Published on 19.04.17 in Vol 5, No 4 (2017): April
Preprints (earlier versions) of this paper are available at http://preprints.jmir.org/preprint/7192, first published Dec 19, 2016.
User Interest in Digital Health Technologies to Encourage Physical Activity: Results of a Survey in Students and Staff of a German University
Background: Although the benefits for health of physical activity (PA) are well documented, the majority of the population is unable to implement present recommendations into daily routine. Mobile health (mHealth) apps could help increase the level of PA. However, this is contingent on the interest of potential users.
Objective: The aim of this study was the explorative, nuanced determination of the interest in mHealth apps with respect to PA among students and staff of a university.
Methods: We conducted a Web-based survey from June to July 2015 in which students and employees from the University of Potsdam were asked about their activity level, interest in mHealth fitness apps, chronic diseases, and sociodemographic parameters.
Results: A total of 1217 students (67.30%, 819/1217; female; 26.0 years [SD 4.9]) and 485 employees (67.5%, 327/485; female; 42.7 years [SD 11.7]) participated in the survey. The recommendation for PA (3 times per week) was not met by 70.1% (340/485) of employees and 52.67% (641/1217) of students. Within these groups, 53.2% (341/641 students) and 44.2% (150/340 employees)—independent of age, sex, body mass index (BMI), and level of education or professional qualification—indicated an interest in mHealth fitness apps.
Conclusions: Even in a younger, highly educated population, the majority of respondents reported an insufficient level of PA. About half of them indicated their interest in training support. This suggests that the use of personalized mobile fitness apps may become increasingly significant for a positive change of lifestyle.
JMIR Mhealth Uhealth 2017;5(4):e51
There is considerable evidence that physical activity (PA) has a positive effect on health [, ]. A sedentary lifestyle, on the other hand, increases the risk of developing cardiovascular, metabolic, or malignant diseases and leads to a reduction in life expectancy [ , ]. According to the World Health Organization (WHO), 150 min per week of moderately intensive endurance activity is currently recommended as the minimum level for promoting health in adults, both in primary and in secondary and tertiary prevention [ ]. Although many individuals are already well aware of the positive effects of PA, a large part of the population does not succeed in continuously implementing the recommendations for increasing PA in their everyday lives [ - ].
The increasingly available mobile health (mHealth) trackers such as wearables or mobile phone apps are usually developed with the motivational aspect in mind and offered to a wide range of potential users. They are an opportunity for improving self-activation and can help overcome implicit barriers for engaging in PA. Over 100,000 of these apps are already available . Many of these apps aim to increase physical capacity and include features such as heart rate monitor, pedometer, activity instructions, and activity monitor [ ]. In addition, they often implement behavioral components such as self-check, feedback mechanisms, or social support features, which can help optimize starting and continuing PA [ ]. Depending on their content, mHealth apps can support the intention to become physically active or to increase the PA level. Moreover, the transfer into everyday life as well as the long-term maintenance of PA may be ensured. Those phases meet the stages of the transtheoretical model (preparation, action, maintenance) [ ]. Concerning the development of model-based, effective apps, the needs of potential users must be considered. However, the basic condition in such apps remains the interest of the target group.
Meta-analyses and systematic reviews that have been conducted were able to prove the effect of mHealth monitors with respect to increasing PA, but according to the authors, long-term randomized controlled trials, especially of larger samples, are lacking thus far [- ]. Furthermore, the training recommendations that are implemented in most of the apps developed until now are not sufficiently evidence-based [ , , , , ]. Additionally, the majority of mHealth systems are not medical products that are clinically evaluated. Therefore, validity and reliability of these systems cannot be assumed [ - ].
But, due to the potential benefit of mHealth monitors for increasing PA, they are particularly significant for stakeholders in the health care system, especially insurance companies. However, before noncommercial apps are developed by health insurers, the acceptance and general interest of potential users and the special interest in individual features of the systems (eg, providing information, documenting measured values, reminders, and instructions) should be determined.
The aim of this study was the explorative, nuanced determination of the interest in mHealth apps with respect to PA among students and staff of a university.
Survey Design and Implementation
In the period from June to July 2015, an Web-based survey was conducted among students and staff of the University of Potsdam. The survey mailing list referred to the students and employee database of the University of Potsdam. Mail addresses of all students, scientific and administration employees were included. The cover letter of the survey mail informed potential participants about the content and aim of the survey, their voluntary participation, the required time, and contained a hyperlink that guided participants to the survey. The survey mail was sent once from the administration department of the University of Potsdam. The survey instrument was created in “UP survey,” which is a platform of the University of Potsdam offering a toolset based on the software Solutions QUAMP (QUAMP qEducation Software, Sociolutions GmbH Potsdam).
The standardized questionnaire included 35 questions (90 items) about PA, the use of or interest in digital fitness apps, sociodemographic parameters such as age, gender, body mass index (BMI), and level of education, and presence of a chronic disease. PA was assessed by questioning patients about their PA during the last 3 months. The question was specified with the terms PA, sports, or fitness during leisure time that led at least to a low increase of respiratory and heart rate. Nordic walking, ball games, jogging, bicycling, swimming, aerobic, and badminton were cited as examples. All questions about PA in the survey were related to this definition.
Chronic disease was assessed by asking participants if they suffer from a chronic disease or a longer lasting health concern persisting for more than six months.
The arrangement of the questions was the result of an optimization process that took the perspective of the surveyed persons into account along with validity aspects and technical requirements (filter questions). By using filter questions, individual questions could be varied depending on the situation and—for certain subgroups of participants—redundant topics were avoided. Groups that were more and less interested in PA or digital media were thus addressed specifically. Accordingly, the questionnaires included sections of questions for participants who were more and less interested in PA and who were additionally differentiated according to the use of or interest in digital fitness apps.
The study used established terminology that was adapted to the target groups in the study or to the topics of this study. Questions about PA and sociodemographic data were based largely on the health survey of the Robert Koch Institute . Questions about digital activity trackers and health or fitness apps were based on a study of mobile software apps [ ].
Survey Functionality and Analysis
The programmed survey was tested several times to ensure its functionality and usability. Using IP-Check, duplicates could be excluded in the analysis. No personal data were collected. Data were anonymously transmitted and descriptively analyzed. The results of the descriptive statistics were presented as percentages for categorical variables and as mean values (standard deviation) for metric variables. Group differences regarding the level of PA (active: ≥3 days/week, less active: 1-2 days/week, inactive: <1 day/week) and the interest in mHealth apps were determined using the chi-square test for categorical variables, the Mann-Whitney U test for ordinal variables, and the t test for metric variables. The level of significance was determined as P<.05. The statistical analyses were calculated using SPSS (IBM SPSS Statistics for Windows, version 22.0; IBM Corporation).
A total of 18,961 students and 2621 employees of the University of Potsdam were invited to take part in the Web-based survey. After data processing and correction, a total of 1217 data sets for students (6.42%) and 485 data sets for staff (18.50%) were evaluated.
Around one-fifth of the students and one-third of employees reported suffering from a chronic disease; those affected within each group were significantly older than healthy members of the group (students: 26.8 vs 25.7 years, P=.01; staff: 46.1 vs 41.0 years, P=.001). In addition, 26.05% (317/1217) of participating students and over 43.1% (209/485) of staff were overweight to obese (BMI ≥25 kg/m2;).
|Characteristics||Students (n=1217)||Staff (n=485)|
|Age in years, mean (SD)||26.0 (4.9)||42.7 (11.7)|
|Gender (male, n %)||398 (32.70)||158 (32.5)|
|High school graduate, n (%)||N/Aa||416 (85.8)|
|University degree, n (%)||N/Aa||357 (73.6)|
|Chronic diseaseb, n (%)||251 (20.62)||145 (29.9)|
|BMIc(kg/m2), mean (SD)||22.7 (9.5)||24.1 (3.6)|
|Normal weight, n (%)||900 (74.00)||276 (56.9)|
|Overweight, n (%)||262 (21.53)||165 (34.0)|
|Obese, n (%)||55 (4.52)||44 (9.1)|
|Extent of PAd, n (%)|
|≥1 day/week||1095 (90.00)||397 (81.9)|
|≥3 days/week||576 (47.33)||145 (29.9)|
aN/A: not applicable.
bChronic diseases or long-term health problems lasting at least six months.
cBody mass index (BMI) classes: normal weight <25 kg/m², overweight 25 to <30 kg/m², obese ≥30 kg/m².
dPA: physical activity.
Some 90.00% (1095/1217) of students and 82% (398/485) of staff engaged in PA at least one day a week; nearly half of the students and one-third of the staff met the WHO’s recommendations for PA (≥3 days or 150 min per week). The level of PA was dependent on gender, that is, among the physically active (eg, ≥3 days/week) participants, the percentage of males was higher both for students and for staff than in the respective group of less active (eg, 1-2 days/week) or not active (eg, <1 day/week) participants (students: 38.8 [224/576] vs 27.2% [174/641], P<.001; staff: 39.3 [57/145] vs 29.5% [100/340], P=.04). Overall, 56.5% (225/398) of the male and 43.3% (355/819) of the female students reported that they engaged in PA at least 3 times a week. Among staff members, 36.7% (58/158) of the men and 27.2% (89/327) of the women met these recommendations for PA. Students who were active in PA were also less often chronically ill than inactive students (17.99 [104/576] vs 23.0% [147/641]; P=.04) and an analogous trend was observed among staff members (26.7 [39/145] vs 31.3% [106/340]; P=.36). Group differences regarding active or inactive participants in BMI class were identified only for staff members. The physically active staff members had a normal weight more often and were less often obese than inactive employees (P=.02;, top). Age had no influence on PA in either group of participants.
Interest in Digital Health Technologies
Data on interest in mobile fitness apps to increase PA were collected only for those participants of the study who were not sufficiently active according to the WHO recommendations. It was found that 53.2% (341/641) of the respective students and 44.2% (150/340) of staff indicated interest in the offers independently of age, gender, or BMI class (, bottom). However, the interest of staff members appeared to be dependent on the extent of previous PA. Of those interested in mobile fitness apps, 30.0% (45/150) were previously inactive, whereas the percentage of previously inactive individuals in the group of those who were not interested was only 15.3% (29/190; P=.003).
A total of 89.7% (575/641) and 85.6% (291/340), respectively, of previously inactive students and staff expressed willingness to pay at least some of the costs of such a fitness app. In fact, 37.8% (242/641) of these less-active or inactive students and 35.8% (122/340) of staff already use fitness apps.
With respect to individual program functions, the greatest interest of those surveyed was in documenting or managing the measured values, instructions, and feedback (). With respect to trust in potential providers of health or fitness apps, the majority of the students and staff surveyed were neutral. They were most likely to trust their physician (33%, 212/641, and 27%, 92/340, respectively) and the sports club or trainer (38%, 244/641, and 27%, 92/340, respectively).
Questions as to the potential access to data stored by the health or fitness apps were also answered by most participants with “undecided” or “opposed.” Here as well, the participants would prefer to allow giving access to their physician (53%, 340/641, of students and 45%, 153/340, of staff) or the sports club or trainer (25%, 160/640, and 12%, 41/340, respectively).
In this survey, the percentage of physically active university members was high, with 90% of students and over 80% of employees. Of these, 57% of the male and 43% of female students and 37% of male and 27% of female staff members met the level of PA of 150 min (or 3 times) a week as recommend by the WHO. Lampert et al  confirmed, based on survey data from the Robert Koch Institute from 2009 for the general population, lower percentages of physically active persons, but simultaneously emphasized the key correlation between PA and social status determined by the level of education and income. With this in mind and taking into account the fact that 10% of the students were majoring in a subject related to sports, the high level of PA among the population in the study is not particularly surprising and is comparable with the results of other internal surveys at universities [ , ].
Around half of the students, who were previously not sufficiently physically active, and one-third of the staff indicated an interest in mHealth apps. Although there was no age dependence within the groups of participants, an age-related dependence of interest in digital solutions may be postulated for the intergroup comparison between students and staff. Recent literature provides extensive evidence that younger individuals in various target groups are more interested in mHealth apps and are more often willing to use such devices [- ]. There has now been a broad discussion of the problems regarding the acceptance of digital devices among older groups, which focused, for example, on the failure to address the needs of these groups and called for the development of apps specifically for these target and age groups in order to increase user acceptance [ , ].
On the other hand, this study identified only negligible differences between students and staff with respect to potentially interesting functions of mHealth apps. Only “documenting and managing measured values” was named by students (approximately 10%) more often than by staff. Overall, these program functions were among the most important features of a mHealth app for both groups aside from “instructions” and “feedback.” This result can most likely be interpreted in view of the current state of technological development and the demand induced by the providers of mHealth apps. Cultural values, such as the current “quantified self” movement, at the center of which is collecting data on various aspects of one’s own life to improve self-understanding, may also play a role [- ].
When differentiating between the previously inactive students and staff members compared with those who got PA once to twice a week, in this survey, the “reminder” function that was of considerably greater interest for the inactive individuals, was especially significant. The reminder feature can be considered a self-check technique and supports adherence. It can be assumed that the inactive individuals are well-aware of their need for PA and thus seek suitable support options to adjust their behavior. Similar behavioral patterns are already known from obesity research .
Regarding trust in the providers of health or fitness apps and with respect to access to the values measured by such apps, it was found that the great majority of those surveyed were undecided or opposed. They were most likely to trust their own physician and the sports club or trainer in this respect. The indecision and opposition expressed are plausible in view of the requirements of trustworthy apps expressed in literature. Dennison et al , for example, emphasize that potential users’ desire confidentiality, expertise of the app providers, and the required transparency regarding the functions of the app and with respect to records of the values measured. Recent studies have shown extensive evidence of general concerns regarding data privacy; here again, some correlation with age and level of education has been postulated in addition to media- and experience-based mistrust [ , , ].
Willingness to assume the partial or full costs for a mHealth app was generally high among the individuals surveyed who were interested in electronic media, corresponding with a recent study . However, the general willingness expressed in an abstract context does not allow a valid conclusion to be drawn about a concrete decision, which may be more complex and take contextual factors such as the financial burden associated with this purchase into consideration. For example, Dennison et al [ ] showed that students expect a health or fitness app to be reasonably priced. Ultimately, the actual willingness to assume the costs of a mHealth app is likely to depend not only on the cost, but also on the anticipated benefit of the concrete app.
The response rate for the study was within the usual range for surveys of this kind. Return rates of less than 10% for students and less than 20% for staff were also reported in other studies of PA at universities [, ]. Nevertheless, it must be assumed that this level of response rates may be associated with a sampling bias. Furthermore, a sampling bias regarding the gender distribution of the investigated population can be assumed. The male proportion among the respondents was approximately 33% both in students and staff members, whereas the proportion of male is higher in the whole population, that is, 42% of all students and 44% of the entire staff of the university in 2015 were male.
The percentage of physically active students is relatively high in this study at 90% and thus comparable with the results of Preuß et al , who found around 80% of physically active students at the University of Bonn. It is assumed that the percentage of physically active students at the University of Potsdam was overestimated due to the high percentage of sport science programs. It is assumed that questions about PA are more likely to be answered by individuals interested in sports. This applies analogously to the group of staff members with 82% of physically active individuals, although comparable data are available in literature here as well [ ].
This study has an explorative nature; the results presented should therefore not be simply transferred to all students and staff at the University of Potsdam or to other populations (such as to population-based cohorts).
Even in younger groups with a high level of education, the majority of individuals do not meet the level of PA recommended by the WHO. However, around half of the individuals in this group of inactive individuals showed interest in mobile apps to encourage activity, indicating that the personalized and age-group-specific use of mHealth apps for optimizing lifestyle through more PA could become increasingly more important.
The study was funded by the GeWINO—Health Research Institute, AOK Nordost—Die Gesundheitskasse. We acknowledge the support of the Deutsche Forschungsgemeinschaft and Open Access Publishing Fund of University of Potsdam.
Conflicts of Interest
- Held C, Iqbal R, Lear SA, Rosengren A, Islam S, Mathew J, et al. Physical activity levels, ownership of goods promoting sedentary behaviour and risk of myocardial infarction: results of the INTERHEART study. Eur Heart J 2012 Feb;33(4):452-466. [CrossRef] [Medline]
- Hills AP, Street SJ, Byrne NM. Physical activity and health: “what is old is new again”. Adv Food Nutr Res 2015;75:77-95. [CrossRef] [Medline]
- Reiner M, Niermann C, Jekauc D, Woll A. Long-term health benefits of physical activity--a systematic review of longitudinal studies. BMC Public Health 2013;13:813 [FREE Full text] [CrossRef] [Medline]
- Lee I, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. The Lancet 2012 Jul;380(9838):219-229. [CrossRef]
- Global Recommendations on Physical Activity for Health. Geneva: World Health Organization; 2010.
- Krug S, Jordan S, Mensink GB, Müters S, Finger J, Lampert T. [Physical activity: results of the German Health Interview and Examination Survey for Adults (DEGS1)]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2013 May;56(5-6):765-771. [CrossRef] [Medline]
- Hallal PC, Andersen LB, Bull FC, Guthold R, Haskell W, Ekelund U. Global physical activity levels: surveillance progress, pitfalls, and prospects. The Lancet 2012 Jul;380(9838):247-257. [CrossRef]
- Xu W, Liu Y. mHealthApps: a repository and database of mobile health apps. JMIR Mhealth Uhealth 2015 Mar 18;3(1):e28 [FREE Full text] [CrossRef] [Medline]
- Modave F, Bian J, Leavitt T, Bromwell J, Harris III C, Vincent H. Low quality of free coaching apps with respect to the American College of Sports Medicine Guidelines: a review of current mobile apps. JMIR mHealth uHealth 2015 Jul 24;3(3):e77. [CrossRef]
- Payne HE, Lister C, West JH, Bernhardt JM. Behavioral functionality of mobile apps in health interventions: a systematic review of the literature. JMIR Mhealth Uhealth 2015 Feb;3(1):e20 [FREE Full text] [CrossRef] [Medline]
- Prochaska JO, Velicer WF. The transtheoretical model of health behavior change. Am J Health Promot 1997 Sep;12(1):38-48. [CrossRef]
- Fanning J, Mullen SP, McAuley E. Increasing physical activity with mobile devices: a meta-analysis. J Med Internet Res 2012 Nov;14(6):e161 [FREE Full text] [CrossRef] [Medline]
- Bort-Roig J, Gilson ND, Puig-Ribera A, Contreras RS, Trost SG. Measuring and influencing physical activity with smartphone technology: a systematic review. Sports Med 2014 May;44(5):671-686. [CrossRef] [Medline]
- Bravata DM, Smith-Spangler C, Sundaram V, Gienger AL, Lin N, Lewis R, et al. Using pedometers to increase physical activity and improve health: a systematic review. JAMA 2007 Nov 21;298(19):2296-2304. [CrossRef] [Medline]
- Higgins JP. Smartphone applications for patients' health and fitness. Am J Med 2016 Jan;129(1):11-19. [CrossRef] [Medline]
- Funk C. Mobile Softwareanwendungen (Apps) im Gesundheitsbereich: Entwicklung, Marktbetrachtung und Endverbrauchermeinung. Stuttgart: ibidem-Verlag; 2013.
- Lee J, Kim Y, Welk GJ. Validity of consumer-based physical activity monitors. Med Sci Sports Exerc 2014 Sep;46(9):1840-1848. [CrossRef] [Medline]
- Evenson KR, Goto MM, Furberg RD. Systematic review of the validity and reliability of consumer-wearable activity trackers. Int J Behav Nutr Phys Act 2015;12(1):159 [FREE Full text] [CrossRef] [Medline]
- An H, Jones GC, Kang S, Welk GJ, Lee J. How valid are wearable physical activity trackers for measuring steps? Eur J Sport Sci 2017 Apr;17(3):360-368. [CrossRef] [Medline]
- Price K, Bird SR, Lythgo N, Raj IS, Wong JY, Lynch C. Validation of the Fitbit One, Garmin Vivofit and Jawbone UP activity tracker in estimation of energy expenditure during treadmill walking and running. J Med Eng Technol 2017 Apr;41(3):208-215. [CrossRef] [Medline]
- Lampert T, Mensink GB, Müters S. [Physical and sporting activity among adults in Germany. Results from the “German Health Update 2009” survey]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2012 Jan;55(1):102-110. [CrossRef] [Medline]
- Preuß M, Preuß P, Kuhlmann K, Ponert M, Mehlis K, Beauducel A. Healthy campus bonn – sport- und bewegungsverhalten der studierenden. In: Göring A, Möllenbeck D, editors. Bewegungsorientierte Gesundheitsförderung an Hochschulen. Göttingen: Universitätsverlag; 2015:37-64.
- Mess F, Gerth D, Hanke J, Rabel M, Walter U. Gesundheitsverhalten und gesundheit bei wissenschaftlichen und nicht-wissenschaftlichen Beschäftigen – ein Vergleich an der Universität Konstanz. In: Göring A, Möllenbeck D, editors. Bewegungsorientierte Gesundheitsförderung an Hochschulen. Göttingen: Universitätsverlag; 2015:115-129.
- Humble JR, Tolley EA, Krukowski RA, Womack CR, Motley TS, Bailey JE. Use of and interest in mobile health for diabetes self-care in vulnerable populations. J Telemed Telecare 2016 Jan;22(1):32-38. [CrossRef] [Medline]
- Torous J, Chan SR, Yee-Marie TS, Behrens J, Mathew I, Conrad EJ, et al. Patient smartphone ownership and interest in mobile apps to monitor symptoms of mental health conditions: a survey in four geographically distinct psychiatric clinics. JMIR Ment Health 2014 Dec;1(1):e5 [FREE Full text] [CrossRef] [Medline]
- Steele GC, Miller D, Kuluski K, Cott C. Tying ehealth tools to patient needs: exploring the use of ehealth for community-dwelling patients with complex chronic disease and disability. JMIR Res Protoc 2014 Nov;3(4):e67 [FREE Full text] [CrossRef] [Medline]
- Schmid A, Dörfler I, Dany F, Böpple O. Analyse der akzeptanzkriterien für mobile anwendungen im bereich gesundheit in der zielgruppe 50+. In: Technologiegestützte Dienstleistungsinnovation in der Gesundheitswirtschaft. Wiesbaden: Gabler Verlag, Springer Fachmedien; 2012:57-82.
- Giesecke S, Hull J, Schmidt S, Strese H, Weiß C, Baumgarten D. Statista. 2005. AAL – ambient assisted living: Country Report Germany URL: https://www.statista.com/outlook/283/137/ambient-assisted-living--aal-/germany [accessed 2017-03-17] [WebCite Cache]
- Banos O, Bilal AM, Ali KW, Afzal M, Hussain M, Kang BH, et al. The Mining Minds digital health and wellness framework. Biomed Eng Online 2016 Jul 15;15 Suppl 1:76 [FREE Full text] [CrossRef] [Medline]
- Fawcett T. Mining the quantified self: personal knowledge discovery as a challenge for data science. Big Data 2015 Dec;3(4):249-266. [CrossRef]
- Gimpel H, Nißen M, Görlitz R. Quantifying the quantified self: a study on the motivation of patients to track their own health. 2013 Dec Presented at: 34th International Conference on Information Systems; 2013; Milan, Italy URL: https://pdfs.semanticscholar.org/7ccb/e2e99078317a8657a2d362cdeb755b323cf4.pdf
- Meister S, Becker S, Simson U. Digitale gesundheit – unterstützung der adipositastherapie durch digitale technologien. Adipositas – Ursachen, Folgeerkrankungen, Therapie 2016;10:38-42.
- Dennison L, Morrison L, Conway G, Yardley L. Opportunities and challenges for smartphone applications in supporting health behavior change: qualitative study. J Med Internet Res 2013 Apr;15(4):e86 [FREE Full text] [CrossRef] [Medline]
- Dehling T, Gao F, Schneider S, Sunyaev A. Exploring the far side of mobile health: information security and privacy of mobile health apps on ios and android. JMIR Mhealth Uhealth 2015 Jan 19;3(1):e8 [FREE Full text] [CrossRef] [Medline]
- Illiger K, Hupka M, von JU, Wichelhaus D, Albrecht U. Mobile technologies: expectancy, usage, and acceptance of clinical staff and patients at a university medical center. JMIR Mhealth Uhealth 2014 Oct 21;2(4):e42 [FREE Full text] [CrossRef] [Medline]
- Mercer K, Giangregorio L, Schneider E, Chilana P, Li M, Grindrod K. Acceptance of commercially available wearable activity trackers among adults aged over 50 and with chronic illness: a mixed-methods evaluation. JMIR Mhealth Uhealth 2016 Jan 27;4(1):e7 [FREE Full text] [CrossRef] [Medline]
|BMI: body mass index|
|mHealth: mobile health|
|PA: physical activity|
|UP: University of Potsdam|
|WHO: World Health Organization|
Edited by C Dias; submitted 19.12.16; peer-reviewed by P Preuß, F Mess; comments to author 20.01.17; accepted 07.02.17; published 19.04.17
©Annett Salzwedel, Sophie Rabe, Thomas Zahn, Julia Neuwirth, Sarah Eichler, Kathrin Haubold, Anne Wachholz, Rona Reibis, Heinz Völler. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 19.04.2017.
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