Current mHealth technologies for physical activity assessment and promotion

Differences in mobile phone affinity between demographic groups: implications for mobile phone delivered interventions and programs

BACKGROUND

The impact of any intervention or program delivered through mobile phones (mHealth) may be influenced by the individual recipient's relationship with his or her mobile phone. However, few studies have assessed the attitudes and preferences of different demographic groups with respect to mobile phone use. This study assessed whether individuals' demographic characteristics [primary demographics (PD): race, ethnicity, gender and age] are influential factors in attitudes and behaviors associated with mobile use pattern, using the Mobile Phone Affinity Scale (MPAS). The MPAS examines six underlying constructs associated with mobile phone use: Connectedness, Productivity, Empowerment, Anxious Attachment, Addiction, and Continuous Use.

METHODS

U.S. adults (n=1,055, mean age 32.5 years, 10% Hispanic, 86.3% white) completed the MPAS and provided information about PD (e.g., race, ethnicity, age) and social demographic (SocD) characteristics (e.g., having children, employment). Chi-square analyses and multivariate analyses were used to assess the relationships among the PD and SocD variables, and MPAS constructs.

RESULTS

Significant differences were found between PD and SocD variables (all P<0.01). Specifically, whites were more likely than non-whites to be married and to be living with children, while non-Hispanics tended to report higher household income and education than Hispanics. Women were more likely to report living with children and less likely to have full-time employment than men (all P<0.01). There was a significant effect of PD characteristics on MPAS constructs in that whites and women tended to score higher on some MPAS constructs than non-whites and men (all P<0.01). Similarly, some SocD characteristics including employment status and living with children were differentially associated with some MPAS constructs (all P<0.01).

CONCLUSIONS

Results indicate that there are differences in attitudes and use preferences to mobile phone use based on some of the primary and SocD demographic characteristics. These findings provide important insights into mHealth intervention components that will increase appeal to different subgroups.

mHealth Technologies to Influence Physical Activity and Sedentary Behaviors: Behavior Change Techniques, Systematic Review and Meta-Analysis of Randomized Controlled Trials

BACKGROUND

mHealth programs offer potential for practical and cost-effective delivery of interventions capable of reaching many individuals.

PURPOSE

To (1) compare the effectiveness of mHealth interventions to promote physical activity (PA) and reduce sedentary behavior (SB) in free-living young people and adults with a comparator exposed to usual care/minimal intervention; (2) determine whether, and to what extent, such interventions affect PA and SB levels and (3) use the taxonomy of behavior change techniques (BCTs) to describe intervention characteristics.

METHODS

A systematic review and meta-analysis following PRISMA guidelines was undertaken to identify randomized controlled trials (RCTs) comparing mHealth interventions with usual or minimal care among individuals free from conditions that could limit PA. Total PA, moderate-to-vigorous intensity physical activity (MVPA), walking and SB outcomes were extracted. Intervention content was independently coded following the 93-item taxonomy of BCTs.

RESULTS

Twenty-one RCTs (1701 participants-700 with objectively measured PA) met eligibility criteria. SB decreased more following mHealth interventions than after usual care (standardised mean difference (SMD) -0.26, 95 % confidence interval (CI) -0.53 to -0.00). Summary effects across studies were small to moderate and non-significant for total PA (SMD 0.14, 95 % CI -0.12 to 0.41); MVPA (SMD 0.37, 95 % CI -0.03 to 0.77); and walking (SMD 0.14, 95 % CI -0.01 to 0.29). BCTs were employed more frequently in intervention (mean = 6.9, range 2 to 12) than in comparator conditions (mean = 3.1, range 0 to 10). Of all BCTs, only 31 were employed in intervention conditions.

CONCLUSIONS

Current mHealth interventions have small effects on PA/SB. Technological advancements will enable more comprehensive, interactive and responsive intervention delivery. Future mHealth PA studies should ensure that all the active ingredients of the intervention are reported in sufficient detail.

Health App Use Among US Mobile Phone Users: Analysis of Trends by Chronic Disease Status

BACKGROUND

Mobile apps hold promise for serving as a lifestyle intervention in public health to promote wellness and attenuate chronic conditions, yet little is known about how individuals with chronic illness use or perceive mobile apps.

OBJECTIVE

The objective of this study was to explore behaviors and perceptions about mobile phone-based apps for health among individuals with chronic conditions.

METHODS

Data were collected from a national cross-sectional survey of 1604 mobile phone users in the United States that assessed mHealth use, beliefs, and preferences. This study examined health app use, reason for download, and perceived efficacy by chronic condition.

RESULTS

Among participants, having between 1 and 5 apps was reported by 38.9% (314/807) of respondents without a condition and by 6.6% (24/364) of respondents with hypertension. Use of health apps was reported 2 times or more per day by 21.3% (172/807) of respondents without a condition, 2.7% (10/364) with hypertension, 13.1% (26/198) with obesity, 12.3% (20/163) with diabetes, 12.0% (32/267) with depression, and 16.6% (53/319) with high cholesterol. Results of the logistic regression did not indicate a significant difference in health app download between individuals with and without chronic conditions (P>.05). Compared with individuals with poor health, health app download was more likely among those with self-reported very good health (odds ratio [OR] 3.80, 95% CI 2.38-6.09, P<.001) and excellent health (OR 4.77, 95% CI 2.70-8.42, P<.001). Similarly, compared with individuals who report never or rarely engaging in physical activity, health app download was more likely among those who report exercise 1 day per week (OR 2.47, 95% CI 1.6-3.83, P<.001), 2 days per week (OR 4.77, 95% CI 3.27-6.94, P<.001), 3 to 4 days per week (OR 5.00, 95% CI 3.52-7.10, P<.001), and 5 to 7 days per week (OR 4.64, 95% CI 3.11-6.92, P<.001). All logistic regression results controlled for age, sex, and race or ethnicity.

CONCLUSIONS

Results from this study suggest that individuals with poor self-reported health and low rates of physical activity, arguably those who stand to benefit most from health apps, were least likely to report download and use these health tools.

A Fitbit and Facebook mHealth intervention for promoting physical activity among adolescent and young adult childhood cancer survivors: A pilot study

BACKGROUND

Physical activity (PA) may be important for preventing chronic diseases for adolescent and young adult (AYA) childhood cancer survivors. Randomized controlled trials (RCTs) of PA interventions for AYA survivors are sparse, but necessary to determine effective programs for increasing PA among this population. Thus, we conducted a pilot RCT, testing the feasibility of a mobile health (mHealth) intervention to promote PA among AYA survivors.

PROCEDURE

We recruited 14- to 18-year-olds who were ≥1-year post cancer therapy from Seattle Children's Hospital. The 10-week intervention consisted of a wearable PA-tracking device (Fitbit Flex) and a peer-based virtual support group (Facebook group). Research staff helped set step goals and awarded badges weekly. Controls received usual care. Baseline assessments occurred before randomization and follow-up assessments occurred during weeks 8-10 of the intervention period. Feasibility criteria are defined below. Qualitative interviews assessed acceptability. Exploratory outcomes included PA, quality of life, and motivation for PA.

RESULTS

All feasibility criteria were met: we recruited 60 survivors, intervention participants wore the Fitbit on the majority (71.5%) of intervention days, and ≥90% of all participants completed questionnaires. Qualitative data confirmed intervention acceptability. Exploratory analyses found no significant adjusted group differences for change in moderate-to-vigorous PA (4.4 vs. 5.0 min/day; P = 0.92) or sedentary time (-4.5 vs. 1.0 min/day; P = 0.73), comparing intervention subjects to controls. Some modest differences were found for select subscales of quality of life and motivation for PA.

CONCLUSIONS

This mHealth PA intervention was feasible and acceptable to AYA childhood cancer survivors and warrants a fully powered RCT.

High-resolution Temporal Representations of Alcohol and Tobacco Behaviors from Social Media Data

Understanding tobacco- and alcohol-related behavioral patterns is critical for uncovering risk factors and potentially designing targeted social computing intervention systems. Given that we make choices multiple times per day, hourly and daily patterns are critical for better understanding behaviors. Here, we combine natural language processing, machine learning and time series analyses to assess Twitter activity specifically related to alcohol and tobacco consumption and their sub-daily, daily and weekly cycles. Twitter self-reports of alcohol and tobacco use are compared to other data streams available at similar temporal resolution. We assess if discussion of drinking by inferred underage versus legal age people or discussion of use of different types of tobacco products can be differentiated using these temporal patterns. We find that time and frequency domain representations of behaviors on social media can provide meaningful and unique insights, and we discuss the types of behaviors for which the approach may be most useful.

Assessing Behavioral Stages From Social Media Data

Important work rooted in psychological theory posits that health behavior change occurs through a series of discrete stages. Our work builds on the field of social computing by identifying how social media data can be used to resolve behavior stages at high resolution (e.g. hourly/daily) for key population subgroups and times. In essence this approach opens new opportunities to advance psychological theories and better understand how our health is shaped based on the real, dynamic, and rapid actions we make every day. To do so, we bring together domain knowledge and machine learning methods to form a hierarchical classification of Twitter data that resolves different stages of behavior. We identify and examine temporal patterns of the identified stages, with alcohol as a use case (planning or looking to drink, currently drinking, and reflecting on drinking). Known seasonal trends are compared with findings from our methods. We discuss the potential health policy implications of detecting high frequency behavior stages.

Carotid femoral pulse wave velocity in type 2 diabetes and hypertension: capturing arterial health effects of step counts

Objective:

Optimal medication use obscures the impact of physical activity on traditional cardiometabolic risk factors. We evaluated the relationship between step counts and carotid-femoral pulse wave velocity (cfPWV), a summative risk indicator, in patients with type 2 diabetes and/or hypertension.

Research design and methods:

Three hundred and sixty-nine participants were recruited (outpatient clinics; Montreal, Quebec; 2011–2015). Physical activity (pedometer/accelerometer), cfPWV (applanation tonometry), and risk factors (A1C, Homeostatic Model Assessment–Insulin Resistance, blood pressure, lipid profiles) were evaluated. Linear regression models were constructed to quantify the relationship of steps/day with cfPWV.

Results:

The study population comprised 191 patients with type 2 diabetes and hypertension, 39 with type 2 diabetes, and 139 with hypertension (mean ± SD: age 59.6 ± 11.2 years; BMI 31.3 ± 4.8 kg/m²; 54.2% women). Blood pressure (125/77 ± 15/9 mmHg), A1C (diabetes: 7.7 ± 1.3%; 61 mmol/mol), and low-density lipoprotein cholesterol (diabetes: 2.19 ± 0.8 mmol/l; without diabetes: 3.13 ± 1.1mmol/l) were close to target. Participants averaged 5125 ± 2722 steps/day. Mean cfPWV was 9.8 ± 2.2 m/s. Steps correlated with cfPWV, but not with other risk factors. A 1000 steps/day increment was associated with a 0.1 m/s cfPWV decrement across adjusted models and in subgroup analysis by diabetes status. In a model adjusted for age, sex, BMI, ethnicity, immigrant status, employment, education, diabetes, hypertension, medication classes, the mean cfPWV decrement was 0.11 m/s (95% confidence interval −0.2, −0.02).

Conclusions:

cfPWV is responsive to step counts in patients who are well controlled on cardioprotective medications. This ability to capture the ‘added value’ of physical activity supports the emerging role of cfPWV in arterial health monitoring.

mHealth Application Areas and Technology Combinations*. A Comparison of Literature from High and Low/Middle Income Countries

BACKGROUND

With the continuous and enormous spread of mobile technologies, mHealth has evolved as a new subfield of eHealth. While eHealth is broadly focused on information and communication technologies, mHealth seeks to explore more into mobile devices and wireless communication. Since mobile phone penetration has exceeded other infrastructure in low and middle-income countries (LMICs), mHealth is seen as a promising component to provide pervasive and patient-centered care.

OBJECTIVES

The aim of our research work for this paper is to examine the mHealth literature to identify application areas, target diseases, and mHealth service and technology types that are most appropriate for LMICs.

METHODS

Based on the 2011 WHO mHealth report, a combination of search terms, all including the word "mHealth", was identified. A literature review was conducted by searching the PubMed and IEEE Xplore databases. Articles were included if they were published in English, covered an mHealth solution/ intervention, involved the use of a mobile communication device, and included a pilot evaluation study. Articles were excluded if they did not provide sufficient detail on the solution covered or did not focus on clinical efficacy/effectiveness. Cross-referencing was also performed on included articles.

RESULTS

842 articles were retrieved and analyzed, 255 of which met the inclusion criteria. North America had the highest number of applications (n=74) followed by Europe (n=50), Asia (n=44), Africa (n=25), and Australia (n=9). The Middle East (n=5) and South America (n=3) had the least number of studies. The majority of solutions addressed diabetes (n=51), obesity (n=25), CVDs (n=24), HIV (n=18), mental health (n=16), health behaviors (n=16), and maternal and child's health (MCH) (n=11). Fewer solutions addressed asthma (n=7), cancer (n=5), family health planning (n=5), TB (n=3), malaria (n=2), chronic obtrusive pulmonary disease (COPD) (n=2), vision care (n=2), and dermatology (n=2). Other solutions targeted stroke, dental health, hepatitis vaccination, cold and flu, ED prescribed antibiotics, iodine deficiency, and liver transplantation (n=1 each). The remainder of solutions (n=14) did not focus on a certain disease. Most applications fell in the areas of health monitoring and surveillance (n=93) and health promotion and raising awareness (n=88). Fewer solutions addressed the areas of communication and reporting (n=11), data collection (n=6), telemedicine (n=5), emergency medical care (n=3), point of care support (n=2), and decision support (n=2). The majority of solutions used SMS messaging (n=94) or mobile apps (n=71). Fewer used IVR/phone calls (n=8), mobile website/email (n=5), videoconferencing (n=2), MMS (n=2), or video (n=1) or voice messages (n=1). Studies were mostly RCTs, with the majority suffering from small sample sizes and short study durations. Problems addressed by solutions included travel distance for reporting, self-management and disease monitoring, and treatment/medication adherence.

CONCLUSIONS

SMS and app solutions are the most common forms of mHealth applications. SMS solutions are prevalent in both high and LMICs while app solutions are mostly used in high income countries. Common application areas include health promotion and raising awareness using SMS and health monitoring and surveillance using mobile apps. Remaining application areas are rarely addressed. Diabetes is the most commonly targeted medical condition, yet remains deficient in LMICs.

Development and Testing of an Intelligent Pain Management System (IPMS) on Mobile Phones Through a Randomized Trial Among Chinese Cancer Patients: A New Approach in Cancer Pain Management

Background

Cancer has become increasingly prevalent in China over the past few decades. Among the factors that determine the quality of life of cancer patients, pain has commonly been recognized as a most critical one; it could also lead to the ineffective treatment of the cancer. Driven by the need for better pain management for cancer patients, our research team developed a mobile-based Intelligent Pain Management System (IPMS).

Objective

Our objective was to design, develop, and test the IPMS to facilitate real-time pain recording and timely intervention among cancer patients with pain. The system’s usability, feasibility, compliance, and satisfaction were also assessed.

Methods

A sample of 46 patients with cancer pain symptoms were recruited at the Oncology Center of Xinhua Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Chongming Branch (hereinafter referred to as “the Oncology Center”). In a pretest, participants completed a pain management knowledge questionnaire and were evaluated using the baseline cancer pain assessment and Karnofsky Performance Status (KPS) evaluation. The participants were then randomly assigned into two groups (the trial group and the control group). After a 14-day trial period, another round of cancer pain assessment, KPS evaluation and pain management knowledge assessment were repeated. In the trial group, the data were fully automatically collected by the IPMS. In the control group, the data were collected using conventional methods, such as phone interviews or door-to-door visits by physicians. The participants were also asked to complete a satisfaction questionnaire on the use of the IPMS.

Results

All participants successfully completed the trial. First, the feasibility of IPMS by observing the number of daily pain assessments recorded among patients was assessed. Second, the users’ satisfaction, effectiveness of pain management, and changes in the quality of their lives were evaluated. All the participants gave high satisfaction score after they used IMPS. Both groups reported similar pain scores and KPS scores at the baseline. At the end of the trial, the mean pain score of the trial group was significantly lower than of the control group (P<.001). The ending KPS score of the trial group was significantly higher than of the control group (P<.001). The improvement of pain management knowledge score in the trial group was more pronounced than that in the control group (P<.001).

Conclusions

This study provided preliminary data to support the potentials of using IPMS in cancer pain communication between patients and doctors and to provide real-time supportive intervention on a convenient basis at a low cost. Overall, the IPMS can serve as a reliable and effective approach to control cancer pain and improve quality of life for patients with cancer pain.

Trial Registration

Clinicaltrials.gov NCT02765269; http://clinicaltrials.gov/ct2/show/NCT02765269 (Archived by WebCite at http://www.webcitation.org/6rnwsgDgv)

Using digital interventions to improve the cardiometabolic health of populations: a meta-review of reporting quality

OBJECTIVES

We conducted a meta-review to determine the reporting quality of user-centered digital interventions for the prevention and management of cardiometabolic conditions.

MATERIALS AND METHODS

Using predetermined inclusion criteria, systematic reviews published between 2010 and 2015 were identified from 3 databases. To assess whether current evidence is sufficient to inform wider uptake and implementation of digital health programs, we assessed the quality of reporting of research findings using (1) endorsement of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, (2) a quality assessment framework (eg, Cochrane risk of bias assessment tool), and (3) 8 parameters of the Consolidated Standards of Reporting Trials of Electronic and Mobile HEalth Applications and onLine TeleHealth (CONSORT-eHEALTH) guidelines (developed in 2010).

RESULTS

Of the 33 systematic reviews covering social media, Web-based programs, mobile health programs, and composite modalities, 6 reported using the recommended PRISMA guidelines. Seven did not report using a quality assessment framework. Applying the CONSORT-EHEALTH guidelines, reporting was of mild to moderate strength.

DISCUSSION

To our knowledge, this is the first meta-review to provide a comprehensive analysis of the quality of reporting of research findings for a range of digital health interventions. Our findings suggest that the evidence base and quality of reporting in this rapidly developing field needs significant improvement in order to inform wider implementation and uptake.

CONCLUSION

The inconsistent quality of reporting of digital health interventions for cardiometabolic outcomes may be a critical impediment to real-world implementation.

Beyond Basic Feedback in Mobile Brief Interventions: Designing SMS Message Content for Delivery to Young Adults During Risky Drinking Events

BACKGROUND

Brief interventions can reduce alcohol consumption in young people through screening and delivery of personally relevant feedback. Recently, Web and mobile platforms have been harnessed to increase the reach of brief interventions. Existing literature on mobile-based alcohol brief interventions indicates mixed use of theory in developing interventions. There is no research available to guide the development of SMS text messaging (short message service, SMS) interventions delivered during risky drinking events.

OBJECTIVE

The aim of this study was to develop and pilot an alcohol-related risk-reduction brief intervention delivered by SMS to Australian young adults during drinking events. This paper describes the development of intervention message content, with specific focus on the context of delivery during drinking events.

METHODS

A sample of 42 young adults attended 4 workshops; these comprised focus-group style discussion on drinking habits and motivations, discussion of intervention design, analysis of existing alcohol media campaigns, and participant development of message content. Data were analyzed thematically.

RESULTS

Participants described a focus on having fun and blocking out any incongruent negative influences during drinking episodes. For content to be acceptable, nonjudgmental and non-authoritative language was deemed essential. A preference for short, actionable messages was observed, including suggestions for reminders around drinking water, organizing transport home, checking on friends, and plans the next day. Participants were excited about the potential for messages to be tailored to individuals, as previous alcohol-related campaigns were deemed too generic and often irrelevant. Normative-based messages were also perceived as largely irrelevant as participants felt that they understood the drinking-related norms of their immediate peers already.

CONCLUSIONS

Findings from this study offer insights into young adults' drinking events and practical advice for designing alcohol-related brief interventions. During our formative development process, we demonstrated a neat correspondence between young people's preferences for alcohol harm reduction interventions and the theoretical principles of brief interventions, including acceptable topics and message style.

Morning self-efficacy predicts physical activity throughout the day in knee osteoarthritis

OBJECTIVE

The purpose of this study was to examine the within-day and cross-day prospective effects of knee osteoarthritis (OA) patients' self-efficacy to engage in physical activity despite the pain on their subsequent physical activity assessed objectively in their natural environment.

METHOD

Over 22 days, 135 older adults with knee OA reported their morning self-efficacy for being physically active throughout the day using a handheld computer and wore an accelerometer to measure moderate activity and steps.

RESULTS

Morning self-efficacy had a significant positive effect on steps and moderate-intensity activity throughout that day, above and beyond the effects of demographic background and other psychosocial factors as well as spouses' support and social control. The lagged effect of morning self-efficacy on the next day's physical activity and the reciprocal lagged effect of physical activity on the next day's self-efficacy were not significant. Positive between-person effects of self-efficacy on physical activity were found.

CONCLUSIONS

Future research should aim to better understand the mechanisms underlying fluctuations in patients' daily self-efficacy, and target patients' daily self-efficacy as a modifiable psychological mechanism for promoting physical activity. (PsycINFO Database Record

Mobile Health Physical Activity Intervention Preferences in Cancer Survivors: A Qualitative Study

BACKGROUND

Cancer survivors are at an elevated risk for several negative health outcomes, but physical activity (PA) can decrease those risks. Unfortunately, adherence to PA recommendations among survivors is low. Fitness mobile apps have been shown to facilitate the adoption of PA in the general population, but there are limited apps specifically designed for cancer survivors. This population has unique needs and barriers to PA, and most existing PA apps do not address these issues. Moreover, incorporating user preferences has been identified as an important priority for technology-based PA interventions, but at present there is limited literature that serves to establish these preferences in cancer survivors. This is especially problematic given the high cost of app development and because the majority of downloaded apps fail to engage users over the long term.

OBJECTIVE

The aim of this study was to take a qualitative approach to provide practical insight regarding this population's preferences for the features and messages of an app to increase PA.

METHODS

A total of 35 cancer survivors each attended 2 focus groups; a moderator presented slide shows on potential app features and messages and asked open-ended questions to elicit participant preferences. All sessions were audio recorded and transcribed verbatim. Three reviewers independently conducted thematic content analysis on all transcripts, then organized and consolidated findings to identify salient themes.

RESULTS

Participants (mean age 63.7, SD 10.8, years) were mostly female (24/35, 69%) and mostly white (25/35, 71%). Participants generally had access to technology and were receptive to engaging with an app to increase PA. Themes identified included preferences for (1) a casual, concise, and positive tone, (2) tools for personal goal attainment, (3) a prescription for PA, and (4) an experience that is tailored to the user. Participants reported wanting extensive background data collection with low data entry burden and to have a trustworthy source translate their personal data into individualized PA recommendations. They expressed a desire for app functions that could facilitate goal achievement and articulated a preference for a more private social experience. Finally, results indicated that PA goals might be best established in the context of personally held priorities and values.

CONCLUSIONS

Many of the desired features identified are compatible with both empirically supported methods of behavior change and the relative strengths of an app as a delivery vehicle for behavioral intervention. Participating cancer survivors' preferences contrasted with many current standard practices for mobile app development, including value-based rather than numeric goals, private socialization in small groups rather than sharing with broader social networks, and interpretation of PA data rather than merely providing numerical data. Taken together, these insights may help increase the acceptability of theory-based mHealth PA interventions in cancer survivors.

Efficacy of interventions that use apps to improve diet, physical activity and sedentary behaviour: a systematic review

BACKGROUND

Health and fitness applications (apps) have gained popularity in interventions to improve diet, physical activity and sedentary behaviours but their efficacy is unclear. This systematic review examined the efficacy of interventions that use apps to improve diet, physical activity and sedentary behaviour in children and adults.

METHODS

Systematic literature searches were conducted in five databases to identify papers published between 2006 and 2016. Studies were included if they used a smartphone app in an intervention to improve diet, physical activity and/or sedentary behaviour for prevention. Interventions could be stand-alone interventions using an app only, or multi-component interventions including an app as one of several intervention components. Outcomes measured were changes in the health behaviours and related health outcomes (i.e., fitness, body weight, blood pressure, glucose, cholesterol, quality of life). Study inclusion and methodological quality were independently assessed by two reviewers.

RESULTS

Twenty-seven studies were included, most were randomised controlled trials (n = 19; 70%). Twenty-three studies targeted adults (17 showed significant health improvements) and four studies targeted children (two demonstrated significant health improvements). Twenty-one studies targeted physical activity (14 showed significant health improvements), 13 studies targeted diet (seven showed significant health improvements) and five studies targeted sedentary behaviour (two showed significant health improvements). More studies (n = 12; 63%) of those reporting significant effects detected between-group improvements in the health behaviour or related health outcomes, whilst fewer studies (n = 8; 42%) reported significant within-group improvements. A larger proportion of multi-component interventions (8 out of 13; 62%) showed significant between-group improvements compared to stand-alone app interventions (5 out of 14; 36%). Eleven studies reported app usage statistics, and three of them demonstrated that higher app usage was associated with improved health outcomes.

CONCLUSIONS

This review provided modest evidence that app-based interventions to improve diet, physical activity and sedentary behaviours can be effective. Multi-component interventions appear to be more effective than stand-alone app interventions, however, this remains to be confirmed in controlled trials. Future research is needed on the optimal number and combination of app features, behaviour change techniques, and level of participant contact needed to maximise user engagement and intervention efficacy.

mHealth or eHealth? Efficacy, Use, and Appreciation of a Web-Based Computer-Tailored Physical Activity Intervention for Dutch Adults: A Randomized Controlled Trial

Background

Until a few years ago, Web-based computer-tailored interventions were almost exclusively delivered via computer (eHealth). However, nowadays, interventions delivered via mobile phones (mHealth) are an interesting alternative for health promotion, as they may more easily reach people 24/7.

Objective

The first aim of this study was to compare the efficacy of an mHealth and an eHealth version of a Web-based computer-tailored physical activity intervention with a control group. The second aim was to assess potential differences in use and appreciation between the 2 versions.

Methods

We collected data among 373 Dutch adults at 5 points in time (baseline, after 1 week, after 2 weeks, after 3 weeks, and after 6 months). We recruited participants from a Dutch online research panel and randomly assigned them to 1 of 3 conditions: eHealth (n=138), mHealth (n=108), or control condition (n=127). All participants were asked to complete questionnaires at the 5 points in time. Participants in the eHealth and mHealth group received fully automated tailored feedback messages about their current level of physical activity. Furthermore, they received personal feedback aimed at increasing their amount of physical activity when needed. We used analysis of variance and linear regression analyses to examine differences between the 2 study groups and the control group with regard to efficacy, use, and appreciation.

Results

Participants receiving feedback messages (eHealth and mHealth together) were significantly more physically active after 6 months than participants in the control group (B=8.48, df=2, P=.03, Cohen d=0.27). We found a small effect size favoring the eHealth condition over the control group (B=6.13, df=2, P=.09, Cohen d=0.21). The eHealth condition had lower dropout rates (117/138, 84.8%) than the mHealth condition (81/108, 75.0%) and the control group (91/127, 71.7%). Furthermore, in terms of usability and appreciation, the eHealth condition outperformed the mHealth condition with regard to participants receiving (t₁₈₂=3.07, P=.002) and reading the feedback messages (t₁₈₁=2.34, P=.02), as well as the clarity of the messages (t₁₈₁=1.99, P=.049).

Conclusions

We tested 2 Web-based computer-tailored physical activity intervention versions (mHealth and eHealth) against a control condition with regard to efficacy, use, usability, and appreciation. The overall effect was mainly caused by the more effective eHealth intervention. The mHealth app was rated inferior to the eHealth version with regard to usability and appreciation. More research is needed to assess how both methods can complement each other.

Trial Registration

Netherlands Trial Register: NTR4503; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=4503 (Archived by WebCite at http://www.webcitation.org/6lEi1x40s)

"Spatial Energetics": Integrating Data From GPS, Accelerometry, and GIS to Address Obesity and Inactivity

To address the current obesity and inactivity epidemics, public health researchers have attempted to identify spatial factors that influence physical inactivity and obesity. Technologic and methodologic developments have led to a revolutionary ability to examine dynamic, high-resolution measures of temporally matched location and behavior data through GPS, accelerometry, and GIS. These advances allow the investigation of spatial energetics, high-spatiotemporal resolution data on location and time-matched energetics, to examine how environmental characteristics, space, and time are linked to activity-related health behaviors with far more robust and detailed data than in previous work. Although the transdisciplinary field of spatial energetics demonstrates promise to provide novel insights on how individuals and populations interact with their environment, there remain significant conceptual, technical, analytical, and ethical challenges stemming from the complex data streams that spatial energetics research generates. First, it is essential to better understand what spatial energetics data represent, the relevant spatial context of analysis for these data, and if spatial energetics can establish causality for development of spatially relevant interventions. Second, there are significant technical problems for analysis of voluminous and complex data that may require development of spatially aware scalable computational infrastructures. Third, the field must come to agreement on appropriate statistical methodologies to account for multiple observations per person. Finally, these challenges must be considered within the context of maintaining participant privacy and security. This article describes gaps in current practice and understanding and suggests solutions to move this promising area of research forward.

The effectiveness of e-& mHealth interventions to promote physical activity and healthy diets in developing countries: A systematic review

Background

Promoting physical activity and healthy eating is important to combat the unprecedented rise in NCDs in many developing countries. Using modern information-and communication technologies to deliver physical activity and diet interventions is particularly promising considering the increased proliferation of such technologies in many developing countries. The objective of this systematic review is to investigate the effectiveness of e-& mHealth interventions to promote physical activity and healthy diets in developing countries.

Methods

Major databases and grey literature sources were searched to retrieve studies that quantitatively examined the effectiveness of e-& mHealth interventions on physical activity and diet outcomes in developing countries. Additional studies were retrieved through citation alerts and scientific social media allowing study inclusion until August 2016. The CONSORT checklist was used to assess the risk of bias of the included studies.

Results

A total of 15 studies conducted in 13 developing countries in Europe, Africa, Latin-and South America and Asia were included in the review. The majority of studies enrolled adults who were healthy or at risk of diabetes or hypertension. The average intervention length was 6.4 months, and text messages and the Internet were the most frequently used intervention delivery channels. Risk of bias across the studies was moderate (55.7 % of the criteria fulfilled). Eleven studies reported significant positive effects of an e-& mHealth intervention on physical activity and/or diet behaviour. Respectively, 50 % and 70 % of the interventions were effective in promoting physical activity and healthy diets.

Conclusions

The majority of studies demonstrated that e-& mHealth interventions were effective in promoting physical activity and healthy diets in developing countries. Future interventions should use more rigorous study designs, investigate the cost-effectiveness and reach of interventions, and focus on emerging technologies, such as smart phone apps and wearable activity trackers.

Trial registration

The review protocol can be retrieved from the PROSPERO database (Registration ID: CRD42015029240).

Electronic supplementary material

The online version of this article (doi:10.1186/s12966-016-0434-2) contains supplementary material, which is available to authorized users.

Promoting Physical Activity among Underserved Populations

Underserved populations, including racial/ethnic minorities, individuals with low socioeconomic status, and individuals with physical disabilities, are less likely to engage in sufficient moderate to vigorous physical activity (MVPA) and are thus at increased risk of morbidity and mortality. These populations face unique challenges to engaging in MVPA. Learning how to overcome these challenges is a necessary first step in achieving health equity through health promotion research. In this review of the literature, we discuss issues and strategies that have been used to promote MVPA among individuals from underserved populations, focusing on recruitment, intervention delivery, and the use of technology in interventions. Physical activity promotion research among these vulnerable populations is scarce. Nevertheless, there is preliminary evidence of efficacy in the use of certain recruitment and intervention strategies including tailoring, cultural adaptation, incorporation of new technologies, and multilevel and community-based approaches for physical activity promotion among different underserved populations.

Strategies to improve effectiveness of physical activity coaching systems: Development of personas for providing tailored feedback

Mobile physical activity interventions can be improved by incorporating behavioural change theories. Relations between self-efficacy, stage of change, and physical activity are investigated, enabling development of feedback strategies that can be used to improve their effectiveness. A total of 325 healthy control participants and 82 patients wore an activity monitor. Participants completed a self-efficacy or stage of change questionnaire. Results show that higher self-efficacy is related to higher activity levels. Patients are less active than healthy controls and show a larger drop in physical activity over the day. Patients in the maintenance stage of change are more active than patients in lower stages of change, but show an equally large drop in level of physical activity. Findings suggest that coaching should at least be tailored to level of self-efficacy, stage of change, and physical activity pattern. Tailored coaching strategies are developed, which suggest that increasing self-efficacy of users is most important. Guidelines are provided.

Validity of the Samsung Phone S Health application for assessing steps and energy expenditure during walking and running: Does phone placement matter?

Background

The emergence of mHealth and the utilization of smartphones in physical activity interventions warrant a closer examination of validity evidence for such technology. This study examined the validity of the Samsung S Health application in measuring steps and energy expenditure.

Methods

Twenty-nine participants (mean age 21.69 ± 1.63) participated in the study. Participants carried a Samsung smartphone in their non-dominant hand and right pocket while walking around a 200-meter track and running on a treadmill at 2.24 m∙s⁻¹. Steps and energy expenditure from the S Health app were compared with StepWatch 3 Step Activity Monitor steps and indirect calorimetry.

Results

No significant differences between S Health estimated steps and energy expenditure during walking and their respective criterion measures, regardless of placement. There was also no significant difference between S Health estimated steps and the criterion measure during treadmill running, regardless of placement. There was significant differences between S Health estimated energy expenditure and the criterion during treadmill running for both placements (both p < 0.001).

Conclusions

The S Health application measures steps and energy expenditure accurately during self-selected pace walking regardless of placement. Placement of the phone impacts the S Health application accuracy in measuring physical activity variables during treadmill running.

Models of Individual Dietary Behavior Based on Smartphone Data: The Influence of Routine, Physical Activity, Emotion, and Food Environment

INTRODUCTION

Smartphone applications (apps) facilitate the collection of data on multiple aspects of behavior that are useful for characterizing baseline patterns and for monitoring progress in interventions aimed at promoting healthier lifestyles. Individual-based models can be used to examine whether behavior, such as diet, corresponds to certain typological patterns. The objectives of this paper are to demonstrate individual-based modeling methods relevant to a person's eating behavior, and the value of such approach compared to typical regression models.

METHOD

Using a mobile app, 2 weeks of physical activity and ecological momentary assessment (EMA) data, and 6 days of diet data were collected from 12 university students recruited from a university in Kunming, a rapidly developing city in southwest China. Phone GPS data were collected for the entire 2-week period, from which exposure to various food environments along each subject's activity space was determined. Physical activity was measured using phone accelerometry. Mobile phone EMA was used to assess self-reported emotion/feelings. The portion size of meals and food groups was determined from voice-annotated videos of meals. Individual-based regression models were used to characterize subjects as following one of 4 diet typologies: those with a routine portion sizes determined by time of day, those with portion sizes that balance physical activity (energy balance), those with portion sizes influenced by emotion, and those with portion sizes associated with food environments.

RESULTS

Ample compliance with the phone-based behavioral assessment was observed for all participants. Across all individuals, 868 consumed food items were recorded, with fruits, grains and dairy foods dominating the portion sizes. On average, 218 hours of accelerometry and 35 EMA responses were recorded for each participant. For some subjects, the routine model was able to explain up to 47% of the variation in portion sizes, and the energy balance model was able to explain over 88% of the variation in portion sizes. Across all our subjects, the food environment was an important predictor of eating patterns. Generally, grouping all subjects into a pooled model performed worse than modeling each individual separately.

CONCLUSION

A typological modeling approach was useful in understanding individual dietary behaviors in our cohort. This approach may be applicable to the study of other human behaviors, particularly those that collect repeated measures on individuals, and those involving smartphone-based behavioral measurement.

Ecological Momentary Assessment in Eating Disorder and Obesity Research: a Review of the Recent Literature

Our current understanding of the etiology and maintenance of eating disorders and obesity continues to be far from complete. Similarly, our understanding of determinants of both successful and unsuccessful weight loss surgery is also quite limited. While a number of research methodologies have been applied to these areas, one methodology that has recently seen a rise in popularity is the use of ecological momentary assessment (EMA). EMA allows one to study a variety of variables of interest in the natural environment. The study of eating disorders, obesity, and bariatric surgery has all been conducted using EMA recently. The current study is a review of these areas and summarizes the recent literature (past 3 years) in eating disorders, obesity, and bariatric surgery using EMA methodology.

Past, Present, and Future of eHealth and mHealth Research to Improve Physical Activity and Dietary Behaviors

Because physical inactivity and unhealthy diets are highly prevalent, there is a need for cost-effective interventions that can reach large populations. Electronic health (eHealth) and mobile health (mHealth) solutions have shown promising outcomes and have expanded rapidly in the past decade. The purpose of this report is to provide an overview of the state of the evidence for the use of eHealth and mHealth in improving physical activity and nutrition behaviors in general and special populations. The role of theory in eHealth and mHealth interventions is addressed, as are methodological issues. Key recommendations for future research in the field of eHealth and mHealth are provided.

Text Messaging for Exercise Promotion in Older Adults From an Upper-Middle-Income Country: Randomized Controlled Trial

Background

Mobile technology to promote exercise is effective; however, most evidence is from studies of younger groups in high-income countries. Investigating if short message service (SMS) texting can affect exercise participation in older adults from an upper-middle-income country is important considering the proliferation of mobile phones in developing regions and the increased interest of older adults in using mobile phones.

Objective

The main objective was to examine the short- and long-term effects of SMS text messaging on exercise frequency in older adults. Secondary objectives were to investigate how SMS text messages impact study participants’ exercise frequency and the effects of the intervention on secondary outcomes.

Methods

The Malaysian Physical Activity for Health Study (_myPAtHS) was a 24-week, 2-arm, parallel randomized controlled trial conducted in urban Malaysia. Participants were recruited via health talks in resident associations and religious facilities. Older Malaysians (aged 55-70 years) who used mobile phones and did not exercise regularly were eligible to participate in the study. Participants randomly allocated to the SMS texting arm received an exercise booklet and 5 weekly SMS text messages over 12 weeks. The content of the SMS text messages was derived from effective behavior change techniques. The non-SMS texting arm participants received only the exercise booklet. Home visits were conducted to collect outcome data: (1) exercise frequency at 12 and 24 weeks, (2) secondary outcome data (exercise self-efficacy, physical activity–related energy expenditure, sitting time, body mass index, grip and leg strength) at baseline and at 12 and 24 weeks. Intention-to-treat procedures were applied for data analysis. Semistructured interviews focusing primarily on the SMS text messages and their impact on exercise frequency were conducted at weeks 12 and 24.

Results

In total, 43 participants were randomized into the SMS texting arm (n=22) and the non-SMS texting arm (n=21). Study-unrelated injuries forced 4 participants to discontinue after a few weeks (they were not included in any analyses). Overall retention was 86% (37/43). After 12 weeks, SMS texting arm participants exercised significantly more than non-SMS texting arm participants (mean difference 1.21 times, bias-corrected and accelerated bootstrap [BCa] 95% CI 0.18-2.24). Interview analysis revealed that the SMS text messages positively influenced SMS texting arm participants who experienced exercise barriers. They described the SMS text messages as being encouraging, a push, and a reminder. After 24 weeks, there was no significant difference between the research arms (mean difference 0.74, BCa 95% CI –0.30 to 1.76). There were no significant effects for secondary outcomes.

Conclusions

This study provides evidence that SMS text messaging is effective in promoting exercise in older adults from an upper-middle-income country. Although the effects were not maintained when SMS text messaging ceased, the results are promising and warrant more research on behavioral mobile health interventions in other regions.

Trial Registration

Clinicaltrials.gov NCT02123342; http://clinicaltrials.gov/ct2/show/NCT02123342 (Archived by WebCite at http://www.webcitation.org/6eGSsu2EI).

Innovations in the Use of Interactive Technology to Support Weight Management

New and emerging mobile technologies are providing unprecedented possibilities for understanding and intervening on obesity-related behaviors in real time. However, the mobile health (mHealth) field has yet to catch up with the fast-paced development of technology. Current mHealth efforts in weight management still tend to focus mainly on short message systems (SMS) interventions, rather than taking advantage of real-time sensing to develop just-in-time adaptive interventions (JITAIs). This paper will give an overview of the current technology landscape for sensing and intervening on three behaviors that are central to weight management: diet, physical activity, and sleep. Then five studies that really dig into the possibilities that these new technologies afford will be showcased. We conclude with a discussion of hurdles that mHealth obesity research has yet to overcome and a future-facing discussion.

Mobile Phone and Web 2.0 Technologies for Weight Management: A Systematic Scoping Review

BACKGROUND

Widespread diffusion of mobile phone and Web 2.0 technologies make them potentially useful tools for promoting health and tackling public health issues, such as the increasing prevalence of overweight and obesity. Research in this domain is growing rapidly but, to date, no review has comprehensively and systematically documented how mobile and Web 2.0 technologies are being deployed and evaluated in relation to weight management.

OBJECTIVE

To provide an up-to-date, comprehensive map of the literature discussing the use of mobile phone and Web 2.0 apps for influencing behaviors related to weight management (ie, diet, physical activity [PA], weight control, etc).

METHODS

A systematic scoping review of the literature was conducted based on a published protocol (registered at

PROSPERO

CRD42014010323). Using a comprehensive search strategy, we searched 16 multidisciplinary electronic databases for original research documents published in English between 2004 and 2014. We used duplicate study selection and data extraction. Using an inductively developed charting tool, selected articles were thematically categorized.

RESULTS

We identified 457 articles, mostly published between 2013 and 2014 in 157 different journals and 89 conference proceedings. Articles were categorized around two overarching themes, which described the use of technologies for either (1) promoting behavior change (309/457, 67.6%) or (2) measuring behavior (103/457, 22.5%). The remaining articles were overviews of apps and social media content (33/457, 7.2%) or covered a combination of these three themes (12/457, 2.6%). Within the two main overarching themes, we categorized articles as representing three phases of research development: (1) design and development, (2) feasibility studies, and (3) evaluations. Overall, articles mostly reported on evaluations of technologies for behavior change (211/457, 46.2%).

CONCLUSIONS

There is an extensive body of research on mobile phone and Web 2.0 technologies for weight management. Research has reported on (1) the development, feasibility, and efficacy of persuasive mobile technologies used in interventions for behavior change (PA and diet) and (2) the design, feasibility, and accuracy of mobile phone apps for behavioral assessment. Further research has focused exclusively on analyses of the content and quality of available apps. Limited evidence exists on the use of social media for behavior change, but a segment of studies deal with content analyses of social media. Future research should analyze mobile phone and Web 2.0 technologies together by combining the evaluation of content and design aspects with usability, feasibility, and efficacy/effectiveness for behavior change, or accuracy/validity for behavior assessment, in order to understand which technological components and features are likely to result in effective interventions.

The Technological Growth in eHealth Services

The infusion of information communication technology (ICT) into health services is emerging as an active area of research. It has several advantages but perhaps the most important one is providing medical benefits to one and all irrespective of geographic boundaries in a cost effective manner, providing global expertise and holistic services, in a time bound manner. This paper provides a systematic review of technological growth in eHealth services. The present study reviews and analyzes the role of four important technologies, namely, satellite, internet, mobile, and cloud for providing health services.

Mobile text messaging for health: a systematic review of reviews

The aim of this systematic review of reviews is to identify mobile text-messaging interventions designed for health improvement and behavior change and to derive recommendations for practice. We have compiled and reviewed existing systematic research reviews and meta-analyses to organize and summarize the text-messaging intervention evidence base, identify best-practice recommendations based on findings from multiple reviews, and explore implications for future research. Our review found that the majority of published text-messaging interventions were effective when addressing diabetes self-management, weight loss, physical activity, smoking cessation, and medication adherence for antiretroviral therapy. However, we found limited evidence across the population of studies and reviews to inform recommended intervention characteristics. Although strong evidence supports the value of integrating text-messaging interventions into public health practice, additional research is needed to establish longer-term intervention effects, identify recommended intervention characteristics, and explore issues of cost-effectiveness.

Using new technologies to improve the prevention and management of chronic conditions in populations

Lifestyle factors are important in the development of chronic diseases, such as heart disease, respiratory disease, and diabetes, and chronic disease risk can be reduced by changes in lifestyle behaviors linked to these conditions. The use of mass media and community-wide strategies targeting these behaviors has been extensively evaluated since the 1970s. This review summarizes some examples of interventions and their use of media conducted within the old communications landscape of the 1970s and 1980s and the key lessons learned from their design, implementation, and evaluation. We then consider the potential and evidence base for using contemporary technology applications and platforms-within the new communications landscape-to improve the prevention and management of lifestyle-related chronic diseases in the future. We discuss the implications and adaptation of lessons derived from the ways in which new technologies are being used in commercial and political contexts and their relevance for public health. Finally, we consider some recent examples of applying new technologies to public health issues and consider some of the challenges in this rapidly developing field.

Commercial Smartphone-Based Devices and Smart Applications for Personalized Healthcare Monitoring and Management

Smartphone-based devices and applications (SBDAs) with cost effectiveness and remote sensing are the most promising and effective means of delivering mobile healthcare (mHealthcare). Several SBDAs have been commercialized for the personalized monitoring and/or management of basic physiological parameters, such as blood pressure, weight, body analysis, pulse rate, electrocardiograph, blood glucose, blood glucose saturation, sleeping and physical activity. With advances in Bluetooth technology, software, cloud computing and remote sensing, SBDAs provide real-time on-site analysis and telemedicine opportunities in remote areas. This scenario is of utmost importance for developing countries, where the number of smartphone users is about 70% of 6.8 billion cell phone subscribers worldwide with limited access to basic healthcare service. The technology platform facilitates patient-doctor communication and the patients to effectively manage and keep track of their medical conditions. Besides tremendous healthcare cost savings, SBDAs are very critical for the monitoring and effective management of emerging epidemics and food contamination outbreaks. The next decade will witness pioneering advances and increasing applications of SBDAs in this exponentially growing field of mHealthcare. This article provides a critical review of commercial SBDAs that are being widely used for personalized healthcare monitoring and management.

Increasing physical activity efficiently: an experimental pilot study of a website and mobile phone intervention

The main objective of this pilot study was to test the effectiveness of an online, interactive physical activity intervention that also incorporated gaming components. The intervention design included an activity planner, progress monitoring, and gamification components and used SMS text as a secondary delivery channel and feedback to improve engagement in the intervention content. Healthy adults (n = 21) recruited through ads in local newspapers (age 35–73) were randomized to the intervention or the control condition. Both groups reported physical activity using daily report forms in four registration weeks during the three-month study: only the experiment condition received access to the intervention. Analyses showed that the intervention group had significantly more minutes of physical activity in weeks five and nine. We also found a difference in the intensity of exercise in week five. Although the intervention group reported more minutes of physical activity at higher intensity levels, we were not able to find a significant effect at the end of the study period. In conclusion, this study adds to the research on the effectiveness of using the Internet and SMS text messages for delivering physical activity interventions and supports gamification as a viable intervention tool.

An evolving scientific basis for the prevention and treatment of pediatric obesity

The 2013 Pennington Biomedical Research Center's Scientific Symposium focused on the treatment and management of pediatric obesity and was designed to (i) review recent scientific advances in the prevention, clinical treatment and management of pediatric obesity, (ii) integrate the latest published and unpublished findings and (iii) explore how these advances can be integrated into clinical and public health approaches. The symposium provided an overview of important new advances in the field, which led to several recommendations for incorporating the scientific evidence into practice. The science presented covered a range of topics related to pediatric obesity, including the role of genetic differences, epigenetic events influenced by in utero development, pre-pregnancy maternal obesity status, maternal nutrition and maternal weight gain on developmental programming of adiposity in offspring. Finally, the relative merits of a range of various behavioral approaches targeted at pediatric obesity were covered, together with the specific roles of pharmacotherapy and bariatric surgery in pediatric populations. In summary, pediatric obesity is a very challenging problem that is unprecedented in evolutionary terms; one which has the capacity to negate many of the health benefits that have contributed to the increased longevity observed in the developed world.