Effects of Three Motivationally Targeted Mobile Device Applications on Initial Physical Activity and Sedentary Behavior Change in Midlife and Older Adults: A Randomized Trial

Digital Behavior Change Intervention Designs for Habit Formation: Systematic Review

BACKGROUND

With the development of emerging technologies, digital behavior change interventions (DBCIs) help to maintain regular physical activity in daily life.

OBJECTIVE

To comprehensively understand the design implementations of habit formation techniques in current DBCIs, a systematic review was conducted to investigate the implementations of behavior change techniques, types of habit formation techniques, and design strategies in current DBCIs.

METHODS

The process of this review followed the PRISMA (Preferred Reporting Item for Systematic Reviews and Meta-Analyses) guidelines. A total of 4 databases were systematically searched from 2012 to 2022, which included Web of Science, Scopus, ACM Digital Library, and PubMed. The inclusion criteria encompassed studies that used digital tools for physical activity, examined behavior change intervention techniques, and were written in English.

RESULTS

A total of 41 identified research articles were included in this review. The results show that the most applied behavior change techniques were the self-monitoring of behavior, goal setting, and prompts and cues. Moreover, habit formation techniques were identified and developed based on intentions, cues, and positive reinforcement. Commonly used methods included automatic monitoring, descriptive feedback, general guidelines, self-set goals, time-based cues, and virtual rewards.

CONCLUSIONS

A total of 32 commonly design strategies of habit formation techniques were summarized and mapped to the proposed conceptual framework, which was categorized into target-mediated (generalization and personalization) and technology-mediated interactions (explicitness and implicitness). Most of the existing studies use the explicit interaction, aligning with the personalized habit formation techniques in the design strategies of DBCIs. However, implicit interaction design strategies are lacking in the reviewed studies. The proposed conceptual framework and potential solutions can serve as guidelines for designing strategies aimed at habit formation within DBCIs.

Combining anchoring with financial incentives to increase physical activity: a randomized controlled trial among college students

The purpose of this study was to: (1) compare the relative efficacy of different combinations of three behavioral intervention strategies (i.e., personalized reminders, financial incentives, and anchoring) for establishing physical activity habits using an mHealth app and (2) to examine the effects of these different combined interventions on intrinsic motivation for physical activity and daily walking habit strength. A four-arm randomized controlled trial was conducted in a sample of college students (N = 161) who had a self-reported personal wellness goal of increasing their physical activity. Receiving cue-contingent financial incentives (i.e., incentives conditional on performing physical activity within ± one hour of a prespecified physical activity cue) combined with anchoring resulted in the highest daily step counts and greatest odds of temporally consistent walking during both the four-week intervention and the full eight-week study period. Cue-contingent financial incentives were also more successful at increasing physical activity and maintaining these effects post-intervention than traditional non-cue-contingent incentives. There were no differences in intrinsic motivation or habit strength between study groups at any time point. Financial incentives, particularly cue-contingent incentives, can be effectively used to support the anchoring intervention strategy for establishing physical activity habits. Moreover, mHealth apps are a feasible method for delivering the combined intervention technique of financial incentives with anchoring.

Age Unplugged: A Brief Narrative Review on the Intersection of Digital Tools, Sedentary and Physical Activity Behaviors in Community-Dwelling Older Adults

This brief narrative review assesses how digital technologies-such as wearables, mobile health apps, and various digital tools such as computers, game consoles, tablets, smartphones, and extended reality systems-can influence sedentary and physical activity behaviors among community-dwelling older adults. Each section highlights the central role of these technologies in promoting active aging through increased motivation, engagement and customized experiences. It underlines the critical importance of functionality, usability and adaptability of devices and confirms the effectiveness of digital interventions in increasing physical activity and reducing sedentary behavior. The sustainable impact of these technologies needs to be further investigated, with a focus on adapting digital health strategies to the specific needs of older people. The research advocates an interdisciplinary approach and points out that such collaborations are essential for the development of accessible, effective and ethical solutions. This perspective emphasizes the potential of digital tools to improve the health and well-being of the aging population and recommends their strategic integration into health promotion and policy making.

Sleep Quality as a Critical Pathway Between Adverse Childhood Experiences and Multimorbidity and the Impact of Lifestyle

OBJECTIVES

This study aims to establish the effects of ACEs on multimorbidity through sleep quality and investigate whether lifestyle factors (e.g., eating habits and exercise) may influence this relationship among middle-aged and older adults.

METHODS

Participants were drawn from a cross-sectional sample of community dwelling older adults (N = 276, 55+) and three waves of data from the Midlife in the United States study (MIDUS, N = 843). We examined the direct and indirect effects of ACEs, sleep quality, and health conditions, as well as the conditional effects of physical activity and eating habits.

RESULTS

Across both samples, sleep quality mediated the relationship between ACEs and chronic health conditions. Moderating effects of unhealthy eating and physical activity differed between samples.

DISCUSSION

Sleep quality is an important pathway connecting ACEs and adult multimorbidity, and health behaviors may provide targets for intervention particularly in older adults.

A pilot test of Internet-delivered brief interactive training sessions for depression: Evaluating dropout, uptake, adherence, and outcome

OBJECTIVE

Uptake, adherence, and dropout rates for Internet-based programs for depression are in need of improvement. Excessive user burden (eg, heavy content, extended duration) may undermine engagement and precipitate dropout. To address this problem, an alternative format was proposed: Brief Interactive Training Sessions (BITS). BITS target a narrow behavioral health skill and require 3-4 hours to complete. A depression-focused version of BITS that provides training in cognitive distortion/restructuring was tested.

PARTICIPANTS

Fifty-two depressed college students were randomly assigned to BITS or to a wait-list.

METHODS

Students competed pre-and post-test (7 week) assessments of depression and cognitive distortions.

RESULTS

Uptake rate was 83%. Among study completers, adherence was 96%. The dropout rate did not improve. Compared to the control group, the experimental group reported a significant reduction in depression and in cognitive distortions.

CONCLUSIONS

The BITS format is a promising vehicle for improving uptake and adherence while achieving positive clinical outcomes.

The Impact of a Mobile Support Group on Distress and Physical Activity in Breast Cancer Survivors: Randomized, Parallel-Group, Open-Label, Controlled Trial

BACKGROUND

While mobile health apps have demonstrated their potential in revolutionizing health behavior changes, the impact of a mobile community built on these apps on the level of physical activity and mental well-being in cancer survivors remains unexplored.

OBJECTIVE

In this randomized controlled trial, we examine the effects of participation in a mobile health community specifically designed for breast cancer survivors on their physical activity levels and mental distress.

METHODS

We performed a single-center, randomized, parallel-group, open-label, controlled trial. This trial enrolled women between 20 and 60 years of age with stage 0 to III breast cancer, an Eastern Cooperative Oncology Group performance status of 0, and the capability of using their own smartphone apps. From January 7, 2019, to April 17, 2020, a total of 2,616 patients were consecutively screened for eligibility after breast cancer surgery. Overall, 202 patients were enrolled in this trial, and 186 patients were randomly assigned (1:1) to either the intervention group (engagement in a mobile peer support community using an app for tracking steps; n=93) or the control group (using the app for step tracking only; n=93) with a block size of 10 without stratification. The mobile app provides a visual interface of daily step counts, while the community function also provides rankings among its members and regular notifications encouraging physical activity. The primary end point was the rate of moderate to severe distress for the 24-week study period, measured through an app-based survey using the Distress Thermometer. The secondary end point was the total weekly steps during the 24-week period.

RESULTS

After excluding dropouts, 85 patients in the intervention group and 90 patients in the control group were included in the analysis. Multivariate analyses showed that patients in the intervention group had a significantly lower degree of moderate to severe distress (B=-0.558; odds ratio 0.572; P<.001) and a higher number of total weekly step counts (B=0.125; rate ratio 1.132; P<.001) during the 24-week period.

CONCLUSIONS

Engagement in a mobile app-based patient community was effective in reducing mental distress and increasing physical activity in breast cancer survivors.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03783481; https://classic.clinicaltrials.gov/ct2/show/NCT03783481.

Leveraging Mobile Technology for Public Health Promotion: A Multidisciplinary Perspective

Health behaviors are inextricably linked to health and well-being, yet issues such as physical inactivity and insufficient sleep remain significant global public health problems. Mobile technology-and the unprecedented scope and quantity of data it generates-has a promising but largely untapped potential to promote health behaviors at the individual and population levels. This perspective article provides multidisciplinary recommendations on the design and use of mobile technology, and the concomitant wealth of data, to promote behaviors that support overall health. Using physical activity as anexemplar health behavior, we review emerging strategies for health behavior change interventions. We describe progress on personalizing interventions to an individual and their social, cultural, and built environments, as well as on evaluating relationships between mobile technology data and health to establish evidence-based guidelines. In reviewing these strategies and highlighting directions for future research, we advance the use of theory-based, personalized, and human-centered approaches in promoting health behaviors.

Qualitative and quantitative evidence of motivation states for physical activity, exercise and being sedentary from university student focus groups

Motivation for physical activity and sedentary behaviors (e.g., desires, urges, wants, cravings) varies from moment to moment. According to the WANT model, these motivation states may be affectively-charged (e.g., felt as tension), particularly after periods of maximal exercise or extended rest. The purpose of this study was to examine postulates of the WANT model utilizing a mixed-methods approach. We hypothesized that: (1) qualitative evidence would emerge from interviews to support this model, and (2) motivation states would quantitatively change over the course of an interview period. Seventeen undergraduate students (mean age = 18.6y, 13 women) engaged in focus groups where 12 structured questions were presented. Participants completed the "right now" version of the CRAVE scale before and after interviews. Qualitative data were analyzed with content analysis. A total of 410 unique lower-order themes were classified and grouped into 43 higher order themes (HOTs). From HOTs, six super higher order themes (SHOTs) were designated: (1) wants and aversions, (2) change and stability, (3) autonomy and automaticity, (4) objectives and impulses, (5) restraining and propelling forces, and (6) stress and boredom. Participants stated that they experienced desires to move and rest, including during the interview, but these states changed rapidly and varied both randomly as well as systematically across periods of minutes to months. Some also described a total absence of desire or even aversion to move and rest. Of note, strong urges and cravings for movement, typically from conditions of deprivation (e.g., sudden withdrawal from exercise training) were associated with physical and mental manifestations, such as fidgeting and feeling restless. Urges were often consummated with behavior (e.g., exercise sessions, naps), which commonly resulted in satiation and subsequent drop in desire. Importantly, stress was frequently described as both an inhibitor and instigator of motivation states. CRAVE-Move increased pre-to-post interviews (p < .01). CRAVE-Rest demonstrated a trend to decline (p = .057). Overall, qualitative and quantitative data largely corroborated postulates of the WANT model, demonstrating that people experience wants and cravings to move and rest, and that these states appear to fluctuate significantly, especially in the context of stress, boredom, satiety, and deprivation.

Software Correction of Speed Measurement Determined by Phone GNSS Modules in Applications for Runners

This paper presents the results of a study on software correction of speed measurements taken by GNSS receivers installed in cell phones and sports watches. Digital low-pass filters were used to compensate for fluctuations in measured speed and distance. Real data obtained from popular running applications for cell phones and smartwatches were used for simulations. Various measurement situations were analyzed, such as running at a constant speed or interval running. Taking a very high accuracy GNSS receiver as the reference equipment, the solution proposed in the article reduces the measurement error of the traveled distance by 70%. In the case of measuring speed in interval running, the error could be reduced by up to 80%. The low-cost implementation allows simple GNSS receivers to approach the quality of distance and speed estimation of very precise and expensive solutions.

Nudging digital physical activity breaks for home studying of university students-A randomized controlled trial during the COVID-19 pandemic with daily activity measures

University students are of particular public health interest because they are at high risk for physical inactivity and sedentary behaviors. In conjunction with the COVID-19 pandemic, sedentariness and physical inactivity were reinforced, as the pandemic led to an increase in home studying. Physical activity (PA) breaks have been identified as promoting factors for university students' physical and mental health. Therefore, the present study explored an approach to nudge students to take PA breaks at home while studying. The purpose was to test the effectiveness of digital nudging for PA breaks for 10 days using a randomized intervention design during the COVID-19 pandemic. It included an intervention group who received daily digital motivational prompts for PA break videos and a minimal intervention control group who got low-level access to PA break videos via a one-time link sent to the media library. Using a sample of university students in the southwest of Germany (n = 57), two-level binary logistic regression models were calculated to predict daily participation in PA breaks during the intervention period depending on the nudging intervention, as well as previous participation in PA breaks, the general PA level of the subjects before the intervention, the time spent on PA and on home studying in a day, the kind of day during the intervention (weekday vs. weekend), and the students' age. Results revealed that the digital nudging intervention did not show any significant effect on the likelihood to participate in PA breaks on a given day (0.69 ≤ β ≤ 0.75, p > 0.3). Instead, an individual-level effect revealed that the longer a student studied at home over the course of a day, the more likely he or she was to take a PA break (1.07 ≤ β ≤ 1.11, p < 0.001). Current findings show that individual characteristics such as daily time spent on home studying, which can change over the course of the intervention phase, are relevant considerations within nudging intervention in university setting. This provides initial insights especially for digital PA breaks for students during home studying.

Long-Term Effectiveness of a Smartphone App and a Smart Band on Arterial Stiffness and Central Hemodynamic Parameters in a Population with Overweight and Obesity (Evident 3 Study): Randomised Controlled Trial

BACKGROUND

mHealth technologies could help to improve cardiovascular health; however, their effect on arterial stiffness and hemodynamic parameters has not been explored to date.

OBJECTIVE

To evaluate the effect of a mHealth intervention, at 3 and 12 months, on arterial stiffness and central hemodynamic parameters in a sedentary population with overweight and obesity.

METHODS

Randomised controlled clinical trial (Evident 3 study). 253 subjects were included: 127 in the intervention group (IG) and 126 in the control group (CG). The IG subjects were briefed on the use of the Evident 3 app and a smart band (Mi Band 2, Xiaomi) for 3 months to promote healthy lifestyles. All measurements were recorded in the baseline visit and at 3 and 12 months. The carotid-femoral pulse wave velocity (cfPWV) and the central hemodynamic parameters were measured using a SphigmoCor System^® device, whereas the brachial-ankle pulse wave velocity (baPWV) and the Cardio Ankle Vascular Index (CAVI) were measured using a VaSera VS-2000^® device.

RESULTS

Of the 253 subjects who attended the initial visit, 237 (93.7%) completed the visit at 3 months of the intervention, and 217 (85.3%) completed the visit at 12 months of the intervention. At 12 months, IG showed a decrease in peripheral augmentation index (PAIx) (-3.60; 95% CI -7.22 to -0.00) and ejection duration (ED) (-0.82; 95% CI -1.36 to -0.27), and an increase in subendocardial viability ratio (SEVR) (5.31; 95% CI 1.18 to 9.44). In CG, cfPWV decreased at 3 months (-0.28 m/s; 95% CI -0.54 to -0.02) and at 12 months (-0.30 m/s, 95% CI -0.54 to -0.05), central diastolic pressure (cDBP) decreased at 12 months (-1.64 mm/Hg; 95% CI -3.19 to -0.10). When comparing the groups we found no differences between any variables analyzed.

CONCLUSIONS

In sedentary adults with overweight or obesity, the multicomponent intervention (Smartphone app and an activity-tracking band) for 3 months did not modify arterial stiffness or the central hemodynamic parameters, with respect to the control group. However, at 12 months, CG presented a decrease of cfPWV and cDBP, whereas IG showed a decrease of PAIx and ED and an increase of SEVR.

Promoting Adherence to Joint Exercise Using the Donation Model: Proof via a Motion-Detecting Mobile Exercise Coaching Application

PURPOSE

Maintaining or increasing user adherence to digital healthcare services is of great concern to service providers. This study aims to verify whether the donation model is an effective strategy to increase adherence to physical exercise using a mobile application.

MATERIALS AND METHODS

A total of 5618 users of a motion-detecting mobile exercise coaching application participated in a donation or self-reward exercise challenge with the same exercise protocol. The workout consisted of 50 squats daily for 14 days. The user's exercise was monitored through a smartphone camera, providing real-time visual and audio feedback. In the donation group, 6 USD was donated to the economically disadvantaged if a participant completed their workout each day. In the self-reward group, three people who completed the program and 20 people who completed ≥12 days of exercise were randomly selected and provided with goods worth 60 USD and 4.3 USD of online currency, respectively.

RESULTS

The average daily exercise completion rate (% of participants who completed daily exercise) in the donation group was 1.8 times higher than that of the self-reward group (donation, 41.7%; self-reward, 22.7%; p<0.0001). The donation group completed more days of the program (donation, 5.8; self-reward, 3.2; p<0.0001). The completion rate of both groups decreased with time and decreased most on day two (donation, -9.9%; self-reward, -14.5%).

CONCLUSION

The donation model effectively promoted adherence to mobile app-based exercise. This donation model is expected to effectively enhance user adherence to digital healthcare services.

Changes in lifestyles, cognitive impairment, quality of life and activity day living after combined use of smartphone and smartband technology: a randomized clinical trial (EVIDENT-Age study)

Background

The aim of this study was to assess the efficacy of the combined use of smartphone and smartband technology for 3-months alongside brief lifestyle counselling, versus counselling alone, in increasing physical activity. As secondary objectives, the effects of the intervention on dietary habits, body composition, quality of life, level of functionality and cognitive performance were assessed.

Methods

This study employed a randomized clinical trial of two-parallel groups design – control group (CG) and intervention group (IG). The study was conducted in 3 Spanish health-centres between October 2018-February 2020. Eligible participants were people of both sexes and aged between 65–80 years attending the health-centres with a score ≥ 24 points on the Mini-Mental State Examination. Key variables included physical activity, dietary pattern, body composition, cognitive performance, level of functionality and quality of life. All variables were measured at baseline and after 3-months. Both groups received a brief nutritional and physical activity advice. Intervention group participants were instructed to use a smartphone application for a period of 3-months. This application integrates information on physical activity received from a fitness bracelet and self-reported information on the patient’s daily nutritional composition.

Results

The study population comprised 160 participants (IG = 81, CG = 79), with a mean age of 70.8 ± 4.0 years (61.3% women). No difference was found in the primary and secondary outcomes analyzed (physical activity (steps/min -0.4 (-1.0 to 0.2) p = 0.174), and dietary habits (Mediterranean diet score 0.0 (-0.6 to 0.6) p = 0.956) that could be attributed to either group after an ANCOVA test. A difference attributable to the intervention was observed in the total Clock test score (0.7 (0.1 to 1.2) p = 0.018.

Conclusions

In a sample of people over 65 years of age, the combined use of the EVIDENT 3 smartphone app and an activity tracking bracelet for 3-months did not result in lifestyles changes related to the amount and level of physical activity or the eating habits, compared to brief lifestyle advice. Other clinical parameters were not changed either, although at the cognitive level, a slight improvement was observed in the score on the Clock test assessing a variety of cognitive functions such as memory.

Trial registration

The study was registered in ClinicalTrials.gov Identifier: NCT03574480. Date of trial Registration 02/07/2018.

Assessing the Pragmatic Nature of mHealth Interventions Promoting Physical Activity: A Systematic Review and Meta-Analysis (Preprint)

BACKGROUND

Mobile health (mHealth) apps can promote physical activity; however, the pragmatic nature (ie, how well research translates into real-world settings) of these studies is unknown. The impact of study design choices, for example, intervention duration, on intervention effect sizes is also understudied.

OBJECTIVE

This review and meta-analysis aims to describe the pragmatic nature of recent mHealth interventions for promoting physical activity and examine the associations between study effect size and pragmatic study design choices.

METHODS

The PubMed, Scopus, Web of Science, and PsycINFO databases were searched until April 2020. Studies were eligible if they incorporated apps as the primary intervention, were conducted in health promotion or preventive care settings, included a device-based physical activity outcome, and used randomized study designs. Studies were assessed using the Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) and Pragmatic-Explanatory Continuum Indicator Summary-2 (PRECIS-2) frameworks. Study effect sizes were summarized using random effect models, and meta-regression was used to examine treatment effect heterogeneity by study characteristics.

RESULTS

Overall, 3555 participants were included across 22 interventions, with sample sizes ranging from 27 to 833 (mean 161.6, SD 193.9, median 93) participants. The study populations' mean age ranged from 10.6 to 61.5 (mean 39.6, SD 6.5) years, and the proportion of males included across all studies was 42.8% (1521/3555). Additionally, intervention lengths varied from 2 weeks to 6 months (mean 60.9, SD 34.9 days). The primary app- or device-based physical activity outcome differed among interventions: most interventions (17/22, 77%) used activity monitors or fitness trackers, whereas the rest (5/22, 23%) used app-based accelerometry measures. Data reporting across the RE-AIM framework was low (5.64/31, 18%) and varied within specific dimensions (Reach=44%; Effectiveness=52%; Adoption=3%; Implementation=10%; Maintenance=12.4%). PRECIS-2 results indicated that most study designs (14/22, 63%) were equally explanatory and pragmatic, with an overall PRECIS-2 score across all interventions of 2.93/5 (SD 0.54). The most pragmatic dimension was flexibility (adherence), with an average score of 3.73 (SD 0.92), whereas follow-up, organization, and flexibility (delivery) appeared more explanatory with means of 2.18 (SD 0.75), 2.36 (SD 1.07), and 2.41 (SD 0.72), respectively. An overall positive treatment effect was observed (Cohen d=0.29, 95% CI 0.13-0.46). Meta-regression analyses revealed that more pragmatic studies (-0.81, 95% CI -1.36 to -0.25) were associated with smaller increases in physical activity. Treatment effect sizes were homogenous across study duration, participants' age and gender, and RE-AIM scores.

CONCLUSIONS

App-based mHealth physical activity studies continue to underreport several key study characteristics and have limited pragmatic use and generalizability. In addition, more pragmatic interventions observe smaller treatment effects, whereas study duration appears to be unrelated to the effect size. Future app-based studies should more comprehensively report real-world applicability, and more pragmatic approaches are needed for maximal population health impacts.

TRIAL REGISTRATION

PROSPERO CRD42020169102; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=169102.

Exploring the effectiveness of a fitness-app prototype for home care service users in Austria and Italy

An infinite number of fitness apps are available on various app stores. However, hardly any of them are fitted to the needs and requirements of care-dependent people. This paper investigates the effectiveness of a customised fitness-app prototype for increasing physical activity in home care service users. Home care service users from Austria and Italy were randomly assigned to two groups. In total, 216 participants were involved in the field trial, 104 received a tablet with the fitness app and an activity tracker (treatment group), 112 did not (control group). Regularity of physical activity, frequency of fitness exercises and walking behaviour were self-reported by participants at baseline, after 4 months and after 8 months. In addition, the frequency of using the prototype was assessed based on the fitness app's logged usage data. We estimated multilevel mixed-effects ordered logistic models to examine the effects of the intervention. After 4 months, the intervention increased the home care users' probability of agreeing strongly with being physically active on a regular basis by 28 percentage points (p < 0.001; 95% CI: 0.20, 0.36) and their probability of reporting to exercise more than once a week by 45 percentage points (p < 0.001; 95% CI: 0.32, 0.57). Walking behaviour was not affected on group-level but improved for frequent users of the activity tracker. Frequent and regular users of the fitness app benefited most and effects persisted until the end of the 8 months controlled trial. Tailoring a fitness-app prototype to the needs of care-dependent people has the potential to support people with functional limitations to engage in a more active lifestyle. Future research is encouraged to seek further insights into how new technologies can support physical activities in people with long-term care needs.

My Wellness Coach: evaluation of a mobile app designed to promote integrative health among underserved populations

Underserved populations, including those from racial and ethnic groups and with low socioeconomic status, often lack access to mobile apps aimed at reducing health risk factors. This study evaluated the feasibility, acceptability, and preliminary effectiveness of the mobile app, My Wellness Coach (MWC), designed to promote behavior change in seven core areas of integrative health among underserved populations. Patients and staff were recruited from clinic and other settings. Some participants used MWC in a weekly group setting (n = 5); others on their own with support from a coordinator (n = 36). Health outcomes were assessed at baseline and 3 months. Mobile app ratings were collected at 5 weeks and 3 months. Goal setting data were analyzed at 3 months. Most participants (76%) set at least one goal, 71% created action steps for goals, and 29% completed a goal. Patients in the group setting had the highest rate of goal completion (60%) compared to patients (20%) and staff (27%) using the app on their own. Significant (p < .05) changes in pre- and post-test scores were documented for overall wellbeing, global physical health, BMI, vigorous physical activity, and eHealth literacy. Most participants (75%-91%) gave MWC high ratings for impact on behavior change, help seeking, intent to change, attitudes, knowledge, and awareness. This study documented preliminary evidence of the potential benefits of MWC among underserved communities. Future evaluations of Spanish and Android versions and comparisons between group and individual administration will inform implementation strategies for scaling MWC-based interventions to reach underserved communities nationally.

Users' Perspective on the AI-Based Smartphone PROTEIN App for Personalized Nutrition and Healthy Living: A Modified Technology Acceptance Model (mTAM) Approach

The ubiquitous nature of smartphone ownership, its broad application and usage, along with its interactive delivery of timely feedback are appealing for health-related behavior change interventions via mobile apps. However, users' perspectives about such apps are vital in better bridging the gap between their design intention and effective practical usage. In this vein, a modified technology acceptance model (mTAM) is proposed here, to explain the relationship between users' perspectives when using an AI-based smartphone app for personalized nutrition and healthy living, namely, PROTEIN, and the mTAM constructs toward behavior change in their nutrition and physical activity habits. In particular, online survey data from 85 users of the PROTEIN app within a period of 2 months were subjected to confirmatory factor analysis (CFA) and regression analysis (RA) to reveal the relationship of the mTAM constructs, i.e., perceived usefulness (PU), perceived ease of use (PEoU), perceived novelty (PN), perceived personalization (PP), usage attitude (UA), and usage intention (UI) with the users' behavior change (BC), as expressed via the acceptance/rejection of six related hypotheses (H1-H6), respectively. The resulted CFA-related parameters, i.e., factor loading (FL) with the related p-value, average variance extracted (AVE), and composite reliability (CR), along with the RA results, have shown that all hypotheses H1-H6 can be accepted (p < 0.001). In particular, it was found that, in all cases, FL > 0.5, CR > 0.7, AVE > 0.5, indicating that the items/constructs within the mTAM framework have good convergent validity. Moreover, the adjusted coefficient of determination (R 2) was found within the range of 0.224-0.732, justifying the positive effect of PU, PEoU, PN, and PP on the UA, that in turn positively affects the UI, leading to the BC. Additionally, using a hierarchical RA, a significant change in the prediction of BC from UA when the UI is used as a mediating variable was identified. The explored mTAM framework provides the means for explaining the role of each construct in the functionality of the PROTEIN app as a supportive tool for the users to improve their healthy living by adopting behavior change in their dietary and physical activity habits. The findings herein offer insights and references for formulating new strategies and policies to improve the collaboration among app designers, developers, behavior scientists, nutritionists, physical activity/exercise physiology experts, and marketing experts for app design/development toward behavior change.

Women's Health Initiative Strong and Healthy (WHISH): A pragmatic physical activity intervention trial for cardiovascular disease prevention

BACKGROUND

National guidelines promote physical activity to reduce cardiovascular disease (CVD); yet, no RCT has tested the effectiveness of physical activity as the sole intervention for primary CVD prevention in older adults. The Women's Health Initiative (WHI) Strong and Healthy (WHISH) trial, a pragmatic trial embedded in the WHI-Extension Study (ES), is testing whether increasing physical activity and decreasing sedentary behavior will reduce major CV events in older women.

METHODS

The randomized consent design was used to assign 49,331 women (aged 68-99 years in 2015) who had consented to ongoing WHI-ES follow-up and for whom CV outcomes were available through WHI-ES procedures (N = 18,985) and/or linkage to the Centers for Medicare and Medicaid Services (N = 30,346) to a physical activity (PA) intervention designed to promote national recommendations (N = 24,657) or "usual activity" comparison (N = 24,674). Women assigned to the intervention provided passive consent to receive the intervention and provide data. A multi-component PA intervention is delivered by seasonal (quarterly) newsletters with targeted inserts (lower, middle, higher) based on self-reported levels of physical functioning (PF) and physical activity; monthly motivational telephone messages; monthly emails; a website; and contact with staff, as requested. Major CV events, myocardial infarction (MI), stroke, or CVD death, collected annually through WHI-ES, comprise the primary outcome. Hip fracture and non-CVD death are primary safety outcomes. Intention-to-treat analyses in all randomized participants will include 8 years of follow-up.

CONCLUSION

Determining whether increased physical activity and decreased sedentary behavior reduce major CV events in older women is of major public health significance.

CLINICAL TRIALS REGISTRATION

ClinicalTrials.govidentifier:NCT02425345.

Efficacy of Mobile Health Applications to Improve Physical Activity and Sedentary Behavior: A Systematic Review and Meta-Analysis for Physically Inactive Individuals

Physical inactivity and sedentary behavior (SB) have attracted growing attention globally since they relate to noninfectious chronic diseases (NCDs) and could further result in the loss of life. This systematic literature review aimed to identify existing evidence on the efficacy of mobile health (mHealth) technology in inducing physical activity and reducing sedentary behavior for physically inactive people. Studies were included if they used a smartphone app in an intervention to improve physical activity and/or sedentary behavior for physically inactive individuals. Interventions could be stand-alone interventions or multi-component interventions, including an app as one of several intervention components. A total of nine studies were included, and all were randomized controlled trials. Two studies involved interventions delivered solely via a mobile application (stand-alone intervention) and seven studies involved interventions that used apps and other intervention strategies (multi-component intervention). Methodological quality was assessed, and the overall quality of the studies was ensured. The pooled data favored intervention in improving physical activity and reducing sedentary behavior. This review provided evidence that mobile health intervention improved physical activity and reduced sedentary behavior among inactive individuals. More beneficial effects can be guaranteed when interventions include multiple components. Further studies that maintain the effectiveness of such interventions are required to maximize user engagement and intervention efficacy.

mHealth-Supported Interventions With Potential to Address Sedentary Behavior in Older Adults: A Scoping Review

Sedentary behavior (SB) is an independent risk for negative health outcomes in older adults. Mobile health (mHealth) technology has potential to address SB in this population. This scoping review aimed to describe, synthesize, and identify gaps in literature on mHealth-supported interventions with potential to reduce sedentariness in older people. Following an iterative search of five major databases, 13 studies were included in the final review. Only three papers described SB-related factors as the primary target of intervention. mHealth-Driven components were frequently paired with nonmobile approaches and aided self-regulation of physical activity as opposed to SB. Most SB-related outcomes were not statistically significant or were inconclusive. This scoping review revealed a paucity of research applying mHealth-supported approaches to directly overcome sedentariness in older people. Protocol studies included in the review provide evidence that efforts to address this research gap continue to be made, but the need for additional high-quality research remains.

A Performance Evaluation Matrix for Measuring the Life Satisfaction of Older Adults Using eHealth Wearables

eHealth wearables can track users’ state of health, record their physiological data, and facilitate self-care. In this study, we examined whether they enhance older adults’ casual exercise willingness and life satisfaction. After reviewing the related literature, the performance and satisfaction of elements for older adults to use eHealth Wearables were determined. The elements were derived from the means–end chain analysis. Three dimensions, product attributes, beneficial consequences, personal values, and responding elements, were identified first. The Performance Evaluation Matrix (PEM) was then established to determine the elements to be improved. A total of 250 questionnaires were distributed, out of which 206 valid questionnaires were completed and returned. In the proposed PEM, the product attributes that were in the priority improvement zone were accessibility, learnability, usability, affordability, positioning, pedometer, heart rate monitor, and data feedback. These elements are the most essential properties in need of improvement.

Daily Level Association of Physical Activity and Performance on Ecological Momentary Cognitive Tests in Free-living Environments: A Mobile Health Observational Study

BACKGROUND

Research suggests that physical activity (PA) has both acute and chronic beneficial effects on cognitive function in laboratory settings and under supervised conditions. Mobile health technologies make it possible to reliably measure PA and cognition in free-living environments, thus increasing generalizability and reach. Research is needed to determine whether the benefits of PA on cognitive function extend from the laboratory to real-world contexts.

OBJECTIVE

This observational study aims to examine the association between daily fluctuations in PA and cognitive performance using mobile health technologies in free-living environments.

METHODS

A total of 90 adults (mean age 59, SD 6.3 years; 65/90, 72% men) with various comorbidities (eg, cardiovascular risk and HIV) and different levels of baseline cognition (ranging from cognitively normal to impaired) completed ecological momentary cognitive tests (EMCTs) on a smartphone twice daily while wearing an accelerometer to capture PA levels for 14 days. Linear mixed-effects models examined the daily associations of PA with executive function and verbal learning EMCTs. Moderation analyses investigated whether the relationship between daily PA and daily performance on EMCTs changed as a function of baseline cognition, cardiovascular risk, and functional status (independent vs dependent).

RESULTS

Days with greater PA were associated with better (faster) performance on an executive function EMCT after covariate adjustment (estimate -0.013; β=-.16; P=.04). Moderation analyses (estimate 0.048; β=.58; P=.001) indicated that days with greater PA were associated with better (faster) executive function performance in individuals who were functionally dependent (effect size -0.53; P<.001) and not in functionally independent adults (effect size -0.01; P=.91).

CONCLUSIONS

EMCTs may be a sensitive tool for capturing daily-level PA-related fluctuations in cognitive performance in real-world contexts and could be a promising candidate for tracking cognitive performance in digital health interventions aimed at increasing PA. Further research is needed to determine individual characteristics that may moderate the association between daily PA and EMCT performance in free-living environments.

Digital Health Promotion and Prevention in Settings: Scoping Review

BACKGROUND

Digital technologies are increasingly integrating into people's daily living environments such as schools, sport clubs, and health care facilities. These settings play a crucial role for health promotion and prevention because they affect the health of their members, as the World Health Organization has declared. Implementing digital health promotion and prevention in settings offers the opportunity to reach specific target groups, lower the costs of implementation, and improve the health of the population. Currently, there is a lack of scientific evidence that reviews the research on digital health promotion and prevention in settings.

OBJECTIVE

This scoping review aims to provide an overview of research targeting digital health promotion and primary prevention in settings. It assesses the range of scientific literature regarding outcomes such as applied technology, targeted setting, and area of health promotion or prevention, as well as identifies research gaps.

METHODS

The scoping review was conducted following the Levac, Colquhoun, and O'Brien framework. We searched scientific databases and gray literature for articles on digital setting-based health promotion and prevention published from 2010 to January 2020. We included empirical and nonempirical publications in English or German and excluded secondary or tertiary prevention and health promotion at the workplace.

RESULTS

From 8888 records, the search resulted in 200 (2.25%) included publications. We identified a huge diversity of literature regarding digital setting-based health promotion and prevention. The variety of technology types extends from computer- and web-based programs to mobile devices (eg, smartphone apps) and telemonitoring devices (sensors). We found analog, digital, and blended settings in which digital health promotion and prevention takes place. The most frequent analog settings were schools (39/200, 19.5%) and neighborhoods or communities (24/200, 12%). Social media apps were also included because in some studies they were defined as a (digital) setting. They accounted for 31.5% (63/200) of the identified settings. The most commonly focused areas of health promotion and prevention were physical activity (81/200, 40.5%), nutrition (45/200, 22.5%), and sexual health (34/200, 17%). Most of the interventions combined several health promotion or prevention methods, including environmental change; providing information, social support, training, or incentives; and monitoring. Finally, we found that the articles mostly reported on behavioral rather than structural health promotion and prevention.

CONCLUSIONS

The research field of digital health promotion and prevention in settings is heterogeneous. At the same time, we identified research gaps regarding the absence of valid definitions of relevant terms (eg, digital settings) and the lack of literature on structural health promotion and prevention in settings. Therefore, it remains unclear how digital technologies can contribute to structural (or organizational) changes in settings. More research is needed to successfully implement digital technologies to achieve health promotion and prevention in settings.

Quality assessment of smartphone fitness apps used to increase physical activity level and improve general health in adults: A systematic review

Introduction

Mobile exercise apps for smartphones have been used with intervention measures to increase physical activity. This study aimed to identify and evaluate the quality of fitness apps for smartphones that were used to increase the level of physical activity and improve the overall health of healthy adults.

Methods

The systematic review was performed in five electronic databases EMBASE, MEDLINE, CINAHL, Academic Research Premier e Cochrane Reviews, and Trials. The search terms were grouped into three categories according to the principles of population, intervention, comparison, and outcomes. The following includes examples of the group terms: population (healthy adults), intervention (smartphone apps), and outcomes (physical activity level).

Results

Of the 3924 potential articles, 74 were read for full-text analysis. Only seven studies were included in the review. The methodological evaluation of the studies and the apps’ quality showed that only one study and one app were evaluated with good quality. All studies used a type of application to improve the level of physical activity (measured by the number of daily steps), reporting an increase and improvement in some general health indices (calorie expenditure, weight, BMI) in healthy adults, regardless of frequency and duration of intervention and applications.

Conclusion

We cannot say that the use of smartphone applications improves the level of physical activity and general health. The low methodological quality of the studies and the possibility to evaluate the applications used (Mars Scale) due to the lack of technical standardization presented in the studies, despite the app used showing positive results in all studies.

Validity of a Global Positioning System-Based Algorithm and Consumer Wearables for Classifying Active Trips in Children and Adults

OBJECTIVE

To investigate the convergent validity of a global positioning system (GPS)-based and two consumer-based measures with trip logs for classifying pedestrian, cycling, and vehicle trips in children and adults.

METHODS

Participants (N = 34) wore a Qstarz GPS tracker, Fitbit Alta, and Garmin vivosmart 3 on multiple days and logged their outdoor pedestrian, cycling, and vehicle trips. Logged trips were compared with device-measured trips using the Personal Activity Location Measurement System (PALMS) GPS-based algorithms, Fitbit's SmartTrack, and Garmin's Move IQ. Trip- and day-level agreement were tested.

RESULTS

The PALMS identified and correctly classified the mode of 75.6%, 94.5%, and 96.9% of pedestrian, cycling, and vehicle trips (84.5% of active trips, F1 = 0.84 and 0.87) as compared with the log. Fitbit and Garmin identified and correctly classified the mode of 26.8% and 17.8% (22.6% of active trips, F1 = 0.40 and 0.30) and 46.3% and 43.8% (45.2% of active trips, F1 = 0.58 and 0.59) of pedestrian and cycling trips. Garmin was more prone to false positives (false trips not logged). Day-level agreement for PALMS and Garmin versus logs was favorable across trip modes, though PALMS performed best. Fitbit significantly underestimated daily cycling. Results were similar but slightly less favorable for children than adults.

CONCLUSIONS

The PALMS showed good convergent validity in children and adults and were about 50% and 27% more accurate than Fitbit and Garmin (based on F1). Empirically-based recommendations for improving PALMS' pedestrian classification are provided. Since the consumer devices capture both indoor and outdoor walking/running and cycling, they are less appropriate for trip-based research.

Long-Term Effects of a Video-Based Smartphone App ("VIDEA Bewegt") to Increase the Physical Activity of German Adults: A Single-Armed Observational Follow-Up Study

As physical inactivity is one of the four leading risk factors for mortality, it should be intensively treated. Therefore, this one-year follow-up study aimed to evaluate the long-term effects of a preventive app to increase physical activity in German adults under real-life circumstances. Data collection took place from July 2019 to July 2021 and included six online questionnaires. Physical activity was studied as the primary outcome based on MET-minutes per week (metabolic equivalent). Secondary outcomes included health-related quality of life based on a mental (MCS) and physical health component summary score (PCS). At the time of publication, 46/65 participants completed the study (median 52 years, 81.5% women). A significant increase of physical activity was observed in people with a low/moderate baseline activity during the first four months of follow-up (median increase by 490 MET-minutes per week, p < 0.001, r = 0.649). Both MCS (median increase by 2.8, p = 0.006, r = 0.344) and PCS (median increase by 2.6, p < 0.001, r = 0.521) significantly increased during the first two months and the BMI significantly decreased during the first six months after the intervention (median decrease by 0.96 kg/m2, p < 0.001, r = 0.465). Thus, this study provides evidence for the medium-term impact of the app, since the effects decreased over time. However, due to the chosen study design and a sizeable loss to follow-up, the validity of these findings is limited.

Internet of things-based lifestyle intervention for prostate cancer patients on androgen deprivation therapy: a prospective, multicenter, randomized trial

Androgen deprivation therapy (ADT) has several adverse effects including loss of libido, osteoporosis, and metabolic complications. We aim to examine whether the Smart After-Care (SAC) service, an Internet of Things (IoT)-based lifestyle intervention, affects clinical outcomes in prostate cancer (PCa) patients on ADT. A prospective, multicenter, randomized trial including 172 patients randomly assigned to the SAC or control group was conducted. The SAC group was provided with a smartphone application providing a personalized exercise program, daily activity monitoring, and diet counselling. The control group was briefly educated on the exercise program using a paper brochure. The primary endpoint was increase in cardiorespiratory endurance assessed using the 2-minute walking test (2MWT). Secondary endpoints included improved muscle strength (hand grip strength test and 30-second chair stand test), short physical performance battery, body composition, and health-related quality of life (EORTC-QLQ-C30 and PR25). Participants in both groups showed significant improvement in the 2MWT and 30-second chair stand test after 12 weeks of intervention. Greater improvement in the 2MWT was observed in the SAC group than in the control group. Significantly increased body fat ratio was observed in both groups; however, decreased skeletal muscle mass was observed only in the control group. Marginal improvement in skeletal muscle mass was observed over time in the SAC group when compared with that in the control group. Both groups showed improvement in all physical scales in the EORTC-QLQ-C30 questionnaire, and the SAC group showed a significant interaction of group and time for social functioning scales. SAC improved cardiorespiratory endurance, sarcopenic obesity, and health-related quality of life in patients with PCa on ADT.

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

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

Mobile health apps to reduce sedentary behavior: a scoping review

Given the continued increase in mobile health applications (apps) aimed at healthcare and the recognition of sedentary behavior (SB) as a public health problem, the goal of this scoping review study was to summarize the effects of interventions based on mobile health apps designed to reduce SB in adults, with a specific focus on SB. The electronic databases PubMed, PsycINFO, SportDISCUS, Web of Science, and manual searches in reference lists were conducted on papers published up to September 2020. Nine out of the 897 studies researched were included and composed the descriptive synthesis. The investigations found in the present study showed a decrease in time spent on television viewing and in total time spent sitting, as well as an increase in the number of SB breaks after interventions based on mobile health apps. In conclusion, despite the growing interest in intervention programs in SB, only nine studies have used smartphone apps as a strategy to reduce SB in adults. Mobile health apps were proved to be effective in SB reduction, as assessed by different parameters, and should be encouraged. However, further studies are needed to verify the long-term effects of the utilization of such applications.

Smartphone-Based Interventions for Physical Activity Promotion: Scoping Review of the Evidence Over the Last 10 Years

Background

Several reviews of mobile health (mHealth) physical activity (PA) interventions suggest their beneficial effects on behavior change in adolescents and adults. Owing to the ubiquitous presence of smartphones, their use in mHealth PA interventions seems obvious; nevertheless, there are gaps in the literature on the evaluation reporting processes and best practices of such interventions.

Objective

The primary objective of this review is to analyze the development and evaluation trajectory of smartphone-based mHealth PA interventions and to review systematic theory- and evidence-based practices and methods that are implemented along this trajectory. The secondary objective is to identify the range of evidence (both quantitative and qualitative) available on smartphone-based mHealth PA interventions to provide a comprehensive tabular and narrative review of the available literature in terms of its nature, features, and volume.

Methods

We conducted a scoping review of qualitative and quantitative studies examining smartphone-based PA interventions published between 2008 and 2018. In line with scoping review guidelines, studies were not rejected based on their research design or quality. This review, therefore, includes experimental and descriptive studies, as well as reviews addressing smartphone-based mHealth interventions aimed at promoting PA in all age groups (with a subanalysis conducted for adolescents). Two groups of studies were additionally included: reviews or content analyses of PA trackers and meta-analyses exploring behavior change techniques and their efficacy.

Results

Included articles (N=148) were categorized into 10 groups: commercial smartphone app content analyses, smartphone-based intervention review studies, activity tracker content analyses, activity tracker review studies, meta-analyses of PA intervention studies, smartphone-based intervention studies, qualitative formative studies, app development descriptive studies, qualitative follow-up studies, and other related articles. Only 24 articles targeted children or adolescents (age range: 5-19 years). There is no agreed evaluation framework or taxonomy to code or report smartphone-based PA interventions. Researchers did not state the coding method, used various evaluation frameworks, or used different versions of behavior change technique taxonomies. In addition, there is no consensus on the best behavior change theory or model that should be used in smartphone-based interventions for PA promotion. Commonly reported systematic practices and methods have been successfully identified. They include PA recommendations, trial designs (randomized controlled trials, experimental trials, and rapid design trials), mixed methods data collection (surveys, questionnaires, interviews, and focus group discussions), scales to assess app quality, and industry-recognized reporting guidelines.

Conclusions

Smartphone-based mHealth interventions aimed at promoting PA showed promising results for behavior change. Although there is a plethora of published studies on the adult target group, the number of studies and consequently the evidence base for adolescents is limited. Overall, the efficacy of smartphone-based mHealth PA interventions can be considerably improved through a more systematic approach of developing, reporting, and coding of the interventions.

Personalized mobile technologies for lifestyle behavior change: A systematic review, meta-analysis, and meta-regression

Given that the one-size-fits-all approach to mobile health interventions have limited effects, a personalized approach might be necessary to promote healthy behaviors and prevent chronic conditions. Our systematic review aims to evaluate the effectiveness of personalized mobile interventions on lifestyle behaviors (i.e., physical activity, diet, smoking and alcohol consumption), and identify the effective key features of such interventions. We included any experimental trials that tested a personalized mobile app or fitness tracker and reported any lifestyle behavior measures. We conducted a narrative synthesis for all studies, and a meta-analysis of randomized controlled trials. Thirty-nine articles describing 31 interventions were included (n = 77,243, 64% women). All interventions personalized content and rarely personalized other features. Source of data included system-captured (12 interventions), user-reported (11 interventions) or both (8 interventions). The meta-analysis showed a moderate positive effect on lifestyle behavior outcomes (standardized difference in means [SDM] 0.663, 95% CI 0.228 to 1.10). A meta-regression model including source of data found that interventions that used system-captured data for personalization were associated with higher effectiveness than those that used user-reported data (SDM 1.48, 95% CI 0.76 to 2.19). In summary, the field is in its infancy, with preliminary evidence of the potential efficacy of personalization in improving lifestyle behaviors. Source of data for personalization might be important in determining intervention effectiveness. To fully exploit the potential of personalization, future high-quality studies should investigate the integration of multiple data from different sources and include personalized features other than content.

Interventions for reducing sedentary behaviour in community-dwelling older adults

BACKGROUND

Older adults are the most sedentary segment of society, often spending in excess of 8.5 hours a day sitting. Large amounts of time spent sedentary, defined as time spend sitting or in a reclining posture without spending energy, has been linked to an increased risk of chronic diseases, frailty, loss of function, disablement, social isolation, and premature death.

OBJECTIVES

To evaluate the effectiveness of interventions aimed at reducing sedentary behaviour amongst older adults living independently in the community compared to control conditions involving either no intervention or interventions that do not target sedentary behaviour.

SEARCH METHODS

We searched the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, CINAHL, PsycINFO, PEDro, EPPI-Centre databases (Trials Register of Promoting Health Interventions (TRoPHI) and the Obesity and Sedentary behaviour Database), WHO ICTRP, and ClinicalTrials.gov up to 18 January 2021. We also screened the reference lists of included articles and contacted authors to identify additional studies.

SELECTION CRITERIA

We included randomised controlled trials (RCTs) and cluster-RCTs. We included interventions purposefully designed to reduce sedentary time in older adults (aged 60 or over) living independently in the community. We included studies if some of the participants had multiple comorbidities, but excluded interventions that recruited clinical populations specifically (e.g. stroke survivors).

DATA COLLECTION AND ANALYSIS

Two review authors independently screened titles and abstracts and full-text articles to determine study eligibility. Two review authors independently extracted data and assessed risk of bias. We contacted authors for additional data where required. Any disagreements in study screening or data extraction were settled by a third review author.

MAIN RESULTS

We included seven studies in the review, six RCTs and one cluster-RCT, with a total of 397 participants. The majority of participants were female (n = 284), white, and highly educated. All trials were conducted in high-income countries. All studies evaluated individually based behaviour change interventions using a combination of behaviour change techniques such as goal setting, education, and behaviour monitoring or feedback. Four of the seven studies also measured secondary outcomes. The main sources of bias were related to selection bias (N = 2), performance bias (N = 6), blinding of outcome assessment (N = 2), and incomplete outcome data (N = 2) and selective reporting (N=1). The overall risk of bias was judged as unclear. Primary outcomes The evidence suggests that interventions to change sedentary behaviour in community-dwelling older adults may reduce sedentary time (mean difference (MD) -44.91 min/day, 95% confidence interval (CI) -93.13 to 3.32; 397 participants; 7 studies; I² = 73%; low-certainty evidence). We could not pool evidence on the effect of interventions on breaks in sedentary behaviour or time spent in specific domains such as TV time, as data from only one study were available for these outcomes. Secondary outcomes We are uncertain whether interventions to reduce sedentary behaviour have any impact on the physical or mental health outcomes of community-dwelling older adults. We were able to pool change data for the following outcomes. • Physical function (MD 0.14 Short Physical Performance Battery (SPPB) score, 95% CI -0.38 to 0.66; higher score is favourable; 98 participants; 2 studies; I² = 26%; low-certainty evidence). • Waist circumference (MD 1.14 cm, 95% CI -1.64 to 3.93; 100 participants; 2 studies; I² = 0%; low-certainty evidence). • Fitness (MD -5.16 m in the 6-minute walk test, 95% CI -36.49 to 26.17; higher score is favourable; 80 participants; 2 studies; I² = 29%; low-certainty evidence). • Blood pressure: systolic (MD -3.91 mmHg, 95% CI -10.95 to 3.13; 138 participants; 3 studies; I² = 73%; very low-certainty evidence) and diastolic (MD -0.06 mmHg, 95% CI -5.72 to 5.60; 138 participants; 3 studies; I² = 97%; very low-certainty evidence). • Glucose blood levels (MD 2.20 mg/dL, 95% CI -6.46 to 10.86; 100 participants; 2 studies; I² = 0%; low-certainty evidence). No data were available on cognitive function, cost-effectiveness or adverse effects.

AUTHORS' CONCLUSIONS

It is not clear whether interventions to reduce sedentary behaviour are effective at reducing sedentary time in community-dwelling older adults. We are uncertain if these interventions have any impact on the physical or mental health of community-dwelling older adults. There were few studies, and the certainty of the evidence is very low to low, mainly due to inconsistency in findings and imprecision. Future studies should consider interventions aimed at modifying the environment, policy, and social and cultural norms. Future studies should also use device-based measures of sedentary time, recruit larger samples, and gather information about quality of life, cost-effectiveness, and adverse event data.

Menstrual patterns and disorders among Chinese women of reproductive age: A cross-sectional study based on mobile application data

Menstruation is an important indicator of women's health. Identification of abnormal menstrual patterns in adolescence may improve early diagnosis of potential health concerns in adulthood. This study aimed to evaluate menstrual patterns and disorders of Chinese women of reproductive age based on an APP.From December 2015 to January 2016, a cross-sectional study was conducted. We utilized a mobile application (APP) to collect information about participants' age at menarche, length of menstruation, duration of menstruation, amount of menstrual flow, regularity of menstrual cycle, prevalence of abnormal uterine bleeding and dysmenorrhoea.A total of 156,055 women (25,716 from the questionnaire survey and 130,000 from the mobile APP users) participated in the study. The average age of the subjects was 26.32 ± 6.97 years (median age, 25 years). Mean age at menarche was 13.08 ± 1.87 years; average length of menstrual cycle, 30.9 ± 4.28 days (median 30 days); and average duration of menstruation, 5.01 ± 1.13 days (median 5 days). Women with irregular menstrual cycles accounted for 36.41%. Women aged < 18 years and > 30 years were more likely to experience irregular menstrual cycles. The prevalence of secondary amenorrhoea was 4.07%. More than 20% of women reported abnormal menstrual flow. About 20.11% of women had abnormal uterine bleeding, and 77.65% had dysmenorrhoea. A hot compress was the most commonly used approach to ameliorate dysmenorrhoea. Women with low education and low income and those with high education and high income tended to have menstrual problems.A mobile APP as a survey tool has the advantages of large sample size, low cost, and high efficiency. The use of a mobile APP is an emerging approach for collecting big data in the field of health research. The results showed that the prevalence of menstrual disorders among Chinese reproductive women was high. Healthcare providers should educate girls and their caregivers about menstrual physiology, normal menstrual pattern, and reproductive health to prevent long-term diseases.

Women's Health Initiative Strong and Healthy Pragmatic Physical Activity Intervention Trial for Cardiovascular Disease Prevention: Design and Baseline Characteristics

BACKGROUND

National guidelines promote physical activity to prevent cardiovascular disease (CVD), yet no randomized controlled trial has tested whether physical activity reduces CVD.

METHODS

The Women's Health Initiative (WHI) Strong and Healthy (WHISH) pragmatic trial used a randomized consent design to assign women for whom cardiovascular outcomes were available through WHI data collection (N = 18 985) or linkage to the Centers for Medicare and Medicaid Services (N30 346), to a physical activity intervention or "usual activity" comparison, stratified by ages 68-99 years (in tertiles), U.S. geographic region, and outcomes data source. Women assigned to the intervention could "opt out" after receiving initial physical activity materials. Intervention materials applied evidence-based behavioral science principles to promote current national recommendations for older Americans. The intervention was adapted to participant input regarding preferences, resources, barriers, and motivational drivers and was targeted for 3 categories of women at lower, middle, or higher levels of self-reported physical functioning and physical activity. Physical activity was assessed in both arms through annual questionnaires. The primary outcome is major cardiovascular events, specifically myocardial infarction, stroke, or CVD death; primary safety outcomes are hip fracture and non-CVD death. The trial is monitored annually by an independent Data Safety and Monitoring Board. Final analyses will be based on intention to treat in all randomized participants, regardless of intervention engagement.

RESULTS

The 49 331 randomized participants had a mean baseline age of 79.7 years; 84.3% were White, 9.2% Black, 3.3% Hispanic, 1.9% Asian/Pacific Islander, 0.3% Native American, and 1% were of unknown race/ethnicity. The mean baseline RAND-36 physical function score was 71.6 (± 25.2 SD). There were no differences between Intervention (N = 24 657) and Control (N = 24 674) at baseline for age, race/ethnicity, current smoking (2.5%), use of blood pressure or lipid-lowering medications, body mass index, physical function, physical activity, or prior CVD (10.1%).

CONCLUSION

The WHISH trial is rigorously testing whether a physical activity intervention reduces major CV events in a large, diverse cohort of older women. Clinical Trials Registration Number: NCT02425345.

Older Adults' Experience With Fall Prevention Recommendations Derived From the STEADI

The Centers for Disease Control and Prevention (CDC) Stopping Elderly Accidents, Deaths & Injuries (STEADI) toolkit is a national effort to prevent falls among older adults. Studies have been conducted on implementation of the STEADI, but no studies have investigated older adults' adherence to or perceptions of fall prevention recommendations delineated within the STEADI algorithm. Semistructured interviews were conducted with a purposive sample of older adults 6 months after attending a falls risk assessment. Seventy-nine percent accurately recalled their fall risk, 57% followed one or more recommendations, and 32% did not recall at least one recommendation correctly. The most common recommendation recalled and adhered to was exercise. No participants recalled or adhered to recommendations including medication review, taking time changing positions, vision check, podiatrist visit, or physical therapy. Thirty-two percent fell. Of these, 55.6% did not follow any recommendations. Interview transcripts were analyzed using comparative methodology following the tenets of thematic analysis. Three themes emerged: participating in fall prevention, barriers to following recommendations, and providers can encourage people to prevent falls. An unexpected facilitator to participation in fall prevention efforts emerged-older adults' perception that they were positively influencing society by participating in research and working with students and the university. This finding provides an opportunity for providers of health education to address the growing public health issue of falls among older adults while also creating opportunities for students to engage in community service and interdisciplinary service learning.

Do smartphone applications and activity trackers increase physical activity in adults? Systematic review, meta-analysis and metaregression

OBJECTIVE

To determine the effectiveness of physical activity interventions involving mobile applications (apps) or trackers with automated and continuous self-monitoring and feedback.

DESIGN

Systematic review and meta-analysis.

DATA SOURCES

PubMed and seven additional databases, from 2007 to 2020.

STUDY SELECTION

Randomised controlled trials in adults (18-65 years old) without chronic illness, testing a mobile app or an activity tracker, with any comparison, where the main outcome was a physical activity measure. Independent screening was conducted.

DATA EXTRACTION AND SYNTHESIS

We conducted random effects meta-analysis and all effect sizes were transformed into standardised difference in means (SDM). We conducted exploratory metaregression with continuous and discrete moderators identified as statistically significant in subgroup analyses.

MAIN OUTCOME MEASURES

Physical activity: daily step counts, min/week of moderate-to-vigorous physical activity, weekly days exercised, min/week of total physical activity, metabolic equivalents.

RESULTS

Thirty-five studies met inclusion criteria and 28 were included in the meta-analysis (n=7454 participants, 28% women). The meta-analysis showed a small-to-moderate positive effect on physical activity measures (SDM 0.350, 95% CI 0.236 to 0.465, I²=69%, T ²=0.051) corresponding to 1850 steps per day (95% CI 1247 to 2457). Interventions including text-messaging and personalisation features were significantly more effective in subgroup analyses and metaregression.

CONCLUSION

Interventions using apps or trackers seem to be effective in promoting physical activity. Longer studies are needed to assess the impact of different intervention components on long-term engagement and effectiveness.

A Mobile Social Networking App for Weight Management and Physical Activity Promotion: Results From an Experimental Mixed Methods Study

BACKGROUND

Smartphone apps, fitness trackers, and online social networks have shown promise in weight management and physical activity interventions. However, there are knowledge gaps in identifying the most effective and engaging interventions and intervention features preferred by their users.

OBJECTIVE

This 6-month pilot study on a social networking mobile app connected to wireless weight and activity tracking devices has 2 main aims: to evaluate changes in BMI, weight, and physical activity levels in users from different BMI categories and to assess user perspectives on the intervention, particularly on social comparison and automated self-monitoring and feedback features.

METHODS

This was a mixed methods study involving a one-arm, pre-post quasi-experimental pilot with postintervention interviews and focus groups. Healthy young adults used a social networking mobile app intervention integrated with wireless tracking devices (a weight scale and a physical activity tracker) for 6 months. Quantitative results were analyzed separately for 2 groups-underweight-normal and overweight-obese BMI-using t tests and Wilcoxon sum rank, Wilcoxon signed rank, and chi-square tests. Weekly BMI change in participants was explored using linear mixed effects analysis. Interviews and focus groups were analyzed inductively using thematic analysis.

RESULTS

In total, 55 participants were recruited (mean age of 23.6, SD 4.6 years; 28 women) and 45 returned for the final session (n=45, 82% retention rate). There were no differences in BMI from baseline to postintervention (6 months) and between the 2 BMI groups. However, at 4 weeks, participants' BMI decreased by 0.34 kg/m² (P<.001), with a loss of 0.86 kg/m² in the overweight-obese group (P=.01). Participants in the overweight-obese group used the app significantly less compared with individuals in the underweight-normal BMI group, as they mentioned negative feelings and demotivation from social comparison, particularly from upward comparison with fitter people. Participants in the underweight-normal BMI group were avid users of the app's self-monitoring and feedback (P=.02) and social (P=.04) features compared with those in the overweight-obese group, and they significantly increased their daily step count over the 6-month study duration by an average of 2292 steps (95% CI 898-3370; P<.001). Most participants mentioned a desire for a more personalized intervention.

CONCLUSIONS

This study shows the effects of different interventions on participants from higher and lower BMI groups and different perspectives regarding the intervention, particularly with respect to its social features. Participants in the overweight-obese group did not sustain a short-term decrease in their BMI and mentioned negative emotions from app use, while participants in the underweight-normal BMI group used the app more frequently and significantly increased their daily step count. These differences highlight the importance of intervention personalization. Future research should explore the role of personalized features to help overcome personal barriers and better match individual preferences and needs.

Analyzing Older Adults' Perceived Values of Using Smart Bracelets by Means-End Chain

To cope with the demands for medical care in an aging society, smart healthcare wearable devices that can measure physiological signals are being regarded as the primary tools in medical care programs, allowing the users to acquire basic health data. Although the smart healthcare wearable devices could be applied to disease management and prevention that could help older adults control their health, older adults must be willing and able to use and continue to use them. In this research, interviews conducted through means–end chain (MEC) and laddering were used to guide the older adults step-by-step by explaining abstract ideas and emphasizing value in their perceptions of specific attributes. A “hierarchical value map” was further constructed to confirm the perceived value of smart healthcare wearable devices to older adults. The research results showed that, in terms of attribute functions, seniors believed that smart bracelets in mobile health devices should have the attributes of safe use, real-time information feedback, correct data, comfortable wear, and clear screen. In terms of consequent benefits, older adults can use smart bracelets in mobile health devices to gain benefits in learning about smart products, understanding technology applications, increasing health awareness and relaxation, and satisfying curiosity. In terms of value goals, older adults want to achieve the value goals of a sense of social belonging, improved quality of life, and healthier bodies. Health is the most important thing for older adults, but previous research has often focused on the use of equipment for physical examinations; relatively few studies have allowed older adults to experience the equipment personally. The device can provide the ultimate value of long-term health promotion for older adults.

Using Mobile Applications to Increase Physical Activity: A Systematic Review

Unhealthy diet and physical inactivity—major risk factors for the main non-communicable diseases—can be addressed by mobile health applications. Using an evidence-based systematic review design, we analysed studies on mobile applications to foster physical activity to determine whether they met the objective of increasing adults’ physical activity. A bibliographic search was conducted in October 2020 using PubMed, Cochrane Library Plus, Biomed Central, Psychology Database, and SpringerLink, retrieving 191 articles. After titles and abstracts were reviewed, 149 articles were excluded, leaving 42 articles for a full-text review, of which 14 met the inclusion criteria. Despite differences in study duration, design, and variables, 13 of the 14 studies reported that applications were effective in increasing physical activity and healthy habits as dietary behaviour. However, further longer-term studies with larger samples are needed to confirm the effectiveness of mobile health applications in increasing physical activity.

The Physical Activity Wearables in the Police Force (PAW-Force) study: acceptability and impact

BACKGROUND

Policing is a highly stressful and increasingly sedentary occupation. The study aim was to assess the acceptability and impact of a mobile health (mHealth) technology intervention (Fitbit® activity monitor and 'Bupa Boost' smartphone app) to promote physical activity (PA) and reduce sedentary time in the police force.

METHODS

Single-group, pre-post, mixed methods pilot study. Police officers and staff (n = 180) were recruited from two police forces in South West England. Participants used the technology for 12 weeks (an 'individual' then 'social' phase) followed by 5 months of optional use. Data sources included Fitbit®-recorded objective step count, questionnaire surveys and semi-structured interviews (n = 32). Outcome assessment points were baseline (week 0), mid-intervention (week 6), post-intervention (week 12) and follow-up (month 8). Paired t-tests were used to investigate changes in quantitative outcomes. Qualitative analysis involved framework and thematic analysis.

RESULTS

Changes in mean daily step count were non-significant (p > 0.05), but self-reported PA increased in the short term (e.g. + 465.4 MET-minutes/week total PA baseline to week 12, p = 0.011) and longer term (e.g. + 420.5 MET-minutes/week moderate-to-vigorous PA baseline to month 8, p = 0.024). The greatest impact on behaviour was perceived by less active officers and staff. There were no significant changes in sedentary time; the qualitative findings highlighted the importance of context and external influences on behaviour. There were no statistically significant changes (all p-values > 0.05) in any secondary outcomes (physical and mental health-related quality of life, perceived stress and perceived productivity), with the exception of an improvement in mental health-related quality of life (SF-12 mental component score + 1.75 points, p = 0.020) from baseline to month 8. Engagement with and perceived acceptability of the intervention was high overall, but a small number of participants reported negative physical (skin irritation) and psychological (feelings of guilt and anxiety) consequences of technology use. Individual app features (such as goal-setting and self-monitoring) were generally preferred to social components (social comparison, competitions and support).

CONCLUSIONS

mHealth technology is an acceptable and potentially impactful intervention for increasing PA in the police force. The intervention was less useful for reducing sedentary time and the impact on secondary outcomes is unclear.

TRIAL REGISTRATION

NCT03169179 (registered 30th May 2017).

Effects of Counseling by Peer Human Advisors vs Computers to Increase Walking in Underserved Populations: The COMPASS Randomized Clinical Trial

IMPORTANCE

Effective and practical treatments are needed to increase physical activity among those at heightened risk from inactivity. Walking represents a popular physical activity that can produce a range of desirable health effects, particularly as people age.

OBJECTIVE

To test the hypothesis that counseling by a computer-based virtual advisor is no worse than (ie, noninferior to) counseling by trained human advisors for increasing 12-month walking levels among inactive adults.

DESIGN, SETTING, AND PARTICIPANTS

A cluster-randomized, noninferiority parallel trial enrolled 245 adults between July 21, 2014, and July 29, 2016, with follow-up through September 15, 2017. Data analysis was performed from March 15 to December 20, 2018. The evidence-derived noninferiority margin was 30 minutes of walking per week. Participants included inactive adults aged 50 years and older, primarily of Latin American descent and capable of walking without significant limitations, from 10 community centers in Santa Clara and San Mateo counties, California.

INTERVENTIONS

All participants received similar evidence-based, 12-month physical activity counseling at their local community center, with the 10 centers randomized to a computerized virtual advisor program (virtual) or a previously validated peer advisor program (human).

MAIN OUTCOMES AND MEASURES

The primary outcome was change in walking minutes per week over 12 months using validated interview assessment corroborated with accelerometry. Both per-protocol and intention-to-treat analysis was performed.

RESULTS

Among the 245 participants randomized, 193 were women (78.8%) and 241 participants (98.4%) were Latino. Mean (SD) age was 62.3 (8.4) years (range, 50-87 years), 107 individuals (43.7%) had high school or less educational level, mean BMI was 32.8 (6.8), and mean years residence in the US was 47.4 (17.0) years. A total of 231 participants (94.3%) completed the study. Mean 12-month change in walking was 153.9 min/wk (95% CI, 126.3 min/wk to infinity) for the virtual cohort (n = 123) and 131.9 min/wk (95% CI, 101.4 min/wk to infinity) for the human cohort (n = 122) (difference, 22.0, with lower limit of 1-sided 95% CI, -20.6 to infinity; P = .02); this finding supports noninferiority. Improvements emerged in both arms for relevant clinical risk factors, sedentary behavior, and well-being measures.

CONCLUSIONS AND RELEVANCE

The findings of this study indicate that a virtual advisor using evidence-based strategies produces significant 12-month walking increases for older, lower-income Latino adults that are no worse than the significant improvements achieved by human advisors. Changes produced by both programs are commensurate with those reported in previous investigations of these behavioral interventions and provide support for broadening the range of light-touch physical activity programs that can be offered to a diverse population.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT02111213.

Enabling Older Adults' Health Self-Management through Self-Report and Visualization-A Systematic Literature Review

Aging is associated with a progressive decline in health, resulting in increased medical care and costs. Mobile technology may facilitate health self-management, thus increasing the quality of care and reducing costs. Although the development of technology offers opportunities in monitoring the health of older adults, it is not clear whether these technologies allow older adults to manage their health data themselves. This paper presents a review of the literature on mobile health technologies for older adults, focusing on whether these technologies enable the visualization of monitored data and the self-reporting of additional information by the older adults. The systematic search considered studies published between 2009 and 2019 in five online databases. We screened 609 articles and identified 95 that met our inclusion and exclusion criteria. Smartphones and tablets are the most frequently reported technology for older adults to enter additional data to the one that is monitored automatically. The recorded information is displayed on the monitoring device and screens of external devices such as computers. Future designs of mobile health technology should allow older users to enter additional information and visualize data; this could enable them to understand their own data as well as improve their experience with technology.

Interventions outside the workplace for reducing sedentary behaviour in adults under 60 years of age

BACKGROUND

Adults spend a majority of their time outside the workplace being sedentary. Large amounts of sedentary behaviour increase the risk of type 2 diabetes, cardiovascular disease, and both all-cause and cardiovascular disease mortality.

OBJECTIVES

Primary • To assess effects on sedentary time of non-occupational interventions for reducing sedentary behaviour in adults under 60 years of age Secondary • To describe other health effects and adverse events or unintended consequences of these interventions • To determine whether specific components of interventions are associated with changes in sedentary behaviour • To identify if there are any differential effects of interventions based on health inequalities (e.g. age, sex, income, employment) SEARCH METHODS: We searched CENTRAL, MEDLINE, Embase, Cochrane Database of Systematic Reviews, CINAHL, PsycINFO, SportDiscus, and ClinicalTrials.gov on 14 April 2020. We checked references of included studies, conducted forward citation searching, and contacted authors in the field to identify additional studies.

SELECTION CRITERIA

We included randomised controlled trials (RCTs) and cluster RCTs of interventions outside the workplace for community-dwelling adults aged 18 to 59 years. We included studies only when the intervention had a specific aim or component to change sedentary behaviour.

DATA COLLECTION AND ANALYSIS

Two review authors independently screened titles/abstracts and full-text articles for study eligibility. Two review authors independently extracted data and assessed risk of bias. We contacted trial authors for additional information or data when required. We examined the following primary outcomes: device-measured sedentary time, self-report sitting time, self-report TV viewing time, and breaks in sedentary time.

MAIN RESULTS

We included 13 trials involving 1770 participants, all undertaken in high-income countries. Ten were RCTs and three were cluster RCTs. The mean age of study participants ranged from 20 to 41 years. A majority of participants were female. All interventions were delivered at the individual level. Intervention components included personal monitoring devices, information or education, counselling, and prompts to reduce sedentary behaviour. We judged no study to be at low risk of bias across all domains. Seven studies were at high risk of bias for blinding of outcome assessment due to use of self-report outcomes measures. Primary outcomes Interventions outside the workplace probably show little or no difference in device-measured sedentary time in the short term (mean difference (MD) -8.36 min/d, 95% confidence interval (CI) -27.12 to 10.40; 4 studies; I² = 0%; moderate-certainty evidence). We are uncertain whether interventions reduce device-measured sedentary time in the medium term (MD -51.37 min/d, 95% CI -126.34 to 23.59; 3 studies; I² = 84%; very low-certainty evidence) We are uncertain whether interventions outside the workplace reduce self-report sitting time in the short term (MD -64.12 min/d, 95% CI -260.91 to 132.67; I² = 86%; very low-certainty evidence). Interventions outside the workplace may show little or no difference in self-report TV viewing time in the medium term (MD -12.45 min/d, 95% CI -50.40 to 25.49; 2 studies; I² = 86%; low-certainty evidence) or in the long term (MD 0.30 min/d, 95% CI -0.63 to 1.23; 2 studies; I² = 0%; low-certainty evidence). It was not possible to pool the five studies that reported breaks in sedentary time given the variation in definitions used. Secondary outcomes Interventions outside the workplace probably have little or no difference on body mass index in the medium term (MD -0.25 kg/m², 95% CI -0.48 to -0.01; 3 studies; I² = 0%; moderate-certainty evidence). Interventions may have little or no difference in waist circumference in the medium term (MD -2.04 cm, 95% CI -9.06 to 4.98; 2 studies; I² = 65%; low-certainty evidence). Interventions probably have little or no difference on glucose in the short term (MD -0.18 mmol/L, 95% CI -0.30 to -0.06; 2 studies; I² = 0%; moderate-certainty evidence) and medium term (MD -0.08 mmol/L, 95% CI -0.21 to 0.05; 2 studies, I² = 0%; moderate-certainty evidence) Interventions outside the workplace may have little or no difference in device-measured MVPA in the short term (MD 1.99 min/d, 95% CI -4.27 to 8.25; 4 studies; I² = 23%; low-certainty evidence). We are uncertain whether interventions improve device-measured MVPA in the medium term (MD 6.59 min/d, 95% CI -7.35 to 20.53; 3 studies; I² = 70%; very low-certainty evidence). We are uncertain whether interventions outside the workplace improve self-reported light-intensity PA in the short-term (MD 156.32 min/d, 95% CI 34.34 to 278.31; 2 studies; I² = 79%; very low-certainty evidence). Interventions may have little or no difference on step count in the short-term (MD 226.90 steps/day, 95% CI -519.78 to 973.59; 3 studies; I² = 0%; low-certainty evidence) No data on adverse events or symptoms were reported in the included studies.

AUTHORS' CONCLUSIONS

Interventions outside the workplace to reduce sedentary behaviour probably lead to little or no difference in device-measured sedentary time in the short term, and we are uncertain if they reduce device-measured sedentary time in the medium term. We are uncertain whether interventions outside the workplace reduce self-reported sitting time in the short term. Interventions outside the workplace may result in little or no difference in self-report TV viewing time in the medium or long term. The certainty of evidence is moderate to very low, mainly due to concerns about risk of bias, inconsistent findings, and imprecise results. Future studies should be of longer duration; should recruit participants from varying age, socioeconomic, or ethnic groups; and should gather quality of life, cost-effectiveness, and adverse event data. We strongly recommend that standard methods of data preparation and analysis are adopted to allow comparison of the effects of interventions to reduce sedentary behaviour.

Oncologists' perceptions of the usefulness of cancer survivorship care plan components

PURPOSE

The Institute of Medicine recommends that cancer patients receive survivorship care plans (SCP) summarizing information important to the individual's long-term care. The various components of SCPs have varying levels of evidence supporting their impact. We surveyed medical oncologists to better understand how they perceived the relative value of different SCP components.

METHODS

Medical oncologists caring for patients in diverse US practice settings were surveyed (357 respondents; participation rate 52.9%) about their perceptions of the usefulness of various components of SCPs to both patients and primary care physicians (PCPs).

RESULTS

Oncologists perceived treatment summaries as "very useful" for PCPs but were less likely to perceive them as "very useful" for patients (55% vs. 40%, p < 0.001). Information about the psychological effects of cancer (41% vs. 29%; p < 0.001) and healthy behaviors (67% vs. 41%; p < 0.001) were considered more useful to patients than to PCPs. From 3 to 20% of oncologists believed that any given component of the SCP was not useful to either PCPs or patients. Oncologists who perceived SCPs to be more useful tended to be female or to practice in settings with a fully implemented electronic health record.

CONCLUSIONS

Oncologists do not perceive all components of SCPs to be equally useful to both patients and PCPs. To be successfully implemented, the SCP should be efficiently tailored to the unique needs and knowledge of patients and their PCPs. A minority of oncologists appear to be late adopters, suggesting that some resistance to the adoption of SCPs remains.

Design of a Low-Cost, Wearable Device for Kinematic Analysis in Physical Therapy Settings

BACKGROUND

Unsupervised home exercise is a major component of physical therapy (PT). This study proposes an inexpensive, inertial measurement unit-based wearable device to capture kinematic data to facilitate exercise. However, conveying and interpreting kinematic data to non-experts poses a challenge due to the complexity and background knowledge required that most patients lack.

OBJECTIVES

The objectives of this study were to identify key user interface and user experience features that would likely improve device adoption and assess participant receptiveness toward the device.

METHODS

Fifty participants were recruited to perform nine upper extremity exercises while wearing the device. Prior to exercise, participants completed an orientation of the device, which included examples of software graphics with exercise data. Surveys that measured receptiveness toward the device, software graphics, and ergonomics were given before and after exercise.

RESULTS

Participants were highly receptive to the device with 90% of the participants likely to use the device during PT. Participants understood how the simple kinematic data could be used to aid exercise, but the data could be difficult to comprehend with more complex movements. Devices should incorporate wireless sensors and emphasize ease of wear.

CONCLUSION

Device-guided home physical rehabilitation can allow for individualized treatment protocols and improve exercise self-efficacy through kinematic analysis. Future studies should implement clinical testing to evaluate the impact a wearable device can have on rehabilitation outcomes.

The effect of e-health interventions promoting physical activity in older people: a systematic review and meta-analysis

INTRODUCTION

The objectives of this review paper were to synthesize the data from randomized controlled trials in the literature to come to a conclusion on the effects of e-health interventions on promoting physical activity in older people.

METHODS

The Medline, CINAHL, Embase, PsycINFO, and SportDiscus databases were searched for articles about studies that 1) recruited subjects with a mean age of > 50 years, 2) tested e-health interventions, 3) employed control groups with no or less advanced e-health strategies, 4) measured physical activity as an outcome, 5) were published between 1st January 2008 and 31st May 2019, and 6) employed randomized controlled trials. The risk of bias in individual studies was assessed using the Physiotherapy Evidence Database scale. To examine the effects of the interventions, variables quantifying the amount of physical activity were extracted. The within-group effects of individual studies were summarized using Hedges g and 95% confidence intervals. Between-group effects were summarized by meta-analyses using RevMan 5.0 with a random effect model.

RESULTS

Of the 2810 identified studies, 38 were eligible, 25 were included in the meta-analyses. The within-group effect sizes (Hedges g) of physical activity in the intervention group at T1 ranged from small to large: physical activity time (0.12 to 0.84), step counts (- 0.01 to 11.19), energy expenditure (- 0.05 to 0.86), walking time (0.13 to 3.33), and sedentary time (- 0.12 to - 0.28). The delayed effects as observed in T2 and T3 also ranged from small to large: physical activity time (0.24 to 1.24) and energy expenditure (0.15 to 1.32). In the meta-analysis, the between-group effect of the e-health intervention on physical activity time measured by questionnaires, physical activity time measured by objective wearable devices, energy expenditure, and step counts were all significant with minimal heterogeneity.

CONCLUSION

E-health interventions are effective at increasing the time spent on physical activity, energy expenditure in physical activity, and the number of walking steps. It is recommended that e-health interventions be included in guidelines to enhance physical activity in older people. Further studies should be conducted to determine the most effective e-health strategies.

Choice architecture interventions to change physical activity and sedentary behavior: a systematic review of effects on intention, behavior and health outcomes during and after intervention

BACKGROUND

Choice architecture interventions, which subtly change the environment in which individuals make decisions, can be used to promote behavior change. This systematic review aimed to summarize studies on micro-environmental choice architecture interventions that encouraged physical activity or discouraged sedentary behavior in adults, and to describe the effectiveness of those interventions on these behaviors - and on related intentions or health outcomes - in presence of the intervention and after removal of the intervention (i.e. post-intervention, regardless of the time elapsed).

METHODS

We systematically searched PubMed, Embase, PsycINFO and the Cochrane Library for (quasi) experimental studies published up to December 2019 that evaluated the effect of choice architecture interventions on physical activity and sedentary behavior, as well as on intentions and health outcomes related to physical activity/sedentary behavior. Studies that combined choice architecture techniques with other behavior change techniques were excluded. All studies were screened for eligibility, relevant data was extracted and two independent reviewers assessed the methodological quality using the QualSyst tool.

RESULTS

Of the 9609 records initially identified, 88 studies met our eligibility criteria. Most studies (n = 70) were of high methodologic quality. Eighty-six studies targeted physical activity, predominantly stair use, whereas two studies targeted sedentary behavior, and one targeted both behaviors. Intervention techniques identified were prompting (n = 53), message framing (n = 24), social comparison (n = 12), feedback (n = 8), default change (n = 1) and anchoring (n = 1). In presence of the intervention, 68% of the studies reported an effect of choice architecture on behavior, whereas after removal of the intervention only 47% of the studies reported a significant effect. For all choice architecture techniques identified, except for message framing, the majority of studies reported a significant effect on behavioral intentions or behavior in presence of the intervention.

CONCLUSIONS

The results suggest that prompting can effectively encourage stair use in adults, especially in presence of a prompt. The effectiveness of the choice architecture techniques social influence, feedback, default change and anchoring cannot be assessed based on this review. More (controlled) studies are needed to assess the (sustained) effectiveness of choice architecture interventions on sedentary behavior and other types of physical activity than stair use.

Designing Ambient Narrative-Based Interfaces to Reflect and Motivate Physical Activity

Numerous technologies now exist for promoting more active lifestyles. However, while quantitative data representations (e.g., charts, graphs, and statistical reports) typify most health tools, growing evidence suggests such feedback can not only fail to motivate behavior but may also harm self-integrity and fuel negative mindsets about exercise. Our research seeks to devise alternative, more qualitative schemes for encoding personal information. In particular, this paper explores the design of data-driven narratives, given the intuitive and persuasive power of stories. We present WhoIsZuki, a smartphone application that visualizes physical activities and goals as components of a multi-chapter quest, where the main character's progress is tied to the user's. We report on our design process involving online surveys, in-lab studies, and in-the-wild deployments, aimed at refining the interface and the narrative and gaining a deep understanding of people's experiences with this type of feedback. From these insights, we contribute recommendations to guide future development of narrative-based applications for motivating healthy behavior.

Iterative four-phase development of a theory-based digital behaviour change intervention to reduce occupational sedentary behaviour

Introduction

As high amounts of occupational sitting have been associated with negative health consequences, designing workplace interventions to reduce sedentary behaviour (SB) is of public health interest. Digital technology may serve as a cost-effective and scalable platform to deliver such an intervention. This study describes the iterative development of a theory-based, digital behaviour change intervention to reduce occupational SB.

Methods

The behaviour change wheel and The Behaviour Change Technique Taxonomy were used to guide the intervention design process and form a basis for selecting the intervention components. The development process consisted of four phases: phase 1 – preliminary research, phase 2 – consensus workshops, phase 3 – white boarding and phase 4 – usability testing.

Results

The process led to the development and refinement of a smartphone application – Worktivity. The core component was self-monitoring and feedback of SB at work, complemented by additional features focusing on goal setting, prompts and reminders to break up prolonged periods of sitting, and educational facts and tips. Key features of the app included simple data entry and personalisation based on each individual’s self-reported sitting time. Results from the ‘think-aloud’ interviews (n=5) suggest Worktivity was well accepted and that users were positive about its features.

Conclusion

This study led to the development of Worktivity, a theory-based and user-informed mobile app intervention to reduce occupational SB. It is the first app of its kind developed with the primary aim of reducing occupational SB using digital self-monitoring. This paper provides a template to guide others in the development and evaluation of technology-supported behaviour change interventions.