Digital Health Solutions for Weight Loss and Obesity: A Narrative Review

Personal exercise programmes have long been used and prescribed for weight loss and the improvement of quality of life in obese patients. While individualised programmes are usually the preferred option, they can be more costly and challenging to deliver in person. A move to digital programmes with a wider reach has commenced, and demand has increased due to the SARS-CoV-2 pandemic. In this review, we evaluate the current status of digital exercise programme delivery and its evolution over the past decade, with a focus on personalisation. We used specific keywords to search for articles that met our predetermined inclusion and exclusion criteria in order to provide valuable evidence and insights for future research. We identified 55 studies in total in four key areas of focus, from the more recent development of apps and personal digital assistants to web-based programmes and text or phone call interventions. In summary, we observed that apps may be useful for a low-intensity approach and can improve adherence to programmes through self-monitoring, but they are not always developed in an evidence-based manner. Engagement and adherence are important determinants of weight loss and subsequent weight maintenance. Generally, professional support is required to achieve weight loss goals.


Introduction
It is widely recognised that overweight and obesity increase the risk of disease and mortality [1]. According to statistics from 2016, 39% of individuals aged 18 and above were overweight, and 13% were obese, resulting in more deaths than underweight individuals in most countries [2]. The most commonly used measure for weight by health organisations is the body mass index (BMI), which is a basic tool to classify groups of populations [3,4]. Statistics from Cancer Research UK reveal that, after smoking, overweight and obesity are the leading causes of preventable cancer in the UK [5].
The SARS-CoV-2 pandemic has had a significant effect on health and wellbeing, as evidenced by Zhu et al. [6]. The impact of mandates to remain at home alongside increased levels of stress and anxiety led to a significant increase in total food intake and a significant decrease in physical activity (PA), resulting in weight gain in many individuals. In addition, Okuyan and Begen [7] have pointed out that many businesses are in all probability looking to make work from home more routine, due to the economic consequences of SARS-CoV-2, the perceived convenience of less travel time, greater autonomy, and potentially reduced costs, for example, rental of large office spaces [8,9]. Consequently, there are more increased sedentary behaviours, including prolonged sitting, which evidence shows causes hypertension, musculoskeletal pain, and the potential for long-term health problems [10][11][12]. This increase in sedentary behaviour has led to reduced levels of PA [13], which in turn can contribute to the development of obesity. Regular or tailored exercise has been shown to have numerous health benefits that can help reverse the health problems associated with inactivity and obesity.
Digital technology has improved many aspects of modern-day life, from advancements in medical science to enhanced healthcare and outcomes in educational settings [14]. However, it is also associated with lessened attention, social isolation, and addiction, and has an impact on sleep [15].
In modern society, there is a requirement to use digital interventions to assist with health [16]. Digital health technologies enable remote monitoring of various health indicators, such as heart rate, diet, PA, and blood glucose levels in individuals with diabetes. They also improve diagnosis and treatment by providing a faster and more personalised service, information on symptoms, and advice [17], thereby reducing pressure on primary care services and physicians [18]. Furthermore, in a previous study [19], authors demonstrated a significant increase in energy expenditure per workday pre to post a digital prompt intervention to interrupt prolonged occupational sitting time and be able to utilise different technologies in a more workplace setting. Moreover, there is a desire from individuals to take advantage of these technologies if they are recognised as helpful and easy to use [20], and friends/family recommend them [21,22]. Therefore, these digital healthcare technologies are being increasingly used for weight loss in obese individuals, either in a generic manner or in a manner specifically tailored to the individual. The impact of weight loss is multifaceted, from a general health and wellbeing aspect to the economics of treatment when required.
This review aims to provide an up-to-date understanding of the digital health solutions for weight loss in overweight and obesity and to identify any potential gaps in this area.

Search Strategy
The database used for the literature search was Web of Science, using the following keywords: "digital health," "digital intervention," "weight loss," "obes*," "e-Health," "m-Health," "online weight management," "online weight loss programme," "web-based intervention," "personal digital assistant." Additional searches were conducted for specific platforms such as NHS, Slimming World, Cell Phone Intervention for You (CITY), MyFitnessPal (MFP), Weight Watchers (WW). The search was limited to articles published between 2011 and 2021 in the English language. We followed previously established frameworks for conducting a narrative literature review [23,24].

Study Selection
Inclusion criteria for the study were individuals aged 18 years or older with a BMI of 25 kg/m 2 or higher, studies that used relevant keywords, mixed methods, process evaluation, randomised, experimental, controlled trial, and/or relevant methods/protocol with a control arm, and outcome measures related to weight loss or digital health. Exclusion criteria included study protocols alone, meta-analysis, systematic reviews, rapid reviews, and narrative reviews.
The literature included was selected based on inclusion criteria, firstly by title, then abstract, and finally by full-text screening [25,26]. The initial search returned 1406 papers, with 107 duplicates and 1151 removed based on title (see Figure 1). From the 148 studies remaining, a further 39 were excluded after abstract screening due to irrelevancies. There were 109 studies remaining for full-text eligibility inspection; 54 were removed due to no control/no results. This left a total of 55 studies available for inclusion in the systematic review. The search strategy was formulated by the co-authors. Once the search was complete, the studies were listed in a spreadsheet, and the titles and abstracts were screened to identify potentially relevant studies. The full text was obtained for all studies that appeared to meet the inclusion criteria or where there was any uncertainty. Subsequently, two authors (L.I. and L.A.M.) examined each full-text manuscript to assess for eligibility, and neither was blinded to the journal titles or study authors, with any disagreement resolved through discussion or consultation with the third author (R.V.V.).
Nutrients 2023, 15, x FOR PEER REVIEW 3 of 32 Subsequently, two authors (L.I. and L.A.M.) examined each full-text manuscript to assess for eligibility, and neither was blinded to the journal titles or study authors, with any disagreement resolved through discussion or consultation with the third author (R.V.V.).

Data Analysis
As proposed by Fah and Aziz [27], articles were inserted into a tabular format to condense information and extract the most relevant information for review. The table included: study authors; participant information; intervention and control; and key outcomes. This step allowed for the differentiation of studies and their key themes.

Results
There were 55 studies out of 1406 results included in the systematic review (see Figure 1). From the available information, there were a total of 17,438 participants, aged 18-72 years, with a BMI of 25-50 kg/m 2 , and four studies had a higher percentage of male participants (57-100%).

Data Analysis
As proposed by Fah and Aziz [27], articles were inserted into a tabular format to condense information and extract the most relevant information for review. The table included: study authors; participant information; intervention and control; and key outcomes. This step allowed for the differentiation of studies and their key themes.

Results
There were 55 studies out of 1406 results included in the systematic review (see Figure 1). From the available information, there were a total of 17,438 participants, aged 18-72 years, with a BMI of 25-50 kg/m 2 , and four studies had a higher percentage of male participants (57-100%).

Themes
Four key themes were derived from the literature, which included the use of an app, a personal digital assistant (PDA), web-based programmes, and text or phone call interventions.
3.1.1. App Table 1 shows the characteristics of those studies. In some cases, the authors were the designers and creators of the app (e.g., [6,28]), while others utilised readily available apps, such as MFP [29,30] and WW apps [31,32]. and control (2.9 ± 3.9 kg) (p < 0.05); there was a significant difference in steps at 12 weeks in the VC group vs. IP and control (p < 0.05) There were no differences in BMI between groups or between intervention groups and control Several studies did not explore weight loss as an outcome measure; for instance, [46,56] used step count as their measure of PA. Mamede et al. [46] used a 10-week intervention consisting of different versions of an app in gamification, nudges, and the follow-up phase (four weeks). They reported participants increased steps/day by 634.0 ± 244.8 vs. control (p = 0.01) in phase one, but this did not continue in phase two (98.2 ± 325.5 steps/day, p = 0.76) or follow-up (53.49 ± 381.7 steps/day, p = 0.89). In Zhou et al. [56], the intervention (personalised steps/day based on past steps and goals) group decreased daily step count −390 ± 490 steps/day between run-in and 10 weeks vs. the control group (10,000 steps/day goal) decrease of −1350 ± 420 steps/day (p = 0.03); a between-group difference of 960 steps/day.
Another study examined the sitting time differences between weight-loss maintainers and weight-stable (obese) participants. Total weekly energy expenditure was significantly greater (p < 0.001) in the intervention group (1835 kcal/week) vs. control (785 kcal/week), and higher BMI was associated with increased sitting hours during weekdays and weekends (p < 0.001) [32].
Furthermore, Ainscough et al. [28] looked at dietary and PA interventions for overweight/obese pregnant women. The app group had healthy eating and exercise advice, whereas the control group received standard care (without advice). At 28 weeks, there was no significant mean difference in self-reported light PA (minutes/week) in the intervention vs. control (13.3 min/week; p = 0.111), but there was a significant difference in moderate activity in the intervention vs. control (26.5 min/week; p = 0.001) and an energy difference of −173.34 kcal in the intervention group vs. the control group (p < 0.001).

Non-Significant Weight Loss Outcomes
Allen et al. [33] investigated how the intensity of counselling may affect weight loss and change in PA; app only −1.8 ± 3.7 kg, IC −2.5 ± 4.1 kg, IC + app −5.4 ± 4.0 kg, less IC + app −3.3 ± 5.9 kg (p = 0.89); self-reported PA of moderate or greater intensity decreased in all groups bar app 0.19 ± 5.1 hrs/week; IC + app −2.0 ± 5.4 hrs/week; less IC + app −3.6 ± 5.5 hrs/week; IC −1.4 ± 7.1 hrs/week (p = 0.51). In a comparison of different self-monitoring and support groups vs. a no-monitoring control group, no significant differences in weight, body composition, or exercise were found between the groups [33].
Whitelock et al. [55] evidenced no significant findings between a food and drink attentive app vs. a dietary advice group. At eight weeks, weight loss for the app was 1.2 ± 3.1 kg vs. control of 1.1 ± 3.4 kg (p = 0.89), and body fat change at 8 weeks was −0.4 ± 1.8% vs. control of −0.5 ± 2.0% (p = 0.81). Brindal et al. [34] used an app with features including food intake recording, rewards, and prompts, but this was not a significant predictor of weight loss (6.67% in the supportive app group vs. 5.41% in the static app condition at 24 weeks (p = 0.36). Additionally, no difference for the interaction between study week and condition was found (p = 0.49). However, 58% of completers lost ≥5% of their body weight (between groups, p = 0.69), a clinically relevant amount of weight. Likewise, Duncan et al. [35] found no difference between a behaviour change app and an enhanced version and wait-list control in average body weight for the pooled intervention vs. control at 12 months (−0.00 kg). Moreover, daily energy intake was insignificantly lower in the pooled intervention than control at 12 months (−913.36 kJ), although this was significant at six months (difference −1037.03 kJ, p < 0.05). Hernández-Reyes et al. [38] found a nonsignificant difference in weight loss in a self-monitoring and push notifications app vs. no notification: −7.9 ± 3.9 kg vs. −7.1 ± 3.4 kg control (p = 0.39), but body fat loss was significantly increased in the intervention group (−12.9 ± 6.7%) vs. control (−7.0 ± 5.7%) (p < 0.001).
Forman et al. [31] reported that the main effect of treatment condition on percent weight loss was not significant (p = 0.70) between conditions WW and WW + OnTrack; however, diet type was an indicator of weight loss (p = 0.002). Gill et al. [37] compared the use of an app and allied coach for improving PA against a control (usual lifestyle), the between-group difference in weight was −0.46 kg (p = 0.63). However, the intervention increased step count (average 3,132 steps/day; p < 0.001) and decreased sitting time (−0.08 min/day; p = 0.03) between groups at 6 months. Hutchesson et al. [40] found differences for a targeted/tailored programme vs. wait-list in body fat in kg (−3.1 kg; p = 0.019), but not for body fat percentage (−2.0%; p = 0.093) or weight loss either measured or self-reported; although within-group weight, BMI, body fat (kg), and waist circumference were all significant in the intervention group. Additionally, a study tested an app in a gamification group given weekly targets, gamification plus physicians given data for feedback, or a basic 10,000 steps/day control. There were no significant differences in weight loss between interventions and control at any time point, but all groups lost significant weight at 12, 24, and 36 weeks [42].
Laing et al. [43] found MFP did not produce significant weight loss in overweight individuals, despite controls gaining weight at both time points. West et al. [54] investigated group-based weight loss via video and text conditions using MFP (−5.0 ± 6.0% vs. −3.0 ± 4.1%, respectively), and no significant self-reported levels of PA per week were observed between video (91.8 ± 107.4 min/week) vs. text (36.6 ± 35.7 min/week, p = 0.92). Monroe et al. [48] reported on participants' self-monitoring using MFP and showed that weight loss did not significantly differ between groups (standard behavioural treatment, 5.30 ± 3.93% vs. enhanced (additional social support), 5.96 ± 5.19%; p = 0.63). There was no difference between groups for adherence to self-monitoring dietary intake (p = 0.37), or for the number of counselling sessions attended (p = 0.13).

Significant Weight Loss Outcomes
Hartman et al. [29] showed the impact of MFP and an accelerometer for individuals with elevated breast cancer risk, with significant reductions in weight −4.4 ± 4.3 kg vs. standard care 0.8 ± 3.8 kg (p = 0.004). Moderate-to-vigorous physical activity (MVPA) increased 15.01 ± 14.2 min/day in the intervention vs. 10.9 ± 10.1 min/day in usual care at 6 months, with the difference at 6 months being statistically significant (p = 0.02), although the difference between the changes in each group was not (p = 0.13). Johnson et al. [30] tested the impact of in-person (IP) vs. video conference (VC) health coaching and control. All groups used MFP and additional software, whereby weight loss was significantly greater (p < 0.05) for VC (8.23 ± 4.5 kg) vs. IP (3.2 ± 2.6 kg) and control (2.9 ± 3.9 kg). VC had 7054.6 ± 2068.7 steps/day at 12 weeks vs. control 5002.4 ± 2640.3 steps/day and IP 6236.2 ± 2393.4 steps/day (p < 0.05).
In [39] four groups were assigned either face-to-face, by app, in combination, or on the waitlist. Significantly more participants in all intervention groups had ≥5% weight loss vs. control, with more participants in the combination vs. app group losing ≥5% (19%; p = 0.06). Significantly reduced total energy intake was reported for all groups except the control. Another study [51] used three intervention groups and control, whereby the NHS weight loss plan and Slimming World did not significantly decrease weight loss compared to the control (−0.4 kg and −0.8 kg, respectively). However, Rosemary Online lost 1.5 kg more than the control (p = 0.001); 19% lost ≥5% in Rosemary Online, and <5 participants lost ≥10% weight in each group [51]. Lin et al. [44] found engagement measures were significant for weight loss at six months and personal coaching at 12 months, with no difference between groups for weight loss >5% at 24 months and large variability in weight loss in each arm. In [53], the app group (support, prompts) was not superior to control at any measurement point. Personal coaching (self-monitoring, group sessions) participants lost significantly more weight than controls at six months (−1.92 kg; p = 0.003), but not at 12 and 24 months. In the first six months, the app group self-weighed 4.0 ± 1.7 times/week vs. 2.2 ± 1.6 times/week in the personal coaching intervention.
Redman et al. [52] considered the impact of an app on reducing weight gain in pregnant women. Participation in a weight management programme either through an app or in-person (paper PA tracking) or usual care, whereby in-person had a mean gestational weight gain of 8.0 ± 1.3 kg vs. usual care 12.8 ± 1.5 kg (p = 0.04) and remote had a mean gestational weight gain of 10.0 ± 1.2 kg vs. usual care (p = 0.07). The proportion of women with excess gestational weight gain was significantly lower in person (56%; p = 0.03) and in remote groups (58%; p = 0.04) vs. usual care (84.6%).

Personal Digital Assistant
Five studies used PDAs as their intervention; Table 2 shows the characteristics of those studies. These publications used the software Dietmate Pro, a dietary self-monitoring programme to track energy and fat intake, with the addition of CalcuFit [57], a PA selfmonitoring programme. Each used two intervention groups: PDA or PDA + tailored feedback (TF) vs. a control group using self-monitoring via a paper record (PR). Three studies [58][59][60] were secondary analyses using data from Burke et al. [61].

Web-Based
We identified 15 studies that applied web-based interventions, and these are summarised in Table 3.  Web-based exercise (WE)-10 weeks web-based weekly progressive interval training programme Control-Supervised exercise (SE); same programme in groups of 8-10 participants under fitness instructor supervision with the same volume increments. Wait-list control with a 10-week washout before WE intervention WE had no significant effect on visceral adipose tissue (p = 0.5), although the SE programme did (p < 0.001), with no between-group differences (p = 0.11) Both groups significantly decreased fat mass, with a significant difference between groups (p = 0.042)    Ballin et al. [66] investigated a web-based training programme (WE) compared to the same programme but in supervised groups at a clinic (SE), with the primary outcome being change in visceral adipose tissue (VAT) in a 50:50 male/female cohort. WE had no significant effect on VAT at 10 weeks compared to baseline 2025 ± 829 g (p = 0.5), and no between-group differences were observed for VAT (p = 0.11), although decreased fat mass was observed in WE (31,863 ± 5752 g (p = 0.034) and SE groups (32,353 ± 6004 g (p < 0.001) that did have between-group differences (p = 0.042).

Non-Significant Weight Loss Outcomes
In Rollo et al. [73], overweight/obese women with recent gestational diabetes enrolled in a web-based programme with either high personalisation, including video calls with a dietitian and exercise physiologist, or low personalisation with normal access to the programme, or wait-list control. At 6 months, the high-personalisation group lost 1.6 kg in comparison to the low-personalisation group, which lost 0.9 kg, and the wait-list control group, which gained 0.75 kg. No significant group-by-time effect was observed for weight when both treatment groups were compared against the control group (p = 0.137), no differences in MVPA were reported (p = 0.158); there was no significant reduction in diabetes risk at the group level.

Significant Weight Loss Outcomes
A study compared four weight loss programmes, whereby significant weight loss was seen for all intervention groups in comparison to the control group: Curves Complete (CC) −4.32 kg, WW −4.31 kg, Jenny Craig −5.34 kg, and Nutrisystem −5.03 kg, with no difference between groups [65]. The control group gained 0.16 kg. Reductions in total energy intake were CC −413 kcal, WW −531 kcal, Jenny Craig −604 kcal, Nutrisystem −631 kcal, and control −103 kcal, with the largest reductions seen in Jenny Craig and Nutrisystem, both web-based interventions where meals were delivered [65]. Beleigoli et al. [67] investigated two interventions a web-based behaviour-change platform with tailored feedback vs. the platform plus additional coaching by a dietitian and a waiting list control with generic PA guidelines. Weight change at 24 weeks was higher using the platform plus additional coaching −1.57 kg vs. control, −0.66 kg (p< 0.001). Additionally, the platform only was 1.08 kg, whereby no difference was observed between intervention groups (p = 0.14). The intervention groups showed a higher proportion ≥5% weight loss, and longer usage of the platform was associated with clinically meaningful weight loss. Platform only and platform plus additional coaching both had improved dietary intake (increased consumption of fruits/vegetables and a reduction in ultraprocessed foods), but changes in PA did not differ between groups. Likewise, Collins et al. [68] found the intervention groups lost significant weight in a basic programme −2.1 ± 3.3 kg and the enhanced programme utilising personalised feedback and reminders lost −3.0 ± 4.1 kg vs. wait-list control weight gain of 0.4 ± 2.3 kg (p < 0.001). There were also significant between-group differences in the proportions of participants who lost 5 to <10% of their baseline weight (enhanced: 17%, basic: 18%, control: 3%; all p < 0.001). In Innes et al. [70], weight loss was significantly reduced post-intervention in all groups. The healthy weight loss programme achieved a weight loss of −5.17 ± 4.22 kg, compared to the free online NHS self-management weight loss tool of -4.19 ± 5.49 kg and gym only (no guidance) of -1.17 ± 3.00 kg; (p < 0.001). The two intervention programmes demonstrated greater reductions compared to gym only (p < 0.05). A separate paper found an adjusted difference of −3.16 kg (p < 0.0001) between WW and standard care control (−6.65 ± 0·43 kg vs. control −3.26 ± 0·3 kg, respectively) for those that completed the 12-month assessment, although at 12-months, 42% of participants had withdrawn from the trial [71]. This attrition differed significantly between countries (p < 0.0001), whereby the number of participants not completing in the UK (64%) was higher than in Australia (41%) and Germany (25%). Thomas et al. [75] investigated WW or WW plus additional behavioural training sessions. Both groups lost significant weight with no difference between groups at 3 months, whereas at 6 months weight loss in WW + ES was nearly twice as big as WW alone (4.7 ± 1.1 kg and 4.9 ± 1.3% vs. 2.6 ± 1.1 kg and 2.5 ± 1.3%, respectively, Ps < 0.047), highlighting the potential of behavioural change web-based VR technology as a tool to improve weight-loss outcomes to complement programmes, but requiring more investigation.
Alencar et al. [63] identified a significant difference between video conference (VC; weekly specialist video) and the control group (baseline guidance) for body weight loss (−7.3 ± 5.2 kg vs. −1.2 ± 3.9 kg, respectively; p < 0.05); and for % body weight loss (−7.16 ± 4.4% vs. −1.5 ± 4.1, respectively; p < 0.05). Furthermore, 9/13 (69.2%) in the intervention group vs. 1/12 (8%) in the control group achieved a clinically significant weight loss of ≥5%. In addition, Alencar et al. [64] found weekly video calls (30 min) increased the healthy rate of weight loss significantly (−0.74 ± 1.8 kg) vs. the self-guided (baseline guidance) control group (0.18 ± 1.8 kg) (p < 0.05). Furthermore, it was reported in [76] that an accelerometer web-based intervention resulted in greater weight loss than a basic wait-list group. The intervention group lost −1.49 ± 0.26 kg in comparison to the control group (−0.82 ± 0.21 kg, p = 0.05), and BMI changes were reported as −0.50 ± 0.09 kg/m 2 vs. control −0.29 ± 0.07 kg/m 2 (p = 0.07). A subset analysis of "successful" intervention participants showed an 80% higher decrease in weight loss than the rest of the intervention group (p < 0.001) and that men were more successful at reaching the personalised targets than women. Additionally, Wijsman et al. [77] used the same intervention vs. wait-list, with findings showing significant weight loss between groups (intervention −1.49 ± 0.26 kg vs. control −0.82 ± 0.21 kg; p = 0.046) and waist circumference (−2.33 ± 0.36 cm vs. control −1.29 ± 0.34 cm; p = 0.036). The web-assisted PA intervention saw a significant increase in daily activity compared to the control alongside parameters of glucose metabolism and high retention rates, demonstrating the feasibility of use in an older population aged 60-70. Conversely, weight loss was higher in the control group (Fitbit only) in [69] at six months −2.54 ± 4.00 kg (p = 0.002) vs. programme + Fitbit −1.71 ± 1.88 kg (p = 0.006) in breast cancer survivors. No significant difference was observed between groups (p = 0.461); three participants in each group achieved ≥5% weight loss. Similarly, Newlands et al. [72] explored a WW group, WW Plus (which included breast cancer-tailored support), and control, which received WW referrals at three months. Although significant weight loss was seen in both WW groups at 12 weeks (p < 0.001) that was not seen in the control group, despite the higher cost and time implications of running the extended programme the results showed no significance for 12-month weight change in WW Plus −1.22 kg (p = 0.436), but the WW and control groups did maintain at 12 months (−5.11 kg; p = 0.002 and −4.22 kg; p = 0.015, respectively). Thomas et al. [74] utilised WW online, or WWO, plus ActiveLink, using a PA tracking device and an informed control. All groups lost weight but no significant differences between groups in weight loss were observed at 12 months. At 12 months, more WW online participants (25.5%) lost ≥5% of their starting weight vs. controls (12.9%; p = 0.04), and neither group differed from WWO, plus ActiveLink (14.3%; p > 0.10) and more frequent engagement with the online platform was associated with superior weight loss.

Tailored Text/Call
Six studies carried out tailored texts or calls as their measure, not including an app, as summarised in Table 4. . At 1 month, weight loss was significant between groups (p = 0.01) but at 6 months, weight loss between groups was not significant (p = 0.78) There was no significant difference between groups for % body fat (p = 0.60) and PA min/wk (p = 0.14) at 6 months Significant between-group differences in PA change from baseline to week 1 for steps (p = 0.01), fairly/very active minutes (p < 0.01), and total active minutes (p = 0.02), but these changes in PA were the short-term and not maintained through weeks 2-6 Significant within-group increase of +4.3 ±2.0 min/week of MVPA from baseline to 6 weeks follow up in the control group (p = 0.04), but no group differences across PA levels  [82] study of 67 participants consisted of a group with a Fitbit One tracker + text message PA prompts vs. Fitbit One only in a short 6-week intervention. The SMS text-messaging effect lasted for a very short-term effect (1 week), whereby at baseline to week one, the intervention group increased by 1266 ± 491 steps vs. −48 ± 240 steps/day control (p = 0.01), MVPA 17.8 ± 8.5 min/week vs. 2.3 ± 4.1 min/week (p < 0.01) and 38.3 ± 15.9 min/week total PA vs. −6.7 ± 11.7 min/week total PA (p = 0.02) and afterward were insufficient in increasing PA. At the 6-week follow-up, there was a within-group significance (p = 0.04) in Fitbit only for MVPA of 4.3 ± 2.0 min/week and a between-group significance (p = 0.33).

Non-Significant Weight Loss Outcomes
Kim et al. [79] compared the effect of personalised text messaging to provide education and motivation, alongside additional offline education and counselling as a worksite weight management programme in comparison with standard care. They found no between-group differences at 6 months; the intervention lost −1.71 kg (p < 0.01) vs. control −1.56 kg (p < 0.001), no between-groups differences (p = 0.78) and no significant difference between groups for body fat percentage (p = 0.60) or PA min/wk (p = 0.14), although there was an overall positive perception of text messaging by participants. Further research found the impact of rewards via direct payment or lottery payment was no different from daily self-weighing and feedback alone (control) at 12 months: lottery vs. control −1.6 kg (p = 0.408); direct payment vs. control −0.5 kg (p = 0.813); lottery vs. direct −1.1 kg (p = 0.494), with the percentage of people who sustained weight loss in the lottery (66%), direct (62%), and control (59%) not significantly different (p > 0.1) [83]. As such, financial incentives provided no additional benefits for weight loss maintenance, but the group was likely highly motivated given their initial weight loss, coupled with additional systems attributable to success such as daily weighing, feedback, and ongoing participation in the WW programme and a trend was observed across arms that those who weighed themselves more frequently had greater weight loss (p < 0.001 at 6 and 12 months) [83].

Significant Weight Loss Outcomes
Fjeldsoe et al. [78] compared a tailored behaviour change text message intervention ('Get Healthy, Stay Healthy') to a standard control group. The intervention improvement in weight loss was significantly greater than controls (−1.35 kg; p = 0.003) and some forms of PA, but not in dietary behaviours. Another study utilised Facebook and text messaging to deliver either personalised (tailored) or generic (targeted) content vs. general healthy body weight content as a control in young adults with overweight/obesity [80]. Similar to other studies, engagement was an important determinant of weight loss, whereby a subset of highly engaged participants (completing >66% of the intervention) in the tailored group lost more weight compared to the control group at 6 months (−2.32 kg; p = 0.004) and 12 months (−2.28 kg; p = 0.04), although between-group differences disappeared by 8 months [80]. This was also shown by Steinberg et al. [81], who demonstrated that adherence was a key outcome whereby women who had ≥80% interactive voice response call completion rate had significantly greater weight loss of −1.97 ± 0.67 kg compared to those who achieved <80% (0.48 ± 0.69 kg; p = 0.01), and older, more educated participants were more likely to achieve a high interactive voice response call completion rate. Similar outcomes were also found for changes in BMI, and participants reported positive attitudes towards interactive voice response self-monitoring [81].

Discussion
The majority of studies used a weight loss of ≥5% of initial body weight as a measure of significant reduction. This value is generally accepted as a marker for health benefits in overweight/obese populations [84,85] and an indication of a strong intervention [86]. There may be an argument that there should be an aim for even greater losses, 5-10% within six months [87]. In contrast, Ryan and Yockey [88] state weight loss is individualised and <5 or >10% may be appropriate, depending on the physical state and health goals.
Data shows that step count can be used to test intervention effectiveness, in line with previous research that shows that step count is a viable and successful method of tracking and increasing PA in overweight populations [89,90]. Additionally, as the rise in wearable technology increases and with increasingly sophisticated technology (e.g., smartphones, smartwatches, smart rings), this offers more opportunities for people to access personal tracking capabilities for targeted physical activity goals. It's suggested that pedometers lead to increases in PA and reduce risk in at-risk individuals without the need for additional counselling or incentives [90]. Further research (Mamede et al. [46]) has included gamification elements and social support features to increase step count in comparison to active control. They only found differences in the first five weeks of their study, which did not continue when physical nudges were used to promote the maintenance of behavioural changes achieved in the gamification phase.
Results indicate that tailored interventions can produce significant findings for weight loss and other health and PA-derived factors. Previous research found tailoring to be more effective in 66.6% (4/6) of studies in supporting weight loss than generic or wait-loss controls [91]. This is supported by [92], in which it was concluded that tailoring had a significant impact on digital interventions (p < 0.001) (see also [93]). The evidence, therefore, suggests there is a place within weight loss programmes for this alone or in addition to technology.
The present findings suggest that apps, in many cases, do not produce significant weight loss compared to controls. This resembles the evidence presented by Ghelani et al. [94], who concluded that apps may be useful as low-intensity approaches or alongside a weight management intervention to have the desired effect. In addition, the publications reviewed here have expressed that there needs to be an awareness that apps are not always developed on a solid evidence-base, and lack essential health aspects, so the addition of professional help may be required to attain weight loss goals [95][96][97]. Furthermore, a few studies tested the feasibility of an intervention plus an app/counselling. For example, Fukuoka et al. [36] found a pedometer plus the use of an app and in-person lifestyle intervention sessions resulted in greater weight loss than a pedometer alone in a diabetes prevention intervention in overweight adults. Equally, another study indicated that the addition of counselling via email increased PA compared to using a sole pedometer [98].
In this review, one of the most frequently used apps for remote recording of dietary intake and exercise for weight loss was MFP. It was shown to be advantageous in some cases, but as the lone intervention, no differences were found [43]. Some indicated MFP should be used more informatively while considering the security risks and safety measures of mobile health apps [99], while others have indicated the app provides precise values for elements of nutrition [100,101]. As the results of the current review suggest, it can be successfully utilised as a guide alongside additional software. For example, it has been used for dietary monitoring [30], whereas a Fitbit enabled accurate PA [29].
Five studies included the use of PDAs ( [58][59][60][61][62]), whereby some weight loss was observed in interventions, but the majority of findings of the present study suggest PDAs are not considerably better than self-monitoring by PR, and additional feedback is not an improvement on PDA alone [102]. A review of technology in overweight and obese people found that the use of PDAs demonstrated an average weight loss of 2.0 kg, with PA monitors being the type of technology that achieved the greatest weight loss at 6.21 kg, followed by virtual reality (4.7 kg), websites (3.75 kg), smartphones (3.44 kg), and DVDs at the lowest (0.48 kg). This suggests that certain technologies can be effective to increase weight loss in patients with obesity while improving treatment adherence through selfmonitoring [103]. Cavero-Redondo et al. [104] concluded, however, that smartphones were the most successful intervention when compared to web-based or PDAs for weight loss and adherence, and likewise, Khokhar et al. [105] found significant weight loss via mobile phone but little evidence for using PDAs vs. controls in promoting weight loss.
Three studies in the web-based theme found the programme alone produced increased weight loss over the programme plus an additional element compared to the control. Moreover, Nakata et al. [106] state the addition of a PA monitor resulted in no significant difference from self-help when looking at weight maintenance following weight loss in a web-based weight loss maintenance programme compared to self-help, although participants with greater increases in their step count lost more weight. A meta-analysis of eight studies found no significant difference between web-based and offline controls, although it reported significant heterogeneity between studies [107]. In contrast, a multitude of studies in the present review and Sorgente et al. [108] report that an enhanced intervention (i.e., one that is more interactive and tailored) contributes to greater weight loss. Furthermore, a review and meta-analysis of web-based versus offline interventions demonstrated the former resulted in greater short-term weight loss but not longer-term [109]. In contrast, Sorgente et al. [108] suggest uncertainty as to whether they are advantageous compared to comparable non-web-based programmes but they are better than a control/minimal intervention.
It was established that tailored text support resulted in greater differences in weight loss and PA outcomes. According to previous studies, long-term text-based interventions are an effective strategy for weight loss [110,111]. A review found significant weight loss/maintenance that could be a scalable intervention option, albeit with a limited understanding of long-term effects [112]. Meanwhile, Spark et al. [113] revealed positive long-term preservation on weight and PA levels using text messages after weight loss intervention. In addition, an evaluation stated that participants find texts and call support a useful intervention, maintaining motivation and encouragement during interventions as a simple and convenient mode of communication [114]. Whilst we present a comprehensive review of the current state of the literature, a limitation here is the exclusive use of studies presented in the English language. There may be pertinent studies published in other languages. Furthermore, there is heterogeneity between studies with a large diversity in population characteristics in terms of age, gender, specific conditions, various platforms/modes of delivery that have considered many different components, from the most basic to the most in depth. These then require varying levels of commitment from participants, alongside differences in programme duration, how much human interaction/cost is required, and also various durations in follow-up time for assessment of weight maintenance, so that it is then difficult to directly compare programme efficacy and success therein. However, given the numerous barriers to exercise and physical activity in obese and clinical populations, digital platforms offer some way into a solution to tackle this given they have the potential to provide convenient and equitable access with longer-term tracking capability. However, this also needs to consider the personal preferences of the individual in terms of how they want to engage with technology and the level of human interaction and support required, whether social elements are required, and behavioural change techniques to prevent weight regain, as it is clear that engagement and adherence are important determinants of weight loss. What is also not considered is whether people paying for access to digital platforms subsequently affect user adherence, engagement, and success, either positively or negatively, and the longer-term use of digital health technology needs to be assessed. The increase in digital platforms also needs to be considered more from the privacy and security of consumers' personal and health information [99]. Nevertheless, the possibility of positively using digital health solutions for obesity and weight loss is clear overall, and future developments should also look to tackle the management of specific conditions alongside weight loss and allow for a more individualised prescription of exercise.

Conclusions
There are many uses for digital interventions in addition to weight loss and maintenance. This narrative review was intended to examine the expanding digital health solutions to weight loss in overweight and obesity and has introduced contemporary evidence of the developments in 'digital health' and what to expect moving forward, for instance, enhanced apps that cater to individualised needs, alongside strategies to promote adherence and motivation to accompany an increase in the utilisation of these tools. The use of technology at present appears to work well, but individuals also require specialist support to achieve their weight goals and then subsequent weight maintenance.

Data Availability Statement:
No new data were created or analysed in this study. Data sharing is not applicable to this article.