Background Physical activity (PA) can increase mental and physical health in adults aged 50 years and older. However, it has been shown that PA guidelines are often not met within this population. Therefore, our research group developed 2 computer-tailored intervention programs in the last decade to stimulate PA: Active Plus and I Move. Although these programs were proven effective, positive effects diminished over time and attrition rates were relatively high. To respond to this, we will integrate 3 interactive mobile elements into the existing programs: activity tracker, ecological momentary intervention program, and virtual coach app. Objective The goal of the research is to define systematic and evidence-based steps for extending our online computer-based PA intervention programs with 3 interactive mobile elements. Methods Components often included in other (eHealth) design models were identified as key components and served as a base for the definition of systematic steps: exploration of context, involvement of the target population, prototype and intervention testing, and implementation. Based on these key components, 10 systematic steps were defined. The initial step is a literature search, with the results serving as a base for development of the low-fidelity prototypes in step 2. The pilot phase comprises the 3rd to 6th steps and includes semistructured interviews, pilot tests, and adaptations of the prototypes with intensive involvement of the target population of adults aged 50 years and older, where particular attention will be paid to lower educated persons. The 7th step is an effect evaluation in the form of a randomized controlled trial. During the 8th step, the most effective intervention programs will be selected and reinforced. These reinforced intervention programs will be used during the design of an implementation plan in the 9th step and the subsequent field study in the 10th step. Results The project will be executed from December 2019 to December 2023. During this period, the systematic approach presented will be practically executed according to the methodological procedures described. Conclusions Based on the 4 identified key components, we were able to design an evidence-based systematic design approach for separately adding 3 mobile elements to our existing online PA intervention programs. The 10 steps are presented as a useful approach to guide future eHealth design studies. International Registered Report Identifier (IRRID) DERR1-10.2196/31677

Purpose: Accurate measurement of energy expenditure (EE) using doubly labeled water depends on the estimate of total body water (TBW). The aims of this study were to 1) assess the accuracy of a new approach for estimating TBW and EE during high-energy turnover and 2) assess the accuracy of day-to-day assessment of EE with this new approach. Methods: EE was measured in six healthy subjects (three male) for 5 consecutive days using three doubly labeled water methods: 1) the plateau, 2) slope-intercept, and 3) overnight-slope method, with whole-room indirect calorimetry as reference method. Urine samples were collected every evening and morning. High EE (physical activity level of >2.5) was achieved by cycling 4 h•d −1 . Results: Physical activity level was 2.8 ± 0.1. TBW values were 41.9 ± 6.1, 38.4 ± 5.7, and 40.4 ± 5.8 L for the plateau, slope-intercept, and overnight-slope methods, respectively. The overnight-slope method showed the highest accuracy in estimated CO 2 production, when compared with indirect calorimetry over the complete 5-d period (mean ± SD difference, 0.9% ± 1.6%). The plateau method significantly overestimated CO 2 production by 4.7% ± 2.6%, whereas the slope-intercept method underestimated CO 2 production (−3.4% ± 2.3%). When CO 2 production was assessed per day, the overnight-slope method showed an average difference of 9.4% ± 4.5% to indirect calorimetry. Conclusions: The overnight-slope method resulted in a more accurate estimation of CO 2 production and EE compared with the plateau or slope-intercept method over a 5-d period in high physical activity conditions. Day-to-day determination of EE using the overnight-slope method was more accurate than diet recall and several standard prediction equations in athletes.

Background Only a minority of adults aged over 50 years meet physical activity (PA) guidelines of the World Health Organization (WHO). eHealth interventions are proven effective tools to help this population increase its PA levels in the short term, among which the Active Plus and I Move interventions have been developed by our own research group. To achieve long-term effects, increase intervention use, and decrease dropout rates, 3 emergent but different mobile elements (an activity tracker, an ecological momentary intervention [EMI] program, and a chatbot) were added separately to Active Plus and I Move. In this study, the prototype development and pilot-testing of these interventions is described. Objective This study aims to enhance 2 existing PA-stimulating computer-based interventions with 3 mobile elements (an activity tracker, an EMI program, or a chatbot) and test the prototypes on usability and appreciation within a target population of adults aged over 50 years. Methods A systematic design protocol consisting of development, evaluation, and adaptation procedures was followed with involvement of the target population. Literature searches separated per mobile element and interviews with the target population (N=11) led to 6 prototypes: Active Plus or I Move including (1) an activity tracker, (2) EMI, or (3) a chatbot. These prototypes were tested on usability and appreciation during pilot tests (N=47) and subsequently fine-tuned based on the results. Results The literature searches and interviews provided important recommendations on the preferences of the target population, which enabled us to develop prototypes. The subsequent pilot tests showed that the mobile elements scored moderate to good on usability, with average System Usability Scale (SUS) scores of 52.2-82.2, and moderate to good on enjoyment and satisfaction, with average scores ranging from 5.1 to 8.1 on a scale of 1-10. The activity tracker received the best scores, followed by EMI, followed by the chatbot. Based on the findings, the activity tracker interventions were fine-tuned and technical difficulties regarding EMI and the chatbot were solved, which is expected to further improve usability and appreciation. Conclusions During this study, 6 prototypes of online PA interventions with added mobile elements were developed and tested for usability and appreciation. Although all prototypes scored moderate to high on usability, enjoyment, and satisfaction, it can be concluded that the integration of an activity tracker with a computer-based PA intervention is the most promising option among the 3 mobile elements tested during this study. The prototype development steps of the systematic design protocol followed can be considered useful and successful for the purposes of this study. The interventions can now be evaluated on a larger scale through a randomized controlled trial. International Registered Report Identifier (IRRID) RR2-10.2196/31677

BACKGROUND Physical activity (PA) can increase mental and physical health in adults aged over 50. However, it has been shown that PA-guidelines are often not met within this population. Therefore, our research group developed two computer tailored intervention programs in the last decade to stimulate PA: Active Plus and I Move. Although these programs were proven effective, positive effects diminished over time and attrition rates were relatively high. To respond to this, three interactive mobile elements are integrated in the existing programs, namely an activity tracker, an ecological momentary intervention program and a virtual coach application. OBJECTIVE To present the systematic approach for extending our online PA intervention programs Active Plus and I Move with three interactive mobile elements. METHODS Components often included in other (eHealth) design models were identified and served as a base for the definition of systematic steps: exploration of context, involvement of the target population, prototype and intervention testing, and implementation were identified as key components. RESULTS The systematic design approach consisting of ten steps is presented. The initial step is a literature search, of which the results serve as a base for development of the low fidelity prototypes in step two. The third to the sixth step are defined as the pilot phase and include semi-structured interviews, pilottests, and adaptations of the prototypes with intensive involvement of the target population of adults aged over 50, where particular attention is paid to lower educated persons. The seventh step is an effect evaluation in the form of a randomized controlled trial. During the eighth step the most effective intervention programs are selected and reinforced. These reinforced intervention programs are used during the design of an implementation plan in the ninth step and the subsequent field study in the tenth step. CONCLUSIONS In this paper, the ten systematic design steps for extending our existing online PA intervention programs with mobile elements are presented. The ten steps are presented as an useful approach to guide future eHealth design studies.

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