Patterns in Weight and Physical Activity Tracking Data Preceding a Stop in Weight Monitoring: Observational Analysis

Psychological and behavioral responses to daily weight gain during behavioral weight loss treatment

Self-weighing is consistently associated with more effective weight control. However, patterns show that participants disengage from their weight control behaviors following weight gain. Women with BMIs in the overweight/obese range (N = 50) enrolled in a long-term behavioral weight loss program completed ecological momentary assessment (EMA) surveys immediately after their daily weigh-ins. Nightly EMA surveys and self-monitoring data through Fitbit measured their weight control behavior that day. On days when participants gained weight (vs. lost or maintained), they reported more negative mood, more guilt/shame, and lower confidence in weight control. Motivation following daily weight gain depended on participants' overall satisfaction with their weight loss so far: more satisfied participants had marginally higher, but less satisfied participants had marginally lower motivation in response to daily weight gain. Greater guilt/shame and lower motivation after the weigh-in predicted less effective weight control behavior that day (e.g., lower likelihood of calorie tracking, fewer minutes of physical activity). Results demonstrate that even small weight gain is distressing and demoralizing for women in BWL programs, which can lead to goal disengagement. These findings have implications for future BWL interventions, including the potential utility of just-in-time adaptive interventions to promote more adaptive responses in the moments after weigh-ins.

Weight Loss with an AI-Powered Digital Platform for Lifestyle Intervention

BACKGROUND

Lifestyle intervention remains the cornerstone of weight loss programs in addition to pharmacological or surgical therapies. Artificial intelligence (AI) and other digital technologies can offer individualized approaches to lifestyle intervention to enable people with obesity to reach successful weight loss.

METHODS

SureMediks, a digital lifestyle intervention platform using AI, was tested by 391 participants (58% women) with a broad range of BMI (20-78 kg/m2), with the aim of losing weight over 24 weeks in a multinational field trial. SureMediks consists of a mobile app, an Internet-connected scale, and a discipline of artificial intelligence called Expert system to provide individualized guidance and weight-loss management.

RESULTS

All participants lost body weight (average 14%, range 4-22%). Almost all (98.7%) participants lost at least 5% of body weight, 75% lost at least 10%, 43% at least 15%, and 9% at least 20%, suggesting that this AI-powered lifestyle intervention was also effective in reducing the burden of obesity co-morbidities. Weight loss was partially positively correlated with female sex, accountability circle size, and participation in challenges, while it was negatively correlated with sub-goal reassignment. The latter three variables are specific features of the SureMediks weight loss program.

CONCLUSION

An AI-assisted lifestyle intervention allowed people with different body sizes to lose 14% body weight on average, with 99% of them losing more than 5%, over 24 weeks. These results show that digital technologies and AI might provide a successful means to lose weight, before, during, and after pharmacological or surgical therapies.

Do monthly coaching calls influence proximal participant adherence in a behavioral weight loss program?

Participants who receive continued coach contact following behavioral weight loss treatment are more successful in maintaining their weight loss long-term. The current study examines whether these contacts have dynamic effects, such that participants are most adherent to the prescribed weight loss behaviors in the days after the call, when motivation and goal salience may be heightened, than they are as time goes on. The current study examined the trajectory of calorie intake, physical activity, weight, and self-monitoring behavior in the fourteen days after a monthly coaching call among participants completing the maintenance phase of a behavioral weight loss trial. For physical activity outcomes, caloric intake, and weight, there were no changes across time. Participants did have the highest adherence and quality of dietary self-monitoring immediately after the call, which diminished over time. Coach contact may continually renew commitment to this burdensome but critical behavior. Likelihood of self-weighing showed an opposite trend, where participants were more likely to weigh themselves in the days more distal from the coach call. Results can inform the timing and content of future coach contact to promote weight control.

The Association Between Goal Setting and Weight Loss: Prospective Analysis of a Community Weight Loss Program

BACKGROUND

Goal setting aids health-related behavior changes; however, the influence of different types of goals on weight loss remains unclear.

OBJECTIVE

We aimed to investigate the association of 3 aspects of goal setting with weight and program dropout over a 24-week period.

METHODS

This study was a prospective longitudinal analysis of participants in a 12-week digital behavioral weight loss program. Weight and engagement data for eligible participants (N=36,794) were extracted from the database. Eligible participants were adults in the United Kingdom who had enrolled in the program, had a BMI ≥25 kg/m², and a weight reading recorded at baseline. Three aspects of goal setting were self-reported at enrollment: weight loss motivation (appearance, health, fitness, or self-efficacy), overall goal preference (low, medium, or high), and percentage weight loss goal (<5%, 5%-10%, or >10%). Weight was measured at 4, 12, and 24 weeks. Mixed models for repeated measures were used to explore the association between goals and weight across the 24-week period. To measure sustained weight change, the primary outcome was weight at 24 weeks. We explored dropout rates over the 24-week period by goal and whether engagement mediated the association between goals and weight loss.

RESULTS

Of the 36,794 participants (mean 46.7, SD 11.1 years; 33,902/36,794, 92.14% female) included in the cohort, 13.09% (n=4818) reported weight at 24 weeks. Most participants set goals of 5%-10% weight loss (23,629/36,794, 64.22%), but setting goals for >10% was associated with greater weight loss (mean difference 5.21 kg, 95% CI 5.01-5.41; P<.001). There was no difference between goals of 5%-10% and <5% (mean difference 0.59 kg, 95% CI 0.00-1.18; P=.05). Appearance was the most prevalent motivational factor (14,736/36,794, 40.05%), but health and fitness were associated with greater weight losses (mean difference health vs appearance 1.40 kg, 95% CI 1.15-1.65; P<.001 and mean difference fitness vs appearance 0.38 kg, 95% CI 0.05-0.70; P=.03). Goal preference had no association with weight. Engagement was an independent predictor of weight loss but not a mediator of the effect of goal setting. At 24 weeks, those who set goals of >10% were less likely to drop out compared with 5%-10% goals (odds ratio [OR] 0.40, 95% CI 0.38-0.42; P<.001); those who liked to set overall high goals were more likely to drop out compared with medium goals (OR 1.20, 95% CI 1.11-1.29; P<.001); and those motivated by fitness or health were less likely to drop out compared with appearance (OR 0.92, 95% CI 0.85-0.995; P=.04 and OR 0.84, 95% CI 0.78-0.89; P<.001, respectively).

CONCLUSIONS

Setting higher weight loss goals and being motivated by health or fitness were associated with greater weight loss and lower likelihood of dropout. Randomized trials for setting these types of goals are required to confirm causality.

Digital phenotyping in young breast cancer patients treated with neoadjuvant chemotherapy (the NeoFit Trial): protocol for a national, multicenter single-arm trial

BACKGROUND

Breast cancer (BC) has particular characteristics in young women, with diagnosis at more advanced stages, a poorer prognosis and highly aggressive tumors. In NeoFit, we will use an activity tracker to identify and describe various digital profiles (heart rate, physical activity, and sleep patterns) in women below the age of 45 years on neoadjuvant chemotherapy for BC.

METHODS

NeoFit is a prospective, national, multicenter, single-arm open-label study. It will include 300 women below the age of 45 years treated with neoadjuvant chemotherapy for BC. Participants will be asked to wear a Withing Steel HR activity tracker round the clock for 12 months. The principal assessments will be performed at baseline, at the end of neoadjuvant chemotherapy and at 12 months. We will evaluate clinical parameters, such as toxicity and the efficacy of chemotherapy, together with quality of life, fatigue, and parameters relating to lifestyle and physical activity. The women will complete REDCap form questionnaires via a secure internet link.

DISCUSSION

In this study, the use of an activity tracker will enable us to visualize changes in the lifestyle of young women on neoadjuvant chemotherapy for BC, over the course of a one-year period. This exploratory study will provide crucial insight into the digital phenotypes of young BC patients on neoadjuvant chemotherapy and the relationship between these phenotypes and the toxicity and efficacy of treatment. This trial will pave the way for interventional studies involving sleep and physical activity interventions.

TRIAL REGISTRATION

Clinicaltrials.gov identifier: NCT05011721 . Registration date: 18/08/2021.

The Impact of Wearable Technologies in Health Research: Scoping Review

Background

Wearable devices hold great promise, particularly for data generation for cutting-edge health research, and their demand has risen substantially in recent years. However, there is a shortage of aggregated insights into how wearables have been used in health research.

Objective

In this review, we aim to broadly overview and categorize the current research conducted with affordable wearable devices for health research.

Methods

We performed a scoping review to understand the use of affordable, consumer-grade wearables for health research from a population health perspective using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework. A total of 7499 articles were found in 4 medical databases (PubMed, Ovid, Web of Science, and CINAHL). Studies were eligible if they used noninvasive wearables: worn on the wrist, arm, hip, and chest; measured vital signs; and analyzed the collected data quantitatively. We excluded studies that did not use wearables for outcome assessment and prototype studies, devices that cost >€500 (US $570), or obtrusive smart clothing.

Results

We included 179 studies using 189 wearable devices covering 10,835,733 participants. Most studies were observational (128/179, 71.5%), conducted in 2020 (56/179, 31.3%) and in North America (94/179, 52.5%), and 93% (10,104,217/10,835,733) of the participants were part of global health studies. The most popular wearables were fitness trackers (86/189, 45.5%) and accelerometer wearables, which primarily measure movement (49/189, 25.9%). Typical measurements included steps (95/179, 53.1%), heart rate (HR; 55/179, 30.7%), and sleep duration (51/179, 28.5%). Other devices measured blood pressure (3/179, 1.7%), skin temperature (3/179, 1.7%), oximetry (3/179, 1.7%), or respiratory rate (2/179, 1.1%). The wearables were mostly worn on the wrist (138/189, 73%) and cost <€200 (US $228; 120/189, 63.5%). The aims and approaches of all 179 studies revealed six prominent uses for wearables, comprising correlations—wearable and other physiological data (40/179, 22.3%), method evaluations (with subgroups; 40/179, 22.3%), population-based research (31/179, 17.3%), experimental outcome assessment (30/179, 16.8%), prognostic forecasting (28/179, 15.6%), and explorative analysis of big data sets (10/179, 5.6%). The most frequent strengths of affordable wearables were validation, accuracy, and clinical certification (104/179, 58.1%).

Conclusions

Wearables showed an increasingly diverse field of application such as COVID-19 prediction, fertility tracking, heat-related illness, drug effects, and psychological interventions; they also included underrepresented populations, such as individuals with rare diseases. There is a lack of research on wearable devices in low-resource contexts. Fueled by the COVID-19 pandemic, we see a shift toward more large-sized, web-based studies where wearables increased insights into the developing pandemic, including forecasting models and the effects of the pandemic. Some studies have indicated that big data extracted from wearables may potentially transform the understanding of population health dynamics and the ability to forecast health trends.

What goes on in digital behaviour change interventions for weight loss maintenance targeting physical activity: A scoping review

OBJECTIVE

To identify the core components of digital behaviour change interventions for weight loss maintenance targeting physical activity, in terms of: (i) behaviour change techniques, (ii) mechanisms of action, (iii) modes of delivery, (iv) dose and (v) tailoring/personalization. In addition, the links between these components were investigated.

METHODS

A literature search was performed in five electronic databases: PubMed, Embase, CINHAL, PsycINFO and Web of Science. Two reviewers independently screened the identified articles and extracted data related with the study characteristics and behaviour change techniques, mechanism of action, mode of delivery, dose, and tailoring, using standardized classifications whenever available (e.g. behaviour change techniques taxonomy).

RESULTS

Seventeen articles reporting 11 original studies were selected. Two studies were protocols, 9 studies presented results for weight change and all but one showed no significant differences between the intervention and control groups. Eight studies (73%) provided adequate information on behaviour change techniques. Five studies (45%) provided partial information about how the behaviour change techniques were linked to mechanisms of action, and only one study (0.9%) described these links for all the techniques. Around half of the studies reported the modes through which behaviour change techniques were delivered. Descriptions of dose were present in most studies, but with minimal information. The use of tailoring or personalization approaches was mentioned in eight studies (73%), but descriptions of what was tailored and how were minimal.

CONCLUSIONS

The compilation of information regarding intervention components was difficult due to the lack of information and systematization in reporting across papers. This is particularly true for the reporting of the links between behaviour change techniques and the other core intervention components. This information is crucial to help us understand in the context of behaviour change interventions what works or does not work, how it works and why.

Home-based digital health technologies for older adults to self-manage multiple chronic conditions: A data-informed analysis of user engagement from a longitudinal trial

Background

Ageing populations are resulting in higher prevalence of people with multiple chronic conditions (multimorbidity). Digital health platforms have great potential to support self-management of multimorbidity, increasing a person's awareness of their health and well-being, supporting a better understanding of diseases and encouraging behaviour change. However, little research has explored the long-term engagement of older adults with such digital interventions.

Methods

The aim of this study is to analyse how 60 older adults with multimorbidity engaged with digital symptom and well-being monitoring through a digital health platform over a period of approximately 12 months. Data analysis focused on user retention, frequency of monitoring, intervals in monitoring and patterns of daily engagement.

Results

Our findings show that the overall engagement with the digital health platform was high, with more than 80% of participants using the technology devices for over 200 days. The submission frequency for symptom parameters (e.g. blood glucose (BG), blood pressure (BP), etc.) was between three and four times per week which was higher than that of self-report (2.24) and weight (2.84). Submissions of exercise (6.12) and sleep (5.67) were more frequent. The majority of interactions happened in the morning time. The most common time of submission for symptom parameters was 10 am, whereas 8 am was the most common time for weight measurements.

Conclusions

The findings indicate the patterns of engagement of older adults with complex chronic diseases with digital home-based self-management systems.

Mobile health assisted self-monitoring is acceptable for supporting weight loss in rural men: a pragmatic randomized controlled feasibility trial

BACKGROUND

Addressing overweight and obesity among men at-risk for obesity-related diseases and disability in rural communities is a public health issue. Commercial smartphone applications (apps) that promote self-monitoring for weight loss are widely available. Evidence is lacking regarding what support is required to enhance user engagement with and effectiveness of those technologies. Pragmatically comparing these apps effectiveness, including rural men's desired forms of support when using them, can lead to greater weight loss intervention impact and reach. This study assessed the feasibility and acceptability of a mobile technology application applied differently across two groups for weight loss.

METHODS

In a two-armed, pragmatic pilot feasibility study, 80 overweight and obese men aged 40-69 were randomized using a 1:1 ratio to either an enhanced Mobile Technology Plus (MT+) intervention or a basic Mobile Technology (MT) intervention. The MT+ group had an enhanced smartphone app for self-monitoring (text messaging, discussion group, Wi-Fi scale) whereas the MT group received a basic app that allowed self-monitoring logging only. Assessments were collected at baseline, 3 and 6 months. App logs were analyzed to track engagement and adherence to self-monitoring. Acceptability was assessed via focus groups. Analysis included descriptive statistics and qualitative content analysis.

RESULTS

Of 80 men recruited, forty were allocated to each arm. All were included in the primary analysis. Recruitment ended after 10 months with a 97.5 and 92.5% (3 month, 6 month) retention rate. Over 90% of men reported via survey and focus groups that Lose-It app and smart scale (MT+) was an acceptable way to self-monitor weight, dietary intake and physical activity. Adherence to daily app self-monitoring of at least 800 dietary calories or more (reported respectively as MT+, MT) was positive with 73.4, 51.6% tracking at least 5 days a week. Adherence to tracking activity via recorded steps four or more days weekly was positive, 87.8, 64.6%. Men also adhered to self-weighing at least once weekly, 64, 46.3%. At 6 months, an observed mean weight loss was 7.03 kg (95% CI: 3.67, 10.39) for MT+ group and 4.14 kg (95% CI: 2.22, 6.06) for MT group, with 42.9 and 34.2% meeting ≥5% weight loss, respectively. No adverse events were reported.

CONCLUSIONS

This National Institutes of Health-funded pilot study using mobile technologies to support behavior change for weight loss was found to be feasible and acceptable among midlife and older rural men. The interventions demonstrated successful reductions in weight, noting differing adherence to lifestyle behaviors of eating, monitoring and activity between groups, with men in the MT+ having more favorable results. These findings will be used to inform the design of a larger scale, clinical trial.

TRIAL REGISTRATION

The trial was prospectively registered with ClinicalTrials NCT03329079 . 11/1/2017.

Frequency of Self-Weighing and Weight Change: Cohort Study With 10,000 Smart Scale Users

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, 66.7% men; mean age 41.5 years; mean BMI 26.8 kg/m2). Of the participants, 4003 (4003/9768, 41.0%), 3748 (3748/9768, 38.4%), and 2017 (2017/9768, 20.6%) were normal weight, overweight, and obese, respectively. During the mean follow-up time of 1085 days, the mean weight change was –0.59 kg, and the mean percentage of days with a self-weigh was 39.98%, which equals 2.8 self-weighs per week. The percentage of self-weighing days correlated inversely with weight change, r=–0.111 (P<.001). Among normal weight, overweight, and obese individuals, the correlations were r=–0.100 (P<.001), r=–0.125 (P<.001), and r=–0.148 (P<.001), respectively. Of all participants, 72.5% (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: 0.58 kg in the normal weight group, 0.93 kg in the overweight group, and 1.37 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.

Wearable Activity Trackers for Monitoring Adherence to Home Confinement During the COVID-19 Pandemic Worldwide: Data Aggregation and Analysis

Background

In the context of home confinement during the coronavirus disease (COVID-19) pandemic, objective, real-time data are needed to assess populations’ adherence to home confinement to adapt policies and control measures accordingly.

Objective

The aim of this study was to determine whether wearable activity trackers could provide information regarding users' adherence to home confinement policies because of their capacity for seamless and continuous monitoring of individuals’ natural activity patterns regardless of their location.

Methods

We analyzed big data from individuals using activity trackers (Withings) that count the wearer’s average daily number of steps in a number of representative nations that adopted different modalities of restriction of citizens’ activities.

Results

Data on the number of steps per day from over 740,000 individuals around the world were analyzed. We demonstrate the physical activity patterns in several representative countries with total, partial, or no home confinement. The decrease in steps per day in regions with strict total home confinement ranged from 25% to 54%. Partial lockdown (characterized by social distancing measures such as school closures, bar and restaurant closures, and cancellation of public meetings but without strict home confinement) does not appear to have a significant impact on people’s activity compared to the pre-pandemic period. The absolute level of physical activity under total home confinement in European countries is around twofold that in China. In some countries, such as France and Spain, physical activity started to gradually decrease even before official commitment to lockdown as a result of initial less stringent restriction orders or self-quarantine. However, physical activity began to increase again in the last 2 weeks, suggesting a decrease in compliance with confinement orders.

Conclusions

Aggregate analysis of activity tracker data with the potential for daily updates can provide information regarding adherence to home confinement policies.