Development and usability evaluation of a nutrition and lifestyle guidance application for people living with and beyond cancer

There is a need to provide accessible information for health care professionals and for people living beyond treatment. Mobile and digital health technologies provide an ideal platform to access diet and nutrition guidance that is both trusted and evidence-based and so that people know how to alter and monitor eating patterns and behaviours to improve the quality of life. Participatory design and usability evaluation approaches have been utilised to develop a nutrition and lifestyle guidance smartphone application for both people living with and beyond cancer, and for health care professionals involved in advising such patients. The challenges centred on the design, development and evaluation of the first version of a new mobile application named ‘Life Beyond’ are presented. This proof of concept application aims to centralise evidence-based nutrition and lifestyle guidance for those living beyond cancer. It enables users to obtain guidance and information, create and track nutrition and activity related goals and track their progress in the completion of these goals. Consistent feedback from participatory design and usability evaluations drove this research and helped to create an initial solution that met the user expectations. The System Usability Scale (SUS) score of 67.69 denotes an ‘average’ usability and hence further development. More research of extensive end user engagement is needed before an optimal solution is disseminated

Systematic review of context-aware digital behavior change interventions to improve health

Health risk behaviors are leading contributors to morbidity, premature mortality associated with chronic diseases, and escalating health costs. However, traditional interventions to change health behaviors often have modest effects, and limited applicability and scale. To better support health improvement goals across the care continuum, new approaches incorporating various smart technologies are being utilized to create more individualized digital behavior change interventions (DBCIs). The purpose of this study is to identify context-aware DBCIs that provide individualized interventions to improve health. A systematic review of published literature (2013-2020) was conducted from multiple databases and manual searches. All included DBCIs were context-aware, automated digital health technologies, whereby user input, activity, or location influenced the intervention. Included studies addressed explicit health behaviors and reported data of behavior change outcomes. Data extracted from studies included study design, type of intervention, including its functions and technologies used, behavior change techniques, and target health behavior and outcomes data. Thirty-three articles were included, comprising mobile health (mHealth) applications, Internet of Things wearables/sensors, and internet-based web applications. The most frequently adopted behavior change techniques were in the groupings of feedback and monitoring, shaping knowledge, associations, and goals and planning. Technologies used to apply these in a context-aware, automated fashion included analytic and artificial intelligence (e.g., machine learning and symbolic reasoning) methods requiring various degrees of access to data. Studies demonstrated improvements in physical activity, dietary behaviors, medication adherence, and sun protection practices. Context-aware DBCIs effectively supported behavior change to improve users' health behaviors

Developing Empirical Decision Points to Improve the Timing of Adaptive Digital Health Physical Activity Interventions in Youth: Survival Analysis

A grant from the One-University Open Access Fund at the University of Kansas was used to defray the author's publication fees in this Open Access journal. The Open Access Fund, administered by librarians from the KU, KU Law, and KUMC libraries, is made possible by contributions from the offices of KU Provost, KU Vice Chancellor for Research & Graduate Studies, and KUMC Vice Chancellor for Research. For more information about the Open Access Fund, please see http://library.kumc.edu/authors-fund.xml.Background: Current digital health interventions primarily use interventionist-defined rules to guide the timing of intervention delivery. As new temporally dense data sets become available, it is possible to make decisions about the intervention timing empirically. Objective: This study aimed to explore the timing of physical activity among youth to inform decision points (eg, timing of support) for future digital physical activity interventions. Methods: This study comprised 113 adolescents aged between 13 and 18 years (mean age 14.64, SD 1.48 years) who wore an accelerometer for 20 days. Multilevel survival analyses were used to estimate the most likely time of day (via odds ratios and hazard probabilities) when adolescents accumulated their average physical activity. The interacting effects of physical activity timing and moderating variables were calculated by entering predictors, such as gender, sports participation, and school day, into the model as main effects and tested for interactions with the time of day to determine conditional main effects of these predictors. Results: On average, the likelihood that a participant would accumulate a typical amount of moderate-to-vigorous physical activity increased and peaked between 6 PM and 8 PM before decreasing sharply after 9 PM. Hazard and survival probabilities suggest that optimal decision points for digital physical activity programs could occur between 5 PM and 8 PM. Conclusions: Overall, the findings of this study support the idea that the timing of physical activity can be empirically identified and that these markers may be useful as intervention triggers.Society of Pediatric Psycholog

USO DE APPS PARA A PROMOÇÃO DOS CUIDADOS À SAÚDE

Introdução: A utilização da tecnologia para monitorar, promover cuidados e maior adesão aos tratamentos de saúde, já é uma realidade que facilita a maior integração entre equipe multiprofissional e usuário/paciente. O que se observa é um fluxo contínuo permeado pela troca constante de informações entre os agentes envolvidos nesse processo. Essa funcionalidade tornou-se possível pelo progresso do ciberespaço mundialmente, associado ao advento dos aplicativos para celulares smartphones [Apps], que possuem, entre suas características, a fácil utilização e o maior acesso a informação pelos usuários, as quais podem favorecer o binômio ensino-aprendizado. Objetivo: Este trabalho buscou investigar produções científicas a respeito da utilização de Apps para promoção da saúde, a partir de uma abordagem que fomente a relação ensino-aprendizagem. Metodologia: Foi realizada revisão de literatura integrativa nas mais importantes bases de dados indexadas, nos idiomas inglês e português, utilizando os descritores App [aplicativos] e cuidados e promoção à saúde, em associação entre si e isolados. Foram encontrados 81 artigos publicados, dos quais 42 se adequaram aos pré-requisitos do estudo. Considerações Finais: O uso de Apps voltados aos cuidados em saúde é crescente com diversas possibilidades em terapia. A utilização de aplicativos dessa natureza tem funcionado de maneira auxiliar na promoção dos cuidados à saúde, principalmente pelo maior acesso a informações, juntamente com a participação do usuário no seu tratamento. Por outro lado, a interface ensino-aprendizagem no que tange ao processo saúde doença ainda é pouco explorada.

USO DE APPS PARA A PROMOÇÃO DOS CUIDADOS À SAÚDE

Introdução: A utilização da tecnologia para monitorar, promover cuidados e maior adesão aos tratamentos de saúde, já é uma realidade que facilita a maior integração entre equipe multiprofissional e usuário/paciente. O que se observa é um fluxo contínuo permeado pela troca constante de informações entre os agentes envolvidos nesse processo. Essa funcionalidade tornou-se possível pelo progresso do ciberespaço mundialmente, associado ao advento dos aplicativos para celulares smartphones [Apps], que possuem, entre suas características, a fácil utilização e o maior acesso a informação pelos usuários, as quais podem favorecer o binômio ensino-aprendizado. Objetivo: Este trabalho buscou investigar produções científicas a respeito da utilização de Apps para promoção da saúde, a partir de uma abordagem que fomente a relação ensino-aprendizagem. Metodologia: Foi realizada revisão de literatura integrativa nas mais importantes bases de dados indexadas, nos idiomas inglês e português, utilizando os descritores App [aplicativos] e cuidados e promoção à saúde, em associação entre si e isolados. Foram encontrados 81 artigos publicados, dos quais 42 se adequaram aos pré-requisitos do estudo. Considerações Finais: O uso de Apps voltados aos cuidados em saúde é crescente com diversas possibilidades em terapia. A utilização de aplicativos dessa natureza tem funcionado de maneira auxiliar na promoção dos cuidados à saúde, principalmente pelo maior acesso a informações, juntamente com a participação do usuário no seu tratamento. Por outro lado, a interface ensino-aprendizagem no que tange ao processo saúde doença ainda é pouco explorada.

Mobile apps for health behaviour change in physical activity, diet, drug and alcohol use, and mental health: a systematic review

Background: With a growing focus on patient interaction with health management, mobile apps are increasingly used to deliver behavioural health interventions. The large variation in these mobile health apps - their target patient group, health behaviour, and behavioural change strategies - has resulted in a large but incohesive body of literature. Objective: The purpose of this systematic review was to assess the effectiveness of mobile apps at improving health behaviours and outcomes, and to examine the inclusion and effectiveness of Behaviour Change Techniques in mobile health apps. Methods: Medline, EMBASE, CINAHL, and Web of Science were systematically searched for articles published between 2014 and 2019 that evaluated mobile apps for health behaviour change. Two authors independently screened and selected studies according to the eligibility criteria. Data was extracted and risk of bias assessed by one reviewer and validated by a second reviewer. Results: 52 randomized controlled trials met the inclusion criteria and were included in analysis - 37 studies focused on physical activity, diet, or a combination of both, 11 on drug and alcohol use, and 4 on mental health. Participant perceptions were generally positive - only one app was rated as less helpful and satisfactory than the control - and the studies that measured engagement and usability found relatively high study completion rates (mean = 83.3%, n = 18) and ease of use ratings (3 significantly better than control, 9/15 rated >70%) . However, there was little evidence of changed behaviour or health outcomes. Conclusions: There was not strong evidence found to support the effectiveness of mobile apps at improving health behaviours or outcomes because few studies found significant differences between the app and control groups. Further research is needed to identify the behaviour change techniques that are most effective at promoting behaviour change. Improved reporting is necessary to accurately evaluate the mobile health app effectiveness and risk of bias

Activity Tracker Measurement of Physical Activity and Sedentary Time in the Workplace Including an Intervention Involving Reminders to Move

Sedentary time and physical inactivity have negative impacts on health and health costs as well as an impact on workplace wellbeing. There is evidence that people are more sedentary and engage in less physical activity on work days. Additionally, sedentary behavior has been found to increase distress and negative mood. Activity trackers such as Fitbits are a useful way to collect and intervene on sedentary behavior and potentially impact other factors of workplace wellbeing in real time and promote self-monitoring. The reminder to move prompts that are now part of Fitbit models provide an innovative and simple way to intervene on workplace sedentary behavior with hourly movement prompts. This study examined the impact of an intervention on sedentary time at work with Fitbit reminders to move and what impact the intervention had on other factors of workplace wellbeing including depression, positive and negative affect, job stress, and productivity. Participants were university employees who wore a Fitbit device for three weeks and completed pre-and post-study measures. For the first week, the Fitbit displayed only the watch screen with no access to other data. This was done to establish baseline data. For the second week, the Fitbit device and Fitbit app allowed for self-monitoring by displaying the activity being tracked, including steps, distance, calories expenditure, and stairs walked. For the third week, the sedentary time reduction was implemented by activating the Fitbit application reminder to move. This caused the Fitbit to vibrate at the 50-minute mark of the hour if the participant had not moved 250 steps in that time. Results show that having the reminders to move prompt activated decreased sedentary time at work and increased steps throughout the day on work days. These changes in sedentary time significantly contributed to decreases in depression. From the start of the study to after the intervention, on average participants reported significantly less depression, negative affect, and stress and more positive affect, affect balance, social functioning, physical functioning, and productivity at work. The benefits of in the moment self-monitoring and an intervention around sedentary time with Fitbits on factors of workplace wellbeing are discussed as well as limitations, and future directions

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