Mobile Health interventions to enhance physical activity. Overview, methodological considerations, and just-in-time adaptive interventions

Physical activity has far-reaching health benefits and contributes to the prevention of noncommunicable diseases like cardiovascular disease, cancer, and diabetes. Today\u27s level of physical activity; however, is below the recommendations of e.g. the World Health Organization for all age groups. This amount of physical inactivity (i.e. not meeting physical activity guidelines) contributes to the rising cases of noncommunicable diseases and is responsible for over 7% of all-cause deaths along with a huge economic toll on the society. Recently, the COVID-19 crisis aggravated matters as many opportunities to be physically active were limited and sports clubs were temporarily closed. Today, effective interventions with a large reach are required to facilitate health behavior change towards more physical activity in the population. Here, even minor changes towards a more physically active lifestyle e.g. going for a daily ten-minute walk or interrupting prolonged physical inactivity can accumulate valuable health benefits over time. There are a variety of evidence-based interventions for different settings which range from individual or group-based face-to-face interventions to digital interventions. While the former is well established in today\u27s physical activity promotion, especially for rehabilitation, the latter is especially promising to promote physical activity on a broad scale due to the availability, fast-evolving technological progress, and ease of use of digital devices in modern society. Digital interventions for health behavior change can be delivered on desktop personal computers (e.g. via DVD), over the internet (e.g. on websites), or on mobile devices (e.g. via text message or mobile application). As nearly every household worldwide has access to and experience with at least one of those devices, the potential reach and cost-efficiency of such interventions are promising. Here, the use of information and communication technologies for health, in general, is defined as electronic health while every health practice supported by mobile devices is defined as mobile health. Recently, technological advances lead to the development of smaller, more convenient, and accurate devices to continuously measure physical activity (e.g. energy expenditure, step count, and classification of physical exertion), physiological (e.g. heart rate, blood sugar, and cortisol), and report psychological (e.g. valence, energetic arousal, and calmness) parameters. This opens up new perspectives using multilevel modeling in longitudinal designs to distinguish between within- and between-person effects and allows for a higher grade of individualization of interventions. One intervention type which greatly benefits from these continuous measurements and the technological advances is just-in-time adaptive interventions. These interventions aim to deliver interventional content (e.g. motivation to be physically active) during the most promising time for the desired health behavior (i.e. physical activity) or during the most vulnerable time for unhealthy behavior (i.e. inactivity) and aim to maximize the usefulness of the intervention while minimizing participant burden. To do so, they rely on high-resolution data to depict opportune moments to deliver the intervention content. Recent progress with machine learning processes also benefits just-in-time adaptive interventions by offering sophisticated decision-making algorithms which can be guided by participants\u27 behavior and preferences. Previous studies on electronic and mobile interventions found heterogenic results for the effectiveness of digital health interventions for physical activity promotion. Here, evidence- and theory-based interventions which are guided by behavior change techniques (e.g. goal-setting or demonstration of behavior) were associated with higher intervention effectiveness. Furthermore, including the social context (e.g. peers, school, work, or family) in the interventions can be beneficial but it is important to distinguish between e.g. collaborative vs competitive settings based on participants\u27 preferences. Finally, a high degree of individualization delivered by e.g. just-in-time adaptive interventions can enhance the effectiveness of mobile health interventions. However, the importance of the different interventional and contextual facets along with additional influences on the evaluation of the effectiveness remains unclear in the fast-developing field of electronic and mobile health behavior change interventions for children, adolescents, and adults. To help close the gap between technological advances and the state of the research in electronic and mobile health interventions for physical activity promotion, this thesis aimed to 1) provide an overview of the effectiveness of electronic and mobile health interventions regarding physical activity promotion and 2) delve into important considerations and research gaps depicted by the overview (i.e. the choice of a measurement tool for physical activity and just-in-time adaptive interventions). In our first paper, we conducted an umbrella review to summarize the evidence on the overall effectiveness of electronic and mobile health interventions along with the association of the key facets of theoretical foundation, behavior change techniques, social context, and just-in-time adaptive interventions with effectiveness. Derived from the eleven included reviews (182 original studies) we found significant benefits in favor of the intervention group (vs. control or over time) in the majority of interventions (59%). Here, the use of theoretical foundations and behavior change techniques were associated with higher effectiveness, the social context was often reported but not evaluated and just-in-time adaptive interventions were not included in any of the studies. One frequently reported shortcoming was the difficulty do compare self-reported and device-based measured results between studies. These findings suggest the potential effectiveness of digital interventions which is very likely facilitated by the key facets. Moreover, these findings helped us to determine promising but understudied facets of intervention effectiveness (i.e. just-in-time adaptive interventions) and depict frequently reported methodological issues (i.e. comparability of different measurement tools) which we could address within our thesis. In our second paper, we explored the reliability, comparability, and stability of self-reported (i.e. questionnaire and physical activity diary) vs. device-based measured physical activity (i.e. analyzed using 10-second and 60-second epochs) in adults and children. We included two independent measurement weeks from 32 adults and 32 children in the control group of the SMARTFAMILY trial to investigate if the differences between measurement tools were systematic over time. Here, participants wore an accelerometer on the right hip during daily life and completed a daily physical activity diary for seven consecutive days. Additionally, the international physical activity questionnaire was completed by participants at the end of each week. Results indicated non-systematic differences between the measurement tools (up to four-fold). Higher associations between the measurement tools were found for moderate than for vigorous physical activity and the results differed between children and adults. These results confirm the importance of carefully considering the measurement tool to be suitable for the research question and target group and the very limited comparability between different measurement tools. Additionally, the differences within accelerometer-derived results (10-second epochs vs. 60-second epochs) point to the need for comprehensive reporting for each measurement tool to compare and replicate the results. In our third paper, we summarized previous frameworks of just-in-time adaptive interventions and pointed out opportunities and challenges within this research field. We combined recommendations of three previous frameworks and refined that just-in-time adaptive interventions should 1) correspond to real-time needs; 2) adapt to input data; 3) be system-triggered. This can be enhanced by 4) be goal-oriented; and 5) be customized to user preferences. By doing so, just-in-time adaptive interventions can achieve a high degree of individualization which is closely fitted to each individual. The main challenge hereby remains the opportune moment identification (i.e. the exact moment when participants are either likely to engage in unhealthy behavior or when they face opportunities to perform healthy behaviors) to timely deliver intervention content. This can be explored using ambulatory assessments and assessing the context of the behavior. The decision-making process can be enhanced by machine learning algorithms. These results guided the reporting and design of the examinations included in our fourth and fifth papers. In our fourth paper, we evaluated the importance of engaging with a just-in-time adaptive intervention triggered after a period of physical inactivity. For this secondary data analysis, 47 adults and 33 children were included in the analysis who wore an accelerometer on the right hip and used our SMARTFAMILY2.0 application during the three-week intervention period of the SMARTFAMILY2.0 trial. Here, we analyzed 907 just-in-time adaptive intervention triggers and compared step and metabolic equivalent count in the hour after occasions when participants answered the trigger (i.e. responded to the question regarding their previous physical inactivity) within 60 minutes ("engaged" condition) with the hour after occasions when they did not answer the trigger within 60 minutes ("not engaged" condition) in the mobile application. Results indicated significantly higher metabolic equivalent and step count for the "engaged" condition within-persons. This shows that if a person engaged with a trigger within 60 minutes, he or she showed significantly higher physical activity in the following hour compared to when the same person did not engage with the trigger. This expands previous research about participants\u27 engagement with the intervention and the importance of an opportune moment identification to enhance this engagement. In our fifth paper, we explored the association of sleep quality and core affect with physical activity during a mobile health intervention period. Based on the same intervention period reported in the fourth paper, but with different inclusion criteria for the data (e.g. minimum wear time of the accelerometer for 8 hours per day instead of 80% of the hour of interest), daily accumulated self-rated mental state was compared to step count and minutes of moderate-to-vigorous physical activity for 49 adults and 40 children in a secondary data analysis. Overall, 996 measurement days of the participants were included in this analysis. Our results showed that higher reported valence and energetic arousal values were associated with more physical activity, while higher reported calmness values were associated with less physical activity within-persons on the same day. No distinct association was found between sleep quality and physical activity. Our results confirm previous ambulatory assessment studies and we suggest that within-person associations of core affect should be considered when designing physical activity interventions for both children and adults. Additionally, core affect might be a promising consideration for opportune moment identifications in just-in-time adaptive interventions to evaluate the feasibility and causality of targeting changes in e.g. valence to improve subsequent and daily physical activity of participants using micro-randomized trials. Based on the current state of knowledge, our results above address important research gaps depicted by our overview in the field of digital interventions for physical activity promotion. One example is the understudied area of just-in-time adaptive interventions for which we provided a framework, evaluated the effect of engaging with such interventions on subsequent physical activity, and explored core affect and sleep quality as facilitators of physical activity behavior. With these findings in mind, we discussed important considerations to progress future mobile health studies for physical activity promotion in general, and just-in-time adaptive interventions in particular at the end of this work. Finally, we aimed to transfer this knowledge into a proposal for designing a just-in-time adaptive intervention in the special group of participants at risk for or with knee osteoporosis who could specifically benefit from this highly individualized approach

Key facets to build up eHealth and mHealth interventions to enhance physical activity, sedentary behavior and nutrition in healthy subjects – an umbrella review

Background Electronic (eHealth) and mobile (mHealth) health interventions can provide a large coverage, and are promising tools to change health behavior (i.e. physical activity, sedentary behavior and healthy eating). However, the determinants of intervention effectiveness in primary prevention has not been explored yet. Therefore, the objectives of this umbrella review were to evaluate intervention effectiveness, to explore the impact of pre-defined determinants of effectiveness (i.e. theoretical foundations, behavior change techniques, social contexts or just-in-time adaptive interventions), and to provide recommendations for future research and practice in the field of primary prevention delivered via e/mHealth technology. Methods PubMed, Scopus, Web of Science and the Cochrane Library were searched for systematic reviews and meta-analyses (reviews) published between January 1990 and May 2020. Reviews reporting on e/mHealth behavior change interventions in physical activity, sedentary behavior and/or healthy eating for healthy subjects (i.e. subjects without physical or physiological morbidities which would influence the realization of behaviors targeted by the respective interventions) were included if they also investigated respective theoretical foundations, behavior change techniques, social contexts or just-in-time adaptive interventions. Included studies were ranked concerning their methodological quality and qualitatively synthesized. Results The systematic search revealed 11 systematic reviews and meta-analyses of moderate quality. The majority of original research studies within the reviews found e/mHealth interventions to be effective, but the results showed a high heterogeneity concerning assessment methods and outcomes, making them difficult to compare. Whereas theoretical foundation and behavior change techniques were suggested to be potential positive determinants of effective interventions, the impact of social context remains unclear. None of the reviews included just-in-time adaptive interventions. Conclusion Findings of this umbrella review support the use of e/mHealth to enhance physical activity and healthy eating and reduce sedentary behavior. The general lack of precise reporting and comparison of confounding variables in reviews and original research studies as well as the limited number of reviews for each health behavior constrains the generalization and interpretation of results. Further research is needed on study-level to investigate effects of versatile determinants of e/mHealth efficiency, using a theoretical foundation and additionally explore the impact of social contexts and more sophisticated approaches like just-in-time adaptive interventions. Trial registration The protocol for this umbrella review was a priori registered with PROSPERO: CRD42020147902

The Tridirectional Relationship among Physical Activity, Stress, and Academic Performance in University Students: A Systematic Review and Meta-Analysis

Higher education students often suffer from physiological and psychological health problems caused by stress, which may negatively impact their academic performance (AP). Physical activity (PA) can be a promising strategy to buffer these stress-induced complaints. Therefore, the aim of this investigation was to summarize evidence for the tridimensional construct of PA, stress, and AP, as well as to quantify the relationships among these variables. Five databases (PubMed, Scopus, SMEI, ERIC, and Web of Science) were systematically searched in November 2019 for publications that examined PA, stress, and AP of university students, without any restrictions regarding the publication period. The systematic review includes four original research studies with a moderate-to-high risk of bias. Results of included studies were narratively summarized and quantified in a meta-analysis using random effect models. Whereas study results point to a positive relation between PA and AP, relationships between PA and stress seem to be negative, while the relation between stress and AP is undecided. The meta-analysis found no significant associations and considerable heterogeneity of the results. Findings indicate a research gap concerning the connection of PA, stress, and AP in university students. Future studies should use validated measuring tools and consider the timepoint of data collection in order to extract truly stressful periods

Comparison of Self-Reported and Device-Based Measured Physical Activity Using Measures of Stability, Reliability, and Validity in Adults and Children

Quantification of physical activity (PA) depends on the type of measurement and analysis method making it difficult to compare adherence to PA guidelines. Therefore, test-retest reliability, validity, and stability for self-reported (i.e., questionnaire and diary) and device-based measured (i.e., accelerometry with 10/60 s epochs) PA was compared in 32 adults and 32 children from the SMARTFAMILY study to examine if differences in these measurement tools are systematic. PA was collected during two separate measurement weeks and the relationship for each quality criteria was analyzed using Spearman correlation. Results showed the highest PA values for questionnaires followed by 10-s and 60-s epochs measured by accelerometers. Levels of PA were lowest when measured by diary. Only accelerometry demonstrated reliable, valid, and stable results for the two measurement weeks, the questionnaire yielded mixed results and the diary showed only a few significant correlations. Overall, higher correlations for the quality criteria were found for moderate than for vigorous PA and the results differed between children and adults. Since the differences were not found to be systematic, the choice of measurement tools should be carefully considered by anyone working with PA outcomes, especially if vigorous PA is the parameter of interest

Sleep quality, valence, energetic arousal, and calmness as predictors of device-based measured physical activity during a three-week mHealth intervention – An ecological momentary assessment study within the SMARTFAMILY trial = Schlafqualität, Valenz, energetische Erregung und Ruhe als Prädiktoren für gerätegestützt gemessene körperliche Aktivität während einer dreiwöchigen mHealth-Intervention – Eine ökologische Momentaufnahme-Studie im Rahmen der SMARTFAMILY-Studie

Physical inactivity is known to be a risk factor for several noncommunicable diseases and has a high prevalence in today’s society. Therefore, it is crucial to understand the psychological factors associated with physical activity (PA). Recent developments in the field of ambulatory assessment and technological advances are promising to enhance our understanding of this relationship by analyzing longitudinal data within- and between-persons. These analyses can reveal important factors to design behavior change interventions to enhance PA. Therefore, this study used an ecological momentary assessment during the 3‑week intervention period in the SMARTFAMILY2.0 trial and aimed to investigate whether valence, calmness, energetic arousal, and sleep quality predict daily steps and moderate to vigorous PA. Overall, 49 adults (35–60 years) and 40 children (5–19 years) were included in this analysis and self-rated their mental state within our smartphone application while also wearing a hip-worn accelerometer for 21 consecutive days (996 days included) during the intervention period. Multilevel analyses were conducted to predict daily PA while considering covariables (e.g., child/adult and non-wear time) both within- and between-persons. The results indicated that higher than average ratings of a person’s valence and energetic arousal on one day predicted increased PA while higher than average calmness predicted decreased PA at the same day within this person. Sleep quality and between-person effects of the affective states showed no clear associations to PA. Overall, these results showed that within-person associations of valence, calmness, and energetic arousal should be considered when designing PA interventions for both children and adults. The influence of sleep quality, as well as between-person effects, should be further explored by future studies

mHealth Interventions to Reduce Physical Inactivity and Sedentary Behavior in Children and Adolescents: Systematic Review and Meta-analysis of Randomized Controlled Trials

Background: Children and adolescents increasingly do not meet physical activity (PA) recommendations. Hence, insufficient PA (IPA) and sedentary behavior (SB) among children and adolescents are relevant behavior change domains for using individualized mobile health (mHealth) interventions. Objective: This review and meta-analysis investigated the effectiveness of mHealth interventions on IPA and SB, with a special focus on the age and level of individualization. Methods: PubMed, Scopus, Web of Science, SPORTDiscus, and Cochrane Library were searched for randomized controlled trials published between January 2000 and March 2021. mHealth interventions for primary prevention in children and adolescents addressing behavior change related to IPA and SB were included. Included studies were compared for content characteristics and methodological quality and summarized narratively. In addition, a meta-analysis with a subsequent exploratory meta-regression examining the moderating effects of age and individualization on overall effectiveness was performed. Results: On the basis of the inclusion criteria, 1.3% (11/828) of the preliminary identified studies were included in the qualitative synthesis, and 1.2% (10/828) were included in the meta-analysis. Trials included a total of 1515 participants (mean age (11.69, SD 0.788 years; 65% male and 35% female) self-reported (3/11, 27%) or device-measured (8/11, 73%) health data on the duration of SB and IPA for an average of 9.3 (SD 5.6) weeks. Studies with high levels of individualization significantly decreased insufficient PA levels (Cohen d=0.33; 95% CI 0.08-0.58; Z=2.55; P=.01), whereas those with low levels of individualization (Cohen d=−0.06; 95% CI −0.32 to 0.20; Z=0.48; P=.63) or targeting SB (Cohen d=−0.11; 95% CI −0.01 to 0.23; Z=1.73; P=.08) indicated no overall significant effect. The heterogeneity of the studies was moderate to low, and significant subgroup differences were found between trials with high and low levels of individualization (χ$^{2}$1=4.0; P=.04; I$^{2}$=75.2%). Age as a moderator variable showed a small effect; however, the results were not significant, which might have been because of being underpowered. Conclusions: Evidence suggests that mHealth interventions for children and adolescents can foster moderate reductions in IPA but not SB. Moreover, individualized mHealth interventions to reduce IPA seem to be more effective for adolescents than for children. Although, to date, only a few mHealth studies have addressed inactive and sedentary young people, and their quality of evidence is moderate, these findings indicate the relevance of individualization on the one hand and the difficulties in reducing SB using mHealth interventions on the other

Sleep quality, valence, energetic arousal, and calmness as predictors of device-based measured physical activity during a three-week mHealth intervention

Physical inactivity is known to be a risk factor for several noncommunicable diseases and has a high prevalence in today’s society. Therefore, it is crucial to understand the psychological factors associated with physical activity (PA). Recent developments in the field of ambulatory assessment and technological advances are promising to enhance our understanding of this relationship by analyzing longitudinal data within- and between-persons. These analyses can reveal important factors to design behavior change interventions to enhance PA. Therefore, this study used an ecological momentary assessment during the 3-week intervention period in the SMARTFAMILY2.0 trial and aimed to investigate whether valence, calmness, energetic arousal, and sleep quality predict daily steps and moderate to vigorous PA. Overall, 49 adults (35–60 years) and 40 children (5–19 years) were included in this analysis and self-rated their mental state within our smartphone application while also wearing a hip-worn accelerometer for 21 consecutive days (996 days included) during the intervention period. Multilevel analyses were conducted to predict daily PA while considering covariables (e.g., child/adult and non-wear time) both within- and between-persons. The results indicated that higher than average ratings of a person’s valence and energetic arousal on one day predicted increased PA while higher than average calmness predicted decreased PA at the same day within this person. Sleep quality and between-person effects of the affective states showed no clear associations to PA. Overall, these results showed that within- person associations of valence, calmness, and energetic arousal should be considered when designing PA interventions for both children and adults. The influence of sleep quality, as well as between-person effects, should be further explored by future studies

Associations between physical activity, physical fitness, and body composition in adults living in Germany: A cross-sectional study

Background & aims Body composition (BC) changes with age and is associated with morbidity and mortality. A physically active lifestyle influences BC and represents an important predictor of successful aging. To emphasize this, the World Health Organization established activity recommendations for all age groups. We describe BC during adulthood using a cross-sectional sample from a German community and investigate the associations between physical activity (PA), physical fitness (PF), and BC. Methods Data from 329 men and women aged 35 to 86 years were analyzed. PA was measured by questionnaire and classified into sport activity and habitual activity. PF was measured through physical performance tests and BC by bioelectrical impedance analysis. Fat mass index (FMI) and fat-free mass index (FFMI) were calculated to represent height-adjusted BC. Associations between PA, PF, and BC were analyzed using linear regression models. Results For both sexes, strength was positively associated with FFMI (♂: ß = 0.313; ♀: ß = 0.213) and phase angle (♂: ß = 0.357; ♀: ß = 0.409). For FMI, a significant negative association with strength was found only in women (ß = -0.189). Cardiorespiratory fitness showed a negative association with FMI (ß = -0.312) and FFMI (ß = -0.201) for men, while in women a positive association was found for FFMI (ß = 0.186). For coordination, a significant association with FMI was observed only in women (ß = -0.190). Regarding PA only one significant relationship between sport activity and FMI among women (ß = -0.170) was found

Toward a deeper understanding of the ecological origins of distance construal

International audienc

Walking and non-motorized vehicle use in adolescents: the role of neighborhood environment perceptions across urbanization levels

Promoting active travel is key to achieving the sustainable development goals of sustainable communities, climate action, and health and well-being. Walking and non-motorized vehicle use (e.g., cycling, longboarding) are influenced by the perceptions of the neighborhood environment. However, most evidence is limited to studies conducted in urban areas. This study aims to assess the relationship between perceived environment and walking as well as non-motorized vehicle use stratified across different levels of urbanicity in adolescents in Germany. Cross-sectional data of 3976 adolescents aged 11–17 (51% female) from the nationwide Motorik-Modul Longitudinal Study in Germany were used. Age, gender, socioeconomic status, neighborhood environment perceptions, duration of walking, and non-motorized vehicle use were assessed via questionnaire. Separate cumulative link mixed models were calculated to analyze the relationships between environment perceptions and walking as well as non-motorized vehicle use across rural areas, small towns, medium-sized towns, and cities. The presence of public sports facilities was related to both walking and non-motorized vehicle use across urbanicity levels. Relationships with other aspects of the perceived environment, such as traffic safety concerns and walking or cycling infrastructure, were more context-specific meaning that associations differed based on active travel mode and urbanicity level. Additionally, non-motorized vehicle use differed considerably across sample points. To conclude, when creating active and sustainable environments for active travel, it is crucial to target specific travel modes and take the urbanicity and regional context into account