Background: Understanding how engagement in mobile health (mHealth) weight loss interventions relates to weight change may help develop effective intervention strategies. Objective: This study aims to examine the (1) patterns of participant engagement overall and with key intervention components within each intervention arm in the Cell Phone Intervention For You (CITY) trial; (2) associations of engagement with weight change; and (3) participant characteristics related to engagement. Methods: The CITY trial tested two 24-month weight loss interventions. One was delivered with a smartphone app (cell phone) containing 24 components (weight tracking, etc) and included prompting by the app in predetermined frequency and forms. The other was delivered by a coach via monthly calls (personal coaching) supplemented with limited app components (18 overall) and without any prompting by the app. Engagement was assessed by calculating the percentage of days each app component was used and the frequency of use. Engagement was also examined across 4 weight change categories: gained (≥2%), stable (±2%), mild loss (≥2% to <5%), and greater loss (≥5%). Results: Data from 122 cell phone and 120 personal coaching participants were analyzed. Use of the app was the highest during month 1 for both arms; thereafter, use dropped substantially and continuously until the study end. During the first 6 months, the mean percentage of days that any app component was used was higher for the cell phone arm (74.2%, SD 20.1) than for the personal coaching arm (48.9%, SD 22.4). The cell phone arm used the apps an average of 5.3 times/day (SD 3.1), whereas the personal coaching participants used them 1.7 times/day (SD 1.2). Similarly, the former self-weighed more than the latter (57.1% days, SD 23.7 vs 32.9% days, SD 23.3). Furthermore, the percentage of days any app component was used, number of app uses per day, and percentage of days self-weighed all showed significant differences across the 4 weight categories for both arms. Pearson correlation showed a negative association between weight change and the percentage of days any app component was used (cell phone: r=−.213; personal coaching: r=−.319), number of apps use per day (cell phone: r=−.264; personal coaching: r=−.308), and percentage of days self-weighed (cell phone: r=−.297; personal coaching: r=−.354). None of the characteristics examined, including age, gender, race, education, income, energy expenditure, diet quality, and hypertension status, appeared to be related to engagement. Conclusions: Engagement in CITY intervention was associated with weight loss during the first 6 months. Nevertheless, engagement dropped substantially early on for most intervention components. Prompting may be helpful initially. More flexible and less intrusive prompting strategies may be needed during different stages of an intervention to increase or sustain engagement. Future studies should explore the motivations for engagement and nonengagement to determine meaningful levels of engagement required for effective intervention. Trial Registration: ClinicalTrials.gov NCT01092364; https://clinicaltrials.gov/ct2/show/NCT01092364 (Archived by WebCite at http://www.webcitation.org/72V8A4e5X)

/pdf/the-association-between-engagement-and-weight-loss-through-17oe8b1p1s.pdf

The Association Between Engagement and Weight Loss Through Personal Coaching and Cell Phone Interventions in Young Adults: Randomized Controlled Trial

Background: Smart scales are increasingly used at home by patients to monitor their body weight and body composition, but scale accuracy has not often been documented.
Objective: The goal of the research was to determine the accuracy of 3 commercially available smart scales for weight and body composition compared with dual x-ray absorptiometry (DEXA) as the gold standard.
Methods: We designed a cross-sectional study in consecutive patients evaluated for DEXA in a physiology unit in a tertiary hospital in France. There were no exclusion criteria except patient declining to participate. Patients were weighed with one smart scale immediately after DEXA. Three scales were compared (scale 1: Body Partner [Tefal], scale 2: DietPack [Terraillon], and scale 3: Body Cardio [Nokia Withings]). We determined absolute error between the gold standard values obtained from DEXA and the smart scales for body mass, fat mass, and lean mass.
Results: The sample for analysis included 53, 52, and 48 patients for each of the 3 tested smart scales, respectively. The median absolute error for body weight was 0.3 kg (interquartile range [IQR] –0.1, 0.7), 0 kg (IQR –0.4, 0.3), and 0.25 kg (IQR –0.10, 0.52), respectively. For fat mass, absolute errors were –2.2 kg (IQR –5.8, 1.3), –4.4 kg (IQR –6.6, 0), and –3.7 kg (IQR –8.0, 0.28), respectively. For muscular mass, absolute errors were –2.2 kg (IQR –5.8, 1.3), –4.4 kg (IQR –6.6, 0), and –3.65 kg (IQR –8.03, 0.28), respectively. Factors associated with fat mass measurement error were weight for scales 1 and 2 (P=.03 and P<.001, respectively), BMI for scales 1 and 2 (P=.034 and P<.001, respectively), body fat for scale 1 (P<.001), and muscular and bone mass for scale 2 (P<.001 for both). Factors associated with muscular mass error were weight and BMI for scale 1 (P<.001 and P=.004, respectively), body fat for scales 1 and 2 (P<.001 for both), and muscular and bone mass for scale 2 (P<.001 and P=.002, respectively).
Conclusions: Smart scales are not accurate for body composition and should not replace DEXA in patient care.
Trial Registration: ClinicalTrials.gov NCT03803098; https://clinicaltrials.gov/ct2/show/NCT03803098

/pdf/accuracy-of-smart-scales-on-weight-and-body-composition-47o5o27r3o.pdf

Accuracy of Smart Scales on Weight and Body Composition: Observational Study.

Background: Sleep is a modifiable lifestyle factor that can be a target for efficient intervention studies to improve the quality of life and decrease the risk or burden of some chronic conditions. Knowing the profiles of individuals with poor sleep patterns is therefore a prerequisite. Wearable devices have recently opened new areas in medical research as potential efficient tools to measure lifestyle factors such as sleep quantity and quality. Objectives: The goal of our research is to identify the determinants of poor sleep based on data from a large population of users of connected devices. Methods: We analyzed data from 15,839 individuals (13,658 males and 2181 females) considered highly connected customers having purchased and used at least 3 connected devices from the consumer electronics company Withings (now Nokia). Total and deep sleep durations as well as the ratio of deep/total sleep as a proxy of sleep quality were analyzed in association with available data on age, sex, weight, heart rate, steps, and diastolic and systolic blood pressures. Results: With respect to the deep/total sleep duration ratio used as a proxy of sleep quality, we have observed that those at risk of having a poor ratio (≤0.40) were more frequently males (odds ratio [OR]female vs male=0.45, 95% CI 0.38-0.54), younger individuals (OR>60 years vs 18-30 years=0.47, 95% CI 0.35-0.63), and those with elevated heart rate (OR>78 bpm vs ≤61 bpm=1.18, 95% CI 1.04-1.34) and high systolic blood pressure (OR>133 mm Hg vs ≤116 mm Hg=1.22, 95% CI 1.04-1.43). A direct association with weight was observed for total sleep duration exclusively. Conclusions: Wearables can provide useful information to target individuals at risk of poor sleep. Future alert or mobile phone notification systems based on poor sleep determinants measured with wearables could be tested in intervention studies to evaluate the benefits. [J Med Internet Res 2017;19(10):e363]

An International Study on the Determinants of Poor Sleep Amongst 15,000 Users of Connected Devices.

Background: To date, the evaluation of diet has mostly been based on questionnaires and diaries that have their limitations in terms of being time and resource intensive, and a tendency toward social desirability. Loyalty card data obtained in retailing provides timely and objective information on diet-related behaviors. In Finland, the market is highly concentrated, which provides a unique opportunity to investigate diet through grocery purchases.
Objective: The aims of this study were as follows: (1) to investigate and quantify the selection bias in large-scale (n=47,066) loyalty card (LoCard) data and correct the bias by developing weighting schemes and (2) to investigate how the degree of loyalty relates to food purchases.
Methods: Members of a loyalty card program from a large retailer in Finland were contacted via email and invited to take part in the study, which involved consenting to the release of their grocery purchase data for research purposes. Participants’ sociodemographic background was obtained through a web-based questionnaire and was compared to that of the general Finnish adult population obtained via Statistics Finland. To match the distributions of sociodemographic variables, poststratification weights were constructed by using the raking method. The degree of loyalty was self-estimated on a 5-point rating scale.
Results: On comparing our study sample with the general Finnish adult population, in our sample, there were more women (65.25%, 30,696/47,045 vs 51.12%, 2,273,139/4,446,869), individuals with higher education (56.91%, 20,684/36,348 vs 32.21%, 1,432,276/4,446,869), and employed individuals (60.53%, 22,086/36,487 vs 52.35%, 2,327,730/4,446,869). Additionally, in our sample, there was underrepresentation of individuals aged under 30 years (14.44%, 6,791/47,045 vs 18.04%, 802,295/4,446,869) and over 70 years (7.94%, 3,735/47,045 vs 18.20%, 809,317/4,446,869), as well as retired individuals (23.51%, 8,578/36,487 vs 31.82%, 1,414,785/4,446,869). Food purchases differed by the degree of loyalty, with higher shares of vegetable, red meat & processed meat, and fat spread purchases in the higher loyalty groups.
Conclusions: Individuals who consented to the use of their loyalty card data for research purposes tended to diverge from the general Finnish adult population. However, the high volume of data enabled the inclusion of sociodemographically diverse subgroups and successful correction of the differences found in the distributions of sociodemographic variables. In addition, it seems that food purchases differ according to the degree of loyalty, which should be taken into account when researching loyalty card data. Despite the limitations, loyalty card data provide a cost-effective approach to reach large groups of people, including hard-to-reach population subgroups.

Characterization and Correction of Bias Due to Nonparticipation and the Degree of Loyalty in Large-Scale Finnish Loyalty Card Data on Grocery Purchases: Cohort Study

Background: Frequent self-weighing is associated with successful weight loss and weight maintenance during and after weight loss interventions Less is known about self-weighing behaviors and associated weight change in free-living settings
Objective: This study aimed to investigate the association between the frequency of self-weighing and changes in body weight in a large international cohort of smart scale users
Methods: This was an observational cohort study with 10,000 randomly selected smart scale users who had used the scale for at least 1 year Longitudinal weight measurement data were analyzed The association between the frequency of self-weighing and weight change over the follow-up was investigated among normal weight, overweight, and obese users using Pearson’s correlation coefficient and linear models The association between the frequency of self-weighing and temporal weight change was analyzed using linear mixed effects models
Results: The eligible sample consisted of 9768 participants (6515/9768, 667% men; mean age 415 years; mean BMI 268 kg/m2) Of the participants, 4003 (4003/9768, 410%), 3748 (3748/9768, 384%), and 2017 (2017/9768, 206%) were normal weight, overweight, and obese, respectively During the mean follow-up time of 1085 days, the mean weight change was –059 kg, and the mean percentage of days with a self-weigh was 3998%, which equals 28 self-weighs per week The percentage of self-weighing days correlated inversely with weight change, r=–0111 (P<001) Among normal weight, overweight, and obese individuals, the correlations were r=–0100 (P<001), r=–0125 (P<001), and r=–0148 (P<001), respectively Of all participants, 725% (7085/9768) had at least one period of ≥30 days without weight measurements During the break, weight increased, and weight gains were more pronounced among overweight and obese individuals: 058 kg in the normal weight group, 093 kg in the overweight group, and 137 kg in the obese group (P<001)
Conclusions: Frequent self-weighing was associated with favorable weight loss outcomes also in an uncontrolled, free-living setting, regardless of specific weight loss interventions The beneficial associations of regular self-weighing were more pronounced for overweight or obese individuals

/pdf/frequency-of-self-weighing-and-weight-change-cohort-study-wqjnqshbkk.pdf

Frequency of self-weighing and weight change: Cohort study with 10,000 smart scale users

Background: Digital self-monitoring, particularly of weight, is increasingly prevalent. The associated data could be reused for clinical and research purposes. Objective: The aim was to compare participants who use connected smart scale technologies with the general population and explore how use of smart scale technology affects, or is affected by, weight change. Methods: This was a retrospective study comparing 2 databases: (1) the longitudinal height and weight measurement database of smart scale users and (2) the Health Survey for England, a cross-sectional survey of the general population in England. Baseline comparison was of body mass index (BMI) in the 2 databases via a regression model. For exploring engagement with the technology, two analyses were performed: (1) a regression model of BMI change predicted by measures of engagement and (2) a recurrent event survival analysis with instantaneous probability of a subsequent self-weighing predicted by previous BMI change. Results: Among women, users of self-weighing technology had a mean BMI of 1.62 kg/m 2 (95% CI 1.03-2.22) lower than the general population (of the same age and height) ( P <.001). Among men, users had a mean BMI of 1.26 kg/m 2 (95% CI 0.84-1.69) greater than the general population (of the same age and height) ( P <.001). Reduction in BMI was independently associated with greater engagement with self-weighing. Self-weighing events were more likely when users had recently reduced their BMI. Conclusions: Users of self-weighing technology are a selected sample of the general population and this must be accounted for in studies that employ these data. Engagement with self-weighing is associated with recent weight change; more research is needed to understand the extent to which weight change encourages closer monitoring versus closer monitoring driving the weight change. The concept of isolated measures needs to give way to one of connected health metrics. [J Med Internet Res 2016;18(1):e17]

Who Self-Weighs and What Do They Gain From It? A Retrospective Comparison Between Smart Scale Users and the General Population in England

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Joffrey Villard

Author Tools

Chat about Author

Papers

Who Self-Weighs and What Do They Gain From It? A Retrospective Comparison Between Smart Scale Users and the General Population in England