengagement on the one hand, and general QOL, disease-specific QOL, life satisfaction and acceptance, on the other hand. Rehabilitation psychologists provided estimates of self-awareness and the extent of motor, communicative and cognitive impairment. RESULTS: Goal reengagement, but not goal disengagement, was positively associated with mental QOL and life satisfaction, after statistically controlling for demographic and impairments. Acceptance mediated the relationship between goal reengagement on the one hand, and mental QOL and life satisfaction, on the other hand. CONCLUSION: After an ABI, reengagement in feasible goals is more important in explaining mental well-being and life satisfaction than disengagement from unattainable goals. Interventions aimed at identifying and pursuing new, feasible goals may be more helpful than strategies focusing on the loss of blocked goals.

Estimation of Controlled Direct Effects in Longitudinal Mediation Analyses with Latent Variables in Randomized Studies.

In a randomized study with longitudinal data on a mediator and outcome, estimating the direct effect of treatment on the outcome at a particular time requires adjusting for confounding of the association between the outcome and all preceding instances of the mediator. When the confounders are themselves affected by treatment, standard regression adjustment is prone to severe bias. In contrast, G-estimation requires less stringent assumptions than path analysis using SEM to unbiasedly estimate the direct effect even in linear settings. In this article, we propose a G-estimation method to estimate the controlled direct effect of treatment on the outcome, by adapting existing G-estimation methods for time-varying treatments without mediators. The proposed method can accommodate continuous and noncontinuous mediators, and requires no models for the confounders. Unbiased estimation only requires correctly specifying a mean model for either the mediator or the outcome. The method is further extended to settings where the mediator or outcome, or both, are latent, and generalizes existing methods for single measurement occasions of the mediator and outcome to longitudinal data on the mediator and outcome. The methods are utilized to assess the effects of an intervention on physical activity that is possibly mediated by motivation to exercise in a randomized study.

Effectiveness of interventions using self-monitoring to reduce sedentary behavior in adults: a systematic review and meta-analysis.

BACKGROUND: Sedentary behavior occurs largely subconsciously, and thus specific behavior change techniques are needed to increase conscious awareness of sedentary behavior. Chief amongst these behavior change techniques is self-monitoring of sedentary behavior. The aim of this systematic review and meta-analysis was to evaluate the short-term effectiveness of existing interventions using self-monitoring to reduce sedentary behavior in adults. METHODS: Four electronic databases (PubMed, Embase, Web of Science, and The Cochrane Library) and grey literature (Google Scholar and the International Clinical Trials Registry Platform) were searched to identify appropriate intervention studies. Only (cluster-)randomized controlled trials that 1) assessed the short-term effectiveness of an intervention aimed at the reduction of sedentary behavior, 2) used self-monitoring as a behavior change technique, and 3) were conducted in a sample of adults with an average age ≥ 18 years, were eligible for inclusion. Relevant data were extracted, and Hedge's g was used as the measure of effect sizes. Random effects models were performed to conduct the meta-analysis. RESULTS: Nineteen intervention studies with a total of 2800 participants met the inclusion criteria. Results of the meta-analyses showed that interventions using self-monitoring significantly reduced total sedentary time (Hedges g = 0,32; 95% CI = 0,14 - 0,50; p = 0,001) and occupational sedentary time (Hedge's g = 0,56; 95% CI = 0,07 - 0,90; p = 0,02) on the short term. Subgroup analyses showed that significant intervention effects were only found if objective self-monitoring tools were used (g = 0,40; 95% CI = 0,19 - 0,60; p < 0,001), and if the intervention only targeted sedentary behavior (g = 0,45; 95% CI = 0,15-0,75; p = 0,004). No significant intervention effects were found on the number of breaks in sedentary behavior. CONCLUSIONS: Despite the small sample sizes, and the large heterogeneity, results of the current meta-analysis suggested that interventions using self-monitoring as a behavior change technique have the potential to reduce sedentary behavior in adults. If future - preferably large-scale studies - can prove that the reductions in sedentary behavior are attributable to self-monitoring and can confirm the sustainability of this behavior change, multi-level interventions including self-monitoring may impact public health by reducing sedentary behavior.

The role of concern about falling on stepping performance during complex activities.

BACKGROUND: There is limited understanding of the underlying mechanisms explaining the role of concern about falling on fall risk in older people. Anxiety is known to interact with cognitive resources and, as people get older, they require more cognitive resources to maintain balance. This might affect an individual's ability to perform cognitive-motor tasks concurrently. The aim of this study was to investigate the effect of a visuospatial dual-task on stepping performance in older people with and without concern about falling and the impact of repeating this task in those with high concern about falling. METHODS: Three-hundred-eight community-dwelling older people, aged 70 to 90 years old, participated in the study. Participants were asked to perform a Choice Stepping Reaction Time (CSRT) task in two conditions; once without any other tasks (single task condition), and once while simultaneously performing a visuospatial task (dual-task condition). Participants were asked to rate their levels of concern and confidence specifically related to each of the 25 stepping trials (before/after). We also measured general concern about falling, affect, and sensorimotor and cognitive functioning. RESULTS: Total stepping reaction times increased when participants also performed the visuospatial task. The relation between general concern about falling and stepping reaction time, was affected by sensorimotor and executive functioning. Generalised linear mixed models showed that the group with moderate to high levels of general concern about falling had slower total stepping reaction times than those with lower levels of concern about falling, especially during the dual-task condition. Individuals with greater general concern about falling showed reduced confidence levels about whether they could do the stepping tasks under both conditions. Repeatedly performing the stepping task reduced the immediate task-specific concern about falling levels and increased confidence in all participants. CONCLUSIONS: These findings reveal that people with higher general concern about falling experienced more difficulties during a dual-task condition than people with lower levels of concern. Of further interest, better sensorimotor and cognitive functioning reduced this effect. Graded exposure has potential to reduce concern about falling during fear-evoking activities, especially in conjunction with therapies that improve balance, mood and cognitive function.

Results of MyPlan 2.0 on Physical Activity in Older Belgian Adults: Randomized Controlled Trial.

BACKGROUND: The beneficial effects of physical activity (PA) for older adults are well known. However, few older adults reach the health guideline of 150 min per week of moderate-to-vigorous PA (MVPA). Electronic health (eHealth) interventions are effective in increasing PA levels in older adults in the short term but, rarely, intermediate-term effects after a period without the support of a website or an app have been examined. Furthermore, current theory-based interventions focus mainly on preintentional determinants, although postintentional determinants should also be included to increase the likelihood of successful behavior change. OBJECTIVE: This study aimed to investigate the effect of the theory-based eHealth intervention, MyPlan 2.0, focusing on pre- and postintentional determinants on both accelerometer-based and self-reported PA levels in older Belgian adults in the short and intermediate term. METHODS: This study was a randomized controlled trial with three data collection points: baseline (N=72), post (five weeks after baseline; N=65), and follow-up (three months after baseline; N=65). The study took place in Ghent, and older adults (aged ≥65 years) were recruited through a combination of random and convenience sampling. At all the time points, participants were visited by the research team. Self-reported domain-specific PA was assessed using the International Physical Activity Questionnaire, and accelerometers were used to objectively assess PA. Participants in the intervention group got access to the eHealth intervention, MyPlan 2.0, and used it independently for five consecutive weeks after baseline. MyPlan 2.0 was based on the self-regulatory theory and focused on both pre- and postintentional processes to increase PA. Multilevel mixed-models repeated measures analyses were performed in R (R Foundation for Statistical Computing). RESULTS: Significant (borderline) positive intervention effects were found for accelerometer-based MVPA (baseline-follow-up: intervention group +5 min per day and control group -5 min per day; P=.07) and for accelerometer-based total PA (baseline-post: intervention group +20 min per day and control group -24 min per day; P=.05). MyPlan 2.0 was also effective in increasing self-reported PA, mainly in the intermediate term. A positive intermediate-term intervention effect was found for leisure-time vigorous PA (P=.02), moderate household-related PA (P=.01), and moderate PA in the garden (P=.04). Negative intermediate-term intervention effects were found for leisure-time moderate PA (P=.01) and cycling for transport (P=.07). CONCLUSIONS: The findings suggest that theory-based eHealth interventions focusing on pre- and postintentional determinants have the potential for behavior change in older adults. If future studies including larger samples and long-term follow-up can confirm and clarify these findings, researchers and practitioners should be encouraged to use a self-regulation perspective for eHealth intervention development. TRIAL REGISTRATION: Clinicaltrials.gov NCT03194334; https://clinicaltrials.gov/ct2/show/NCT03783611.

Efficacy of a Self-Regulation-Based Electronic and Mobile Health Intervention Targeting an Active Lifestyle in Adults Having Type 2 Diabetes and in Adults Aged 50 Years or Older: Two Randomized Controlled Trials.

BACKGROUND: Adopting an active lifestyle plays a key role in the prevention and management of chronic diseases such as type 2 diabetes mellitus (T2DM). Web-based interventions are able to alter health behaviors and show stronger effects when they are informed by a behavior change theory. MyPlan 2.0 is a fully automated electronic health (eHealth) and mobile health (mHealth) intervention targeting physical activity (PA) and sedentary behavior (SB) based on the Health Action Process Approach (HAPA). OBJECTIVE: This study aimed to test the short-term effect of MyPlan 2.0 in altering levels of PA and SB and in changing personal determinants of behavior in adults with T2DM and in adults aged ≥50 years. METHODS: The study comprised two randomized controlled trials (RCTs) with an identical design. RCT 1 was conducted with adults with T2DM. RCT 2 was performed in adults aged ≥50 years. Data were collected via face-to-face assessments. The participants decided either to increase their level of PA or to decrease their level of SB. The participants were randomly allocated with a 2:1 ratio to the intervention group or the waiting-list control group. They were not blinded for their group allocation. The participants in the intervention group were instructed to go through MyPlan 2.0, comprising 5 sessions with an interval of 1 week between each session. The primary outcomes were objectively measured and self-reported PA (ie, light PA, moderate-to-vigorous PA, total PA, number of steps, and domain-specific [eg, transport-related] PA) and SB (ie, sitting time, number of breaks from sitting time, and length of sitting bouts). Secondary outcomes were self-reported behavioral determinants for PA and SB (eg, self-efficacy). Separate linear mixed models were performed to analyze the effects of MyPlan 2.0 in the two samples. RESULTS: In RCT 1 (n=54), the PA intervention group showed, in contrast to the control group, a decrease in self-reported time spent sitting (P=.09) and an increase in accelerometer-measured moderate (P=.05) and moderate-to-vigorous PA (P=.049). The SB intervention group displayed an increase in accelerometer-assessed breaks from sedentary time in comparison with the control group (P=.005). A total of 14 participants of RCT 1 dropped out. In RCT 2 (n=63), the PA intervention group showed an increase for self-reported total PA in comparison with the control group (P=.003). Furthermore, in contrast to the control group, the SB intervention group decreased their self-reported time spent sitting (P=.08) and increased their accelerometer-assessed moderate (P=.06) and moderate-to-vigorous PA (P=.07). A total of 8 participants of RCT 2 dropped out. CONCLUSIONS: For both the samples, the HAPA-based eHealth and mHealth intervention, MyPlan 2.0, was able to improve only some of the primary outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT03291171; http://clinicaltrials.gov/ct2/show/NCT03291171. ClinicalTrials.gov NCT03799146; http://clinicaltrials.gov/ct2/show/NCT03799146. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/12413.

How Users Experience and Use an eHealth Intervention Based on Self-Regulation: Mixed-Methods Study.

BACKGROUND: eHealth interventions show stronger effects when informed by solid behavioral change theories; for example, self-regulation models supporting people in translating vague intentions to specific actions have shown to be effective in altering health behaviors. Although these theories inform developers about which behavioral change techniques should be included, they provide limited information about how these techniques can be engagingly implemented in Web-based interventions. Considering the high levels of attrition in eHealth, investigating users' experience about the implementation of behavior change techniques might be a fruitful avenue. OBJECTIVE: The objective of our study was to investigate how users experience the implementation of self-regulation techniques in a Web-based intervention targeting physical activity and sedentary behavior in the general population. METHODS: In this study, 20 adults from the general population used the intervention for 5 weeks. Users' website data were explored, and semistructured interviews with each of the users were performed. A directed content analysis was performed using NVivo Software. RESULTS: The techniques "providing feedback on performance," "action planning," and "prompting review of behavioral goals" were appreciated by users. However, the implementation of "barrier identification/problem solving" appeared to frustrate users; this was also reflected by the users' website data-many coping plans were of poor quality. Most users were well aware of the benefits of adopting a more active way of living and stated not to have learned novel information. However, they appreciated the provided information because it reminded them about the importance of having an active lifestyle. Furthermore, prompting users to self-monitor their behavioral change was not sufficiently stimulating to make users actually monitor their behavior. CONCLUSIONS: Iteratively involving potential end users offers guidance to optimally adapt the implementation of various behavior change techniques to the target population. We recommend creating short interventions with a straightforward layout that support users in creating and evaluating specific plans for action.

Paul Eelen: Reflections on Life and Work.

This manuscript is part of a special issue to commemorate professor Paul Eelen, who passed away on August 21, 2016. Paul was a clinically oriented scientist, for whom learning principles (Pavlovian or operant) were more than salivary responses and lever presses. His expertise in learning psychology and his enthusiasm to translate this knowledge to clinical practice inspired many inside and outside academia. Several of his original writings were in the Dutch language. Instead of editing a special issue with contributions of colleagues and friends, we decided to translate a selection of his manuscripts to English to allow wide access to his original insights and opinions. Even though the manuscripts were written more than two decades ago, their content is surprisingly contemporary. This introductory article presents a reflection on Paul's career and legacy and introduces the selected manuscripts that are part of this special issue.

A Self-Regulation-Based eHealth Intervention to Promote a Healthy Lifestyle: Investigating User and Website Characteristics Related to Attrition.

BACKGROUND: eHealth interventions can reach large populations and are effective in increasing physical activity (PA) and fruit and vegetable intake. Nevertheless, the effects of eHealth interventions are overshadowed by high attrition rates. Examining more closely when users decide to leave the intervention can help eHealth developers to make informed decisions about which intervention components should be reshaped or simply removed. Investigating which users are more likely to quit an intervention can inform developers about whether and how their intervention should be adapted to specific subgroups of users. OBJECTIVE: This study investigated the pattern of attrition in a Web-based intervention to increase PA, fruit, and vegetable intake. The first aim was to describe attrition rates according to different self-regulation components. A second aim was to investigate whether certain user characteristics are predictors for start session completion, returning to a follow-up session and intervention completion. METHODS: The sample consisted of 549 adults who participated in an online intervention, based on self-regulation theory, to promote PA and fruit and vegetable intake, called "MyPlan 1.0." Using descriptive analysis, attrition was explored per self-regulation component (eg, action planning and coping planning). To identify which user characteristics predict completion, logistic regression analyses were conducted. RESULTS: At the end of the intervention program, there was an attrition rate of 78.2% (330/422). Attrition rates were very similar for the different self-regulation components. However, attrition levels were higher for the fulfillment of questionnaires (eg, to generate tailored feedback) than for the more interactive components. The highest amount of attrition could be observed when people were asked to make their own action plan. There were no significant predictors for first session completion. Yet, two subgroups had a lower chance to complete the intervention, namely male users (OR: 2.24, 95% CI=1.23-4.08) and younger adults (OR: 1.02, 95% CI=1.00-1.04). Furthermore, younger adults were less likely to return to the website for the first follow-up after one week (OR: 1.03, 95% CI=1.01-1.04). CONCLUSIONS: This study informs us that eHealth interventions should avoid the use of extensive questionnaires and that users should be provided with a rationale for several components (eg, making an action plan and completing questions). Furthermore, future interventions should focus first on motivating users for the behavior change before guiding them through action planning. Though, this study provides no evidence for removal of one of the self-regulation techniques based on attrition rates. Finally, strong efforts are needed to motivate male users and younger adults to complete eHealth interventions.

The effect of experimental low back pain on lumbar muscle activity in people with a history of clinical low back pain: a muscle functional MRI study.

In people with a history of low back pain (LBP), structural and functional alterations have been observed at several peripheral and central levels of the sensorimotor pathway. These existing alterations might interact with the way the sensorimotor system responds to pain. We examined this assumption by evaluating the lumbar motor responses to experimental nociceptive input of 15 participants during remission of unilateral recurrent LBP. Quantitative T2 images (muscle functional MRI) were taken bilaterally of multifidus, erector spinae, and psoas at several segmental levels (L3 upper and L4 upper and lower endplate) and during several conditions: 1) at rest, 2) upon trunk-extension exercise without pain, and 3) upon trunk-extension exercise with experimental induced pain at the clinical pain-side (1.5-ml intramuscular hypertonic saline injections in erector spinae). Following experimental pain induction, muscle activity levels similarly reduced for all three muscles, on both painful and nonpainful sides, and at multiple segmental levels (P = 0.038). Pain intensity and localization from experimental LBP were similar as during recalled clinical LBP episodes. In conclusion, unilateral and unisegmental experimental LBP exerts a generalized and widespread decrease in lumbar muscle activity during remission of recurrent LBP. This muscle response is consistent with previous observed patterns in healthy people subjected to the same experimental pain paradigm. It is striking that similar inhibitory patterns in response to pain could be observed, despite the presence of preexisting alterations in the lumbar musculature during remission of recurrent LBP. These results suggest that motor output can modify along the course of recurrent LBP.

Attentional bias to pain-relevant body locations: New methods, new challenges.

In a recent issue of Consciousness and Cognition, Filbrich, Torta, Vanderclausen, Azanon, and Legrain (2016) commented on a paper in which we used a tactile Temporal Order Judgment (TOJ) task to show that expecting pain on a specific body location biased attention to that location (Vanden Bulcke, Crombez, Durnez, & Van Damme, 2015). Their main criticism is that the effects are likely to reflect response bias rather than genuine attentional bias. We agree that the TOJ task used may be susceptible to response bias, and welcome the authors' methodological suggestions to control for such bias. However, we feel that certain aspects of our work are misrepresented in their paper. Most importantly, we contest their argument that our instructions made the threat location task-relevant, thereby increasing risk of response bias. Further, we reply to other methodological and theoretical issues raised by these authors.

Effectiveness of the self-regulation eHealth intervention 'MyPlan1.0.' on physical activity levels of recently retired Belgian adults: a randomized controlled trial.

The study purpose was to test the effectiveness of the self-regulation eHealth intervention 'MyPlan1.0.' to increase physical activity (PA) in recently retired Belgian adults. This study was a randomized controlled trial with three points of follow-up/modules (baseline to 1-week to 1-month follow-up). In total, 240 recently retired adults (intervention group [IG]: n = 89; control group [CG]: n = 151) completed all three modules. The IG filled in evaluation questionnaires and received 'MyPlan1.0.', an intervention focusing on both pre- and post-intentional processes for behavioural change. The CG only filled in evaluation questionnaires. Self-reported PA was assessed using the long International Physical Activity Questionnaire, usual week version. Repeated-measures multivariate analysis of variances were conducted in SPSS 22.0. On the short-term (baseline to 1 week), the intervention significantly increased walking for transport (IG: +11 min/week, CG: -6 min/week; P < 0.01). On the intermediate-term (baseline to 1 month), the intervention increased transport-related walking (IG: +14 min/week, CG: +6 min/week; P < 0.01), leisure-time walking (IG: +26 min/week, CG: -14 min/week; P < 0.10), leisure-time vigorous PA (IG: +16 min/week, CG: -4 min/week; P < 0.01), moderate-intensity gardening (IG: +4 min/week, CG: -34 min/week; P < 0.10) and voluntary work-related vigorous PA (IG: +28 min/week, CG: +13 min/week; P < 0.10). Results show that our eHealth intervention is effective in recently retired adults. Future studies should include long-term follow-up to examine whether the effects persist over a longer period.